Microbial Associations in the Coffee Berry Borer, Hypothenemus hampei (Ferrari) (Coleoptera: : Scolytinae)

A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

TROPICAL PLANT PATHOLOGY

May 2021

By

Sayaka Aoki

Dissertation committee:

Mohammad Arif, Chairperson

Brent Sipes Koon-hui Wang Fernando E. Vega Shefali Dobhal Ikkei Shikano Birendra Mishra, University Representative

1

© 2021, Sayaka Aoki

ii

Acknowledgements

First and foremost, I am extremely grateful to my committee members; Dr. Mohammad Arif, Dr. Brent S. Sipes, Dr. Koon-hui Wang, Dr. Fernando E. Vega, Dr. Ikkei Shikano, Dr. Shefali Dobhal, and Dr. Birendra Mishra for their kind support and their thoughtful guidance during my Ph.D. in Tropical Plant Pathology. I would like to express a special acknowledgment to my advisor, Dr. Arif, who had accepted me as a transfer student and kindly supported me throughout my Ph.D. With his expertise and immense knowledge in bacteriology and bacterial , I was able to achieve my goal of conducting research from the bacterial side of the story in the -symbiont system. Dr. Sipes and Dr. Wang had been highly supportive throughout my Ph.D. During this worldwide pandemic, I have faced difficulties in navigating my degree; however, with their support, I was able to complete my degree on time. I am extremely grateful for Dr. Vega, who has been kindly guiding me through my Ph.D. since 2016. My interest in symbionts and insect associations sparked when I came across one of his publications. I was ecstatic when he joined my committee panel. With his deep knowledge in coffee berry borer research, I was able improve my project to a level that I hoped for. I am also highly grateful to Dr. Dobhal, who has provided me with much guidance through my experiments. Especially with her vast knowledge in chemistry, I was able to navigate challenging experiments in the lab. I would also like to express my gratitude to Dr. Shikano and Dr. Mishra. I appreciate their inputs on my research, as well as providing me with many of their insights from various perspectives. I am immensely grateful for my supervisors and directors at the Hawaii agriculture research center, Dr. Chifumi Nagai, Dr. Ming-Li Wang, Stevie Warren, and Tyler Jones. I am extremely thankful for Hawaii agricultural research center for funding me through my Ph.D. and providing me with an opportunity to work as a research assistant in a coffee germplasm project. I am especially grateful for Dr. Nagai, who has been my mentor in science for many years. She provided me with support and guidance through my most difficult times. I also would like to express my gratitude to Dr. Wang. She had been understanding and supportive when I was having difficulty balancing work and experiments.

iii I would like to thank all of the graduate students from the Tropical Plant Pathology program especially, lab members from Plant bacteriology; Gamze Boluk, Dario Arizala Quinto, Sujan Padel, Diksha Klair, Shu-Cheng Chuang, and Hanul Nathaniel Seo, for sharing their outstanding knowledge and skills in the whole genome sequencing, assembly, annotation, and another analytical part of my study. I am immensely grateful for their help, especially towards the end of chapter 4, my lab members provided me with much help to complete my experiments on time. I am very thankful for Dr. Mark Wright (Entomology) and Dr. Ania Wiecozrek (TPSS) for their support for my chapter 1, and Dr. Gordon M. Bennet (U.C Merced) for his guidance for the fluorescent in situ Hybridization assay and the analysis for chapter 2. I would like to express special gratitude for many other people who had helped me with experiments; Tina Weatherby and Dr. Marilyn Dunlap at the UH biological microscopy lab for assisting with confocal laser scanning microscopy assays, Dr. Anne Alvarez from Plant bacteriology lab for her kind guidance through my defense, and Andrea Kawabata, Jen Burt, and Marc Maisner at UH Kainaliu research station for sharing their insights on where to sample the coffee cherries on the Big Island. I am also grateful for all of the coffee growers who had kindly invited me to their farms to sample coffee berries. Representing all the coffee growers, I would like to give a special acknowledgment for Pepe Miranda, Tommy Greenwell (Kona), and John Ah Sun (Kau) I am grateful for my parents, Nobuhiko Aoki, M.D., Ph.D., and Sayoko Aoki for their support. My dear friend, Dr. Christine Lynch had stood by my side during the most challenging time of my life. With her support, I was able to achieve my goal. I also would like to give a special acknowledgment to my significant other, Robert Anthony Parr, who had been my rock throughout my Ph.D. He has provided me with moral, technical support, and many pieces of advice to navigate through my journey.

This research was supported by NIH-COBRE, USDA, HARC, HDOA, and CTHAR.

Sayaka Aoki 5/13/2021

iv Abstract Coffee (Coffea arabica and C. canephora; Rubiaceae) is one of the most important agricultural commodities in tropical and subtropical regions in the world, generating industries that surpass an estimated US$170 billion annually. The coffee berry borer (CBB), Hypothenemus hampei, is the most devastating insect pest of coffee worldwide. The insect was first reported in 1901, and it has invaded most coffee-producing countries, causing severe economic losses surpassing more than US$500 million annually. The coffee berry borer was first reported on Kona, island of Hawaii in 2010, and by 2020, it had been reported on Oahu, Maui, Kauai, and Lanai, causing significant economic loss in the coffee industry in Hawaii. Determining how the coffee berry borer became a coffee pest has been an area of interest due to the fact that it is the only insect to consume the coffee seeds inside the berry. Survival on the seeds therefore implies a mechanism to degrade caffeine. have evolved a diversity of strategies to overcome challenges imposed by plants. One of the strategies to mitigate these challenges is to establish mutualistic associations with symbiotic microorganisms that could enable insects to thrive and reproduce within the unfavorable environments. These microbial symbionts associated with insects play pivotal roles in host survival, reproduction, host metabolism, and affect hosts’ biology and phenotypes via a multitude of functions, providing vital nutrients such as essential amino acids, nitrogen, vitamins, and sterols, breaking down cellulose and lignin materials that are hard to digest, influencing host plant usage, and mediating interactions with natural enemies. Recent studies have provided strong evidence of coffee berry borer-bacterial associations and the mechanisms, revealing the coffee berry borer has a range of associations with its bacterial symbionts for the survival and possibly affected its evolution and adaptations. Vega et al. (2002), revealed coffee berry borer associations with the maternally inherited bacterium Wolbachia from many countries. Ceja-Navarro et al. (2015) demonstrated that the coffee berry borer relies on caffeine-degrading bacterial symbionts in the alimentary canal in order to live in caffeine-rich conditions that are unfavorable for other insects. The caffeine-degrading microbiome reported by Ceja-Navarro et al. (2015) consisted of 13 bacterial species, and only one of these , i.e., fulva, had the caffeine demethylase ndmA gene (methylxanthine N-demethylase A). When the insect was fed an artificial diet containing

v antibiotics, they lost the ability to degrade caffeine, and their reproductive fitness was negatively affected (Ceja-Navarro et al. (2015). Additional investigations on the role of bacteria on the fitness of the coffee berry borer are needed in order to better understand the biology of the insect. The objectives of this study are to: (1) Determine the identity of bacterial species associated with the coffee berry borer in commercial coffee farms and wild coffee in Hawaii; (2) Explore the transmission mode of caffeine degrading bacteria by visualizing the bacteria within the insect eggs by fluorescent in situ hybridization (FISH) targeting the caffeine demethylation gene (ndmA); (3) Isolate and identify bacterial species associated with the eggs and determine if they are capable of breaking down caffeine; and (4) Conduct whole genome sequencing to identify them into species-level, and understand genome biology of bacterial species isolated from CBB eggs.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS……………………………………………….……………………...iii ABSTRACT…………………………………………………………………………………….....v LIST OF TABLES……………………………………………………………………………..…ix LIST OF FIGURES………………………………………………………………………….…....x APPENDICES………………………………………………………………………………..…xiv

CHAPTER 1. Bacterial taxa associated with the Coffee berry borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae: Scolytinae), in Hawai‘i

Abstract………………………………………………………………………………...….1 Introduction…………………………………………………………………………..……2 Materials and Methods………………………………………………………………..…...7 Results………………………………………………………………………..……………9 Discussion………………………………………………………………….…………….13 Conclusion……………………………………………………………………………….15 Figures……………………………………………………………………………………17 Reference………………………………………………………………………..……….33 Appendix………………………………………………………………………...……….38

CHAPTER 2. Coffee berry borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae: Scolytinae), association with caffeine degrading bacteria in their eggs: exploring bacterial mode of transmission

Abstract………………………………………………………………………….……….46 Introduction..…………………………………………………………………..…………47 Materials and Methods………………………………………………………..………….55 Results. ……………………………………………………………………….………….62 Discussion.………………………………………………………………………..……...65 Conclusion….……………………………………………………………………..……..66

vii Figures...………………………………………………………………..….…..…………68 Reference………………………………………………………………….…..…………87

CHAPTER 3. Isolation of endophytic bacteria from eggs of coffee berry borer, Hypothenemus hampei (Ferrari), and their association with caffeine degradation

Abstract……………………………………………………………………………..……93 Introduction.………………………………………………….……………….………….94 Materials and Methods.…………………………………………………..….…………...97 Results……………………………………………………………………………...…...100 Discussion.………………………………………………………………...…………....101 Conclusion.…………………………………………………………..………………....104 Tables..…………………………………………………………………..……………...105 Figures…………………………………………………………….………………….....107 Reference...…………………………………………………...………………………...118

CHAPTER 4. Genome-wide analyses of caffeine-degrading bacteria associated with the eggs of the Coffee berry borer, Hypothenemus hampei (Ferrari): a species-level identification of caffeine-degrading bacterial taxa isolated from coffee berry borer eggs

Abstract..………………………………..…………………….………………………...122 Introduction.………………………………….………………………………………....123 Materials and Methods. ……………………...………………………………………....126 Results.…………………………………………………….…………………………....130 Discussion..…………………………………..………….……………………………...135 Conclusion..…………………………………………………..………………………...141 Tables..……………………………………………………….………….……………...142 Figures..……………………………………………………………………….………...145 Reference…………………………………………………………..…………………...172 Appendix..…………………………………………………………….………………...178

viii LIST OF TABLES Chapter 3: Table 1. Summary of Sanger sequencing result………………………………….……...105 Table 2. List of bacterial genera isolated from coffee berry borer eggs and used for caffeine degradation experiment………………………………………………………...105 Table 3. Caffeine degradation result…………………………………………………….106

Chapter 4: Table 1. MinION sequence result………………………………………………………..142 Table 2. MinION sequencing result summary…………………………………………..143 Table 3: Summary of bacteria used for the analysis and the result……………………...144

ix LSIT OF FIGURES Chapter 1: Figure 1. Relative abundance of bacterial (%) Phylum detected………………………....17 Figure 2. Relative abundance of bacteria (%) by Class detected………………………....18 Figure 3. Relative abundance of bacteria (%) by Order detected. …….………………....19 Figure 4. Relative abundance of bacteria (%) Family-level detected…..………………...20 Figure 5. Relative abundance of bacterial genera (%): Top 50 genera…………………...21 Figure 6. Relative abundance of bacterial genera (%): Top 50 to 100 genera…………....22 Figure 7. Relative abundance of bacterial Species (%): Top50 species…………………..23 Figure 8. Relative abundance and hierarchical clustering dendrogram by genus-level...... 24 Figure 9. Principal Coordinate Analysis (PCoA) in Species-level……………………….25 Figure 10. Principal Coordinate Analysis (PCoA) in Genus-level……………………….26 Figure 11. Principal Coordinate Analysis (PCoA) in Family-level………………………27 Figure 12. Principal Coordinate Analysis (PCoA) in Order-level………………………..28 Figure 13. Principal Coordinate Analysis (PCoA) in Class-level………………………...29 Figure 14. Principal Coordinate Analysis (PCoA) in Phylum-level……………………...30 Figure 15. Principal Coordinate Analysis (PCoA) in Kingdom -level…………………....31 Figure 16. Number of reads obtained by 16S rRNA amplicon analysis of reproductive manipulating bacteria…………………………………………………………………….32

Chapter 2: Figure 1. Autofluorescence test of outer layer of un-fixed coffee berry borer egg……….68 Figure 2. Autofluorescence test of an un-fixed coffee berry borer egg (Cross-sectional view)………………………………………………………………………………………69 Figure 3. Autofluorescence test of un-fixed coffee berry borer egg (Cross-sectional view)………………………………………………………………………………………70 Figure 4. Autofluorescence test of un-fixed coffee berry borer egg (Cross-sectional view)………………………………………………………………………...…………….71 Figure 5. Autofluorescence test of un-fixed coffee berry borer egg……………...………72 Figure 6. Negative control-1: No probe control…………………………………………..73 Figure 7. Negative Control 2-1: Competitive suppression assay…………………………74

x Figure 8. Negative control 2-2: Competitive suppression assay………………………….75 Figure 10. Tropical nut borer egg with CBBcdmF and Alexa 647……………………….76 Figure 11. Tropical nut borer egg with CBBcdmF and Alexa 647……………………….77 Figure 12. Negative control 3-1: Fluorescent in situ Hybridization of Tropical nut borer, Hypothenemus obscurus, egg-1…………………………………………………………...78 Figure13. Negative control 3-2: Fluorescent in situ Hybridization of Tropical nut borer, Hypothenemus obscurus, egg-2…………………………………………………………...79 Figure 14. A coffee berry borer egg hybridized with CBBcdmF labeled with Alexa Fluor 647………………………………………………………………………………………...80 Figure 15. Z-stack slices of a coffee berry borer egg, hybridized with CBBcdmF labeled with Alexa Fluor 647……………………………………………………………………...81 Figure 16. A coffee berry borer egg hybridized with Eubacterial primer F labeled with Alexa 488………………………………………………………………………………....82 Figure 17. A coffee berry borer egg hybridized with Eubacterial primer, 515F labeled with Alexa 488………………………………………………………………………………....83 Figure 18.A coffee berry borer egg hybridized with Eubacterial primer, 515F labeled with Alexa 488………………………………………………………………………………....84 Figure19. A coffee berry borer egg hybridized with Eubacterial primer, 515F labeled with Alexa 488……………………………………………………………………………….85 Figure 20. PCR amplification of caffeine demethylation gene, ndmA, using CBBcdm primer sets on bacterial DNA isolated from coffee berry borer eggs…………………….86

Chapter 3: Figure.1 Negative control of caffeine concentration experiment with Ralstonia pseudosolanacearum……………………………………………………………………………..107 Figure 2. Serratia sp. caffeine plate assay……………………………………………….108 Figure 3. Pseudomonas sp. 2. caffeine plate assay ……………………………………..110 Figure 4. Pantoea sp. caffeine plate assay……………………………………………....112 Figure 5. Pseudomonas sp. 1 caffeine plate assay………………………………………114 Figure 6. Ochrobactrum sp. caffeine plate assay………………………………………..115 Figure 7. Microbacterium sp. caffeine plate assay……………………………………....116

xi Figure 8. Achromobacter sp. caffeine plate assay……………………………………….117

Chapter 4: Figure 1. Pairwise heatmap of Pseudomonas sp………………………………………...145 Figure 2. GBDP tree of Pseudomonas sp. (16S rDNA gene sequence-based)………….146 Figure 3. GBDP tree of Pseudomonas sp. (whole-genome sequence-based)…………...147 Figure 4-1. Pairwise heatmap of Serratia sp…………………………………………….148 Figure 4-2. ANI of Serratia nematodiphila and S. marcescens (16S rRNA-based)…….149 Figure 5-1. GBDP tree of Serratia sp. (16S rDNA gene sequence-based)……………...150 Figure 5-2. Partial GBDP tree of Serratia sp. (16S rDNA gene sequence-based) focused on the cluster with Barcode 23 in Figure. 5-1…………………………………………...151 Figure 6. GBDP tree of Serratia sp. (whole-genome sequence-based)…………………152 Figure 7. Pairwise heatmap of Ochrobactrum sp………………………………………..153 Figure 8: GBDP tree of Ochrobactrum sp. (16S rDNA gene sequence-based)…………154 Figure 9: GBDP tree of Ochrobactrum sp. (whole-genome sequence-based)…………..155 Figure 10. Pairwise heatmap of Achromobacter sp……………………………………..156 Figure 11. GBDP tree of Achromobacter sp. (16S rDNA gene sequence-based)……….157 Figure12. GBDP tree of Achromobacter sp.(whole-genome sequence-based)………….158 Figure 13. Pairwise heatmap of Microbacterium sp…………………………………….159 Figure 14. GBDP tree of Microbacterium sp. (16S rDNA gene sequence-based)……...160 Figure 15. GBDP tree of Microbacterium sp. (whole-genome sequence-based)……….161 Figure 16. Pairwise heatmap of Pantoea sp-1…………………………………….…….162 Figure 17: Pairwise heatmap of Pantoea sp-2……………………………………...…....163 Figure 18. GBDP tree of Pantoea sp. (16S rDNA gene sequence-based)………………164 Figure 19. GBDP tree of Pantoea sp.(whole-genome sequence-based)………………...165 Figure 20. Pairwise heatmap of Pantoea sp…………………………………………….166 Figure 21. GBDP tree of Pantoea sp.(16S rDNA gene sequence-based)……………….167 Figure 21. GBDP tree of Pantoea sp.(whole-genome sequence-based.)………………..168 Figure 23. Pairwise heatmap of Pseudomonas sp...... 169 Fig 24. GBDP tree of Pseudomonas sp.(16S rDNA gene sequence-based)…………….170 Figure 25. GBDP tree of Pseudomonas sp. (whole-genome sequence-based)………….171

xii APPENDICES

Appendix 1-1. Information of sampled locations on Oahu and Hawaii……….………….33 Appendix 1-2. Map of Hawaii island sampled……………………………………………34 Appendix 1-3. Illumina Miseq result…………………………………………….……….35 Appendix 1-4 .Erwinia species yielded homologies……………………………………...36 Appendix 1-5. List of Enterobacter species yielded homologies………………………...36 Appendix 1-6. List of Pseudomonas species yielded homologies………………………..37 Appendix 1-7. List of Corynebacterium species yielded homologies database…………..40 Appendix 4-1. Pairwise comparison of Pseudomonas sp. 2 against 24 Pseudomonas species……………………………………………………………………………...……178 Appendix 4-2. Pairwise comparison of Pseudomonas sp. 2 against 15 Pseudomonas species…………………………………………………………………………………...179 Appendix 4-3. Pairwise comparison of Pseudomonas sp. 2 against 10 Pseudomonas species.. …………………………………………………………………………………179 Appendix 4-4. Pairwise comparison of Pseudomonas sp. 2 against 12 Pseudomonas species.. …………………………………………………………………………………180 Appendix 4-5. Pairwise comparison of Pseudomonas sp. 2 against 11 Pseudomonas species.. …………………………………………………………………………………180 Appendix 4-6. Pairwise comparison of Pseudomonas sp. 2 against 14 Pseudomonas species………………………………………………………………………………...…181 Appendix 4-7. Pairwise comparison of Pseudomonas sp. 1 against 9 Pseudomonas species………………………………………………………………………………...…181 Appendix 4-8. Pairwise comparison of Pseudomonas sp. 1 against 9 Pseudomonas species…………………………………………………………………………………...182 Appendix 4-9. Pairwise comparison of Pseudomonas sp. 1 against 7 Pseudomonas species.. …………………………………………………………………………………182 Appendix 4-10. Pairwise comparison of Pseudomonas sp. 1 against 9 Pseudomonas species…………………………………………………………………………………...183 Appendix 4-11. Pairwise comparison of Pseudomonas sp. 1 against 9 Pseudomonas species…………………………………………………………………………………...183

xiii Appendix 4-12 List of type strains compared to Pseudomonas sp.1 TYGS………….…184 Appendix 4-13. List of type strains compared to Serratia sp.1 TYGS……………….....185 Appendix 4-14. List of type strains compared to Ochrobactrum sp.1 TYGS…………..187 Appendix 4-15. List of type strains compared to Achromobacter sp.1 TYGS………….188 Appendix 4-16. List of type strains compared to Microbacterium sp.1 TYGS………....189 Appendix 4-17. List of type strains compared to Pantoea sp. TYGS……………….….192 Appendix 4-18. List of type strains compared to Pseudomonas sp. 2 TYGS………..….195

xiv

CHAPTER 1: BACTERIAL TAXA ASSOCIATED WITH THE COFFEE BERRY BORER, HYPOTHENEMUS HAMPEI (FERRARI) (COLEOPTERA: CURCULIONIDAE: SCOLYTINAE), IN HAWAI‘I

Abstract The coffee berry borer, Hypothenemus hampei (Ferrari), is the most devastating insect pest of coffee worldwide. The insect was first reported in Kona (island of Hawaii) in 2010 and has since spread to Oahu, Maui, Kauai, and Lanai, causing significant economic losses to the local farms and the economy in Hawaii. A bacterial community analysis was conducted to investigate the community structures and abundance of bacteria associated with the coffee berry borer in Hawaii. Insects from commercial coffee farms and wild coffee populations on the island of Hawaii and Oahu were sampled. Illumina Miseq, an amplicon sequencing platform, was used for the bacterial screening targeting regions V3 and V4 of bacterial 16SrRNA. Operational Taxonomic Units (OTU) were clustered at the 97% level by using the Illumina BaseSpace 16S pipeline and compared to the modified version of the Greengenes database. A total of 28 phyla, 57 classes, 114 orders, 258 families, 761 genera, and 2,337 species of bacteria were identified. The dominant bacterial taxa belong to , Actinobacteria, and Firmicutes. The most abundant bacterial genera were Erwinia, Enterobacter, and Pseudomonas. The relative abundance and hierarchical clustering and Principal Coordinate Analysis were conducted by sites and revealed the geographical patterns in the bacterial abundance between 33 locations on Hawaii and Oahu. There was a difference between the bacterial communities associated with the insect between two islands and also within the geographical regions of Hawaii.

1 Introduction Coffee Overview Coffee is one of the most important agricultural commodities in tropical and subtropical regions in the world (Ricketts et al., 2004; Food and Agriculture Organization, and United Nations Statistics Division (FAOSTAT), 2020). Coffee is cultivated on more than 10 million hectares by approximately 20 million families in over 80 countries and generates industries that surpass US $170 billion annually (Baker et al., 2002; Vega et al., 2003; Aristizábal et al., 2016; Vega et al., 2015; ICO, 2020). Even though there are 124 species in the genus Coffea (Rubiaceae) (Davis et al. 2006, 2011), commercial production is based on C. arabica L., which accounts for ca. 70% of the world production, and C. canephora Pierre ex A. Froehner (commonly known as robusta), which accounts for ca. 30% (USDA, 2020).

Coffee Production in Hawaii Commercial coffee production in Hawaii started in the early 1800s, producing mainly C. arabica cv. Typica (Bittenbender et al., 2004), which accounts for < 1% of total world commercial production (Aristizábal et al., 2016). In Hawaii, coffee is cultivated in more than 960 farms covering ca. 8,000 acres, and generates farm revenue of US$30 million annually, which makes coffee the second largest agricultural commodity in the state (Fleming and Mauri, 2001; USDA 2017). Due to the favorable and unique environmental conditions to grow coffee, the coffee produced in Kona and Kau regions of Hawai’i are known as high-quality specialty coffee, ranking among the best in the world (Bittenbender and Smith, 2004; USDA, 2017).

Coffee berry borer damage and history Among over 3,000 insect and mite species that are associated with coffee plant (Waller, 2007), the coffee berry borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae: Scolytinae), is the most devastating insect pest of coffee worldwide (Damon, 2000; Waller et al., 2007; Vega et al., 2015). Although the insect does not damage the leaves, the branches, or the stem, it causes premature fall of berries as well as qualitative and quantitative losses by boring galleries in the endosperm (seeds) within coffee berries (Le Pelley, 1968; Vega et al., 2015).

2 The insect was first reported in Gabon in 1901 and has now been reported in most coffee producing countries (Duque-Orrego et al., 2002; La Pelley, 1968; Vega et al., 2015). Due to its cryptic life cycle inside the coffee berry, controlling the insect in the field is extremely challenging (Messing, 2012; Vega et al., 2015).

Coffee berry borer in Hawaii Hawaii has been relatively free of serious coffee diseases and insect pests, mostly due to the geographical separation from the rest of the coffee-growing regions of the world, and strict quarantine procedures taken by the state. The coffee in Kona, Hawaii, suffered devastating damage from the Kona coffee root-knot nematode, Meloidogyne konaensis, in the 1960s (Schmitt et al., 2001). One of the most devastating plant pathogens of coffee, coffee leaf rust (Hemileia vastatrix) was not found in Hawaii until recently. Coffee leaf rust has been reported on Hawaii, Maui, Oahu, and Lanai in late 2020 to 2021. The coffee berry borer was first reported in Kona, Hawaii, in September 2010, and detected in Ka‘u, and Hamakua on the island of Hawai’i in 2011. The presence of coffee berry borer was confirmed in a commercial coffee field in Waialua, Oahu, in 2013, imposing significant loss in crop yields and revenue (Aristizábal et al., 2016). According to a survey conducted by HDoA, coffee berry borer was also found in six non-commercial coffees in west, north, and east of Oahu in 2016 (Kaneaki Heiau, Makaha Valley, Waianae Kai Forest Reserve, Wahiawa, Waialua, and Hawaii Agriculture Research Center fields: Kunia and Maunawili locations). The coffee berry borer was also found on the island of Maui in September 2016 in Hana and Kipahulu (http://hdoa.hawaii.gov/blog/main/nr17-1-coffee berry boreronmaui/). Subsequently, coffee berry borer was reported from residential areas on Kauai, and wild coffees on Lanai island in 2020.

Coffee berry borer biology and life cycle The coffee berry borer life cycle starts when an adult female invades a coffee fruit, usually through the floral disc on the upper region of the fruits. Colonization occurs approximately 120 to 150 days after flowering of coffee when fruits are in the green stages and penetration commences after the dry content in the fruit is 20% or higher (Baker, 1984).

3 Once inside the fruit, the colonizing female builds galleries throughout the seeds, where it oviposits the eggs. The early life stage of the beetle consists of egg (4 days), larva (15 days), followed by the pre-pupal stage, and pupa (7 days) depending on the temperature (Baker, 1984). There are two instars in females and one in males (Bergamin, 1943; Gómez et al., 2015 ). Completing the life cycle for the female coffee berry borer takes approximately 28 to 34 days (Damon, 2002). The life expectancy of a female is ca.157 days, and 20 – 87 days for a male (Barrera, 1994). When larvae hatch into adults, there is sibling mating and a skewed sex ratio favoring females. Colonization of each berry is usually achieved by a single female per fruit, although under heavy infestations more than one female can colonize a fruit (Jaramillo et al., 2006). Population dynamics of the coffee berry borer vary depending on the climatic factors, e.g., precipitation, temperature, and relative humidity, as well as the physiology of the coffee plants (Jaramillo et al., 2006). Temperature is an important element that affects development and the number of eggs that could be laid by the female . According to Damon (2000), a female coffee berry borer will lay between 31 to 119 eggs within a berry during its lifetime at 27°C (Damon, 2000). The optimum temperature for their egg to adult development occurs between 20°C to 30°C, whereas the upper and lower threshold is approximately 14.9°C to 32°C (Jaramillo et al., 2009). The sex ratio of coffee berry borer is significantly skewed towards females as it is commonly found in Scolytinae tribes (e.g., Corthylini, Cryphalini, Dryocoetini, Hyorrhynchini, and Xyleborini). Female to male ratio was initially believed to be an average of approximately 10:1 (Kirkendall, 1998). This skewed sex ratio could be an attribute of their reproductive mode of functional haplodiploidy (pseudo-arrhenotoky, or paternal genome elimination). In the pseudo-arrhenotokous reproductive system, both females and males are initially diploid; however, males become functionally haploid after a paternal set of chromosomes condense into a mass of chromatin, therefore, failing to be incorporated into sperm during spermatogenesis or inactivated in the somatic cells (Brun et al., 1995; Vega et al., 2015) The main function of males is to mate, and males never leave the nesting fruits. In addition, male coffee berry borers have rudimentary wings (Vega et al., 2015b) , significantly reduced number of ommatidia when compared to females (Vega et al., 2014), and have a smaller size (0.9 mm long) than females (1.4 to 1.6mm long) (Jaramillo et al., 2006).

4

Coffee Berry Borer Bacterial Associations Bacterial symbionts could play essential roles in insect’s host survival, reproduction, host metabolism, and could also affect the hosts’ biology and phenotypes via a number of mechanisms, including the provision of vital nutrients, breaking down food materials, influencing host plant usage, and mediating interactions with natural enemies, among others (Itoh et al., 2014). The range of association varies depending on the functions of the symbionts in conjunction to the biology of insect species from obligate (primary symbionts) to facultative (secondary symbionts) (Kikuchi et al., 2007). The coffee berry borer is the only insect species able to reproduce and complete their development and life cycle consuming coffee seeds inside of the coffee berries (Vega et al., 2015). Two main reasons that make coffee berries an unlikely host of the insects in general are the presence of an indigestible polysaccharide matrix and high level of defensive secondary metabolites. The green coffee endosperm has high level of hemicellulose, pectin, and cellulose that are indigestible for most insects; however, it could serve as a vital nutrient source if the complex polysaccharide matrix is able to be degraded (Vega et al., 2015). Coffee also contains a high concentration of secondary metabolites, e.g., chlorogenic acid derivatives that are highly toxic to many insects (Vega et al., 2003). Caffeine (1,3,7 trimethylxanthine) is a purine alkaloid present in coffee seeds, at ca. 1.0% of dry weight in C. arabica and 1.7% in C. canephora (Ashihara et al., 1999). It has been hypothesized that the main purpose of the caffeine in the plant is to be used as defensive mechanisms by the plants to protect itself from the insect attacks (Uefuji et al., 2005; Kim et al., 2006; Wright et al., 2013). Recent studies provided evidence of coffee berry borer associations with various microorganisms e.g., bacteria, fungi, and yeasts (Carrión and Bonet, 2004; Pérez and Vega, 2005; Vega and Dowd, 2005; Acuña et al., 2012; Ceja-Navarro et al., 2015; Vega et al. 2021). The first evidence of an interesting bacterial association with the coffee berry was reported by Vega et al. (2002), after identifying the maternally inherited intracellular proteobacterium Wolbachia in Benin, Brazil, Colombia, Ecuador, El Salvador, Honduras, India, Kenya, Mexico, Nicaragua, and Uganda (Vega et al., 2002). Acuña et al. (2012) reported that bacteria may have had a long association with the coffee berry borer and possibly shaped its evolution and adaptation in an unfavorable environment.

5 One of the most important findings by Acuña et al. (2012) was the identification HhMAN1, a gene encoding a mannanase, i.e., a digestive enzyme that could break down galactomannan in the coffee seed, thereby making the polysaccharide available to the insect. HhMAN1was found to be similar to a Bacillus circulans protein identified from a cDNA of the coffee berry borer, indicating that the gene was horizontally transferred to the insect by bacteria (Bacillus sp (Acuña et al., 2012). Vega et al. (2015) reported the first draft genome of coffee berry borer. This research provided information that revealed several functions in the coffee berry borer’s genome. According to one of its findings, there are at least ten bacterial candidates for horizontal gene transfer into the coffee berry borer’s genome. These genes were found to be more closely related to bacteria than eukaryotic genes (Vega et al., 2015). Furthermore, a caffeine demethylation gene (ndmA), responsible for caffeine detoxification in the coffee berry borer, was not found in the coffee berry borer genome suggesting the function of caffeine demethylations has not been transferred to the insects from bacteria (Vega et al., 2015) Ceja-Navarro et al. (2015) demonstrated that 13 bacterial species isolated from the coffee berry borer digestive tract are responsible for mediating caffeine detoxification in the alimentary canal (discussed in more detail in Chapter 2). The caffeine degrading bacteria were Brachybacterium rhamnosum, Enterobacter sp., Jonesiae, Kosakonia cowanii, Ochrobactrum sp., Novosphingobium sp., Microbacterium binotii, Pseudomonas sp., P. fulva, P. fluorescens, Pantoea vagans, P. septica, P. eucalypti, and Stenotrophomonas maltophilia (Ceja-Navarro et al., 2015). Vega et al. (2021) identified 25 caffeine degrading bacterial species associated with the coffee berry borers in Hawai’i, Mexico, and a laboratory colony in Maryland, and found to have caffeine degradation genes in their genome. (Vega et al., 2021). Mariño et al. (2018) conducted an analysis of microbial associations with coffee berry borers in Puerto Rico and compared the effect of shade-grown coffee vs full sun-grown coffee on the bacterial community found in the eggs and adult females by sequencing the V4 regions of the 16Sr RNA (Mariño et al., 2018). Pseudomonas and Pantoea dominated the bacterial community associated with then insect. Additionally, by using comparative functional inferences with Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUS),

6 their data suggested that samples from the field were enriched for genes involved in carbohydrate and protein digestion, absorption, melanization, and caffeine metabolism (Mariño et al., 2018).

Objectives The aim of this study is to investigate the community structure and abundance of bacteria associated with the coffee berry borer in Hawaii. The main objectives of this study are to 1) determine abundance of bacterial populations; and 2) identify bacterial species associated with coffee berry borers from commercial coffee farms and wild coffee population in Hawaii. Insects were collected from infested ripe coffee berries at commercial coffee farms and from wild coffee trees in the Kona and Kau’ regions in the island of Hawaii and Waialua commercial coffee field in Oahu. Total genomic DNA was extracted from insects, and Illumina Miseq (Illumina, San Diego, CA), an amplicon sequencing platform, was used to sequence the V3 and V4 regions (~300 bp) of the bacterial 16S rRNA region. Operational Taxonomic Units (OTUs) were clustered at the 97% level by using Illumina BaseSpace 16S pipeline (Illumina, San Diego, CA) and compared to the modified version of the Greengenes database (Illumina, San Diego, CA ).

Materials and Methods Sample Collection A targeted total of 2,500 ripe coffee cherries/raisins were collected from 33 commercial coffee farms and wild coffee trees in the Kona and Ka‘u’ regions of Hawaii and a Waialua farm on Oahu throughout 2016. The sampled cherries were randomly hand-picked throughout farms until targeted numbers were reached. Only cherries with small holes on the apex indicating entry of coffee berry borer into the cherries were picked during the collection. The cherries were bagged in double Ziploc bags, and placed in a cooler while being transported to the Kainaliu extension center from farm locations in Kona and Ka’u’, and frozen at -20 °C immediately after arrival. The samples were transferred to the Kona office of the HDoA in a cooler box within several days after the collection. The samples were maintained at -20°C for 48 hours under the supervision of HDoA inspectors. After inspection, the frozen samples containing dead coffee berry borers were placed in the cooler box, sent to the University of Hawaii at Manoa on Oahu and transferred to a -20° C freezer until dissection. The coffee cherries were cut by scapula, and approximately 150 female coffee berry borers per location were extracted from the cherries using

7 a small paint blush and stored in 90% EtOH at -20 °C. Adult females were collected from 33 locations. Males were not present in all sampling locations; however, males were found in large quantities from the Waialua, Oahu location. Therefore, 150 samples of males collected from Waialua were added to the analysis. Larvae and pupae were found in small quantities from each field. Ten larvae and 10 pupae were collected from all locations and pooled into one sample each.

DNA extraction The coffee berry borer samples were submerged in the 70% EtOH for 5 minutes with gentle agitation them washed with sterilized H2O for 30 seconds for twice for sterilization. Approximately 136 female beetles/site, 150 males from Oahu, and 100 larvae and pupae for all locations were surface sterilized by Whole insect body homogenates were obtained by bead- beating for 5 minutes at 30 hz from 50 mg (of insect tissue samples. Total genomic DNA was extracted from homogenates using a DNeasy Blood and Tissue sample kit (Qiagen, Valencia, CA) by following the protocol developed for psyllid sample extraction for use in citrus greening or HLB (Huanglongbing) Molecular Diagnostic Assays (USDA-APHIS Doc.WI-B-T-1-16. Rev.6). The sample from the Waialua coffee farm on Oahu, was replicated three times in order to examine consistency in sequence reading among samples.

Library Preparation and Sequencing Extracted DNA samples were sent to SeqMatics (Fermont, CA) for sequencing. DNA samples were PCR amplified by targeting the V3 and V4 regions of bacterial 16S rRNA creating a single amplicon of approximately ~460 bp by using a specific amplicon primer pair (Illumina, 16S Metagenomic Sequencing library preparation protocol) The Illumina sequencing adaptors and dual index barcodes were added to amplicon targets, and the targets were pooled and sequenced on the Illumina Miseq platform using paired 300-bp reads (16S Metagenomic Sequencing library preparation protocol). Sequenced data were clustered into Operational Taxonomic Units (OTUs) at the 97% level by using Illumina BaseSpace 16S pipeline and were compared to the modified version of the Greengenes Consortium Database, Version 1.0, May 2013 release).

8

Data analysis A principal coordinate analysis (PCoA) was conducted to visualize the variation in bacterial community structures among samples. The PCoA charts (Figures 10-16) were created by using classical multidimensional scaling (MDs) on a Pearson covariance distance matrix generated from per-sample normalized classification abundance vectors (16S Metagenomics App; 16S Metagenomics V1.1.0 APP online help; Wickelmaier, 2003). The full range of taxonomic levels, including Kingdom, Phylum, Class, Order, Family, Genus, and Species of PCoA were generated by applying the same approach. Relative abundance and hierarchical clustering by genus-level was also conducted. A per- sample classification vector was created for each sample using the methods described in 16S Metagenomics App (16S Metagenomics App, p14). Pearson’s Correlation Distance Matrix was calculated as described in a Pearson Correlation Distance Matrix was calculated (16S Metagenomics App, p14; 16S Metagenomics V1.1.0 APP online help). Finally, standard Unweighted Pair Group method with Arithmetic mean (UPGMA ) hierarchical clustering was used to generate the dendrogram (16S Metagenomics App, P14).

Results Relative Abundance of Bacteria in Different Taxonomic Levels The Greengenes database (Illumina, CA) yielded homologies

Twenty-eight phyla were associated with the coffee berry borer. Majorities of Phyla detected are represented in Figure 1 (Fig. 1). The majority of bacterial phyla of coffee berry borer was composed of Proteobacteria (74%) followed by Actinobacteria (16%), Firmicutes (5%), Bacteroidetes (3%), Cyanobacteria (0.9%), and Verrucomicrobia (0.5%). Other phyla associated with coffee berry borer found in small fractions (>0.2% each phyla) were Fusobacteria, Acidobacteria, Planctomycetes and Armatimonadetes. Fifty-seven classes yielded homologies. Seventeen phyla that represented the majority were summarized in Figure 2 (Fig. 2). Bacterial class found frequently in our samples were the Gamma (55.8%) and Alpha (14.8%) class of Proteobacteria and Actinobacteria (15.7%). Small fractions of bacteria detected were from the Betaproteobacteria (3.4%), Bacilli (2.9%), Sphingobacteria (1.8%), Clostridia (1.6%),

9 and Flavobacteriia (1.0%). Twenty-eight other bacterial species were detected in small fractions (<1%), including Nostocophycidae, Bacteroidia, Spartobacteria, Deltaproteobacteria, Verrucomicrobia, Fusobacteria and Acidobacteria. The Greengenes database (Illumina, San Diego, CA) yielded homologies with 114 orders (Fig. 3). The Order Enterobacteriales was found to dominate our samples (41%), followed by Actinomycetales (15%), Rhizobiales (9%), (8%), Xanthomonadales (4%), Burkholderiales (3%), Rhodospirillales (3%), and Sphingobacteriales (2%). Other bacterial orders were Lactobaccilales (2%), Clostridiales (2%), Bacillales (1%), and Flavobacteriales (1%). A total of 258 families yielded homology to Greengenes database (Fig. 4). The most abundant family that dominated our sample was Enterobacteriaceae (42%). was found in the next abundant family (8%). Other bacterial families were Corynebacteriaceae (7%), Xanthomonadaceae (4%), Methylobacteriaceae (3%), Sphingomonadaceae (3%), Acetobacteraceae (3%), Mycobacteriaceae (2%), Phizobiaceae (2%) and Nocardiaceae (2%). Greengenes database yielded homologies with 761 genera (Figs. 5 and 6). The relative abundance of bacterial genera found in high frequency in our samples were: Erwinia (16%), Enterobacter (13.7%), Pseudomonas (8.6%), Corynebacterium (7.9%) Methylobacterium (4%), Stenotrophomonas (2.6%), Mycobacterium (2.1%), Sphingomonas (1.9%), Trabulsiella (1.9%), Rhodococcus (1.8%), Gluconobacter (1.7%), Luteibacter (1.7%), Ochrobactrum (1.5%), Agrobacterium (1.4%), Acetobacter (1.1%), Burkholderia (1.1%), Klebsiella (1.1%) and Serratia (0.8%). A total of 2337 bacterial species were associated with the coffee berry borer. Two , Erwinia billingiae and E. tasmaniensis, represented the highest abundance (10.4% and 8.5%, respectively) followed by E. amnigenus (5.4%), E. cowanii (4.2%), E. nickellidurans (3.4%), Corynebacterium nuruki (2.5%), Trabulsiella odontotermitis (2.4%), Pseudomonas purafulva (1.8%), Stenotrophomonas retroflexus (1.2%), Agrobacterium larrymoorei (1.2%), Enterobacter hormaechei (1.2%), Serratia entomophila (1.2%), Pseudomonas lutea (1.1%), Acetobacter pasteurianus (1.1%), Pseudomonas entomophila (1.1%), Corynebacterium variabile (1.1%), Corynebacterium ammoniagenes (0.9%), Ochrobactrum thiophenivorans (0.9%), Ochrobactrum pseudogrignonense (0.9%), Sphingobacterium multivorum (0.7%) Sphingomonas oligophenolica (0.6%), and Corynebacterium freiburgense (0.5%) (Fig. 7).

10 Relative abundance and hierarchical clustering by genus level Hierarchical clustering analysis by genus-level revealed a geographical pattern in bacterial community structure and abundance (Fig. 8). The locations with similar bacterial community structures were grouped into three major geographical locations; 1) Waialua, Oahu, 2) Kau, and 3) Kona. The Waialua samples were dissimilar to all other Hawaiian island samples. The difference was the high relative abundance of Corynebacterium (Fig. 8). Even though Oahu male samples represented similar bacterial abundance and structures in terms of high abundance of Corynebacterium, the high abundance of Pseudomonas and Erwinia made the male samples different than female samples. Throughout all of the Hawaii island samples, Erwinia was the most abundant group from the majority of the locations except samples from Kau, Cloud Rest and Pear Tree (3, 29, 4, 32 and 10; Fig. 9). Kau locations including Cloud Rest and Pear Tree had dissimilarities in bacterial abundance from the rest of the Hawaii island locations (Fig. 8). The most abundant bacterial genera commonly found throughout Kau was Methylobacterium (2.68% to 8.63%), whereas this bacterium was not abundant in one of the locations (#3) in Pear Tree (Fig. 8). Sample number 3 showed the highest relative abundance with Rhodococcus (5.45%), and the Methylobacterium that was found to be abundant in other Kau fields were not abundant in this sample. Therefore, it makes this particular sample dissimilar from the rest of Ka‘u samples. Sample number 17 from lower Keei was still in the Kona cluster; however, this location was found to have a dissimilar bacterial composition from the rest of Kona due to the highest relative abundance of Pseudomonas (6.81%). Sample numbers 33, 16, 20, 24, 26, 13, 30, 23, 21, and 15 were collected from upper Holualoa to Koa road (south of Kona). These locations are similar due to the higher relative abundance of Erwinia (4.25% to 15.75%) compared to the rest of the locations. Sample numbers 5, 31, 27, 6, and 22 from Middle Keei locations were clustered together as similar bacterial abundance with a high relative abundance of Enterobacter (4.29% to10.79%). The middle Keei bacterial relative abundance was somewhat similar to Kalaoa (9) and Upper Holualoa samples (19, and 12) in terms of the presence of Enterobacter and Erwinia; however, the percentage of the Enterobacter detected in these Kalaoa and Upper Holualoa were lower (1.41% to 5.62%) than middle Keei, therefore, its own clade. Samples 1, 28, 35, 25, 36, 7, and 14 were clustered together (Fig. 8) even though they had

11 no geographical connections. Additionally, this cluster includes larvae and pupae samples collected sporadically through all Hawaii locations (35 and 36).

Principal Coordinate Analysis at Different Taxonomic Levels The differences in the analysis were detected between Oahu and Hawaii in the scatter plot of PCoA in all taxa, species to kingdom (Figs. 9-15). PCoA of family to kingdom-level analysis revealed a similar trend in terms of dissimilarity of relative abundance of bacteria between two islands. Oahu and Hawaii were represented in two separate clusters. Oahu, female coffee berry borer clusters with three replicates showed no difference (Samples 2, 37, and 38). Oahu, female and male (Sample 34) showed dissimilarity in the abundance in all taxonomic levels. As the level of taxonomy increases the sample 32 from Ka‘u, Cloud rest showed significant dissimilarity from the rest of Hawaii island samples. In the species-level analysis (Fig. 9), the Hawaii island samples, and middle Kona, and Ka‘u, North and South Kona represented two different clusters. Middle Kona included: sample 21, 23, 24, 30, 26, 13, 18, 15, 20, 19 and 36 (Pupae, mix of all locations). Ka‘u locations included: sample 10, 11, 29, and 32. North and South Kona included samples 5, 6, 7, 9, 13, 14, 16, 17, 21, 24, 25, 28, 31, and 33. PCoA of genus-level revealed clear distinctions of clusters by the sites (Fig. 10). PCoA of genus-level analysis yielded similar results in two major clusters of bacterial abundance between Oahu and Hawaii island samples. Among the Hawaii samples, Ka‘u samples (3, 4, 10, 20. 29, and 32) show similarity, but Kona samples were clearly separated from Ka‘u samples as its own cluster.

Reproductive Manipulating Bacteria Wolbachia pipientis was detected in 13 locations in the Hawaii island (samples 4, 5, 6, 9, 11, 12, 18, 19, 25, 30, 33, 35, and 36), but not from the Oahu. Most of the locations had low frequency for this bacterium (1-1,699 reads among a total of 2,754 reads) (Fig. 16). However, some locations were detected with a high frequency of W. pipientis in the sample populations. The locations found with the high frequency of Wolbachia pipientis were 6 (1699 reads), and 12 (757 reads). The second reproductive manipulator, Rickettsia (Zchori-Fein and Bourtzis, 2012) was detected from 29 locations in the small fractions (1- 2891 reads among a total of 3,164reads)

12 (Fig. 17). There were 11 species detected in this genus: Rickettsia aeschlimanii, R. asiatica, R. felis, R. hulinii, R. marmionii, R. monacensis, R. tamurae, R. grylli, R. melolonthae, R. tipulae, and R. microfusus. Other reproductive manipulators, i.e, Arsenophonus, Cardinium, and Spiroplasma (Zchori-Fein and Bourtzis, 2012) were not detected from our samples.

Bacterial genera in larvae and pupae Coffee berry borer pupae and larvae samples were collected from all locations from Hawaii island and Oahu and pooled into “pupae” (sample number 36) and “larvae” (sample number 35). The total number of bacterial genera found from larvae and pupae were as low as 451 and 431 compared to adult females from all locations of 751 genera.

Discussion The Greengenes database (Illumina, San Diego, CA) yielded homologies with a high relative abundance of the bacterial taxa associated with coffee berry borer. The bacterial phyla of coffee berry borer found with high abundance in the samples were Proteobacteria (74%) followed by Actinobacteria (16%), Firmicutes (5%), Bacteroidetes (3%), Cyanobacteria (0.9%), and Verrucomicrobia (0.5%). Among the high diversity of Proteobacteria, Gammaproteobacteria dominated the bacteria detected from Hawaii island and Oahu samples. Proteobacteria, were previously reported by Mariño et al. (2018) as predominant phylum in coffee berry borer microbiota in Puerto Rico, where 90% of their samples were comprised of Proteobacteria, followed by Bacteroides (6%), and Firmicutes (3%) (Mariño et al., 2018). Among 80 species in Pseudomonas detected from our samples, the most abundant species are P. parafulva, P. lutea, and P. entomophila, P. benzenivorans, and P. plecoglossicida. According to Vega et al. (2021), P. purafulva was found to possess caffeine degradation genes (Vega et al., 2021). Additionally, P. parafulva was previously isolated from coffee berry borer eggs in Hawaii. This bacterium was able to degrade up to 3 g/L of caffeine in mineral media (Chapter 3). Pseudomonas fulva and P. fluorescens previously reported to be capable of degrading caffeine in the CBB digestive tracts (Ceja-Navarro et al., 2015) were found only at low relative abundance and low frequency from a few locations in Hawaii, indicating these

13 Pseudomonas species are not the main bacteria providing coffee berry borer with caffeine detoxification in our samples from Hawaii. Enterobacter was one of the most abundant genera after Erwinia followed by Pseudomonas. It was reported with high relative abundance in coffee berry borer populations from Puerto Rico (Mariño et al., 2018). E. amnigenus, E. cowanii, and E. nickellidurans were third to fifth most abundant in our samples. Previous studies have reported Wolbachia associated with coffee berry borers from many countries (Vega et al., 2002; Ceja-Navarro et al., 2015). Mariño et al. (2017 and 2018), have also reported a high frequency of Wolbachia from their field collected samples and lab-reared samples from Puerto Rico. However, this bacterium was only detected in low frequencies and low relative abundance from most of our samples whereas a few locations detected with a comparatively high relative abundance of Wolbachia. Both sample 5 and 6 were collected from the Lion’s Gate property, but Wolbachia was not detected from sample 5 whereas sample 6 had a high reading of this bacteria with 757 reads. This could be due to Wolbachia occurring sporadically throughout the populations and individuals of insect host species (Mariño et al., 2017). An eleven species of Rickettsia sp., also reported to be involved in reproductive manipulation (ref), was detected from 29 locations. This bacterium was reported as one of the core bacterial genera from seven major coffee producing countries (Ceja-Navarro et al., 2015). This bacterium also was detected in low frequencies from most of the locations sampled in the present study, with sample 19 having 2,891 reads. A majority of the reads in this location among Rickettsia sp. was dominated by R. melolonthae with 2,787 reads. This indicates that double infections by Wolbachia and Rickettsia can occur within the same coffee berry borer populations. Other reproductive manipulators, such as Arsenophonus, Cardinium, and Spiroplasma were not detected from our samples. Corynebacterium, found at high relative abundance in Oahu, has not been reported to have an association with coffee berry borer in previous studies. The relative abundance, hierarchical clustering, and Principal Coordinate Analysis were conducted by sites and revealed the geographical patterns in the bacterial abundance between 33 locations on Hawaii island and Oahu. Hawaii and Oahu exhibited two main clusters of bacterial compositions in all levels. This could be explained by the different local climatic and environmental conditions where the coffee grows in the two islands: hot and dry conditions of

14 Waialua, Oahu to low temperature, wet, and humid conditions in Kona, Hawaii with acidic volcanic soils. The elevational differences between two islands are Waialua site at 484 m (286- 671 m) and Kona is only 134 m above sea-level (Appendix: Table. 1). Annual average temperature of Oahu is 25 ºC and reaches the highest at 27.6ºC in September, whereas Kona has an average temperature of 19.3 ºC, and peaks in August at 23.1ºC. Annual precipitation of Waialua is 28 mm in contrast to high precipitation throughout the year in Kona, which reaches up to 3058 mm. (Climate-data.org, year). The elevation of Kau, Hawaii, is on average of 521 m above sea-lelves, and the annual precipitation is much lower than Kona at 1026.6 mm/year with an average temperature of 22 ºC (National Weather Service, year). A major factor that could contribute to the difference in bacterial compositions between these two locations could be a seasonal coffee production and the inherent bacterial populations present in the areas sampled. The Kau region of coffee production is all year long, whereas Kona has two harvesting seasons annually. Therefore, coffee berry borer in Kau has an undisrupted life cycles, which could possibly affect bacterial compositional difference in Kona. In addition, the females and male samples from Oahu were in the same cluster but showed dissimilarities between the bacterial compositions in all taxonomic-levels. This could be due to the cryptic lifestyle of male coffee berry borers, which spend their life strictly in the coffee fruits, whereas the female coffee berry borer will depart from their nests, picking up bacteria from the environment. Pantoea (Enterobacteriaceae) was capable of subsisting on media where caffeine is the only source of C and N (Ceja Navarro et al. 2015). Pantoea was reported as the most second abundant bacterial genera in Puerto Rico, as well as in Hawaii (Marino et al., 2018; Vega et al., 2021). Pantoea was also isolated from eggs of coffee berry borer collected from Oahu and confirmed for their ability to degrade caffeine (Chapter 3). However, our analysis failed to detect this bacterium from our samples. Greengenes database (Version 1.0) not having the sequence in the database at the time of analysis.

Conclusion PCoA of the normalized relative abundance of bacteria detected in coffee berry borer in all taxonomic levels by site reviled geographical patterns in the abundance of bacteria in Hawaii and Oahu, bacterial communities associated with the coffee berry borer differ between the islands and also within the geographical regions of the island of Hawaii. The relative abundance and

15 hierarchical clustering by genus-level conducted by sites also yielded similar results to the PCoA. This chapter could be a first step in assessing bacterial diversity in Hawaii, and subsequently narrowing down bacterial presence internally, which if consistent, could reveal the candidate bacterium that could be studied for determinations of possible roles, which could lead to understanding bacteria-coffee berry borer mutualisms. Additionally, the bacterial survey could also simply reflect endemic bacterial diversity exist in the environment in the surveyed areas.

16 Figures and Tables

Figure 1: Relative abundance (%) of bacterial Phylum detected from coffee berry borers in all samples of Hawaii island and Oahu. Illumina Miseq, an amplicon sequencing platform, was used to sequence V3 and V4 regions (~300 bp) of bacterial 16S rRNA region. Operational Taxonomic Units (OUT) were clustered at the 97% level by using Illumina BaseSpace 16S pipeline and compared to the modified version of the Greengenes database. The bacterial phyla with relative abundance of less than 0.01% are enclosed in “others:”

17

Figure 2: Relative abundance (%) of bacteria by Class detected from coffee berry borers in all samples of Hawaii island and Oahu. Illumina Miseq, an amplicon sequencing platform, was used to sequence V3 and V4 regions (~300 bp) of bacterial 16S rRNA region. Operational Taxonomic Units (OUT) were clustered at the 97% level by using Illumina BaseSpace 16S pipeline and compared to the modified version of the Greengenes database. Bacterial class found less than 0.1 % are enclosed in “other”.

18

Figure 3: Relative abundance by order detected from coffee berry borers in all samples of Hawaii island and Oahu. Illumina Miseq, an amplicon sequencing platform, was used to sequence V3 and V4 regions (~300 bp) of bacterial 16S rRNA region. Operational Taxonomic Units (OUT) were clustered at the 97% level by using Illumina BaseSpace 16S pipeline and compared to the modified version of the Greengenes database. The bacterial class less than 0.1 % are enclosed in “other”.

19

Figure 4: Relative abundance of bacteria (%) Family-level detected from coffee berry borers in all samples of Hawaii island and Oahu. Illumina Miseq, an amplicon sequencing platform, was used to sequence V3 and V4 regions (~300 bp) of bacterial 16S rRNA region. Operational Taxonomic Units (OUT) were clustered at the 97% level by using Illumina BaseSpace 16S pipeline and compared to the modified version of the Greengenes database. The bacterial family with less than 1% relative abundance in enclosed in “other”

20

Figure 5: Relative abundance of bacterial genera (%). Top 50 genera found abundant in coffee berry borer in all samples of Hawaii island and Oahu. Illumina Miseq, an amplicon sequencing platform, was used to sequence V3 and V4 regions (~300 bp) of bacterial 16S rRNA region. Operational Taxonomic Units (OUT) were clustered at the 97% level by using Illumina BaseSpace 16S pipeline and compared to the modified version of the Greengenes database.

21

Figure 6: Relative abundance of bacterial genera (%). Top50 to 100 genus that are found abundant in coffee berry borers in all samples of Hawaii island and Oahu. Illumina Miseq, an amplicon sequencing platform, was used to sequence V3 and V4 regions (~300 bp) of bacterial 16S rRNA region. Operational Taxonomic Units (OUT) were clustered at the 97% level by using Illumina BaseSpace 16S pipeline and compared to the modified version of the Greengenes database.

22

Figure 7: Relative abundance of bacterial Species (%). Top50 species that are found abundant in coffee berry borers in all samples of Hawaii island and Oahu. Oahu. Illumina Miseq, an amplicon sequencing platform, was used to sequence V3 and V4 regions (~300 bp) of bacterial 16S rRNA region. Operational Taxonomic Units (OUT) were clustered at the 97% level by using Illumina BaseSpace 16S pipeline and compared to the modified version of the Greengenes database.

23

Figure 8: Relative abundance and hierarchical clustering dendrogram was created by sites. Dendrogram shows a hierarchical clustering of samples based on genus-level classifications. Standard UPGMA hierarchical clustering was used to generate the dendrogram. The bar chart of each sample shows the relative abundance of its genus-level classifications.

24 Oahu

Figure 9: Principal Coordinate Analysis (PCoA) of the normalized relative abundance of bacteria detected in coffee berry borers by site (species level) was generated to visualize the variation in bacterial community structures among samples. PcoA in species level showing clear distinction of bacterial abundance between Oahu and Hawaii. Within the Hawaii sample, Middle Kona, and Kau, North/South Kona had dissimilarity in bacterial relative abundance. The males and females sample collected from Waialua; Oahu showed dissimilarity

25

Figure 10: Principal Coordinate Analysis (PCoA) of the normalized relative abundance of bacteria detected in coffee berry borers by site (genus level) was generated to visualize the variation in bacterial community structures among samples. PcoA of genus level showing clear distinction of bacterial abundance between two islands of Oahu and Hawaii. Within the Hawaii sample, Kona and Kau had dissimilarity in bacterial relative abundance. The male and female sample collected from Waialua; Oahu showed dissimilarity.

26

Figure 11: Principal Coordinate Analysis (PCoA) of the normalized relative abundance of bacteria detected in coffee berry borers by site (family level) was generated to visualize the variation in bacterial community structures among samples. PcoA of genus level showing clear distinction of bacterial abundance between two islands of Oahu and Hawaii. Within the Hawaii sample, Kona and Kau had dissimilarity in bacterial relative abundance. The male and female sample collected from Waialua; Oahu showed dissimilarity.

27

Figure 12: Principal Coordinate Analysis (PCoA) of the normalized relative abundance of bacteria detected in coffee berry borers by site (order level) was generated to visualize the variation in bacterial community structures among samples. PcoA of genus level showing clear distinction of bacterial abundance between two islands of Oahu and Hawaii. Within the Hawaii sample, Kona and Kau had dissimilarity in bacterial relative abundance. The male and female sample collected from Waialua; Oahu showed dissimilarity.

28

Figure 13: Principal Coordinate Analysis (PCoA) of the normalized relative abundance of bacteria detected in coffee berry borers by site (class level) was generated to visualize the variation in bacterial community structures among samples. PcoA of genus level showing clear distinction of bacterial abundance between two islands of Oahu and Hawaii. Within the Hawaii sample, Kona and Kau had dissimilarity in bacterial relative abundance. The male and female sample collected from Waialua; Oahu showed dissimilarity.

29

Figure 14: Principal Coordinate Analysis (PCoA) of the normalized relative abundance of bacteria detected in coffee berry borers by site (phylum level) was generated to visualize the variation in bacterial community structures among samples. PcoA of genus level showing clear distinction of bacterial abundance between two islands of Oahu and Hawaii. Within the Hawaii sample, Kona and Kau had dissimilarity in bacterial relative abundance. The male and female sample collected from Waialua; Oahu showed dissimilarity.

30

Figure 15: Principal Coordinate Analysis (PCoA) of the normalized relative abundance of bacteria detected in coffee berry borers by site (kingdom level) was generated to visualize the variation in bacterial community structures among samples. PcoA of genus level showing clear distinction of bacterial abundance between two islands of Oahu and Hawaii. Within the Hawaii sample, Kona and Kau had dissimilarity in bacterial relative abundance. The male and female sample collected from Waialua; Oahu showed dissimilarity.

31

Figure 16: Number of reads obtained by 16S rRNA amplicon analysis of reproductive manipulating bacteria. Wolbachia pipientis was found from coffee berry borers collected from 13 locations on the island of Hawaii. Eleven species in genus Rickettsia (Rickettsia aeschlimanii, R. asiatica, R. felis, R. hulinii, R. marmionii, R. monacensis, R. tamurae, R. grylli, R. melolonthae, R. tipulae, and R. microfusus) were detected from 29 locations from Hawaii and Oahu.

32

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37 Appendix

Appendix 1-1: Information of sampling locations including, 1) assigned location numbers, 2) name of farm and name of owner, 3) location name, 4) elevation of the farm (metera above sea-level, 5) coordinates of the farm (latitude and longitude) and 6) the treatments of coffee berry borer used in the farm. “Lacticacid” should read “Lactic acid”

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Appendix 1-2 : 32 locations of Hawaii island, sampled for coffee berries. The map was created by using ArcGIS app. See more info at ArcGIS https://arcg.is/1j4WGL

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Appendix 1-3: Illumina sample information for farms listed in Table 1, showing number of reads per location and % reads that are classified to genus-level.

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Appendix 1-5: List of Erwinia species yielded homology to Greengenes database (Illumina). Number of reads and % of the species within the genus were listed by each species.

Appendix 1-6: List of Enterobacter species yield homologies to Greengenes database (Illumina). Number of reads and % of the species within the genus were listed by each species.

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Appendix 1-7: List of Pseudomonas species yield homologies to Greengenes database (Illumina). Table Number of reads and % of the species within the genus were listed by each species.

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Appendix 1-8:: List of Corynebacterium species yielding homologies to Greengenes database (Illumina). Number of reads and % of the species within the genus were listed by each specie

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CHAPTER2: COFFEE BERRY BORER, HYPOTHENEMUS HAMPEI (FERRARI) (COLEOPTERA: CURCULIONIDAE: SCOLYTINAE), ASSOCIATION WITH CAFFEINE DEGRADING BACTERIA IN THEIR EGGS: EXPLORING BACTERIAL MODE OF TRANSMISSION

Abstract A fluorescent in situ hybridization assay was carried out to explore a possible vertical transmission route of the bacteria responsible for caffeine degradation in coffee berry borer eggs. The forward primer of oligonucleotide sequence, CBBcdm, used for amplification of the methylxanthine N-demethylase (ndmA) regions in bacteria, was labeled with Alexa Fluor 647 fluorescence label, and hybridized with coffee berry borer eggs in order to localize the bacteria within the beetle eggs. The fluorescent in situ hybridization of universal eubacteria has provided evidence of bacterial presence within the coffee berry borer eggs. Furthermore, the signals of ndmA were successfully detected at 647 nm with emission 654-776 nm visualizing the location of bacteria in the coffee berry borer egg. When the regions of ndmA were amplified by the PCR in the bacteria isolated from coffee berry borer eggs, Pseudomonas species yielded a positive band (~ 400 bp). These findings strongly suggest the caffeine degrading bacteria, essential for the coffee berry borer’s survival, is present in the eggs in the early stage of oogenesis and may be transmitted via vertical transmission from mother to the offspring. The significance of this mechanism in the coffee berry borer’s biology is the larvae could be already equipped with the bacterial symbionts to degrade caffeine upon egg hatching and ready for breaking down the caffeine inside of the coffee seeds rather than acquiring the bacteria with this function from the environment after hatching.

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Introduction Insect association with microbial symbiont overview: Importance of microbial symbionts in host insects Plants are unfavorable food sources for insects and . In order to feed on and survive on a strictly plant-based diet, insects have evolved a diversity of strategies to overcome challenges imposed by plants. One strategy insects use to mitigate these challenges is to establish associations with symbiotic microorganisms that enable the insects to thrive and reproduce within the unfavorable environments (Pieterse and Dicke, 2007; Frago et al., 2012). These microbial symbionts associated with insects play pivotal roles in host survival, reproduction, host metabolism, and affect hosts’ biology and phenotypes via a multitude of functions; providing vital nutrients such as essential amino acids, nitrogen, vitamins, and sterols (Oliver et al., 2010; Douglas, 2015; Lu et al., 2016), degrading(?) cellulose and lignin materials that are difficult to digest (Sudakaran et al., 2017), influencing host plant usage, and mediating interactions with natural enemies (Oliver et al., 2010; Itoh et al., 2014). Microbial symbionts are classified into two major groups 1) obligate or primary symbionts that are indispensable to their hosts and required for the reproduction and survival by the host insect species (Vavre and Braig, 2012) and 2) secondary or facultative symbionts that are not obligate in nature but whose infection can take various forms within and between the host populations or individuals and are not required for the reproduction and survival of the host (Oliver et al., 2010; Vavre and Braig 2012).

Primary (Obligate) symbionts of insects Obligate symbionts are often associated with insects that have nutritionally limited diets such as plant phloem sap, vertebrate blood, or woody materials (Douglas, 1989; Kikuchi, 2009). Approximately 10-15% of insects are known to harbor obligate symbionts that facilitate insect reproduction and survival (Oliver et al., 2010; Vavre and Braig, 2012). Furthermore, a majority of phloem sap-feeding, piercing-sucking types of insect species, such as Hemipterans, harbor obligate symbionts in their guts (Vavre and Braig, 2012). Those host insects and the obligate symbionts tend to have a mutualistic relationship. Insects are provided with nutrient supplements by symbionts. The symbionts supply nutrients that are deficient in the host’s plant diet. The symbionts are protected from the environment and actively transported by insect hosts

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(Sadakaran et al., 2017). The host insects often suffer retarded growth, sterility, and even death when they are experimentally deprived of their symbionts (Douglas, 1989). Obligate symbionts are typically harbored intercellularly in specialized organs in the host insect body; bacteriocytes for bacteria and mycetocytes for fungi and yeasts. Obligate symbionts can be transferred vertically through ovaries to the cytoplasm of the eggs (Bridges, 1981; Douglas, 2015; Lu et al., 2016; Sudakaran et al., 2017). For example, in the /Buchnera symbiont system, Buchnera is harbored in a bacteriocyte in the aphid abdominal hemocoel (Vavre and Braig, 2012). The obligate symbionts tend to be the dominant bacteria in bacteriocytes, and the bacteria are entirely dependent on the host system for their survival. Subsequently, some obligate symbionts have lost their ability to live outside of the host cells (Douglas, 2015). Some of these symbionts eliminated their genome size over the years and have smaller genome sizes compared to their free-living relatives (Bennett and Moran, 2014). In the case of Buchnera symbionts, the symbiont reduced its genome size to the point where the bacteria resemble organelles rather than independent organisms (Frago et al., 2012).

Secondary (Facultative) symbionts of insects In contrast to obligatory primary symbionts, secondary symbionts are often facultative, generally not required for host reproduction or development (Ishikawa, 2003). These symbionts exist sporadically throughout the insect host populations or within individuals and can be located intra- or extracellularly (Douglas, 2015). However, facultative symbionts may play various functions in the host biology, including manipulating host reproduction, providing benefits such as defense mechanisms from their natural enemies or extreme temperatures, or provisioning of vital nutrition (Oliver et al., 2012; Sadakaran et al., 2017). Therefore, secondary symbionts can be categorized as either mutualistic or reproductive manipulators depending on their phenotypic effects on their hosts (Vavre and Braig, 2012). In the case of extracellular locations, microbes may colonize host insects on the exoskeletons, in the gut lumen, or in gut-associated sac known as gastric caeca depending on the functions in the insect biology and the relationship with the insect host (Douglas 2015; Sadakaran et al., 2017). In extreme cases, insects such as beetles and wasps possess “mycangia,” cuticular structures that carry fungal propagules and mycelia in order

47 to transport and protect their beneficial microbial symbionts against abiotic factors and contaminations (Paine et al., 1997: Slippers et al., 2015; Lu et al., 2016). The most studied facilitative bacterial symbiont, found in a large percentage of arthropod species (estimate of 65% in insects), is the maternally inherited alpha- Proteobacteria Wolbachia (Hilgenboecker et al., 2008; Werren et al., 2008). This bacterial endosymbiont is known to manipulate host reproduction via cytoplasmic incompatibility (CI), feminization (F), male-killing (MK), or parthenogenesis (P), eventually increasing the number of females in the host population (Engelstädter and Hurst, 2009). These reproductive distortions are known as “selfish strategies” where bacterial symbionts drive host populations to increase the frequency of infected females due to their maternally inherited property at the expense of host fitness (Bourtzis and Miller, 2002; Kikuchi, 2009; Moran et al., 2019). Facultative symbionts can also occur in conjunction with obligate symbionts in the same insect species and provide host insects with additional biological functions. In addition to the nutrient provisioning Buchnera symbiont, aphids often carry several other facultative bacterial symbionts which provide significant fitness benefits to their host species (Moran and Dunbar, 2006; Oliver et al., 2012). In the case of pea aphids (Acyrthosiphon pisum), three members of the Enterobacteriaceae are found at high frequency from field populations. The facultative symbiont Hamiltonera defensa provided host insects defense from the endoparasitoid wasp Aphidius ervi (Godfray, 1994; Moran and Dunbar, 2006). Serratia symbiotica facilitated heat tolerance for aphids in extreme temperatures (Chen et al., 2000; Moran and Dunbar 2006) and Regiella insecticola provided aphids with defense against spores of the aphid specific entomopathogenic fungus Pandora neoaphidis (Ferrari et al., 2001; Moran and Dunbar 2006). As an additional example, Tsetse flies carry an obligate symbiont, Wigglesworthia, a facultative symbiont, Sodalis glossinidius, and a maternally inherited symbiont, Wolbachia (De Vooght et al., 2014).

Modes of transmission In order to ensure the acquisition of essential microbial symbionts by their offspring, insects have evolved a multitude of mechanisms in disseminating their symbionts. These modes of transmission are categorized into two major strategies distinguishing between parental or non-parental transmission routes. Vertical transmission involves parent(s) to

48 offspring. Horizontal mode of transmission involves environmental acquisition and physical means of transmission such as sexual transmission (Itoh et al., 2014; Salem et al., 2015). Additionally, a less studied mode of transmission combines both strategies is known as mixed- mode of transmission (Ebert, 2013).

Vertical transmission of primary symbionts The obligate bacteria can be maternally (vertically) transmitted to the offspring in the early stages of oogenesis or embryogenesis via diverse mechanisms (Salem et al., 2015). For example, Blochmannia, an obligate intracellular endosymbiont of carpenter ants, Camponotus sp., is vertically transmitted via an acute intracellular infection of the host ovaries, and consequently incorporated into the eggs (Feldhaar et al., 2007). Buchnera, a symbiont in the pea aphid, Acyrthosiphon pisum, is maternally inherited by “transovarial transmission” where symbionts transferred from maternal bacteriocytes to adjacent blastulae at ovariole tips infect the developing embryo. This indicates that the transmission of the symbionts occurs within the maternal body (Mira and Moran, 2002; Braendle et al., 2003). Wigglesworthia, a bacterium in tsetse flies (Diptera: Glossinidae) that provides vitamin B, is transmitted through secretion of their milk gland while their larvae develop in utero (Attardo et al., 2008). Florez et al. (2007) demonstrated one of the most intriguing and extremely unique mechanisms of vertical transmission of symbionts. The South African soybean pest, Largria villosa (Coleoptera:Tenebrionidae: Largriinae), uses antimicrobial compounds produced by the bacterial symbiont Burkholderia gladioli to protect the vulnerable egg and larval stages against detrimental microbes. This extracellular bacterial symbiont is harbored in accessory glands that are connected to the adult female reproductive organs. The symbiont is transmitted vertically via the egg surface during oviposition. Bacteria will enter the egg through the chorion shortly before the larvae hatch. The bacteria then begin to colonize the invaginations of the cuticle located in the embryo and later in the larva. Therefore, larvae already possess this symbiotic bacterium upon hatching (Florez et al., 2007).

Horizontal and vertical transmissions of secondary symbionts Facultative symbionts are vertically transmitted through mechanisms similar to obligate symbionts. However, many of the symbionts are transmitted horizontally (Sadakaran et

49 al., 2017). There are variable extracellular routes of horizontal transmission depending on the insect species that possess specific bacterial symbionts and their functions. For example, most members of Coreidea and super families harbor Burkholderia symbionts belonging to stinkbug associated beneficial environmental (SBE) groups that are free-living in the environment and acquired from the environment every host generation (environmental acquisition) (Kikuchi et al., 2007; Kikuchi et al., 2009). Several insect groups, including , Blattodea, and Isoptera, acquire symbionts by probing their conspecific’s feces after their symbionts are shed from the gut lumen along with food and excreted (coprophagy) (Bourtzis and Miller, 2000; Salem, 2015). One of the most commonly described extracellular routes of transmission is the smearing of brood cells or egg surfaces containing bacterial symbionts in various insects such as Diptera, Coleoptera, Hymenoptera and Hemiptera (Salem et al., 2015). Many social and subsocial insect species such as bees, ants, and termites, transmit symbionts through social acquisition while exchanging their foods or fluids through mouth to mouth or anus to mouth (trophallaxis) routes (Wilson, 1971). A unique route of transmission of extracellular symbionts is the dissemination of bacteria via symbiont enclosing capsules laid by female stink bugs (capsule transmission) (Fukatsu and Hosokawa, 2002; Hosokawa et al., 2006), and infection through jelly-like secretions deposited by females in Urostylididae (jelly transmission) (Kaiwa et al., 2014). In both cases, bacteria are acquired by their offspring upon consumption of symbiont-containing materials shortly after their hatching (Salem et al., 2015). Although vast numbers of intra/extracellular symbionts are transmitted via mothers to their offspring, some studies are reporting paternal transmission of symbionts to their offspring. The study conducted by Watanabe et al. (2014) has demonstrated vertical transmission routes; not only maternal ovarial passage but also paternal transmission via intrasperm passage of the facultative endocellular bacterial symbiont, Rickettsia, in green rice , Nephotettix cincticeps (Uhler) (Hemiptera: Cicadellidae) (Watanabe et al., 2014).

Mixed modes of transmissions The mode of transmission does not seem to have clear distinctions between vertical or horizontal routes in some insect species. Hence the insects can use both strategies (Ebert, 2013). By combining these two modes of transmission strategies, symbionts can maximize the range of

50 ecological conditions where the symbionts can persist and increase the rate of successful infection by stretching host density achieved by horizontal route of transmission (Ebert, 2013). Previous studies by Moran and Dubar (2006) demonstrated that beneficial symbionts of aphids could be transmitted vertically as well as horizontally. Facultative symbionts in aphids are transmitted maternally and may also be sexually transmitted. Male borne symbionts are acquired by female conspecifics during sexual reproduction and passed down to their offspring (Moran and Dubar 2006). Another example of mixed-mode transmission was observed between Burkholderia and Oriental chinch bug, Cavelerius saccharivorous (Heteroptera: Blissidae). In short, symbionts are environmentally and vertically transmitted (Itoh et al., 2014).

Bark beetle and microbe interactions species (Coleoptera: Curculionidae: Scolytinae) that colonize living trees mainly belong to Dendroctonus sp. and are considered as the most destructive forest pests in North America. Bark beettles cause a substantial economic loss in the forest industry; thus, they are one of the best-studied insect species (Coulson et al., 1982; Popa et al., 2012). In order for these wood-dwelling bark beetles to overcome tree defenses, establish their gallery, and survive within the host trees, bark beetles developed a range of associations with varieties of microorganisms, including fungi, bacteria, viruses, and algae (Hofstertter et al., 2015). These microbial symbionts play pivotal roles in the bark beetle development and survival, affecting their co-evolution, coadaptation, and speciation of bark beetles (Aanen et al., 2009; Durand et al, 201). The bacterial symbionts of the bark beetles render essential functions to the beetles, while creating complex tripartite interactions between bacteria, fungi, and beetles (Popa et al., 2013; Hofstertter et al., 2015). Scott et al. (2008) have demonstrated the Southern pine beetle, Dendroctonus frontalis, carries bacterial symbionts that protect the beneficial fungal symbiont, Entomocorticium sp., against its antagonistic fungal symbiont, Ophiostoma minus. Aside from fungal interactions, the bacteria itself plays critical roles in bark beetle biology (Popa et al., 2013). Facultative symbionts such as Serratia and Erwinia associated with the gut of Dendroctonus valens are known to facilitate nitrogen fixation and carbohydrate fermentation (Morales-Jimenez et al., 2009). Other vital functions mitigated by bacterial symbionts in the bark beetles are provisioning vital nutrients including amino acids, nitrogen and vitamins (Miao et al., 2003; Douglas, 2009), production of pheromones (Brand et al., 1975; Hunt and Borden,

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1990), detoxification of plant defense compounds (Gibson et al., 2010), digestion of food materials by breaking down cellulose (Morales-Jimenez et al., 2013), and metabolizing phenolic compounds found in trees (Adams et al., 2011: Adams et al., 2013). These associations with bark beetle species and bacterial symbionts can vary from obligate mutualist to commensal to parasitic (Kikuchi, 2009).

Coffee berry borer and microbe interactions: Coffee as an improbable host for insects and the challenge imposed by caffeine for herbivorous insects According to Waller et al. (2007), more than 3,000 insect and mite species have associations with coffee; however, the Coffee Berry Borer (CBB), Hypothenemus hampei, is the only insect species able to reproduce and complete their development and life cycles inside of the coffee endosperm upon their consumption (Vega et al., 2015). Caffeine (1,3,7 trimethylxanthine) is the purine alkaloid in coffee endosperms, comprising approximately 1.0 % of dry weight of C. arabica and 1.7 % of C. canephora (Ashihara et al., 1999). Due to its high toxicity, caffeine is used as a defense mechanism by the plants to protect themselves from insect attacks. It is further speculated that the insects capable of feeding on the plants containing secondary metabolites possess specialized mechanisms that detoxify and overcome the plant defensive mechanisms (Vega et al., 2015).

Coffee berry borer associations with bacterial symbionts Recent studies provided strong evidence of coffee berry borer-bacterial interactions and the mechanisms, revealing the coffee berry borer has a range of associations with its bacterial symbionts for the survival (Ceja-Navarro et al., 2015), and has possibly affected its evolution and adaptations as seen in the related bark beetle subfamilies (Acuña et al., 2012). A previous study conducted by Vega et al. (2002) positively identified coffee berry borer associations with the maternally inherited intracellular proteobacterium, Wolbachia, in many countries including Benin, Brazil, Colombia, Ecuador, El Salvador, Honduras, India, Kenya, Mexico, Nicaragua, and Uganda. Marino et al. (2017) reported Wolbachia could be responsible for the reproductive success of the coffee berry borer; when coffee berry borers were treated with tetracycline, coffee berry borer population growth rate (λ), net reproductive rate (R0), and mean generation time (T) were negatively affected.

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One of the most important findings could be the discovery of the recombinant protein HhMAN1, a digestive enzyme of mannanase gene in the coffee berry borer’s genome. HhMAN1 is responsible for hydrolyzing coffee berry galactomannan that is speculated as food of the coffee berry borer. This specific gene was similar to a Bacillus circulans protein identified from a cDNA of coffee berry borer and also a xylanase, highly similar to Streptomyces bingchengensis that was isolated from a coffee berry borer genomic clone. This suggests that there is a likely pathway of horizontal gene transfer of the HhMAN1 gene of bacteria (Bacillus sp.) to the insect. However, the gene was not detected in the closely related Tropical Nut Borer, H. obscurus, that does not possess that gene nor is able to colonize coffee endosperms (Acuña et al., 2012). The compelling evidence suggests that the coffee berry borer has had long interactions with its bacterial symbionts and has even incorporated the bacterial genome into its own genome over years with the bacterial symbionts. Vega et al. (2015) reported the first draft genome of coffee berry borer in 2015. This research provided information that reveals several biological functions in the coffee berry borer’s genome. According to one of its findings, there are at least ten candidates of Horizontal Gene Transfer (HGT) integrated into the coffee berry borer’s genome. These genes were found to be more closely related to bacteria than eukaryotic genes based on the phylogenetic evidence. Additionally, caffeine demethylation homologous gene (ndm), responsible for caffeine detoxification in coffee berry borer, was not found in the coffee berry borer genome, suggesting the function of caffeine demethylations has not been transferred to the insects from bacteria via horizontal gene transfer, unlike mannanase; thus, this mechanism remains in the bacterial community (Vega et al., 2015),

Coffee berry borer association with caffeine degrading bacteria The study by Ceja-Navarro et al. (2015) demonstrated an important role of bacterial symbionts in the coffee berry borer. This study successfully isolated core microbiomes of coffee berry borers collected from multiple coffee-producing countries, isolated 13 bacterial species from coffee berry borer alimentary canals that are able to degrade caffeine and identified the presence of the methylxanthine N-demethylase A (ndmA), coding for alpha-subunit of caffeine demethylase, expressed in Pseudomonas fulva. When bacteria were removed from the coffee berry borer digestive system by administering combinations of antibiotics, not only did coffee berry borers lose the ability to degrade caffeine, but it also affected the insects’ reproductive

53 fitness. Pseudomonas fulva was capable of subsisting on caffeine as a source of carbon and nitrogen subsequently after the degradation of the caffeine in a laboratory setting. In this experiment, CBBcdm was used to amplify the ndmA regions of the bacterial genome by qPCR and yielded a 400 bp amplicon with P. fulva strain. This evidence strongly suggests that the coffee berry borer relies on the caffeine detoxification of the bacterial symbionts in their midgut, allowing the coffee berry borer to live in caffeine rich conditions that are unfavorable for the insect species lacking this mechanism (Ceja-Navarro et al., 2015; Vega et al., 2015).

Objective If bacterial symbionts play essential roles in host-insect biology and are required for their survival and reproduction, the symbionts could be transmitted vertically from their parent(s) to offspring. It is also a possibility that obligate bacterial symbionts can be disseminated via mixed modes of transmission to ensure positive acquisition by their offspring as seen in pea aphids and stink bugs. The coffee berry borer is the only insect species that can live in, feed on, and ultimately complete their life cycle in caffeine-rich seeds of coffee. In order to overcome toxic secondary metabolites, coffee berry borers adapted a unique mechanism to detoxify caffeine by harboring symbiotic bacterial species that are capable of breaking down caffeine (Ceja-Navarro et al., 2015). A previous study successfully isolated thirteen bacterial species with caffeine-degrading ability from coffee berry borers’ digestive tracts and identified methylxanthine N-demethylase (ndmA) in one of the bacteria, P. fulva (Ceja-Navarro et al., 2015). However, the transmission/acquisition mechanism of caffeine degrading bacteria by coffee berry borers is understudied and still unknown. In order to get a better understanding of how these symbionts are transmitted, it is crucial to explore the association of bacteria within the coffee berry borer’s eggs. Hence, our objective in this chapter was to localize caffeine demethylating bacteria, harboring caffeine demethylation gene (ndmA), within the coffee berry borer’s eggs using the fluorescent in situ hybridization (FISH) technique.

Materials and Methods Sample collection Green coffee fruits, approximately 160 days after flowering, with holes on the floral disk, indicating infestation with coffee berry borer were obtained from 2 locations (upper and lower

54 fields) of Maunawili stations at Hawaii Agriculture Research Center on the island of Oahu, Hawaii. The sampling was conducted during October 2019, November 2019, and March 2020. Two to three-hundred green coffee fruits were hand-picked from 30 trees from each field, double bagged in two Ziplock bags, stored in a cooler box, and then transferred to the University of Hawaii (St. John, Room 303). Green coffee fruits were cut in half without disrupting the entry holes of coffee berry borer by a scapula to observe the presence of eggs in the galleries and to remove female coffee berry borer from the coffee seeds. Once observed in the seeds, eggs were picked by a sterile small painting brush and transferred into sterilized 1.5 ml Eppendorf tubes. One hundred-fifty eggs were obtained from green coffee fruits with 60 arbitrarily selected eggs transferred to Carnoy’s solution and used immediately. The remaining eggs were placed in 100% acetone in 1.5 ml Eppendorf tube and stored at -20 ºC for later use.

Characterization of autofluorescence in coffee berry borer eggs and selection of fluorescent probes For the determination of the strength of autofluorescence in egg specimens as well as to determine the emission and excitation spectra of the outer surface of the eggs, yolks, and larvae (in advanced eggs), a trial run without fluorescent probes was conducted. Ten eggs collected from Maunawili, Oahu were dissected from green coffee fruits following the procedure described and mounted on glass slides using the same procedure described for confocal microscopy but without probes or fixation reagents. One to 3 drops of Slowfade-Antifade (without DAPI) (Vector Laboratories, Inc, Burlingame, CA) was applied between two silicon strips. The sample slides were brought to the University of Hawaii, Biological Microscopy laboratory, and viewed under a TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) (See Image acquisition and adjustment). The sample coffee berry borer eggs were screened across a wide range of wavelengths (405 nm – 631 nm excitation maxima) for autofluorescence in order to differentiate from background fluorescence noise. This result was used to determine the range of emission/excitation spectra for fluorescent probes for FISH.

Oligonucleotide and fluorescent labelled probes Based on results , the Alexa Fluor 488 fluorophore and Alexa Fluor 647 fluorophore were

55 selected. Oligonucleotides probes 515F (5’-GTGCCAGCAGCCGCGGTAA-3’) (Itoh et al., 2014), universally targeting bacterial 16SrRNA gene, and CBBcdmF (5’- TGGCATCCCGTWTGTACYGT-3’) (Ceja-Navarro, et al.,2015), specifically targeting the caffeine demethylation gene (ndmA), were labeled with fluorescent probes (Invitrogen, Custom Standard Oligos, https://www.thermofisher.com/order/custom-standard-oligo) with a synthesis scale of 50 nmole, purification of HPLC, 100µM in water with 5’ modification with selected Alexa labels. Oligonucleotide probe 515F was labeled with Alexa Fluor® 488 fluorophore (Invitrogen, Carlsbad, CA) and CBBcdmF was labelled with the Alexa Fluor 647-infrared fluorescent probe (Invitrogen, Carlsbad, CA). Nuclei of the host cells were counterstained with Vectasheild, slow-fade antifade solution with DAPI (Vector Laboratories, Inc, Burlingame, CA).

Fluorescent in situ Hybridization (FISH) Sample fixation and bleaching: quenching of autofluorescence Sixty coffee berry borer eggs were soaked in Carnoy’s solution (6 volumes of 100% EtOH, 3 volumes of chloroform, and 1 volume of glacial acetic acid) at 25 ºC on a rotator plate with gentle agitation for 12 hours. After 12 hours, sample tissues were soaked in 80% EtOH and washed for 10 minutes with gently agitation twice. The eggs were transferred into a solution of

1:4 30% H2O2 to 100% EtOH in a 1ml Eppendorf tube and washed with gentle agitation for 10 minutes. This process was repeated twice. The solution was changed each time by using a 1 ml pipet. After the sample tissues were washed, the eggs were placed in fresh 1:4 ratio 30% H2O2 to

100% EtOH and then soaked in the solution for 2 days for bleaching. The solution was replaced every day for 2 days.

In situ hybridization The eggs were soaked in 1 ml hybridization buffer (20 mM Tris-HCL (pH 8.0), 0.9 M NaCl. 0.01% sodium dodecyl sulfate (SDS), 30% formamide) in a 1ml Eppendorf tube and washed for 10 minutes with gentle agitation on the rotator plate at 25 ºC prior to the hybridization. This process was repeated three times, replacing the solutions each time. After this process, 20 eggs each for three treatments of 1) CBBcdm F oligonucleotide sequence + Alexa 647, 2) Universal EUBacterial primer + Alexa Fluor 488, and 3) Control, were placed in the 1.5 ml Eppendorf tubes. A 0.5 ml aliquot of hybridization buffer containing 5 µl (10µM) of

56 fluorescent probes + oligonucleotide sequence was added to each tube. The control was prepared with 0.5 ml of hybridization buffer without any probes. The samples were incubated for 12 hours at 25 ºC with the gentle agitation on the rotator plate. In order to avoid photobleaching of fluorophores in the Alexa probes, addition of the Alexa probe into the hybridization buffer and hybridization process were conducted in a darkroom with minimum light exposure. The 1.5 ml Eppendorf tubes containing samples with hybridization buffer and probes were wrapped in the small piece of aluminum foil during the 12 hour incubation period. After Incubation, the samples were washed in 1 ml of phosphate buffer saline with 0.3% Triton X-100 (PBSTx) for 5 minutes on the rotator plate with gentle agitation at 25 ºC for three times, replacing the reagent for each time.

Slide preparation for confocal microscopy In order to avoid photobleaching, this process was conducted in a darkroom with minimum light exposure. Two strips of silicone (0.8 cm x 0.2 cm x 0.01 mm) were placed on the center of each glass slide with approximately 3 mm distance from each other to support the edge of the cover glass. One to 3 drops of Vectashield, slow-fade antifade solution with 4′,6- diamidino-2-phenylindole (DAPI) (Vector Laboratories, Inc, Burlingame, CA) were placed on the channel between the silicon strips. Twenty sample eggs/treatment were gently scooped from the PBSTx solution with a sterile small painting brush then placed gently onto the Slowfade- Antifade with DAPI, avoiding introducing air bubbles. Coverslips were placed on top of the silicone strips to embed the tissue without introducing air bubbles.

Image acquisition and adjustment The sample slides were placed in a black 25 x 75 mm microscope slide box (Thermo Fisher Scientific, Kapolei, Hawaii) and covered with aluminum foil. The box was transferred to the Biological Electron Microscope Facility at the University of Hawaii at Manoa. Twenty coffee berry borer eggs/treatments were examined with a TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) equipped with a white light laser plus a 405 nm UV laser, mounted on a Leica DF6 CFS upright microscope. Observations were made with HC Plan Flotar 10 x/0.3 (no-immersion), HC Plan Apo 20 x/0.75

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IMM (multi-immersion), and HC Plan Apo 63 x/1.3 NA (glycerol immersion) lens (Leica Microsystems, Inc, Buffalo Grove, IL). DAPI was excited with a 405 nm laser and the emission was detected at 410 nm to 494 nm. FISH probe Universal EUBacterial primer + Alexa Fluor 488 was excited with an emission maximum of 519 nm and detected at 504 nm to 632 nm with a HyD SMD4 detector. Alexa Fluor 647 was excited at 665 nm with a detected emission at 654 nm to 776 nm with HYD 5 detector. DAPI, Alexa Fluor 488, and Alexa Fluor 647 were excited sequentially to exclude crosstalk. Z- stacks were obtained by collecting optical slices at 13.4 micrometer intervals. Confocal fluorescence images were acquired with Leica LAS X navigator software (Leica Microsystems, Inc, Buffalo Grove, IL). Three eggs/treatment without any sign of the physical damage from sample fixation were used for the photography.

Negative controls (3) 1. No probe controls Negative controls (no probe control) with no fluorescent probes and target oligonucleotide sequences were used to discriminate between autofluorescence of the specimen and the fluorescent probe signals (Engle et al., 2013). Approximately 20 eggs containing no fluorescent probes were brought each time of the experiment to determine the adequacy of specimen bleaching and set range of excitation spectrum prior to the screening of eggs hybridized with oligonucleotide probes conjugated with fluorescent tags. The control eggs were prepared by following the procedure described in the Sample fixation and bleaching, and In situ hybridization section, then a Slow-fade antifade solution with DAPI to mimic the experimental setup following Slide preparation for confocal microscopy. Image acquisition was done by following the same procedure described in Image acquisition and adjustment.

2. Competitive suppression control In order to ensure sequence-specific, competitive suppression of hybridization signals, competitive suppression control (Engle et al., 2013) was conducted with 50 times more volume of the same unlabeled oligonucleotide with labeled oligonucleotide. 10 coffee berry borer eggs/treatment were fixed by following the same protocol, Sample fixation and bleaching, then washed in the hybridization buffer for 10 minutes with gentle agitation on the rotator plate at 25

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ºC prior to the hybridization. This process was repeated three times with replacing the solutions for each time. After this process, 10 eggs each for three treatments of 1) Unlabeled CBBcdm F (250 μl) and labeled CBBcdmF with Alexa 647 (5 μl) , 2) Unlabeled Universal EUBacterial primer (250 μl) and labeled Universal EUBacterial primer with Alexa Fluor 488 (5 μl), 3) Control, were placed in the 1.5 ml Eppendorf tubes. a 0.5 ml of hybridization buffer containing 5 µl (10 µM) of Fluorescent probes conjugated were added to each tube. The control was prepared in the 0.5 ml of hybridization buffer without adding any probes. The samples were incubated for 12 hours at 25 ºC with the gentle agitation on the rotator plate. In order to avoid photobleaching of fluorophores in the Alexa probes, adding the Alexa probe into the Hybridization buffer, and hybridization process were done in a darkroom with minimum light exposure. The tubes containing samples with hybridization buffer and fluorescent probes were wrapped in the aluminum foils during the 12 hours incubation period. After the incubation, the samples were washed in 1 ml of Phosphate buffer saline with 0.3% Triton X-100 (PBSTx) for 5 minutes on the rotator plate with gentle agitation at 25 ºC for three times, replacing the reagent for each time. The sample slides were prepared following Slide preparation for confocal microscopy then, images were obtained by following the same protocol described in Image acquisition and adjustment.

3. Fluorescent in situ hybridization of tropical nut borer, (Hypothenemus obscurus) In order to further investigate the legitimacy of signals detected from methylxanthine N- demethylase (ndmA) in the coffee berry borer samples, eggs of the tropical nut borer, Hypothenemus obscurus, a closely related beetle species to coffee berry borer, were hybridized with CBBcdmF labeled with Alexa Fluor 647 fluorophore. Approximately 1 kg of macadamia nuts with holes, indicating the infestation of Tropical nut borers, were collected from University of Hawaii, Waimanalo research station, Oahu, by a personal from Entomology department of University of Hawaii at Manoa, and transferred to University of Hawaii laboratory (St. John, rm #303) in a Ziploc bag. Hard outer shells of the macadamia nuts were broken by smashing the shells by a hummer to obtain the nuts inside of the shells. The nuts were examined for the presence of the adult Tropical nut borers, then carefully shaved with sterilized scapula to search for the eggs in the insect galleries. Once the eggs were found in the galleries, the eggs were removed carefully into a 1 ml Eppendorf tube. Ten eggs were obtained and processed for sample fixation by following the same procedure mentioned in Sample fixation and bleaching followed

59 by In situ hybridization. Slide preparation was done by following Slide preparation for confocal microscopy. Ten coffee berry borer eggs were hybridized with the same Oligonucleotide and fluorescent probe by following the same protocol described above to be used as a positive control. The same set up of the laser lines and detectors as with coffee berry borer control was used to observe the signals at 647 nm in the TNB eggs. DAPI (405 nm) was used as an insect nuclear counterstain (Laser set up in detail in Appendix Figures 10-12). The sample slides were prepared following Slide preparation for confocal microscopy then, images were obtained by following the same protocol described in Image acquisition and adjustment.

PCR amplification of ndmA gene in bacteria isolated from coffee berry borer eggs Bacterial Isolation Twenty eggs obtained from Maunawili, Hawaii, by following the same protocol described in the section, Sample collection, were collected into a 1.5 ml Eppendorf tube, sterilized in 70% EtOH solution for 30 seconds with gentle agitation, then rinsed with sterilized

H2O twice for 30 seconds each. The coffee berry borer eggs were thoroughly macerated in 1 ml sterilized H2O. A 100 µl of macerated solution was plated on mineral media containing the following: 9.5 mM KH2PO4, 4.8 mM MgSO4, 0.1 mM CaCl2, 0.8 mM Na2HPO4, and 20 g/L bacto agar supplemented with 1.5 g/L caffeine (Ceja-Navarro et al., 2015). The bacterial cultures were incubated at 28ºC for 5 days to allow the bacteria to grow. After incubation, bacteria growth was observed on the mineral media. Single colonies were isolated based on their morphological characteristics, streaked on Tryptic Soy Agar (TSA) media containing 17 g tryptone, 3 g soytone, 2.5 g dextrose, 5 g NaCl, 2.5 g K2HPO4, and 15 g/L agar, and incubated at 28ºC for 2 days. The single colony streaking was repeated four times on TSA agar in order to obtain pure bacterial cultures. After several days of growth, the bacteria were preserved in 25% glycerol solution and frozen at -80°C for long term storage.

PCR amplification of ndmA gene The DNA of bacteria isolated from coffee berry borer eggs was extracted by using Qiagen, DNeasy, Blood and Tissue kits, following manufactures’ instruction (Qiagen, Germantown, MD), and ndmA regions of bacterial genome were amplified with CBBcdm ’ ’ forward and reverse primer set (CBBcdmF, 5 -TGGCATCCCGTWTGTACYGT-3 ; CBBcdmR,

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’ ’ 5 -CTTGKATAACRATTCG CAACC-3 ) (Ceja-Navarro, et al., 2015). DNA extracted from the coffee berry borer’s eggs was used as positive control due to the presence of a bacterial genome associated with the eggs. A 1 μl drop of H2O was added to the master mix as a negative control. The PCR reactions were prepared with 1 µl of DNA as template, 10 μl of Gotaq Green master mix (Promega Corporation, Madison, WI), 1 μl of each primer, and 7 μl of H2O to a final volume of 20 μl. The PCR conditions were 95°C for 3 min, followed by 45 cycles of 95°C for 10 s, 57°C for 30 s and 72°C for 30 s, and a final extension step of 72°C for 3 min. The electrophoresis was conducted at 100 volts for 30 minutes. Positive reactions were defined with the presence of a ~400 bp band on the 1.5 % agarose gel containing 5 μl of ethidium bromide gel stain (Invitrogen, Carlsbad, CA).

Identification of caffeine degrading bacteria to genus-level: 16s rRNA gene amplification and sequencing. The bacteria isolated from eggs of coffee berry borer were identified to genus-level by targeting 16SrRNA regions in Sanger sequencing (Aoki, 2021)

Results Characterization of autofluorescence The outer layers of coffee berry borer eggs, chorions, generated autofluorescence in a wide range of spectra (emission: 409 nm to 639 nm (Fig. 1). The cross-sectional views demonstrated that the chorions, yolks, and developing embryos generate strong autofluorescence in the range of red (564-629 nm) (Fig. 2-5). With this information, fluorophores with the range of 592-629 nm (red) should be avoided considering quenching autofluorescence in the outer layer of the egg should be the main focus in order to examine inner contents of the eggs. Alexa Fluor (488 nm Emission maxima, 519 nm, green) and Alexa Fluor 647 (EM maxima 665 nm, infrared) were selected for eubacterial signals and caffeine demethylating bacterial signals.

Negative controls (3) No probe controls No autofluorescence was detected from the egg chorions, yolks, or developing embryos inside of the eggs from excitation at 488 nm (Fig. 6, Channel 2) and 647 nm (Figure. 6, Channel

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3) without the hybridization with the fluorescent-labels and oligonucleotide, indicating that specimen bleaching successfully quenched autofluorescence from the coffee berry borer eggs at 488 nm and 647 nm (Fig. 6). Since DAPI was administered in the mounting media for this study in order to use the same mounting media for control and test subjects, a signal was detected at 406 nm excitation illuminating insect nuclei (Fig. 6, Channel 1).

Competitive suppression control By hybridizing the sample tissues with 50 times more volume (250 μl) of CBBcdmF unlabeled oligonucleotide followed by hybridization of the same tissues with CBBcdmF conjugated with Alexa Fluor probe (5 μl), the signal at the excitation of 647 nm (Fig. 7, Channel 2) was successfully suppressed. For the universal eubacterial primer, the samples tissues were hybridized with 50 times more volume of unlabeled oligonucleotide sequence (250 μl), universal eubacterial forward primer sequence, 515 F, followed by of the same oligonucleotide sequence conjugated with Alexa 488 nm (5μ l). The signals at 488 nm were also successfully suppressed (Fig. 8: Negative control 2-2). This result ensured the oligonucleotide sequences used in this experiment were successfully bound to the corresponding sequences found in the coffee berry borer eggs. Therefore, it is concluded that the signals detected from excitation at 488 nm and 647 nm were true signals.

Fluorescent in situ hybridization of Tropical nut borer, (Hypothenemus obscurus) In order to further investigate the legitimacy of methylxanthine N-demethylase (ndmA) signals in the coffee berry borer samples, the eggs of the tropical nut borer, a closely related beetle species, were hybridized with CBBcdmF conjugated with Alexa Fluor 647 probe. The signal from CBBcdmF was detected at 647 nm with emission of 654-776 nm in the coffee berry borer positive control eggs hybridized with CBBcdm F conjugated with Alexa Fluor 647 probe (Fig. 10, Channel 2 in magenta, Fig. 11, Channel 2 in red). The insect nuclei were also successfully detected at excitations of 410-494 nm (Fig. 10, Channel 1 in cyan, Fig. 11, Channel 1 in blue). This same setting of the laser lines (excitation at Channel 1 at 405 nm and Channel 2 at 647 nm), and detectors Channel 1: HyD SMD2 (Emission: 410-494 nm) and Channel 2: HyD5 (Emission: 654-776 nm) was used to observe the signals at 647 nm in the tropical nut borer eggs. There were no caffeine demethylase signals detected in the tropical nut borer eggs at

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647 nm (Fig. 12, Channel 2, Fig. 13, Channel 2 for CBBcdmF).

Localization of bacterial symbionts in coffee berry borer eggs: Fluorescent in situ hybridization (FISH) CBBcdmF with Alexa Fluor 647 signals was detected at 647 nm with emission from 654-776 nm (Fig. 14, Channel 2) in magenta color visualizing the location of the caffeine demethylating gene (ndmA) with CBBcdmF oligonucleotide sequence in the coffee berry borer egg (Fig.14, Channel 2). DAPI signal was detected at 488 nm in the middle of the egg with emission of 410-494 nm (Fig. 14, Channel 1) inside of the egg chorion in cyan, visualizing the location of insect tissues (Fig. 13, Channel 1). The Z-stack slices provide cross-sectional views of the sample egg with a laser scanning of the sample from the top of the egg close to the cover glass (Fig. 15, Pictures. 1, 2, 3, and 4) toward the lateral side of the eggs (Fig.15, Pictures 5 and 6), ensured the signals detected were from inside of the egg and not from the outer surface of egg chorion (Fig. 15). Universal bacterial primers with Alexa Fluor 488 signals were detected at 488 nm with emission of 504-632 nm from inside the egg (Fig. 16, Channel 2). DAPI was detected at 405 nm with emission of 410-494 nm (Fig. 16, Channel 1). The same egg used for Fig. 16 was pressed between a coverslip and glass slide in order to get the signal from a narrower focal plane with better resolutions of 20x (Figs. 17 and 18) and 64x (Fig. 19). Image was acquired by applying the same emission and excitation spectrum with Fig. 17. Universal bacterial primer conjugated with Alexa Fluor 488 signals were detected at 488 nm with emission of 504-632 nm from inside the egg in green (Figs. 17, 18, and 19, Ch2). DAPI was detected at 405 nm with emissions of 410-494 nm in blue (Figs. 17, 18, and 19, Channel 1).

Amplification of ndmA gene in bacteria isolated from Coffee berry borer eggs by PCR PCR amplification of ndmA gene region successfully yielded ~400 bp amplicon of 11) a positive control, DNA extracted from coffee berry borer eggs, and 4) one of the bacteria isolated from the eggs (Fig. 20). This bacterium was later identified as a Pseudomonas sp. (NCBI GenBank accession#: KF913767.1) based on the 16S rRNA region of the bacterial genome with 99.69% pairwise homology.

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Discussion Fluorescent in situ hybridization assay of coffee berry borer eggs has successfully demonstrated the presence of the caffeine demethylating bacteria within the eggs by targeting methylxanthine N-demethylase (ndmA). These findings strongly suggest the caffeine degrading bacteria, essential for the coffee berry borer’s survival, is present in the eggs in the early stages of oogenesis and may be transmitted via vertical transmission from mother to the offspring. The significance of this mechanism in the coffee berry borer’s biology is the larvae could be already equipped with the bacterial symbionts to degrade caffeine upon hatching and ready for breaking down the caffeine inside of the coffee seeds rather than acquiring the bacteria with this function from the environment after hatching. The amplification of caffeine demethylation gene, from bacteria isolated from coffee berry borer eggs have shown that the bacteria found in the coffee berry borer eggs were Pseudomonas species. Approximately 71 bacterial strains in 27 genera, including Pseudomonas species, from various habitats were previously reported to be involved in caffeine degradation (Vega et al., 2021). A majority of these bacteria belong to Pseudomonas species (Summers et al., 2015). The Pseudomonas species with caffeine degradation associated with coffee berry borer reported thus far are P. fulva, P. fluorescences, P. Monteilii, P. purafulva, P. plecoglossicida, P. putida, P. and P. aeruginosa (Ceja-Navarro et al., 2015; Vega et al., 2021). Although a few reports (Mario et al., 2018; Vega et al., 2021) have indicated bacterial associations with coffee berry borer eggs, this could be the first report that successfully demonstrated caffeine degrading bacterial presence within the coffee berry borer’s eggs, implicating the vertical transmission mode of essential bacteria in the early stages of the coffee berry borer. However, acquisition mechanisms of the bacteria into the beetle eggs are unknown. In an unreported, preliminary experiment, the bacteria were present in the fertilized eggs, but no bacteria were detected in unfertilized eggs (Aoki, unpublished). Based on this information, it is hypothesized that the bacterial route of transmission could be via beetle spermatozoa with entry through the chorions via micropyles. Scanning Electron Microscopy of the micropyle of fertilized eggs and staining of the micropyle in both fertilized and unfertilized eggs with Coomassie Brilliant Blue G, a nonspecific protein-staining dye (Yanagimachi et al., 2013) failed to detect the micropyles on the beetle eggs (unpublished data). The minute nature of the coffee berry borer eggs, approximately 500 to 600 μm, made these observations even more difficult.

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Another possibility allowing bacterial entry to the eggs would be the smearing of the egg surface containing bacterial symbionts by the female coffee berry borer after egg deposition. As it was discussed in the earlier chapter, this transmission mode is the most commonly described extracellular route of bacterial transmission found in many insects, including Diptera, Coleoptera, Hymenoptera, and Hemiptera (Salem et al., 2015). The grooming behavior involving oral secretions over the eggs was also observed in coffee berry borer by Vega et al. (2017) as well as in the ambrosia beetles in the tribe Xyleborini (Kingsolver and Norris 1977; Beidermann and Taborsky 2011; Vega et al., 2017). Vega et al. (2021) also reported that the discovery of several Pseudomonas species from the head of female coffee berry borer. This evidence would suggest that the bacteria enter the eggs via the oral secretion deposited by the female borer. However, the possible route of entry via aeropyles (air passages in the chorion for the oxygen exchange), or micropyles, remains unclear. Here, we have demonstrated that an essential bacterial symbiont of the coffee berry borer may be transmitted via vertical mode of transmission. On the other hand, the transmission of the bacterium could not be limited to a particular mode of transmission. There could be a possibility that the bacterium may be acquired by insects via a mixed mode of transmission, both vertical and horizontal, depending on their life stages. The insects could be retaining the bacterium for caffeine degradation upon hatching (vertical transmission) as well as acquiring the bacterium during their development from the environment (horizontal transmission) to regain or supplement the bacterium later on in their life. In fact, the bacterial species capable of degrading caffeine exist in the environment as free-living were summarized in Summers et al. (2015), as well as endophytic bacteria, exist in the coffee plants including P. putida, were described in Vega et al. (2005). This could be a plausible explanation of bacterial acquisition by insects based on the information that insects typically shed the lining of the foregut and hindgut and end up eliminating most or all gut contents each molt (Hammer and Moran 2019; Vega et al., 2021). Bacteria previously present in the insect gut would be purged during molting and then re- acquired from the environment (Vega et al., 2021).

Conclusion The fluorescent in situ hybridization of coffee berry borer eggs successfully detected the presence of the caffeine degrading bacteria within the insect eggs by targeting methylxanthine N-

65 demethylase (ndmA). When the regions of ndmA were amplified using CBBcdm primer sets in the bacteria isolated from coffee berry borer eggs, Pseudomonas species yielded a positive band (~400 bp). The fluorescent in situ hybridization of universal eubacteria has also provided evidence of possible secondary bacterial infection within the coffee berry borer eggs. Although the identity of this bacterium is still unknown, the bacterial colonization was observed on the lateral side of the developing larvae. This could be an implication of possible dual bacterial infections in the coffee berry borer eggs. Although fluorescent in situ hybridization of ndmA gene and eubacterial primer oligonucleotide sequence have revealed that the presence of the bacteria within the insect egg, implicating the possible route of vertical transmission of bacteria via mother to offspring, the mechanism of this bacteria entry to the eggs remains unknown.

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Figures

Figure 1: Autofluorescence test of outer layer of un-fixed coffee berry borer egg: A sample of un-fixed coffee berry borer egg was screened with a wide range of laser lines to determine range of autofluorescence emitted from outer layer of eggs by TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a 20 x/0.75 IMM (multi-immersion) lens. Following laser lines were used to excite the samples: 1) Ch1 (Top left): 405 nm, Blue, 2) Ch2 (Top middle): 488 nm, Green, 3) Ch3 (Top right): 590 nm, Red, 4) Ch4 (Bottom left): 631nm, Cyan, and 5) Ch5 (Bottom middle): Combined view of all windows. The emission spectra were detected by the detectors as followed: 1) Ch1: PMT1: (409 nm-485 nm), 2) Ch2: HyD SMD2 (496 nm-556 nm), 3) Ch3: HyD SMD4 (565 nm-587 nm), and 4) Ch4: HyD (639 nm-786 nm)

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Figure 2: Autofluorescence test of an unfixed coffee berry borer egg showing strong autofluorescent signals in the chorion and developing larvae in the egg at the emission spectra of 564 nm -629 nm (Channel 3 and 4) in red when screened with a wide range of laser lines to determine range of autofluorescence emitted from the chorion and developing larvae of coffee berry borer eggs. The sample egg was observed under TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Apo 20 x/0.75 IMM (multi-immersion) lens. Cross sectional view of the eggs was achieved by the laser reaching inside of the eggs on the top one-third depth of the egg from a coverslip. Following laser lines were used to excite the samples: 1) Ch1 (Top left): 405 nm, Blue, 2) Ch2 (Top middle): 488 nm, Green, 3) Ch3 (Top right): 561 nm, Red, 4) Ch4 (Bottom left): 590 nm, Red, 5) CH5 (Bottom middle): 631 nm, Infrared (in Cyan), and 6) Ch6 (Bottom left): Combined view of all windows. The emission spectra were detected by the detectors and the range of emissions as followed: 1) Ch1: PMT1: (409 nm-485 nm), 2) Ch2: HyD SMD2 (496 nm-556 nm), 3) Ch 3: PMT 3 (564 nm-590 nm, 3) Ch3: HyD SMD4 (592 nm-629 nm), and 4) Ch4: HyD (639 nm-786 nm).

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Figure 3: Autofluorescence test of un-fixed coffee berry borer egg showing strong autofluorescent signals in the chorion and developing larvae in the egg at the emission spectra of 564 nm -629 nm (Channel 3 and 4) in red when an unfixed coffee berry borer egg was screened to determine range of autofluorescence from the chorion and a developing larvae of coffee berry borer egg. The sample egg was observed under TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Apo 20 x/0.75 IMM (multi-immersion) lens. Cross sectional view of the eggs was achieved by the laser reaching inside of the eggs on the middle from a cover slip Following laser lines were used to excite the samples: 1) Ch1 (Top left): 405 nm, Blue, 2) Ch2 (Top middle): 488 nm, Green, 3) Ch3 (Top right): 561 nm, Red, 4) Ch4 (Bottom left): 590 nm, Red, 5) CH5 (Bottom middle): 631 nm, Infrared (in Cyan), and 6) Ch6 (Bottom left): Combined view of all windows. The emission spectra were detected by the detectors and the range of emissions as followed: 1) Ch1: PMT1: (409 nm-485 nm), 2) Ch2: HyD SMD2 (496 nm-556 nm), 3) Ch 3: PMT 3 (564 nm-590 nm, 3) Ch3: HyD SMD4 (592 nm -629 nm), and 4) Ch4: HyD (639 nm-786 nm).

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Figure 4: Autofluorescence test of un-fixed coffee berry borer egg showing strong autofluorescent signals at the emission spectra of 564 nm -629 nm (Channel 3 and 4) in red when an unfixed coffee berry borer egg was screened to determine range of autofluorescence from the chorion and a developing larvae of coffee berry borer egg. The sample egg was observed under TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Apo 20 x/0.75 IMM (multi-immersion) lens. Cross sectional view of the eggs was achieved by the laser reaching inside of the eggs towards the bottom of the egg from a coverslip. Following laser lines were used to excite the samples: 1) Ch1 (Top left): 405 nm, Blue, 2) Ch2 (Top middle): 488 nm, Green, 3) Ch3 (Top right): 561 nm, Red, 4) Ch4 (Bottom left): 590 nm, Red, 5) CH5 (Bottom middle): 631 nm, Infrared (in Cyan), and 6) Ch6 (Bottom left): Combined view of all windows. The emission spectra were detected by the detectors and the range of emissions as followed: 1) Ch1: PMT1: (409 nm-485 nm), 2) Ch2: HyD SMD2 (496 nm-556 nm), 3) Ch 3: PMT 3 (564 nm-590 nm, 3) Ch3: HyD SMD4 (592 nm -629 nm), and 4) Ch4: HyD (639 nm-786 nm).

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Figure 5: Autofluorescence test of un-fixed coffee berry borer eggs with embryo showing strong autofluorescence emitted from the outer layer of the egg and the developing embryo at emission spectra of 556-587 nm (Ch2) in red. Autofluorescence was detected from the chorion of the egg at emission spectra in Ch 1 (496 nm -556 nm) in green and Ch3 (595-786 nm) in infrared in cyan with a small amount . The autofluorescence from the embryo was undetectable at these two emission spectra. A sample was observed under TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion lens. Cross sectional view of the eggs was achieved by the laser reaching inside of the eggs on the middle from a cover slip Following laser lines were used to excite the samples: 1) Ch1(Top left): 488 nm, Green, 2) Ch2 (Top right): 561 nm, Red, 3) Ch3 (Bottom left): 631 nm, infrared Cyan, and 4) Ch 4 (Bottom right): Combined view of all channels. The emission spectra were detected by the detectors and the range of emissions as followed: 1) Ch1: PMT1: (496 nm- 556 nm), 2) Ch2: HyD SMD2 (556 nm-587 nm), and 3) CH3: PMT3 (595 nm-786 nm).

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Figure 6: A fixed coffee berry borer egg with no probe added was examined under TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Apo 20 x/0.75 IMM (multi- immersion) lens to ensure autofluorescence of the samples were quenched by bleaching at the emission/ excitation spectra of the selected fluorophores (Alexa 488 and Alexa 647) for the experiment. Ch 2 at emission spectra of 504- 621 nm and Ch3 at 654 nm-77 nm showing the autofluorescence was quenched adequately. Following laser lines were used to excite the samples:1) Ch1 (Top left): 405 nm, DAPI as insect nuclei stain, 2) Ch2 (Top right): 488 nm, 3) Ch3 (Bottom left): 647 nm, and 4) combined view of all 3 channels. The emission spectra were detected by the detectors and the range of emissions as followed: 1) Ch1: HyD SMD2: (410-494 nm), 2) Ch2: HyD SMD4 (504 - 621 nm), and 3) CH3: HyD (654nm-776 nm).

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Figure 7: A coffee berry borer egg was hybridized with 50x volume of unlabeled CBBcdmF oligonucleotide sequence (250 μl) followed by hybridization with CBBcdmF (5 μl) conjugate with Alexa Fluor 647. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion) lens was used for the observation. Channel 1(top left) shows the DAPI, an insect nuclear counterstain at emission 395-589 nm in blue. Channel 2 (top right) shows absence of the signals at emission 644-782 nm after the suppression of an oligonucleotide sequence labeled with Alexa Fluor 647. Channel 3 (bottom left) is transmitted light. Channel 4 is the combined view of all channels. Laser lines used to excite the sample were 1) Ch1:405 nm, 2) Ch2: 647 nm, and 3) Ch3: Transmission light

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Figure 8: A coffee berry borer egg was hybridized with 50x volume of 515F (250 μl) of unlabeled oligonucleotide sequence followed by hybridization with 515F (5 μl) labeled with Alexa Fluor 488. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion) lens was used for the observation. Channel 1(top left) shows the DAPI, an insect nuclear counterstain at emission 410-494 nm in blue. Channel 2 (top right) shows absence of the signals at emission 504-632 nm after the suppression of oligonucleotide sequence, universal eubacterial primer, 515F. Channel 3 (bottom left) is transmitted in light. Channel 4 is the combined view of all channels. Laser lines used to excite the sample tissue were:1) Ch1:405 nm, 2) Ch2: 488 nm, and 3) Ch3: transmitted light.

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Figure 10: A egg of coffee berry borer used to set the excitation and emission spectra for the tropical nut borer . Sample egg was hybridized with CBBcdmF labeled with Alexa Fluor 647. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion) lens was used for the observation. Channel 1 (top left) shows the DAPI, insect nuclear counterstain illuminating the insect nuclei at emission 410 -494 nm in cyan. Channel 2 (top right) shows the signal, CBBcdmF oligonucleotide sequence at emission 654- 776 nm in magenta. Channel 3 (bottom left) is transmitted light. Channel 4 is the combined view of all channels. Laser lines used to excite the sample tissue were: 1) Ch1: HyD SMD2 (410-494 nm), 2) Ch2: HyD5 (654-776 nm), and 3) CH3: PMT3 Transmitted light.

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Figure 11: egg of coffee berry borer used to set the excitation and emission spectra for the tropical nut borer negative control. Sample egg was hybridized with CBBcdmF labeled with Alexa Fluor 647. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion) lens was used for the observation. Channel 1 (top left) shows the DAPI, insect nuclear counterstain illuminating the insect nuclei at emission 410-494 nm in Blue. Channel 2 (top right) shows the signal, CBBcdmF oligonucleotide sequence at emission 654-776 nm in red. Channel 3 (bottom left) is transmitted light. Channel 4 is the combined view of all channels. Laser lines used to excite the sample tissue were: 1) Ch1: HyD SMD2 (410-494 nm), 2) Ch2: HyD5 (654-776 nm), and 3) CH3: PMT3 Transmitted light.

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Figure 12: Negative control 3-1: Fluorescent in situ Hybridization of Tropical nut borer, Hypothenemus obscurus, egg-1. Sample egg was hybridized with CBBcdmF labeled with Alexa Fluor 647. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion) lens was used for the observation. Channel 1 (top left) shows the DAPI, an insect nuclear counterstain illuminating the insect nuclei at emission 410-494 nm in cyan. Channel 2 (top right) shows the signal, CBBcdmF oligonucleotide sequence at emission 654-776 nm in magenta. Channel 3 (bottom left) is transmitted light. Channel 4 is the combined view of all channels. Laser lines used to excite the sample tissue were: 1) Ch1: HyD SMD2 (410-494 nm), 2) Ch2: HyD5 (654-776 nm), and 3) CH3: PMT3 Transmitted light.

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Figure13: Negative control 3-2: Fluorescent in situ Hybridization of Tropical nut borer, Hypothenemus obscurus, egg-1. Sample egg was hybridized with CBBcdmF labeled with Alexa Fluor 647. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion) lens was used for the observation. Channel 1 (top left) shows the DAPI, insect nuclear counterstain illuminating the insect nuclei at emission 410-494 nm in cyan. Channel 2 (top right) shows the signal, CBBcdmF oligonucleotide sequence at emission 654-776 nm in magenta. Channel 3 (bottom left) is transmitted light. Channel 4 is the combined view of all channels. Laser lines used to excite the sample tissue were: 1) Ch1: HyD SMD2 (410-494 nm), 2) Ch2: HyD5 (654-776 nm), and 3) CH3: PMT3 Transmitted light.

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Figure 14: A coffee berry borer egg hybridized with CBBcdmF labeled with Alexa Fluor 647. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion) lens was used for the observation. After the egg was scanned by laser reaching from top (from cover slip) to bottom (glass slide), an overlay of 48 flames were made by Leica, LAS X navigator software. Channel 1 shows DAPI detected at emission of 410-494nm in cyan. Channel 2 shows the presence of the signal from CBBcdmF and Alexa Fluor 647 conjugate at emission of 410-494nm in magenta. Channel 3 is the transmitted light of the sample. Channel 4 is the combined view of all 3 channels. Laser lines used to excite the sample tissue were: 1) Ch1:405nm, 2) Ch2: 647nm, and 3) Ch3: transmitted light.

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Figure 15: Z-stack slices of a coffee berry borer egg. Z-stack slice views of a coffee berry borer egg hybridized with CBBcdmF labeled with Alexa Fluor 647 showing the signal detected was from inside of the egg rather than outer layer of egg. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a 10x lens was used for the observation. Z-stack slices of combined view from Figure 14 showing the reach of lasers within the sample egg (a total of 48 flames, showing first picture to thirty-second picture). Picture 1 was taken with the depth of laser at -126.16 μm, Picture 2, at -91.56 μm, Picture 3 at -52.96 μm, Picture 4 at -24.36 μm, Picture 5 at 0.84 μm, and Picture c6 at 8.84 μm. Length (Z position: 201.46 μm. Starting -125.16 μm and ending at 76.3 μm. The physical length of the sample is 201.46 μm).

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Figure 16: A coffee berry borer egg hybridized with Eubacterial primer F labeled with Alexa 488. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Flotar 10 x/0.3 (no-immersion) lens was used for the observation. Channel 1 shows DAPI detected at emission 410-494 nm in blue. Channel 2 shows the presence of the signal, universal eubacterial primer, 515F, conjugate with Alexa Fluor 488 at emission 504-632 nm in green. Channel 3 is the transmitted light of the sample. Channel 4 is the combined view of all 3 channels. Laser lines used to excite the sample tissue were: 1) Ch1:405nm, 2) Ch2: 488nm, 3) Ch3: transmitted light.

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Figure 17: A coffee berry borer egg hybridized with Eubacterial primer, 515F labeled with Alexa 488, and smashed between cover glass and glass slide. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Apo 20 x/0.75 IMM (multi-immersion) lens was used for the observation. Channel 1 shows DAPI detected at emission 410-494 nm in blue. Channel 2 shows the presence of the signal, universal eubacterial primer, 515F, conjugate with Alexa Fluor 488 at emission 504-632 nm in green. Channel 3 is the transmitted light of the sample. Channel 4 is the combined view of all 3 channels. Laser lines used to excite the sample tissue were: 1) Ch1:405nm, 2) Ch2: 488nm, 3) Ch3: transmitted light.

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Figure 18: A coffee berry borer egg hybridized with Eubacterial primer, 515F labeled with Alexa 488, and smashed between cover glass and glass slide. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Apo 20 x/0.75 IMM (multi-immersion) lens was used for the observation. Channel 1 shows DAPI detected at emission 410-494 nm in blue. Channel 2 shows the presence of the signal, universal eubacterial primer, 515F, conjugate with Alexa Fluor 488 at emission 504-632 nm in green. Channel 3 is the transmitted light of the sample. Channel 4 is the combined view of all 3 channels. Laser lines used to excite the sample tissue were: 1) Ch1:405nm, 2) Ch2: 488nm, 3) Ch3: transmitted light.

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Figure19: A coffee berry borer egg hybridized with Eubacterial primer, 515F labeled with Alexa 488, and smashed between cover glass and glass slide. TCS Leica SP8 X confocal laser scanning microscope (Leica Microsystems, Inc, Buffalo Grove, IL) with a HC Plan Apo 63x/1.3 NA (glycerol immersion) lens was used for the observation. Channel 1 shows DAPI detected at emission 410-494 nm in blue. Channel 2 shows the presence of the signal, universal eubacterial primer, 515F, conjugate with Alexa Fluor 488 at emission 504-632 nm in green. Channel 3 is the transmitted light of the sample. Channel 4 is the combined view of all 3 channels. Laser lines used to excite the sample tissue were: 1) Ch1:405nm, 2) Ch2: 488nm, 3) Ch3: transmitted light.

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Figure 20: PCR amplification of caffeine demethylation gene, ndmA, using CBBcdm primer sets on bacterial DNA isolated from coffee berry borer eggs. 1) ladder, 2) Achromobacter sp., 3) Serratia sp., 4) Pseudomonas sp., 5) Achromobacter sp., 6) Microbacter sp., 7) Microbacter sp., 8) Unknown bacteria sp., 9) Negative control (H2O) 10) Adult, and 11) Positive control DNA extracted from eggs. 1) Positive control, and 2) Pseudomonas sp. successfully yielded ~ 400 bp. Amplicons.

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CHAPTER 3: ISOLATION OF ENDOPHYTIC BACTERIA FROM EGGS OF COFFEE BERRY BORER, HYPOTHENEMUS HAMPEI (FERRARI), AND THEIR ASSOCIATION WITH CAFFEINE DEGRADATION

Abstract Bacterial symbionts associated with coffee berry borer (Hypothenemus hampei) are known to assist the host beetle with the degradation of caffeine, otherwise, a toxic substance to various insect species. Although previous studies have reported the presence of the caffeine degradation genes in the genome of bacteria isolated from the coffee berry borer, this research provides tangible evidence that these bacteria are, in fact, capable of degrading caffeine in the host insect’s system. The bacteria associated with coffee berry borer eggs were isolated from eggs of coffee berry borer, identified into genus-level, and further characterized for their caffeine tolerance. When seven bacterial genera isolated from coffee berry borer eggs were tested for their abilities to tolerate caffeine with the gradient of caffeine concentrations ranging from 0 g/L to 25 g/L, five bacterial genera were able to grow colonies at following concentrations: Pseudomonas sp. 2 at 15 g/L, Serratia sp. at 15 g /L, Pantoea sp. at 10 g/L, Pseudomonas sp 1. at 3 g /L, and Ochrobactrum sp at 1 g/L. The presence of bacterial caffeine degradation capabilities examined may indicate that these bacteria associated with the beetle eggs could assist the coffee berry borer for the caffeine degradation as the larvae emerge.

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Introduction

Caffeine (1,3,7 trimethylxanthine) and related N- methylxanthines, including theobromine (3,7-dimethylxanthine) and theophylline (1.3-dimethylxanthine) are natural purine alkaloids and occur in varieties of plants including Coffea (coffee), Theobroma (cacao) and Camellia (Tea) (Ashihara, and Crozier 1999; Dash and Gummadi, 2007). Due to their stimulatory effect, methylxanthines are often consumed by humans in the forms of foods and beverages such as coffee, soft drinks, chocolate, and tea (Dash and Gummadi, 2007). They are also used for a variety of applications in pharmaceutical preparations as a neurological, cardiac, and respiratory stimulant (Gokulakrishnan et al., 2005; Summers et al., 2011). On the other hand, caffeine and related methylxanthine compounds are one of the major agricultural and industrial wastes. Large quantities of byproducts are released to the environment as liquid effluents and solid forms from processing facilities of coffee and tea, causing severe environmental pollution (Nayak et al., 2011; Summers et al., 2011). Especially, coffee pulps and husks released from coffee estates are significant contributors to environmental pollution, affecting soil fertility and contaminating aquatic ecosystems in the surrounding areas (Adams and Dougan, 1981; Nayak et al., 2011). Moreover, caffeine in liquid effluents of coffee and tea industries released into the drinking water has been a major health hazard to humans. Prolonged consumption of caffeine causes a variety of deleterious effects to human bodies, such as a headache, fatigue, and even malformation of the fetus and could reduce fertility rate during pregnancy (Gummadi et al., 2009; Gummadi et al., 2012). Because of this adverse effect on the environment and human health, the properties of microbes to degrade caffeine has gained increasing research interest in recent years as an ecologically sound means of bioremediation/bio-decaffeination alternative to chemical decaffeination. Although the ability of bacteria to grow in the presence of caffeine by utilizing the compound as a sole source of carbon and nitrogen has been known and studied since the 1970s, the enzyme and genes responsible for the caffeine degradation in bacteria have not been studied until recently (Summer et al., 2015).

Fungi (Stemphylium sp., Penicillium sp. and Aspergillus sp.) and bacteria have developed the ability to grow in the caffeine in their environment (Kurtzman and Schwimmer, 1971;

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Schwimmer et al., 1971; Roussos et al., 1995; Hakil et al., 1998; Hakil et al., 1999; Mazzafera, 2002). Serratia marcescens (Mazzafera et al., 1994) and many Pseudomonas sp. (Asano et al., 1994; Blecher and Lingens, 1977; Yamaoka-Yano and Mazzafera, 1998) are well known for their caffeine degrading abilities. Although caffeine at high concentrations is antimicrobial, resulting in inhibition of growth and death in most bacterial strains, some bacteria possess the ability to utilize caffeine as the sole source of carbon and nitrogen. They can metabolize it via N- demethylation or C-8 oxidation (Harm 1967; Mazzafera, 2002; Dash and Gummadi, 2007; Dash and Gummadi, 2010; Gummadi et al., 2012; Nayak et al., 2012; Summers et al., 2015). According to Gokulakrishnan et al. (2005), caffeine concentration greater than 2.5 mg/g in the growth media has been reported to inhibit the growth of many bacterial species (Gokulakrishnan et al., 2005). Therefore, the survival of the microorganisms in the presence of caffeine would be solely dependent on their capacity to degrade this alkaloid (Mazzafera, 2002). According to Vega et al. (2021), over 71 bacterial strains in 27 genera possess the ability to degrade caffeine. These caffeine-degrading bacterial species are geographically dispersed and found in various environments, including coffee fields (Yamaoka-Yano and Mazzafera, 1998), wastewater streams (Ogunseitan, 1996), and garden soil (Blecher and Lingens, 1997). A majority of these bacteria belong to Pseudomonas species, primarily P. putida that metabolizes caffeine via the N-demethylation pathway (Summers et al., 2011; Vega et al., 2021). Other bacterial genera known to degrade caffeine are Klebsiella sp. (Madyastha and Sridhar, 1999), Rhodococcus sp. (Madyastha and Sridhar, 1998), Coryneform sp. (Yamaoka-Yano and Mazzafera, 1998), Acinetobacter sp. (Yamaoka-Yano and Mazzafera, 1998; Sarath Babu et al., 2005), Flavobacterium sp. (Yamaoka-Yano and Mazzafera, 1998), Moraxella sp. (Yamaoka- Yano and Mazzafera, 1998), and Alcaligenes sp. (Babu et al., 2005; Mohapatra et al., 2006). The second bacterial species that is known for caffeine degradation is be Serratia marcescens. Serratia marcescens is enterobacteria and previously known as an opportunistic pathogen of human patients in hospitals (Starr et al., 1979). Mazzafera and Sandberg demonstrated the isolates from the soil of coffee farm in Brazil can degrade up to 1.2 g/L caffeine but showed optimal growth at 600 mg/L. Several reports demonstrated the abilities of bacteria to grow in media, supplemented with different concentrations of caffeine. Dash and Gummadi (2007) isolated a bacterium species from the soil of a coffee plantation area in India and demonstrated that it was capable of

94 degrading caffeine in an initial concentration of 5 g/L within 48 hours. This bacterium was later identified as P. putida (NCIM 5235) based on 16S rRNA analysis (Dash and Gummadi 2007). One of the highest caffeine concentrations reported to be tolerated by bacteria was 20 g/L by Pseudomonas sp. (Middelhoven and Bakker, 1982; Middelhoven and Lommen, 1984). Yamaoka-Yano and Mazzafera also reported P. putida, isolated from soil under coffee tree in Brazil, was capable of degrading up to 25 g/L in liquid medium and 50 g/L in solid medium (Middelhoven and Bakker, 1982; Yamaoka-Yano and Mazzafera, 1998). This was an important report since the degradation of caffeine by any microorganisms had not been reported for concentration higher than 5 g/L (Dash and Gummadi, 2007). Subsequently, Gummadi and Santhosh, (2009) further demonstrated that P. putida (NCIM 5235) could degrade caffeine at a concentration of 0. 3g/L to 15 g/L. Moreover, their study also demonstrated that this strain could degrade caffeine at a concentration of up to 20 g/L in the immobilized cell (Gummadi et al., 2009). Caffeine is found in coffee endosperms at approximately 1.0% on the dry weight of C. arabica (10-12 mg/g per fruit) and 1.7% in C. canephora (Carvalho et al., 1965). Caffeine is hypothesized to serve as defensive mechanisms by the plants to protect themselves from insect attacks due to its high toxicity to insect species (Mazzafera et al., 2005; Vega et al., 2015). Other possible explanations for the biosynthesis of caffeine could be inhibiting plant matter (Waller, 1989) as well as improving pollination by enhancing pollinator’s memory (Wright et al., 2013; Summers et al., 2015) Evidence suggests that the coffee berry borer relies on its bacterial symbionts to mediate caffeine detoxification (More details discussed in Chapter 2). The study conducted by Ceja- Navarro et al. (2015), isolated core microbiomes of coffee berry borer collected from a laboratory colony and from seven coffee-producing countries including Kenya, Indonesia, India, Puerto Rico, Hawaii, Guatemala, and Mexico. Furthermore, they successfully isolated 13 bacterial species from coffee berry borer alimentary canals that were able to degrade caffeine. These caffeine degrading bacteria are namely, Brachybacterium rhamnosum, Enterobacter sp., Jonesiae sp., Kosakonia cowanii, Ochrobactrum sp., Novosphingobium sp., Microbacterium binotii, Pseudomonas sp., P. fulva, P. fluorescens, Pantoea vagans, P. septica, P. eucalypti, and Stenotrophomonas maltophilia (Ceja- Navarro et al., 2015). This study further detected the presence of the methylxanthine N-

95 demethylase A, (ndmA) gene, responsible for caffeine degradation, expressed in P. fulva. When bacteria were removed from the coffee berry borer digestive system by administering combinations of antibiotics, not only did coffee berry borer lose the ability to degrade caffeine, but it also impacted the insects’ reproductive fitness. Furthermore, P. fluva was found to be capable of subsisting on caffeine as a source of carbon and nitrogen in the laboratory (Ceja- Navarro et al., 2015). Vega et al. (2021) detected caffeine degrading genes, N-demethylation genes and C-8 oxidation genes, in many bacterial species isolated from coffee berry borers. In this study, a total of 25 caffeine degrading bacterial species were isolated from heads of adult coffee berry borer, larvae, and eggs from Hawai’i, Mexico, and a laboratory colony in Maryland. Bacterial species reported here are Acinetobacter septicus, Bacillus aryabhattai, B. cereus group, B. wiedmannii, Delftia lacustris, Erwinia billingiae, E. tasmaniensis, Kosakonia cowanii, Klebsiella oxytoca, Ochrobactrum grignonense, Paenibacillus flavisporus, Pantoea allii, P cinetobacter septicus, P. rwandensis, Pseudomonas aeruginosa, P. monteilii, P. putida, P. parafulva, P. plecoglossicida, and Stenotrophomonas maltophilia, (Vega et al., 2021). Although there are a few previous studies have demonstrated associations between coffee berry borer and caffeine degrading bacteria (Ceja-Navarro et al., 2015; Vega et al., 2021), the ability of these bacteria to degrade caffeine needs to be examined in order to ensure the function of bacteria is in fact operational, regardless of the presence of caffeine degradation genes in the bacterial genome.

Objectives The objectives are: 1) to isolate endophytic bacterial species associated with the eggs of coffee berry borer, Hypothenemus hampei (Ferrari), 2) to identify the bacterial isolates to genus- level, and 3) to determine the caffeine degradation abilities of isolated bacterial strains. Two main foci for the third objective are 1) to test caffeine degradation capability (present /absent) of each bacterial species isolated from eggs, and 2) to determine maximum concentrations of caffeine that each bacterium can degrade in mineral media with limited carbon and nitrogen sources.

Materials and Methods

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Sample collection bacteria from coffee berry borer eggs Two hundred green coffee fruits with holes on the floral discs, indicating entry by adult coffee berry borer females, were hand-picked from 2 locations (upper and lower fields) of Maunawili stations at Hawaii Agriculture Research Center on the island of Oahu in March 2019. The cherries were double bagged, stored in a cooler box, and transferred to the University of Hawaii laboratory. The outer skins of the cherries were sterilized by submerging the cherries in 70 % EtOH in a sterilized glass beaker for 1 min with gentle agitation, stirring the sample with sterilized spatula. The cherries were dried on the autoclaved paper towel for 5 min in a Laminar Flow Hood. The green cherries were dissected with a sterilized scalpel and examined for the presence of the eggs in the galleries created by adult female coffee berry borers. Once eggs were observed in the seeds, 20 eggs were picked with a sterilized small painting brush and transferred into sterilized 1.5 ml Eppendorf tubes. The 20 eggs were sterilized in 70% EtOH solution for 30 seconds with gentle agitation then rinsed with sterilized H2O twice for 30 s, changing H2O each time. The coffee berry borer eggs were thoroughly macerated in 1 ml sterilized H2O in the 1.5 m/L Eppendorf tube by using a sterilized pipette tip. A 100 µl aliquot of macerated solution was plated on mineral media containing 9.5 mM KH2PO4, 4.8 mM MgSO4, 0.1 mM CaCl2, 0.8 mM Na2HPO4, and 20 g/L bacto agar supplemented with 1.5 g/L caffeine (Ceja-Navarro et al., 2015). The pH of the medium was adjusted to 5.6 and autoclaved. The bacterial cultures were incubated at 28ºC for five days to allow the bacteria to grow. The bacterial growth was observed daily until the optimal growth of the bacterial colonies was achieved, 5 days of incubation. Single colonies were isolated based on their morphological characteristics and streaked on Tryptic Soy Agar (TSA) media containing17 g tryptone, 3 g soytone, and incubated at 28ºC for 2 days. The single colony streaking was repeated four times on TSA agar in order to obtain pure bacterial cultures. After several days of growth, the bacteria were preserved in 30% glycerol solution and frozen at -80°C for long term storage .

Identification of caffeine degrading bacteria to genus-level: 16s rRNA gene amplification and sequencing. The DNA of bacteria isolated from coffee berry borer eggs was extracted using Qiagen DNeasy, Blood and Tissue kits (Qiagen, Germantown, MD) following manufactures’ instruction.

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The 16S rRNA regions of the bacterial genomes were amplified by PCR with 16S rRNA primers P16s-F1 (5’-AGACTCCTACGGGAGGCAGCA-3’) and P16s-R1 (5’- TTGACGTCATCCCCACCTTCC-3’) (Dobhal et al., 2019; Larea-Sarmiento et al, 2019). The PCR reactions were prepared as follows: 1 µl of DNA as template, 10 µl of GoTaq Green master mix, 1 µl of each primer and 7 µl of H2O to a final volume of 20 µl. The PCR was performed using a Bio-Rad T100 thermal cycler (Bio-Rad, Hercules, CA) with cycling conditions were set as initial denaturation at 94°C for 5 min and 35 cycles of the following denaturation at 94°C for 20 s, annealing at 58°C for 30 s, extension at 72°C for 1 min, and a final extension at 72°C for 3 min. An electrophoresis was performed by using the setup of 100 volts for 30 min. PCR amplicons were visualized on 1.5% agarose gel containing ethidium bromide gel stain (Invitrogen, Carlsbad, CA) (Dobhal et al., 2019). The PCR amplicons were further sequenced by Sanger sequencing. Prior to the PCR amplicons were sent to the sequencing facility, 5 µl of the PCR product was treated with 2 µl of ExoSAP-IT (Affymetrix Inc, Santa Clara, CA) following the manufacturer’s instructions. The clean PCR products were sequenced using both sense and antisense P16s primers at GENEWIZ facility (Genewiz, La Jolla, CA) (Dobhal et al., 2019). The sequenced bacterial genomes were edited and aligned manually by using Geneious prime software 10.1.3 (Kearse et al., 2012; Dobhal et al., 2019). BLASTn tool was used to compare the sequences against the GenBank database of National Center for Biotechnology Information (Altschul et al., 1990).

Screening of negative control Four plant pathogenic bacterial species were used as negative controls. Dikeya zeae (PL 65) isolated from taro, Pectobacterium parmentieri (PL30) isolated from potato, Ralstonia syzygii (5719) and Ralstonia pseudosolanacearum (19229) were tested for their ability to grow on the mineral media supplemented with caffeine concentrations at 0 g/L and 1 g/L. After bacteria were streaked, the plates were incubated at 28°C for 2 days. Dikeya zeae and P. parmentieri did not exhibit any colony growth at either caffeine concentrations. Ralstonia syzygii and R. .pseudosolanacearum were able to grow on the 0 g/L caffeine concentration plates but did not produce colonies on the 1 g/L concentration. Ralstonia syzygii growth on the TSA plate was slow. It did not grow overnight on the TSA plate by the time of experiment to be started. Therefore, R. pseudosolanacearum was selected to be used as a negative control.

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Caffeine concentration experiment Seven bacterial genera isolated from coffee berry borer eggs were tested for their abilities to grow on caffeine concentrations of 0 g/L, 1 g/L, 2 g/L, 3 g/L, 5 g/L, 7.5 g/L, 10 g/L, 15 g/L. 20 g/L and 25 g/L in mineral agar media (Ceja-Navarro et al., 2015). A plant pathogenic bacterial species, R. pseudosolanacearum was used as a negative control. Each caffeine concentration was replicated three times. One loop-full of each bacterial colony was streaked on a TSA agar plate. The plates were incubated for 2 days at 28ºC. This process was repeated four times to obtain pure bacterial colonies. A single colony of each bacterium was picked out with a loop and transferred to 5 mL TSB media in 50 mL test tubes. The bacterial colonies were incubated at 28ºC overnight. After overnight incubation, a loop-full of bacteria was streaked onto the mineral media (pH 5.7) with gradients of caffeine concentrations described above. The plates were incubated at 28ºC. The bacterial growth was observed everyday till the optimal growth of the bacterial colonies were achieved. After 5 days of incubation, optimal growth of bacteria was observed and pictures of the bacterial colonies on the plates were taken.

Results Isolation of caffeine degrading bacteria from coffee berry borer eggs Fifteen different bacterial colonies were isolated from the coffee berry borer eggs based on their morphological characteristics. These bacterial colonies were transferred to TSA media individually and transferred four times to new TSA media to obtain pure bacterial cultures for further studies.

Identification of caffeine degrading bacteria to genus-level BLASTn search for samples 1, 5, and 7 yielded homologies with Achromobacter sp. (GenBank accession#: KP973962.1) with 99.87% to 100% similarity (Table 1). Samples 2 and 9 yielded homologies of 99.87% to 100% with Serratia sp. (GenBank accession#: MN733354.1) (Table 1). Samples 3 and 4 yielded homologies of 99.34 to 99.47% similarity to Pantoea sp. (GenBank accession#: KR265436.1) (Table 1). Sample 6 was Pseudomonas sp. (GenBank accession#: KF913767.1) with 99.69% similarity (Table 1). Samples 8 and 10

99 were Microbacterium sp. (GenBank accession #:MN577329.1) with 99.71 to 99.73% similarity (Table 1). Samples 12, 13, and 14 were Ochrobactrum sp. (GenBank accession#: MN082062.1) with 99.86 to 100% similarity and sample 15 was also Pseudomonas sp. (GenBank accession#: MT075806.1) with 99.87% similarity (Table 1). Although sample 11 was sequenced twice, it did not yield a result. This is probably due to 16s primers mismatch (Table 1).

Caffeine concentration experiment Achromobacter sp. (GenBank accession#: KP973962.1), Serratia sp. (GenBank accession#: MN733354.1), Pantoea sp. (GenBank accession#: KR265436.1), Pseudomonas sp.1 (GenBank accession#: KF913767.1), Ochrobactrum sp. (GenBank accession#: MN082062.1), Pseudomonas sp. 2 (GenBank accession#: MT075806.1), and Microbacterium sp.(MN577329.1) (Table 2) were tested for their abilities to grow on concentrations of caffeine ranging from 0 g/L, 1 g/L, 2 g/L, 3 g/L, 5 g/L, 7.5 g/L, 10 g/L, 15 g/L. 20 g/L, and 25 g/L in mineral agar media (Ceja-Navarro et al., 2015). Negative control with R. pseudosolanacearum had shown positive bacterial growth on the 0 g/L caffeine plates but failed to grow on any of the caffeine concentrations (1 g/L to 25 g/L) (Figure 1 and Table 3). Serratia sp., Pseudomonas sp 2., Ochrobactrum sp., Pseudomonas sp. 1, and Pantoea sp. had yielded positive bacterial growth on the mineral media supplemented with caffeine (Figs. 2- 6). However, Microbacterium sp. and Achromobacter sp. did not yield bacterial growth (Figs. 7- 8). There was no bacterial growth observed at the caffeine concentrations of 20 g/L and 25 g/L for any of the bacteria tested. Serratia sp. and Pseudomonas sp. 2 (GenBank accession#: MT075806.1) grew at the caffeine concentration of 15 g/L (Figs. 2 -3). Growth on 10g/L was observed in Pantoea sp. (Fig/. 4). Pseudomonas sp.1 (GenBank accession#: KF913767.1) grew on 3 g/L (Figures 5). Ochrobactrum sp. grew only at the lowest caffeine concentration of 1 g/L.

Discussion The highest concentration of the caffeine degraded was 15 g/L by Serratia sp. and Pseudomonas sp 2. The most likely species of this Serratia culture may be S. marcescens based on homologies with the NCBI GenBank database. S. marcescens is an enterobacterium, previously known as an opportunistic pathogen of human patients in hospitals (Starr et al., 1979). S. marcescens was previously isolated from the soil of coffee farms in Brazil,

100 and was able to degrade up to 1.2 g/L of caffeine in M9 media (Mazzafera et al., 1994).The Serratia sp. isolated from coffee berry borer eggs in this experiment was able to degrade an approximately ten-fold higher concentration of caffeine at 15 g/L compared to a previous experiment (Mazzafera et al., 1994). One interesting species in the genus Serratia, previously reported to have a symbiotic relationship with bark beetle species, is S. proteamaculans. Although there has been no report of this Serratia species associated with coffee berry borer, S. proteomaculans was previously isolated from bark beetles, Dendroctonus rhizophagus and D. valens along with several other bacterial species (Raoultella terrigena, R. aquatilis, and P. fluorescens) responsible for Nitrogen-fixation and nitrogen recycling in the beetle’s gut (Morales-Jimenez et al., 2013). S. proteomaculans in that study was reportedly able to degrade uric acid in the gut of larvae, pupae, and adults of D. rhizophagus and D. valens, contributing to nitrogen balance in the insect gut (Morales-Jimenez et al., 2013). In addition, S. liquefaciens was reported to be an endophyte of coffee, isolated from coffee seeds in Colombia (Vega et al., 2005) although its function in the plants was unknown (Vega et al., 2005). The second bacterial genus able to degrade caffeine at 15 g/L was Pseudomonas sp. 2. Pseudomonas has been identified as the major caffeine degrading genus among 71 isolates within 25 genera that could reportedly degrade caffeine in various environments. Pseudomonas fulva, and P. fluorescens isolated from the insect digestive tracts were previously reported by Ceja-Navarro to be caffeine degrading symbionts of coffee berry borer (Ceja-Navarro et al., 2015). Pseudomonas aeruginosa, P. monteilli, P. parafulva, P. putida, and P. plecoglossicida were also isolated from coffee berry borer and were found to possess the genes for both N-demethylation and C-8 oxidation. Pseudomonas aeruginosa, P. putida, and P. plecoglossicida were isolated from coffee berry borer eggs, whereas P. purafulva was isolated from the frass of coffee berry borer. Pseudomonas monteilii and a second P. aeruginosa culture were isolated from the head (Vega et al., 2021). Pantoea sp. degraded caffeine at a concentration of 10 g/L in our experiment. Pantoea, an enterobacterium, is known to form symbiotic associations with a number of insect species and has the capability of degrading and utilizing many types of plant materials including caffeine (Chen et al, 2016; Marino et al., 2018; Vega et al., 2021). This bacterium was reported in Marino et al. (2018) as the second abundant bacterial genera associated with coffee berry borer in Puerto Rico. Vega et al. (2021) also reported in Hawaii. Pantoea vagans, P. septica,

101 and P. eucalypti has been previously isolated from coffee berry borer’s digestive tracts (Hawaii, Kealakekua, and Chiapas, Mexico) . Pantoea allii and P. rwandensis were previously isolated from the head of coffee berry borer collected in Hawaii and reportedly possesses genes for caffeine degradation by C-8 oxidation. Pantoea rwandensis was also found to have genes coding for N-demethylation (Vega et al., 2021). Genbank nucleotide database, BLASTn has indicated two Pseudomonas sp. with different homologies based on 16S rRNA analysis. Therefore, these Pseudomonas sp. could potentially belong to separate Pseudomonas species. In regard to the caffeine degradation capability, Pseudomonas sp. 1 and 2 had shown contrasting results. Pseudomonas sp 1. degraded caffeine concentration of 3 g/L, which was five-folds less caffeine concentration degraded compared to the Pseudomonas sp 2. of 15 g/L. This result could indicate there is a variability in caffeine degrading capability among the genera of Pseudomonas. Ochrobactrum sp. degraded the minimum concentration of caffeine at 1 g/L. This bacterium was previously isolated from the digestive tracts of coffee berry borer obtained from Hawaii and Mexico and was able to degrade caffeine on 1.0 g/L to 2.5 g/L caffeine plates (Ceja- Navarro et al., 2015). Additionally, Ochrobactrum grignonense isolated from coffee berry borer eggs from Mexico was found to possess C-8 oxidation pathway (Vega et al., 2021). Microbacterium sp. and Achromobacter sp. failed to degrade caffeine at the concentrations tested in this experiment. The bacteria did not grow at 0 g/L caffeine with mineral media, whereas all other bacteria in this experiment had positive growth even at 0 g/L. Microbacterium sp. was not previously reported to degrade caffeine. Achromobacter sp. is known to be an environmental bacterium and clinical pathogen, previously isolated from lungs of a cystic fibrosis patient (Ridderberg et al., 2015). There has been no previous report of Achromobacter sp. associated with caffeine degradation. Additionally, there has not been any report of this bacterium associated with coffee berry borer. The bacteria used for this experiment were isolated from insect eggs on the 1.5 g/L caffeine mineral media plate. This implies that all bacteria selected for this experiment should possess the ability to degrade caffeine at least 1.5 g/L. The explanation of this lack of caffeine degrading ability could be that this bacterium may further degrade the compounds produced by other bacteria associated with them during the degradation process. Therefore, it may be that these bacteria did not grow in the caffeine because of the absence of other caffeine degrading bacteria in caffeine plates. This could be a likely

102 scenario since all bacteria in this experiment were isolated together in the shared caffeine plate at the start. Furthermore, this could indicate the bacterial caffeine degradation may be carried out by a consortium of bacteria present in the environment and that they break down the caffeine into final products together; each taking a different part of the caffeine degradation pathway. A second possibility is that caffeine degradation gene/genes present in this bacterium have become nonfunctional. Additionally, Pseudomonas sp1 did not degrade any caffeine whereas Pseudomonas sp2 was able to degrade caffeine at 15 g/L.

Conclusion Among the genera able to degrade caffeine, Serratia sp. and Pseudomonas sp. 2 tolerated caffeine at the highest concentration of 15g/L. Serratia sp. degraded a ten-fold higher concentration of caffeine compared to the previous report. There are no reports of these bacteria associated with coffee berry borer. The properties of caffeine degradation by those bacterial species may have potential practical applications in the future, such as; 1) the bioremediation of caffeine released in the environment to reduce the adverse effects to the environment and human health and 2) decaffeination of coffee by industries to remove the caffeine for non-caffeine coffee drinks for the consumers. Considering that Serratia sp. and Achromobacter sp. were previously reported to be isolated from the environment, it could be that the coffee berry borer not only relies on the bacteria that are transferred vertically, but also may utilize the available bacterial species in the environment to aid caffeine degradation in their system (horizontal transmission). In addition, since the surface of the eggs was sterilized prior to the experiment it is still unclear whether those bacterial genera isolated from the eggs in this experiment were present in the outer surface of the eggs or within the eggs. Therefore, the bacterial genera isolated in this experiment need to be further examined by Fluorescent in situ Hybridization with species- specific primers to localize the bacteria within the eggs. Whole genome sequencing will be carried out to identify these bacteria into species-level will be reported in the next chapter.

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Tables and Figures

Table 1: Bacteria isolated from coffee berry borer eggs were identified at the genus-level. The 16S rRNA regions of bacterial genomes were amplified and sequenced and compared with the NCBI GenBank database. Sample number 10 was sequenced twice, however, failed to confirm the identity.

Sample # Homologous nucleotide Query cover Percent ident Genbank accession # 1 Achromobacter sp. 100% 100% KP973962.1 2 Serratia sp. 100% 99.87% MN733354.1 3 Pantoea sp. 100% 99.34% KR265436.1 4 Pantoea sp. 100% 99.84% KR265436.1 5 Achromobacter sp. 100% 99.87% KP973962.1 6 Pseudomonas sp. 100% 99.69% KF913767.1 7 Achromobacter sp. 100% 100% KP973962.1 8 Microbacterium sp. 100% 99.87% MN577329.1 9 Serratia sp. 100% 100% MN733354.1 10 Microbacterium sp. 100% 99.86% MN577329.1 11 N/A N/A N/A N/A 12 Ochrobactrum sp. 100% 99.86% MN082062.1 13 Ochrobactrum sp. 100% 100.00% MN082062.1 14 Ochrobactrum sp. 100% 100.00% MN082062.1 15 Pseudomonas sp. 100% 99.87% MT075806.1

Table 2: List of bacterial genera isolated from coffee berry borer eggs and used for caffeine degradation experiment. The bacterial genera mentioned in Table 1 were consolidated according to the same genera.

Sample # Homologous nucleotide Query cover Percent ident Genbank accession # 1 Achromobacter sp. 100% 100% KP973962.1 2 Serratia sp. 100% 99.87% MN733354.1 3 Pantoea sp. 100% 99.34% KR265435.1 4 Pseudomonas sp. 1 100% 99.69% KF913767.1 5 Microbacterium sp. 100% 99.87% KU560433.1 6 Ochrobactrum sp. 100% 99.86% MN082062.1 7 Pseudomonas sp. 2 100% 99.87% MT075806.1

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Table 3: List of bacterial genera used for caffeine degradation experiment showing caffeine degradation + (positive) and – (negative) and maximum concentrations of caffeine degraded by each bacterium.

Caffeine degradation Maximum caffeine Sample # Homologous nucleotide (+/-) degraded (g/L) 1 Achromobacter sp. + 0 2 Serratia sp. + 15 3 Pantoea sp. + 10 4 Pseudomonas sp. 1 + 3 5 Microbacter sp. - 0 6 Ochrobactrum sp. + 1 7 Pseudomonas sp. 2 + 15 8 Ralstonia pseudosolanacearum (Negative control) - 0

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Figure 1: Negative control of caffeine concentration experiment was carried out with Ralstonia pseudosolanacearum on the mineral media with gradients of 0 g/L to 25 g/L caffeine concentrations. The 0 g/L exhibiting bacterial growth on the top whereas 1 g/L did not have any bacterial growth on the lower; the experiment was performed in three replicates.

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Figure 2 Serratia sp. isolated from coffee berry borer eggs were examined for their caffeine degradation capability on the gradient of caffeine (1 g/L -25 g/L) in the mineral media with 3 replications/concentrations. Bacterial colony growth were observed with caffeine concentration 0 g/L to 15 g/L

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Figure 3: Pseudomonas sp 2. Isolated from coffee berry borer eggs were examined for their caffeine degradation capability on the gradient of caffeine (1 g/L -25 g/L) in the mineral media with 3 replications/concentrations Bacterial growth was observed with caffeine concentration 0 g/L to 15 g/L

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Figure 4: Pantoea sp. from coffee berry borer eggs were examined for their caffeine degradation capability on the gradient of caffeine (1 g/L -25 g/L) in the mineral media with 3 replications/concentrations Bacterial colony growth were observed with caffeine concentration 0 g/L to 10 g/L.

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Figure 5: Pseudomonas sp. 1 from coffee berry borer eggs were examined for their caffeine degradation capability on the gradient of caffeine (1 g/L -25 g/L) in the mineral media with 3 replications/concentrations Bacterial colony growth were observed with caffeine concentration 0 g/L to 3 g/L.

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Figure 6: Ochrobactrum sp. from coffee berry borer eggs were examined for their caffeine degradation capability on the gradient of caffeine (1 g/L -25 g/L) in the mineral media with 3 replications/concentrations. Bacterial colony growth were observed with caffeine concentration 0 g/L to 1 g/L

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Figure 7: Microbacterium sp. from coffee berry borer eggs were examined for their caffeine degradation capability on the gradient of caffeine (1 g/L -25 g/L) in the mineral media with 3 replications/concentrations. Bacterial colony growth was not observed with any caffeine concentrations.

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Figure 8: Achromobacter sp. from coffee berry borer eggs were examined for their caffeine degradation capability on the gradient of caffeine (1 g/L -25 g/L) in the mineral media with 3 replications/concentrations. Bacterial colony growth was not observed in any caffeine concentrations.

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CHAPTER4: GENOME-WIDE ANALYSES OF CAFFEINE-DEGRADING BACTERIA ASSOCIATED WITH THE EGGS OF THE COFFEE BERRY BORER, HYPOTHENEMUS HAMPEI (FERRARI): A SPECIES-LEVEL IDENTIFICATION OF CAFFEINE-DEGRADING BACTERIAL TAXA ISOLATED FROM COFFEE BERRY BORER EGGS

Abstract Pseudomonas sp. was detected within the eggs of coffee berry borer by localizing the caffeine demethylase, Methylxanthine N-demethylase (ndmA) regions of the bacterial genome using Fluorescent in situ hybridization. This demonstrates a possible route of vertical transmission of this bacterium in coffee berry borer’s system. Furthermore, among the seven bacteria genera isolated from coffee berry borer’s eggs, five bacteria were characterized with caffeine-degrading capabilities. The main focus is to further investigate the identities of caffeine-degrading bacteria into species-level. The bacteria isolated from coffee berry borer eggs were subjected to the whole genome sequencing using the Oxford Nanopore MinION. The sequenced genomes were assembled and polished by Qiagen CLC genomic workbench, then identified to species-level by comparing Average Nucleotide Identity (ANI) and digital DNA-DNA hybridization (dDDH) values. Phylogenetic trees of each bacterium were generated by GBDP distance calculated from 16S rDNA and the whole genome sequences for further analyses. At a cut-off value of >95% and >70% as the species delineation framework for ANI and dDDH, respectively, three bacterial species were identified Pseudomonas parafulva, Serratia nematodiphila, and Ochrobactrum pseudogringnonse. Four bacterial strains were identified as potential novel species: Microbacterium sp., Erwinia sp., Pseudomonas sp. 2, and Achromobacter sp. Furthermore, the identity of the Pseudomonas species detected within the eggs of coffee berry borer was determined as Pseudomonas parafulva, and/or novel Pseudomonas sp.

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Introduction Caffeine degradation in bacteria Caffeine (1,3,7-trimethylxanthine) is naturally occurring purine alkaloids present in many species of plants, including Coffea (coffee) and Theobroma (cacao) (Aashihara, and Crozier 1999; Dash and Gummadi, 2006; Summers et al., 2011). Caffeine is toxic to bacteria. A relatively low concentration of caffeine is antimicrobial and results in the inhibition of growth and death of some bacterial strains such as E. coli (Harm, 1967; Ramanaviciene et al., 2003). On the other hand, some bacterial species are capable of subsisting on caffeine as a sole source of carbon and nitrogen (Summers et al., 2015). According to Vega et al. (2021), over 71 bacterial strains in 27 genera were reported to possess the genes involved in caffeine-degradation. These caffeine-degrading bacterial species are geographically dispersed and found in various environments, including coffee fields (Yamaoka-Yano and Mazzafera, 1998), wastewater streams (Ogunseitan, 1996), and also garden soil (Blecher and Lingens, 1997). These bacteria are Pseudomonas (Woolfolk, 1975; Blecher and Lingens, 1977; Middelhoven and Bakker, 1982), Serratia marcescens (Mazzafera et al., 1996), Klebsiella (Madyastha and Sridhar, 1998), Rhodococcus (Madyastha and Sridhar, 1998), Coryneform (Yamaoka-Yano and Mazzafera, 1998), Acinetobacter (Yamaoka-Yano and Mazzafera, 1998; Babu et al., 2005), Flavobacterium sp. (Yamaoka-Yano and Mazzafera, 1998), Moraxella sp. (Yamaoka-Yano and Mazzafera, 1998) and Alcaligenes sp. (Babu et al., 2005; Mohapatra et al., 2006). Among the bacteria with caffeine-degrading capability, the majority belong to Pseudomonas, primarily Pseudomonas putida strains (Summers et al., 2011; Vega et al., 2021). Other Pseudomonas species that have been reported with caffeine-degrading capabilities are P. monteilii (Aimurti et al., 2018), P. putida, and P. chlororaphis (Nunes and de Melo, 2006). Both bacteria are endophytes of coffee, isolated from coffee pulps, stems, roots, and leaves (Nunes and de Melo, 2006; Aimurti et al., 2018). P. alcaligenes (Babu et al., 2005) and P. putida (Yamaoka-Yano and Mazzafera, 1999) were isolated from coffee plantation soils. Another bacterial species that is well known for caffeine degradation is Serratia marcescens. Serratia marcescens is an Enterobacteria, and previously known as an opportunistic environmental pathogen, originally isolated from human patients in hospitals (Starr et al., 1979).

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Bacterial caffeine degradation pathways and genes Recent metabolic studies revealed two distinctive catabolic pathways of caffeine degradation by bacteria:1) N-Demethylation and 2) C-8 oxidation (Summers et al., 2015). The N-demethylation pathway has been observed in over 80% of bacterial isolates that are reported, therefore, N-demethylation is believed to be the most common pathway for bacterial caffeine degradation (Summers et al., 2105). Five novel enzymes involved in the N-demethylation pathways are ndmA, ndmB, ,ndmC, ndmD, and ndmE (Summers et al., 2011; Summers et al., 2013). Pseudomonas putida CBB5 reportedly uses a full complement of five enzymes (Summers et al., 2011; Summers et al., 2013). In the C-8 oxidation pathway, two enzymes are so far purified and characterized. Caffeine dehydrogenase (Cdh) transforms caffeine into 1,3,7- trimethylurioc acid (TMU) and a 58-kDA of heterotrimeric caffeine dehydrogenase (cdh) enzyme have been found in Pseudomonas sp. CBB1 (Yu et al., 2008). Trimethyluric acid monooxygenase (TmuM) which converts TMU to 1,2,7-trimethyle-5 hydroxyisourate, a 43-kDa NADH- dependent trimethyluric acid monooxygenase (TmuM). TmuM was also isolated from P. putida CBB1 (Mohanty et al., 2013). C-8 oxidation was also observed in mixed cultures of Klebsiella and Rhodococcus (Madyastha and Sridhar, 1998) and also pure bacterial isolates of Alcaligenes sp. (Mohapatra et al., 2006), and Pseudomonas sp. CBB1 (Yu et al., 2008; Mohanty et al., 2012).

Coffee berry borer and caffeine-degrading bacterial symbionts There is compelling evidence that the coffee berry borer relies on bacterial symbionts to mediate caffeine detoxification; otherwise, caffeine is a toxic substance to the insect species (Mazzafera et al., 2005). The study conducted by Ceja-Navarro et al. (2015) successfully isolated 13 bacterial species from coffee berry borer alimentary canals that were able to degrade caffeine. These caffeine-degrading bacteria are Brachybacterium rhamnosum, Enterobacter sp., Jonesiae sp., Kosakonia cowanii, Ochrobactrum sp., Novosphingobium sp., Microbacterium binotii, Pseudomonas sp., P. fulva, P. fluorescens, Pantoea vagans, P. septica, P. eucalypti, and Stenotrophomonas maltophilia (Ceja- Navarro et al., 2015).

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Vega et al. (2021) have previously explored the bacteria associated with coffee berry borers in caffeine degradation from Hawaii, Mexico, or a laboratory colony in Maryland, examining a possible scenario of vertical transmission of the caffeine-degrading bacteria by coffee berry borer. In this article, 938 bacteria were isolated from the heads of adult, larvae, eggs, and frass of coffee berry borer on the Nutrient broth (NB). For the characterization of caffeine demethylase and identification of bacteria, 16S rRNA and ndmA regions of the caffeine demethylase were amplified by Polymerase Chain Reaction (PCR) with primer sets, 27F and 1392R, and CBBcdmF and CBBcdmR (Ceja-Navarro et al., 2015). PCR products obtained from 16S rRNA regions and ndmA were sequenced by Dye-terminator sequencing (Applied Biosystems/Life Technologies, Carlsbad, CA) (Vega et al., 2021). Furthermore, 55 selected colonies of bacteria were sequenced using a high throughput sequencing platform, Illumina NextSeq, to annotate caffeine-degradation genes (Illumina, San Diego, CA). In this study, a total of 25 bacterial species were identified based on 16S rRNAs and ANI with cutoff values of > 95%. Methylxanthine N-demethylase (ndmA, ndmB, ndmC, ndmD, and ndmE) regions were detected in Acinetobacter sp. S40, S54, S55, Bacillus aryabhattai, Delftia lacustris, Erwinia sp. S38, S43, S63, Klebsiella oxytoca, Ochrobactrum sp. S45, S46, Pantoea sp. S61, Pseudomonas aeruginosa, P. parafulva, and Pseudomonas sp. S30, S31, S32, S37, S44, S60, S75. Moreover, N- demethylase was detected in the bacterial genome from the whole eggs and macerated eggs of coffee berry borers, suggesting the possible involvement of these bacteria in vertical transmission of caffeine degradation. The bacterial genes found from eggs were ndmC and ndmD in Acinetobacter sp. S 40, S54 and S55 isolated from whole eggs, ndmA in Bacillus aryabhattai from macerated eggs, ndmA and ndmD in Erwinia sp. S 38 from whole eggs, ndmE in Ochrobactrum sp. S45 and S46 from macerated eggs, ndmD and ndmE in P. aeruginosa from macerated eggs, ndm A, B, C, D, and E in Pseudomonas sp. (S31, S 32, and S 37), and ndmD and ndmE in Pseudomonas sp. S44 from whole eggs (Vega et al., 2021). A bacterial community analysis was conducted with the coffee berry borer samples collected from the islands of Hawaii and Oahu, revealing that the microbiota associated with coffee berry borer in Hawaii is dominated by bacterial genera belong to Erwinia, Enterobacter, and Pseudomonas (Aoki, 2021). A Pseudomonas sp. within the eggs of coffee berry borer was detected by localizing the Methylxanthine N-demethylase (ndmA) regions of the bacterial genome using Fluorescent in situ Hybridization. These findings strongly suggest the caffeine-

124 degrading bacteria, essential for the coffee berry borer’s survival, is present in the eggs in the early stage of oogenesis and may be transmitted via vertical transmission from mother to the offspring (Aoki, 2021). The bacterial strains isolated from coffee berry borer eggs were identified to genus-level by targeting 16S rRNA regions, and the caffeine-degradation abilities of isolated bacterial strains were examined. The seven bacterial strains identified were Achromobacter sp., Serratia sp., Pantoea sp., Pseudomonas sp.1, Ochrobactrum sp., Pseudomonas sp. 2, and Microbacterium sp. (Aoki, 2021). When the bacterial strains were tested for their ability to degrade caffeine/, five bacterial genera were able to degrade caffeine. Pseudomonas sp. and Serratia sp. were the most efficient being able to degrade 15 g caffeine/L. Pantoea sp. was less efficient degrading only 10 g caffeine/L. Pseudomonas sp. 1 was even less efficient degrading only 3 g caffeine/L and Ochrobactrum sp. could degrade only 1 g caffeine/L (Aoki, 2021).

Objectives Although the bacterial strains isolated from the coffee berry borer eggs have been identified to genus-level and their caffeine-degrading capabilities characterized , further investigations into their genomic biology is needed. The objectives of this chapter are to: 1) to sequence the whole genome of the bacteria isolated from coffee berry borer eggs , and 2) identify the bacteria to species-level and elucidate their taxonomic relationships. The information obtained could be used to reveal the identities of the Pseudomonas species and possibly a second bacterium in the eggs of coffee berry borer that are potentially passed to offspring via vertical transmission. Furthermore, the bacterial genome sequenced could be used for the further analysis of annotations and the mapping of caffeine-degradation genes in bacteria for a better understanding of caffeine-degradation mechanisms by bacteria that mitigate caffeine degradation in coffee berry borer’s system.

Materials and methods Sample collection Two-hundred green coffee fruits with holes on the floral discs, indicating entry by adult coffee berry borer females, were hand-picked from 2 locations (upper and lower fields) of the Maunawili station at Hawaii Agriculture Research Center, Oahu, Hawaii in March 2019. The

125 cherries were double-bagged, stored in a cooler box, and transferred to the University of Hawaii laboratory. The outer skins of the cherries were sterilized by submerging the cherries in 70 % EtOH in a sterilized glass beaker for 1 min with gentle agitation. The sample was stirred with a sterilized spatula and then dried on an autoclaved paper towel for 5 min in a laminar flow hood. The green cherries were dissected with a sterilized scalpel and examined for the presence of eggs in the galleries created by adult female coffee berry borers. Once eggs were observed in the seeds, 20 eggs were picked by a small sterilized paint brush and transferred into sterilized 1.5 ml Eppendorf tubes .

Bacterial isolation The 20 eggs were sterilized in 70% EtOH solution for 30 seconds with gentle agitation then rinsed with sterilized H2O twice for 30 s, changing H2O each time. The coffee berry borer eggs were thoroughly macerated in 1 ml sterilized H2O in the 1.5 m/L Eppendorf tube by using a sterilized pipette tip. A 100 µl aliquot of macerated solution was plated on mineral media containing 9.5 mM KH2PO4, 4.8 mM MgSO4, 0.1 mM CaCl2, 0.8 mM Na2HPO4, and 20 g/L bacto agar supplemented with 1.5 g/L caffeine (Ceja-Navarro et al., 2015). The pH of the medium was adjusted to 5.6 and autoclaved. The bacterial cultures were incubated at 28ºC for five days to allow the bacteria to grow. The bacterial growth was observed daily until the optimal growth of the bacterial colonies was achieved, 5 days of incubation. Single colonies were isolated based on their morphological characteristics and streaked on Tryptic Soy Agar (TSA) media containing 17 g tryptone, 3 g soytone, and incubated at 28ºC for 2 days. The single colony streaking was repeated four times on TSA agar in order to obtain pure bacterial cultures. After several days of growth, the bacteria were preserved in 30% glycerol solution and frozen at -80°C for long term storage .

Whole genome DNA extraction Seven bacterial strains from coffee berry borer eggs were tested. Strains of Achromobacter sp., Serratia sp., Pantoea sp.,Pseudomonas sp., Ochrobactrum sp., Pseudomonas sp. 2, and and Microbacterium sp. were subjected to whole genome DNA extraction by using QIAGEN Genomic-tips by following the manufacturer’s instructions (Qiagen, Germantown, MD). DNA

126 was quantified with a Qubit 4 Fluorometer (Thermo Fisher Scientific, Carlsbad, CA). AMPure XP was used to clean and concentrate the genomic DNA according to the manufacturer’s manual (Agencourt Bioscience Corporation, Beverly, MA)

Whole genome sequence The Rapid Barcoding Kit (SQK-RBK004) and the Ligation Sequencing Kit (SQK- LSK109) were used to prepare the DNA library. The libraries were sequenced with a R9.3 single flow cell and the flow cells were monitored and controlled by MinKNOW software (version 4.0.20) (Oxford Nanopore Technology, Oxford, UK). In total, 339,586 MinION reads with an average length of ~5,500,000 bp were obtained (Table 2).

Assembly and polishing The reads were de novo assembled using “long-reads (beta)” and the assembled genomes were polished using “polish long reads (beta)” by Qiagen CLC Genomic workbench (Qiagen, Hilden, Germany). A total of 26 contigs, for all seven genomes, were obtained that were between 50,053 - 6,792,192 bp, with a total of 38,538,272 bp after polishing (Tables 1 and 2).

Species Identification: Average Nucleotide Identity (ANI) and digital DNA-DNA hybridization (dDDH) The contigs of each bacterial genome were screened by selecting 3 kb and comparing the selected region to the NCBI GenBank database. The BLASTn tool was used to obtain percent identity values and quarry coverages to determine the homologies of the short genomic regions to the existing bacterial sequences in the genome database. This was to ensure the purity of the assembly, and that the correct contigs will be used for analysis. A BLASTn search indicating bacteria with 85-100% identities and query coverages was used to select the bacterial species for pairwise comparison by Average ANI and dDDH. The approximate sizes of complete genomes for each bacterial species were: 1) Ochrobactrum sp., ~ 4.6 Mb (Pan et al., 2017), 2) Achromobacter sp., ~ 6.24 Mb (Jacobsen et al., 2013), 3) Pantoea sp., ~ 4.5 Mb (Hara et al., 2012), 4) Pseudomonas sp., ~5.5 Mb to 7 Mb (Klockgether et al., 2011), 5) Microbacterium sp., ~7 Mb (Rakhashiya et al., 2015) 6) Serratia sp., ~5 Mb (Khan et al., 2017). The bacterial type strains and complete genome sequences were retrieved from NCBI GenBank genome database in

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FASTA formats. ANI values were calculated by CLC Genomic Workbench (Create Average Nucleotide Identity Comparison). The sequences were subsequently submitted to the Type (Strain) Genome Server (TYGS), a bioinformatics platform (https://tygs.dsmz.de). FASTA formats were uploaded to the TYGS for a dDDH whole genome-based taxonomic analysis. Cut- off value of >95 % (Richter et al., 2009; Goris et al., 2007; Kim et al., 2014; Chun et al., 2018; Wang et al. 2020) and >70% (Wayne 1987; Goris et al., 2007) were assigned as the species delineation framework for ANI and dDDH. Both values were compiled into a Microsoft 365 Excel spreadsheet in a single matrix, then visualized as a color-coded heatmap using DISPLAYR (https://www.displayr.com/). dDDH The TYGS analysis was subdivided into the following steps:

Determination of Closely related species The determination of the closest type strain genomes was conducted in two complementary ways. First, all genomes were compared against all type strain genomes available in the TYGS database via the MASH algorithm, a fast approximation of intergenomic relatedness (Ondov et al., 2016), and the ten type strains with the smallest MASH distances were chosen per user genome. Second, an additional set of ten closely related type strains was determined via 16S rDNA gene sequences. These were extracted from the user genomes using RNAmmer (Kagesen et al., 2007) and each sequence was subsequently BLASTed (Camacho et al., 2009) against the 16S rDNA gene sequence of each of the 14,309 type strains available in the TYGS database. This was used as a proxy to find the best 50 matching type strains (according to the bitscore) for each user genome and to subsequently calculate precise distances using the Genome BLAST Distance Phylogeny approach (GBDP) under the algorithm ‘coverage” and distance formula d5 (Meier-Kolthoff et al., 2013). These distances were finally used to determine the ten closest type strain genomes for each of the user genomes.

Pairwise comparison of genome sequences For the phylogenomic inference, all pairwise comparisons among the sets of genomes were conducted using GBDP and accurate intergenomic distances inferred under the algorithm

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‘trimming’ and distance formula d5 (Meier-Kolthoff et al., 2013). One hundred distance replicates were calculated. Digital DDH values and confidence intervals were calculated using the recommended settings of the GGDC 2.1 (Meier-Kolthoff et al., 2013).

Phylogenetic inference

The resulting intergenomic distances were used to infer a balanced minimum evolution tree with branch support via FASTME 2.1.6.1 including SPR postprocessing (Lefort et al., 2015). Branch support was inferred from 100 bootstrap replicates. The trees were rooted at the midpoint (Farris, 1972) and visualized with PhyD3 (Kreft et al., 2017).

Results Species Identification:

Pseudomonas sp. 1 The type-based species clustering using a 70% dDDH radius around each of the 40 type strains (Appendix, Table 1) was done as previously described (Kolthoff and Goker, 2019). The most frequently detected 12 species with high percent identities (90-100%) by manual screening were determined as a core species. The 12 type strain species used for the analysis were as followed: Pseudomonas putida NBRC 14164T, P. monteilii DSM 14164T, P. mosselii DSM17497T, P. soli LMG27941T, P. plecoglossicida DSM 15088T, P taiwanensis DSM 21245T, P. asiatica RYU5T, P. cremoricolorata DSM 17059T, P. fulva DSM 17717T, P. alkylphenolica KL 28T, P. parafulva DSM 17004T, and P. entomophila L48T. ANI value of Pseudomonas sp.1 yielded 98.26 % homology to P. parafulva (DSM17004T). dDDH value yielded 81.9 % to P. parafulva (Fig.1). A total 44 strains of Pseudomonas species and 8 strains of other genera were used to generate 16S rDNA gene sequence-based (Fig. 2) and whole-genome sequence-based (Fig. 3) phylogenetic trees from Type Strains Server (GBDP). All strains of Pseudomonas species were on a separate branch from out of genus with high bootstrap values. In 16S rDNA gene sequence-based phylogenetic tree, Pseudomonas sp. 1 (barcode 22) groups with P. fulva and P. parafulva (Fig. 2). On the other hand, Pseudomonas sp. 1 (barcode 22) clusters with only P. parafulva based on the whole genome sequence-based phylogenetic tree (Fig. 3)

Serratia sp.

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The type-based species clustering using a 70% dDDH radius around each of the 38 type strains (Appendix, Table 2) was done as previously described (Kolthoff and Goker, 2019). Four main species were the most frequently detected with high percent identities (90-100%) from manual screening. A total of 5 genomes (4 type strains and 1 complete genome) were subjected to ANI analysis with Serratia sp. (Serratia_barcode_23): S. marcescens XRSC 14T, S. liquefaciens ATCC 27592T, S. symbiotica CWBI2.3T, S. nematodiphila PH-SO1, and S. nematodiphila DSM 21420T. dDDH values of S. nematodiphila PH-SO1 and S. nematodiphila PH-SO1 were 72.5% and 72.4%, respectively. ANI values yielded 97.6 % and 96.87% homologies to the same species (Fig. 4-1). Subspecies clustering was done using a 79% dDDH threshold as previously introduced (Meier-Kolthoff et al., 2014). The dDDH values of S. nematodiphila PH -SO1 and DSM 21420T at 72.5% and 72.4 % indicate that this strain could be a subspecies of S. nematodiphila (Fig. 4-1). A total 21 strains of Serratia species and 30 strains of other genus were used to generate 16S rDNA gene sequence-based (Fig. 5-1 and 5-2) and whole-genome sequence-based (Fig. 6) phylogenetic trees from Type Strains Server (GBDP). In a 16S rDNA gene sequence-based phylogenetic tree, Serratia sp. (Barcode23) is in the same clusters with S. ureilytica, S. marcescens, S. rubidaea, S. nematodiphila, and Skermanella stibiiresitens with branch support at 92% bootstrap value (Fig. 5-1 and 5-2). The whole genome sequence-based phylogenetic tree supports Serratia sp. (Barcode 23) was clustered together with from S. ureilytica, S. marcescens, and S. nematodiphila with branch support at 92% bootstrap value nematodiphila with a bootstrap value of 98% (Fig. 6).

Ochrobactrum sp. The type-based species clustering using a 70% dDDH radius around each of the 32 type strains (Appendix, Table 3) was done as previously described (Kolthoff and Goker, 2019). The most frequently detected by manual screening of the genome revealed the main 6 species. A total of 7 genomes (3 type stains and 4 complete genomes) subjected to the analysis with Ochrobactrum sp. (Ochrobactrum_Barcode 1_Contings123) were following: O. anthropi NCTC 12168T, O. anthropi ATCC 49188, O. pseudogrignonense CCUG 30717T, O. pseudogrignonense KB, Brucella pituitosa CCUG 508899, O. grignonense OgA9aT, and B. intermedia ZJ 499. ANI yielded 97.6% and 97.78% to O. pseudogrignonense (CCUG30717T and KB) (Fig. 7). dDDH values indicated 97.9% and 80.3% homologies to the same species (Fig. 7). A total 35 strains of

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Ochrobactrum and Brucella and 3 strains of other genera were used to generate 16S rDNA gene sequence-based (Fig. 8) and whole-genome sequence-based (Fig. 9) phylogenetic trees from Type Strains Server (GBDP). All strains of Ochrobactrum/Brucella species were on separate branches from out of genus with high bootstrap values. In the 16S rDNA gene sequence-based phylogenetic tree, Ochrobactrum sp. (Ochrobactrum_contig123) grouped with O. pseudogrignonense KB and CCUG 30717T with a bootstrap value of 100% (Fig. 8). Whole genome sequence-based phylogenetic tree also showed that the Ochrobactrum sp. (Ochrobactrum_contig123) is grouped together with O. pseudogrignonense with a high bootstrap value (Fig. 9).

Achromobacter sp. The type-based species clustering using a 70% dDDH radius around each of the 35 type strains (Appendix, Table 4) was done as previously described (Kolthoff and Goker, 2019). manual screening of the genome revealed 7 main species. A total of 9 genomes (5 type strains and 4 complete genomes) were subjected to analysis with Achromobacter sp. (Achromobacter_Barcode2_Contig 2): A. ruhlandii LMG 1866T, A. denitrificans USDA 56712, A. denitrificans NBRC 15125T A. insolitus DSM 23807, A. pestifer LMG 3431T, A. deleyi FDAAPGOS 1050, A. deleyi LMG 3458T, A. spanius DSM 2806, and A. xylosoxidans NTCT 10807T (figure 10). ANI value of Achromobacter sp. (Barcode 2-Contig 2) yielded 97. 2% homology to A. deleyi FDAAPGOS 105097 (Fig. 10). dDDH value yielded 72.4 % homology to the same species. A total 28 strains of Achromobacter species and 7 strains of other genera were used to generate 16S rDNA gene sequence-based (Fig. 11) and whole-genome sequence-based (Fig. 12) phylogenetic trees from Type Strains Server (GBDP). 16S rDNA based phylogenetic trees did not yield sufficient resolution to separate them from each other, however, the whole genome sequence-based phylogenetic tree showed the same trends that the Achromobacter sp. (barcode2_Contig 1) is grouped together with A. deleyi FDAAPGOS_1050 with a high bootstrap value, but not with A. deleyi LMG 3458 in a distant cluster (Fig. 11 and 12).

Microbacterium sp. Type-based species clustering using a 70% dDDH radius around each of the 78 type strains (Appendix, Table 5) was done as previously described (Kolthoff and Goker, 2019). Based

131 on the frequently detected Microbacterium species by manual screening of the genome, and the top hit species suggested by TYGS, 15 Microbacterium species were subjected to ANI analysis, and dDDH values were obtained from TYGS. A total of 21 genomes (12 type strains and 9 complete genomes) subjected to analysis with Microbacterium sp. (Microbacterium_Barcode 5_Contig1): M. oleivorans A9, M. oleivorans NBRC 103075T, M. hominis SJTG1, M. aurum KACC 19, M. aurum DSM 8600T, M. amylolyticum DSM 24221T, M. chocolatum SIT 101, M. testaceum stLBo37, M. pygmaeum DSM 23142T, M. esteraromaticum DSM8609T , M. lemovicicum ViU22T, M. lushaniae L031, M. foliorum DSM 1296T, M. wangchenii dK512T, M. oryzae MB10T, M. oryzae MB10, M. paludicola DSM 1695T , M. gubbeenense DSM 15944, M. sorbitolivorans CGMCC 15228, M. nanhaiense CGMCC 5.7181, and M. sediminis YLB 01T(Figure13). All ANI values were less than 95% (Figure 13). dDDH values were also less than 70% (Fig. 13). The ANI values of the Microbacterium species ranged from 82.4% to 87.85%. dDDH values were between 18.3% to 33.3%. A total 94 strains of Microbacterium and one other strain belonging to another genus were used to generate 16S rDNA gene sequence (Fig. 14) and whole genome sequence-based (Fig. 15) phylogenetic trees from Type Strains Server (GBDP). In the 16S rDNA gene sequence-based phylogenetic tree, Microbacterium sp. (Barcode 5_contig 1) grouped with M. nanhaiense with a bootstrap value of 83% (Fig. 14). On the other hand, Microbacterium sp. (Barcode 5_contig 1) grouped with M. sorbitolivorans with a bootstrap value of 71% on the whole genome based phylogenetic tree (Fig. 15).

Pantoea sp. The type strain-based species clustering using a 70% dDDH radius around each of the 32 type strains was done as previously described (Kolthoff and Goker, 2019). For the ANI, all 20 Pantoea species that have been previously identified (Walterson and Starvrinides, 2015) (Appendix, Table 6) were downloaded from NCBI GenBank and compared to Barcode3_Contig4. The type stains and the complete genomes used were: P. eucalypti LMG24197, P. cypripedii LMG2657T, P. eucrina LMG5346T, P. dispersa DSM32899, P. hericii JZB2120024T, P. wallisii LMG26277T P. brenneri LMG5343T , P. dispersa CCUG25232T, P. stewartii ZJ FGZX1, P. stewartia CCUG26359T, P. ananatis R100, P. alhagi LTYR112, P. conspicua LMG 24534, P. rodasii LMG 26, P. agglomerans C410P1T, P. vegans LM G 24199T, P. rwandensis NDO4, P. septica LMG 5345T, P. endophytica 596, P. rwandensis LMG26275T,

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P. allii LMG24248T, and P. anthophila LMG2558T. All dDDH values were less than 95%. dDDH values were also less than 70% (Figs. 16 and 17). The ANI of 20 Pantoea species were between 82.03% to 83.99%. dDDH values were between 19.3% to 21%. A total 31 strains of Pantoea species, 7 strains of Erwinia sp., and 16 strains of other genera were used to generate 16S rDNA gene sequence-based (Fig. 18) and whole-genome sequence-based phylogenetic trees (Fig. 19) from the Type Strains Server (GBDP). Both phylogenetic trees infer that the species was misidentified as Pantoea sp.. The species found more closely related to the Erwinia species (Figs.18 and 19). The closely related Erwinia species were E.billingiae, E. tasmaniensis, E. tracheiphila, E. pyliforiae, E. amylovora, and E. persicina. Mixta sp. were also detected in the target genome with low frequencies. The species of Mixta were M. intestinalis, M. theicola, M. calida, and M gaviniae. When Erwinia sp. and Mixta sp. were analyzed with ANI and dDDH, the values yielded were lower than the expected values. The values were 83.1% to 83.59% and 20.3% to 22.1 % for ANI and dDDH, respectively, indicating that they are not the same species (Fig. 20). A total 18 strains of Pantoea sp., 16 strains of Erwinia sp., 9 strains of Mixta sp., and 29 other genera were used to generate 16S rDNA gene sequence (Fig. 21) and whole genome sequence- based phylogenetic trees (Fig. 22) from Type Strains Server (GBDP). In the 16S rDNA sequence based phylogenetic tree, Pantoea sp. (Barcode3_contig 4) was grouped with P. wallisii with a bootstrap value of 63% (Fig. 21), contrary to the whole genome sequence based phylogenetic tree. The whole genome sequence-based phylogenetic tree places Pantoea sp. (Barcode3_contig 4) in the cluster with other Erwiana species after separating it from E. billingiae with 100% bootstrap value (Fig. 22). However, the tree indicates that the Pantoea sp. (Barcode3_contig 4) is in an outgroup from the cluster that includes E. pyliforiae, E. amylovora, E. tasmaniensis, E. pririflorinigrans, E. persicina, and E. aphidicola with a bootstrap value of 100% (Fig. 22). Although E. tasmaniensis was not able to be included in ANI analysis because only short partial genomes were available in GenBank, the phylogenetic trees infer the genome of interest is not E. tasmaniensis (Figs. 18, 19, 21, and 22).

Pseudomonas sp. 2. The type-based species clustering using a 70% dDDH radius around each of the 59 type strains (Appendix, Table. 7) was done as previously described (Kolthoff and Goker, 2019).

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Combining the information of Type Strain genome Server and the manually screened bacterial genome, the most frequently found 12 species with high percent identities (90-100%) were selected as core species for the analysis. The 12 type stains used for the analysis were: Pseudomonas putida NBRC 14164T, P. monteilii DSM 14164T, P. mosselii DSM17497T, P. soli LMG27941T, P. plecoglossicida DSM 15088T, P taiwanensis DSM 21245T, P. asiatica RYU5T, P. cremoricolorata DSM 17059T, P. fulva DSM 17717T, P. alkylphenolica KL 28T, P. parafulva DSM 17004T, and P. entomophila L48T. P. aeruginosa NCTC 10332T and P. fluorescens NCTC10038T were also added to the analysis based on the previous report (Ceja-Navarro et al., 2015) (Figure 23), indicating associations with coffee berry borer and the presence of caffeine degradation genes. Pseudomonas sp. 1 (Barcode 22) was also added to the analysis in order to determine whether it is the same species as Pseudomonas sp. 2 (Barcode4_Contig 1). ANI and dDDH of all type strains compared to the Barcode 4_Contig 1 were less than 95% and 70%, respectively (Fig. 23). The range of ANI and dDDH values obtained were between 83.29% to 89.64% and 21.6% to 38.8%, respectively. ANI of Pseudomonas sp. 1 and Pseudomonas sp. 2 was 86.73%, and dDDH was 30.8% (Fig. 23). This indicates these two Pseudomonas species in fact belong to different species. A total 64 strains of Pseudomonas species, and 8 strains of other genera were used to generate 16S rDNA gene sequence-based (Fig. 24) and whole-genome sequence-based phylogenetic trees (Fig. 25) from Type Strains Server (GBDP). In the whole genome sequence-based phylogenetic tree, Pseudomonas sp. 2 (Barcode4_Contig 1) was grouped together with P. putida and P. monteilii with the bootstrap value of 75% (Fig. 25).

Discussion ANI and dDDH analysis revealed the identities of bacteria isolated from coffee berry borer eggs at cut-off values of >95% and >70%, respectively. The 16S rDNA sequence-based and whole genome sequence-based phylogenetic trees were also used to analyze the phylogenetic relationship of the bacterial species, however, we found whole genome sequenced-based trees to be more reliable for the species-level analysis compared to 16S rDNA sequence-based trees. Three bacterial species were identified in this analysis: Pseudomonas parafulva, Serratia nematodiphila, and Ochrobactrum pseudogrignonense (Table 3). Microbacterium sp., Pantoea sp., and Pseudomonas sp. 2 yielded less than the expected ANI and dDDH cut-off value and therefore are predicted to be potential new species (Table 3).

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Pseudomonas parafulva was one of the most abundant bacterial species detected from coffee berry borers in Hawaii (Aoki, 2021). Pseudomonas parafulva was previously isolated from coffee berry borer frass of reared colonies and found to have caffeine demethylase genes ndmA, ndmB, ndmC, ndmD, and ndmE (Vega et al., 2021). When the caffeine-degradation capability of this bacteria was isolated from coffee berry borer eggs and examined , the bacteria was able to degrade 3 g/L of caffeine in the mineral media. There has been no previous report demonstrating coffee berry borer associations with S. nematodiphila or the caffeine-degradation capability by this species. Serratia nematodiphila, was originally isolated from the intestine of the entomopathogenic nematode, gen nov. and reported to be an essential symbiont for growth and survival when the nematode was infecting the target insect species (Zhang et al., 2009), as has been seen in the Photorhabdus and Xenorhabdus endosymbionts of the entomopathogenic nematode, Heterorabditis and Steinernema (Kwak et al., 2015). According to Zhang et al. (2009), S. nematodiphila DSM 21429T also demonstrated virulent mortality on insect hosts and nematode-bacteria symbioses (Zhang et al., 2009). Because of insecticidal properties, S. nematodiphila has gained interest as a biocontrol agent of pest insect species (Patil et al., 2012; Xu et al., 2017). Furthermore, the bacterial strains belonging to S. nematodiphila seem to have multi-dimensional properties. The strains have been reported to degrade sulfur (Malarkodi et al., 2013), have bactericidal activity against species such as Bacillus subtilis and Klebsiella planticola (Malarkodi et al., 2013), have a Gibberellin plant growth-promoting effect, (Dastager et al., 2011; Kang et al., 2015), and degrade steroid estrogens released into environment (Zhao et al., 2020). Although S. marcescens was previously isolated from coffee plantation soil, and demonstrated caffeine degradation at 1.2 g/L (Mazzafera et al., 1999), there has been no report regarding caffeine degradation by the closely related species, S. nematodiphila. S. nematodiphila isolated from coffee berry borer eggs was tested for caffeine-degradation capability, and it demonstrated a high caffeine degradation ability at 15 g/L (Aoki, 2021). Hence, this is the first report of S. nematodiphila association with coffee berry borer and caffeine degradation. Furthermore, based on the 79% dDDH threshold for the subspecies clustering, S. nematodiphila isolated from coffee berry borer eggs may belong to a new subspecies. The 16S rDNA gene- and whole genome-based phylogenetic trees also support this conclusion. In addition, S. nematodiphila was not detected when 16S rRNA amplicon sequencing of bacteria associated with coffee berry borer was conducted. On the other hand, S.

135 marcescens was detected with high abundance and high frequency from coffee berry borers sampled from the islands of Hawaii and Oahu (Aoki, 2021). Although S. nematodiphila (EU036987.1) was in the Illumina Greengenes database (Version 1.0) at the time of analysis, it is impossible to distinguish S. nematodiphila from S. marcescens based only on the 16S rRNAs, which give ANI value of >99% similarities between those two species. Furthermore, ANI values based on the whole genome- sequences of these species indicate that they are essentially the same species based on the values of 95.33% and 95.3%, whereas dDDH values of 27.2% separates them as two distinct species at the whole genome-level. Therefore, it could be a possibility that the S. nematodiphila was misidentified as S. marcescens due to the similarity of these species. ANI and dDDH values of Ochrobactrum sp. indicate that this species belongs to O. pseudogrignonense. The whole genome-based phylogenetic tree and 16S rDNA sequence-based phylogenetic tree also support those two species being identical with the bootstrap value of 100% . This bacterium was highly abundant in our samples from the islands of Hawaii and Oahu (Aoki, 2021) and demonstrated caffeine degradation at 1 g/L (Aoki, 2021). Unknown species of bacteria belonging to Ochrobactrum sp. were previously isolated from the gut of coffee berry borers from Hawaii confirmed the capability of caffeine degradation (Ceja-Navarro et al. 2015). Vega et al. (2021) also isolated Ochrobactrum sp. from macerated eggs of coffee berry borers from Mexico and detected the presence of ndmE. The ANI value of Achromobacter sp. yielded 97.2% homology to A. deleyi FDAAPGOS 105097. However, the ANI value of A. deleyi type strain LMG3458T was 89.98%. The dDDH value of A deleyi FDAAOGOS 105097 compared to the target Achromobacter sp. was 72.4%, whereas A. deleyi LMG3458T was 36.9%. This implies the Achromobacter sp. belongs to A. deleyi FDAAOGOS 105097, but not A. deleyi LMG 3458T. Therefore, since this Achromobacter sp. does not belong to the type strain of A. deleyi, Achromobacter sp. is considered as a new species. The whole-genome sequence-based phylogenetic tree of Achromobacter sp. also supports that Achromobacter sp. from our experiment and A. deleyi FDAAOGOS 195097 are not the same species with a bootstrap value of 99%. In addition, based on the ANI and dDDH values of A. deleyi LMG3458T and FDAAOGOS at 89.9% and 34.3%, indicates that these two bacterial stains do not belong to the same species. Whole genome phylogenetic tree also showed that those

136 two species are distantly related. This indicates that A. deleyi FDAAOGOS 105097 is misidentified in the NCBI GenBank database. The bacterial species previously identified as Pantoea sp. by targeting 16S rRNA was found to be more closely related to Erwinia sp. than Pantoea sp. Erwinia billingiae and E. tasmaniensis were most associated with coffee berry borer samples from Oahu and the island of Hawaii (Aoki, 2021). However, the dDDH, ANI, and phylogenetic trees indicated that the bacteria of interest do not belong to these species . Both phylogenetic trees, 16S rDNA and whole genome sequences, also support that this species is not the same species as the two Erwinia species mentioned above. Based on these analyses, this could be another potential new species. In addition, ndmA and ndmD were detected in Erwinia sp. S 38 from whole eggs of coffee berry borer in the previous experiment (Vega et al., 2021). This Erwinia strain was able to degrade 10 g/L of caffeine (Aoki, 2021). ANI and dDDH values of Microbacterium sp. yielded lower than expected values , meaning this could be another potential new species. The 16S rDNA and whole genome-based phylogenetic trees also support the result . Microbacterium binotii was previously isolated from the gut of coffee berry borers and reported to have caffeine-degradation ability (Ceja-Navarro et al., 2015). Only a partial 16S rRNA sequence (1,406 bp) was available for this bacterium in the NCBI database, which was not suitable for the whole genome based dDDH and ANI comparison with other Microbacterium species. Therefore, 16S rRNA of M. binotii was analyzed separately. When the 16S rRNA of the isolated Microbacterium sp., was compared to M. binotii CIP 1031303T, ANI yielded a value of 95.06%, whereas the cutoff value of 16S rRNA of ANI should be close to 100% to determine these to be the same species. Although the analysis has extensively screened for the Microbacterium species currently deposited in the NCBI GenBank database for the potential match, it did not yield identification of the Microbacterium sp. Therefore, this bacterium is predicted to be a potential new species. Aside from the 16 species of Pseudomonas that were analyzed using ANI and dDDH and the 59 type strains analyzed by TYGS based on the similarity of dDDH values, an additional 24 bacterial strains found in low frequencies were downloaded from the NCBI database and compared to Pseudomonas sp. 2 by ANI (Appendix. Figure 1). This analysis also did not yield an expected value of >95% ANI for any of the species. Pseudomonas. alcaligenes was previously reported from coffee plantation soils (Babu et al., 2005), however, ANI values of

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Pseudomonas sp. 2 of 83.22 % indicates that they are not the same species . An additional 55 bacterial stains were analyzed using ANI for the further analysis (Appendix. Figures 2 to 6), but none of them yielded the ANI value of >95%. Based on the analysis of dDDH, ANI, and phylogenetic trees created with 16S rDNA and whole genome sequences , this is also a potential new species. In addition, Pseudomonas sp. 2 was able to degrade caffeine at a high concentration of 15 g/L, whereas the P. parafulva only degraded 3 g/L of caffeine. This indicates that there are variations in caffeine-degradation abilities within the same genus (Aoki, 2021). Pseudomonas sp. was previously detected within the eggs of coffee berry borers by Fluorescent in situ hybridization, the Pseudomonas species isolated from the eggs demonstrated caffeine-degrading capabilities. The identities of the bacteria were P. parafulva and a potential new Pseudomonas species. Since CBBcdm, the primers used to localize the bacteria within the eggs were found to be Pseudomonas species-specific primers, both P. parafulva and the new Pseudomonas sp., or at least one of them could be in the eggs of the coffee berry borer. Although the identity and the function of this bacteria is unknown, the universal eubacterial primer labeled with an Alexa fluorescence probe revealed the potential presence of an unknown secondary bacterial species in the coffee berry borer eggs in Chapter 2. Therefore, there needs to be further examination by Fluorescent in situ Hybridization using the primers that are specific to each Pseudomonas species as well as the other five bacterial species isolated from the eggs to 1) articulate which bacteria are present within the eggs 2) analyze the clusters of caffeine degradation genes in the bacterial genomes, and 3) investigate the mechanisms of their caffeine degradation. My chapters have focused mainly on exploring the vertical transmission mode of bacterial species that are involved in the caffeine degradation in coffee berry borer’s system; however, we cannot disregard addressing the possibilities of horizontal transmission/acquisition of these bacteria by the insect from the environment where coffee berry borers inhabit. As it was discussed in the introduction, many bacterial species equipped with caffeine-degradation capabilities have been isolated from the environment including coffee plantation soils, and coffee plant tissues (Yamaoka-Yano and Mazzafera, 1999; Summer et al., 2015). These bacteria species are namely, P. putida (Yamaoka-Yano and Mazzafera, 1999; Summers et al., 2011), P. monteilii (Aimurti et al., 2018), P. chlororaphis (Nunes and de Melo, 2006), P. alcaligenes (Babu et al., 2005), and S. marcescens (Starr et al., 1979).

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Although their caffeine-degradation capabilities were not clear for some of the species in the following studies, there have been several studies isolated and identified endophytic and epiphytic bacteria from coffee plant tissues (Miguel et al., 2013;Vega et al., 2005). Nineteen bacterial genera were previously isolated from coffee plant tissues including leaves, stems, roots, berry pulps and seeds (Vega et al., 2005) and identified as endophytes and epiphytes from Colombia, Hawai’i, and Mexico (Vega et al., 2005). These bacteria were Bacillus, Burkholderia, Clavibacter, Curtobacterium, Escherichia, Micrococcus, Pantoea, Pseudomonas, Serratia, Stenotrophomonas, Pseudomonas sp., P. chloroaphis, and P. putida (Vega et al., 2005). Furthermore, Miguel et al. (2013) have isolated 17 bacterial endophytes from Coffea canephora plant tissues (berries) from Brazil, and identified as Microbacterium sp., Kocuria turfanensis, Ochrobactrum sp., Agrobacterium tumefaciens, Janibacter melonis, Chryseobacterium sp., Bacillus pumilus, Bacillus subtilis, Bacillus amyloliquefaciens, Paenibacillus sp., Bacillus firmus, Klebsiella oxytoca, Enterobacter hormaechei, Escherichia coli, Citrobacter freundii, Pantoea vagans, and Pantoea eucrina . Considering that Microbacterium sp., Ochrobactrum sp., Pantoea sp., Serratia sp., and Pseudomonas sp. were previously isolated from the environment as it was mentioned above, it could be that the coffee berry borer not only relies on the bacteria that are transferred vertically, but also may utilize the available bacterial species in the environment to aid caffeine degradation in their system. Therefore, it is a mixed mode of transmission. The possible scenario regarding transmission/acquisition of bacteria by coffee berry borer via mixed mode has been discussed in the Chapter 2. The essential symbionts may be acquired by insects via both vertical and horizontal, depending on their life stages. The coffee berry borer larvae could be already equipped with the caffeine-degrading bacteria passed on from the mothers (via vertical transmission) while they are in the eggs, then acquire more from the environment (via horizontal transmission) during their development, regaining more bacteria to supplement the function of caffeine degradation in their system. This could be a plausible explanation of bacterial acquisition by insects based on the information that insects typically shed the lining of the foregut and hindgut and end up eliminating most or all gut contents each time they molt (Hammer and Moran, 2019). Therefore, the bacteria previously present in their gut would be purged during their molting and then re-acquired from the environment later on (Hammer and Moran, 2019; Vega et al., 2021). In order to determine these mechanisms of essential bacterial transmission by the insect, there needs to be further investigations into the

139 identities of endophytic/epiphytic bacteria of coffee and the capability of these bacteria to degrade caffeine in the future.

Conclusion Three bacterial species previously isolated from the eggs of coffee berry borer with demonstrated caffeine degradation were identified as Pseudomonas parafulva, Serratia nematodiphila, and Ochrobactrum pseudogrignonense. Four bacterial strains were determined to be potential novel species:Microbacterium sp., Pantoea sp., Erwinia sp., and Pseudomonas sp. 2. For further investigation, the bacterial genomes sequenced will be used for future analysis of annotations and mapping of caffeine-degradation genes to achieve a better understanding of caffeine degradation mechanisms by bacteria in coffee berry borers, focusing on not only the genes involved in the N-demethylation, but also the C-8 oxidation pathway, which is less studied catabolic pathway of caffeine degradation in bacteria.

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Figures and Tables

Table 1: The bacterial genera isolated from coffee berry borer eggs were subjected to whole genome sequencing using MinION platform. The barcode numbers were assigned for each genus for the identification (first column). The numbers of contigs obtained after polishing were listed in the second column after the genera. Contigs used for the analysis were listed in the fourth column with *, followed by the length of contigs in the fifth column. %GC of the combined contigs that are used for the analysis is summarized in the last.

Bacterial genera Barcode Contig used Length of %GC of (# of contigs # for analysis contigs (bp) combined * obtained) Ochrobactrum sp. 1 1* 53.57 (4) 1,685,059 2* 236,7033 3* 1,044,406 4 124,892 Achromobacter sp. 2 5* 68.17 (5) 6,792,192 1 50,053 2 63,842 3 74,412 4 118,562 3 Pantoea sp. (4) 4* 5,212,005 54.8

Pseudomonas sp. 1 4 1* 62.02 (1) 5,421,947 Microbacterium sp. 5 1* 68.18 (2) 3,618,519 2 17,916 Pseudomonas sp. 2 22 1* 62.28 (1) 6,480,860 23 Serratia sp. (9) 1* 1,607,377 59.7 2* 1,220,079 3* 164,325 4* 135,213 5* 421,558 6* 614,842 7* 445,492 8* 147,321 9* 7,1311

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Table 2: The number of reads/ assigned barcodes, number of contigs, and total nucleotide lengths obtained from MinION sequencing is summarized above.

Barcode # Reads # contigs Total nucleotides (bp) Barcode 1 70,457 4 5,221,390 Barcode 2 54,105 5 7,099,061 Barcode 3 31,276 4 5,851,061 Barcode 4 72,103 1 6,480,860 Barcode 5 55,970 2 3,636,435 Barcode22 5,019 1 5,421,947 Barcode 23 50,656 9 4,827,518 Total 339,586 26 38,538,272 Average 4,8512 5,505,467

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Table 3: Summary of bacteria used for the analysis. The column 1 summarizes the seven genera identified into genus-level by targeting 16S rRNA in chapter 3. The column 2 summarizes the bacterial species identified targeting the whole genome at cut-off values of 95% and 70% of ANI and dDDH for species delineation framework followed by the ANI and dDDH values of each strain obtained in column 4 and 5. Maximum caffeine degradation capabilities for each bacterium tested in Chapter 3 is summarized in column.

Bacterial genera Bacterial species identified (Whole ANI dDDH Barcode # identified genome) (%) (%) (16S rRNA) 1 Ochrobactrum sp. Ochrobactrum pseudogrignonense 97.6 79 2 Achromobacter sp. Novel Achromobacter sp. x x 3 Pantoea sp. Novel Erwinia sp. x x 4 Pseudomonas sp. 1 Novel Pseudomonas sp. x x 5 Microbacterium sp. Novel Microbacterium sp. x x 22 Pseudomonas sp. 2 Pseudomonas parafulva 98.26 81.9 23 Serratia sp. Serratia nematodiphila 97 72.5

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Figure 1:Pseudomonas sp. 1 were compared against 12 Pseudomonas species. Pairwise heatmap of Pseudomonas species were created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as percentage among 13 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Pseudomonas sp. 1 against 12 other Pseudomonas species. The left row represents dDDH values of Pseudomonas sp. 1.

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Figure 2: GBDP tree of Pseudomonas sp. (16S rDNA gene sequence-based) A total 44 strains of Pseudomonas species and 8 strains of other genus were used to generate 16S rDNA gene sequence-based phylogenetic trees from Type Strains Server (GBDP). Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from 16S rDNA gene sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 56.3 %. The tree was rooted at the midpoint.

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Figure 3: GBDP tree of Pseudomonas sp. (whole-genome sequence-based). A total 44 strains of Pseudomonas species and 8 strains of other genus were used to generate whole-genome sequence-based phylogenetic trees from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 53.4 %. The tree was rooted at the midpoint.

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Figure 4: Serratia sp. (Serratia_Barcode_23) was compared against 4 Serratia species. Pairwise heatmap of Serratia species were created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as percentage among 5 Serratia species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Serratia sp. against 4 other Serratia species. The left row represents dDDH values of Serratia sp.

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Figure 4-1: ANI of Serratia nematodiphila and S. marcescens based on the 16S rRNA downloaded from NCBI Genbank. Pairwise heatmap of Serratia species were created based on the Average Nucleotide Identity (ANI) values of 16S rRNA. S. nematodiphila EU036987.1 and S. marcescens AB061685.1 was used for the analysis in Chapter 1.

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Figure 5-1: GBDP tree of Serratia sp. (16S rDNA gene sequence-based) A total 21 strains of Serratia species and 30 strains of other genus were used to generated 16S rDNA gene sequence-based phylogenetic trees from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from 16S rDNA gene sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 61.0 %. The tree was rooted at the midpoint.

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Figure 5-2: Partial GBDP tree of Serratia sp. (16S rDNA gene sequence-based) focused on the cluster with Barcode 23 in Figure. 5-1.

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Figure 6: GBDP tree of Serratia sp. (whole-genome sequence-based) A total 21 strains of Serratia species and 30 strains of other genus were used to generate whole-genome sequence-based phylogenetic trees from Type Strains Server (GBDP). Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 70.1 %. The tree was rooted at the midpoint.

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Figure 7: Ochrobactrum sp. (Ochrobactrum_barcode 1_Contig 123) was compared against 5 other species of Ochrobactrum. Pairwise heatmap of Ochrobactrum species were created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as a percentage among 6 species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Ochrobactrum sp. against 5 other species. The left row represents dDDH values of Ochrobactrum sp.

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Figure 8: GBDP tree of Ochrobactrum sp. (16S rDNA gene sequence-based) A total 35 strains of Ochrobactrum and Brucella species and 3 strains of other genus were used to generate 16S rDNA gene sequence-based phylogenetic trees from Type Strains Server (GBDP). Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from 16S rDNA gene sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 61.0 %. The tree was rooted at the midpoint.

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Figure 9: GBDP tree of Ochrobactrum sp. (whole-genome sequence-based) A total 35 strains of Ochrobactrum and Brucella species and 3 strains of other genus were used to generated and whole-genome sequence-based phylogenetic trees from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 70.1 %. The tree was rooted at the midpoint.

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Figure 10: Achromobacter sp. (Achromobacter_Barcode2_contig 2) was compared to 7 other Achromobacter species. Pairwise heatmap of Achromobacter species were created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as percentage among 8 Achromobacter species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Achromobacter sp. against 7 other species. The left row represents dDDH values of Achromobacter sp.

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Figure 11: GBDP tree of Achromobacter sp. (16S rDNA gene sequence-based) A total 28 strains of Achromobacter species and 7 strains of other genus were used to generated 16S rDNA gene sequence-based phylogenetic trees from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from 16S rDNA gene sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 53.2 %. The tree was rooted at the midpoint.

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Figure12: GBDP tree of Achromobacter sp.(whole-genome sequence-based) A total 28 strains of Achromobacter species and 7 strains of other genus were used to generated whole-genome sequence-based phylogenetic trees from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 82.7 %. The tree was rooted at the midpoint.

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Figure 13:Microbacterium sp. (Microbacteirum_Barcode5_cintig 1) was compared to 21 others. Pairwise heatmap of Microbacterium species were created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as percentage among 22 Microbacterium species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Microbacterium sp. against 15 other species. The left row represents dDDH values of Microbacterium sp.

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Figure 14: GBDP tree of Microbacterium sp. (16S rDNA gene sequence-based) A total 94 strains of Microbacterium species and 1 other strain belonging to other genera were used to generate 16S rDNA gene sequence-based phylogenetic trees from Type Strains Server (GBDP). Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from 16S rDNA gene sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 62.7 %. The tree was rooted at the midpoint.

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Figure 15: GBDP tree of Microbacterium sp. (whole-genome sequence-based) A total 94 strains of Microbacterium species and 1 other strain belong to other genera were used to generated whole-genome sequence-based phylogenetic trees from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 66.9 %. The tree was rooted at the midpoint.

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Figure 16: Pantoea sp. (Pantoea_Barcode_3contig 4) was compared to 11 others. Pairwise heatmap of Pantoea species were created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as percentage among 12 Pantoea species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Pantoea sp. against 11 other species. The left row represents dDDH values of Pantoea sp.

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Figure 17: Pantoea sp. (Pantoea_Barcode_3contig 4) was compared to 10 others Pairwise heatmap of Pantoea species were created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as a percentage among 11 Pantoea species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Pantoea sp. against 10 other species. The left row represents dDDH values of Pantoea sp.

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Figure 18: GBDP tree of Pantoea sp. (16S rDNA gene sequence-based) A total 31 strains of Pantoea species 7 strains of Erwinia sp., and 16 strains of other genus were used to generated 16S rDNA gene sequence-based from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from 16S rDNA gene sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 39.4 %. The tree was rooted at the midpoint.

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Figure 19: GBDP tree of Pantoea sp.(whole-genome sequence-based) A total 31 strains of Pantoea species 7 strains of Erwinia sp., and 16 strains of other genus were used to generated -genome sequence-based phylogenetic trees from Type Strains Server (GBDP). Tree inferred with FastME 2.1.6.1from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support 61.7 %. The tree was rooted at the midpoint.

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Figure 20 Pantoea sp. (Pantoea_Barcode_3contig 4) was compared to 6 Erwinia sp. and 4 Mixta sp. Pairwise heatmap was created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as a percentage among 10 species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Pantoea sp. against 9 other species. The left row represents dDDH values of Pantoea sp

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Figure 21: GBDP tree of Pantoea sp.(16S rDNA gene sequence-based) A total 18 strains of Pantoea species 16 strains of Erwinia species, and 9 strains of Mixta species, and 29 other genera were used to generated 16S rDNA gene sequence-based from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support 59.9 %. The tree was rooted at the midpoint

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Figure 21: GBDP tree of Pantoea sp.(whole-genome sequence-based) A total 18 strains of Pantoea species 16 strains of Erwinia species, and 9 strains of Mixta species, and 29 other genera were used to generated whole-genome sequence-based phylogenetic trees from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 87.9 %. The tree was rooted at the midpoint.

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Figure 23: Pseudomonas sp. (Barcode 4_contig 1) was compared to 16 other Pseudomonas species. Pairwise heatmap of Pseudomonas species were created based on the Average Nucleotide Identity (ANI) values and digital DNA-DNA Hybridization (dDDH) values. Both ANI and dDDH are represented as percentage among 17 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico dDDH data. The cut off values of species delineation are 95% and 70% for ANI and dDDH. The top column represents ANI values of Pseudomonas sp. against 16 other species. The left row represents dDDH values of Pseudomonas sp.

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Fig 24: GBDP tree of Pseudomonas sp.(16S rDNA gene sequence-based) A total 64 strains of Pseudomonas species, and 8 strains of other genera were used to generated 16S rDNA gene sequence-based phylogenetic tree from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support 65 %. The tree was rooted at the midpoint

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Figure 25: GBDP tree of Pseudomonas sp. (whole-genome sequence-based) A total 64 strains of Pseudomonas species, and 8 strains of other genera were used to generated 16S rDNA gene sequence-based phylogenetic trees from Type Strains Server (GBDP).Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers above branches are GBDP pseudo-bootstrap support values > 60 % from 100 replications, with an average branch support of 64.0 %. The tree was rooted at the midpoint

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Appendix

Appendix 4-1: Pairwise comparison of Pseudomonas sp. 2 (Top row) against 24 Pseudomonas species that were found in low frequencies from Pseudomonas sp. 2 genome. The heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 25 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

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Appendix 4-2: Pairwise comparison of Pseudomonas sp. 2 (Top row) against 15 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 16 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

Appendix 4-3: Figure 2: Pairwise comparison of Pseudomonas sp. 2 (Top row) against 10 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 11 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

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Appendix 4-4: Pairwise comparison of Pseudomonas sp. 2 (Top row) against 12 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 13 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

Appendix 4-5: Pairwise comparison of Pseudomonas sp. 2 (Top row) against 11 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 12 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

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Appendix 4-6 Pairwise comparison of Pseudomonas sp. 2 (Top row) against 14 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 15 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

Appendix 4-7: Pairwise comparison of Pseudomonas sp. 1 (Top row) against 9 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 10 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

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Appendix 4-8: Pairwise comparison of Pseudomonas sp. 1 (Top row) against 9 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 10 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

Appendix 4-9: Pairwise comparison of Pseudomonas sp. 1 (Top row) against 7 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 8 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

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Appendix 4-10: Pairwise comparison of Pseudomonas sp. 1 (Top row) against 7 Pseudomonas species. heatmap of Pseudomonas species were created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 8 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

Appendix 4-11: Pairwise comparison of Pseudomonas sp. 1 (Top row) against 9 Pseudomonas species. A heatmap of Pseudomonas species was created based on the Average Nucleotide Identity (ANI) values and Alignment Percentage (AP) values. Both ANI and AP are represented as percentage among 10 Pseudomonas species. The upper diagonal displays ANI data, whereas lower diagonal depicts the in silico AP data. The cut off value of species delineation is 95%.

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Bioproject Biosample Assembly Preferred name Deposit accession accession accession NBRC Aerosticca soli 112897T PRJDB6914 SAMD00115724 GCA_003967035 Pseudomonas putida NBRC 14164 PRJDB191 SAMD00061028 GCA_000412675 Pseudomonas putida NBRC 14164T Pseudomonas asiatica JCM 32716T PRJDB7090 SAMD00127555 GCA_009932335 Pseudomonas asiatica RYU5T NH517510T Pseudomonas reidholzensis CCOS 865 PRJEB28254 SAMEA4836241 GCA_900536025 Pseudomonas cremoris WS 5106T PRJNA224116 SAMN14516828 GCF_014230465 Pseudomonas cremoricolorata DSM 17059 PRJNA188911 SAMN02441180 GCA_000425745 Pseudomonas cremoricolorata DSM17059T Pseudomonas vranovensis DSM 16006 PRJNA188914 SAMN02441181 GCA_000425805 Pseudomonas parafulva DSM 17004 PRJNA188912 SAMN02441530 GCA_000425765 Barcorde22 Pseudomonas parfulva DSM17004T Pseudomonas alkylphenolica JCM 16553 PRJNA224116 SAMN02929205 GCF_000746525 Pseudomonas alkylphenolica KL28T Lampropedia cohaerens CT6 PRJNA282900 SAMN03580769 GCA_001005215 Pseudomonas taiwanensis DSM 21245 PRJNA188913 SAMN02440864 GCA_000425785 Pseudomonas taiwanensis DSM21245T Pseudomonas eucalypticola NP-1 T PRJNA639687 SAMN15248734 GCA_013374995 Cupriavidus gilardii CCUG 38401 PRJNA563568 SAMN12697570 GCA_008801915 Pseudomonas qingdaonensis JJ3 PRJNA419834 SAMN08099659 GCA_002806685 Pseudomonas huaxiensis WCHPs060044 PRJNA224116 SAMN09302770 GCF_003231275 Pseudomonas Pseudomonas inefficax sp. JV551A3 PRJEB24815 SAMEA104569200 GCA_900277125 Achromobacter veterisilvae LMG 30378T PRJEB24293 SAMEA4780387 GCA_900496975 Pseudomonas tructae SNU WT1 PRJNA522009 SAMN10922936 GCA_004214895 Pseudomonas monteilii DSM 14164 PRJNA221052 SAMN02743984 GCA_000621245 Pseudomonas monteilli DSM14164T Pseudomonas plecoglossicida DSM 15088 PRJNA221049 SAMN02743900 GCA_000688275 Pseudomonas plecogrossicida DSM15088T Pseudomonas fulva DSM 17717 PRJNA221053 SAMN02743980 GCA_000621265 Psuedomonas fulva DSM17717T Pseudomonas mosselii DSM 17497 PRJNA221051 SAMN02743979 GCA_000621225

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Pseudomonas wadenswilerensis CCOS 864 PRJEB27830 SAMEA4796555 GCA_900497695 Azomonas agilis DSM 375 PRJNA262246 SAMN05660238 GCA_007830255 Pigmentiphaga kullae DSM 13608 PRJNA520317 SAMN10866310 GCA_004216695 Pseudomonas sichuanensis WCHPs060039 PRJNA415331 SAMN09302769 GCA_003231305 Stenotrophomonas nitritireducens DSM 12575 PRJNA284361 SAMN03701629 GCA_001431425 Pseudomonas glycinae MS586 PRJNA309549 SAMN04435621 GCA_001594225 Pseudomonas japonica DSM 22348 PRJNA331484 SAMN05444352 GCA_900188455 Pseudomonas japonica NBRC 103040 PRJDB212 SAMD00018431 GCA_000730585 Cupriavidus metallidurans CH34 PRJNA250 SAMN02598450 GCA_000196015 Pseudomonas mosselii DSM17497T Pseudomonas hutmensis XWS2T PRJNA474423 SAMN09303047 GCA_004025535 Pseudomonas donghuensis HYS PRJNA89717 SAMN02472130 GCA_000259195 Pseudomonas abietaniphila ATCC 700689 PRJEB15899 SAMN05216605 GCA_900100795 Pseudomonas guariconensis LMG 27394 PRJEB15687 SAMN05216185 GCA_900102675 Pseudomonas fuscovaginae ICMP 5940 PRJDB1420 SAMD00000756 GCA_000467065 Pseudomonas asplenii ATCC 23835 PRJEB16404 SAMN05216598 GCA_900105475 Pseudomonas mucidolens LMG 2223 PRJEB16499 SAMN05216202 GCA_900106045 Pseudomonas entomophila L48 PRJNA16800 SAMEA3138225 GCA_000026105 Pseudomonas entomophila L48T Pseudomonas soli LMG 27941 PRJEB16975 SAMN05216230 GCA_900110655 Pseudomonas soli LMG27941T Appendix 4-12: List of type strains that are compared to the Pseudomonas sp.1 for dDDH by TYGS

Bioproject Biosample Assembly Preferred name Deposit accession accession accession Serratia inhibens S40 PRJNA491277 SAMN10068376 GCA_003591175 Serratia proteamaculans CCUG 14510 PRJNA563568 SAMN12839049 GCA_008830365 Enterobacter dykesii E1T PRJNA224116 SAMN12560198 GCF_008364625 Candidatus Moranella endobia PCIT PRJNA224116 SAMN02604229 GCF_000219175 Escherichia hermannii NBRC 105704T PRJDB14 SAMD00041803 GCA_000248015 Candidatus Arsenophonus lipoptenae CB PRJNA306001 SAMN04338047 GCA_001534665 Acinetobacter cumulans WCHAc060092 PRJNA387062 SAMN08772519 GCA_003024525 Acinetobacter oleivorans JCM 16667 PRJNA224116 SAMN02603211 GCF_000196795 Serratia quinivorans NCTC 11544 PRJEB6403 SAMEA3282965 GCA_900457075 Rouxiella badensis DSM 100043 PRJNA224116 SAMN06101559 GCF_002093665 Raoultella planticola ATCC 33531 PRJNA65511 SAMN02743268 GCA_000735435

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Klebsiella trevisanii DSM 2688 PRJNA500331 SAMN10362885 GCA_004345285 Serratia marcescens subsp. sakuensis KCTC 42172 PRJNA484649 SAMN09767465 GCA_003428265 Serratia marcescens ATCC 13880 PRJNA59561 SAMN02743269 GCA_000735445 Serratia nematodiphila DSM 21420 PRJNA257492 SAMN02952129 GCA_000738675 Serratia nematodiphila DSM21420T Serratia nematodiphila PH SO1 Serratia odorifera DSM 4582 PRJNA40087 SAMN00120576 GCA_000163595 Pectobacterium wasabiae CFBP 3304 PRJNA159625 SAMN02471791 GCA_000291725 Enterobacter asburiae ATCC 35953 PRJNA285282 SAMN03742638 GCA_001521715 Providencia heimbachae ATCC 35613 PRJNA210924 SAMN04875434 GCA_001655055 Klebsiella singaporensis LMG 23571 PRJNA309027 SAMN04419546 GCA_001969305 Klebsiella variicola DSM 15968 PRJNA272370 SAMN01174581 GCA_000828055 Pectobacterium peruviense IFB5232 PRJNA313959 SAMN04526254 GCA_002847345 Serratia symbiotica CWBI-2.3 PRJEB6106 SAMEA2581874 GCA_000821185 Serratia symbiotica CWBI 2.3 Skermanella stibiiresistens SB22 PRJNA214805 SAMN02951888 GCA_000576635 Idiomarina zobellii KMM 231 PRJNA291100 SAMN03939991 GCA_001294745 Buttiauxella brennerae ATCC 51605 PRJNA210901 SAMN04870318 GCA_001654925 Kosakonia oryzendophytica REICA_082 PRJEB15027 SAMN04487791 GCA_900094925 Hafnia psychrotolerans CGMCC1.12806 PRJDB10509 SAMD00244972 GCA_014639435 Serratia ficaria NBRC 102596 PRJDB1514 SAMD00046907 GCA_001590885 Serratia grimesii NBRC 13537 PRJDB267 SAMD00046909 GCA_001590905 Serratia plymuthica NBRC 102599 PRJDB268 SAMD00046908 GCA_001590925 Serratia rubidaea NBRC 103169 PRJDB269 SAMD00046903 GCA_001598675 Pantoea ananatis LMG 2665 PRJNA240762 SAMN02680126 GCA_000661975 Klebsiella pneumoniae subsp. ozaenae ATCC 11296 PRJEB7845 SAMEA3141919 GCA_000826585 Rickettsia prowazekii Breinl PRJNA188530 SAMN01983876 GCA_000367405 Serratia fonticola LMG 7882 PRJNA213314 SAMN02471347 GCA_000469035 Yersinia bercovieri ATCC 43970 PRJNA16104 SAMN00005351 GCA_000167975 Xenorhabdus koppenhoeferi DSM 18168 PRJNA329748 SAMN05421784 GCA_900116635 Kluyvera cryocrescens NBRC 102467 PRJDB285 SAMD00046725 GCA_001571285 Serratia liquefaciens ATCC 27592 PRJNA208332 SAMN02604177 GCA_000422085 Serratia liquefaciense ATCC27592 Enterobacter chengduensis WCHECl-C4 PRJNA415108 SAMN06249239 GCA_001984825 Barcode 23 19 Barcode23

185

Serratia symbiotica Cinara cedrii Serratia marcescnes XRSC 14 Serratia ureilytica CC119

Appendix 4-13: List type strains compared to Serratia sp. for dDDH by TYGS.

Bioproject Biosample Assembly Preferred name Deposit accession accession accession Rhizobium esperanzae CNPSo 668 PRJNA378648 SAMN06555453 GCA_002204185 Brucella pituitosa CCUG 50899 PRJNA445841 SAMN08800193 GCA_003049685 Ochrobactrum4 Ochrobactrum soli BO-7 PRJNA494976 SAMN10187550 GCA_003664555 Brucella gallinifaecis Iso196 PRJNA224116 SAMN11998090 GCA_006476605 Brucella rhizosphaerae PR17 PRJNA391102 SAMN07258022 GCA_002252475 Ochrobactrum contig123 Brucella pseudogrignonensis CCUG 30717 PRJNA391102 SAMN07258024 GCA_002252525 Ochrobactrum2 Ochrobactrum3 Brucella thiophenivorans DSM 7216 PRJNA224116 SAMN07258021 GCF_002252445 Brucella grignonensis OgA9a PRJNA224116 SAMN07258023 GCF_002252505 Paenochrobactrum gallinarii DSM 22336 PRJNA546778 SAMN12025069 GCA_014205685 Brucella daejeonensis DSM 26944 PRJNA546777 SAMN12025165 GCA_014199265 Ochrobactrum quorumnocens A44 T PRJNA224116 SAMN07259827 GCF_002278035 Ensifer fredii USDA 205 PRJNA211973 SAMN04301590 GCA_001461695 Brucella lupini LUP21 PRJNA391246 SAMN07259926 GCA_002252535 Brucella anthropi ATCC 49188 PRJNA19485 SAMN02598421 GCA_000017405 Ochrobactrum Ochrobactrum1 Brucella inopinata BO1 PRJNA41855 SAMN02472076 GCA_000182725 Brucella canis ATCC 23365 PRJNA20243 SAMN02604295 GCA_000018525 Brucella ovis ATCC 25840 PRJNA12514 SAMN02604024 GCA_000016845 Brucella suis 1330 PRJNA70695 SAMN02604297 GCA_000223195 Brucella microti CCM 4915 PRJNA32233 SAMN02603148 GCA_000022745 Brucella abortus 544 PRJNA254449 SAMN02904715 GCA_000739315 Brucella melitensis 16M PRJNA180 SAMN02603416 GCA_000007125 Brucella vulpis F60 PRJEB12176 SAMEA3715143 GCA_900000005 Brucella pecoris 08RB2639 PRJNA548045 SAMN11998268 GCA_006376675 Brucella pecoris DSM 23868

186

Brucella tritici LMG 18957 PRJNA573682 SAMN12821298 GCA_008932295 Brucella intermedia LMG 3301 PRJNA37725 SAMN02472089 GCA_000182645 Brucella ciceri DSM 22292 PRJNA558852 SAMN12500706 GCA_012103155 Ochrobactrum5 Brucella oryzae NBRC 102588 PRJNA558852 SAMN12500703 GCA_012103035 Brucella cytisi LMG 22713 PRJNA558852 SAMN12500709 GCA_012103075 Brucella haematophila CCUG 38531 PRJNA544772 SAMN11855631 GCA_005938105 Appendix 4-14. List of type strains that were compared to the Ochrobactrum sp. to obtain dDDH by TYGS.

Achromobacter sp. Bioproject Biosample Assembly Preferred name Deposit accession accession accession Achromobacter mucicolens LMG 26685 PRJEB37567 SAMEA6647240 GCA_902859725 Achromobacter xylosoxidans NBRC 15126 PRJNA209573 SAMN02641513 GCA_000508285 Achromobacter xylosoxidans NTCT10807T Achromobacter spanius DSM 23806 PRJNA419110 SAMN08043154 GCA_002812705 Achromobacter spanius DSM2806 Pleomorphomonas carboxyditropha SVCO-16 PRJNA224116 SAMN07510660 GCF_002770725 Achromobacter veterisilvae LMG 30378T PRJEB24293 SAMEA4780387 GCA_900496975 Achromobacter insolitus DSM 23807 PRJNA358463 SAMN06174288 GCA_001971645 Achromobacte insolitus DSM23807 Extensimonas perlucida HX2-24 PRJNA551535 SAMN12272682 GCA_007655255 Achromobacter anxifer LMG 26857 PRJEB37567 SAMEA6647246 GCA_903652925 Achromobacter agilis LMG 3411 PRJEB27656 SAMEA4780388 GCA_900496965 Achromobacter marplatensis B2 PRJNA391539 SAMN07270369 GCA_002209535 Pandoraea apista DSM 16535 PRJNA305052 SAMN04316586 GCA_001465595 Curvibacter delicatus NBRC 14919 PRJDB1347 SAMD00047220 GCA_001592265 Achromobacter piechaudii NBRC 102461 PRJDB239 SAMD00046723 GCA_001571245 Achromobacter denitrificans NBRC 15125 PRJDB698 SAMD00046748 GCA_001571365 Achromobacter denitrificans NBRC15125T Luteimonas huabeiensis HB2 PRJNA232942 SAMN02952968 GCA_000559025 Thauera aminoaromatica DSM 14742 PRJNA175413 SAMN02470715 GCA_000310185 Achromobacter kerstersii LMG 3441 PRJEB37567 SAMEA6647248 GCA_902859595 Achromobacter animicus LMG 26690 PRJEB37567 SAMEA6647241 GCA_902859585 Delftia lacustris LMG 24775 PRJEB16626 SAMN05421547 GCA_900107225 Achromobacter insuavis LMG 26845 PRJEB37567 SAMEA6647244 GCA_902859645 Achromobacter ruhlandii LMG 1866 PRJEB37567 SAMEA6647237 GCA_902859695

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Achromobacter denitrificans USDA56712 Achromobacter pestifer LMG 3431 PRJEB37567 SAMEA6647247 GCA_902859625 Achromobacter pestifer LMG3431T Achromobacter deleyi LMG 3458 PRJEB37567 SAMEA6647249 GCA_902859705 Achromobacter deleyi LMG3458T Achromobacter aegrifaciens LMG 26852 PRJEB37567 SAMEA6647245 GCA_902859735 Achromobacter pulmonis LMG 26696 PRJEB37567 SAMEA6647242 GCA_902859765 Achromobacter deleyi FDAAPGOS 1050 Barcode 3 Achromobacter ruhlandii LMG1866T Appendix 4-15: List of Achromobacter types strains that are used by TYGS to compare dDDH.

Microbacterium sp. Bioproject Biosample Assembly Preferred name Deposit accession accession accession GCA_01419726 Microbacterium invictum DSM 19600 PRJNA347140 SAMN05877815 5 GCA_00365122 Microbacterium telephonicum S2T63 PRJNA440731 SAMN08775494 5 Microbacterium phyllosphaerae DSM 13468 Microbacterium imperiale DSM 20530 Microbacterium amylolyticum DSM 24221 GCA_01104697 Microbacterium amylolyticum DSM 24221 PRJNA608228 SAMN14167854 5 Microbacterium amylolyticum DSM24221T Microbacterium barkeri DSM 20145 GCA_00399187 Microbacterium lemovicicum ViU22 PRJNA419105 SAMN09748166 5 Microbacterium lemovicicum ViU22T Microbacterium GCA_00297097 halophytorum YJYP303 PRJNA427966 SAMN08278568 5 GCA_00801741 Microbacterium hatanonis JCM 14558 PRJNA224116 SAMN12558880 5 GCA_01300456 Microbacterium ulmi JCM 14282 PRJNA622446 SAMN14517836 5 GCA_01175970 Microbacterium ulmi CECT 5976 PRJNA546709 SAMN12025065 5 GCA_01132672 Microbacterium excoecariae CBS5P-1 PRJNA610138 SAMN14278075 5 GCA_00338757 Microbacterium bovistercoris NEAU-LLE PRJNA484679 SAMN09767657 5

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Microbacterium GCA_01340974 pseudoresistens DSM 22185 PRJNA347132 SAMN05877827 5 GCA_00042238 Microbacterium indicum DSM 19969 PRJNA188869 SAMN02440686 5 GCA_00042240 Microbacterium luticocti DSM 19459 PRJNA188870 SAMN02440801 5 GCA_00872777 Microbacterium lushaniae L-031 PRJNA543373 SAMN12791242 5 Microbacterium lushaniae L031 GCA_00872775 Microbacterium caowuchunii ST-M6 PRJNA543373 SAMN12791222 5 Microbacterium rhizomatis JCM 30598 PRJNA224116 SAMN12792678 GCF_008710745 GCA_00456435 Microbacterium wangchenii dk512T PRJNA529067 SAMN11254709 5 Microbacterium wangchenii dK512T GCA_00379783 Kocuria soli M5W7-7 PRJNA501788 SAMN10345596 5 SAMEA10465189 GCA_90029207 Microbacterium timonense Marseille-P5731 PRJEB24954 8 5 GCA_00886800 Microbacterium algeriense G1T PRJNA573086 SAMN12799030 5 GCA_90009688 Microbacterium enclense NIO-1002 PRJEB15469 SAMN05216418 5 GCA_00145695 Microbacterium enclense DSM 25125 PRJNA302088 SAMN04263881 5 GCA_00886802 Microbacterium oxydans DSM 20578 PRJNA573493 SAMN12811485 5 GCA_00456407 Microbacterium sediminis MCCC 1A06153 PRJNA528380 SAMN11180679 5 GCA_00274199 Microbacterium sediminis YLB-01 PRJNA261778 SAMN03075694 5 GCA_00168991 Microbacterium sediminis DSM 23767 PRJNA320460 SAMN04939663 5 Microbacterium sediminis YLB 01T GCA_00042274 Microbacterium gubbeenense DSM 15944 PRJNA185602 SAMN02440649 5 GCA_00653914 Microbacterium testaceum NBRC 12675 PRJDB3283 SAMD00097165 5 Microbacterium GCA_00801743 saccharophilum K-1 PRJNA559781 SAMN12559021 5 Microbacterium GCA_00799245 saccharophilum NBRC108778 PRJDB6314 SAMD00172671 5 GCA_00798882 Microbacterium aerolatum NBRC 103071 PRJDB6193 SAMD00166027 5 GCA_01463500 Microbacterium aerolatum CCM 4955 PRJDB10511 SAMD00244843 5 GCA_00653976 Microbacterium liquefaciens NBRC 15037 PRJDB5973 SAMD00097552 5 GCA_01464875 Microbacterium liquefaciens JCM 3879 PRJDB10510 SAMD00245378 5

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GCA_00325464 Microbacterium suaedae YZYP306 PRJNA427968 SAMN08278656 5 GCA_00312130 Microbacterium ureisolvens KCTC 39802 PRJNA459494 SAMN09064720 5 GCA_01413798 Microbacterium halimionae DSM 27576 PRJNA547256 SAMN12024942 5 Microbacterium radiodurans DSM 25564 PRJNA224116 SAMN12792677 GCF_008710705 Microbacterium GCA_01419241 proteolyticum CECT 8356 PRJNA546708 SAMN12025117 5 Microbacterium GCA_90010520 hydrocarbonoxydans DSM 16089 PRJNA303690 SAMN04489807 5 GCA_90010088 Microbacterium pygmaeum DSM 23142 PRJNA303688 SAMN04489810 5 Microbacterium esteraromaticum DSM8609T GCA_90010533 Microbacterium paraoxydans DSM 15019 PRJNA303689 SAMN04489809 5 GCA_00080230 Microbacterium mangrovi MUSC 115 PRJNA261100 SAMN03070121 5 GCA_00155380 Microbacterium hominis LCDC 84-209 PRJNA306491 SAMN04350845 5 GCA_00159212 Microbacterium hominis NBRC 15708 PRJDB1325 SAMD00047214 5 Microbacterium hominis LCDC 84 0209T Microbacterium hominis SJTG1 GCA_00155247 Microbacterium oleivorans NBRC 103075 PRJDB453 SAMD00046492 5 Microbacterium oleivolans NBRC103075T Microbacterium oleivolans A9 GCA_01464601 Microbacterium nanhaiense CGMCC 4.7181 PRJDB10509 SAMD00245182 5 GCA_01463518 Microbacterium murale CCM 7640 PRJDB10511 SAMD00244852 5 GCA_00076337 Microbacterium profundi Shh49 PRJNA257114 SAMN02947407 5 GCA_01464369 Microbacterium album CGMCC1.15794 PRJDB10509 SAMD00245088 5 Microbacterium resistens NBRC 103078 PRJDB431 SAMD00046494 GCA_001552355 Microbacterium immunditiarum DSM 24662 PRJNA347121 SAMN05878372 GCA_013409785 Microbacterium lacticum DSM 20427 PRJNA547276 SAMN12025723 GCA_006716815 Microbacterium lacticum NBRC 14135 PRJDB5976 SAMD00097244 GCA_006539445 Microbacterium lacticum JCM 1379 PRJDB10510 SAMD00245243 GCA_014646835 Microbacterium ginsengisoli DSM 18659 PRJNA224116 SAMN03266139 GCF_000956535 Microbacterium trichothecenolyticum DSM 8608 PRJNA224116 SAMN03266141 GCF_000956465 Microbacterium foliorum DSM 12966 PRJNA224116 SAMN03256325 GCF_000956415

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Microacterium foliorum DSM1296T Microbacterium arborescens DSM 20754 PRJNA224116 SAMN09428468 GCF_003339645 Microbacterium paludicola DSM1695T Microbacterium CGMCC sorbitolivorans 1.15228 PRJDB10509 SAMD00245028 GCA_014641475 Microbacterium sorbitolivorans C1.15228 PRJNA480549 SAMN09640157 GCA_003327285 Microbacterium kyungheense DSM 105492 PRJNA537961 SAMN11511978 GCA_006783905 Microbacterium karelineae TRM 80801 PRJNA577614 SAMN13030836 GCA_009745985 Microbacterium oryzae MB10 PRJNA224116 SAMN10053049 GCA_009735645 Microbacterium oryzae MB10T Microbacterium keratanolyticum DSM 8606 Microbacterium dextranolyticum DSM 8607 Microbacterium esteraromaticum DSM 8609 Microbacterium laevaniformans DSM 20140 Microbacterium insulae DSM 23024 Microbacterium aurum DSM 8600 Microbacterium aurum DSM8600T Microbacterium aurum KACC 19 Microbacterium ketosireducens DSM 12510 PRJNA224116 SAMN03266140 GCF_000956575 Microbacterium terrae DSM 8610 Microbacterium thalassium DSM 12511 Microbacterium marinum DSM 24947 PRJNA347154 SAMN05877737 GCA_014204835 barcode5 contig1 Micreobacterium chocolatum SIT101 Microbacterium testacean stLBo37 Appendix 4-16: List of Microbacterium species types strains that were used to compare dDDH by TYGS.

Bioproject Biosample Assembly Preferred name Deposit accession accession accession GCF_00869291 Pantoea dispersa CCUG 25232 PRJNA224116 SAMN12771129 5 GCF_00836462 Enterobacter dykesii E1T PRJNA224116 SAMN12560198 5 GCA_01477348 Erwinia aphidicola JCM 21238 PRJNA663353 SAMN16130814 5

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Salmonella enterica subsp. GCA_90063553 salamae NCTC 5773 PRJEB6403 SAMEA2665118 5 SAMEA10411391 GCA_90018590 Enterobacter cancerogenus ATCC 33241 PRJEB9821 6 5 GCA_90070678 Enterobacter taylorae NCTC 12126 PRJEB6403 SAMEA2548140 5 GCA_00649435 Pantoea eucalypti LMG 24197 PRJNA549756 SAMN12097183 5 GCA_01415579 Pantoea hericii JZB 2120024T PRJNA515154 SAMN10743378 5 Pantoea eucarypti LMG24197 Pantoea hericii JZB2120024T GCA_00649437 Pantoea anthophila LMG 2558 PRJNA549748 SAMN12097134 5 GCA_00880169 Pantoea stewartii CCUG 26359 PRJNA563568 SAMN12697580 5 Pantoea stewartii subsp. GCA_00075740 indologenes LMG 2632 PRJNA252992 SAMN02905159 5 Pantoea stewartii CCUG26359T Pantoea stewartii ZJ FGZX1 GCA_00024801 Escherichia hermannii NBRC 105704T PRJDB14 SAMD00041803 5 GCA_00479241 Pantoea vagans LMG 24199 PRJNA505269 SAMN10414043 5 GCA_00651762 Mixta tenebrionis BIT-26 PRJNA550228 SAMN12116280 5 GCF_00151740 Erwinia gerundensis E_g_EM595 PRJNA224116 SAMEA3650399 5 GCA_00295319 Mixta gaviniae DSM 22758 PRJNA430362 SAMN08369925 5 GCF_90060431 Erwinia mediterraneensis Marseille- P5165 PRJNA224116 SAMEA4979743 5 GCA_00289592 Mixta theicola DSM 29212 PRJNA407784 SAMN07663080 5 GCA_00295021 Shigella flexneri ATCC 29903 PRJNA218110 SAMN08330274 5 GCA_00209547 Pantoea rwandensis LMG 26275 PRJNA252996 SAMN05907793 5 GCA_00209557 Pantoea septica LMG 5345 PRJNA252991 SAMN05915835 5 GCA_00209530 Pantoea brenneri LMG 5343 PRJNA252984 SAMN05728469 5 Pantoea brenneri LMG5343 GCA_00209531 Pantoea conspicua LMG 24534 PRJNA252986 SAMN05757366 5 GCA_00209554 Pantoea allii LMG 24248 PRJNA252983 SAMN05908195 5 GCA_00209538 Pantoea eucrina LMG 5346 PRJNA252988 SAMN05757669 5

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Pantoea eucrina LMG5346T GCA_00209535 Mixta calida LMG 25383 PRJNA252985 SAMN05757634 5 GCA_00209548 Pantoea wallisii LMG 26277 PRJNA252993 SAMN05907794 5 Pantoea wallisii LMG26277T GCA_00209553 Pantoea cypripedii LMG 2657 PRJNA252995 SAMN05915831 5 Pantoea cypripedii LMG2657T GCA_00209546 Pantoea rodasii LMG 26273 PRJNA252990 SAMN05907788 5 GCA_00209537 Pantoea deleyi LMG 24200 PRJNA252987 SAMN05757637 5 GCA_00036762 Erwinia amylovora CFBP 1232 PRJEB604 SAMEA2271944 5 GCA_00002698 Erwinia pyrifoliae DSM 12163 PRJEA37877 SAMEA2272353 5

Klebsiella pneumoniae GCA_00016345 subsp. rhinoscleromatis ATCC 13884 PRJNA40083 SAMN00120581 5 Klebsiella quasipneumoniae subsp. GCA_00061322 similipneumoniae 07A044 PRJEB5159 SAMEA3138998 5 GCA_00165498 Kluyvera georgiana ATCC 51603 PRJNA210915 SAMN04875430 5 GCA_00210139 Pantoea alhagi LTYR-11Z PRJNA374633 SAMN06329872 5 Pantoea alhagi LTYR112 GCA_00159088 Serratia ficaria NBRC 102596 PRJDB1514 SAMD00046907 5 GCA_00159092 Serratia plymuthica NBRC 102599 PRJDB268 SAMD00046908 5 GCA_00371024 Curtobacterium plantarum LMG 16222 PRJNA498371 SAMN10290473 5 GCA_01415661 Pantoea pleuroti JZB 2120015 PRJNA513927 SAMN10716182 5 GCA_00159847 Pantoea agglomerans NBRC 102470 PRJDB388 SAMD00046726 5 GCA_00131288 Kosakonia cowanii JCM 10956 PRJDB901 SAMD00016805 5 GCA_00082000 Aeromonas piscicola LMG 24783 PRJEB7033 SAMEA2752415 5 GCA_00046311 Franconibacter helveticus LMG 23732 PRJNA215373 SAMN02318523 5 GCA_00046315 Siccibacter turicensis LMG 23730 PRJNA215386 SAMN02318531 5 GCA_00066197 Pantoea ananatis LMG 2665 PRJNA240762 SAMN02680126 5 Pantoea ananatis R100 GCA_00095113 Rouxiella chamberiensis 130333 PRJNA263737 SAMN03106102 5

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GCA_00157128 Kluyvera cryocrescens NBRC 102467 PRJDB285 SAMD00046725 5 GCA_00002618 Erwinia tasmaniensis Et1/99 PRJEA20585 SAMEA2272215 5 GCF_00285893 Pantoea endophytica 596 PRJNA224116 SAMN08195396 5 GCA_00434672 Phytobacter diazotrophicus DSM 17806 PRJNA500337 SAMN10362891 5 Pantoea dispersa DSM32899 Appendix 4-17: List of Type strains that are used compared to Pantoea sp. dDDH by TYGS.

Bioproject Biosample Assembly Preferred name Deposit accession accession accession NBRC GCA_0039670 Aerosticca soli 112897T PRJDB6914 SAMD00115724 35 GCA_0004126 Pseudomonas putida NBRC 14164 PRJDB191 SAMD00061028 75 Pseudomonas putida NBRC 14164T GCA_0099323 Pseudomonas asiatica JCM 32716T PRJDB7090 SAMD00127555 35 Pseudomonas asiatica RYU5T NH517510T GCA_9005360 Pseudomonas reidholzensis CCOS 865 PRJEB28254 SAMEA4836241 25 Pseudomonas GCA_0058779 nicosulfuronedens LAM1902T PRJNA533733 SAMN11465145 05 GCF_01423046 Pseudomonas cremoris WS 5106T PRJNA224116 SAMN14516828 5 GCA_0088073 Pseudomonas lalkuanensis MCC 3792 PRJNA561124 SAMN12607303 75 GCA_9001118 Pseudomonas otitidis DSM 17224 PRJEB17072 SAMN05216263 35 GCA_9001123 Pseudomonas citronellolis LMG 18378 PRJEB17188 SAMN05216577 75 GCA_0017482 Pseudomonas humi CCA1 PRJDB5092 SAMD00057624 65 GCA_9001156 Pseudomonas toyotomiensis JCM 15604 PRJEB17410 SAMN05216177 95 Pseudomonas GCA_0004257 cremoricolorata DSM 17059 PRJNA188911 SAMN02441180 45 Pseudomonas cremoricolorata DSM17059T GCA_0004258 Pseudomonas vranovensis DSM 16006 PRJNA188914 SAMN02441181 05 GCA_0004257 Pseudomonas parafulva DSM 17004 PRJNA188912 SAMN02441530 65 Barcorde22 Pseudomonas parfulva DSM17004T GCF_00074652 Pseudomonas alkylphenolica JCM 16553 PRJNA224116 SAMN02929205 5

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Pseudomonas alkylphenolica KL28T GCA_0010052 Lampropedia cohaerens CT6 PRJNA282900 SAMN03580769 15 GCA_0004257 Pseudomonas taiwanensis DSM 21245 PRJNA188913 SAMN02440864 85 Pseudomonas taiwanensis DSM21245T GCA_9001884 Pseudomonas japonica DSM 22348 PRJNA331484 SAMN05444352 55 GCA_0007305 Pseudomonas japonica NBRC 103040 PRJDB212 SAMD00018431 85 GCA_0133749 Pseudomonas eucalypticola NP-1 T PRJNA639687 SAMN15248734 95 GCA_0088019 Cupriavidus gilardii CCUG 38401 PRJNA563568 SAMN12697570 15 GCA_0028066 Pseudomonas qingdaonensis JJ3 PRJNA419834 SAMN08099659 85 WCHPs06004 GCF_00323127 Pseudomonas huaxiensis 4 PRJNA224116 SAMN09302770 5 Pseudomonas SAMEA10456920 GCA_9002771 Pseudomonas inefficax sp. JV551A3 PRJEB24815 0 25 GCA_9004969 Achromobacter veterisilvae LMG 30378T PRJEB24293 SAMEA4780387 75 GCA_0042148 Pseudomonas tructae SNU WT1 PRJNA522009 SAMN10922936 95 GCA_0006212 Pseudomonas monteilii DSM 14164 PRJNA221052 SAMN02743984 45 Pseudomonas monteilli DSM14164T GCA_0006882 Pseudomonas plecoglossicida DSM 15088 PRJNA221049 SAMN02743900 75 Pseudomonas plecogrossicida DSM15088T GCA_0006212 Pseudomonas fulva DSM 17717 PRJNA221053 SAMN02743980 65 Psuedomonas fulva DSM17717T GCA_0006212 Pseudomonas mosselii DSM 17497 PRJNA221051 SAMN02743979 25 GCA_9001671 Pseudomonas aeruginosa DSM 50071 PRJNA235084 SAMN02745748 95 Pseudomonas aeruginosa NCTC10332T Pseudomonas GCA_9004976 wadenswilerensis CCOS 864 PRJEB27830 SAMEA4796555 95 GCA_0078302 Azomonas agilis DSM 375 PRJNA262246 SAMN05660238 55 GCA_0042166 Pigmentiphaga kullae DSM 13608 PRJNA520317 SAMN10866310 95 WCHPs06003 GCA_0032313 Pseudomonas sichuanensis 9 PRJNA415331 SAMN09302769 05 Pseudomonas GCA_0020917 pseudoalcaligenes NBRC 14167 PRJDB224 SAMD00046936 75

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GCA_0020917 Pseudomonas nitroreducens NBRC 12694 PRJDB220 SAMD00046934 55 GCA_0109941 Pseudomonas nitritireducens WZBFD3-5A2 PRJNA589232 SAMN13268851 65 GCA_0004671 Pseudomonas alcaligenes NBRC 14159 PRJDB200 SAMD00041817 05 GCA_0006894 Pseudomonas knackmussii B13 PRJEB1565 SAMEA3139009 15 Stenotrophomonas GCA_0014314 nitritireducens DSM 12575 PRJNA284361 SAMN03701629 25 GCA_0015942 Pseudomonas glycinae MS586 PRJNA309549 SAMN04435621 25 GCA_0001960 Cupriavidus metallidurans CH34 PRJNA250 SAMN02598450 15 Pseudomonas mosselii DSM17497T GCA_0040255 Pseudomonas hutmensis XWS2T PRJNA474423 SAMN09303047 35 GCA_0002591 Pseudomonas donghuensis HYS PRJNA89717 SAMN02472130 95 GCA_9000996 Pseudomonas peli DSM 17833 PRJEB15912 SAMN05216370 45 GCA_9001007 Pseudomonas abietaniphila ATCC 700689 PRJEB15899 SAMN05216605 95 GCA_9000999 Pseudomonas delhiensis CCM 7361 PRJEB15961 SAMN05216189 45 GCA_9000997 Pseudomonas panipatensis CCM 7469 PRJEB15946 SAMN05216272 85 GCA_9001026 Pseudomonas guariconensis LMG 27394 PRJEB15687 SAMN05216185 75 GCA_0004670 Pseudomonas fuscovaginae ICMP 5940 PRJDB1420 SAMD00000756 65 GCA_9001054 Pseudomonas asplenii ATCC 23835 PRJEB16404 SAMN05216598 75 GCA_9001038 Pseudomonas jinjuensis JCM 21621 PRJEB16237 SAMN05216193 45 GCA_9001060 Pseudomonas mucidolens LMG 2223 PRJEB16499 SAMN05216202 45 GCA_0002620 Pseudomonas furukawaii KF707 PRJNA83639 SAMN02471394 65 GCA_0000261 Pseudomonas entomophila L48 PRJNA16800 SAMEA3138225 05 Pseudomonas entomophila L48T GCA_0058768 Pseudomonas nosocomialis A31/70 PRJNA475667 SAMN09398993 55 GCA_9001106 Pseudomonas soli LMG 27941 PRJEB16975 SAMN05216230 55 Pseudomonas soli LMG27941T GCA_0027411 Pseudomonas sediminis PI11 PRJNA389586 SAMN07203179 05 Barcode 4 Contig1 Appendix 4-18: List of type strains that are compared to the Pseudomonas sp.2 for dDDH by TYGS

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