Characterisation of Opportunistic Bacterial Pathogens of the Marine Macroalga Delisea Pulchra Vipra Kumar Doctor of Philosophy

Home , Agarivorans, Alteromonadaceae, Alteromonas marina, Cellulophaga algicola, Delisea, Delisea pulchra

Characterisation of opportunistic bacterial pathogens of the marine macroalga Delisea pulchra

Vipra Kumar

A thesis in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Biotechnology and Biomolecular Sciences Faculty of Science The University of New South Wales Sydney, Australia

August 2015

THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet

Surname or Family name: Kumar

First name: Vipra Other name/s: Nandani

Abbreviation for degree as given in the University calendar: PhD

School: Biotechnology and Biomolecular Sciences Faculty: Science

Title: Characterisation of opportunistic bacterial pathogens of the marine macroalga Delisea pulchra

Abstract

Macroalgae, the major habitat-formers of temperate marine ecosystems are susceptible to disease, yet in many cases the aetiological agent/s remain unknown. The red macroalga Delisea pulchra suffers from a bleaching disease, which can be induced by the bacteria Nautella italica R11 and Phaeobacter sp. LSS9 under laboratory conditions. However recent analyses suggest that these strains are not representative of the dominant pathogens in the environment. Therefore, the aim of this thesis was to determine if multiple pathogens of D. pulchra exist and to further understand the microbial dynamics of bleaching in D. pulchra. To achieve this aim, a culture collection of bacterial strains present on bleached and adjacent-to-bleached tissues of D. pulchra was generated. Bacterial strains that were also overrepresented in previous culture independent studies were subsequently assessed for virulence-related traits, including motility, biofilm-formation, resistance to chemical defences of D. pulchra and the degradation of algal polysaccharides. Ten bacterial isolates with the broadest range of virulence traits were thereafter tested for the ability to induce in vivo bleaching of D. pulchra following the development of a new infection assay. Two Alteromonas sp. (LSS17 and BL110), two Aquimarina sp. (AD1 and BL5) and one Agarivorans sp. (BL7) were identified as new pathogens of D. pulchra. Deep-sequencing of 16S rRNA gene amplicons from the microbial community of D. pulchra demonstrated an increased abundance of Aquimarina sp. AD1 and Alteromonas sp. BL110 strains in diseased hosts post infection with these pathogens providing evidence for Koch’s postulates. Moreover reduced diversity and increased dispersion of microbial communities was observed for diseased algae compared to healthy individuals. Finally, insights into the molecular mechanisms of disease was obtained through a comparative genomic analysis of the newly identified pathogens. Mechanisms for virulence unique to representatives of Alteromonadaceae included chemotaxis, motility and secretion systems whereas por-secretion systems, gliding motility and specific surface appendages were found to be unique to Aquimarina sp. In conclusion, this work provides evidence that multiple opportunistic pathogens of a single host organism are more common in marine systems than previously anticipated and further highlights the complex nature of host-pathogen-microbiome interactions.

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I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.

I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only).

31 August 2015

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FOR OFFICE USE ONLY Date of completion of requirements for Award:

Originality Statement

‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’

Signed: Date: 31 August 2015

‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.'

Signed: Date: 31 August 2015

Authenticity Statement

‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’

Signed: Date: 31 August 2015

Abstract

List of Publications

Egan S, Fernandes ND, Kumar V, Gardiner M & Thomas T (2014) Bacterial pathogens, virulence mechanism and host defence in marine macroalgae. Environmental Microbiology 6: 925-38.

Egan S, Kumar V, Nappi J & Gardiner M (2014) Microbial interactions with seaweeds. In: Algae and Cyanobacteria Symbiosis (Grube M, Muggia L & Seckbach, J. eds). Nova Publishers, New York, USA.

Kumar V, Zozaya-Valdes E, Kjelleberg S, Thomas T & Egan S (2015) Multiple opportunistic pathogens in the bleaching disease of the red seaweed Delisea pulchra, submitted.

Kumar V, Tebben, J & Egan S (2015) Development of an alternative in vivo infection model in the red algae Delisea pulchra, in prep.

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Acknowledgements

Foremost, I would like to acknowledge the guidance and directions provided by my supervisors, Dr. Suhelen Egan and Prof. Staffan Kjelleberg. It has been my utmost privilege to work under their inspiring mentorship and receive unceasing support and encouragement throughout my candidature. The opportunity to engage in multidisciplinary research at the Centre for Marine Bio-innovation – an international leader in the field of marine holobionts, has been extremely fulfilling.

Appreciation is extended also to the Australian Research Council, the School of Biotechnology and Biomolecular Sciences and the Graduate Research School at UNSW for grants to carry out research and present findings at two prestigious international conferences. Timely assistance from the administrative team has been greatly valued, for which I am thankful to Adam Abdool, Kirsty Collard, Esra Ertan, Leena Koop and Kylie Jones.

I have been extremely fortunate to collaborate with and receive guidance from Prof. Torsten Thomas, who with Prof. Hazel Mitchell, also provided helpful feedback and advice during the span of my candidature. Appreciation is conveyed to all collaborators within the Disease Team, i.e. Prof. Peter Steinberg, Dr. Alexandra Campbell, Dr. Ezequiel Marzinelli, Dr. Mariana Pinto, Dr. Adriana Vergés, Dr. Melissa Gardiner and Dr. Neil Fernandes for inspiring discussions. Sincere gratitude is extended to Enrique Zozaya-Valdes for collaborative and experimental assistance rendered at the Sydney Institute of Marine Science. I extend thanks to Dr. Tilmann Harder also for advice on chemical analyses and to Dr. Jan Tebben for analytical assistance.

For technical support required at SIMS, I thank Josh Aldrich and Ulysse Bove. Appreciation is conveyed also to the staff at the Histology and Microscopy Unit for support with imaging. Much of the credit for the work presented in this thesis goes to the diving team for their sampling efforts and for this I thank Rebecca Neumann, Dr. Ross Hill, Dr. Suzanna Evans, Matt Skye and Doug Beattie. I am most grateful to Tamsin Peters for her persistent help during both algal sampling and document proof-reading. For the helpful discussions and advice on analytical procedures and data analysis, I thank Dr. Sharon Longford, Dr. Shaun Nielsen, Dr. Brendan Colley, Dr. Rajesh Thangamani, Dr. Ana Esteves, Dr. Tim Lachnit, Dr. Sohail Siddiqui, Dr. James Grasela, Dr. Sabrina Beckmann and Dr. Carla Lutz.

Hearty thanks also to the teams in Labs 304, 306, 351 and 602 for providing a supportive and lively atmosphere, which contributed immensely towards enhancing research productivity. Special thanks to Adam Bournazos, Nural Cokcetin, Meera Esvaran, Marina Garcia, Harry Tan,

Jun Ye, Galaxy Qiu, Valentina Wong, Lucy Jo, Alexandra Roth, David Reynolds, Mary Nguyen, Nas Mohamad, John Webster and Divya Srinivasa. I am grateful also to Jackson Walburn for analytical assistance and to the rest of the Friday Coffee Club goers, including An Grobler and Claudia Maturana, for productive and insightful discussions over coffee and birthday cakes.

The journey undertaken during this PhD established friendships that are sure to last a life-time and for this, I am utterly grateful. My sincerest thanks to Blosia Sun, Lifu Sheng, Jadranka Nappi, Carolina Campi, Giampiero Batani, Dr. Michael Roggenbuck, Laurence Delina, Dr. Nahid Sultana, Marwan Majzoub and Raymond Regalia for their continuous assistance, encouragement, philosophical discussions and most importantly, for their incomparable friendship, which made this journey thoroughly satisfying and most enjoyable.

Finally, I acknowledge with utmost gratitude the patience and concerns of my family. Their love is unmatched, for which I will remain forever grateful. Heartiest appreciation to Radhika, Aunt Vidya and little Rishan for their unceasing support and care over the years. I could not be happier to share the momentous completion of this PhD with my dearest brother – congratulations Dr. Shivendra Kumar! I convey highest gratitude to my pillars of strength – Mum and Dad. Their selfless sacrifices and teachings have most definitely been the biggest contributors towards this achievement. This work is dedicated to them and to my most beloved Swami, without whose will none of this would be imaginable!

Table of Contents

Originality Statement ...... i Abstract ...... ii

List of Publications ...... iii

Acknowledgements ...... iv

List of Tables ...... xii

List of Figures ...... xiv

List of Abbreviations ...... xvii

Chapter One: Literature review

1.1 Bacteria in the marine environment ...... 1

1.1.1 Surface-attachment through biofilm-formation ...... 1 1.1.2 Patterns in bacterial diversity on macroalgae ...... 4 1.2 Interactions between microbes and macroalgal hosts ...... 6

1.2.1 Beneficial interactions...... 6 1.2.2 Pathogenic Interactions ...... 8 1.3 Macroalgal chemical defence ...... 12

1.3.1 Production of secondary metabolites ...... 12 1.3.2 Defence triggered by pathogens ...... 12 1.4 Opportunistic pathogens, host defence and environmental factors ...... 13

1.4.1 Opportunistic pathogens and their virulence mechanisms ...... 14 1.4.2 Koch’s postulates ...... 15 1.5 Life history stages and model of disease in Delisea pulchra ...... 16

1.6 Thesis aims ...... 20

Chapter Two: Isolation of putative opportunistic bacterial pathogens associated with bleaching of Delisea pulchra

2.1 Introduction ...... 22

2.2 Materials and methods ...... 23

2.2.1 Sample collection ...... 23 2.2.2 Culturing of bacteria ...... 23 2.2.3 Generation of a culture collection ...... 24 2.2.4 PCR amplification ...... 24 2.2.5 DNA sequencing ...... 25 2.2.6 Phylogenetic analysis ...... 25 2.2.7 Sequence-based search of cultured isolates in culture-independent studies ...... 26 2.3 Results ...... 26

2.3.1 Isolates cultured from diseased D. pulchra ...... 26 2.3.2 Over-representation of specific bacterial groups during bleaching of D. pulchra .. 31 2.3.3 Over-representation of specific bacterial groups during bleaching of E. radiata .. 34 2.4 Discussion ...... 36

2.4.1 Abundance and diversity of isolates in the culture collection ...... 37 2.4.2 Contribution of cultured commensal bacteria in bleaching events of D. pulchra .. 39 2.4.3 Association of cultured isolates in disease of other host systems ...... 40 2.4.4 Conclusions and future work ...... 43

Chapter Three: Assessment of virulence-related traits of selected putative pathogens of Delisea pulchra

3.1 Introduction ...... 44

3.2 Materials and methods ...... 45

3.2.1 Strains and culture conditions ...... 45 3.2.2 Examination of cell motility ...... 45 3.2.3 Ability of isolates to form a biofilm on surfaces ...... 46 3.2.4 Resistance to total furanone extracts from D. pulchra ...... 46 3.2.5 Resistance to oxidative stress ...... 47 3.2.6 Ability to degrade host cell wall-related polymers ...... 47 3.3 Results ...... 48

3.3.1 Bacterial motility ...... 48 3.3.2 Biofilm attachment ...... 49 3.3.3 Growth inhibition by D. pulchra total furanone extracts ...... 50 3.3.4 Growth reduction by hydrogen peroxide ...... 52

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3.3.5 Degradation of polymers present in host cell wall ...... 53 3.4 Discussion ...... 55

3.4.1 Motility and biofilm-formation are prevalent characteristics in putative pathogens ...... 55 3.4.2 Resistance to chemical defence strategies of the host...... 57 3.4.3 Production of extracellular enzymes ...... 59 3.4.4 Conclusions and future work ...... 60

Chapter Four: Bleaching effects of putative pathogens on temperature-stressed and furanone-reduced juveniles of Delisea pulchra

4.1 Introduction ...... 62

4.2 Materials and methods ...... 63

4.2.1 Sample collection ...... 63 4.2.2 Maintenance of D. pulchra in the aquarium ...... 63 4.2.3 Effect of temperature on maintenance of juvenile D. pulchra in a closed system . 64 4.2.4 Effect of bacterial inoculation on juvenile D. pulchra ...... 65 4.2.5 Effect of bromide manipulation on furanone content in juvenile D. pulchra ...... 66 4.2.6 Effect of bromide-deficient media and amended inoculation plan ...... 67 4.2.7 Effect of putative pathogens in in vivo bleaching of D. pulchra ...... 67 4.2.8 Microscopy of biofilm-associated bleached tissues of D. pulchra ...... 68 4.3 Results ...... 68

4.3.1 Health of juveniles in comparison to adult D. pulchra ...... 68 4.3.2 Health of D. pulchra at an increased temperature and in the presence of inoculum ...... 71 4.3.3 Bromide-deficient media reduces total furanones in juvenile D. pulchra ...... 71 4.3.4 Bleaching as a result of amended inoculation plan and use of bromide-deficient media ...... 72 4.3.5 Putative pathogens induce in vivo bleaching in D. pulchra ...... 74 4.4 Discussion ...... 78

4.4.1 Juvenile but not adult D. pulchra persist under laboratory test conditions ...... 78 4.4.2 Temperature stress solely does not induce bleaching in D. pulchra ...... 79 4.4.3 Bromide-deficient media improves assay sensitivity ...... 80 4.4.4 Putative pathogens demonstrate bleaching in stressed juveniles of D. pulchra .... 80

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4.4.5 Conclusions and future work ...... 81

Chapter Five: Detection of putative pathogens and bacterial community structure on Delisea pulchra

5.1 Introduction ...... 82

5.2 Materials and methods ...... 83

5.2.1 DNA extraction from the microbial community on D. pulchra ...... 83 5.2.2 Experimental design for 16S rRNA gene sequencing ...... 84 5.2.3 Quality-filtering of sequences ...... 85 5.2.4 Sequence analysis ...... 85 5.2.4.1 Detection of putative- and known pathogens on bleached and unbleached D. pulchra ...... 85 5.2.4.2 Analysis of the total bacterial community ...... 86 5.3 Results ...... 87

5.3.1 Quality-filtered sequences ...... 87 5.3.2 Abundance of putative- and known pathogens on bleached and unbleached D. pulchra ...... 88 5.3.3 Total bacterial community on bleached and unbleached D. pulchra ...... 92 5.3.3.1 Alpha-diversity ...... 92 5.3.4 Bacterial community structure and diversity...... 95 5.4 Discussion ...... 99

5.4.1 Detection of inocula in fulfilment of Koch’s postulates ...... 100 5.4.2 Putative- and known- pathogens present in the background communities on D. pulchra ...... 101 5.4.3 Bleaching-associated alteration to bacterial community ...... 102 5.4.4 Effect of introducing putative pathogens on the total bacterial community ...... 103 5.4.5 Conclusions and future work ...... 104

Chapter Six: Comparative genome analysis of opportunistic pathogens reveals potential mechanisms of virulence towards seaweeds

6.1 Introduction ...... 106

6.2 Materials and methods ...... 107

6.2.1 Isolates and DNA extraction ...... 107 ix

6.2.2 Sequencing and genome assembly ...... 108 6.2.3 Genomes comparative analysis ...... 108 6.3 Results and discussion ...... 109

6.3.1 Features in the genomes of Agarivorans sp., Alteromonas sp. and Aquimarina sp...... 109 6.3.2 Putative mechanisms for virulence in pathogenic Alteromonadaceae ...... 112 6.3.2.1 Surface colonisation (motility and host adhesion) ...... 112 6.3.2.2 Secretory systems...... 113 6.3.2.3 Enzymes for degradation of algal cell wall components ...... 118 6.3.2.4 Resistance against host chemical defence ...... 123 6.3.2.5 Natural products...... 124 6.3.3 Putative mechanisms for virulence in Aquimarina sp. AD1 and BL5 ...... 128 6.3.3.1 Predicted proteins unique to pathogenic Aquimarina sp...... 129 6.3.4 Shared virulence genes of pathogenic Alteromonadaceae and Aquimarina sp. .. 137 6.3.5 Conclusions and future work ...... 138

Chapter Seven: General discussion

7.1 Environmental stressors, opportunistic pathogens and macroalgal hosts ...... 139

7.2 Presence of opportunistic pathogens on macroalgal hosts ...... 140

7.3 Significance of model systems and the development of a new infection model ...... 141

7.4 Unique patterns of disease in opportunistic pathogens of D. pulchra ...... 143

7.5 Proposed model of virulence in opportunistic pathogens of macroalgal hosts ...... 145

7.6 Conclusions and future perspectives ...... 147

References ...... 149

Appendix I ...... 188

Appendix II ...... 190

Appendix III ...... 197

Supplementary Information_S1 ...... 198

Supplementary Information_S2 ...... 215

Supplementary Information_S3 ...... 223

Supplementary Information_S4 ...... 225

Supplementary Information_S5 ...... 230

Supplementary Information_S6 ...... 232

List of Tables

Table 1.1: Bacterial-mediated disease of macroalgae ...... 10

Table 2.1: The number of isolates successfully subcultured and sequenced ...... 27

Table 2.2: ID of unique cultured isolates and corresponding OTU (classified at 97%) in the culture-independent community dataset of D. pulchra ...... 32

Table 2.3: ID of unique cultured isolates and corresponding OTU (classified at 97%) in the culture-independent community dataset of E. radiata ...... 34

Table 2.4: Affiliation of bacteria cultured from D. pulchra in disease of other marine host ..... 42

Table 3.1: Characterisation of motility in test isolates ...... 49

Table 3.2: Sensitivity of test isolates to three test concentrations of total furanone extracts of D. pulchra...... 52

Table 3.3: IC50 values of test isolates calculated from exposure to increasing concentrations of

H2O2 ...... 53

Table 3.4: Ability of test isolates to degrade host cell wall associated polymers ...... 54

Table 3.5: Putative pathogens proposed for further investigation of virulence traits ...... 611

Table 5.1: Summary of the OTU-based alpha diversity measurements of sequenced samples 92

Table 5.2: PERMANOVA table of results for the effect of treatment and bleaching outcome on abundances of OTUs (97% identity classification) ...... 98

Table 6.1: General genomic characteristics of the sequenced bacterial isolates obtained from the surface of D. pulchra ...... 110

Table 6.2: Secretion systems present in genomes of pathogenic Alteromonadaceae ...... 113

Table 6.3: Comparisons between genes comprising T6SSs in Agarivorans sp. BL7 and two characterised phytopathogens ...... 117

Table 6.4: Carbohydrate-active enzymes encoded in genome of Agarivorans sp. BL7 ...... 120

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Table 6.5: Carbohydrate-active enzymes encoded in genome of Alteromonas sp. BL110 ...... 121

Table 6.6: Carbohydrate-active enzymes encoded in genome of Alteromonas sp. LSS17 ...... 122

Table 6.7: Comparisons of the genes shared between Alteromonas sp. BL10 and LSS17; and Marinobacter hydrocarbonclasticus in a putative siderophore gene cluster...... 127

Table 6.8: BLASTP analysis of predicted proteins unique to pathogenic Aquimarina sp...... 134

Table 6.9: List of non-redundant COGs and corresponding genes shared only between pathogenic representatives of Alteromonadaceae and Aquimarina sp...... 137

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List of Figures

Fig. 1.1: Stages involved in colonisation of an immersed surface ...... 2

Fig. 1.2: Life-history stages of the temperate red macroalga D. pulchra ...... 187

Fig. 1.3: Mid-thallus bleaching in the temperate red macroalga D. pulchra ...... 18

Fig. 1.4: Microscopic examination of the effect of inoculating sporelings of D. pulchra with N. italica R11...... 19

Fig 2.1: Proportion of bacterial isolates representing different families...... 28

Fig. 2.2: Evolutionary relationships of the cultured isolates to known taxa ...... 30

Fig. 2.3: Log abundances of OTUs (classified at 97%) on bleached (B) and healthy (H) D. pulchra ...... 33

Fig. 2.4: Log abundances of OTUs (classified at 97%) on diseased (D) and healthy (H) E. radiata ...... 35

Fig. 2.5: Flowchart summarising steps involved in the isolation of bacteria ...... 37

Fig. 3.1: Biofilm-forming ability of test isolates ...... 50

Fig. 3.2: Bacterial growth inhibition by total furanone extracts of D. pulchra...... 51

Fig. 3.3: Examples of assay plates displaying polymer degradation by test isolates ...... 54

Fig. 4.1: Experimental setup at the SIMS aquarium...... 64

Fig. 4.2: Closed-system setup used for monitoring effect of temperature stress on juvenile D. pulchra...... 65

Fig. 4.3: Deterioration in health of adult D. pulchra...... 69

Fig. 4.4: Monitoring of health of juvenile D. pulchra ...... 70

Fig. 4.5: Comparisons of total furanone quantities in D. pulchra ...... 72

Fig. 4.6: Bleaching of algae incubated in bromine-deficient media ...... 73

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Fig. 4.7: An outline of the steps required to test putative pathogens for bleaching effects on juvenile D. pulchra...... 75

Fig 4.8: Bleaching effects of candidate pathogens on juvenile D. pulchra...... 76

Fig. 4.9: Microscopic images of D. pulchra from representative samples of each treatment. .. 77

Fig. 5.1: Experimental design used for sequencing the microbial community on D. pulchra. ... 84

Fig. 5.2: Box plots showing the relative abundances of the putative pathogens ...... 89

Fig. 5.3: Relative abundance of sequences having 99-100% identity with sequences of putative- and known opportunistic pathogens of D. pulchra...... 91

Fig. 5.4: Rarefaction curves of quality- and abundance-filtered sequence data...... 93

Fig. 5.5: Inverse Simpson diversity indices ...... 94

Fig. 5.6: nMDS plot based on Bray-Curtis measure of square-root transformed OTU (97% similarity cut-off) abundances ...... 95

Fig. 5.7: Relative abundance of bacterial phyla on unbleached and bleached samples of D. pulchra...... 96

Fig. 5.8: Relative abundances of bacterial orders in each treatment ...... 97

Fig. 5.9: SIMPER graph showing phylogenetic affiliation of OTUs ...... 99

Fig. 6.1: Functional comparison of genomes of Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17 and Aquimarina sp. AD1, AD10 and BL5...... 111

Fig. 6.2: Schematic representation of the gene cassettes encoding T6SSs in A. tumefaciens and Agarivorans sp. BL7 ...... 116

Fig. 6.3: Genomic context for mexAB-oprM operon...... 124

Fig. 6.4: Schematic representation of the lantipeptide biosynthetic cluster neighbourhood in the genome of Alteromonas sp. BL110 ...... 125

Fig. 6.5: Schematic representation of the gene clusters involved in biosynthesis of a petrobactin-like siderophore in Alteromonas sp. BL110 and LSS17, and Marinobacter hydrocarbonclasticus...... 127

Fig. 6.6: Venn diagram representing numbers of CDS genes shared between the three Aquimarina genomes...... 129

Fig. 6.7: COG function categories representing the genes unique to pathogen genomes...... 130

Fig. 6.8: Schematic representation of the putative flexirubin biosynthetic cluster in Aquimarina sp. AD1 and BL5 ...... 133

Fig 7.1: Schematic model of virulence traits in opportunistic pathogens of D. pulchra...... 146

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List of Abbreviations

°C degrees Celsius µ micro (10-6) AD Adjacent-to-bleached D. pulchra tissue AHL N-acyl homoserine lactone BL Bleached D. pulchra tissue BLAST Basic Local Alignment Search Tool bp basepair(s) c-di-GMP 3'-5'cyclic diguanylic acid CDS coding sequences(s) CFU colony forming units cm centimetre(s) COG Clusters of Orthologous Group(s) CV crystal violet DNA deoxyribonucleic acid EDTA ethylene diamine tetra acetic acid, trisodium salt EPS extracellular polymeric substance FSW filter-sterilised seawater g gram(s) GH glycosyl hydrolase GLM generalized linear model HCl hydrochloric acid HMA half-strength DifcoTM Marine Broth 2216 solidified with 1.5% agar hr hour(s) HR hypersensitive response IBF Intermediate biofilm former IMG Integrated Microbial Genomes kb kilobase(s) l litre(s) log logarithmic m milli (10-3) M molar MA DifcoTM Marine Broth 2216 solidified with 1.5% agar MB DifcoTM Marine Broth 2216

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min minute(s) mol moles n nano (10-9) NBF Non-biofilm former NCBI National Centre for Biotechnology Information OD optical density OTU operational taxonomic unit PCR polymerase chain reaction PL polysaccharide lyase p pico(10-12) QS quorum sensing RCG Ramaciotti Centre for Genomics ROS reactive oxygen species rpm revolutions per minute rRNA ribosomal ribonucleic acid SBF strong biofilm former sec second(s) sp. species TBDR tonB-dependent receptor V volts v/v volume per volume w/v weight per volume WBF weak biofilm former x g gravitational force

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Chapter One:

Literature review

1.1 Bacteria in the marine environment

Microorganisms, including bacteria, are ubiquitous in the marine environment. Playing key roles in biogeochemical processes, such as carbon and nutrient cycling, bacteria also form the basis of marine food webs (Falkowski et al., 1998). The oceanic waters support a huge genetic diversity of microbes, numbering 104 to 106 per millilitre of seawater (Delille, 2004, Azam & Malfatti, 2007, Sunagawa et al., 2015). The abundance, diversity and metabolic activity of the ocean is dominated by bacteria (Azam & Malfatti, 2007) and a large proportion belongs to the “rare” biosphere (Rappé & Giovannoni, 2003, Rusch et al., 2007).

Our understanding of microbial diversity has historically been based on cultivation-dependent techniques, which vastly under-represent the true diversity (Pace, 1997, Rappé & Giovannoni, 2003, Keller & Zengler, 2004). An improved account of microbial genetic diversity in the marine environment is now possible through the introduction of cultivation-independent techniques that rely on molecular methods such as sequencing bacterial 16S ribosomal RNA (rRNA) genes (Pace, 1997, Keller & Zengler, 2004). Moreover, new comparative genomics and assembly methods using sequencing information and metadata has provided valuable insights into other aspects, such as the biochemical diversity of marine bacteria (Rusch et al., 2007). Consequently, the role of marine microbes within specific micro-environments in the ocean has gained renewed interest with increasing awareness of bacterial genetic and biochemical diversity.

1.1.1 Surface-attachment through biofilm-formation

While planktonic bacteria are present throughout the water column, surface-associated microbes are prevalent on marine animals, seaweeds and in sediments, (Ma et al., 2009, Zinger et al., 2011). Biotic surfaces, such as macroalgae (seaweeds), provide a nutrient-rich habitat for the settlement of bacteria (Armstrong et al., 2001). Surface colonisation in the marine environment is a three-stage process that includes adsorption of dissolved organic molecules (molecular fouling), colonisation by microorganisms such as bacteria and diatoms (micro- fouling) and the subsequent recruitment of macro-foulers, such as algal spores and invertebrate larvae (macro-fouling) (Wahl, 1989, Aldred & Clare, 2008). Initial colonising bacteria attach to surfaces through the formation of biofilms - a complex consortium of cells, embedded in an

extracellular matrix (Dang et al., 2008). The formation of these highly elaborate structures occurs through a series of steps which involves cell attachment, microcolony formation, biofilm maturation and eventually cell dispersal (Stoodley et al., 2002) (Fig 1.1).

Fig. 1.1: Stages involved in colonisation of an immersed surface: molecular-, micro- and macrofouling. The second stage, micro-fouling, involves biofilm-formation, which can be further subdivided into four phases, surface attachment, microcolony-formation, biofilm-maturation and differentiation and finally cell dispersal.

Bacterial biofilms have been likened to the cell-specialization system observed in structurally complex organisms (Costerton et al., 1994, Stoodley et al., 2002). Each stage within a biofilm necessitates a specific function that enables bacterial cells to effectively participate as members of the biofilm community. For example, in the microcolony-formation stage, planktonic cells and cell flocs attach to aggregated or pre-attached cells and swarm towards existing microcolonies. Existing resident bacteria also increase microcolony size by cell division (Tolker-Nielsen et al., 2000, Stoodley et al., 2001, Stoodley et al., 2002). Once maturation is reached, the microcolonies form mushroom-shaped structures, segregated into distinct clusters, which are interconnected by exopolymers (Sauer et al., 2002). Cells within the microcolonies exchange nutrients and metabolic waste via the highly-permeable water channels that run within the matrix and act as a “circulatory system,” (Costerton et al., 1994). Finally, dispersal involves cells detaching,

migrating and exploring new surfaces as nutrients are depleted or as surfaces become overcrowded (Stoodley et al., 2002). Isolated cell aggregates are also released from the biofilm following ‘programmed cell-death’, the death of specific regions of the microcolony (Mai- Prochnow et al., 2004, Mai-Prochnow et al., 2006, Mai-Prochnow et al., 2008).

In many bacteria, the second messenger 3′-5′-cyclic dimeric guanosine monophosphate (c-di- GMP) regulates a transition from the motile to a sessile state, allowing the establishment of multicellular biofilm communities (Römling et al., 2013). By modulating intracellular concentrations of c-di-GMP, functions including motility, adhesion, biofilm formation, and virulence are regulated (Ryan et al., 2006). Increased concentrations of c-di-GMP promote surface attachment, biofilm-formation and the production of extracellular polymeric substances (EPS), while reduced concentrations are linked to increased motility via flagella motors and attenuation of virulence factors (Jenal & Malone, 2006). The cellular concentration of c-di-GMP is controlled by enzymes diguanylate cyclase (DGC) and phosphodiesterase (PDE), synthesis and degradation of which are mediated respectively by GGDEF and EAL domain-containing proteins (Schirmer & Jenal, 2009).

Quorum sensing (QS) is yet another system used by some bacteria to regulate biofilm- development (Parsek & Peter Greenberg, 1999). The extracellular chemical signalling system is population dependant and involves cell-to-cell communication, which is achieved through the secretion of signalling molecules called autoinducers (Nealson & Hastings, 1979, Dobretsov et al., 2009). There are numerous different QS signalling systems employed by gram negative bacteria but the best characterized involves N-acyl homoserine lactones (AHLs) as the signalling molecule. AHLs are autoinducers produced by homologues of AHL synthase LuxI and each AHL differs in length and degree of saturation (Parsek & Peter Greenberg, 1999, Fuqua et al., 2001). After being produced and having reached a certain cellular threshold concentration, AHLs bind to LuxR type receptor leading to the formation of active dimers. The complex binds to specific regions and activates transcription of QS regulated genes. In addition to controlling biofilm- related characteristics, QS systems regulate traits in various bacteria such as bioluminescence (Sitnikov et al., 1995, Ravn et al., 2001, Romero et al., 2010), virulence factors (Büttner & Bonas, 2010, da Silva et al., 2014), swarming (Eberl et al., 1996), and conjugation (Brimacombe et al., 2013). Moreover, these molecules are also involved in inter-kingdom communication such as mediating induction of macroalgal spore settlement (discussed in section 1.2.1). In a broader sense, the interactions between macroalgae and their associated microbes, such as surface colonisation and signalling systems are only just being understood. Advancing this

understanding involves resolving the structure and composition of an alga’s biofilm which, in turn, relates to the chemical nature of the host alga.

1.1.2 Patterns in bacterial diversity on macroalgae

Knowledge of algal-bacterial interactions has expanded as a consequence of continued research that has made invaluable contributions to various fields, including drug discovery and bacterial- mediated algal diseases (Gachon et al., 2010, Hollants et al., 2012, Wahl et al., 2012, Egan et al., 2013, Goecke et al., 2013, Singh & Reddy, 2014). In addition to improving our understanding of bacterial diversity in the pelagic environment, recent molecular techniques have enhanced knowledge of epiphytic communities on macroalgal surfaces. For example, a first application of catalysed reporter deposition fluorescence in situ hybridisation (CARD-FISH) protocol quantified the bacterial community on macroalgal surfaces (Tujula et al., 2006). Similarly, previously undetected but substantial microbial diversity on macroalgal surfaces has been uncovered with the help of other culture-independent methods. For example, phylogenetic examination of denaturing gradient gel electrophoresis (DGGE) analysis and 16S rRNA gene libraries demonstrated the presence of Alpha-, Delta- and Gamma-proteobacteria in libraries of the green alga, Ulva australis and the red alga, Delisea pulchra (Longford et al., 2007). Another study confirmed that phylogenetic profile of the bacterial community on U. australis was dominated by Alpha-proteobacteria (Burke et al., 2011). The latter study also highlighted the presence of Bacteroidetes.

Patterns in bacterial colonisation on macroalgal surfaces have begun to immerge as a result of various studies. For example, the cosmopolitan algal genus Ulva remains relatively free of surface-foulers (Egan et al., 2000, Rao et al., 2006, Burke et al., 2011a, Kumar et al., 2011, Lachnit et al., 2011). Moreover, investigations of epiphytic communities of other groups of algae such as D. pulchra, Fucus sp., and Laminaria sp., suggest the occurrence of specific core bacterial communities on some of these algae (Longford et al., 2007, Staufenberger et al., 2008, Bengtsson & Øvreås, 2010, Bengtsson et al., 2010, Tujula et al., 2010, Burke et al., 2011a, Lachnit et al., 2011, Bondoso et al., 2014, Lage & Bondoso, 2014). In some cases distinct microbial communities have been detected between different regions of the macroalgal thallus, reflecting the various micro-niches available to colonising microorganisms (Staufenberger et al., 2008, Bengtsson et al., 2012). The studies have further highlighted that algal epiphytic microflora not only differ from the surrounding planktonic community but temporal and species-specific differences also occur. A study comparing the epiphytic community composition of three co- occurring macroalgae, F. vesiculosus, Gracilaria vermiculophylla and U. intestinalis, found algae 4

of the same species that were exposed to the same pool of bacterial colonisers to have different microflora when sampled over different seasons (Lachnit et al., 2011). In addition, Lachnit et al. (2011) found compositional patterns re-occurred during the same season for two consecutive years, whereby 7-16% of the sequences remained specific to the host alga. Likewise, another study reported up to 40% differences when comparing individuals of U. australis from within the same, and between three different, tidal pools separated by <20m (Tujula et al., 2010). Outcomes of the two studies point towards regulation of surface communities and possible species-specific bias during colonisation. This species specificity is further supported by the observations that disturbances of the microbial communities associated with seaweeds lead to succession effects whereby the native surface community is restored over time (Longford, 2007).

While some macroalgae maintain core groups of associated bacteria, microbial colonisation of others tends to be stochastic. A comprehensive analysis of U. australis libraries indicated a relatively variable associated microbial community with the absence of a consistent sub- population of bacterial species (Burke et al., 2011a). The study by Burke et al. (2011a) involved construction of gene libraries using six samples of U. australis (5293 sequences) and ten samples of local seawater (10884 sequences). Inconsistencies between findings of earlier studies and the findings of Burke et al. (2011a) were attributed to technical limitations of earlier technologies, including DGGE, which warranted a re-modelling of the current understanding of surface colonisation. A follow-up metagenomic study aiming to link community structure with function on algal surfaces revealed that although phylogenetic diversity was high (only 15% similarity between samples) with broad functional composition (70%), core functional genes were present that were consistent across the variety of taxa (Burke et al., 2011b). Hence, the findings implied that gene content of the bacterial community was more consistent than the phylogenetic diversity. Thus, with improved sequencing technology, further insights have been gained into the patterns of macroalgal surface colonisation, with the scope to investigate further processes, such as community composition on different macroalgae during disease-driven community shifts (Longford, 2007).

However, despite culture-independent techniques having offered numerous benefits, including contributing considerably to our current understanding of bacterial diversity, culturing the ‘unculturable’ organisms in vitro for physiological and pathological analyses remains a challenge (Vartoukian et al., 2010, Pham & Kim, 2012). Lack of culturability has been attributed to loss of cell viability (Janssen et al., 2002), missing growth factors (D'Onofrio et al., 2010) and over-

concentrated nutrients experienced by environment bacteria when cultivated on artificial media (Rappé et al., 2002, Davis et al., 2005). Recent techniques of selective culturing, specific to a respective isolates’ growth requirements may thus offer better alternatives to isolating ‘unculturable’ bacteria (Köpke et al., 2005, Ferrari & Gillings, 2009, D'Onofrio et al., 2010, Montalvo et al., 2014, Ringeisen et al., 2015). Taken together, while the modern culture- independent tools have provided insights into previously unknown microbial diversity, the traditional culture-dependent techniques remain vital for physiological characterisations of the newly discovered microbes. Hence more meaningful information can be acquired by using a combination of both techniques.

1.2 Interactions between microbes and macroalgal hosts

1.2.1 Beneficial interactions

Bacteria on macroalgal surfaces engage in a range of activities to interact with their macroalgal host (Hollants et al., 2012, Wahl et al., 2012, Egan et al., 2013, Hollants et al., 2013, Singh & Reddy, 2014). For example, bacterial biofilms can act as important settlement cues for higher organisms and as such can promote unwanted epibiosis for the host (Qian et al., 2007). Excessive epibiosis consequently impairs the host’s ability to exchange gases and nutrients, increases physical drag, and impedes growth and photosynthesis (Jagels, 1973, Sand-Jensen, 1977, Silberstein et al., 1986). Some macroalgae, such as D. pulchra, have defence mechanisms that involve the production of secondary metabolites to prevent unwanted epibiosis (Dworjanyn et al., 2006). Defence systems are energy-demanding for macroalgae and it has been suggested that some algae form mutualistic or commensal relationships with bacteria, which through the production of inhibitory compounds prevent unwanted host surface colonisation (Armstrong et al., 2001, Holmström et al., 2002).

In the case of Ulva sp., epiphytic bacteria rich in secondary metabolites are harboured by the relatively chemically benign alga and are thought to protect it from macrofouling (Egan et al., 2000, Kumar et al., 2011, Lachnit et al., 2011). The bacterial community in return is sheltered from possible grazers and provided with nutrients for survival. Members of the bacterial genus Pseudoalteromonas are examples of epiphytic bacteria commonly isolated from biotic surfaces, including the surface of Ulva sp. (Egan et al., 2000, Holmström et al., 2002, Kumar et al., 2011). P. tunicata in particular produces a range of compounds that are active against the settlement of algal spores, invertebrate larvae, bacteria and fungi (Egan et al., 2002, Holmström et al., 2002, 6

Bowman, 2007). Similarly, Phaeobacter inhibens, formerly classified as Phaeobacter gallaeciensis is a widespread marine colonizer shown to have antibacterial activity presumably against the colonising bacteria that contribute to fouling of the host’s surface (Rao et al., 2005, Brinkhoff et al., 2008). Other examples of epiphytic bacterial groups known to have anti-fouling activity include Vibrio sp., Shewanella sp., Micrococcus sp., Bacillus sp., Flavobacterium sp. etc. (Penesyan et al., 2009, Kumar et al., 2011, Singh et al., 2015). On the global front, algal-bacterial symbioses and the associated metabolites are of particular interest to maritime industries, for which biofouling costs millions of dollars annually (Schultz et al., 2011). Such industries are thus venturing into isolating natural products from algal-bacterial interactions, which are promising to provide greener anti-fouling technologies (Yebra et al., 2004).

Perhaps the hallmark of algal-bacterial interactions has been the discovery that bacteria play a key role in algal spore settlement and morphogenesis (Provasoli & Pintner, 1980, Patel et al., 2003, Matsuo et al., 2005, Tait et al., 2005, Joint et al., 2007, Singh & Reddy, 2014, Wichard, 2015). The production of morphogenetic factors by bacteria are indispensable to the normal development of some algae (Matsuo et al., 2005). For instance, the physical association between representatives of Marinomonas sp. and Bacillus sp. and the alga, U. fasciata is critical for normal algal morphogenesis and growth (Singh et al., 2011). Moreover, a suitable surface is needed for the settlement of zoospores, a vital stage in the algal lifecycle, and zoospores respond to settlement cues produced by bacteria (Patel et al., 2003). For example, the zoospores of Ulva sp. respond to the QS system of Vibrio anguillarum for selecting sites of permanent attachment (Tait et al., 2005). Weinberger et al. (2007) observed that spore release by the red algal epiphyte Acrochaetium sp. was dependent on bacterial AHLs, which were often present exogenously on the algal host G. chilensis. More recently, members of the bacterial orders Bacillales, Pseudomonadales, Alteromonadales, Actinomycetales and Enterobacteriales were discovered to produce different types of AHLs capable of inducing carpospores liberation in the red macroalga G. dura (Singh et al., 2015). Thus, the studies together contribute towards an increasing global appreciation for the inter-dependence and intimate associations between bacteria and macroalgal hosts.

1.2.2 Pathogenic Interactions

Mutualistic symbioses generally safeguard algae from undesirable relations, however deleterious microbe-host interactions sometimes occur. Although bacteria may be the most widely reported of all algal pathogens, infectious agents such as viruses, fungi, protists and other eukaryotes are also linked with diseases in macroalgae (Andrews, 1976, Sekimoto et al., 2008, Gachon et al., 2010, Goecke et al., 2010, Schroeder, 2011). The pathogenic interactions of microorganisms generally result in characteristic symptoms such as rot, bleaching, lesions or malformations, sometimes resulting in sudden extinctions of natural populations (Correa, 1996, Goecke et al., 2010, Campbell et al., 2014). The global impact of disease is expected to be catastrophic, given that macroalgae act as ecosystem engineers that support the growth and development of a diverse range of organisms (Stachowicz, 2001, Bouma et al., 2005, Byers et al., 2012, Wahl et al., 2014).

Despite descriptions of multiple algal diseases to-date (Table 1.1), in the majority of cases, the mechanisms and abiotic factors that influence disease manifestation and its impact on the host and ecosystem are not clearly understood (Egan et al., 2013, Egan et al., 2014). For example, bacteria of the Roseobacter lineage have been implicated in the tumour-like (gall-formation) disease of the red alga Prionitis lanceolata (Ashen & Goff, 1996). Galls appear to be a result of interactions with specific Roseobacter ribotypes that infect P. lanceolata through a wound site and colonise intracellular spaces by means of overproduction of indole-3-acetic acid, a phytohormone (Ashen & Goff, 1996, Ashen et al., 1999). While gall-formation in P. lanceolata remains one of the best characterised algal diseases, the ecological impacts of the disease is yet to be assessed.

Microorganisms have also been linked with disease outbreaks during the intensive farming of macroalgae, including Porphyra sp. (including nori) and Laminaria sp. (Largo et al., 1995, Ding & Ma, 2005, Gachon et al., 2010, Hollants et al., 2012). The traditional methods of seaweed aquaculture involve the use of propagules (Reddy et al., 2008, Yong et al., 2014, Radulovich et al., 2015). Hence, the genetic implication of clonal propagation means entire populations of algal species may become susceptible to pathogens. The disease, ‘ice-ice’, which affects the red macroalga Kappaphycus alvarezii has caused commercial losses in seaweed farms in the Philippines since 1974 (Largo et al., 1995). The strains Vibrio sp. P11 and Cytophaga sp. P25, isolated from the alga showed pathogenic activity towards the stressed host, where motility- driven attachment of pathogens to the host was suggested to be an important factor for infection (Largo et al., 1995, Largo et al., 1999). Studies of the ‘ice-ice’ disease condition in K. 8

alvarezii have provided important insights into the role of environmental stressors, opportunistic pathogens and associated virulence factors in disease induction of macroalgal hosts. However, despite the vast number of reported disease symptoms and resulting pathogen diversity, the understanding of bacterial-induced macroalgal diseases is thought to still be in its infancy.

Table 1.1: Bacterial-mediated disease of macroalgae*

Macroalga (Phylum) Disease syndrome Putative bacterial Environmental conditions Reference pathogen affecting disease Chondrus crispus (Rhodophyta) Green rot disease Dark orange bacterial Ammonium concentration. (Correa et al., 1994, Craigie colony, designated DOR & Correa, 1996) isolate Delisea pulchra (Rhodophyta) Mid-thalli Nautella italica sp. R11, Increased temperature and (Case et al., 2011, bleaching Phaeobacter sp. LSS9 reduced host chemical Fernandes et al., 2012) defence. Gracilaria verrucosa (Rhodophyta) Rotten thallus Vibrio sp. Na (Lavilla-Pitogo, 1992, syndrome Beleneva & Zhukova, 2006) Gracilariopsis lemaneiformis White tip disease Thalassospira sp., Vibrio Na (Sun et al., 2012) (Rhodophyta) parahaemolyticus Gracilaria gracilis (Rhodophyta) Cell-wall Pseudoalteromonas Na (Schroeder et al., 2003) degradation gracilis B9

Gracilaria conferta (Rhodophyta) White tip disease Bacterial strain OR-I1 Temperature, light intensity (Friedlander & Gunkel, & brown points and organic nutrients. 1992, Weinberger et al., disease 1994) Iridaea laminarioides (Mazzaella Deformative gall Cyanobacteria – Higher prevalence in (Correa et al., 1994, Correa laminarioides) (Rhodophyta) disease Pleurocapsa sp. summer. et al., 1997)

Kappaphycus alvarezii Ice-ice whitening Cytophaga P25, and Reduced salinity, light (Largo et al., 1995, Largo et (Rhodophyta) Vibrio sp.P11 intensity, water movement al., 1999) and increased temperatures. Porolithon onkodes and other Coralline lethal Planococcus sp. Increased temperatures. (Cervino et al., 2005) coralline algae (Rhodophyta) orange disease Bacillus sp. (CLOD) Pseudomonas sp Porphyra leucosticta Suminori Flavobacterium sp. Na (Kusuda et al., 1992) (Rhodophyta) Porphyra yezoensis (Rhodophyta) Anaaki Flavobacterium sp. LAD-1 Na (Sunairi et al., 1995)

Macroalga (Phylum) Disease syndrome Putative bacterial pathogen Environmental conditions Reference affecting disease Porphyra yezoensis (Rhodophyta) Suminori Gaetbulibacter Tissue damage. (Mine, 2009) saemankumensis Porphyra sp.(Rhodophyta) White rot disease Beneckea/Vibrio sp., Unspecified environmental (Tsukidate, 1983) Achromobacter, conditions induce amino acid Flavobacterium/Cytophaga, exudation which when available Pseudomonas sp. to pathogen leads to vigorous growth resulting in disease. Prionitis sp.(Rhodophyta) Gall-formation Roseobacter sp. Na (Apt & Gibor, 1989, Ashen & Goff, 2000) Laminaria japonica (Heterokontophyta) Hole-rotten disease Pseudoalteromonas, Na (Wang et al., 2008) Vibrio, Halomonas Laminaria japonica (Heterokontophyta) Red spot disease Pseudoalteromonas Na (Sawabe et al., 1998) bacteriolytica Laminaria japonica (Heterokontophyta) Spot-wounded Pseudoalteromonas Na (Sawabe et al., 1992) fronds elyakovii Laminaria religiosa (Heterokontophyta) Lesions Alteromonas sp. Reduced salinity and increased (Vairappan et al., 2001) temperature. Saccharina japonica (synonymous Swollen Alteromonas sp. A-1. Increase in temperature. (Peng & Li, 2013) for Laminaria japonica) gametophyte and (Heterokontophyta) filamentous fading na – not available * Table modified from Egan et al. (2014)

1.3 Macroalgal chemical defence

1.3.1 Production of secondary metabolites

Secondary metabolites produced by macroalgae deter attachment, grazing and disease induction by colonisers and herbivores, thus preventing premature decomposition of the thalli. Target organisms for the inhibitory activity of several secondary metabolites studied to date include bacteria, fungi, viruses, algal spores, invertebrate larvae and larger grazers (Kolender et al., 1995, Hellio et al., 2000, Engel et al., 2006, Paul et al., 2006, Lachnit et al., 2009, Salaün et al. 2012, Egan et al., 2013). The majority of secondary metabolite production has been observed for representatives of the algal divisions, Rhodophyta and Heterokontophyta (Goecke et al., 2010, Egan et al., 2013). For example, the red alga Bonnemaisonia hamifera produces a poly- brominated 2-heptanone antibacterial metabolite shown to be particularly effective against gram positive bacteria and flavobacteria (Nylund et al., 2008). Similarly, another red alga, D. pulchra has significantly lower bacterial abundance compared to co-occurring algae and it achieves this by secreting furanone compounds, secondary metabolites analogous to the bacterial AHL (Maximilien et al., 1998). Furanones produced by D. pulchra interfere with the bacterial AHL-mediated gene expression by competing for the LuxR binding site within bacterial cells and thus preventing the formation of AHL:LuxR complex (Manefield et al., 1999). The furanone-LuxR complex eventually undergoes proteolytic degradation, dampening the QS response (Kjelleberg et al., 1997, Manefield et al., 2002). The defence system employed by D. pulchra is a remarkable antifouling strategy that provides researchers with a model system for studying defence relationships between macroalgae and bacteria, including potential pathogens. Similarly, the brown alga, Lobophora variegata is chemically defended by lobophorolide, a lactone active against pathogenic and saprophytic marine fungi (Kubanek et al., 2003). Increasing evidence suggests that the activity of secondary metabolites may be targeted towards specific pathogens that pose a threat to the host, such as disease or resource competition in the same niche (Lam et al., 2008, Sneed & Pohnert, 2011, Saha et al., 2012). Moreover, the production of secondary metabolites is of particular interest to researchers as macroalgal metabolites are novel sources of bioactive compounds that have vast biotechnological applications (Kubanek et al., 2003, Reddy et al., 2008).

1.3.2 Defence triggered by pathogens

To protect themselves from pathogens, some macroalgae employ pathogen-induced defences, which involve specific recognition of bacteria through discrimination between self and non-self

cells, followed by the activation of defence mechanisms (Weinberger, 2007). As for plants and animals, two defence strategies can be observed for macroalgae: 1) microbial-associated molecular patterns (MAMPs) and 2) pathogen-induced molecular patterns (PIMPs) (Nürnberger et al., 2004, Zipfel & Felix, 2005, Vera et al., 2011). While MAMPs are not pathogen-specific and perceive microorganisms generally, including pathogens, PIMPs perceive the breakdown on the host cell wall as a result of phytopathogenic activity via direct interaction with host defence receptors (Zipfel & Felix, 2005). The same pathways and responses are thought to be activated in certain macroalgae, mainly in the orders Rhodophyta and Heterokontophyta, although any such receptors have yet to be isolated and characterized (Weinberger, 2007).

Although the mechanisms behind pathogen-triggered algal defences remain to be elucidated, the existence and relevance of such systems in macroalgal hosts has been demonstrated. For instance, the model pathosystem between the host alga, Chondrus crispus and pathogenic endophyte, Acrochaete operculata, has been used to show that elicitation with pathogen extracts generates hydroperoxides of the two oxylipins, C18 and C20, which consequently induce invasion resistance (Bouarab et al., 2004). Oxylipin signalling increases the expression of stress related genes in red algae (Collén et al., 2006) and triggers oxidative burst activity in kelps (Küpper et al., 2009). The oxidative burst response, is a transient generation of reactive oxygen species (ROS), which includes production of hydrogen peroxide, superoxide or hydroxyl radicals (Küpper et al., 2006, Weinberger, 2007). In the brown alga L. digitata and the red alga G. conferta, degradation of cell walls triggers an oxidative burst response that eliminates the associated bacteria (Weinberger et al., 1999, Küpper et al., 2001). Moreover, as a consequence of alginate-triggered oxidative bursts in L. digitata, significant resistance is induced against infection by the endophytic pathogen Laminariocolax tomentosoides (Küpper et al., 2002). Hence, together secondary metabolite production and the pathogen-triggered defence systems demonstrate strategies employed by macroalgal hosts to defend themselves against pathogen activity, which are crucial factors in the overall health of macroalgae.

1.4 Opportunistic pathogens, host defence and environmental factors

The majority of well-studied diseases in the marine environment are understood to be caused by disturbances in the overall relationship between the host and microorganisms, which interact together as a functional entity or holobiont (Chapin III et al., 1993, Harder et al., 2012, Apprill et al., 2013, Burge et al., 2013, Egan et al., 2013). Changing climatic conditions such as increases in

seawater temperature and ocean acidification, in addition to anthropogenic factors, such as nutrient input, correlate with changes in host physiology, abundance, diversity and community structure (Altizer et al., 2013, Becherucci et al., 2014). For example, a study investigating effects of nutrient enrichment and metal pollution found that abundance of the sensitive canopy- forming algae Sargassum muticum and Bifurcaria bifurcata reduced in response to subtle levels of pollution (Rubal et al., 2014). Therefore, although in itself ecological conditions may not directly lead to disease, the manifestation of adverse conditions could render an organism more vulnerable to infections (Karsten et al., 2001, Toohey & Kendrick, 2007). For example, reduced salinity, irradiance, water movement and temperature are factors crucial for the development of ‘ice-ice’ disease in K. alvarezii (Largo et al., 1995). Diseases in corals, including the white syndrome and black- and yellow band diseases, are also linked with elevated seawater temperatures (Jones et al., 2004, Bruno et al., 2007, Closek et al., 2014). These findings suggest that environment-driven stressors contribute factors to disease susceptibility during host- microbe interactions, thus having negative implications on the health of marine organisms, including macroalgae.

1.4.1 Opportunistic pathogens and their virulence mechanisms

Opportunistic pathogens are organisms that associate with healthy hosts as commensals, but are implicated in pathogenesis under specific conditions, such as during wounding or aging and on immunocompromised hosts (Becker et al., 2004, Wang et al., 2008, Campbell et al., 2011, Case et al., 2011, Seyedsayamdost et al., 2011a, Seyedsayamdost et al., 2011b, Harder et al., 2012, Burge et al., 2013, Egan et al., 2014). The study of opportunistic pathogens can help elucidate possible causes of shifts in host-microbe relationships from commensal to pathogenic interactions (Scully & Bidochka, 2006). One of the better studied opportunistic pathogens is Pseudomonas aeruginosa, a versatile pathogen, capable of virulence in a variety of hosts, including humans, plants and animals (Lyczak et al., 2000, Stover et al., 2000, Ryder et al., 2007, Matz et al., 2008). Genome analyses reveal that the success of P. aeruginosa as an opportunistic pathogen can be attributed to a combination of pathogenic traits, including resistance to antibiotics and disinfectants, environment adaptability and an exceptionally high proportion of regulatory genes (Stover et al., 2000, Lee et al., 2006). Many microorganisms in the marine environment are opportunistic pathogens with the capacity to engage in virulence against the host (Largo et al., 1995, Jaffray et al., 1997, Weinberger, 2007). For example, Phaeobacter gallaeciensis switches the production of growth stimulants to selective algicides, in response to lignin breakdown products from the microalgal host Emiliania huxleyi (Seyedsayamdost et al.,

2011b). Similarly, when microorganisms originally isolated from healthy specimens of the kelp Macrocystis pyrifera were re-introduced to axenic kelp its capacity to perform oxidative bursts was impaired and the kelp was rapidly degraded by the resident microbes (Küpper et al., 2002).

The virulence mechanisms of pathogenic bacteria that are particularly relevant against macroalgal hosts include strategies for adhesion to the host, evasion of host defence system, production of extracellular polysaccharides for the degradation of host cell walls (refer to section 1.4.2), biofilm formation and coordinated gene regulation through quorum-sensing or c-di-GMP systems (refer to section 1.1.1) (Cotter & Stibitz, 2007, Defoirdt, 2013). Specific virulence determinants that have been used to predict virulence in bacteria include secretion systems (Tseng et al., 2009), flagella, pili and surface lipoproteins (Merz et al., 2000, Mattick, 2002, Bahar et al., 2009); and the production of biosynthetic compounds, such as toxins (Seyedsayamdost et al., 2011b) and siderophores (Carniel, 2001, Lamont et al., 2002, West & Buckling, 2003, Oide et al., 2006). While virulence factors were previously thought to be maintained in pathogens to aid parasitic exploitation (Erken et al., 2013), increasing evidence shows that selection of virulence factors may in fact be for non-parasitic or generalist strategies and phenotypic plasticity (Brown et al., 2012). Moreover, homologues of virulence genes have been detected in a high proportion of phylogenetically diverse genomes of marine bacteria with no known association to infectious disease (Rusch et al., 2007, Persson et al., 2009). Thus, together these findings have strong implications on the abundance of opportunistic pathogens in the marine environment and their role in seaweed diseases.

1.4.2 Koch’s postulates

In the medical field, the identification of causative agents of disease has traditionally required a rigorous criteria in fulfilment of Koch’s postulates (Blevins & Bronze, 2010). The criteria includes demonstrating that a) the putative pathogen is always present in diseased individuals and absent in healthy individuals b) the putative pathogen can be isolated in pure culture from a diseased individual c) introducing the putative pathogen to a healthy individual causes the disease and d) the putative pathogen can be re-isolated from the newly diseased individual. Demonstrating Koch’s postulates has been challenging, especially in diseases involving opportunistic pathogens, which may persist on hosts both under healthy and diseased states (Falkow, 2004). However, with the advancement of molecular techniques, the original postulates have been revised and sequence-based identification mechanisms are being used to demonstrate disease causality (Fredericks & Relman, 1996, Falkow, 2004).

The fulfilment of Koch’s postulates and mechanisms of infection in most cases remain poorly understood despite the large number of macroalgal diseases identified (Goecke et al., 2010, Egan et al., 2014). This is partly due to the difficulty in distinguishing opportunistic pathogens from saprophytic secondary colonisers feeding on decaying matter (Egan et al., 2014). Algal cell walls are comprised of a diverse range of polysaccharides and some bacteria produce a variety of enzymes such as cellulases, alginases, fucoidanases, pectinases and agarases that breakdown algal cell walls and other substances as part of the natural biotransformation process (Goecke et al., 2010, Domozych, 2011, Martin et al., 2014). While the primary function of these enzymes may be nutrient cycling, repeated demonstrations of enzyme-mediated pathogenesis in seaweeds highlight their role also as a potential virulence trait (Lavilla-Pitogo, 1992, Sawabe et al., 1992, Ivanova et al., 2002). Hence, together the findings indicate that disease in macroalgae involves a complex interplay between the virulence strategies of pathogens, host defence and environmental factors.

1.5 Life history stages and model of disease in Delisea pulchra

The genus Delisea belongs to the family Bonnemaisoniaceae, classified amongst red seaweeds (Rhodophyta) and is abundant in temperate habitats around Australia, New Zealand and Japan (Bonin & Hawkes, 1988). Life history studies have reported isomorphic free-living haploid (gametophyte) and diploid (tetrasporophyte) stages in D. pulchra growing in Australia and New Zealand (Chihara, 1962). However the gametophyte stage is missing in > 90% populations of D. pulchra in south-eastern Australia, indicating maintenance by asexual reproduction of tetraspores (Wright, 2000). Whilst tetraspores harvested from adult tetrasporophytes have been used to culture sporelings of D. pulchra (Campbell et al., 2011), attempts to maintain adults for prolonged periods under laboratory conditions have been unsuccessful (personal observation). Fig. 1.2 illustrates the three distinct life history stages in D. pulchra.

Fig. 1.2: Life history stages of the temperate red macroalga, D. pulchra. The three distinct stages include (a) sporeling (b) juvenile and (c) reproductive adult phase.

During summer, D. pulchra undergoes a bleaching disease involving loss of photosynthetic pigment from mid-thallus sections of the plant (Fig. 1.3) (Campbell et al., 2011, Case et al., 2011). As a consequence of bleaching, affected individuals experience decreased fecundity and size reductions and a corresponding increase in herbivory (Campbell et al., 2014). Numerous contributing factors in the manifestation of disease have been identified such as environmental stress, reduced chemical defences in the host, the presence of pathogens and possibly differential gene expression (Campbell et al., 2011, Case et al., 2011, Fernandes et al., 2011, Gardiner et al., 2015, Zozaya-Valdes et al., 2015). A metagenomic comparison of the bacterial communities on D. pulchra found that bleaching involved a shift in microbial community composition, where distinct assemblages characterised the microbial communities on healthy, adjacent-to-bleached and bleached tissues of the alga (Fernandes et al., 2012). The study by Fernandes et al. (2012) also found that while the healthy samples showed less than 20% similarity with the other two sample types, bleached and adjacent-to-bleached samples shared at least 35% of the sequences found, indicating that changes in the microbial community occurred prior to manifestation of the bleaching phenotype.

Fig. 1.3: Mid-thallus bleaching in the temperate red macroalga D. pulchra (arrow indicates bleached region).

There is on-going research on the role of the bacterium, Nautella italica R11 in inducing bleaching in D. pulchra. The opportunistic pathogen, originally isolated from the surface of D. pulchra in Sydney, belongs to the Alpha-proteobacteria phylum and is a member of the Roseobacter clade (Case, 2006). Characterisation of N. italica R11 by Case et al. (2011) found that at elevated temperatures during summer, the bacterium formed biofilms, invaded tissues and caused bleaching in ‘undefended’ (furanone-free) sporelings of D. pulchra (Fig. 1.4). An analysis of the genome sequence of N. italica R11 implied that virulence in the bacterium was attributed to a combination of factors, such as the type III secretion system, toxins, proteases, iron sequestration systems and a QS system (Fernandes et al., 2011). Recent studies have found roles for two QS-dependant transcriptional regulators (VarR and RaiR) and the stress resistance enzyme, glutathione peroxidase in mediating virulence in N. italica R11 (Gardiner, 2014, Gardiner et al., 2015). Similar to N. italica R11, the bacterium Phaeobacter sp. LSS9, is also a member of the Roseobacter clade, capable of inducing bleaching in D. pulchra (Fernandes et al., 2011). Preliminary data also shows that in vitro bleaching, similar to that induced by N. italica R11 and Phaeobacter sp. LSS9 were caused by Alteromonas sp. LSS17, Cellulophaga sp. ESS16 and Microbulbifer sp. U156, of which Microbulbifer sp. U156 had the capacity to induce bleaching also in furanone-producing sporelings of D. pulchra (Fernandes, 2011).

Fig. 1.4: Microscopic examination of the effect of inoculating sporelings of D. pulchra with N. italica R11. Images represent (a) furanone free (-) D. pulchra at 19°C, (b) furanone producing D. pulchra at 19°C, (c) furanone (-) D. pulchra at 24°C and (d) furanone (+) D. pulchra at 24°C. Bacterial biofilms are indicated with arrows. Image adapted with permission from Case et al. (2011).

An investigation of the abundances of pathogens involved in community shifts during multiple natural bleaching events on D. pulchra found that while Phaeobacter sp. LSS9 was over- represented on bleached tissues, a similar increase in the abundance of N. italica R11 was not detected (Zozaya-Valdes et al., 2015). The study by Zozaya-Valdes et al. (2015) further showed that disease-associated community shifts involved clear increases in the abundance of bacterial species belonging to the families Rhodobaceraceae and Flavobacteraceae, suggesting an involvement of yet uncharacterised opportunistic pathogens of D. pulchra.

1.6 Thesis aims

To date N. italica R11 and Phaeobacter sp. LSS9 are the only well-characterised bacterial pathogens causing temperature-induced bleaching disease in D. pulchra. However, a low detection of at least N. italica R11 and clear increases in the abundance of bacterial species belonging to Rhodobacteraceae and Flavobacteraceae in bleaching events on D. pulchra suggests the involvement of other as yet unknown pathogens. In addition, homologues of virulence genes are abundant in marine systems (Persson et al., 2009). With increasing environmental stress (e.g. due to temperature, urbanisation etc.) placed on host organisms (Harvell et al., 2009, Doney et al., 2012, Wernberg et al., 2013), many opportunistic bacteria may emerge with the capacity to cause algal disease, however this remains to be tested. This thesis aims to address the hypothesis that other opportunistic pathogens are involved in the bleaching disease of D. pulchra. To investigate this hypothesis, opportunistic pathogens of marine macroalgae will be characterised by investigating their prevalence and virulence mechanisms. Detailed aims for each chapter are outlined as follows:

Chapter two focuses on isolating bacteria that contribute towards a microbial community shift in diseased D. pulchra. In addition to the traditional targeted approach, a randomised approach for isolate-sampling is used to obtain estimates of isolate abundances. The phylogenetic identity of isolates are determined via sequencing and analysis of 16S rRNA gene and the isolate sequences that contributed towards bacterial community shifts associated with disease events on macroalgae in the field are identified.

Chapter three investigates the potential of isolates over-represented on diseased D. pulchra to function as opportunistic pathogens of macroalgae. The isolates identified as putative pathogens in Chapter two are characterised for virulence-related traits through the assessment of motility, biofilm-forming capabilities, resistance to host chemical defence (furanones), resistance to oxidative stress, i.e. hydrogen peroxide and the degradation of algal cell wall components.

Chapter four tests the putative pathogens for bleaching effects on D. pulchra, using a new method developed for the in vivo screening of isolates. Putative pathogens that expressed the broadest range of virulence-related traits in Chapter three are tested for their ability to induce bleaching in D. pulchra.

Chapter five determines the effect of introducing selected putative pathogens on stressed D. pulchra and the impact made on the resident microbial community. Sequences with matching

identity to the putative pathogens are detected in partial fulfilment of Koch’s postulates and the presence of putative and known pathogens within the background community on D. pulchra is determined. In addition, the overall bacterial community is assessed to determine the effect of treatment (pathogen introduction) and the bleaching outcome on the overall abundance of bacterial species.

Chapter six compares the genomes of selected opportunistic pathogens of D. pulchra in order to explore likely mechanisms of virulence employed against the host. Virulence determinants are identified that are either specific to phylogenetic relatives or common between non- relatives.

Chapter seven provides a general overview of the major findings of this thesis. A hypothetical virulence model involving key virulence genes and pathogenic traits is provided. In addition, the implications of these findings and directions for future research are discussed.

Chapter Two:

Isolation of putative opportunistic bacterial pathogens associated with bleaching of Delisea pulchra

2.1 Introduction

Much of fundamental aspects of bacterial physiology have been understood through culture- based studies. Bacterial diversity, the conditions for optimal growth and expression of virulence characteristics - in the case of pathogens, have all been established in the past by relying on studies that utilise pure cultures. However, following the recent revolution in molecular biology and nucleic acid sequencing there has been a shift towards culture-independent approaches to study microbial communities (Rappé & Giovannoni, 2003, Keller & Zengler, 2004, Achtman & Wagner, 2008). No doubt the introduction of culture-independent approaches has greatly advanced our understanding of bacterial diversity and microbial interactions with different host- types (Harvell et al., 1999, Jones et al., 2004, Bourne, 2005, Angermeier et al., 2011). In addition, the advent of culture independent tools means that confirming the membership of a microorganism in a particular niche or determining how the abundance of that organism changes under different circumstances no longer depends on its cultivability. Comprehensive analyses of the changes in microbial communities during disease progression have also become less challenging (Harvell et al., 1999, Jones et al., 2004, Bourne, 2005, Angermeier et al., 2011). However, despite the numerous benefits that culture-independent analyses offer, a fundamental component of understanding pathogen physiology and the conditions leading to pathogenicity still largely depends on having representative pathogens in culture (Harvell et al., 1999, Joint et al., 2010, Stewart, 2012).

Originally cultured from Delisea pulchra, the bacteria, Nautella italica R11 and Phaeobacter sp. LSS9, are the only known opportunistic pathogens of D. pulchra to-date (Case et al., 2011, Fernandes et al., 2011). Both bacteria have the capacity to reproduce the characteristic bleaching disease in vitro (see Fig. 1.3). The abundances of these pathogens in natural bacterial communities on healthy and diseased D. pulchra were recently investigated using culture- independent analyses (Zozaya-Valdes et al., 2015). Zozaya-Valdes et al. (2015) found Phaeobacter sp. LSS9 to be over-represented in the bacterial communities associated with bleached as compared to healthy individuals across multiple disease events, however could not detect a similar increase in the abundance of N. italica R11. The low detection of the currently 22

known pathogens in naturally occurring D. pulchra disease events suggested the presence of yet other unknown opportunistic pathogens. Indeed a clear increase in the abundance of bacterial species belonging to the families Rhodobacteraceae and Flavobacteriaceae was observed between healthy and diseased D. pulchra, suggesting that these bacterial groups are likely to contain additional pathogens capable of causing bleaching in D. pulchra and possibly other hosts. For example, the kelp Ecklonia radiata, which shares habitat with D. pulchra, is also prone to disease, however no known pathogens exist to-date.

In order to test the hypothesis that other opportunistic pathogens exist, the objective of this chapter was to obtain cultures of bacteria that contribute towards a community shift on diseased D. pulchra. To address this objective, the first aim was to culture bacteria from diseased tissues of D. pulchra using two methods to sample for bacterial isolates – a standard targeted approach and a randomised method, which provided estimates of isolate abundances. The second aim was to determine the phylogenetic identity of isolates via sequencing and analysis of their 16S rRNA gene and determine the isolate sequences that contribute towards the bacterial community shifts associated with disease events on macroalgae in the field.

2.2 Materials and methods

2.2.1 Sample collection

Sampling of D. pulchra was carried out in February 2012 (late summer). Six individual alga showing bleaching in mid thalli regions were collected from Long Bay at Malabar, Sydney (151ᵒ14’42’’ E, 33ᵒ58’19’’ S) from depths of eight meters. Samples were transported to the laboratory in bags pre-filled with seawater within twenty minutes of collection.

2.2.2 Culturing of bacteria

Approximately 2 cm lengths of 7 bleached and 3 directly adjacent pigmented tissues of D. pulchra were cut using sterile scalpels and rinsed individually in filter-sterilized seawater (FSW) to remove loosely attached bacteria. Sections were swabbed using sterile cotton applicators, transferred to tubes containing 10 ml FSW and vortexed vigorously for a minute. Sections with weak tissue integrity were transferred directly to 10 ml FSW and vortexed without swabbing. Serial dilutions were performed up to 10-2 and a 100 µl aliquot of each dilution was spread plated on the culture media. Half-strength DifcoTM Marine Broth 2216 (hereafter referred to as HMA) solidified with 1.5% Bacteriological Agar was used throughout the culturing exercise. HMA plates 23

were incubated at room temperature for a period of 4 days before bacterial colonies were isolated and subcultured. In addition to the traditional targeted approach, a random technique of sampling for colonies was adopted. Under the random technique for colony isolation, dilution plates with bacterial colony counts with 30-300 CFU were selected. Quadrats measuring 1 x 1 cm were marked on each replicate plate for the selected dilutions. Out of a total of 65 quadrats, 10 were randomly chosen using a random number generator and all colonies within the quadrats were subcultured on HMA. The remaining colonies were subjected to the traditional method of isolation where colonies with particularly interesting pigmentation or agar-degrading morphotypes, not already chosen through the random approach, were isolated.

2.2.3 Generation of a culture collection

All isolates chosen were uniquely labelled and subcultured at least twice on HMA to ensure purity. Isolates were transferred to tubes containing half-strength MB - DifcoTM Marine Broth 2216 and incubated up to 48 hr at room temperature on platform shakers set at 200 rpm. Glycerol stocks were prepared by aliquoting 300 µl of sterile 80% v/v glycerol in 2 ml cryotubes and adding 700 µl of the broth culture. The solution was mixed thoroughly before tubes were transferred to labelled boxes and stored at -80 °C.

2.2.4 PCR amplification

The 16S rRNA gene was amplified by carrying out polymerase chain reaction (PCR) in reaction volumes of 25 µl containing 12.5 µl Econotaq® PLUS 2X Master Mix (Lucigen), 0.2 µM F27 and R1492 primers (Appendix I), 1 µl bacterial broth culture as DNA template (refer to 2.2.3 for growth conditions) and molecular grade water to a final volume of 25 µl. The DNA was exposed to an initial denaturation step of 94 °C for 2 min, followed by 30 cycles of denaturation at 94 °C for 30 sec, annealing at 50 °C for 30 sec and extension at 72 °C for 2 min. A final extension step was performed at 72 °C for 5 min and samples were held at 4°C.

PCR product concentration was estimated by electrophoresis on 1% (w/v) agarose gel, which contained 0.01% Biotium GelRedTM Nucleic Acid Stain 10000 X. Using Gene RulerTM 1 kb DNA molecular weight marker, the size and concentration of the amplified PCR product was estimated. Gels were run in 1 X sodium borate buffer (diluted from a 20 X stock, Appendix II) at 80 V for 45 min and visualized using the Gel-Doc Imaging system (BioRad).

2.2.5 DNA sequencing

PCR products obtained in 2.2.4 were purified using Zymo Research DNA Clean & ConcentratorTM- 5 Kit as per manufacturer’s instructions and the purified product was examined by gel electrophoresis as mentioned above (section 2.2.4). The purified PCR products were sequenced unidirectionally using 1 µl BigDye® Terminator v3.1 Cycle Sequencing mix (Applied Biosystems), 10-20 ng DNA, 3.2 pmol F27 primer (Appendix I), 3.5 µl of 5 X CSA buffer (Applied Biosystems) and molecular grade water in a final volume of 20 µl. Amplifications were conducted using the following thermoprofile: 96 °C for 10 sec, 50 °C for 5 sec and extension at 60 °C for 4 min in 99 cycles. To purify the BigDye PCR products, 5 µl of 125 mM EDTA and 60 µl of 100% ethanol were added and vortexed briefly. The extension products were left to precipitate for 30 min at room temperature. Tubes were centrifuged at 14000 rpm for 20 min and the supernatant was aspirated. The pellet was washed twice with 70% ethanol (v/v), tubes briefly vortexed and spun at 4 °C, at maximum speed for 10 min. Samples were dried by placing in a heat block set at 90 °C for 1 min. Sequencing was performed on an ABI 3730 DNA Capillary Sequencer at the Ramaciotti Centre for Genomics (RCG, UNSW).

2.2.6 Phylogenetic analysis

Sequences obtained were checked for purity of bases and contiguous read lengths using Sequence Scanner v1.0 (Applied Biosystems). Each sequence was manually trimmed at both ends to remove low purity bases. Sequences were then compared against the NCBI (National Centre for Biotechnology Information) BLAST 2.2.30+ database (Zhang et al., 2000) in November 2014, to identify homologies to known sequences.

A phylogenetic tree was constructed using <700 nucleotide bases of the unique sequences, together with the closest relatives. The sequences were aligned on the integrated software for Molecular Evolutionary Genetics Analysis, MEGA6, using the in-built alignment generator program, Muscle (Edgar, 2004). Using default parameters, the 112 aligned nucleotide sequences were tested by bootstrapping using a thousand replications. The percentage of trees in which the associated taxa clustered together was noted. Evolutionary history of the sequences were inferred using the Maximum Likelihood method (Tamura & Nei, 1993) and the evolutionary distances were computed using the Maximum Composite Likelihood method (Tamura et al., 2004).

2.2.7 Sequence-based search of cultured isolates in culture-independent studies

The 16S rRNA sequences obtained in section 2.2.5 were used to carry out a search against the culture-independent study of surface bacteria from D. pulchra, described in Zozaya-Valdes et al. (2015). The study included 16S rRNA gene sequences from bacterial communities on healthy and diseased D. pulchra from two separate disease events in two locations, Long Bay and Bare Island. To compare the near full-length Sanger sequences to the former study, sequences were trimmed down to 100 bp (coding the V4 region) and aligned against the Silva reference database through the analysis pipeline of Mothur (Schloss et al., 2009). The resulting 37 unique Sanger sequences were queried against the former dataset and OTUs with 100% BLAST identities and coverage to the query sequences were selected for further analysis. The standardized abundances of the selected OTUs (classified at 97%) were fitted to a Generalized Linear Model (GLM), using a negative binomial distribution, in the R mvabund software package (Wang et al., 2012). The GLMs were then used to test the significance of difference in abundances of the selected OTUs between healthy and bleached samples using analysis of deviance. The same analysis was repeated by querying the sequences obtained in section 2.2.5 against the 16S rRNA gene community of E. radiata, described in Marzinelli et al. (2015). Sequence data of the bacterial community on healthy and diseased tissues of the kelp was available for two separate disease events in two locations, Long Bay and Kurnell. This analysis was conducted in collaboration with Enrique Zozaya-Valdes, UNSW.

2.3 Results

2.3.1 Isolates cultured from diseased D. pulchra

In order to sample bacterial colonies cultured from the surface of diseased D. pulchra, two techniques – a traditional targeted method and a randomised method were used. With both techniques combined, 242 bacterial isolates were initially obtained, however 17% of the isolates became unviable during subsequent subculturing. Thus, in total, 119 isolates from bleached and 81 from directly adjacent pigmented tissues were successfully maintained in culture, with the 16S rRNA gene being successfully sequenced from 156 isolates (Table 2.1).

Table 2.1: The number of isolates successfully subcultured and sequenced

Technique Tissue Initially Isolated Subcultured Sequenced Random Adjacent-1 36 29 24 Adjacent-2 29 21 19 Adjacent-3 30 19 19 Bleached-1 34 26 20 Bleached-2 51 48 28 Bleached-3 28 25 20 Targeted Adjacent-1 2 2 2 Adjacent-2 10 8 8 Adjacent-3 3 2 1 Bleached-1 8 7 7 Bleached-2 3 7 2 Bleached-3 2 2 2 Bleached-4 1 1 1 Bleached-5 1 1 1 Bleached-6 1 0 0 Bleached-7 3 2 2 Total 242 200 156

The majority (80%) of the sequenced isolates belonged to the phylum Proteobacteria (Alpha- and Gamma-proteobacteria), with a smaller proportion of Bacteroidetes (20%) (Fig. 2.1). The Alpha-proteobacteria were comprised entirely of Rhodobacteriaceae, while the majority (60%) of Gamma-proteobacteria was comprised of members of Vibrionaceae. Bacteroidetes was entirely represented by Flavobacteriaceae.

When comparing the sequenced isolates obtained from bleached and adjacent-to-bleached tissues, similarities in bacterial diversity were observed. However, patterns in abundance of individual bacterial families obtained from the two tissue-types varied (Fig. 2.1). A similar trend was observed when comparing random and targeted approaches of isolate sampling, where 130 and 26 isolates, respectively, were obtained.

Comparisons of the culturing effort with a previous culture-independent study showed that three out of sixteen bacterial families detected in the former study were present also in the

culture collection obtained here. Detailed information on the closest match identities of all isolates is provided in Table 2.2 (Appendix II). Phylogenetic analysis of the isolates indicated similarities to various known marine isolates (Fig. 2.2).

Fig. 2.1: Proportion of bacterial isolates representing different families. Figure legend continued on next page.

Fig. 2.1 continued: Diversity and abundances of bacterial families obtained from (a) adjacent- to-bleached and (b) bleached tissues of D. pulchra, obtained through the random approach of isolate sampling; and from (c) adjacent-to-bleached and (d) bleached tissues of D. pulchra, obtained through the targeted approach of isolate sampling. The total number of isolates obtained were 130 and 26 through random and targeted approaches respectively. Taxonomic classification of the sequences were obtained using the BLAST tool 2.2.30+ database available through the NCBI (Zhang et al., 2000). Diversity and abundance of bacterial families detected on (e) adjacent-to-bleached and (f) bleached tissues of D. pulchra, in a former culture-independent study; graph constructed using data from Fernandes et al. (2012), where sequences contributing to the top 90% of the total community composition were included.

Gamma- proteobacteria

Alpha- proteobacteria

Bacteroidetes

Fig. 2.2: Evolutionary relationships of the cultured isolates to known taxa. Figure legend continued on next page.

Fig. 2.2 continued: Maximum likelihood tree of the cultured isolates from bleached and adjacent-to-bleached tissues of D. pulchra (indicated with blue dots) and the closest relatives, obtained from GenBank, in November 2014. The tree was constructed in MEGA6 using the aligned partial 16S rRNA gene sequences (approximately 700 nucleotide bases). Bootstrap method, with a thousand replications was used as the test of phylogeny. The phyla to which the strains belong are indicated on the right. The tree is drawn to scale with branch lengths representing the number of base substitutions per nucleotide position.

2.3.2 Over-representation of specific bacterial groups during bleaching of D. pulchra

In order to determine if the isolates obtained through culturing were involved in the observed disease-associated community shifts on D. pulchra described earlier (section 2.2.7), the 16S rRNA gene sequences corresponding to each isolate was searched for in the culture- independent dataset. The 66 unique cultured isolate sequences of near-full length (>700 bp) were aligned using 100 bp of the V4 region (to correspond with sequences in the culture- independent study), which resulted in 37 sequences unique in the V4 region. Of these, 28 sequences matched to 18 OTUs of the culture-independent study (Table 2.2). Nine sequences could not be matched to any OTUs.

Table 2.2: ID of unique cultured isolates and corresponding OTU (classified at 97%) in the culture-independent community dataset of D. pulchra

ID of unique cultured isolates a Corresponding OTU (97% classification) in the culture- independent dataset b AD1,AD10 DP_OTU00001 AD44,BL0115 DP_OTU00020 AD65,AD0104,AD098,AD47, DP_OTU00036 AD95,BL0114,BL89 BL18 DP_OTU00043 BL110,BL86 DP_OTU00052 PB1 DP_OTU00053 AD2,AD8 DP_OTU00258 BL8 DP_OTU00262 AD82 DP_OTU00427 BL54 DP_OTU00463 BL23 DP_OTU00499 BL7 DP_OTU00909 AD28 DP_OTU01153 AD39 DP_OTU01380 BL112 DP_OTU01464 BL103 DP_OTU12243 AD88 DP_OTU14430 PB21 DP_OTU15411 a BLAST sequence identity and accession ID available in Table 2.2 (Appendix II) b Full description of the dataset available in (Zozaya-Valdes et al., 2015)

The culture-independent community composition data for bleached and healthy D. pulchra included two disease events. With both disease events combined, the bleaching-associated community shift involved significant increases in the abundances of DP_OTU00001 (Aquamarina sp. AD1 and AD10), DP_OTU00262 (Shimia sp. BL8) (P<0.001), DP_OTU00499 (Dokdonia sp. BL23), DP_OTU00909 (Agarivorans sp. BL7) and DP_OTU00463 (Winogradskyella sp. BL18) (P<0.05). Consistently during both disease events, the abundance of DP_OTU00001 (Aquamarina sp. AD1 and AD10) and DP_OTU00262 (Shimia sp. BL8) were significantly higher on bleached samples. The abundances of OTUs on healthy and bleached D. pulchra during respective disease events at Long Bay and Bare Island are illustrated in Figures 2.3 (a) and (b).

(a) (b)

Fig. 2.3: Log abundances of OTUs (classified at 97%) on bleached (B) and healthy (H) D. pulchra from: (a) Long Bay where a significant increase in abundances of DP_OTU00001, DP_OTU00043, DP_OTU00262, DP_OTU00052, DP_OTU00036, DP_OTU00258, DP_OTU00053 and DP_OTU00463 (P<0.05) was observed and (b) Bare Island, where significant increases in abundances of DP_OTU00001, DP_OTU00262 and DP_OTU00499 (P<0.05) were observed. OTUs with significant increases between healthy and bleached samples are highlighted in blue. Analysis on OTU abundances was conducted using mvabund hypothesis tests (Wang et al., 2012). *Refer to Table 2.3 for ID of other isolates included in the OTU.

2.3.3 Over-representation of specific bacterial groups during bleaching of E. radiata

To determine if the cultured isolates were involved in disease-associated community shifts of other marine hosts, the analysis conducted in section 2.3.4 was repeated against a diseased and healthy, culture-independent community dataset from the brown kelp E. radiata (Marzinelli et al., 2015). Of the 37 sequences – with unique sequences in the V4 region (described in section 2.3.4), 22 could be matched to 14 OTUs of the culture-independent study (Table 2.3). Fifteen sequences could not be matched to any OTUs.

Table 2.3: ID of unique cultured isolates and corresponding OTU (classified at 97%) in the culture-independent community dataset of E. radiata

ID of unique cultured isolates a Corresponding OTU (97% classification) in the culture-independent dataset AD1,AD10 ER_OTU0010 BL18 ER_OTU0033 AD65,AD0104,AD098,AD47, ER_OTU0051 AD95,BL0114,BL89 BL23 ER_OTU0085 AD44,BL0115 ER_OTU0092 PB1 ER_OTU0145 BL110 ER_OTU0220 BL103 ER_OTU0401 AD2 ER_OTU0554 AD096 ER_OTU0751 AD82 ER_OTU1621 BL112 ER_OTU1929 BL8 ER_OTU2434 BL54 ER_OTU3435 a BLAST sequence identity and accession ID available in Table 2.2 (Appendix II)

The culture-independent community composition data for diseased and healthy E. radiata included two disease events. With both disease events combined, the bleaching-associated community shift involved significant increases in the abundances of ER_OTU0010 (Aquimarina sp. AD1 and AD10), ER_OTU0751 (Maribacter sp. AD096), ER_OTU0033 (Winogradskyella sp. BL18) and ER_OTU0085 (Dokdonia sp. BL23) (P<0.05). Individual sites had at least one of the aforementioned OTUs significantly over-represented on bleached tissues. The respective abundances of OTUs on healthy and diseased E. radiata during disease events at Long Bay and Kurnell are illustrated in Figures 2.4 (a) and (b). 34

(a) (b)

Fig. 2.4: Log abundances of OTUs (classified at 97%) on diseased (D) and healthy (H) E. radiata from: (a) Long Bay where a significant increase in abundance of ER_OTU0751 (P<0.05) and (b) Kurnell, where significant increase in abundances of ER_OTU0085 and ER_OTU0033 (P<0.05) was observed. OTUs with significant increases between healthy and bleached samples are highlighted in blue. Analysis on OTU abundances were conducted using mvabund hypothesis tests (Wang et al., 2012). *Refer to Table 2.3 for ID of other isolates included in the OTU.

2.4 Discussion

Culture-independent techniques are instrumental in understanding correlations between deterioration of host-health and shifts in the associated microbiota (Mao-Jones et al., 2010, Closek et al., 2014). However, to implicate disease causality in the host, characterisation of virulence against the host is crucial and this can only be achieved by having the pathogen in culture. The disease-associated community shift on D. pulchra, as determined using culture- independent approaches, involves an over-representation of a number of bacterial groups, for which in vitro bleaching has been demonstrated for Phaeobacter sp. LSS9 (Fernandes et al., 2011, Zozaya-Valdes et al., 2015). In order to isolate additional pathogens of macroalgae, this study established a culture collection of bacteria from bleached and adjacent-to-bleached tissues of D. pulchra. In addition, the abundance of the cultured isolates in separate disease events of two macroalgae, the red alga D. pulchra and the brown alga E. radiata, were investigated. Figure 2.5 summarises the workflow adapted in this chapter to organise sequences of the isolates for phylogenetic and abundance analyses.

Fig. 2.5: Flowchart summarising steps involved in the isolation of bacteria and sequence analyses performed in this study. Near full-length sequences were used for phylogenetic analysis. Sequences were re-aligned to correspond to 97% classified OTUs in former culture-independent studies of D. pulchra and E. radiata.

2.4.1 Abundance and diversity of isolates in the culture collection

In this study, bacteria cultured from the surface of bleached D. pulchra using HMA, was found to comprise largely of Proteobacteria (80%), followed by Bacteroidetes (20%). This finding is supported by previous studies where using non-selective media with reduced nutrient concentrations, representatives of both Proteobacteria and Bacteroidetes were readily cultured from marine surfaces (Ivanova et al., 2004, Bowman & Nichols, 2005, Cain & LaFrentz, 2007, Finnegan et al., 2011, Margassery et al., 2012, Montalvo et al., 2014, Penterman et al., 2014).

In total, abundances of Flavobacteriaceae and Vibrionaceae increased (from 15% on to 23% and from 21% to 34% respectively) while Alteromonadaceae and Rhodobacteraceae decreased on bleached tissues (from 15% on to 5% and from 45% to 35% respectively), relative to their abundances on adjacent-to-bleached tissues. These findings suggest that although a similar bacterial diversity exist on bleached and adjacent-to-bleached tissues of D. pulchra, changes in

abundances of the individual bacterial groups can be correlated with the physiological condition of bleaching. For example, many Bacteroidetes species, such as Flavobactericeae, are known for their ability to enzymatically hydrolyse polysaccharides present in algal cell walls and thrive on the nutrients released (Ivanova et al., 2004, Goecke et al., 2010, Hollants et al., 2012). Therefore this may explain the increased abundance of Flavobacteriaceae on bleached tissues, however experimental verification remains to be carried out. Moreover, although abundances of the individual representatives of Proteobacteria varied, an overall decline was observed on bleached tissues, suggesting that the adjacent-to-bleached tissues of the host was preferred by representatives of this phyla. Together the findings suggest that bleaching in D. pulchra involves a mechanistic succession of bacteria, where changes in the relative abundances of dominant species can be linked with disease progression in the host.

Using the random approach of sampling for isolates, the majority of the bacteria cultured comprised of Rhodobacteriaceae (46%), followed by close to equal proportions of Vibrionaceae (22%) and Flavobacteriaceae (21%). A similar diversity was observed when isolates were sampled using the targeted approach, although the largest contribution was ascribed to Vibrionaceae (58%), followed by Flavobacteriaceae (15%) and Rhodobacteraceae (8%). In a previous culture-independent study by Fernandes et al. (2012), the bacterial community on bleached and adjacent-to-bleached tissues of D. pulchra comprised largely of Flavobacteriaceae, Pelagibacteraceae and Rhodobacteraceae (15%, 12% and 11% respectively), whereas Vibrionaceae was absent. The bias towards members of Vibrionaceae in the current study could be due to cultivation periods that favoured selection of fast-growing r-strategist bacteria. Earlier studies have classified Vibrionaceae as an r-strategist on the basis of growth response to nutrient additions (Eilers et al., 2000, Weinbauer et al., 2006). Moreover, high abundances particularly observed in the targeted approach, was likely due to members of Vibrionaceae expressing diverse morphotypes, which were falsely assumed to be unique isolates when sampled. Phase variation in colony morphology within members of Vibrio sp. has been observed and linked with biofilm-formation and predation resistance (Matz et al., 2005). A previous study also demonstrated inconsistencies between culture-dependent and -independent analyses that used Vibrio as indicator species in corals with lesions (Apprill et al., 2013). Hence, for abundance estimations, in instances where variation in colony morphology is expected, targeted approaches have the tendency to artificially inflate true abundances. Nevertheless given that the diversity of isolates obtained using both methods were similar the traditional targeted approach of sampling for colonies, on the basis of pigmentation or agar-degrading properties, is less laborious and sufficient to provide a rapid method for capturing the culturable diversity. 38

As indicated earlier, the culture-independent analysis conducted by Fernandes et al. (2012), found that members of the family Pelagibacteriaceae contributed 12% to the total microbial community composition on bleached and adjacent-to-bleached tissues of D. pulchra. Pelagibacteriaceae are members of the recently discovered SAR11 clade, which are unable to grow on nutrient-rich synthetic media and require a ‘dilution-to-extinction’ approach in order to be cultured (Rappé et al., 2002, Vartoukian et al., 2010). Hence, unsurprisingly, representatives of Pelagibacteriaceae and possibly other bacterial families that were detected by Fernandes et al. (2012) could not be cultured in this study.

In addition to differing nutrient requirements of certain environmental bacteria, the lack of culturability in other bacteria has been attributed also to the inability of the microorganisms to grow independently of biofilms, mostly due to missing growth factors and unfavourable incubation conditions (Janssen et al., 2002, Rappé et al., 2002, Davis et al., 2005, D'Onofrio et al., 2010, Vartoukian et al., 2010, Penterman et al., 2014). Bacteria persist on the surface of macroalgae through the formation of biofilms and utilize host exudates as a major source of nutrients (Rao et al., 2005, Burke et al., 2011, Lachnit et al., 2011, Egan et al., 2013). Hence it is possible that the absence of exudates from D. pulchra and/or suboptimal culture conditions resulted in a limited diversity of bacteria being isolated. Culturability of previously unculturable bacteria has been enhanced using techniques such as diluted enrichment broths, reduced inoculum size and increased incubation periods (Janssen et al., 2002, Rappé et al., 2002, Davis et al., 2005, Vartoukian et al., 2010) and future culturing efforts of D. pulchra symbionts will benefit from making use of these novel techniques.

2.4.2 Contribution of cultured commensal bacteria in bleaching events of D. pulchra

A culture-independent analysis by Zozaya-Valdes et al. (2015), specifically investigating bacterial groups driving shifts on bleached D. pulchra, found that Proteobacteria and Bacteroidetes were the most abundant phyla, contributing 52% and 44% to the community respectively. More than half of the sequenced isolates (56%) from this study corresponded to 10 OTUs, which were described by Zozaya-Valdes et al. (2015) to be over-represented in at least one of two bleaching events on D. pulchra (Fig. 2.3). The over-represented OTUs belonged to families - Rhodobacteraceae, Flavobacteriaceae, Vibrionaceae, Alteromonadaceae and Pseudoalteromonadaceae. The contributions of Rhodobacteraceae and Flavobacteriaceae were the largest i.e. within the top 5% difference between healthy and bleached D. pulchra. In comparison and whilst still significant, the contributions of Vibrionaceae, Alteromonadaceae and Pseudoalteromonadaceae was relatively less. 39

The increased prevalence of specific bacterial families during bleaching events suggests that these groups potentially include opportunistic pathogens. Moreover, the presence of these bacterial strains on healthy alga (Fig. 2.3) indicates capability to live in a commensal relationship with the host. It is commonly reported that a collapse in commensal interactions occur when the host becomes compromised (Case et al., 2011, Brown et al., 2012, Casadevall & Pirofski, 2014, Egan et al., 2014). In the compromised state of the host, bacteria that are opportunistic in nature take advantage of the reduced host defence often resulting in disease (Harder et al., 2012). However, distinction between opportunistic pathogens and the background community continues to be a challenge, especially in the presence of saprophytes or secondary colonisers that benefit from decaying macroalgal material (Egan et al., 2014). Hence the demonstration of disease-causality – both in manipulative and field experiments, can provide valuable information that makes implicating specific roles to bacteria that exist on the surfaces of diseased hosts easier.

2.4.3 Association of cultured isolates in disease of other host systems

One of the characteristics of opportunistic pathogens is the ability to grow in diverse environments and thus have a capacity to induce disease in multiple hosts (Lyczak et al., 2000, Stover et al., 2000, Álvarez et al., 2008, Brown et al., 2012). In this study, patterns observed in the disease-associated community shifts on D. pulchra could be extended to E. radiata, where three OTUs that contributed significantly towards disease-associated community shifts were common to both algae. The three OTUs were representatives of the family Flavobacteriaceae, which comprised 15% of the total isolates sequenced in the current study. The concept of opportunistic pathogens having an extended host range has been observed in plants. For example, the prominent opportunistic pathogen Ralstonia solanacearum survives in moist soil until encountering a susceptible host where it produces wilt-type symptoms in multiple agricultural crops, over a broad geographical range (Álvarez et al., 2008). Similarly, Pseudomonas aeruginosa is another opportunistic pathogen, which utilizes diverse environmental compounds as energy sources whilst possessing a variety of virulence factors that permit colonisation and infection of susceptible hosts (Lyczak et al., 2000, Stover et al., 2000). Closely-related bacterial groups have a relatively similar gene pool and it is possible that some members share virulence genes (da Silva et al., 2002, Kim et al., 2005).

Although the remaining bacterial families isolated from D. pulchra in this study were found not to be significantly over-represented on E. radiata, their affiliation with macroalgal hosts other than D. pulchra has been recorded previously (Largo, 2002, Egan et al., 2014). Vibrionaceae and 40

Pseudoalteromonadaceae have been implicated in disease of other red macroalgae, such as Gracilaria verrucosa and Gracilaria gracilis, whereas both families and Alteromonadaceae cause hole-rots and fading in the brown macroalgae Laminaria japonica (Kusuda et al., 1992, Lavilla- Pitogo, 1992, Sawabe et al., 1998, Vairappan et al., 2001, Beleneva & Zhukova, 2006, Wang et al., 2008, Sun et al., 2012). Moreover, the disease-affiliated host range of bacterial families isolated in this study was not restricted to macroalgae but apply also on other marine organisms, such as corals and invertebrates (Table 2.4). For example, Flavobacteriaceae, which was detected in high abundances on both D. pulchra and E. radiata in this study, was also found to be affiliated with disease of the lobster Homarus americanus (Chistoserdov et al., 2012). Similarly, Rhodobacteraceae, which was detected to be over-represented on bleached D. pulchra in the current study, was associated with cell lysis in the dinoflagellate Alexandrium catenella (Amaro et al., 2005). Taken together, these findings suggest that commensal bacteria, which have attributes such as being involved in disease-associated community shifts on D. pulchra, display versatility via their affiliation with disease of other host systems and are hence likely to be true opportunistic pathogens. Moreover, whilst the limitations of using broad taxonomic groups for comparisons is acknowledged, research focus on these bacterial families provides a promising strategy for identifying new opportunistic pathogens.

Table 2.4: Affiliation of bacteria cultured from D. pulchra in disease of other marine host

Isolate Homology to known Associated marine Description of Reference ID pathogen host association AD1 Aquimarina latercula Lobster - Homarus Occasionally found to be (Chistoserdov (strain sequence not americanus present in lesion sites of et al., 2012) available for lobster hosts comparison) AD2 and Ruegeria sp. Dinoflagellate – The bacterium produces (Amaro et al., BL54 (99% identity and 64% Alexandrium a compound that lyses 2005) query coverage) catenella the dinoflagellate host BL8 Shimia marina Scleractinian coral Consistently found on (Séré et al., (strain sequence not – Porites lutea diseased regions 2013) available for comparison) AD10 Aquimarina sp. Lobster - Homarus Present on lesions (Chistoserdov (95% identity and 39% americanus et al., 2012) query coverage) AD47, Various closely related Sea cucumber - Causes skin ulceration (Becker et al., AD65, Vibrio sp. Holothuria scabra, (white disease) in H. 2004, Gay et AD95, mollusc - scabra, al., 2004, BL89, Crassostrea gigas, lesions in C. gigas and Cervino et AD098, prawns – Penaeus sp. and Yellow al., 2008, AD0104, Penaeus monodon Band Disease in Indo- Cano‐Gómez and and P. monodon, Pacific and Carribean et al., 2009, BL0114 zooxanthellae - corals by directly Séré et al., Symbiodinium sp. attacking Symbiodinium 2013) and scleractinian sp. Also associated with coral – Porites lutea Porites White Patch Syndrome BL18 Winogradskyella sp. Salmon – Salmo Presence causes (Embar- (strain sequence not salar increased severity of Gopinath et available for Amoebic Gill Disease in al., 2006) comparison) salmonids BL86 Alteromonas sp. Kelp – Saccharina Causes swelling in (Peng & Li, and (97% identity and japonica gametophyte cells 2013) BL110 100% query coverage) followed by filamentous fading PB1 Pseudoalteromonas Kelp – Saccharina Causes red spot disease (Sawabe et bacteriolytica japonica in S. japonica al., 1998) (90% identity and 96% query coverage)

2.4.4 Conclusions and future work

This chapter involved the generation of an extensive culture-collection of bacteria involved in bleaching events on D. pulchra. In addition to the traditional targeted approach for colony- sampling, in order to obtain estimates of isolate abundances a randomised approach was used. Sequenced data of cultured isolates indicated a similar diversity of isolates captured using the two approaches however examples where targeted approaches could introduce bias in reporting was highlighted. Inconsistencies in diversity between a previous culture-independent anlaysis by Fernandes et al. (2012) and this study may be attributed to the absence of growth factors, in the form of host exudates and suboptimal growth conditions. It is expected that by integrating some the recent improvements of culturing techniques, such as the incorporation of host components to simulate natural conditions (Bollmann et al., 2007, Ben‐Dov et al., 2009, Ferrari & Gillings, 2009), the culturable diversity from D. pulchra could be expanded.

More than half of the isolates cultured in this study were detected to be significantly over- represented during bleaching events of D. pulchra and/or E. radiata. Representatives of the families Rhodobacteraceae, Flavobacteriaceae, Vibrionaceae, Alteromonadaceae and Pseudoalteromonadaceae were detected on both D. pulchra and E. radiata. In addition, the bacterial families had affiliations in disease of other marine hosts (Table 2.4). By virtue of over- representation during bleaching events on one of two macroalgae and by representing families which have known implications in marine diseases, these isolates are being proposed for further investigation for their role as opportunistic pathogens.

In conclusion, the work described in this chapter demonstrates an example of combining information obtained from advanced culture-independent techniques with traditional culture- dependent approaches so as to streamline subsequent analyses. Thus, the following chapter will involve characterising the isolates shortlisted in Table 2.5 for virulence-related traits in order to determine their potential to function as opportunistic pathogens of D. pulchra.

Chapter Three: Assessment of virulence-related traits of selected putative pathogens of Delisea pulchra

3.1 Introduction

Changing climatic conditions are associated with diverse consequences in the marine ecosystem (Harvell et al., 2009, Hoegh-Guldberg & Bruno, 2010, Burge et al., 2014). Environmental stress, such as increased seawater temperatures, acidity, and drops in salinity during storm surges, influence health of habitat-forming species (Peters & Raftos, 2003, Hoegh-Guldberg & Bruno, 2010). There is also evidence to suggest that virulence-related activity in marine bacteria increases with changing environmental conditions (Campbell et al., 2011, Case et al., 2011, Crisafi et al., 2013).

Opportunistic pathogens are non-obligate and/or non-specialist parasites of a given host (Brown et al., 2012). Representative opportunistic pathogens generally engage in commensal interactions with healthy hosts. However, opportunistic pathogens may become virulent towards the host following perturbations (Brown et al., 2012), such as caused by wounds (Campbell et al., 2014), immunodeficiency (Case et al., 2011) and ageing (Seyedsayamdost et al., 2011b). Typically, pathogens use specialised virulence determinants, such as toxins (Kuehne et al., 2010), adhesins (Kern & Schneewind, 2010), polysaccharides (Russo et al., 2010) and degradative enzymes (Schneider et al., 2010) to induce disease in hosts. It is now recognised that some of the virulence characteristics of bacteria are also important factors for survival, which likely evolved as protective mechanisms for evading predation (Matz et al., 2004, Begun et al., 2007, Brüssow, 2007, Brown et al., 2012, Erken et al., 2013). Thus, the implication of bacteria with enhanced fitness strategies is that a high proportion of environmental bacteria are possibly virulent (Persson et al., 2009).

In addition to the characterised pathogens, Nautella italica R11 and Phaeobacter sp. LSS9, that cause bleaching disease of D. pulchra, it has previously been suggested that bleaching of D. pulchra may be also caused by yet other unknown opportunistic pathogens (Fernandes et al., 2012, Zozaya-Valdes et al., 2015). Indeed a clear increase in the abundance of bacterial species belonging to the families Rhodobacteraceae and Flavobacteriaceae was observed between healthy and diseased D. pulchra, suggesting that these bacterial groups likely contain additional pathogens capable of causing bleaching in the alga (Zozaya-Valdes et al., 2015). In Chapter 2, bacteria that contribute to disease-associated community shifts on D. pulchra were isolated

from the alga. Moreover, most of these bacterial isolates belonged to families with known disease implications in other marine hosts, including kelps, coral, marine invertebrates and a dinoflagellate (Sawabe et al., 1998, Amaro et al., 2005, Chistoserdov et al., 2012, Peng & Li, 2013, Séré et al., 2013). Thus, the objective of this chapter was to investigate the potential of isolates over-represented in diseased D. pulchra to function as opportunistic pathogens of macroalgae. To address this objective, isolates identified in Chapter 2 were characterised for virulence- related traits through the assessment of (1) motility, (2) biofilm-forming capabilities, (3) resistance to host chemical defence, determined using total furanone extracts, (4) resistance to oxidative stress, determined using hydrogen peroxide, and (5) host cell wall-related polymer degradation.

3.2 Materials and methods

3.2.1 Strains and culture conditions

The thirteen isolates identified to be over-represented on diseased tissues of D. pulchra and E. radiata (Chapter 2) were chosen for characterisation of virulence-related traits. Also chosen for characterisation was the strain Aquimarina sp. BL5, identical to Aquimarina sp. AD1 in the V4 region of the 16S rRNA gene (refer to Table 2.2, Appendix II). However, while Aquimarina sp. AD1 was isolated from adjacent-to-bleached tissues, the strain Aquimarina sp. BL5 was obtained from bleached tissues. Included in the assays were two previously identified pathogens of D. pulchra, i.e. N. italica R11 and Phaeobacter sp. LSS9, and a third isolate, Alteromonas sp. LSS17, all three isolated originally from healthy tissues of the alga (Longford, 2007, Case et al., 2011). Prior to use in assays, bacterial cultures were grown in DifcoTM Marine Broth 2216 (MB). Cultures were incubated at 25°C overnight (approx. 18 hr) with agitation at 150 rpm on an orbital shaker (Ratek, Australia).

3.2.2 Examination of cell motility

Motility of the bacterial cells was observed using the conventional wet mount method (Rosengarten & Kirchhoff, 1987). Bacterial cultures were grown as previously indicated (refer to 3.2.1). Following an 18 hr incubation period, cell motility was determined by observing cell movement, using a bright-field Olympus CX31 microscope with a 100X oil objective.

3.2.3 Ability of isolates to form a biofilm on surfaces

The capacity of bacterial isolates to form biofilms on surfaces was observed using the procedure outlined by Penesyan (2010), with slight modifications. Briefly, ten microliters of exponential- phase cultures (refer to 3.2.1 for growth conditions) were inoculated in 90 μl MB, in each lane of a 96 well microtitre plate. Control wells comprised of media only. The inoculated plates were incubated on a horizontal shaker (120 rpm) at 25 °C for 24 hr. After incubation, liquid from the wells was discarded and wells were washed twice with sterile deionised water. One hundred microliters of the crystal violet (CV) 0.1% solution (Sigma-Aldrich) were added to each well and incubated for 15 min. The CV stain was removed and the wells were washed twice. The remaining CV (i.e. bound to bacteria attached to wells) was extracted using 200 μl of 100 % ethanol, diluted 1:8 fold for all isolates and transferred to a fresh 96 well microtitre plate.

Absorption was measured (OD550) using an automatic plate reader, Wallac 1420 multilabel counter. The absorption reading from wells containing blank medium was used as reference. A total of three independent experiments were conducted. The procedure used by Sahal & Bilkay (2014) was adopted to classify isolates based on the respective biofilm-forming abilities, using mean OD. The four categories included: OD = 0, non-biofilm former (NBF), 0 < OD < 0.4, weak biofilm former (WBF), 0.4 ≤ OD < 0.8, intermediate biofilm former (IBF) and OD ≥ 0.8, strong biofilm former (SBF). Mean and standard error was calculated using GraphPad Prism 6.03 (San Diego, CA, USA).

3.2.4 Resistance to total furanone extracts from D. pulchra

Extraction of total furanones was carried out in collaboration with Tilmann Harder (UNSW). D. pulchra samples were collected from Long Bay (151ᵒ14’42’’ E, 33ᵒ58’19’’ S), Sydney, in September 2013 from 3-meter depths. The extraction was adapted according to de Nys et al. (1993). Briefly, 416 g wet weight algae were freeze-dried and extracted with dichloromethane and hexane. The total furanone extract obtained was separated by vacuum liquid chromatography over silica gel using an eluent consisting of 1:45 ratio of hexane and ethyl acetate and subsequently used to conduct furanone sensitivity assays (described below).

In order to account for the non-uniform distribution of furanones across D. pulchra thalli (Dworjanyn et al., 1999), three strengths of total furanone extracts (0.1X, 1X and 10X), corresponding to 0.4, 4 and 40 µg/µl were prepared from the original concentrate, using hexane and ethyl acetate mix as the diluent. Agar disc diffusion assays were performed using the three extracts. Briefly, 28 µl of the three test concentrations of furanone, i.e. 0.4, 4 and 40 µg/µl, and

hexane-ethyl acetate mix control were added to sterile antibiotic assay discs, 0.6 cm in diameter (Thermo Fisher Scientific, Australia), and allowed to dry. Bacterial cultures (refer to 3.2.1 for growth conditions) were swabbed uniformly on MA before the seeded discs were placed on it. Each isolate was tested in triplicates and the entire experiment was repeated three times. Following 4 days of incubation, the plates were assessed for the presence of growth inhibition zones. Based on the number of test concentrations against which sensitivity was expressed, i.e. out of 0.4, 4 and 40 µg/µl, isolates were classified as either resistant, semi-resistant or sensitive to furanones. Mean and standard error in measurements of inhibition zones were calculated using GraphPad Prism 6.03 (San Diego, CA, USA).

3.2.5 Resistance to oxidative stress

To test for resistance against oxidative stress, the isolates were inoculated in media supplemented with increasing concentrations of hydrogen peroxide (H2O2). One hundred and forty-eight microliters DifcoTM Marine Broth 2216 were added to each well of 96 well microtitre plates, followed by 5 µl concentrated H2O2 solution and 2 µl bacterial cultures as the inocula (bacterial cultures were grown overnight as indicated in section 3.2.1). Following incubation, 1 ml aliquots of the cell suspensions were centrifuged for 5 min at 10 000 x g, washed twice and resuspended in sterile water to an OD600 = 0.1. The concentrated H2O2 solution (inhibitor) added to each lane of the microtitre plate had consecutive lanes containing a final concentration (dose) of 0 µM, 1 µM, 3 µM, 10 µM, 30 µM, 100 µM and 300 µM of H2O2 respectively. The final lane consisted of control wells, which contained media only. Measurement of OD600, as an indicator of bacterial cell density, was carried out at the beginning of the experiment followed by incubation of the microtitre plates at 25 °C on a horizontal shaker (120 rpm). The final OD reading was measured after 6 hr. The experiment was repeated thrice.

Growth rate (response) of the test isolates to each dose of the inhibitor was determined by calculating change in OD for the 6 hr period, using the three phase linear model of Buchanan et al. (1997), as described in McKellar & Delaquis (2011). Inhibitor doses were log transformed and nonlinear regression was used to fit dose vs response data in GraphPad Prism 6.03 (San Diego, CA, USA). Best fits to the inhibitor dose versus bacterial response curve was used to compute the IC50 value (the concentration of H2O2 at which bacterial growth was reduced to 50%).

3.2.6 Ability to degrade host cell wall-related polymers

Candidate pathogens were tested qualitatively for the ability to degrade a range of polymers, which would be indicative of enzyme production. Enzymes that were tested included agarase, 47

alginate-lyase, amylase, carrageenase, cellulase, xylanase and protease, using the method by Devasia & Muraleedharan (2012) with slight modifications. Experiments were conducted in collaboration with Jackson Walburn (UNSW). Briefly, marine agar plates supplemented with 0.5% of the respective substrates were spot inoculated using 10 µl of the overnight bacterial cultures (refer to 3.2.1 for growth conditions) and incubated for 4 days. Following incubation the plates were flooded with the suitable dye and observed for zones of clearance around the spotted region, indicating the production of the respective enzyme. Agarase and amylase activity in inoculated plates were detected by flooding agarose and starch containing plates with Lugol’s iodine solution. Alginate-lyase activity was detected by flooding inoculated alginic acid plates with hydrochloric acid for 5 mins followed by 0.5 M sulphuric acid. Carrageenase was detected using 0.5% w/v cetyl pyridinium on inoculated - carrageenan plates. The Congo red dye (0.1%) was used to determine cellulase and xylanase activities in inoculated plates containing cellulose and xylan from beechwood respectively. Protease activity was determined directly by the presence of a clear halo surrounding the spotted region on plates containing skim milk. Two sets of control plates were set up for each substrate, first observing inoculum only, without the addition of dye and the second with no inoculum but observing reaction of the dye with the substrate. All plates were inspected for the presence of clearing zones that determined enzymatic breakdown of the substrates.

3.3 Results

3.3.1 Bacterial motility

In order to characterise the test isolates for motility, wet mounts of the respective bacterial suspensions were observed. More than half of the total number of isolates tested were motile (Table 3.1).

Table 3.1: Characterisation of motility in test isolates

Isolate Motility Aquimarina sp. AD1 Non-motile Ruegeria sp. AD2 Motile Aquimarina sp. AD10 Non-motile Vibrio sp. AD65 Motile Maribacter sp. AD096 Motile Aquimarina sp. BL5 Non-motile Agarivorans sp. BL7 Motile Shimia sp. BL8 Motile Winogradskyella sp. BL18 Non-motile Dokdonia sp. BL23 Non-motile Ruegeria sp. BL54 Non-motile Alteromonas sp. BL110 Motile Pseudoalteromonas sp. PB1 Motile Nautella italica R11 Motile* Phaeobacter sp. LSS9 Motile* Alteromonas sp. LSS17 Motile *Determined previously by Fernandes et al. (2011)

3.3.2 Biofilm attachment

To determine the relative ability of each bacterial isolate to attach to a surface and form biofilms, absorption of the CV stain (OD550) was measured. The majority of the isolates (63%) were classified as strong biofilm formers (OD > 0.8), including Aquimarina sp. AD1 and AD10, Agarivorans sp. BL7, Shimia sp. BL8, Winogradskyella sp. BL18, Pseudoalteromonas sp. PB1, Phaeobacter sp. LSS9, Alteromonas sp. LSS17 and BL110, and Maribacter sp. AD096 (Fig. 3.1). The isolates classified as intermediate biofilm formers (0.4 < OD < 0.8) included Ruegeria sp. AD2, Dokdonia sp. BL23 and Aquimarina sp. BL5 (19%) whereas weak biofilm formers (0 < OD < 0.4) included the isolates, N. italica R11 and Ruegeria sp. BL54 (12%). Only one isolate, Vibrio sp. AD65, (6%) was classified as a non-biofilm former (OD = 0).

Fig. 3.1: Biofilm-forming ability of test isolates, plotted in descending order. Using absorption readings (OD550) of CV stained bacterial cells forming biofilms in microtitre plate wells, isolates were classified into four categories. Blue bars represent isolates demonstrating strong biofilm- forming abilities (OD > 0.8), green bars represent intermediate biofilm-forming abilities (0.4 < OD < 0.8), orange bars indicate weak biofilm-forming abilities (0 < OD < 0.4) and no bars indicate a non-biofilm former (OD = 0). Data includes results from three independent experiments where mean + standard error values were calculated in GraphPad Prism 6.03 (San Diego, CA, USA).

3.3.3 Growth inhibition by D. pulchra total furanone extracts

The test isolates were characterised for sensitivity against three concentrations of furanone extracts, i.e. 0.4, 4 and 40 µg/µl (Table 3.2). The group of resistant isolates, i.e. expressing sensitivity to only the highest test concentration of furanones, included Alteromonas sp. BL110 and LSS17, Agarivorans sp. BL7 and Winogradskyella sp. BL18. Semi-sensitive isolates, i.e. expressing sensitivity to two test concentrations of furanones, included Vibrio sp. AD65, Pseudoalteromonas sp. PB1, Shimia sp. BL8, Maribacter sp. AD096, Phaeobacter sp. LSS9, and Aquimarina sp. AD1 and BL5. The isolates, N. italica R11, Ruegeria sp. BL54 and AD2, Aquimarina sp. AD10 and Dokdonia sp. BL23 were classed as sensitive since sensitivity was expressed at all three test concentrations of furanones. Examples of inhibitory activity displayed against the isolates by the three test concentrations of furanones is presented in Fig. 3.2.

Fig. 3.2: Bacterial growth inhibition by total furanone extracts of D. pulchra. Disc diffusion assay plates showing examples of 6 isolates tested against three concentrations of total furanone extracts of D. pulchra (0.4 µg/µl, 4 µg/µl and 40 µg/µl) and the negative control - hexane-ethyl acetate mix, represented with ‘(1)’, ‘(2)’, ‘(3)’ and ‘(4)’ respectively. Displayed plates include solates that were: sensitive to furanones, i.e. (a) Ruegeria sp. AD2, (b) Aquimarina sp. AD10, (c) Ruegeria sp. BL54 and (d) N. italica R11; semi-resitant to furanones, i.e. (e) Pseudoalteromonas sp. PB1; and resistant to furanones, i.e. (f) Alteromonas sp. LSS17.

Table 3.2: Sensitivity of test isolates to three test concentrations of total furanone extracts of D. pulchra

Isolate Zone of inhibition (mm)* Classification of 0.4 µg/µl 4 µg/µl 40 µg/µl response Alteromonas sp. BL110 0 + 0 0 + 0 1.6 + 0.1 Resistant Alteromonas sp. LSS17 0 + 0 0 + 0 1.7 + 0.1 Resistant Agarivorans sp. BL7 0 + 0 0 + 0 5.9 + 0.3 Resistant Winogradskyella sp. BL18 0 + 0 0 + 0 6.6 + 0.2 Resistant Vibrio sp. AD65 0 + 0 1.7 + 0.3 6.9 + 1.1 Semi-resistant Pseudoalteromonas sp. PB1 0 + 0 2.9 + 0.8 10.8 + 0.8 Semi-resistant Shimia sp. BL8 0 + 0 3.1 + 0.5 9.1 + 0.2 Semi-resistant Maribacter sp. AD096 0 + 0 3.2 + 0.7 6.7 + 0.7 Semi-resistant Phaeobacter sp. LSS9 0 + 0 3.6 + 0.2 7.9 + 0.3 Semi-resistant Aquimarina sp. AD1 0 + 0 6.3 + 0.3 16.4 + 0.5 Semi-resistant Aquimarina sp. BL5 0 + 0 7.1 + 0.2 13.3 + 0.2 Semi-resistant Nautella italica R11 1.4 + 0.4 13.6 + 0.4 19.3 + 0.5 Sensitive Ruegeria sp. BL54 2.1 + 0.4 7.7 + 0.4 14.4 + 0.7 Sensitive Ruegeria sp. AD2 3.9 + 0.6 11.3 + 0.7 16.7 + 0.7 Sensitive Aquimarina sp. AD10 6.2 + 1.2 16.1 + 1.2 24.0 + 0.9 Sensitive Dokdonia sp. BL23 6.3 + 0.6 14.3 + 0.6 19.5 + 0.4 Sensitive *Mean + standard error

3.3.4 Growth reduction by hydrogen peroxide

In order to investigate the extent of oxidative stress resistance displayed by the isolates, the respective IC50 values were calculated using responses of individual isolates to increasing concentrations of H2O2. The highest IC50 values were observed for Alteromonas sp. BL110 and

LSS17, Vibrio sp. AD65, Maribacter sp. AD096 and Shimia sp. BL8, each with an IC50 value of >500 µM (Table 3.3).

Table 3.3: IC50 values of test isolates calculated from exposure to increasing concentrations of

H2O2

Isolate IC50 (µM)* 95% confidence interval (µM)* Alteromonas sp. BL110 5114.0 4227.0 to 6187.0 Vibrio sp. AD65 1376.0 1265.0 to 1496.0 Alteromonas sp. LSS17 1150.0 906.7 to 1459.0 Maribacter sp. AD096 910.5 721.9 to 1148.0 Shimia sp. BL8 522.7 438.9 to 622.7 Pseudoalteromonas sp. PB1 462.5 422.2 to 506.5 Dokdonia sp. BL23 390.7 343.3 to 444.7 Agarivorans sp. BL7 334.7 294.1 to 381.0 Winogradskyella sp. BL18 311.8 261.5 to 371.9 Aquimarina sp. AD10 238.0 61.7 to 918.0 Ruegeria sp. AD2 210.6 168.3 to 263.6 Aquimarina sp. BL5 198.1 127.7 to 307.4 Nautella italica R11 164.4 109.1 to 247.7 Phaeobacter sp. LSS9 147.0 121.9 to 177.4 Ruegeria sp. BL54 88.8 65.0 to 121.4 Aquimarina sp. AD1 61.6 48.3 to 78.6 *Calculated in GraphPad Prism 6.03 (San Diego, CA, USA)

3.3.5 Degradation of polymers present in host cell wall

All test isolates were screened for the production of a range of enzymes, which inferred degradation of polymers present in cell walls of red and brown algae. All isolates, except Shimia sp. BL8 and Phaeobacter sp. LSS9 degraded at least one polymer. The isolates Aquimarina sp. AD1 and AD10 degraded six out of seven polymers, the highest number of polymers degraded by any one isolate (Table 3.4). Examples of polymer degradation, as detected in the different isolates, are presented in Fig. 3.3.

Fig. 3.3: Examples of assay plates displaying polymer degradation by test isolates. In the top row – Aquimarina sp. AD1 and Ruegeria sp. AD2 and in the bottom row – Vibrio sp. AD65 and Aquimarina sp. AD10, being tested for degradation of: (a) agar, (b) alginate (c) starch and (d) cellulose. The experiments were repeated three times before activity was confirmed.

Table 3.4: Ability of test isolates to degrade host cell wall associated polymers

Detection of extracellular enzyme Isolate Agar Alginate Starch Carrageenan Cellulose Protein Xylan Aquimarina sp. AD1 + + + + + + - Aquimarina sp. AD10 + + + + + + - Aquimarina sp. BL5 + + + - + + - Agarivorans sp. BL7 + + + + - + - Alteromonas sp. BL110 + + + - - + - Alteromonas sp. LSS17 + + + - - + - Vibrio sp. AD65 + - - + - + + Winogradskyella sp. + - + - - + - BL18 Dokdonia sp. BL23 - + - - - + + Pseudoalteromonas sp. - - + - - + - PB1 Ruegeria sp. AD2 - - - - - + - Marinacter sp. AD096 ------+ Ruegeria sp. BL54 - + - - - - - Nautella italica R11 - - + - - - - Shimia sp. BL8 ------Phaeobacter sp. LSS9 ------‘+’ Enzyme activity inferred ‘-’ Enzyme activity not inferred

3.4 Discussion

Bacterial pathogens, with opportunistic characteristics, employ a combination of known virulence determinants and enhanced fitness strategies to induce disease in hosts (Persson et al., 2009, Egan et al., 2014). Data presented in Chapter 2 showed that 50% of the bacterial isolates cultured from D. pulchra were commonly involved in disease-associated community shifts on the alga. Moreover, the isolates represented families, with known disease implications in other marine hosts. In order to determine if the isolates had potential to function as opportunistic pathogens of D. pulchra, this study investigated the virulence-associated traits of motility, biofilm formation, resistance to host chemical defence, i.e. furanones, resistance to oxidative stress and host cell wall-related polymer degradation.

3.4.1 Motility and biofilm-formation are prevalent characteristics in putative pathogens

Motility has long been recognised as a mediator of the interaction between pathogenic bacteria and various eukaryotic hosts, such as plants, invertebrates and humans (Josenhans & Suerbaum, 2002). One of the implications of motility in virulence is the advantage conferred to pathogenic bacteria in providing increased access to the host. Habitat-forming organisms such as macroalgae and corals release significant amounts of organic matter or exudates, which serve as attractants for microbes in the marine environment (Haas et al., 2011, de Oliveira et al., 2012, Tout et al., 2015). Hence, motility is often associated with chemotaxis, whereby cell movement is regulated through the perception of environmental stimuli, including attractants such as host exudates (Stocker et al., 2008, Arora et al., 2015, Tout et al., 2015).

In addition to motility displayed by N. italica R11 and Phaeobacter sp. LSS9, as previously determined by Fernandes et al. (2011), the current study found that the putative algal pathogens Vibrio sp. AD65, Ruegeria sp. AD2, Maribacter sp. AD096, Agarivorans sp. BL7, Shimia sp. BL8, Alteromonas sp. BL110 and LSS17 and Pseudoalteromonas sp. PB1 were also motile. This finding is in support of previous studies where taxonomically-related bacteria have been reported to be motile (Romanenko et al., 2003, Kurahashi & Yokota, 2004, Yoon et al., 2005, Choi & Cho, 2006, Vandecandelaere et al., 2008, Vandecandelaere et al., 2009). Moreover, virulence links have been made whereby the pathogenic V. cholerae uses motility and chemotactic responses to travel from the lumen of the small intestine to the preferred intestinal epithelium where virulence-related activities, such as toxin production, are carried out (Butler & Camilli, 2004).

Findings in the current study showed that members of the bacterial phyla Bacteroidetes, i.e. Aquimarina sp. AD1, AD10 and BL5, Dokdonia sp. BL23, and Winogradskyella sp. BL18 and the 55

Gamma-proteobacteria, Ruegeria sp. BL54, were non-motile. Studies by Nedashkovskaya et al. (2009) and Yu et al. (2013) observed that whilst flagella may or may not be present in Aquimarina sp. and Winogradskyella sp., representatives of both genera expressed capacity to glide on surfaces. Similarly Dokdonia sp. has also been demonstrated to express gliding motility (González et al., 2011). Since gliding-motility cannot be determined by conventional wet mounts alone, it is likely that some of the isolates classified here as non-motile are instead capable of gliding-motility. Techniques, such as direct observations of pre-inoculated glass-slides coated with soft agar, will be useful in investigating the ability of the isolates to move across surfaces by gliding (Bowman, 2000).

Interestingly, the two Ruegeria species, AD2 and BL54 (members of the Roseobacter clade), behaved differently with respect to their motility. Whilst Ruegeria sp. AD2 was motile, isolate Ruegeria sp. BL54 was observed to be non-motile in the current study. This difference may be due to genetic differences or suboptimal growth conditions for flagella production in Ruegeria sp. BL54. For example, Silicibacter sp. strain TM1040 is another member of the Roseobacter clade, which forms symbiotic associations with unicellular phytoplankton and has a biphasic ‘swim or stick’ lifestyle (Sule & Belas, 2013). Variation in motility amongst other members of the Ruegeria sp. have also been reported previously (Kim et al., 2014). It is also possible that transcription level regulation of the genes involved in motility resulted in varied responses by members of the same genus. Moreover, since motility appears to be a dominant trait among the putative algal pathogens, future studies should address how chemotaxis and motility behaviours are affected in the presence of the host.

It is interesting to note that all isolates shown to be motile in this study, including isolates that potentially expressed gliding motility, were correspondingly strong biofilm-formers. The group of strong biofilm-formers included the isolates, Aquimarina sp. AD1 and AD10, Phaeobacter sp. LSS9, Shimia sp. BL8, Winogradskyella sp. BL18, Alteromonas sp. LSS17 and BL110, Pseudoalteromonas sp. PB1, Agarivorans sp. BL7 and Maribacter sp. AD096. Members of the Flavobacteriaceae family, such as Aquimarina and Winogradskyella, and members of Alteromonadaceae family, such as Alteromonas and Agarivorans, frequently colonise diverse marine hosts (Sawabe et al., 1997, Dang et al., 2008, Chistoserdov et al., 2012, Margassery et al., 2012, Meres et al., 2012). Moreover, members of the Roseobacter clade, which include Shimia and Phaeobacter, are reputed for competitive colonisation of surfaces of both - macro- and microalgae (Choi & Cho, 2006, Rao et al., 2006, Seyedsayamdost et al., 2011b).

As is the case for motility, important traits for effective surface colonisation include cell attachment and biofilm-formation. Flagella and pili, which are used for motility, also aid attachment to host surfaces, along with the use of capsules, adhesins, lipoproteins and extracellular polymeric substances (EPS) (Dalisay et al., 2006, Conrad, 2012, Utada et al., 2014). Biofilm-formation has been linked with persistence on surfaces through improved protection against host defences, which contributes to pathogen survival during latent infections (Baldi et al., 2012, Gardiner et al., 2014, Tercero-Alburo et al., 2014, Xu et al., 2014). For example, mutants of Edwardsiella tarda impaired in motility, also exhibit reduced biofilm formation, trait that is crucial for colonisation of and pathogenicity against the zebrafish host, Epithelioma papulosum cyprini (Xu et al., 2014). The findings of the current study indicate that both motility and biofilm-formation are highly prevalent amongst bacteria isolated from diseased tissues of D. pulchra. Since both flagella and pili directly aid attachment to host surfaces, a positive correlation between the two traits is not unexpected. In addition, correlations between motility, biofilm-formation and virulence in other host systems, such as plants (Bahar et al., 2009), suggest that the presence of both traits in isolates involved in disease-associated community shifts on D. pulchra is important for pathogenicity against the macroalga.

3.4.2 Resistance to chemical defence strategies of the host

D. pulchra produces halogenated furanone compounds, which differentially inhibit bacteria from diverse niches within the marine environment by targeting activities, such as attachment, swarming and growth (Maximilien et al., 1998). However, a study by Campbell et al. (2011) has shown that bleaching in stressed D. pulchra correlates with reduced levels of furanones. Hence, it is anticipated that opportunistic pathogens take ‘advantage’ of reduced host defence and dominate bacterial communities during host stress conditions. In support of this scenario, data presented in the current study showed that seven isolates (44%) which were inhibited by the 1X concentration of total furanones (4 µg/µl) demonstrated no sensitivity to the 0.1X concentration (0.4 µg/µl) (Table 3.2). This finding indicates that the group of bacterial isolates categorised as ‘resistant’ and ‘semi-resistant’ in Table 3.2 are likely to also include opportunistic pathogens of D. pulchra.

It is likely that the isolates that were classified as ‘resistant’, i.e. Alteromonas sp. BL110 and LSS17, Agarivorans sp. BL7 and Winogradskyella sp. BL18, have specific strategies that confer tolerance to furanones. Whilst the mechanisms of resistance to furanones have not been assessed in these strains it is possible that features in the bacterial cell membranes, such as efflux pumps and specific binding-proteins prevent entry of the potentially toxic compound into 57

the cell cytoplasm. In support of this, a study by Maeda et al. (2011) demonstrated that P. aeruginosa has the capacity to evolve resistance to furanones through mutations in mexR and nalC encoding for repressors of the mexAB-oprM multi-drug resistance operon (Maeda et al., 2011). Interestingly, these mutations have been reported to be similar to that observed in bacteria associated with chronic cystic-fibrosis conditions (Oliver & Mena, 2010, Maeda et al., 2011). Such observations are examples of enhanced fitness strategies for survival in bacteria however alarmingly highlight the fact that resistance can be acquired easily and hence give algal pathogens a chance to obtain an additional virulence factor against the host.

Another common response of most macroalgae, including D. pulchra, towards stress and infection caused by potentially pathogenic organisms is the release of reactive oxygen species

(ROS), e.g. H2O2, in a process referred to as oxidative burst (Dring, 2005). The hypersensitive response involves a chemically-induced recognition of either the pathogen or specific elicitor compounds, such as fragments of the host cell wall, to generate the oxidative burst response, leading to localised death of the host cells (Wojtaszek, 1997, Weinberger & Friedlander, 2000). Bacteria residing on the surfaces of macroalgal hosts may protect themselves from oxidative stress-induced physiological damages, through production of enzymes such as catalase and superoxide dismutase, which neutralise reactive oxygen species and which are also required for carrying out aerobic respiration (Pigeolet et al., 1990, Baatout et al., 2006).

In the current study, the highest tolerance to H2O2, calculated from IC50 values, were observed for the isolates Alteromonas sp. BL110 and LSS17, Vibrio sp. AD65, Maribacter sp. AD096 and Shimia sp. BL8, indicating that these bacteria potentially have the enzymes required to breakdown H2O2. A previous study reported tolerance for species of the genus Alteromonas at levels as high as 700 µM H2O2, a feature attributed to the presence of enzymes, such as glutaredoxin and thioredoxin (Math et al., 2012). A comparatively higher oxidative stress response of Alteromonas sp. BL110 and LSS17 observed in this study is likely due to the combined effect of a number of detoxification enzymes produced by the respective isolates. Resistance against ROS has been demonstrated to be necessary for full virulence of N. italica R11 (Gardiner et al., 2015) and a number of plant pathogens, including R. solanacearum (Brown & Allen, 2004) and the rice blast fungus, Magnaporthe oryzae (Huang et al., 2011). Thus, by having the ability to resist different defence strategies employed by the host D. pulchra, bacteria involved in the disease-associated community shifts of the algal microbiome express traits that can have virulence-related consequences for the host. It is worth noting that whilst resistance against ROS is an important virulence factor in N. italica R11 (Gardiner et al., 2015), the isolate

is in fact amongst the most sensitive to furanones and H2O2 (Tables 3.2 and 3.3). This finding suggests lack in expression of pathogenicity factors by opportunistic pathogens in the absence of a host, further highlighting the limitation associated with in vitro assays.

3.4.3 Production of extracellular enzymes

The cell walls of red algae are typically composed of cellulose, starch, xylan, agar, carageenan (sulfated galactans) and lignin whereas laminarin and alginate constitute brown algal cell walls (Percival, 1979, Davis et al., 2003, Domozych, 2011). Thus, an ability to degrade algal cell wall components can have potentially detrimental impacts on the host. In the current study, the polymers most commonly degraded included protein, agar and starch, thus inferring protease, agarase and amylase-related activity in isolates cultured from bleached D. pulchra. A recent study by Gomaa et al. (2014) found that in order to utilise biomass from macroalgal hosts, fungi that colonised Palisada perforata (Rhodophyceae) and Sargassum sp. (Phaeophyceae) also produced protease, agarase and amylase. In addition, other studies showed protease secretion in bacteria to be linked to algicidal activities against microalgae (Lee et al., 2000, Paul & Pohnert, 2011). In the current study, the isolates Aquimarina sp. AD1, AD10 and BL5, Agarivorans sp. BL7, Vibrio sp. AD65 and Alteromonas sp. BL110 and LSS17 were found to degrade the largest number of polymers (Table 3.4). Member species of the genera Vibrio and Alteromonas have been reported to be associated with macroalgal diseases in the past (Lavilla-Pitogo, 1992, Largo et al., 1999, Vairappan et al., 2001, Beleneva & Zhukova, 2006, Sun et al., 2012, Peng & Li, 2013) and a member of Aquimarina sp. has been linked with lesions in the shell of lobsters (Chistoserdov et al., 2012). Whilst the mechanisms behind virulence of these bacteria have not been fully elucidated, it is possible that the production of extracellular enzymes contributes towards infection of the host. The isolate Agarivorans sp. BL7, cultured in the current study, demonstrated ability to degrade agar, alginate, starch, carrageenan and protein. The presence of agarase (Fu et al., 2008, Hu et al., 2009) and alginate-lyase (Kobayashi et al., 2009, Uchimura et al., 2010) in Agarivorans sp. has been of biotechnological interest with this study suggesting a virulence role for the polymer-degrading enzymes in Agarivorans sp.

Although the ability to degrade algal tissue is likely to be an important virulence trait for opportunistic pathogens, this characteristic is equally important for those bacteria contributing to the remineralisation of dead algal material (Ohta & Hatada, 2006, Arnosti, 2011, Yao et al., 2013). Seaweeds contribute large amounts of organic material to the worlds’ oceans and bacteria play a significant role in nutrient transformation through the production of various enzymes (Ramaiah & Chandramohan, 1992, Goecke et al., 2010). Extracellular enzymes 59

produced by bacteria often initiate remineralisation of the organic matter by hydrolysing complex compounds to sufficiently smaller sizes that are easily transported across cell membranes of consumers (Ohta & Hatada, 2006, Arnosti, 2011, Yao et al., 2013). Thus a distinction between true pathogens and saprophytic bacteria is often challenging and additional virulence features, such as other virulence traits of the pathogen, host susceptibility and environmental conditions need to be considered before disease causality can be established.

3.4.4 Conclusions and future work

The ability of a particular bacterium to cause disease is determined by its virulence traits whereby the total ‘cards of virulence’ possessed determine relative capacity to induce disease in a host (Casadevall, 2006). Bacteria also use fitness-related traits, such as motility and biofilm- formation as virulence-determinants (Matz et al., 2004, Begun et al., 2007, Brüssow, 2007, Brown et al., 2012, Erken et al., 2013). Hence an understanding of both – known virulence traits and fitness-potential - can provide useful insights as to whether a bacterial isolate may function as an opportunistic pathogen.

This chapter investigated virulence-related traits of bacterial isolates cultured from diseased tissues of D. pulchra in order to predict their capacity to function as opportunistic pathogens of macroalgae. The outcome of the assays that characterised isolates for motility, biofilm formation, resistance to the defence mechanisms of D. pulchra, i.e. furanones and oxidative bursts, and the production of host tissue-degrading enzymes found some isolates to be ‘all- rounders’. These ‘all-rounder’ isolates demonstrated a high level of activity in most of the virulence-related traits tested and hence may have a stronger potential to function as opportunistic pathogens of D. pulchra. Based on the number of times an isolate demonstrated high levels of virulence-related activity in the different assays conducted in this study, putative pathogens are proposed for further investigations of their virulence traits (Table 3.5).

Table 3.5: Putative pathogens proposed for further investigation of virulence traits

Isolate Motility Biofilm- Resistance Resistance Degradation of formation to to oxidative host cell wall- furanones bursts related polymers Alteromonas sp. LSS17 + + + + + Alteromonas sp. BL110 + + + + + Agarivorans sp. BL7 + + + + Aquimarina sp. AD1 (+) + + Aquimarina sp. AD10 (+) + + Winogradskyella sp. BL18 (+) + + Vibrio sp. AD65 + + + Shimia sp. BL8 + + + Maribacter sp. AD096 + + + Phaeobacter sp. LSS9 + + + Highest level of activity expressed amongst all isolates tested (+) May express gliding motility

Thus, the next chapter will aim to test the effects of some of these isolates on D. pulchra in vivo. This will involve the development of a new infection assay, which tests the susceptibility of D. pulchra to putative pathogens under close-to-natural circumstances, such as in the presence of the natural surface microbiota.

Chapter Four: Bleaching effects of putative pathogens on temperature-stressed and furanone-reduced juveniles of Delisea pulchra

4.1 Introduction

Disease in D. pulchra, is characterised by bleaching in the mid-thalli region of the red macroalga. Higher frequencies of bleaching in D. pulchra populations are correlated positively with warmer seawater temperatures and negatively with levels of furanones – compounds which provide the chemical defence system of the alga (Campbell et al., 2011). Moreover, bleaching involves a shift in the surface microbial community, resulting in an over-representation of specific bacterial groups (Fernandes et al., 2012, Zozaya-Valdes et al., 2015). To-date, two pathogens of D. pulchra, i.e. Nautella italica R11 and Phaeobacter sp. LSS9, have been identified (Case et al., 2011, Fernandes et al., 2011), however, only Phaeobacter sp. LSS9 was significantly over- represented in multiple bleaching events on D. pulchra (Zozaya-Valdes et al., 2015). In addition, bacterial groups within the families Rhodobacteracae and Flavobacteriaceae were also over- represented on bleached tissues (Zozaya-Valdes et al., 2015). The findings suggest that yet other opportunistic pathogens may also be responsible for causing bleaching of D. pulchra, however this remains to be investigated.

Generally, opportunistic pathogens can persist in commensal relationships with healthy hosts (Apprill et al., 2013, Achermann et al., 2014), including macroalgae (Goecke et al., 2012). However, increasing evidence shows that changing environmental conditions, such as increasing temperatures and reduction in seawater salinity, drive host-commensal interactions towards pathogenesis (Peters & Raftos, 2003, Case et al., 2011, Tout et al., 2015). Opportunistic pathogens often utilise virulence determinants, such as toxins, adhesins and degradative enzymes, in combination with enhanced fitness strategies, such as motility, biofilm-formation and resistance to host defence mechanisms to cause disease in compromised hosts (Josenhans & Suerbaum, 2002, Persson et al., 2009, Egan et al., 2014). Hence, in order to gain meaningful insights into pathogenesis, an understanding of the interactions between hosts, traits of pathogens and environmental factors is highly beneficial.

To investigate if putative opportunistic pathogens of macroalgae are capable of inducing disease symptoms, an experimental system needs to be in place that (1) targets adult alga, i.e. the life- history stage observed to undergo bleaching in the field, (2) is reproducible and (3) is high-

throughput. The existing experimental system used to test pathogenesis against D. pulchra depends on harvesting algal spores and requires prolonged culturing under laboratory conditions until the sporeling stage is reached (Case et al., 2011). Therefore, due to the fact that the sporeling life-history stage is targeted, whereby its susceptibility to bleaching in the field is unknown, and that the design is relatively low-throughput, the existing experimental system needs to be improved.

Data presented in Chapter 3 showed that a number of bacterial isolates cultured from diseased D. pulchra demonstrated virulence-related traits, such as motility, biofilm-formation, resistance to host defence responses and degradation of algal cell wall components. Based on the range of virulence-related traits expressed, putative pathogens were suggested for further investigation of their potential to function as opportunistic pathogens. Thus, in order to investigate effects of the putative pathogens on D. pulchra, the aims of this study were to (1) develop a method for in vivo screening of isolates, and (2) use the newly-developed infection assay to test putative pathogens for their ability to induce bleaching in D. pulchra.

4.2 Materials and methods

4.2.1 Sample collection

Sampling of juvenile and adult D. pulchra was carried out between April 2013 and September 2014. Whilst juvenile D. pulchra had an average length of 6 cm and were the smallest recognisable individuals found, adult conspecifics were fully grown and varied in length. Samples were collected from between three and eight meter depths, from two sites around Sydney: Long Bay (151ᵒ14’42’’ E, 33ᵒ58’19’’ S) and Bare Island (151ᵒ13’50’’ E, 33ᵒ59’32’’ S). Within an hour of collection, samples were transported to the aquarium or the laboratory in buckets pre-filled with seawater. The algae were rinsed in filtered seawater and inspected for any visual signs of bleaching or fouling. Only healthy (unbleached and un-fouled) individuals were used in the experiments.

4.2.2 Maintenance of D. pulchra in the aquarium

Adult D. pulchra were maintained at the Sydney Institute of Marine Sciences (SIMS) aquarium over a period of 12 days. Ten replicate adults were attached to plastic mesh using nylon strings, wrapped loosely around the holdfast of the algae. The mesh was held to the bottom of 10 X 4 l plastic buckets using ceramic weights. Each bucket was arranged to receive 0.4 µm filtered 63

seawater at a continuous and steady rate of 180 L/h. Excess water was allowed to overflow into a larger holding tray. The seawater temperature was maintained at 20 °C and a 15:9 light-dark photoperiod was used (Fig. 4.1). All algae were rinsed at days 5, 7 and 12 and buckets were cleaned of depositing debris. The algae were photographed and visually assessed for overall health, i.e. the presence of mid-thalli bleaching, fading of tips and tissue integrity. Using the same conditions, the experiment was repeated with twenty-eight juvenile D. pulchra, over a period of 14 days. The experimental set up was done in collaboration with Enrique Zozaya- Valdes (UNSW).

Statistical analyses were conducted to determine the developmental stage of the alga, i.e. adult or juvenile, that could be maintained in healthy conditions for a longer period of time. A two- way analysis of variance (ANOVA) was used to compare two outcomes, i.e. healthy and unhealthy, at three and four time-points for juveniles and adults respectively. Individual comparisons between time points were done using unpaired t-tests. All analyses were conducted in GraphPad Prism 6.03 (San Diego, CA, USA).

Fig. 4.1: Experimental setup at the SIMS aquarium. Each bucket contained an individual adult D. pulchra. Seawater flowed into buckets through 0.6 cm plastic tubing and the excess overflowed into a larger tray. Insulation coverings were used to maintain constant temperature within the buckets. Lighting was regulated from racks above the buckets.

4.2.3 Effect of temperature on maintenance of juvenile D. pulchra in a closed system

The effect of temperature stress on juvenile D. pulchra was investigated by incubating the algae at two temperatures, representing typical upper and lower limits of the summer seawater

temperature around Sydney, i.e. 25 °C and 19 °C respectively. Individual algae were transferred to sterile 25 cm2 Corning® polystyrene tissue culture flasks with vented caps, which contained 50 ml 0.22 µm filtered and autoclaved seawater (Fig. 4.2). Flasks were incubated at the two temperatures indicated and agitated at 80 rpm within orbital shaker/incubators (Ratek, Fisher- Biotec, Australia). A 15:9 hour light dark cycle was regulated from racks above the transparent cabinet of the incubators. Algae were visually assessed for presence or absence of mid-thalli bleaching after 5 days of incubation. The experiment was conducted once with five replicates per treatment. A two-way ANOVA was used to compare two outcomes, i.e. bleached and unbleached, at the two temperatures tested. Individual comparisons were done using unpaired t-tests. All analyses were conducted in GraphPad Prism 6.03 (San Diego, CA, USA).

Fig. 4.2: Closed-system setup used for monitoring effect of temperature stress on juvenile D. pulchra. Culture flasks containing juvenile D. pulchra and 50 ml filtered and autoclaved seawater were incubated at 19 °C and 25 °C and assessed for presence or absence of mid-thalli bleaching after 5 days of incubation.

4.2.4 Effect of bacterial inoculation on juvenile D. pulchra

The effect of bacterial inoculation on juvenile D. pulchra, incubated at an elevated temperature (25 °C) was monitored for bleaching. Briefly, on day 0, algae were transferred to sterile individual 25 cm2 Corning® polystyrene tissue culture flasks with vented caps – containing 50 ml 0.22 µm filtered and autoclaved seawater and permitted to acclimatise. On day 1, seawater in the flasks was replaced and replicate algae were inoculated with Microbulbifer sp. U156, an isolate

observed to cause bleaching in furanone-positive sporelings in preliminary studies (Fernandes, 2011). Procedural controls were set up.

Bacterial cell preparation included inoculating 10 ml DifcoTM Marine Broth 2216 with 100 µl of respective log-phase culture and incubating at 25 °C with agitation at 160 rpm. After an 18 hr incubation period, absorbance of the bacterial culture was measured using a Wallac 1420 multilabel counter. Tubes containing bacterial cultures were centrifuged at 3000 x g for 10 min and the cell pellet was washed twice to remove media. The pellet was finally resuspended in sterile seawater to an OD600 = 0.5. One hundred microliters of this culture (referred to from herein as the inoculum) were used per 50 ml culture flask (corresponding to a final concentration of 106 cfu/ml).

Flasks were incubated at 25 °C under 15:9 light dark cycle and agitated at 80 rpm. The algae were assessed on days 10 and 18 for the presence or absence of bleaching. At day 10, seawater in the flasks was replaced, followed by re-inoculation with a fresh preparation of the inoculum (as outlined above). The experiment was conducted once with six replicates per treatment. A two-way ANOVA was used to compare two outcomes, i.e. bleached and unbleached, at the three time-points tested. Individual comparisons were done using unpaired t-tests. All analyses were conducted in GraphPad Prism 6.03 (San Diego, CA, USA).

4.2.5 Effect of bromide manipulation on furanone content in juvenile D. pulchra

In order to determine if furanones could be reduced by manipulation of culture media, juveniles of D. pulchra were maintained in artificial seawater deficient in bromide. Briefly, algae were kept in individual 25 cm2 Corning® polystyrene tissue culture flasks with vented caps – containing either of 50 ml filtered and autoclaved seawater or bromide-deficient artificial seawater. Flasks were incubated at 25 °C under 15:9 light dark cycles and agitated at 80 rpm. At days 0 and 5, replicate algae were rinsed and gently shaken to remove excess water. Liquid nitrogen was used to snap freeze samples following which the algae were stored at -80 °C until chemical analysis was performed.

Furanone quantification was carried out in collaboration with Jan Tebben (UNSW). Briefly, total furanones were analysed on Agilent model 6890N equipped with a splitless injection port and interfaced with a HP5973N mass selective detector. The injection port temperature was set to 250 °C and the interface to 280 °C. Injections were made at 220 °C in splitless mode with a purge- off time of 1.5 min. The oven temperature program was as follows: Initial temperature 190 °C for 1 min, an increase at 10 °C to 280 °C held for 4 min and an increase at 30 °C to 300 °C. The 66

transfer line was set to 280 °C. The carrier gas was methane in chemical ionization (CI) mode. Data was acquired in total ion chromatogram mode and masses for individual ions were extracted. The dwell time was 50 msec and solvent delay was set to 2 min. A HP5-Ms (30 m x 0.25 µm) column was used. The experiment was conducted once with five replicates per treatment and time point. To compare furanone levels of algae in the different treatments, unpaired t-tests were carried out in GraphPad Prism 6.03 (San Diego, CA, USA).

4.2.6 Effect of bromide-deficient media and amended inoculation plan

To test the effect of inoculation when bromide-deficient media was used, the procedure in section 4.2.4 was repeated with slight modifications. The amended assay involved day 0 procedures to be exactly as outlined in section 4.2.4. From day 1 onwards, bromide-deficient artificial seawater was used and media was replaced on days 3 and 5. On days 1 and 3, the algal replicates were inoculated with fresh preparation of the inoculum - Microbulbifer sp. U156 or Alteromonas sp. LSS17. The inoculum concentration was increased such that 1 ml of the washed

7 cells with OD600 = 0.1 was added per 50 ml flask (corresponding to a final concentration of 10 cfu/ml). The bacterial culture was prepared as outlined in section 4.2.4, however the cells were washed and finally re-suspended in bromide-deficient artificial seawater. Control flasks contained media alone.

All flasks were incubated at 25 °C under 15:9 light dark cycles and agitated at 80 rpm to promote aeration. Algal replicates were assessed for the presence or absence of bleaching on days 5 and 7. To determine consistency between experiments, the setup was repeated twice with 6 replicates per treatment. Due to the overall deterioration of algal health post day 5, the second and third independent experiments were concluded at day 5. A two-way ANOVA was used to test for consistency between independent experiments. An unpaired t-test was used to test the effect of inoculation within individual experiments. All analyses were carried out in GraphPad Prism 6.03 (San Diego, CA, USA).

4.2.7 Effect of putative pathogens in in vivo bleaching of D. pulchra

In addition to strains Microbulbifer sp. U156 and Alteromonas sp. LSS17 (section 4.2.5), five putative pathogens, identified on the basis of total virulence-related traits in Chapter 3 (Table 3.5) were investigated for their effect on D. pulchra. These putative pathogens were Alteromonas sp. BL110, Agarivorans sp. BL7, Aquimarina sp. AD1 and AD10 and Winogradskyella sp. BL18. Additional strains tested included Aquimarina sp. BL5, which shared 16S rRNA gene sequence with Aquimarina sp. AD1 and was isolated from bleached D. pulchra tissues. The algal 67

pathogen, N. italica R11 (Case et al., 2011); and Microbulbifer sp. D250, which shared 16S rRNA gene sequence with Microbulbifer sp. U156 were also included (Penesyan et al., 2009). The protocol used for the infection assay was as outlined in section 4.2.6. For each putative pathogen, three independent experiments were conducted with six replicates per experiment and statistical analyses were conducted as outlined in section 4.2.6. Independent experiments were pooled to determine the effect of treatments relative to the respective uninoculated control using unpaired t-tests. Statistical analyses were conducted in GraphPad Prism 6.03 (San Diego, CA, USA).

4.2.8 Microscopy of biofilm-associated bleached tissues of D. pulchra

Microscopy of representative bleached tissues was conducted to determine if the bleaching was bacterial biofilm-associated. No prior sectioning of tissues was carried out to prevent disturbance to biofilm structures and imaging was done using the 100X magnification on a standard light microscope (Olympus BX51-DP70 colour digital camera).

4.3 Results

4.3.1 Health of juveniles in comparison to adult D. pulchra

To determine if adult D. pulchra could be maintained in healthy conditions in the aquarium, visual inspections of all replicate algae were carried out on days 5, 7 and 12. A decline in the overall health, indicated by tissue fading of most individuals over the duration of the experiment was observed. Figure 4.3(a) illustrates deterioration in health of an adult D. pulchra, photographed at three time points. Fifty percent of total algal replicates became visibly unhealthy by day 5, a further 20% deteriorated in health by day 7 and at day 12 all algae were visibly unhealthy (Fig. 4.3b). Juvenile D. pulchra were also inspected to identify healthy and unhealthy individuals and those with mid-thalli bleaching on days 7 and 14 (Fig. 4.4a). Eighteen percent of the total algal replicates became visibly unhealthy by day 7 and at day 14 a further 18% deteriorated in health (Fig. 4.4b).

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Fig. 4.3: Deterioration in health of adult D. pulchra. (a): A representative adult alga. Tissue fading (highlighted with dotted circles), which indicated decline in overall health, was observed on days 5 and 7. On day 12, loss of faded tissue, due to breakage was observed. (b) Proportion of adult D. pulchra that either remained healthy or became unhealthy, at the four time-points observed. Data are shown as the mean ± standard error from one experiment, performed using ten replicates. Between the start of the experiment (day 0) and day 5, there was a significant decrease in the proportion of replicates that remained healthy (P=0.008). At day 7 there was a further decrease in the proportion of replicates that were healthy (P=0.0002). Statistical analyses were conducted in GraphPad Prism 6.03 (San Diego, CA, USA).

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Fig. 4.4: Monitoring health of juvenile D. pulchra. (a): An example of a healthy and an unhealthy juvenile D. pulchra as observed on days 7 and 14. The ‘healthy’ alga remained unchanged on day 14. The ‘unhealthy’ alga developed localised bleaching in mid-thalli region (indicated with dotted circles) prior to developing an ‘unhealthy’ phenotype (indicated by tissue fading). (b) The proportion of healthy and unhealthy juvenile D. pulchra observed at the three time-points. Data are presented as the mean ± standard error from one experiment, performed using 28 replicates. Between the start of the experiment (day 0) and day 7, there was a significant decrease in the proportion of replicates that remained healthy (P=0.02). At day 14 there was a further reduction in the proportion of healthy replicates (P=0.0003). Statistical analyses were conducted in GraphPad Prism 6.03 (San Diego, CA, USA).

4.3.2 Health of D. pulchra at an increased temperature and in the presence of inoculum

After determining that juvenile D. pulchra could be effectively maintained in healthy states for longer periods of time than adults (Fig 4.3 and Fig 4.4), all subsequent assays were conducted using juveniles. To reduce the amount of handling, assays were downscaled and conducted in controlled temperature rooms where the algae were contained in sterile flasks instead of open buckets in the aquarium.

No significant difference in the bleaching outcome was observed between the two incubation temperatures, i.e. 25 °C and 19 °C, after 5 days of incubation (P=0.346). This indicated that under the test conditions, bleaching would not result from temperature stress alone thus permitting the remaining experiments to be conducted at 25 °C.

Under 25 °C incubation conditions, bleaching of juvenile D. pulchra was also not observed when inoculated with the bacterial strain, Microbulbifer sp. U156, and there was no significant difference between inoculated and uninoculated algae after 10 and 18 days of incubation (P=0.341 and P=0.145, respectively). These observations were contrary to the previous experimental system, where the sporeling life-history stage of D. pulchra was targeted and where Microbulbifer sp. U156 induced bleaching in defended algae (Fernandes, 2011). The inability of Microbulbifer sp. U156 to cause bleaching in the current experimental system, which targeted the juvenile life-history stage of D. pulchra suggested underlying differences between the two experimental systems. It is likely that algal defence was heightened in the juvenile life- history stage, where increased surface area of the thallus could be a contributing factor.

4.3.3 Bromide-deficient media reduces total furanones in juvenile D. pulchra

In order to increase the sensitivity of the juvenile infection assay, culture media was manipulated in an effort to reduce furanone levels. Algae incubated in artificial seawater deficient in bromide were analysed for levels of total furanones. Chemical analysis of algae kept in the two media types (Br +/-) over time showed that furanone concentrations could be effectively reduced by day 5 by incubating algae in Br(-) media (Fig. 4.5).

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Fig. 4.5: Comparisons of total furanone quantities in D. pulchra harvested fresh from field versus incubated in Br (+/-) media over a period of 5 days. Data are shown as the mean ± standard error from one experiment, performed using five replicates per treatment. At day 5, while no significant change was observed in algae incubated in Br (+) media (P=0.46), in comparison to fresh algae (day 0), levels of total furanones in algae incubated in Br (-) media were significantly reduced (P=0.003). The level of total furanones in algae incubated in Br (-) media for 5 days was significantly reduced also in comparison to algae in Br (+) media, incubated for the same length of time (P=0.011). Unpaired t-tests were conducted between treatments in GraphPad Prism 6.03 (San Diego, CA, USA).

4.3.4 Bleaching as a result of amended inoculation plan and use of bromide-deficient media

The use of bromide-deficient media to maintain D. pulchra, in combination with increased inocula concentration was associated with development of bleaching in inoculated algae. Microbulbifer sp. U156 caused no change whilst a significant bleaching effect as a result of inoculation with Alteromonas sp. LSS17 was observed (P=0.01). The experiment was repeated twice to check for consistency in outcomes (Fig. 4.6). No significant interaction between factors, i.e. time and bleaching outcome in treatments inoculated with Microbulbifer sp. U156 (P=0.22) and Alteromonas sp. LSS17 (P=>0.99) was observed. This indicated that all three independent experiments were consistent with no effect of time or interaction between factors.

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Fig. 4.6: Bleaching of algae incubated in bromine-deficient media after inoculation with Microbulbifer sp. U156 and Alteromonas sp. LSS17. Figure legend continued on next page.

Fig. 4.6 continued: Representative images of juvenile D. pulchra representing: (a) a healthy uninoculated control alga (b) a bleached alga inoculated with Microbulbifer sp. U156 and (c) a bleached alga inoculated with Alteromonas sp. LSS17. Bleached regions are indicated with dotted circles. Comparisons of bleaching effects between uninoculated algae and those inoculated with Microbulbifer sp. U156 and Alteromonas sp. LSS17 in three independent experiments, represented in (d), (e) and (f) respectively. Data are shown as the mean ± standard error using six replicates per treatment. (d) Microbulbifer sp. U156 caused no change, while Alteromonas sp. LSS17 caused significant bleaching, P=0.01, (e) both Microbulbifer sp. U156 and Alteromonas sp. LSS17 caused significant bleaching, P=0.049 and P=0.01 respectively, and (f) neither Microbulbifer sp. U156 nor Alteromonas sp. LSS17 caused significant bleaching, P=0.55 and P=0.26 respectively. Unpaired t-tests were conducted in GraphPad Prism 6.03 (San Diego, CA, USA).

4.3.5 Putative pathogens induce in vivo bleaching in D. pulchra

The significant presence of bleaching as a result of inoculation with putative pathogens and its absence in uninoculated controls indicated that the infection assay developed was suitable for testing other bacterial isolates. Moreover, consistent outcomes in the independent experiments indicated that the protocol was reproducible. To improve precision of the assay, the number of independent experiments was maintained at three, with six replicates per treatment in each experiment. Figure 4.7 illustrates the steps in the final protocol, which were used to test remaining putative pathogens for bleaching effects on D. pulchra.

Fig. 4.7: An outline of the steps required to test putative pathogens for bleaching effects on juvenile D. pulchra. The procedural control required all steps to be the same however inocula to be replaced with the same volume of media blank. The protocol was verified by testing multiple isolates, in at least three independent experiments, with six replicates per treatment in each experiment.

The finalised protocol was used to test the remaining putative pathogens for bleaching effects on D. pulchra. No differences were detected between independent experiments for each isolate tested (Table 5.1, Appendix III), therefore outcomes of the three experiments were pooled before comparing with the respective uninoculated control treatments. Of the ten candidate pathogens chosen for testing, four were able to cause significant amounts of bleaching in juvenile D. pulchra. These included Aquimarina sp. AD1, Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 (Fig. 4.8). Microscopy on representative bleached and unbleached thalli was carried out to determine if the bleaching observed was bacterial biofilm-associated. Figure 4.9 illustrates examples of un-sectioned tissues visualised following inoculation assays.

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Fig 4.8: Bleaching effects of candidate pathogens on juvenile D. pulchra. Data represent the mean ± standard error values calculated from three independent experiments. Eighteen replicates per treatment were observed over time. Statistically significant difference in comparison to the uninoculated controls were observed in treatments inoculated with Aquimarina sp. AD1, Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 (P=0.006, P=0.005, P=0.004, P=0.0003 respectively). Results from independent experiments were compared using Tukey’s multiple comparisons test to determine consistency. With no significant differences observed (Table 4.1, Appendix III), independent experiments were then pooled and the effect of candidate pathogen inocula was compared relative to the uninoculated control using unpaired t-tests. Statistical analyses were conducted in GraphPad Prism 6.03 (San Diego, CA, USA).

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(b) (e) Fig. 4.9: Microscopic images of D. pulchra from Faded region representative samples of each treatment. (a) Healthy alga from the field, (b) healthy uninoculated control alga, (c) faded alga from field - non-biofilm associated, (d) biofilm-associated bleaching after inoculation with Aquimarina sp.

20µm 20µm AD1, (e) fading upon inoculation with Aquimarina sp. AD10 – in a non-biofilm associated manner, (f) (c) Pigmented region (f) biofilm-associated bleaching after inoculation with Aquimarina sp. BL5, (g) biofilm-associated Faded region Bleached region bleaching after inoculation with Alteromonas sp. BL110. Images of algal cell walls were taken using Biofilm an Olympus light microscope, without any sectioning of tissue. Scale bar: 20 µm at 100X 20µm 20µm

magnification.

4.4 Discussion

Disease in D. pulchra is linked to environmental stress, host resilience and the presence of pathogens (Steinberg et al., 2011, Harder et al., 2012). Bacterial community shifts associated with bleached D. pulchra show an increased representation of specific bacterial groups, suggesting the involvement of opportunistic pathogens (Zozaya-Valdes et al., 2015). However, in order to fully investigate the virulence potential of putative pathogens, a sensitive, simple and reliable in vivo infection assay is required. Data presented in Chapter 3 indicated that the bacterial isolates cultured from bleached D. pulchra, possessed a combination of virulence- related traits that may be utilised against the host. Ten putative pathogens were proposed for an investigation of their roles as opportunistic pathogens of D. pulchra. To determine the pathogenic potential of the isolates, this study aimed to develop a new infection assay and subsequently screen the putative pathogens for their ability to induce in vivo bleaching in D. pulchra.

4.4.1 Juvenile but not adult D. pulchra persist under laboratory test conditions

An investigation of the preferred life-history stage of D. pulchra for subsequent use in infection assays led to the finding that adult D. pulchra could not persist in the aquarium for a period of up to 5 days without becoming significantly unhealthy (P=0.008). In comparison, findings of the following experiment indicated that juvenile D. pulchra were able to persist up to 7 days with a relatively smaller proportion becoming unhealthy (P=0.02). Moreover, while at day 12, all the adult D. pulchra were visibly unhealthy, greater than 50% of the juveniles remained healthy on day 14 (Fig. 4.3 and Fig. 4.4). Together, the findings of the two experiments indicate that juveniles, and not adults of D. pulchra persist in a healthier state under laboratory conditions.

Ontogenetic (juveniles vs. adult) studies on plants have indicated that some species have higher tolerance to stressors in the early life stages in comparison to the adult reproductive stages. For example, the wild gourd plant, Cucurbita pepo subsp. texana has higher tolerance during most of the growth stages, in contrast to the peak reproductive periods where damage due to herbivory significantly impacts reproduction in the plant (Du et al., 2008). Similarly, juveniles of four species of Antarctic marine invertebrates were shown to be more resilient to changing environmental conditions, such as warming, in comparison to adults of the same species (Peck et al., 2013). A possible reason for the longer survival of juvenile D. pulchra in this study could be better adaptive traits (acclimatisation responses) and tolerance to artificial environments, such as that of an aquarium. In comparison, adult D. pulchra that are in or near reproductive 78

life-history stage may be less tolerant due to high energy demands of the associated life stage. The ‘resource-allocation’ theory, based on terrestrial plants, suggests that reproduction involves a ‘cost’ and often there may be ‘trade-offs’ between various functions in the plant including reproduction, growth and defence (Bazzaz et al., 1987). Indeed ‘trade-offs’ between growth and reproductive output has been observed for the giant kelp, Macrocystis pyrifera (Graham, 2002). Hence, the reproductive life stage of adult D. pulchra may also impose ‘costs’ on the alga, which reduces persistence. However, ‘trade-offs’ between stress response and persistence remain to be investigated in D. pulchra.

4.4.2 Temperature stress solely does not induce bleaching in D. pulchra

In order to include temperature as a consistent stress factor in the infection experiments, juvenile D. pulchra incubated at 25 °C and 19 °C were assessed for bleaching after 5 days of incubation. The lack of observable difference in bleaching outcomes between the algae incubated at the two temperatures (4.3.2) indicated that temperature stress in itself was insufficient to induce bleaching in D. pulchra, thus temperature could be used as a consistent stress factor during infection experiments.

Studies have shown that environmental stress plays a crucial role in disease (Largo et al., 1995, Closek et al., 2014). While in itself environmental stressors may or may not induce disease, by exceeding tolerance limits, organismal physiology is affected, which increases susceptibility to infection (Bruno et al., 2007, Toohey & Kendrick, 2007). Using an experimental system that targeted the sporeling life-history stage of D. pulchra, Case et al. (2011) demonstrated that although temperature stress in itself did not induce bleaching, it was necessary for disease induction by the pathogen N. italica R11 (Fig. 1.3). In field surveys, involving adult D. pulchra, Campbell et al. (2011) found that despite the consistent association of warmer temperatures with high proportions of bleaching, water temperature explained only <10% of bleaching observed in the alga. The role that temperature plays in disease-mediation of D. pulchra has been achieved only from studies of sporelings (Case et al., 2011) and adults (Campbell et al., 2011), however it is unknown if juvenile D. pulchra are affected in a similar manner. Thus, whilst increased temperature may induce host stress and increase susceptibility to disease, findings of the current study show that in itself, temperature was not linked with bleaching in juvenile D. pulchra.

4.4.3 Bromide-deficient media improves assay sensitivity

An earlier study by Dworjanyn et al. (1999) showed that although the removal of bromide from culture media did not have morphological consequences it was essential for production of furanones in sporelings of D. pulchra. In the current study, the incubation of juvenile D. pulchra in bromide-deficient artificial seawater, over a five-day period was successful in reducing the levels of total furanones (P=0.003). In addition, D. pulchra incubated in bromine-deficient artificial seawater when inoculated with Alteromonas sp. LSS17 was observed to undergo significant bleaching relative to the uninoculated controls (Fig. 4.8). Thus, conditions used in the experimental protocol were considered appropriate for the detection of pathogenic activity against D. pulchra. In order to improve precision of the experimental outcome, three independent experiments were conducted. The occasional bleaching of uninoculated controls highlighted biological variation amongst replicate algae. Physiological variation in chemical defence, health state and the pre-existence of opportunistic pathogens within natural surface communities may have contributed towards the natural bleaching of algal samples. Given the conditions of the infection assay, i.e. presence of temperature stress and reduced host defence, opportunistic pathogens present in the background microbial community were also provided with optimal conditions to induce disease in the stressed D. pulchra hosts. Similarly, D. pulchra inoculated with putative pathogens may have had pre-existing opportunistic pathogens present in the background microbial community, which could have contributed false positive results. Hence, six algal replicates, in three independent experiments were tested before pathogenicity was confidently determined for the individual bacterial strains.

4.4.4 Putative pathogens demonstrate bleaching in stressed juveniles of D. pulchra

Using the newly developed infection assay, putative pathogens identified in Chapter 3 were tested for their ability to induce bleaching in juvenile D. pulchra. Significant amounts of bleaching were caused by isolates Alteromonas sp. LSS17 and BL110, Aquimarina sp. AD1 and Agarivorans sp. BL7. Microscopic analysis of representative thalli indicated localised bacterial- associated bleaching on the periphery of infected thalli (Fig. 4.11). Future studies directed at quantifying pathogens comprising algal biofilms through use of specific FISH probes could be beneficial. Although statistically insignificant, bleaching was also associated with inoculation of isolates Aquimarina sp. BL5 (P=0.053) and Microbulbifer sp. U156 (P=0.066), suggesting that these bacteria may also be capable of pathogenic activity against D. pulchra. No bleaching was observed upon inoculation with Aquimarina sp. AD10 and Winogradskyella sp. BL18, indicating that these bacteria were non-pathogenic towards D. pulchra. Whilst data presented in Chapter 80

3 showed that both Aquimarina sp. AD10 and Winogradskyella sp. BL18 had various virulence- related traits, it is possible that these traits are not expressed under the conditions tested here, are effective against organisms other than D. pulchra or that the virulence-related traits detected earlier are related to functions other than pathogenesis.

The strain N. italica R11, which causes bleaching in furanone-deficient sporelings (Case et al., 2011) and in damage-inflicted adults of D. pulchra in situ (Campbell et al., 2014) was unable to induce significant levels of disease in juveniles of the alga (P=0.44). These results indicate that some opportunistic pathogens may have varied consequences for hosts during different life- history stages. For example the reduced susceptibility of D. pulchra to N. italica R11 in the current study maybe due to a more resilient juvenile life-history stage of D. pulchra, in comparison to sporelings or adult life-history stages. These results are consistent with the observation that juvenile algae were also more tolerant to growth in the aquarium than adults. However resilience to pathogens in the juvenile life-history stage, in comparison to early sporeling or reproductive adult life-history stages of D. pulchra remains to be fully investigated.

4.4.5 Conclusions and future work

While algal pathosystems involving viruses (Sharoni et al., 2015), ooymycetes (Sekimoto et al., 2008, Gachon et al., 2009), protists (Letcher et al., 2013) and endophytic algae (Bourab et al., 2001) have been established, studies on the involvement of bacterial pathogens in macroalgal model systems is scarce (Harder et al., 2012). This chapter involved the development of an infection assay for testing putative bacterial pathogens, isolated from bleached D. pulchra, for their ability to induce bleaching in the alga. The earlier stages of protocol development highlighted that juveniles of D. pulchra were more resilient to changes in environmental conditions than adults of the alga. Temperature, which was used as a consistent stress factor in the infection assay was found to have no bleaching effects on D. pulchra, in the absence of pathogens.

The newly developed infection assay was instrumental in identifying at least four bacterial isolates, Aquimarina sp. AD1, Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17, which caused significant amounts of bleaching in juvenile D. pulchra. In addition, the study indicated that previously identified pathogens of D. pulchra may not induce bleaching in all life-history stages of the alga. The next chapter will address the detection of the newly-identified pathogens post-infection, in partial fulfilment of Koch’s postulates, which in the classical context of disease is a requirement for demonstrating disease causality (Blevins & Bronze, 2010).

Chapter Five: Detection of putative pathogens and bacterial community structure on Delisea pulchra

5.1 Introduction

Bacterial communities can be considered as signatures of a host’s well-being where distinct community assemblages have been linked to healthy, intermediate or diseased states of an organism (Fernandes et al., 2012, Closek et al., 2014, Galimanas et al., 2014, Olson et al., 2014). Under healthy conditions, the homeostasis of host-microbe interactions are maintained and greatly influenced by the underlying host physiology and natural host defence systems (Hooper et al., 2012, Hacquard et al., 2015). However, environmental change and/or anthropogenic factors often induce host stress, which can result in susceptibility to infectious pathogens (Jones et al., 2004, Closek et al., 2014).

The bleaching disease in D. pulchra is temperature-dependent. Under healthy conditions, the macroalga is chemically defended against unwanted surface colonisation by the production of furanone secondary metabolites (Givskov et al., 1996, Maximilien et al., 1998, Case et al., 2011). A reduction in furanone levels in bleached individuals has been observed (Campbell et al., 2011). Moreover, analysis of the bacterial community of the alga shows distinct bacterial assemblages present on healthy, bleached and intermediate regions (Campbell et al., 2011, Fernandes et al., 2012). It has been hypothesised that the bacterial groups involved in driving the community shift on bleached tissues may have a role to play in pathogenesis of the host (Zozaya-Valdes et al., 2015).

In the medical field, confirmation of pathogenicity has traditionally involved fulfilment of Koch’s postulates (Blevins & Bronze, 2010). The guidelines for Koch’s postulates require demonstration of disease-causation in healthy hosts upon introduction of the pathogen, followed by its re- isolation from diseased individuals (Evans, 1976, Blevins & Bronze, 2010). However, where the pathogen has not been isolated in pure culture, or in the case of opportunistic pathogens which exist in commensal relationships with healthy hosts, the fulfilment of Koch’s postulates is challenging (Karrasch et al., 2007, Sutherland et al., 2011, Egan et al., 2014). Nevertheless recent amendments to Koch’s postulates allowing for PCR-based systems for pathogen tracking have now led to more accommodative guidelines for demonstrating disease causality (Blevins & Bronze, 2010, Breitschwerdt et al., 2013).

The ability of putative pathogens to induce bleaching in temperature-stressed D. pulchra was investigated in Chapter 4 of this thesis and the data presented therein suggested that Agarivorans sp. BL7, Aquimarina sp. AD1 and Alteromonas sp. BL110 and LSS17 were able to cause a significant proportion of the algal replicates to bleach. However in order to establish a causal link between putative pathogens and disease, the inocula on bleached individuals remained to be detected. The effect of introducing putative pathogens to the natural bacterial community on D. pulchra is also unknown. Therefore, the objective of this chapter was to further investigate the effect of introducing these specific pathogens to stressed D. pulchra and determining impact on the resident microbial community. To address this objective, the specific aims of the chapter were to 1) detect sequences with identity to the inocula, in partial fulfilment of Koch’s postulates, 2) determine the presence and abundance of putative- and known pathogens of D. pulchra within the background community, and 3) assess the overall community to determine the effect of treatment and the bleaching outcome on overall abundances of bacterial species.

5.2 Materials and methods

In Chapter 4 of this thesis, putative pathogens were inoculated on healthy D. pulchra in order to test their capacity to induce bleaching in the alga. Bleached and unbleached algae, inoculated with selected putative pathogens (i.e. Aquimarina sp. AD1 and Alteromonas sp. BL110 and LSS17), were analysed in this chapter to investigate the effect of inoculation on the surface microbial communities.

5.2.1 DNA extraction from the microbial community on D. pulchra

DNA was extracted from all samples at the endpoint of the inoculation assay, described in section 4.2.7. The DNA extraction protocol was adapted from Burke et al. (2009). Briefly, the algal samples (average length of 6 cm) were rinsed twice in artificial seawater to remove unattached bacteria. Samples were placed in 2 ml calcium and magnesium-free artificial seawater containing 0.45 M NaCl, 10 mM KCl, 7 mM Na2SO4, and 0.5 mM NaHCO3 and supplemented with 10 mM EDTA. Twenty microliters of filter-sterilized 3M Rapid Multienzyme cleaner (Sydney, Australia) were added to the samples and incubated at room temperature with agitation at 80 rpm. Following an hour of incubation, the liquid from the sample was filtered using a 3 µm filter to remove diatoms and small algal fragments. DNA extraction was performed by adding an equal volume of phenol:choloroform:isoamy alcohol (25:24:1 mix was used). Tubes 83

were inverted to mix the contents, followed by centrifugation at 10 000 x g for 10 min. Supernatant was removed and added to new tubes. To precipitate the DNA, sodium acetate to a final concentration of 0.3 M and 1 volume isopropanol was added before samples were incubated overnight at 4°C.

Following incubation, samples were centrifuged at 20 000 x g at 4 °C for 30 min and supernatant was removed. The DNA pellet was washed once with 70% ethanol. The precipitation step was repeated once after which the pellet was again air-dried and resuspended in 20 µl molecular grade water. DNA was RNase treated by adding 0.2 mg/ml RNase A and samples were incubated at 4°C overnight, before being stored at -20 °C until sequencing was performed.

5.2.2 Experimental design for 16S rRNA gene sequencing

The experimental design chosen for sequencing the microbial community on D. pulchra was based on the samples from which sufficient amounts of DNA could be extracted and amplified following the inoculation assay, described in section 4.2.7 (Fig. 5.1).

Fig. 5.1: Experimental design used for sequencing the microbial community on D. pulchra. Sequencing of bleached and unbleached representatives from both inoculated and uninoculated control treatments were carried out. Four samples from each combination of treatment and bleaching outcome were chosen, except for unbleached LSS17 where only three samples were available.

Sequencing of the paired-end 16S rRNA gene from the bacterial community was carried out on the MiSeq platform (Caporaso et al., 2012), at the Ramaciotti Centre for Genomics (RCG, UNSW). The primers, 27F (AGAGTTTGATCMTGGCTCAG) and 519R (GWATTACCGCGGCKGCTG) were used to amplify the V1-V3 regions of the 16S rRNA gene. The sequencing facility followed instructions as per the MiSeq System User Guide (Illumina, 2013).

5.2.3 Quality-filtering of sequences

Sequence analysis was performed following the Miseq SOP pipeline in Mothur (Schloss et al., 2009, Kozich et al., 2013). Briefly, contigs were made using the forward and reverse reads of each sample. Sequences with ambiguous bases and lengths longer than 525 bp were removed. Unique sequences were identified and aligned against the SILVA reference database (Pruesse et al., 2007). Sequences that did not overlap during alignment and homopolymers greater than 8 bp in length were also removed. Filtering was performed to remove sequences overhanging outside the paired-end regions and those that consisted of gap characters only. Unique sequences were identified and pre-clustered – allowing 1 difference per 100 bp of sequence, followed by an abundance-based sorting. Singleton sequences, which likely represented sequencing errors, were removed (Tedersoo et al., 2010). The Uchime algorithm was used to remove chimeric sequences (Edgar et al., 2011). Taxonomic assignment was given by classifying sequences against the Greengenes reference taxonomy (Werner et al., 2012), using the naïve Bayesian approach (Wang et al., 2007). Sequences assigned as chloroplast, mitochondria, archaea, eukaroytes and unknowns were removed.

5.2.4 Sequence analysis

5.2.4.1 Detection of putative- and known pathogens on bleached and unbleached D. pulchra

In order to detect sequences of putative pathogens with the highest phylogenetic resolution possible, quality-filtered sequences were clustered into OTUs at 100% identity with consensus taxonomy, using the default average neighbour algorithm. An additional abundance-based OTU filter was applied to remove spuriously generated OTUs, as a result of sequencing large number of reads. In range with previously verified filter cut-offs (Bokulich et al., 2013), an abundance threshold of 0.0003% was used.

To identify the putative pathogens, near full-length 16S rRNA gene sequences, obtained previously through Sanger sequencing, were queried against the 100%-defined OTUs. The query was executed locally using BLAST2.2.30+ (Altschul et al., 1990). Sequences with 99-100% similarity to the query sequence were verified by checking phylogenetic similarities and conducting pairwise alignments against sequences of the respective putative pathogen, using the NCBI database (Zhang et al., 2000). Abundance patterns of the putative pathogens were determined by retrieving the corresponding OTU identity numbers and using the samples-by- OTU table, generated in Mothur, for further analysis (Schloss et al., 2009).

To test the effect of inoculation on abundance of the putative pathogens, the R mvabund software package was used (Wang et al., 2012). Data from the ‘treatment’ factor was adjusted to a multivariate generalised linear model (MGLM), where each OTU was treated as a variable and fitted into separate generalised linear models, using a negative binomial distribution. Significance of the difference in abundances of OTUs corresponding to putative pathogens were compared between the inoculated treatments and the uninoculated control treatment using analysis of deviance for MGLM fits for abundance data. Univariate test statistics were calculated using ordinary unadjusted P values, for which values of less than 0.05 were considered significant. Abundance plots of the OTUs in corresponding inoculated-treatments and the uninoculated controls were visualized using box plots in GraphPad Prism 6.03 (San Diego, CA, USA).

To determine if the presence of pathogens in the background community could have contributed to bleaching of uninoculated samples in the control treatment, abundances of putative- and known pathogens of D. pulchra in corresponding samples were determined. As with the detection of putative pathogens, described earlier, sequences with 100% identity to the known pathogens Nautella italica R11 and Phaeobacter sp. LSS9 were identified. Contributions of OTUs of putative- and known pathogens were used to plot a stacked bar chart illustrating possible additive effects in each sample, in addition to the uninoculated control samples.

5.2.4.2 Analysis of the total bacterial community

The total bacterial community present in each sample was further analysed by clustering OTUs at 97% similarity to consensus taxonomy. An OTU abundance threshold of 0.0003%, as described in section 5.2.4.1, was applied (Bokulich et al., 2013). Samples with extremely low number of sequences (<2000) were removed. Sequences were standardised to account for different sequencing depths by subsampling to the sequence counts of the smallest sample (here 33,889). Rarefaction data for quality- and abundance-filtered sequences was generated in Mothur (Schloss et al., 2009) and plotted in the computational software, R (Ihaka & Gentleman, 1996), to observe sampling efficiency.

In order to make comparisons between bleached and unbleached D. pulchra, which was not biased by large proportions of inocula, sequences of the inoculated pathogens were removed from corresponding OTUs using Mothur (Schloss et al., 2009), before further analysis was carried out. To determine effects of treatment (i.e. inoculation with putative pathogen) and the bleaching outcome using inverse-Simpson diversity indices, statistical analyses of the diversity 86

data generated in Mothur (Schloss et al., 2009) were performed in GraphPad Prism 6.03 (San Diego, CA, USA). P values of less than 0.05 were considered significant. Taxonomic diversity within the community on bleached and unbleached samples were visualized using pie charts and stacked bar graphs.

The effect of treatment and the bleaching outcome on the overall community, comprising of OTUs defined at 97% similarity, was tested using PRIMER (Clarke & Gorley, 2006). The OTU-by- sample table generated in Mothur (Schloss et al., 2009) was imported in PRIMER (Clarke & Gorley, 2006) and the community structure (abundance data) and composition (presence/absence data) were compared. Bray-Curtis similarity coefficient for square root- transformed sequence counts was calculated to analyse community structure. Multivariate patterns were illustrated using a non-metric multidimensional scaling (nMDS) plot. A Jaccard resemblance matrix was created to compare community composition. Multivariate analysis of the effect of treatment and the bleaching outcome on the overall abundances of OTUs were tested using PERMANOVA+ (Anderson, 2001). Univariate analysis, within PERMANOVA (Anderson, 2001) was used to test for differences within treatments and bleaching outcomes. In order to test whether differences of assemblages were due to different multivariate dispersion between samples, PERMDISP was used (Anderson, 2001). P values of less than 0.05 were considered significant. Similarity percentage (SIMPER) analysis (Clarke, 1993, Clarke & Gorley, 2006) was used to identify the OTUs contributing most towards Bray-Curtis dissimilarities between bleaching outcomes.

5.3 Results

5.3.1 Quality-filtered sequences

A total of 17,551,413 reads were obtained from the sequencing of 31 samples. Removal of ambiguous sequences resulted in 11,494,964 sequences, of which 2,312,199 (20%) were unique. The removal of misaligned reads and pre-clustering, with a difference of 1 per 100 bp allowance, grouped and further reduced the unique sequences to 255,611. In the pre-clustered unique sequences, 202,939 singletons (79%), 27,162 chimeras (10%) and 36 irrelevant or unknown lineages (0.01%) were identified and removed. A total of 10,533,247 sequences remained post quality trimming, of which 25,474 were unique and had an average length of 434 bp.

5.3.2 Abundance of putative- and known pathogens on bleached and unbleached D. pulchra

In order to match the sequences of putative pathogens to OTUs with the highest phylogenetic resolution possible and to analyse the respective abundance patterns within treatments, OTUs were defined at an identity cut-off of 100%, which resulted in 25,474 OTUs. Application of an abundance threshold to remove spurious OTUs reduced the total number of OTUs to 2,569. Upon querying the sequences of putative pathogens against OTUs, a sequence each with 100% identity to Aquimarina sp. AD1 and Alteromonas sp. LSS17 was found (OTU18472 and OTU08723 respectively). The closest match sequence for Alteromonas sp. BL110 had 99% identity (OTU08694), as a result of 2 mismatches between the query and hit sequences. The relative abundance of sequences having 99-100% identity with putative pathogens, in bleached and unbleached samples of inoculated treatments and uninoculated controls were determined (Fig. 5.2).

(a) (b)

(c)

Fig. 5.2: Box plots showing the relative abundances of the putative pathogens in inoculated treatments and uninoculated control treatments of (a) OTU18472 (with 100% identity to Aquimarina sp. AD1). An increase in the abundance of OTU18472 as a result of inoculation (P=0.059) was determined. (b) OTU08694 (with 99% identity to Alteromonas sp. BL110). A significant increase in the abundance of OTU08694 as a result of inoculation (P=0.022) was determined. (c) OTU08723 (with 100% identity to Alteromonas sp. LSS17). A significant increase in the abundance of OTU08723 as a result of inoculation (P=0.023) was determined. The mean of the data is represented with ‘+’. Mvabund hypothesis test was used to determine the effect of treatment on OTU abundances (Wang et al., 2012).

To investigate if there were pathogens present in the background community, which could have contributed to bleaching of uninoculated samples in the control treatment, abundances of putative- and known- pathogens of D. pulchra in the corresponding samples were determined. Near full-length sequences of N. italica R11 and Phaeobacter sp. LSS9 (NCBI accession GU176618 and GQ906799 respectively) were queried against OTUs clustered at 100% similarity cut-off. A sequence each with 100% identity to N. italica R11 and Phaeobacter sp. LSS9 was found (OTU00341 and OTU00058 respectively). The abundance of OTU00341 and OTU00058, in addition to the OTUs corresponding to putative pathogens, was used to plot a stacked bar chart illustrating possible additive effects of putative- and known pathogens in each sample (Fig. 5.3).

C_78 Aquimarina sp. AD1 C_69 Alteromonas sp. BL110

C_56 Control

bleached Alteromonas sp. LSS17 C_21 Nautella italica R11 C_48 Phaeobacter sp. LSS9 C_47

C_42 Control

unbleached C_39 LSS17_8 LSS17_17 LSS17_12

LSS17 bleached LSS17 LSS17_10 LSS17_7

LSS17_15 LSS17

LSS17_11 unbleached BL110_3 BL110_2

BL110 BL110_19 bleached BL110_15 BL110_24

Samples within treatmentsSamples within BL110_17

BL110 BL110_14

unbleached BL110_13 AD1_20 AD1_19 AD1_17

AD1bleached AD1_16 AD1_24 AD1_22

AD1 AD1_18

unbleached AD1_13

0 10 20 30 40 50 60 70

Relative abundance (%)

Fig. 5.3: Relative abundance of sequences having 99-100% identity with sequences of putative- and known opportunistic pathogens of D. pulchra. These include Aquimarina sp. AD1, Alteromonas sp. BL110 and LSS17, N. italica R11 and Phaeobacter sp. LSS9. Red arrows indicate the 4 samples out of 31 in which no known putative- or known- pathogens were detected.

5.3.3 Total bacterial community on bleached and unbleached D. pulchra

5.3.3.1 Alpha-diversity

To gain insights on effects of inoculation with putative pathogens on the total bacterial community, OTUs were re-defined at 97% similarity cut-off, resulting in the identification of 3,071 OTUs. Removal of potential spurious OTUs reduced the total number to 987. Samples with low number of reads were filtered out, resulting in the removal of two samples – C_56 and C_78 (Table 5.1). Subsampling to the size of the smallest sample, i.e. 33,889, resulted in 984 OTUs remaining. Rarefaction curves were plotted to estimate species richness. Asymptotic curves for the majority of the samples suggested good sampling coverage (Fig. 5.4).

Table 5.1: Summary of the OTU-based alpha diversity measurements of sequenced samples

Sample ID/treatment Outcome No. of sequences post No. of observed quality filtering OTUs (97% ID) AD1_13 Unbleached 568576 164 AD1_16 Bleached 397423 158 AD1_17 Bleached 441230 125 AD1_18 Unbleached 519093 211 AD1_19 Bleached 452267 124 AD1_20 Bleached 360268 187 AD1_22 Unbleached 100298 83 AD1_24 Unbleached 509778 111 BL110_13 Unbleached 613406 75 BL110_14 Unbleached 596394 128 BL110_15 Bleached 319322 95 BL110_17 Unbleached 532649 136 BL110_19 Bleached 60996 114 BL110_2 Bleached 33889 50 BL110_24 Unbleached 391569 124 BL110_3 Bleached 136848 15 C_21 Bleached 105177 28 C_39 Unbleached 248190 259 C_42 Unbleached 407246 131 C_47 Unbleached 291173 113 C_48 Unbleached 481644 228 C_56 Bleached 1095 Sample removed C_69 Bleached 74810 23 C_78 Bleached 66 Sample removed LSS17_10 Bleached 445842 89 LSS17_11 Unbleached 415401 98 LSS17_12 Bleached 522351 118 LSS17_15 Unbleached 181663 94 LSS17_17 Bleached 462503 49 LSS17_7 Unbleached 461732 108 LSS17_8 Bleached 389220 117

(a) (b)

Fig. 5.4: Rarefaction curves of quality- and abundance-filtered sequence data. Samples comprising treatments AD1, BL110, LSS17 and the control, respectively are represented in plots (a)-(d). Sequences were defined at a 16S rRNA gene sequence identity of 97%. The curves were generated with data from every 100 sequences, re-sampled without replacement.

Comparison of the inverse Simpson diversity index between all bleached and unbleached samples showed a significant decrease (P=0.04) in OTU-defined species diversity within samples with the bleached outcome (Fig. 5.5a). However, treatment with specific pathogens alone did not have a significant effect (P=0.6116) on species diversity (Fig 5.5b).

(a)

(b)

Fig. 5.5: Inverse Simpson diversity indices : (a) between all bleached (blue) and unbleached (green) samples. Using an unpaired t-test, an overall significant reduction in diversity in bleached samples was calculated, P=0.04 (b) between bleached (blue) and unbleached (green) samples, as functions of treatment. No overall effect of treatment was observed using one-way ANOVA, P=0.6116. OTUs were clustered at 97% similarity cut-off. The mean of the data is represented with ‘+’. Statistical analyses were conducted in GraphPad Prism (San Diego, CA, USA).

5.3.4 Bacterial community structure and diversity

To visualise the overall patterns of similarities between samples with respect to both factors, i.e. treatment and outcome, a non-metric multidimensional scaling plot was constructed. An overlap was observed between samples from different treatments and also between unbleached and bleached samples, of which the latter was more dispersed (Fig. 5.6).

Fig. 5.6: nMDS plot based on Bray-Curtis measure of square-root transformed OTU (97% similarity cut-off) abundances on unbleached (filled symbols) and bleached (unfilled symbols) samples of D. pulchra. Treatments are represented as follows: AD1 – blue upright triangles, BL110 – green squares, LSS17 – red inverted triangles and the control – black circles.

In order to gain further insights into the taxonomic distribution of bacteria within the samples, overall comparisons between bleached and unbleached samples were made. Both bleached and unbleached samples were dominated by Proteobacteria (Fig. 5.7). Approximately equal proportions of Alpha- and Gamma-proteobacteria comprised the communities on bleached and unbleached D. pulchra. Samples from individual treatments, within bleached and unbleached groups, had varying proportions of bacteria representing different orders (Fig. 5.8a and b).

Unbleached Bleached

Acidobacteria Actinobacteria Acidobacteria Actinobacteria Bacteroidetes Chlamydiae Bacteroidetes Chlamydiae Chlorobi Chloroflexi Chlorobi Chloroflexi Cyanobacteria Firmicutes Cyanobacteria Firmicutes Planctomycetes Proteobacteria Planctomycetes Proteobacteria TM6 TM7 TM6 TM7 Verrucomicrobia WPS-2 Verrucomicrobia WPS-2 Thermi unclassified Thermi unclassified

Fig. 5.7: Relative abundance of bacterial phyla on unbleached and bleached samples of D. pulchra. The phylum Proteobacteria comprised >75% of the bacterial communities on both sample types.

(a)

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Unbleached Bleached Unbleached Bleached Unbleached Bleached Unbleached Bleached AD1 AD1 BL110 BL110 LSS17 LSS17 Control Control

BD7-3 Caulobacterales Kiloniellales Kordiimonadales Rhizobiales Rhodobacterales Rhodospirillales Rickettsiales Sphingomonadales unclassified

(b)

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Unbleached Bleached Unbleached Bleached Unbleached Bleached Unbleached Bleached AD1 AD1 BL110 BL110 LSS17 LSS17 Control Control

Alteromonadales Chromatiales Enterobacteriales HTCC2188 Legionellales Oceanospirillales Pasteurellales Pseudomonadales Thiohalorhabdales Thiotrichales Vibrionales Xanthomonadales Marinicellales unclassified

Fig. 5.8: Relative abundances of bacterial orders in each treatment that comprised the Alpha- and Gamma-proteobacteria in (a) and (b) respectively. Varying patterns in abundance of bacteria representing different orders were observed for the individual treatments.

The effect of treatment (i.e. inoculation with specific strains) and bleaching outcome factors on the presence and abundance of OTUs (97% similarity cut-off) was tested using PERMANOVA (Anderson, 2001), where a significant overall effect of both factors was determined. However a significant interaction in community structure data suggested that the effect of outcome varied within individual treatments (Table 5.2). Here, the strongest contribution towards the overall difference in outcomes i.e. between bleached and unbleached samples, was due to OTU abundances within the BL110-treatment (P=0.054). The treatment - LSS17 made the largest contribution to the overall difference observed as a result of inoculation with putative pathogens (P=0.03), followed by community composition in the AD1-treatment (P=0.055).

PERMDISP analysis indicated that multivariate differences between bleached and unbleached samples were also influenced by data dispersion between the two groups (P=0.028), whereby bleached samples were more dispersed, as indicated by the nMDS plot (Fig. 5.6). SIMPER analysis revealed that an increase in Alteromonadales abundances and variable responses from multiple representatives of Rhodobacterales and two Rhizobiales OTUs were primarily responsible for the top 20% difference between communities on bleached and unbleached samples (Fig. 5.9).

Table 5.2: PERMANOVA table of results for the effect of treatment and bleaching outcome on abundances of OTUs (97% identity classification)

Treatments Factor Structure Composition compared (p value) (p value) Overall treatment 0.001* 0.006* outcome 0.002* 0.002* treatment x outcome 0.024* 0.134

AD1 vs Control treatment 0.294 0.055 BL110 vs Control treatment 0.553 0.293 LSS17 vs Control treatment 0.03* 0.225

Control outcome 0.107 0.182 AD1 outcome 0.954 0.671 BL110 outcome 0.054 0.272 LSS17 outcome 0.203 0.176 * denotes p-values below significance level of 0.05

Thalassomonas (Otu00021) Rhodobacteraceae (Otu00015) Bleached Unbleached Alteromonas sp. (Otu00007) Phaeobacter gallaeciensis (Otu00018) Thalassomonas (Otu00010) Labrenzia sp. (Otu00014) Rhizobiales (Otu0004) Ruegeria sp. (Otu00003) Nautella sp. (Otu00016) Rhodobacteraceae (Otu00009) Alteromonas sp. (Otu00001) Phylogenetic affiliation Rhodobacteraceae (Otu00005) Phaeobacter gallaeciensis (Otu00008) Nautella sp. (Otu00002) 0 2 4 6 8 10 12 14 16 18 Contribution (%)

Fig. 5.9: SIMPER graph showing phylogenetic affiliation of OTUs contributing a minimum of 1% to observed dissimilarities between bleached and unbleached samples. Analysis was conducted in PRIMER (Clarke, 1993, Clarke & Gorley, 2006).

5.4 Discussion

Changes in host physiology are often reflected in the structure and composition of the associated microbial community (Mao-Jones et al., 2010, Closek et al., 2014, Jakobsson et al., 2014). The bleaching disease of D. pulchra involves a shift in the bacterial community characterised by an over-representation of specific members of the existing bacterial assemblage (Fernandes et al., 2012, Zozaya-Valdes et al., 2015). Rhodobacteraceae and Flavobacteriaceae are amongst the bacterial families that contribute to the difference between healthy and bleached D. pulchra in the field (Zozaya-Valdes et al., 2015). Data presented in Chapter 4 of this thesis confirms such field data and here using manipulative experiments, for the first time, show members of Flavobacteriaceae and Alteromonadaceae, to also be involved in in vivo bleaching of the macroalga. In particular, the isolates Agarivorans sp. BL7, Aquimarina sp. AD1, and Alteromonas sp. BL110 and LSS17 caused a significant number of D. pulchra individuals to bleach. Thus, the aim of this chapter was to analyse the bacterial community found on D. pulchra samples used in the in vivo experiments in Chapter 4, in order to 1) detect the inocula and establish a causative link to bleaching disease in this macroalga and 2) investigate the impact of adding putative pathogens and bleaching-associated changes on the total bacterial community.

5.4.1 Detection of inocula in fulfilment of Koch’s postulates

The establishment of causative links between putative pathogens and disease involves the fulfilment of Koch’s postulates, and has traditionally required pathogens to be uniquely present on diseased hosts (Evans, 1976, Blevins & Bronze, 2010). Thus, the demonstration of disease- causality becomes challenging, especially in environmental systems, where the opportunistic pathogens involved are detectable even in the absence of disease (Karrasch et al., 2007, Sutherland et al., 2011). Revisions to the original Koch’s postulates have allowed for PCR-based detections, which compare abundances of pathogen sequences under healthy and diseased states of the host (Fredericks & Relman, 1996). The revision offered by Fredericks & Relman (1996) states that “a nucleic acid sequence belonging to a putative pathogen should be present in most cases of an infectious disease; and fewer, or no, copy numbers of pathogen-associated nucleic acid sequences should occur in hosts without disease”. In line with this revised guideline, data presented here show that all three putative pathogens, i.e. Aquimarina sp. AD1 and Alteromonas sp. BL110 and LSS17, inoculated on D. pulchra, could be detected in their respective treatments post-infection (Fig. 5.2). Moreover, an increase in numbers of Aquimarina sp. AD1 (P=0.059) and Alteromonas sp. BL110 sequences (P=0.022) occurred on bleached, compared to unbleached samples (Figs. 5.2), thus partly fulfilling the revised Koch’s postulates for these strains. However, the abundance of the most aggressive pathogen Alteromonas sp. LSS17 (Chapter 4, Fig. 4.8) was reduced on bleached samples (P=0.023) in comparison to unbleached samples (Fig. 5.2). Interestingly, despite the high abundance (16% of total community) of Alteromonas sp. LSS17 on unbleached D. pulchra, samples remained asymptomatic. Latent infections, which do not necessarily result in the symptomatic diseased phenotype have been noted in other Gamma-proteobacteria, including plant pathogens Pseudomonas sp., Erwinia sp. and Xanthomonas sp. (Hayward, 1974, Hayward, 1991). Alternatively, it is possible that reduced abundance of Alteromonas sp. LSS17 on bleached in comparison to unbleached alga is attributed to a mechanistic ‘shedding’ of the pathogen from infected hosts as has been demonstrated for other opportunistic pathogens (Madetoja et al., 2000, Merikanto et al., 2012). Thus, taken together, despite Koch’s postulates only being partially met for Alteromonas sp. LSS17 a causal link between this bacterium and bleaching in D. pulchra cannot be completely ruled out.

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5.4.2 Putative- and known- pathogens present in the background communities on D. pulchra

The background communities on uninoculated control samples that demonstrated signs of bleaching were investigated for the presence of putative- and known- pathogens that may have contributed to disease in the alga. Indeed in half of these samples (two out of four bleached controls), the putative pathogen – Alteromonas sp. LSS17 and the known pathogen N. italica R11 (Case et al., 2011) were detected (Fig. 5.3). However, no putative- or known- pathogens were detected on bleached samples in the remaining 2 uninoculated bleached controls. Likewise, neither, putative nor known pathogen/s were detected for one of the replicates inoculated with Aquimarina sp. AD1 (sample AD1_17). These findings suggest the presence of yet uncharacterised opportunistic pathogens in the marine environment. The prevalence of opportunistic pathogens in the marine environment is currently thought to be underappreciated (Egan et al., 2014). Moreover, it is estimated that over 8% of planktonic bacteria contain virulence genes, with the highest proportions in productive coastal waters that are inhabited by macroalgae (Persson et al., 2009). Hence given that macroalgae are a rich source of nutrients, the prevalence of other uncharacterised pathogens on D. pulchra may not be surprising.

Present also in the background community (i.e. not as a result of inoculation) was the known pathogen Phaeobacter sp. LSS9 (Fernandes et al., 2011) and the putative pathogens- Aquimarina sp. AD1 and Alteromonas sp. BL110 and LSS17 (Fig. 5.3). The ecological significance of disease- severity as a result of multiple opportunistic pathogens on macroalgal hosts is currently unknown. However, given that bacteria often co-exist in multi-species biofilms on macroalgal surfaces (Longford, 2007, Tujula et al., 2010, Burke et al., 2011, Lachnit et al., 2011, Singh & Reddy, 2014), the possibility of synergistic effects caused by multiple opportunistic pathogens is of particular interest. Co-infection by multiple pathogens has previously been demonstrated to induce disease in plant and animal hosts that is sometimes more aggressive than disease caused by one pathogen alone (Romansic et al., 2011, Buono et al., 2014, Chen et al., 2014, da Silva et al., 2014). For example, metabolic complementarity and interspecies signalling between Pseudomonas savastanoi pv. savastanoi and Erwinia toletana allows synergistic behaviour, which results in more aggressive symptoms in the olive knot disease (Hosni et al., 2011, da Silva et al., 2014). Similarly, Burkholderia cepacia enhances pathogenicity in cystic fibrosis patients and forms mixed biofilms by utilizing signalling molecules produced by the opportunistic pathogen P. aeruginosa (McKenney et al., 1995, Riedel et al., 2001). Understanding the role of

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the co-occurrence of multiple opportunistic pathogens on disease incidences and severity in macroalgae will require further investigation.

Detection of the abundance of opportunistic pathogens on host surfaces is important for measuring disease-severity and understanding the dynamics of disease progression. Data presented in this chapter show 29% and 52% abundance of the known pathogens N. italica R11 and Phaeobacter sp. LSS9, respectively, in background communities on juvenile D. pulchra. Moreover, the abundance of Phaeobacter sp. LSS9 was significantly increased on bleached samples in comparison to abundance of the pathogen on unbleached algae (P=0.028). The detection of Phaeobacter sp. LSS9 associated with bleaching of D. pulchra in the field has been inconsistent (Fernandes et al., 2012, Zozaya-Valdes et al., 2015) and together with the results of the current study it is suggested that opportunistic pathogens in natural communities can have varying patterns of occurrence over time. Whilst the population dynamics of pathogens on macroalgae are not known, a number of factors such as varying levels of infectivity during different life stages of the host and changes in environmental conditions are known to be common factors regulating pathogen abundances in other host systems (Bruno et al., 2007, Du et al., 2008). These factors may therefore also regulate the occurrence and thus pathogenesis of N. italica R11 and Phaeobacter sp. LSS9 on the surface of D. pulchra.

5.4.3 Bleaching-associated alteration to bacterial community

To investigate if general differences in the microbial community between bleached and unbleached samples exist, the taxonomic composition and community structure of both groups were compared. A significant reduction in overall species diversity was observed on bleached samples (Fig. 5.5a). Moreover, the top 20% difference between bleached and unbleached samples were due to changes in abundances of Alteromonadales, Rhodobacterales and Rhizobiales (Fig. 5.9). While Alteromonadales increased on bleached samples, individual representatives of Rhodobacterales and Rhizobiales had varying changes in abundance patterns (Fig. 5.8, Fig. 5.9). Differences between samples were also influenced by increased dispersion in data on bleached samples (Fig. 5.6). In other ecological systems, increases in dispersion have been correlated with disturbance to the natural community structure (O’Connor, 2013, Séguin et al., 2013, Rubal et al., 2014). For example, disturbance induced by draining rock pools resulted in increased within-group-dissimilarity in the communities of marine benthic algae and sessile animals inhabiting the pools (Séguin et al., 2013). Increased dispersion was also observed on the kelp Ecklonia radiata, where the characteristic ‘core’ community associated with healthy hosts was replaced with taxonomically diverse microbial communities following host stress (Marzinelli 102

et al., 2015). Thus, findings of this study contributes to increasing evidence that host condition impacts the structure of associated microbial communities, which may be linked to disturbances, such as pathogen activity.

Depending on the host system, the overall microbial community can either suffer a loss or a further diversification as a result of disturbance, such as that caused by the introduction of pathogens (Barman et al., 2008, Hajishengallis et al., 2011, Closek et al., 2014). The manipulative method used in this study involved a closed system, which investigated the effect of the factors inoculation and the bleaching outcome without interference by further planktonic colonisation. Hence, in such a system, only losses to diversity can be observed since there is no influx of incoming bacteria. The effect of inoculation with pathogens and the bleaching outcome may or may not have a different consequence on diversification if performed in situ. Nonetheless, bleaching-associated community shifts are evident regardless of whether bleaching is induced artificially in a closed system (Table 5.2) or observed naturally in the field (Campbell et al., 2011, Fernandes et al., 2012, Zozaya-Valdes et al., 2015). Moreover, both field and laboratory-based systems indicate that shifts in the bacterial community on bleached samples are driven by changes in the abundance of specific bacterial families. Studies by Zozaya-Valdes et al. (2015) showed that Flavobacteriaceae and Rhodobacteracae were over-represented on bleached D. pulchra. Using a manipulative experiment, the findings of the current study shows that Alteromonadales may also be involved in driving the observed shifts within bacterial communities on bleached D. pulchra.

5.4.4 Effect of introducing putative pathogens on the total bacterial community

The effects that pathogens have on the structure of microbial communities can be used to gain insights into how they interact with the native microbiota in order to induce disease in a host (Kamada et al., 2013). In this study, inoculation with Alteromonas sp. LSS17 resulted in a significant change in the bacterial community structure (P=0.03, Table 5.2) but not in species diversity (P=0.19, Fig. 5.5b) and no difference between bleached and unbleached samples was observed (P=0.203, Table 5.2). In comparison, inoculation with Alteromonas sp. BL110 did not cause a significant change in the bacterial community structure (P=0.553, Table 5.2) and species diversity (P=0.47, Fig. 5.5b), although significant differences were observed between bleached and unbleached samples (P=0.054, Table 5.2). A shift in the microbial community as a consequence of introduction of Alteromonas sp. BL110 and LSS17 suggests interactions between the existing microbial community and the opportunistic pathogens. The effect of opportunistic pathogens on the existing microbiota depends to a great extent on specific competitive traits 103

and inherent virulence strategies of the bacteria (Schluter et al., 2014). For example, competitive colonisers produce inhibitory compounds that result in exclusion of bacteria from the existing microbiota (Rao et al., 2005, Rao et al., 2010) and members of Alteromonas sp. display a broad spectrum of inhibitory activity that target bacterial colonisers (Gauthier & Flatau, 1976, Barja et al., 1989, Long et al., 2003). It is likely that a similar growth inhibition mechanism is employed by Alteromonas sp. BL110 and LSS17 against the existing D. pulchra microbiota, which resulted in the observed community shifts (Table 5.2).

In comparison to Alteromonas sp. BL110 and LSS17, comparatively lower occurrence of Aquimarina sp. AD1 was detected post-infection on inoculated hosts (Fig. 5.2) and a smaller reduction in mean species diversity on bleached individuals was observed (P=0.56, Fig. 5.5b). Moreover, neither the treatment nor bleaching outcome resulted in a significant shift in the total microbial community structure (P=0.294 and P=0.954, respectively), although treatment caused a shift in community composition (P=0.055) on D. pulchra when inoculated with Aquimarina sp. AD1 (Table 5.2). Interestingly, in all cases of bleaching where Aquimarina sp. AD1 was detected, other opportunistic pathogens were also present. Together, these observations indicate that Aquimarina sp. AD1 likely has a unique disease etiology, which does not involve extensive presence of the pathogen or a major re-organisation of the existing microbial community for disease induction. Moreover, it is likely that the virulence strategy of Aquimarina sp. AD1 involves co-operation with the existing surface microbiota and other opportunistic pathogens of D. pulchra.

5.4.5 Conclusions and future work

In fulfillment of Koch’s postulates, this chapter investigated the presence of the pathogens Aquimarina sp. AD1 and Alteromonas sp. BL110 and LSS17, in D. pulchra that developed a diseased symptoms post inoculation. Whilst the criteria to classify Aquimarina sp. AD1 and Alteromonas sp. BL110 as opportunistic pathogens of D. pulchra were successfully met, only partial fulfilment was determined for Alteromonas sp. LSS17. Furthermore, the findings suggested that despite each pathogen being capable of inducing bleaching in D. pulchra, likely unique disease etiologies were involved, which were reflected in the unique patterns of pathogen abundances and changes to total community structure and diversity as a result of inoculation. The background community on D. pulchra showed presence of Aquimarina sp. AD1, Alteromonas sp. BL110 and LSS17 and previously identified pathogens, N. italica R11 and Phaeobacter sp. LSS9 (Case et al., 2011, Fernandes et al., 2011). The finding emphasised the

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natural occurrence of these bacteria on D. pulchra, in addition to suggesting the presence of yet uncharacterised opportunistic pathogens of the alga.

Comparisons between the overall structure and compostion of bacterial commuinties on bleached and unbleached D. pulchra confirmed previous findings that distinct bacterial community assemblages exist on bleached and healthy tissue-types (Campbell et al., 2011, Fernandes et al., 2012, Zozaya-Valdes et al., 2015). In addition, findings of this study indicated an involvement of Alteromonadales in bleaching associated community shifts on D. pulchra.

Overall, this study has demonstrated that disease-associated community shifts can provide valuable insights into possible interactions of pathogenic bacteria with the host. The presence of specific virulence determinants can influence the impact that a pathogen has on the overall health of the host. Thus, an understanding of the virulence traits in an opportunistic pathogen can help predict mechanisms for disease induction. The following chapter will explore likely mechanisms of virulence, employed by the newly identified pathogens in disease-related activity against macroalgae through a comparative analysis of their genomes.

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Chapter Six: Comparative genome analysis of opportunistic pathogens reveals potential mechanisms of virulence towards seaweeds

6.1 Introduction

Genomes are more than just a collection of genes as their analysis can provide insights into the metabolic and cellular functions of the organism. In particular, hints on organismal processes, such as nutrient acquisition and metabolism (Rohmer et al., 2011); cell structure and motility (Tercero-Alburo et al., 2014); host interaction and disease induction (Ryan et al., 2011, Za et al., 2014) can be acquired by exploring genomes. As a consequence of improved sequencing technologies, genomic analysis is much more accessible and has significantly improved our understanding of bacterial biology, making invaluable contributions to the study of bacterial pathogenicity (Guzmán et al., 2008, Donkor, 2013).

The genomes of bacterial pathogens encode specific features that enable the organism to engage in virulence-related activities (Stover et al., 2000). For example, adhesive structures permit attachment and colonisation of the host (Petrova & Sauer, 2012) and secretory systems allow the delivery of toxins and effector molecules, often directly into the host cytoplasm (Preston et al., 2005, Persson et al., 2009, Xin & He, 2013). Yet another common feature prevalent in phytopathogens, is the production of cell-wall degrading enzymes (Pérombelon, 2002, González et al., 2007). The production of degradative enzymes not only permit invasion of the host cell wall but also provide nutrients for the subsequent proliferation of pathogens (Hématy et al., 2009, Rohmer et al., 2011). Mechanisms to evade host defence systems also feature in pathogen genomes (Lesser, 2006, Maeda et al., 2011). Thus, bacterial pathogens are likely to possess both generalised and host-specific virulence traits that facilitate disease induction.

The red macroalga, D. pulchra undergoes a mid-thalli bleaching disease and two bacterial pathogens, N. italica R11 and Phaeobacter sp. LSS9, have previously been implicated in disease of the alga (Case et al., 2011, Fernandes et al., 2011). The genome sequence of N. italica R11 revealed features common to phytopathogenic bacteria, including the plant hormone indole acetic acid, cellulose fibrils, succinoglycan and the nodulation protein L, which in Rhizobium sp. NGR234 suppresses innate plant defence response (Fernandes et al., 2011). Of particular interest is a signal transduction system, demonstrated to regulate surface colonisation and

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virulence in the bacterium (Gardiner et al., 2015). Data presented in Chapter 4 showed that three representatives of the bacterial family, Alteromonadaceae, i.e. Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17 and two representatives of the Flavobacteriaceae, i.e. Aquimarina sp. AD1 and BL5, induced bleaching in D. pulchra. Interestingly, a closely related strain, Aquimarina sp. AD10 was incapable of inducing bleaching of the macroalga. Moreover, characterisation of these isolates in virulence-related assays showed that the isolates were strong biofilm-formers and degraded cell wall components (Chapter 3). Thus, the aim of this chapter was to further investigate the mechanisms behind virulence-related activity in the newly-identified pathogens of D. pulchra. This aim was achieved through (1) analysis of putative virulence mechanisms specific to the individual phylogenetic groups, i.e. Alteromonadaceae and Flavobacteriaceae (Aquimarina sp.) and (2) detection of factors that were present broadly amongst pathogens of the two phylogenetic groups.

6.2 Materials and methods

6.2.1 Isolates and DNA extraction

Genomes of bacterial isolates that induced bleaching in D. pulchra in Chapter 4 were chosen for sequencing. These included Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17 and Aquimarina sp. AD1 and BL5. For insights into the mechanisms underpinning disease in members of the Aquimarina genus, the non-pathogenic Aquimarina sp. AD10 was also chosen for genome sequencing.

DNA extraction from the bacterial cultures was conducted using the XS-buffer method (Tillett & Neilan, 2000). Briefly, 2 mL of bacterial cultures, grown in DifcoTM Marine Broth 2216 for 24 hr were pelleted in a microfuge tube. The supernatant was discarded and cells were resuspended in 1 ml XS-buffer (Appendix I) before being incubated at 70 °C for 60 min. After incubation, tubes were vortexed for 10 sec and incubated on ice for 30 min. Tubes were then centrifuged at 21 000 x g for 10 min. The supernatant was carefully removed into a fresh 2 ml microfuge tube, one volume of isopropanol was added and gently inverted to precipitate the DNA. Tubes were incubated at room temperature for 5 min and then centrifuged at 21 000 x g for 10 min. After the supernatant was removed the DNA pellet was washed with 70% ethanol (v/v) and resuspended in 20 μl of molecular grade water.

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6.2.2 Sequencing and genome assembly

Genomes were sequenced at the Ramaciotti Centre for Genomics (RCG, UNSW), using the Illumina MiSeq sequencer (Caporaso et al., 2012) at a 2 x 300 bp read length configuration. Genome assemblies were carried out by Torsten Thomas (UNSW). Briefly, reads were assembled with the SPADE 3.1 pipeline (Bankevich et al., 2012), which included read error correction, de Bruijn graph assembly and mismatch correction of contigs and scaffolding. The parameters setting – aimed to reduce the number of mismatches, short indels and kmers - 39, 61, 83, 105 and 127 were used. Only scaffolds greater than 1000 bp were considered for further analysis. Scaffolds were taxonomically classified with RAIphy using the iterative refinement mode (Nalbantoglu et al., 2011) and those with a taxonomic assignment consistent with the 16S rRNA gene sequence were retained. Those with different taxonomy were, in addition, searched with blastn and/or blastx against NCBI’s nucleotide sequence database and/or non-redundant protein sequence database to confirm their taxonomic assignment and before deciding to retain or discard scaffolds (Johnson et al., 2008). The quality of these scaffolds were then further checked with Quast (Gurevich et al., 2013) using the available Aquamarina, Agarivorans and Alteromonas genomes as references. Open reading frames (ORFs) for the genomes were determined with GLIMMER (Delcher et al., 1999) and each ORF was searched against the COG, KEGG, TIGRFam and curated SwissProt databases using an in-house annotation pipeline (Thomas et al., 2010).

6.2.3 Genomes comparative analysis

For comparative analysis, the genomes were uploaded into IMG – the integrated microbial genome annotation system (Markowitz et al., 2009). The uploaded genomes were assessed using the tools available in the system between February and August 2015, using default settings, unless specified. IMG‘s gene object identifiers were used for making references to the genes. The Uniprot database (Apweiler et al., 2004) was used to carry out BLASTP analysis and identify proteins with homology to query proteins. References to the CAZy database were made to identify families that the enzymes belonged to (Lombard et al., 2014). Gene clusters in Alteromonadaceae that were predicted by IMG with a probability of >0.6 (Markowitz et al., 2009) were verified using the biosynthetic cluster analysis software, antiSMASH v.3.0.1 (Weber et al., 2015).

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6.3 Results and discussion

The interaction between pathogenic bacteria and a host depends on a number of factors, including the presence of virulence determinants, pathogen fitness and host defence (Beceiro et al., 2013). In Chapter 4, a number of putative pathogens were inoculated on the host D. pulchra, in order to investigate their bleaching effects on the alga. Amongst the newly-identified pathogens, three isolates, i.e. Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17, belonged to the Alteromonadaceae family and two members of Flavobacteriaceae, i.e. Aquimarina sp. AD1 and BL5 were identified (Chapter 4, Fig. 4.8). To gain insights into the mechanisms behind the virulence interactions of these two bacterial groups with the host D. pulchra, this chapter aimed to identify virulence determinants that were (1) specific to representatives of Alteromonadaceae and Aquimarina sp., and (3) present broadly across both phylogenetic groups.

6.3.1 Features in the genomes of Agarivorans sp., Alteromonas sp. and Aquimarina sp.

A comparison of general features in the newly sequenced genomes showed that the three Aquimarina sp. were larger (average size - 5.7 Mb) than both Alteromonas sp. (average size - 4.5 Mb) and the Agarivorans sp. (4.8 Mb) (Table 6.1). Certain features of the genomes were more prominent in a specific phylogenetic group. For example, comparison of COG categories showed that genomes of the pathogenic Alteromonadaceae, i.e. including Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17, had more genes allocated to functions related to cell motility, energy production and conversion, intracellular trafficking and signal transduction mechanisms. In contrast, a greater number of genes for secondary metabolite production were present in representatives of Flavobacteriaceae, i.e. Aquimarina sp. AD1, AD10 and BL5 genomes.

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Table 6.1: General genomic characteristics of the sequenced bacterial isolates obtained from the surface of D. pulchra (based on IMG server)

Genome Name Agarivorans Alteromonas Alteromonas Aquimarina Aquimarina Aquimarina sp. BL7 sp. BL110 sp. LSS17 sp. AD1 sp. BL5 sp. AD102 Taxon ID 2619618870 2576861406 2576861404 2576861407 2576861405 2576861408 Status Draft Draft Draft Draft Draft Draft Genome Size 4818812 4437258 4535151 5294442 5882103 6048390 Gene Count 4403 3857 3960 4808 5112 5187 Scaffold Count 13 10 20 519 353 88 CRISPR Count 1 0 0 0 0 1 GC Count 2130101 1954118 1976878 1701684 1935371 1957088 GC 0.44 0.44 0.44 0.32 0.33 0.32 Coding Base Count 4286259 3905692 4010132 4791707 5265901 5415781 Coding Base Count 88.95 88.04 88.43 90.51 89.52 89.55 CDS% 1 Count 4299 3772 3875 4742 5039 5125 CDS % 88.95 97.80 97.85 98.63 98.57 98.80 RNA Count 104 85 85 66 73 62 rRNA Count 13 8 12 5 3 5 tRNA Count 77 64 57 52 51 50 Enzyme Count 1090 972 1012 867 917 912 Enzyme % 24.76 25.2 25.56 18.03 17.94 17.58 Signal Peptide 600 497 501 502 590 641 SignalCount Peptide % 13.63 12.89 12.65 10.44 11.54 12.36 1Coding sequence 2Non-pathogen (did not cause bleaching in D. pulchra).

Translation

Transcription

Signal transduction

Secondary metabolites

Replication and repair

Posttranslational modification

Nucleotide metabolism

Lipid metabolism

Intracellular trafficking

Ion transport

Function unknown COG COG categories

Energy

Defense

Coenzyme transport

Cell wall

Cell motility

Cell cycle

Carbohydrate metabolism

Amino acid metabolism

0 50 100 150 200 250 300 350 Gene count

Aquimarina sp. BL5 Aquimarina sp. AD10 Aquimarina sp. AD1 Alteromonas sp. LSS17 Alteromonas sp. BL110 Agarivorans sp. BL7

Fig. 6.1: Functional comparison of genomes of Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17 and Aquimarina sp. AD1, AD10 and BL5. Absolute abundances of gene counts within COG categories were extracted from IMG (Markowitz et al., 2009), using a minimum identity cut-off of 30% and a maximum expectancy of 1e-5.

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6.3.2 Putative mechanisms for virulence in pathogenic Alteromonadaceae

To predict specific mechanisms behind virulence-related activity in the pathogenic Alteromonadaceae, comparative analysis between the genomes of Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 was carried out. In total, 1840 genes were common between the three genomes, of which, genes encoding putative virulence traits were investigated. These included CDSs encoding genes for motility and host adhesion (Petrova & Sauer, 2012), secretion systems (Tseng et al., 2009), enzymes production (Pérombelon, 2002, González et al., 2007), strategies for avoidance of host defence (Gardiner et al., 2015) and production of bioactive compounds (Seyedsayamdost et al., 2011b), all of which have been suggested to be important traits in pathogenic bacteria.

6.3.2.1 Surface colonisation (motility and host adhesion)

Bacterial motility and chemotaxis play an important role in mediating interactions with hosts (Josenhans & Suerbaum, 2002). One of the best understood modes of motility in bacteria is cell movement facilitated by flagella (Minamino et al., 2011, Yonekura et al., 2011, Utada et al., 2014, Xu et al., 2014). Data presented in Chapter 3 showed that Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 were motile (Table 3.1) and genome analysis confirmed that all three isolates contained gene clusters encoding proteins for flagella assembly and rotation (Supplementary information_S1). In addition, genes encoding for chemotaxis-related methyl- accepting proteins were found in genomes of Agarivorans sp. BL7 (0.6% of CDSs) and Alteromonas sp. BL110 and LSS17 (0.1% and 0.2% of CDSs respectively). Studies have demonstrated that directed motility, as a result of coordination between chemotactic responses and cell movement are required for full virulence in the plant pathogens, Ralstonia solanacaerum (Yao & Allen, 2006) and Dickeya dadantii 3937 (ex Erwinia chrysanthemi 3937) (Antúnez-Lamas et al., 2009). Whilst chemotaxis was not investigated in the current study, it is possible that exudates released by the host D. pulchra serve as chemoattractants for pathogens, resulting in movement towards the host. A similar phenomenon has been seen for the plant pathogen Salmonella enterica serovar Typhimurium, where in addition to functioning as a chemotaxis signal, host lettuce exudates were responsible for an increase in expression of virulence related genes in the pathogen (Klerks et al., 2007).

Bacteria commonly utilise adhesion factors, such as surface proteins (Russo et al., 2010), curli, fimbriae, pili (Mattick, 2002, Dalisay et al., 2006) and flagella (Xu et al., 2014) and other strategies such as EPS production and biofilm-formation (Petrova & Sauer, 2012) to effectively attach and colonise host surfaces. Numerous studies have shown that host attachment and 112

colonisation are crucial also for optimal virulence in both plant and animal pathogens (Craig et al., 2004, Bahar et al., 2009, Baldi et al., 2012, Petrova & Sauer, 2012). Data presented in Chapter 3 showed that Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 were strong biofilm- formers (Fig. 3.1) and genome analysis further indicated that attachment to surfaces was likely to be facilitated by extracellular structures encoded in the genomes of the three pathogens. The adhesive structures included proteins for curli, surface lipoproteins and type IV pilus (Tfp), including mannose-sensitive hemagglutinin (MSHA) pili and toxin co-regulated pili (TCP) (Supplementary information_S1). Moreover, microscopic examination of D. pulchra during infection experiments showed algae inoculated with Alteromonas sp. BL110 to contain bacterial biofilms on the periphery of bleached tissues (Fig. 4.9(g)). Hence the adhesion factors present in the pathogenic Alteromonadaceae may serve as a prerequisite for bleaching disease in D. pulchra by facilitating bacterial colonisation of the host. D. pulchra.

6.3.2.2 Secretory systems

The genomes of pathogenic bacteria encode protein secretion systems for the export of virulence factors, toxins and enzymes. Secretion pathways involved in virulence-related activity include the type I secretion system (T1SS), T2SS, T3SS, T4SS, T5SS and T6SS (Persson et al., 2009, Xin & He, 2013), the twin-arginine translocation (Tat) system (De Buck et al., 2008, Xin & He, 2013) and the Sec translocase system (Preston et al., 2005). Many of these secretion systems are encoded in the genomes of Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17, and these may play important roles in secretion of specific virulence molecules and proteins (Table 6.2).

Table 6.2: Secretion systems present in genomes of pathogenic Alteromonadaceae

Isolate T1SS T2SS T3SS T4SS T5SS T6SS TAT Sec Agarivorans sp. BL7 1 1 1 - - 1 2 1 Alteromonas sp. BL110 1 1 1 - - - 1 1 Alteromonas sp. LSS17 1 1 1 - - - 1 1 Digit denotes number of times a secretion system occurs in a genome ‘-’ denotes not detected

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Bacterial T1SSs comprise of a membrane fusion protein, an ABC-type transporter and an outer TolC membrane protein, which together mediate the secretion of a large variety of protein substrates. In pathogenic gram-negative bacteria, the secreted products are often adhesins, S- layer proteins, nutrient-acquisition proteins, such as lipase, protease, iron-scavenger proteins, and virulence proteins, such as the pore-forming haemolysin, bacteriocins and RTX toxins (Kanonenberg et al., 2013, Thomas et al., 2014). Each of the pathogenic Alteromonadaceae genomes investigated in this study encoded T1SSs, however based on gene annotation the type of molecule secreted could not be predicted.

The T2SSs are found in a wide variety of both pathogenic and non-pathogenic gram-negative bacteria and are involved in assembly of surface organelles (Ayers et al., 2010, Campos et al., 2013) and secretion of hydrolysing enzymes and toxins (Nivaskumar & Francetic, 2014). The presence of proteins for biogenesis and assembly of MSHA and type IV pilus in the genomes of all three Alteromonadaceae suggests that these bacteria use the T2SSs for the assembly of surface organelles (discussed in section 6.3.2.1) that likely has a role in host attachment and pathogenesis. In addition the T2SS is possibly involved in enzyme secretion for nutrient acquisition through the degradation of biopolymers and this is discussed in section 6.3.2.3.

Bacterial T3SSs are encoded by a large number of bacterial species that are either symbiotic or pathogenic to plant and animal hosts and function by transmitting bacterial proteins directly into host cells (Galán & Wolf-Watz, 2006). Of plant pathogens, effector transmission via the T3SS has been implicated in virulence of Xanthomonas sp. (Marois et al., 2002, Hotson et al., 2003), Pseudomonas sp. (Schoehn et al., 2003) and Ralstonia solanacearum (Meng, 2013). However, avirulence (avr) genes, which are a class of phytopathogenic effectors when recognized by plant hosts elicit a defence response involving hypersensitive response (HR) - a type of programmed cell death in the host (Greenberg, 1997). Data presented here shows that in addition to having T3SSs, the genomes of the pathogenic Alteromonadaceae isolates contain proteins with homology to HopJ type III effectors, belonging to the hypersensitive response outer protein (Hop) family, (gene ID 2619817145, 2578287810 and 2578277680, in Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17 respectively). The HopJ effector is present also in the phytopathogenic Pseudomonas syringae pv. maculicola (UniProt entry Q8RP07), to which the proteins of Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17 have 41%, 42% and 45% similarities (82%, 87% and 87% sequence coverage, respectively). Previous studies show that in P. syringae, the Hop family of proteins are involved in suppressing the basal immune system of the host, where the pathway targeted depends on the effector molecule and the type of plant

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host (Hauck et al., 2003, de Torres‐Zabala et al., 2007, Fu et al., 2007, Zhou et al., 2011). HR is common in macroalgae also, where the basal immunity responds with oxidative bursts after pathogen attack is perceived (Weinberger, 2007). It is thus of interest to investigate if T3SSs of the pathogenic Alteromonadaceae is used to deliver the HopJ effector molecules/proteins into the cytoplasm of D. pulchra. Moreover, depending on the nature of the effector, it will be interesting to explore whether the HopJ effectors target pathways involved in perception of pathogen-induced molecular patterns in D. pulchra, hence leading to the suppression of defence responses.

The T6SS translocates toxic effector proteins into eukaryotic and prokaryotic cells and has a pivotal role in pathogenesis and bacterial competition (Ho et al., 2014, Zoued et al., 2014). Superfamilies of the T6SS effectors known to date include T6S amidase effectors (Tae), T6S glycosidase hydrolase effectors (Tge), T6S lipase effectors (Tle) and T6S DNase effectors (Tde) (Russell et al., 2014). Recent studies show that at least the Tle family of effectors are over- represented in specific environmental niches and mostly where hosts are involved (Barret et al., 2013, Egan et al., 2015). In the current study, only the genome of Agarivorans sp. BL7 encoded a T6SS. Comparisons of conserved proteins known to comprise T6SS loci (Boyer et al., 2009, De Maayer et al., 2011) in Agarivorans sp. BL7 against two characterised phytopathogenic bacteria showed greater similarities to the T6SSs of Agrobacterium tumefaciens than Pantoea ananatis LMG 2665 (Table 6.3). Similarities were also observed in the gene arrangements comprising T6SSs in Agarivorans sp. BL7 and A. tumefaciens (Fig. 6.2). In A. tumefaciens, the Tde family of effectors are deployed for inter-bacterial competition where competitive-advantage is maximal when colonising the host plant Nicotiana benthamiana (Ma et al., 2014). Similarly, a mutant of Pantoea ananatis LMG 2665, lacking one of two T6SSs is no longer pathogenic to the host onion plant and is less competitive, thus highlighting the role of T6SS in pathogenicity and intercellular competition (Shyntum et al., 2015). Taken together, whilst the role of the T6SS in Agarivorans sp. BL7 has not been experimentally investigated, based on similarities with other phytopathogens, a role in pathogenesis either due to competitive advantage over other surface- colonisers or effector-delivery in host cytoplasm is likely a mechanism of virulence in the pathogen.

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Fig. 6.2: Schematic representation of the gene cassettes encoding T6SSs in A. tumefaciens and Agarivorans sp. BL7 (indicated within red dotted lines). Homologs of COGs in A. tumefaciens and Agarivorans sp. BL7 are represented with arrows of the same colour. Off-white arrows indicate proteins with no COGs assigned. Numbers denote nucleotide co-ordinates in the respective genomes. Gene products corresponding to COGs are provided in Table 6.3. Image was acquired and modified from IMG (Markowitz et al., 2009).

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Table 6.3: Comparisons between genes comprising T6SSs in Agarivorans sp. BL7 and two characterised phytopathogens

COG ID Gene Name Agarivorans sp. BL7 Agrobacterium tume- Similarity Protein Pantoea ananatis Similarity Protein (IMG taxon ID faciens C58 (IMG taxon to BL7 cover- LMG 2665 (IMG taxon to BL7 cover- 2619618870) ID 2558860256) age ID 2585427963) age COG3456 FHA domain protein Ga0074152_103129 Atu4335 27% 47% - NA - COG3521* T6S lipoprotein, VC_A0113 family Ga0074152_103130 - NA - EL29DRAFT_01999 26% 78% COG3522* T6S protein, VC_A0114 family Ga0074152_103131 Atu4334 43% 98% EL29DRAFT_01998 38% 97% COG3455* T6SS peptidoglycan-associated domain Ga0074152_103132 Atu4333 33% 79% EL29DRAFT_01997 35% 91% COG3523* T6S protein IcmF Ga0074152_103133 Atu4332 30% 96% EL29DRAFT_01996 33% 100% COG3913 T6S protein, BMA_A0400 family Ga0074152_103134 Atu4331 29% 28% EL29DRAFT_01995 29% 89% COG0631 Serine/threonine protein phosphatase Ga0074152_103135 Atu4331 25% 51% - NA - COG3515 T6S -associated protein, ImpA family Ga0074152_103136 Atu4343 31% 93% EL29DRAFT_01994 29% 50% COG3516* T6S protein, VC_A0107 family Ga0074152_103137 Atu4342 59% 93% EL29DRAFT_01993 39% 92% COG3517* T6S protein, EvpB/VC_A0108 family Ga0074152_103139 Atu4341 39% 88% EL29DRAFT_01992 38% 84% COG3517* T6S protein, EvpB/VC_A0108 family Ga0074152_103138 Atu4340 33% 89% - NA - COG4455 Protein of avirulence locus involved in Ga0074152_103140 Atu4339 27% 80% - NA - temperature-dependent protein secretion COG3518* T6SS lysozyme-like protein Ga0074152_103141 Atu4338 35% 86% - NA - COG3519* T6S protein, VC_A0110 family Ga0074152_103142 Atu4337 36% 100% - NA - COG3520* T6S protein, VC_A0111 family Ga0074152_103143 Atu4336 22% 67% - NA - COG0542* T6S ATPase, ClpV1 family Ga0074152_103144 Atu4344 46% 96% - NA - COG3157* T6SS component Hcp (secreted Ga0074152_103145 Atu4345 37% 93% EL29DRAFT_01990 25% 67% cytotoxin) COG3501* Rhs element Vgr protein Ga0074152_103146 Atu4348 31% 83% - NA - COG4104 Zn-binding Pro-Ala-Ala-Arg (PAAR) Ga0074152_103147 Atu4352 44% 92% - NA - domain, involved in T6S COG3209 RHS repeat-associated core domain Ga0074152_103149 - NA - - NA - Reference (Ma et al., 2014) (Shyntum et al., 2015) *denotes conserved protein ‘-’ denotes COG as absent

The Sec and the Tat pathways have been implicated in pathogenesis of hosts via protein transport across the bacterial cytoplasmic membrane (Büttner & Bonas, 2010). While the Sec pathway exports unfolded proteins across the cytoplasmic membrane, the Tat pathway is responsible for transport of folded proteins (De Buck et al., 2008). The genomes of Agarivorans sp. B7 and Alteromonas sp. BL110 and LSS17 encode both Tat and Sec pathways, putative roles of which are discussed in 6.3.2.3.

6.3.2.3 Enzymes for degradation of algal cell wall components

The cell walls of macroalgae are composed of a diverse array of polymers including alginates, carrageenan, agar, cellulose, laminarin, pectin, protein and xylan (Percival, 1979, Davis et al., 2003, Domozych, 2011). The production of cell wall-degrading enzymes by surface-associated bacteria can thus have important virulence-related consequences on macroalgal hosts. Data presented in Chapter 3 showed that the isolates, Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 degraded algal cell wall components (Table 3.4). Genome analysis confirmed enzymes encoded in the genomes of the pathogenic Alteromonadaceae isolates, which could be linked to the breakdown of algal cell wall components (Tables 6.4, 6.5 and 6.6). The genomes of Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 also encoded T2SSs, Tat and Sec pathways and TonB-dependent receptors (TBDR). As demonstrated in Xanthomonas sp., many of the enzymes required for cell wall degrading activities are translocated outside of the bacterial cell via the T2SS and the Tat and Sec pathways (Sandkvist, 2001, Büttner & Bonas, 2010). Moreover, CAZymes are often clustered in operon-like ‘polysaccharide utilization loci’ (PULs) with at least one TBDR (Neumann et al., 2015).

Of particular relevance to D. pulchra, is the production of agarase, cellulase and xylanase, which specifically degrades cell wall components in red algae (Percival, 1979, Domozych, 2011). In this study, genes encoding for beta-agarases and xylanases were detected only in Agarivorans sp. BL7 whereas cellulases were detected in all three pathogenic Alteromonadaceae representatives (Tables 6.4, 6.5 and 6.6). In addition, of all polysaccharide-degrading enzymes detected, alginate lyases were the most abundant. The cell walls of brown algae in particular are comprised of alginate (Mabeau & Kloareg, 1987) and laminarin (Elyakova & Zvyagintseva, 1974). A previous study showed that five alginate lyases were present in the surface water isolate A. macleodii strain 83-1 (Neumann et al., 2015). Varying numbers of alginate lyases in this study also, indicated plasticity within Alteromonadaceae, which permits adaptation in diverse environments. In addition to alginate lyases, the presence of genes encoding for laminarinases

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provides further evidence of the capacity for these bacterial strains to degrade cell wall components of brown algae.

The production of extracellular cell wall-degrading enzymes are important ‘trademarks’ of phytopathogens. For example, determinants of virulence in Erwinia carotovora subsp., the causative agents of soft rot disease in potato include the production of extracellular plant cell wall-degrading enzymes (PCWDEs) that cause extensive tissue maceration during the latter stages of infection (Pérombelon, 2002). In particular, the genome of E. carotovora subsp. SCRI1043 has twenty putative pectinase genes, eleven of which were previously unknown in E. carotovora subsp. and a further seven CDSs may encode other PCWDEs, including cellulases, four of which are novel to the Erwinia genus (Bell et al., 2004). Thus in the current study, in addition to the range of degradative enzymes encoded in the genomes of Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17, mechanisms to enable their secretion across cellular membranes suggest similarities in functions with known phytopathogens. Whilst the enzymes may also have saprophytic functions, if used as virulence strategies, the pathogenic Alteromonadaceae representatives will have detrimental effects on macroalgal hosts.

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Table 6.4: Carbohydrate-active enzymes encoded in genome of Agarivorans sp. BL7

Enzyme Core gene IMG Gene CAZy Adjacent transport Homology (% Uniprot ID family and CAZyme ID/coverage) entry genes* Agarase β-agarase 2619815226 GH16 Sugar transporter Agarivorans albus A8W969 (87/99) β-agarase 2619816950 GH16 Not detected Agarivorans albus A8W969 (41/98) β-agarase 2619817161 GH16 Not detected Agarivorans albus A8W969 (49/94) β-agarase 2619818541 GH16 Not detected Agarivorans albus A8W969 (46/57) β-agarase 2619818789 GH16 Not detected Agarivorans albus A8W969 (89/40) Alginate Alginate 2619816353 PL7 Not detected Klebsiella Q59478 lyase lyase pneumoniae (55/95) Alginate 2619818962 PL7 Not detected Klebsiella Q59478 lyase pneumoniae (61/93) Alginate 2619817863 PL7 Not detected Klebsiella Q59478 lyase pneumoniae (61/90) Alginate 2619818197 PL7 Sugar transporter Klebsiella Q59478 lyase pneumoniae (52/98) Alginate 2619817333 PL7 Not detected Klebsiella Q59478 lyase pneumoniae (51/92) Alginate 2619818399 PL7 Alginate lyase (PL7) Klebsiella Q59478 lyase pneumoniae (31/90) Amylase α-amylase 2619817273 GH13 α-amylase (GH13), Aeromonas P22630 β-xylosidase (GH43) hydrophila (49/96) α-amylase 2619815507 GH13 α-amylase (x3) Pseudoalteromonas P29957 (GH13) haloplanktis (49/70) α-amylase 2619815019 GH13 α-amylase (GH13) Escherichia coli strain P25718 K12 (43/78) α-amylase 2619815835 GH13 Sugar transporter Escherichia coli strain P25718 K12 (55/82) Cellulase β- 2619814847 GH1 Sugar transporter, Paenibacillus P22505 glucosidase maltoporin polymyxa (44/98) β- 2619816230 GH5 α-amylase (GH13) Streptomyces lividans P51529 glucosidase (45/82) Endo- 2619814697 GH5 Tat system Clostridium A3DJ77 glucanase thermocellum strain ATCC 27405 (30/83) Laminari- β- 2619817360 GH16 TBDR, β-glucanase Rhodothermus P45798 nase glucanase (GH16), endo-β- marinus (37/86) 1,3-glucanase (GH17) Xylanase Endo-1,4- 2619816799 GH10 α-glycosidases Clostridium P10478 β -xylanase (GH31), TBDR, thermocellum strain sugar transporter ATCC 27405 (43/49) Endo-1,4- 2619816159 GH10 Maltoporin Cellvibrio japonicus Q59675 β -xylanase (44/65) *TBDR: tonB-dependent receptor; GH: glycosyl hydrolase; PL: polysaccharide lyase

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Table 6.5: Carbohydrate-active enzymes encoded in genome of Alteromonas sp. BL110

Enzyme Core gene IMG Gene CAZy Adjacent transport Homology (% Uniprot ID family and CAZyme ID/coverage) entry genes* Alginate Alginate 2578286577 PL7 β-galactosidase Klebsiella Q59478 lyase lyase (GH2) pneumoniae (62/94) Alginate 2578287930 PL7 α -1,4-glucan Klebsiella Q59478 lyase (GH13) pneumoniae (34/92) Alginate 2578287631 PL7 TBDR Klebsiella Q59478 lyase pneumoniae (34/86) Amylase α-amylase 2578288041 GH13 Not detected Aeromonas P22630 hydrophila (57/98) Cellulase β- 2578286729 GH1 TBDR, sugar Paenibacillus P22505 glucosidase transporter (x2), α- polymyxa (42/100) glucosidase (GH31), β-glucanase (GH16), β- glucosidase (GH3) β- 2578286971 GH1 TBDR, β-glucanase Paenibacillus P22505 glucosidase (GH16) polymyxa (43/99) Endo-1,4- 2578286091 GH8 Not detected Cellulomonas uda P18336 β-D- (41/74) glucanase Laminari- β- 2578286722 GH16 TBDR, β- Rhodothermus P45798 nase glucanase glucosidase (GH3) marinus (35/97) β- 2578287337 GH16 TBDR, β- Bacillus subtilis strain P04957 glucanase glucosidase (GH3) 168 (31/90) *TBDR: tonB-dependent receptor; GH: glycosyl hydrolase; PL: polysaccharide lyase

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Table 6.6: Carbohydrate-active enzymes encoded in genome of Alteromonas sp. LSS17

Enzyme Core gene IMG Gene CAZy Adjacent transport Homology (% Uniprot ID family and CAZyme ID/coverage) entry genes* Alginate Alginate 2578278297 PL7 TBDR, sugar Klebsiella Q59478 lyase lyase transporter, pneumoniae (54/91) alginate lyase (PL7) Alginate 2578278301 PL7 TBDR, sugar Klebsiella Q59478 lyase transporter, pneumoniae (55/89) alginate lyase (PL7) Alginate 2578278490 PL7 Not detected Klebsiella Q59478 lyase pneumoniae (34/97) Alginate 2578278064 PL7 TBDR Klebsiella Q59478 lyase pneumoniae (34/87) Amylase α-amylase 2578279521 GH13 Not detected Aeromonas P22630 hydrophila (36/37) Cellulase β- 2578279583 GH13 Sugar transporter Agrobacterium sp. P12614 glucosidase strain ATCC 21400 (47/93) β- 2578279838 GH13 TBDR Agrobacterium sp. P12614 glucosidase strain ATCC 21400 (48/93) Endo-1,4- 2578280800 GH8 Not detected Cellulomonas uda P18336 β-D- (38/76) glucanase Laminari- β- 2578279605 GH16 TBDR Rhodothermus P45798 nase glucanase marinus (39/97) *TBDR: tonB-dependent receptor; GH: glycosyl hydrolase; PL: polysaccharide lyase

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6.3.2.4 Resistance against host chemical defence

Macroalgae are known to defend themselves against incoming pathogens through oxidative bursts, a transient production of reactive oxygen species (ROS) (Weinberger, 2007). In order to detoxify ROS, bacteria produce enzymes such as catalase, superoxide dismutase and peroxidases (Lesser, 2006, Cabiscol et al., 2010). A number of catalase and superoxide dismutase enzymes are encoded in the genomes of Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17 (Supplementary information_S2). In addition, two proteins in Alteromonas sp. BL110 (41/73 % and 89/34 % sequence identity/coverage, respectively) and one protein in Alteromonas sp. LSS17 (36/91 % sequence identity/coverage) have similarity to a glutathione perioxidase gene, gpoA, in N. italica R11 (R11_201). Glutathione peroxidase has been demonstrated to be important for oxidative stress response in N. italica R11 and also plays a role in the bleaching disease of D. pulchra (Gardiner et al., 2015). Thus, the findings of this study further emphasise that strategies, such as ROS detoxification play an important role in pathogenesis of D. pulchra.

The macroalga, D. pulchra, produces furanone compounds that function to prevent herbivory, have antimicrobial activity and act as antagonists to bacterial signalling systems (Kjelleberg et al., 1997, Maximilien et al., 1998, Manefield et al., 2002). Various efflux pumps, capable of removing toxic material are encoded in the genomes of Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 (Supplementary information_S2). Whilst defence/resistance mechanisms towards furanones remain to be investigated in the bacterial pathogens, it is possible that these inhibitory metabolites are removed from bacterial cells via efflux pumps. Indeed the genomes of both Alteromonas sp. BL110 and LSS17 encode two operons each, with homology to the 6.5 kb mexAB-oprM operon in P. aeruginosa PA14, which is involved in efflux of the brominated furanone C-30 molecule (Fig. 6.3). A study by Maeda et al. (2011) showed that mutations in the operon repressor, mexR (genbank accession WP_004365054.1), resulted in increased efflux of C-30, where the mutant also increased pathogenicity towards the nematode Caenorhabditis elegans. Thus, presence of the mexAB-oprM operon in addition to other efflux pumps in genomes of Alteromonas sp. BL110 and LSS17 suggest mechanisms for defence against furanones. Moreover, various enzymes for the detoxification of ROS provide strategies for representatives of Alteromonadaceae to counteract oxidative bursts in the host, D. pulchra.

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Fig. 6.3: Genomic context for mexAB-oprM operon. A schematic of the P. aeruginosa PA14 genome showing the relative positions of mexR, mexA, mexB and oprM within the operon. Homologs of mexA, mexB and oprM in Alteromonas sp. BL110 and LSS17 are labelled with IMG locus tags (Markowitz et al., 2009) and represented with arrows of the same colour. The translated amino acid BLASTP sequence identities and coverage are listed within parentheses. Grey arrows indicate hypothetical proteins. Scale bar represents 500 bp.

6.3.2.5 Natural products

The production of toxins, bioactive compounds and siderophores are some common strategies used my marine surface colonisers to overcome competition and successfully acquire nutrients from the environment (Goecke et al., 2010, Vraspir & Butler, 2009). Targets of the natural products synthesised by marine bacteria include other surface colonisers (Bowman, 2007) and eukaryotic hosts, such as algae (Seyedsayamdost et al., 2011b) and marine animals (Austin & Zhang, 2006). The genomes of Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 encode natural products, of which those with relevance to colonisation of macroalgal hosts are discussed below.

The genome of Alteromonas sp. BL110 includes a 20 kb region encoding a lantipeptide gene cluster (gene cluster ID 160904005, Fig. 6.4). Previously thought to be present only in gram- positive bacteria, lantipeptide biosynthetic gene clusters have recently been found in gram- negative bacteria also (Goto et al., 2010). The biological activity of lantipeptides is exerted through inhibition of cell wall synthesis and disruption of membrane integrity through pore- formation (Knerr & van der Donk, 2012). Whilst the activity of the lantipeptide in Alteromonas 124

sp. BL110 remains to be tested, protein sequence analysis against homologues in other bacteria suggested that the peptide (gene ID 2578286445) belongs to the class IIb bacteriocin group (Fig. 6.4). Genes encoding for similar class IIb bacteriocins-like proteins could not be detected in Agarivorans sp. BL7 and Alteromonas sp. LSS17 or other representatives of Alteromonadaceae. In addition, the presence of a transposase (gene ID 2578286449) in the biosynthetic cluster is suggestive that the cluster was horizontally acquired. On the basis of known biological activity of the peptide in other bacteria, it is likely that this predicted bacteriocin of Alteromonas sp. BL110, targets competing surface-colonising bacteria and thus contributes to the overall competitive fitness of this pathogen.

Fig. 6.4: Schematic representation of the lantipeptide biosynthetic cluster neighbourhood in the genome of Alteromonas sp. BL110 (indicated within red dotted lines). Numbers above the diagram denote nucleotide co-ordinates within the genome of Alteromonas sp. BL110 and labels below the line refer to IMG locus tags. Corresponding gene product names are indicated below the schematic. Off-white arrows without locus tags denote hypothetical proteins. Image was acquired and modified from IMG (Markowitz et al., 2009).

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Siderophores are iron-binding complexes that are secreted from bacteria in order to sequester iron, which is vital for cellular processes (Skaar, 2010). The genomes of Alteromonas sp. BL110 and LSS17 (gene cluster ID 160904014 and 160903954, respectively) both contain gene clusters for the production of siderophore-like compounds, with homology to petrobactin-like siderophores in the soil pathogen, Bacillus anthracis (Pfleger et al., 2008) and the marine hydrocarbon degrading bacterium, Marinobacter hydrocarbonoclasticus (Barbeau et al., 2002). A detailed comparison of the shared COGs between the siderophore biosynthesis cluster of M. hydrocarbonoclasticus (gene cluster ID 160699499) and Alteromonas sp. BL110 and LSS17 revealed homologies (Table 6.7) and similar gene arrangements (Fig. 6.5) between the three genomes. Representatives of Marinbacter sp. also engage in mutualistic interactions that promote toxic phytoplanktonic blooms through the assimilation of iron and where the organic molecules released from the algal cells are used to promote bacterial growth (Amin et al., 2007, Amin et al., 2009). Also present in the genomes of Alteromonas sp. BL110 and LSS17 were genes encoding for multiple TonB-dependent siderophore receptors for the transport of siderophore- iron complexes into the bacterial periplasm. Iron is known to be scarce in the marine environment (Jickells et al., 2005) and various studies have shown evidence of iron scavenging through siderophore production to be linked to virulence (Carniel, 2001, Lamont et al., 2002, West & Buckling, 2003, Oide et al., 2006). In addition to the petrobactin-like cluster in the genomes of Alteromonas sp. BL110 and LSS17, genes with homology to siderophore synthase components are encoded in the genome of Agarivorans sp. BL7 (gene cluster ID 161511797), however further investigation is required to determine its function. Thus, by having strategies for iron-acquisition, the pathogenic Alteromonadaceae isolates may enhance their competitiveness during conditions of low iron availability in addition to using siderophore production as a virulence determinant against macroalgal hosts.

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Fig. 6.5: Schematic representation of the gene clusters involved in biosynthesis of a petrobactin- like siderophore in Alteromonas sp. BL110 and LSS17, and Marinobacter hydrocarbonclasticus. COGs conserved in the three bacterial genomes are represented with arrows of the same colour and corresponding gene product names are colour-coded and indicated below image. Off-white arrows indicate hypothetical proteins. Numbers above the diagram denote nucleotide co- ordinates in the respective genomes. Image was acquired and modified from IMG (Markowitz et al., 2009).

Table 6.7: Comparisons of the genes shared between Alteromonas sp. BL10 and LSS17; and Marinobacter hydrocarbonclasticus in a putative siderophore gene cluster.

COG ID Gene Name M. hydro- Alteromonas Homology Alteromonas Homology carbonclasticus sp. BL110 (%ID/ sp. LSS17 (%ID/ (IMG taxon ID (IMG taxon ID coverage) (IMG taxon ID coverage) 2540341173) 2576861406) 2576861404) COG1082 Sugar MARHY3427 ALTBL110_ 47/96 ALTLSS17_ 49/97 phosphate 02258 01405 isomerases/ epimerases COG0236 Acyl carrier MARHY3425 ALTBL110_ 35/16 ALTLSS17_ 39/18 protein 02259 01406 COG0318 Acyl-CoA MARHY3424 ALTBL110_ 42/99 ALTLSS17_ 43/98 synthetases 02260 01407 COG4264 Siderophore MARHY3423, ALTBL110_ 37/96, ALTLSS17_ 40/95, synthetase MARHY3422 02261, 43/95 01408, 46/99 component ALTBL110_ ALTLSS17_ 02262 01409 COG0534 Efflux MARHY3420 ALTBL110_ 43/88 ALTLSS17_ 46/99 protein 02263 01410 127

The genome of Agarivorans sp. BL7 encodes a 26 kb region predicted to be involved in O-antigen biosynthesis (gene cluster ID 161511798). The O-antigens are components of the outer membrane in gram-negative bacteria. Genes encoded in the cluster represent three sub-groups, i.e. genes for nucleotide sugar biosynthesis, O-antigen processing and glycosyl transferases, of which the last group is highly variable (Samuel & Reeves, 2003). All three groups are present in the Agarivorans sp. BL7 O-antigen biosynthesis cluster. In the opportunistic phytopathogen, Pectobacterium atrosepticum SCRI1043, regulation of the O-antigen biosynthesis pathway by the quorum-sensing (QS) system has been shown to control surface swarming behaviour (Bowden et al., 2013). Comparisons of the gene clusters involved in O-antigen biosynthesis between genomes of Agarivorans sp. BL7 and P. atrosepticum SCRI1043 (gene cluster ID 161540352) showed that the organisms had only one COG in common, i.e. an O-antigen ligase (COG3307). O-antigen biosynthesis clusters are also present in other related Agarivorans sp. strains and in P. aeruginosa 3578 (gene cluster ID 160925271), however these gene clusters have not been characterised. Hence, whilst it can be predicted that the O-antigen biosynthesis cluster of Agarivorans sp. BL7 controls surface swarming characteristics, further investigation is required.

6.3.3 Putative mechanisms for virulence in Aquimarina sp. AD1 and BL5

Comparisons between the genomes of Aquimarina sp. AD1 and BL5 were made in order to gain insights into the putative mechanisms of virulence within this phylogenetic group. General features potentially involved in disease induction included genes for surface colonisation, gliding motility, biofilm-formation, secretory pathways, enzymes for degradation of host cell wall components, biosynthesis of secondary metabolites and strategies for the evasion of host defence systems (Supplementary information_S1-S5).

Data presented in Chapter 2 showed that the pathogenic isolates Aquimarina sp. AD1 and BL5 shared identical 16S rRNA gene sequence, whereas sequence of the non-pathogenic AD10 represented a different species within the same genera (Table 2.2). Comparative genome analysis of coding sequences showed that in total, 3413 genes were common between the three Aquimarina genomes, with Aquimarina sp. AD1 and BL5 sharing the highest numbers of genes (Fig. 6.6).

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Fig. 6.6: Venn diagram representing numbers of CDS genes shared between the three Aquimarina genomes. The pathogenic isolates, Aquimarina sp. AD1 and BL5 shared the highest number of genes. Numbers outside the Venn diagram indicate total number of genes (in parentheses) for each strain.

6.3.3.1 Predicted proteins unique to pathogenic Aquimarina sp.

In order to identify specific molecular determinants putatively involved in virulence of Aquimarina sp., genomes of the pathogenic representatives, i.e. Aquimarina sp. AD1 and BL5, were analysed for their similarities and contrasted against the genome of the non-pathogen, Aquimarina sp. AD10. In total, 636 genes were unique to the pathogen genomes (Fig. 6.6, Supplementary information_S6). Due to the relatively large number of genes shared (636), with possible redundancy in functions, COGs and not gene IDs were used for further comparisons, unless otherwise stated. An investigation on the functional profiles of genes unique to pathogen genomes indicated that 42 non-redundant COG functions were present only in the pathogenic Aquimarina sp. AD1 and BL5 genomes. The unique functions represented 11 COG function categories (Fig. 6.7).

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Amino acid transport and metabolism Carbohydrate transport and metabolism 10% 5% 10% Cell wall/membrane/envelope biogenesis 5% 2% Coenzyme transport and metabolism 2% 7% Energy production and conversion 2% Function unknown 7% General function prediction only 24% Lipid transport and metabolism Nucleotide transport and metabolism 26% Posttranslational modification, protein turnover, chaperones

Replication, recombination and repair

Fig. 6.7: COG function categories representing the genes unique to pathogen genomes. A total of 42 non-redundant COG functions were detected only in the genomes of pathogenic Aquimarina sp. AD1 and BL5 and missing in the non-pathogenic, Aquimarina sp. AD10. The COG functions were extracted from IMG (Markowitz et al., 2012), using a minimum identity cut-off of 30% with ≥ 50% alignment over the length of the sequence. A maximum expectancy of 1e-5 was used.

Relative to their original contributions in genomes of Aquimarina sp. AD1 and BL5, six out of the eleven COG functional categories containing genes unique to pathogen genomes, were over- represented. The over-represented categories included carbohydrate transport and metabolism (4.7% increase), cell wall/membrane/envelope biogenesis (3% increase), energy production and conversion (2.3% increase), function unknown (20.5% increase), general function prediction only (13.6% increase) and replication, recombination and repair (6.4% increase). All individual functions (i.e. 42 in total) within the eleven COG categories and predicted proteins with no assigned COG functions (i.e. 91 in total) were searched for homologues against the Uniprot database (Apweiler et al., 2004). On the basis of the search, results of analysis for all 42 COG functions, in addition to 5 putative proteins that shared amino acid sequence similarities with proteins predicted to be linked to virulence-related activities are listed in Table 6.8.

Within COG functions and predicted proteins unique to the pathogen genomes were genes with known virulence-related activities including polysaccharide-utilisation, toxin synthesis, membrane biogenesis and attachment to the host. Particularly noticeable was a 22-27 kb region 130

encoding a putative flexirubin biosynthesis cluster (gene cluster ID 160904025 and 160903985 in Aquimarina sp. AD1 and BL5, respectively) that was present only in the pathogenic isolates. The biosynthetic cluster included the non-redundant COG4261 (Fig. 6.8, Table 6.8), which corresponded to gene ID 2578289971 and 2578283334 in Aquimarina sp. AD1 and BL5, respectively. Moreover, comparisons of the putative flexirubin biosynthetic clusters of Aquimarina sp. AD1 and BL5 showed that 41% and 55% of genes were shared with the flexirubin biosynthetic cluster of Flavobacterium johnsoniae (Fig. 6.8). Flexirubins are orange pigments made of a 2,5- dialkylresorcinols (DAR) esterified with a non-isoprenoid arylpolyene carboxylic acid and are used as chemotaxonomic markers for bacteria belonging to the Bacteroidetes phylum (Schöner et al., 2014). Other derivatives of DAR have been described as antibiotics (Joyce et al., 2008), cytotoxins (Kronenwerth et al., 2014), signalling molecules (Brameyer et al., 2015), free radical scavengers involved in protection against lipid peroxidation and photooxidative damage (Schöner et al., 2014) and growth-stimulating factors (Imai et al., 1993). Situated also in the flexirubin biosynthetic cluster of Aquimarina sp. AD1 (but not shared with Flavobacterium johnsoniae) are COG5498 and COG2273, which encode por-secretion system proteins (gene ID 2578289983 and 2578289984, respectively); and COG1472 and pfam00722, which encode polysaccharide-degrading enzymes (gene ID regions 2578289985 to 2578289987). In Aquimarina sp. BL5, COG2273 (gene ID 2578283321) is located at the 5’-end of the flexirubin biosynthetic cluster whilst the remaining proteins are located on a different scaffold (gene ID regions 2578284174 to 2578284176). Por-secretion systems are widespread amongst members of the phylum Bacteroidetes (McBride & Zhu, 2013). This protein translocation system has been linked to gliding motility and chitinase secretion in Flavobacterium johnsoniae (Sato et al., 2010) and in pathogenesis by Porphyromonas gingivalis, a major pathogen in severe forms of periodontal disease (Shoji et al., 2011). Thus, in context of macroalgal disease, it is a possibility that the putative flexirubin biosynthetic clusters of Aquimarina sp. AD1 and BL5 contribute towards pathogenicity by providing tolerance against oxidative burst responses of the host. Functional contributions of the por-secretion system proteins and the polysaccharide-degrading enzymes towards the role of the flexirubin biosynthetic cluster is unclear and further investigation will shed light on the exact function of the biosynthetic gene cluster found unique to pathogenic Aquimarina isolates.

As highlighted above for the Alteromonadaceae pathogens, polysaccharide degrading enzymes are likely to play an important role in microbial interactions with seaweed hosts. Whilst polysaccharide degrading enzymes were present in genomes of all three Aquimarina sp. (Supplementary information_S3), certain isozymes (different forms of the enzymes) were

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unique to the pathogens (COG3693, pfam00457 and pfam02927, Table 6.8). It is possible that these isozymes target different polysaccharide components of macroalgal hosts and are thus important determinants of virulence in Aquimarina sp. In addition to degradative enzymes, various COGs unique to the pathogen genomes encode proteins involved in the biosynthesis of surface appendages, such as lipopolysaccharides and O-antigens (COG0381, COG1089, COG1232, COG1835 and COG4261, Table 6.8). Another surface appendage encoding protein, i.e. with homology to surface lipoproteins in Spirochaetales (pfam12103) shared sequence similarity with the LipL32 protein in the bacterium Pseudoalteromonas tunicata (locus tag PTD2_05920). The ptlL32 gene mediates colonisation of the algal host, U. australis (Gardiner et al., 2014). In phytopathogens, lipopolysaccharides are important virulence determinants, mediating host adhesion (Yang et al., 2013), inducing oxidative burst responses (Meyer et al., 2001) and inducing the expression of defence-related genes in the host (Newman et al., 2007). Thus, the findings presented in the current study suggest that surface appendages, such as lipoproteins and lipopolysaccharides, encoded in the pathogenic Aquimarina sp. genomes play important roles in host attachment and mediate subsequent virulence interactions with macroalgal hosts.

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Fig. 6.8: Schematic representation of the putative flexirubin biosynthetic cluster in Aquimarina sp. AD1 and BL5 (gene cluster ID 160904025 and 160903985, respectively), compared against a homologous gene cluster in Flavobacterium johnsoniae (antiSMASH reference BGC0000838). Similarities (%) between the genomes compared are provided within the respective boxes. Shared COGs (excluding hypothetical proteins) are labelled and indicated with arrows of the same colour in the genomes being compared. COG4261, which is highlighted in yellow (gene ID 2578289971 and 2578283334 in Aquimarina sp. AD1 and BL5, respectively) is non-redundant in pathogenic Aquimarina genomes (Table 6.8). COG5498 and COG2273 encode por- secretion system proteins and pfam00722 and COG1472 encode polysaccharide-degrading enzymes. The image was acquired and modified from antiSMASH v.3.0.1 (Weber et al., 2015).

Table 6.8: BLASTP analysis of predicted proteins unique to pathogenic Aquimarina sp.

Function IMG Gene ID IMG Name UniProt homologous Uniprot Predicted function Homology to: % sequence ID in AD1* protein name entry ID/coverage COG0160 2578290874 4-aminobutyrate Aminotransferase A0A0C1L7T7 Aminotransferase Flavihumibacter 41/96 aminotransferase solisilvae COG0312 2578290303 Predicted Zn-dependent Putative Zn-dependent F0C4P7 Hydrolase, protease Xanthomonas 62/97 protease or its inactivated protease-like protein gardneri ATCC 19865 homolog COG0381 2578293873 UDP-N-acetylglucosamine UDP-N- P27828 Enterobacterial Escherichia coli strain 60/98 2-epimerase acetylglucosamine 2- common antigen K12 epimerase biosynthesis COG0401 2578290455 Uncharacterized Hydrogenase A0A0A7K9B0 Integral component Cellulophaga baltica 92/100 membrane protein YqaE, expression protein of membrane 18 homolog of Blt101 COG0464 2578292686 AAA+-type ATPase Stage V sporulation P27643 Sporulation Bacillus subtilis 168 39/86 protein K COG0813 2578293692 Purine-nucleoside Purine nucleoside A6TVU0 Glycosyltransferase Alkaliphilus 63/98 phosphorylase phosphorylase DeoD- metalliredigens type QYMF COG1032 2578293988 Radical SAM superfamily Uncharacterized O58549 Methyltransferase Pyrococcus horikoshii 27/81 enzyme YgiQ, UPF0313 methyltransferase activity ATCC 700860 family PH0819 COG1089 2578294257 GDP-D-mannose GDP-mannose 4,6- Q9JRN5 LPS O-antigen Aggregatibacter 68/100 dehydratase dehydratase biosynthesis (OM) actinomycetemcomitans COG1112 2578292685 Superfamily I DNA and/or Uncharacterized ATP- Q57568 Nucleotide binding Methanocaldococcus 25/56 RNA helicase dependent helicase jannaschii ATCC MJ0104 43067 COG1232 2578290232 Protoporphyrinogen Probable UDP- Q48485 O-antigen Klebsiella 27/46 oxidase galactopyranose biosynthetic process pneumoniae mutase

Function IMG Gene ID in IMG Name UniProt homologous Uniprot Predicted function Homology to: % sequence ID AD1* protein name entry ID/coverage COG1254 2578293690 Acylphosphatase Acylphosphatase Q3JCP6 Hydrolase Nitrosococcus oceani 43/100 ATCC 19707 COG1284 2578291369 Uncharacterized membrane- UPF0750 membrane P54478 Integral component Bacillus subtilis 168 33/93 anchored protein YitT protein YqfU of membrane COG1312 2578292672 D-mannonate dehydratase Mannonate Q8A7U2 Glucuronate Bacteroides 65/99 dehydratase catabolic process thetaiotaomicron COG1409 2578293898 3',5'-cyclic AMP 3',5'-cyclic-nucleotide L8K0C6 Hydrolase Fulvivirga imtechensis 45/98 phosphodiesterase CpdA phosphodiesterase AK7 COG1835 2578293253 Peptidoglycan/LPS O- O-acetyltransferase Q79ZY2 Acyltransferase Staphylococcus aureus 25/52 acetylase OafA/YrhL OatA MW2 COG1904 2578290765 Glucuronate isomerase Uronate isomerase A5FC03 Glucuronate Flavobacterium 57/99 isomerase activity johnsoniae ATCC 17061 COG1979 2578294122 Alcohol dehydrogenase YqhD, Alcohol Q46856 NADP-dependent Escherichia coli K12 50/99 Fe-dependent ADH family dehydrogenase YqhD ADH activity. COG2102 2578291683 Diphthamide synthase (EF-2- ATP-binding protein W7QQ84 ATP binding Cellulophaga geojensis 65/99 diphthine--ammonia ligase) KL-A COG2304 2578290827 Secreted protein containing Uncharacterized P76481 The epithelial Escherichia coli K12 42/83 bacterial Ig-like & vWFA protein YfbK chloride channel (e- domain clc) family COG2388 2578292900 Predicted acetyltransferase, Uncharacterized P39274 N-acetyltransferase Escherichia coli K12 38/58 GNAT superfamily protein YjdJ activity COG2513 2578292184 2-Methylisocitrate lyase and Uncharacterized P9WLN9 Catalytic activity Mycobacterium 32/92 related enzymes protein Rv1998c tuberculosis COG2801 2578290327 Transposase InsO and Insertion element P16940 DNA integration Shigella sonnei 33/95 inactivated derivatives IS600 uncharacterized protein COG3007 2578291565 Trans-2-enoyl-CoA reductase Putative reductase A5FE91 Probable reductase Flavobacterium 78/100 Fjoh_3464 johnsoniae COG3209 2578291457 Uncharacterized conserved RHS repeat-associated E1YQR4 Self-proteolysis Parabacteroides sp. 35/78 protein RhaS-with 28 RHS core domain protein 20_3 repeats COG3216 2578292861 Uncharacterized conserved Glycosyl transferase, M7MIH4 transferase activity Formosa sp. AK20 59/96 protein, DUF2062 family family 2

Function IMG Gene IMG Name UniProt homologous Uniprot Predicted function Homology to: % sequence ID ID in AD1* protein name entry ID/coverage COG3227 2578292032 Zn-dependent Por secretion system C- V6S4E8 Unknown Flavobacterium 54/98 metalloprotease terminal sorting domain cauense R2A-7 COG3361 2578290974 Uncharacterized protein YqjF, Uncharacterized protein L8JNZ7 Acetoacetate Fulvivirga 51/99 DUF2071 family YqjF decarboxylase-like imtechensis AK7 COG3371 2578293695 Uncharacterized membrane Putative membrane S3FI05 Unknown Exiguobacterium sp. 30/96 protein protein S17 COG3392 2578293769 Adenine-specific DNA Modification methylase P24582 DNA restriction- Neisseria lactamica 67/99 methylase NlaIII modification system COG3528 2578293557 Uncharacterized protein Uncharacterized protein K4IJ57 Unknown Psychroflexus torquis 34/99 ATCC 700755 COG3550 2578294523 HipA, toxin component of the HipA-like protein U6ZJ48 Unknown Dickeya solani D 47/96 HipAB toxin-antitoxin module s0432-1 COG3569 2578290459 DNA topoisomerase IB DNA topoisomerase 1B Q7T6X9 DNA topological change Acanthamoeba 36/96 polyphaga mimivirus COG3693 2578292490 Endo-1,4-beta-xylanase, GH35 Endo-beta-1,4-xylanase Q59675 Cleaves internal linkages Cellvibrio japonicus 33/72 family Xyn10C on the xylan backbone COG3698 2578290493 Uncharacterized protein YigE, Putative periplasmic V6SLG6 Unknown Flavobacterium 57/97 DUF2233 family protein limnosediminis JC2902 COG3738 2578291155 Uncharacterized protein YijF, Uncharacterized protein P32668 Unknown Escherichia coli K12 54/73 DUF1287 family YijF COG3751 2578292345 Proline 4-hydroxylase No hits found - - - - COG4076 2578294656 Predicted RNA methylase Uncharacterized protein Q58847 protein methylation Methanocaldococcus 24/98 MJ1452 jannaschii ATCC 43067 COG4258 2578292862 Predicted exporter Glycerol acyltransferase A0A086AVE1 Transferase activity Chryseobacterium sp. 44/96 JM1 COG4261 2578289971 Predicted acyltransferase, Lipid A biosynthesis A0A0A2MAK0 Acyltransferase Flavobacterium 49/99 LPLAT superfamily acyltransferase suncheonense DSM 17707 COG4325 2578290462 Uncharacterized membrane Putative membrane I3C0T6 Unknown Joostella marina DSM 36/95 protein protein 19592

Function ID IMG Gene IMG Name UniProt homologous Uniprot Predicted function Homology to: % sequence ID in AD1* protein name entry ID/coverage COG4980 2578290469 Gas vesicle protein Conserved hypothetical G0L866 Unknown Zobellia galactanivorans 44/93 membrane protein DSM 12802 COG5586 2578294302 Uncharacterized Conserved hypothetical C0GE72 Unknown Dethiobacter alkaliphilus 46/97 protein protein AHT 1 pfam00457 2578293916 Glycosyl hydrolases Endo-1,4-beta-xylanase Q06562 Xylan-degrading enzyme Cochliobolus carbonum 33/94 family 11 I pfam02927 2578290141 N-terminal ig-like Xyloglucan-specific A7LXT3 Cleaves the backbone of Bacteroides ovatus ATCC 41/93 domain of cellulase endo-beta-1,4- xyloglucans 8483 glucanase BoGH9A pfam12103 2578292062 Surface lipoprotein of LipL32 A4CDZ2 protein insertion into Pseudoalteromonas 41/86 Spirochaetales order membrane tunicata D2 pfam13529 2578290392 Peptidase_C39 like Peptidase A0A023BUK8 Peptidase Aquimarina sp. 22II-S11- 26/22 family z7 pfam14099 2578291707 Polysaccharide lyase Polysaccharide lyase A0A015QHY2 Lyase Bacteroides fragilis 42/86 family protein strain 3397 T10 * Corresponding gene IDs of the non-redundant COGs in Aquimarina sp. AD1, which are shared with Aquimarina sp. BL5 but absent in Aquimarina sp. AD10 (30% minimum identity cut-off with ≥ 50% alignment over the length of the sequence and a maximum expectancy of 1e-5 was used).

6.3.4 Shared virulence genes of pathogenic Alteromonadaceae and Aquimarina sp.

To investigate if genomes of the five pathogens, representing 2 distinct phylogenetic groups, had universal features that contributed to virulence against macroalgal hosts, COG functions that were common in the five genomes were identified. Only three non-redundant COG functions were detected that were common in pathogenic representatives of both Alteromonadaceae and Flavobacteriaceae (i.e. Aquimarina sp.). The proteins from individual genomes that represented the three universal COG functions are listed in Table 6.9. Closer inspection of these genes failed to determine an obvious link between their predicted functions, thus further studies are required to determine if the unique representation of these genes in the pathogenic strains is related to virulence or is purely incidental.

Table 6.9: List of non-redundant COGs and corresponding genes shared only between pathogenic representatives of Alteromonadaceae and Aquimarina sp.

Pathogen Alcohol dehydrogenase 2-Methylisocitrate lyase Trans-2-enoyl-CoA YqhD, Fe-dependent and related enzymes, PEP reductase (COG3007) ADH family (COG1979) mutase family (COG2513) Agarivorans sp. BL7 2619816275* 2619814671, 2619814930 2619815674, 2619818103 Alteromonas sp. BL110 2578287908 2578287504 2578286136 Alteromonas sp. LSS17 2578277169 2578278431 2578280773 Aquimarina sp. AD1 2578294122 2578292184 2578291565 Aquimarina sp. BL5 2578285398 2578282444 2578284781 Aquimarina sp. AD10 absent absent absent *Numbers in table are the unique IMG gene Identification numbers for each gene

The lack of obvious roles for the unique genes (listed in Table 6.9) across the pathogenic strains speaks to the broader view of the nature of opportunistic pathogens, which is that opportunistic pathogens may not have a unique set of virulence genes (Hilker et al., 2015) but rather rely on the coordinated regulation of traits under specific conditions. It is therefore possible that such virulence regulation in the newly-identified bacterial pathogens is dependent on external cues, such as environmental or host conditions. For example, in N. italica R11, pathogenesis could not be linked to expression of unique virulence proteins, however quorum-sensing (QS) regulated

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functions, such as host attachment and colonisation were prompted in presence of algal polysaccharides (Gardiner et al., 2015). Global regulatory systems, such as quorum sensing regulate the expression of a number functions, including cell aggregation, host colonisation and biofilm-formation, which have an overall virulence implication on the host (Cotter & Stibitz, 2007, Crépin et al., 2012, Alasil et al., 2015). In the current study a QS system in Aquimarina sp. (including in Aquimarina sp. AD10) and both QS and the 3′-5′-cyclic dimeric guanosine monophosphate (c-di-GMP) systems in Alteromonadaceae were detected (Supplementary information_S1). Thus, it is a possibility that regulatory signal transduction systems in Alteromonadaceae and Aquimarina sp. co-ordinate gene expression and ultimately control pathogenic behaviour towards macroalgal hosts.

6.3.5 Conclusions and future work

This chapter involved a comparative analysis of the genome sequences of five opportunistic pathogens of D. pulchra and a non-pathogen in order to gain insights into virulence mechanisms putatively used against the macroalgal host. Analysis of common gene features among specific phylogenetic groups, i.e. Alteromonadaceae and Aquimarina sp. highlighted a list of potential mechanisms, with both general functions, such as chemotaxis, motility and surface attachment, and specific functions such as production of degradative enzymes and secretion of effector molecules. It is anticipated that the information presented in this chapter will guide future research aimed at characterising (e.g. via gene-knock out experiments) the exact role of the selected genes and pathways in disease induction of macroalgal hosts.

A comparison of all pathogenic strains, beyond phylogenetic grouping, suggested that virulence in the opportunistic pathogens was not limited to the presence of specific determinants but rather involved regulation of several traits. The finding supports a paradigm shift in the way scientists view disease and virulence whereby a holistic view of disease includes opportunism, environmental influences and the health of the host (Casadevall & Pirofski, 2001, Casadevall & Pirofski, 2002, Brown et al., 2012, Lorenzo, 2015). Therefore in order to further elucidate the mechanisms of virulence in these macroalgal pathogens, future work will benefit from a detailed analysis of the transcriptional and metabolic profiles of pathogens during interactions with the host.

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Chapter Seven: General discussion

7.1 Environmental stressors, opportunistic pathogens and macroalgal hosts

Marine ecosystems provide nurseries, fishing grounds and resource for aquaculture however infectious disease outbreaks often threaten the well-being of this natural resource (Ramaiah, 2006, Gachon et al., 2010, Heron et al., 2010, Altizer et al., 2013, Burge et al., 2014). Changing climatic conditions, such as rising global seawater temperatures, nutrient input and ocean acidification correlate with increased physiological stress in resident organisms (Harvell et al., 2009, Doney et al., 2012, Wernberg et al., 2013). As a consequence of such physiological stress, habitat-forming organisms, including macroalgae, are more vulnerable to infections caused by microbial pathogens (Gachon et al., 2010, Campbell et al., 2011). Thus, disease in macroalgae is likely to be a result of a combination of factors, including physiological condition of the host, environmental stressors and microbial pathogens (Burge et al., 2013, Egan et al., 2014). The infectious agents of macroalgae include bacteria, fungi, protists, and viruses (Gachon et al., 2010). Of these, bacteria are well-studied due to their contribution towards the functioning of algal holobionts (Hollants et al., 2012, Egan et al., 2013, Singh & Reddy, 2014). However, despite the recognised role of bacteria in mediating both positive and negative interactions with macroalgal hosts, there remains a paucity in information on opportunism amongst bacterial pathogens (Egan et al., 2014).

The red macroalga, Delisea pulchra, is a model organism used to demonstrate bacterial-induced bleaching at elevated sea temperatures by the pathogens, Nautella italica R11 and Phaeobacter sp. LSS9 (Case et al., 2011, Fernandes et al., 2011, Campbell et al., 2014). Moreover, analysis of the microbial communities on D. pulchra reveals that bleached tissues are characterised by increased prevalence in bacteria of the families Flavobacteriaceae and Rhodobacteriaceae, suggesting a role for multiple pathogens in disease of the alga (Zozaya-Valdes et al., 2015). Hence the objective of this thesis was to investigate if multiple opportunistic pathogens are involved in the bleaching disease of D. pulchra through an assessment of their disease-inducing capacities.

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7.2 Presence of opportunistic pathogens on macroalgal hosts

The introduction of next generation sequencing has revolutionised molecular biology (Grada & Weinbrecht, 2013). Access to high-throughput culture-independent techniques has allowed for a comprehensive understanding of the microbial diversity in different environments (Harvell et al., 1999, Jones et al., 2004, Bourne, 2005, Angermeier et al., 2011), including the discovery of previously unknown microorganisms (Rappé et al., 2002, Rappé & Giovannoni, 2003). However, in order to study the role of microbes in specific environments, little can be achieved without having the organisms in culture (Harvell et al., 1999, Joint et al., 2010, Stewart, 2012). Consequently, after a decade of predominately molecular based microbial exploration, there is now an increased awareness on the need to culture organisms, thus resulting in a renaissance in studies involving classical culturing (Margassery et al., 2012, Pham & Kim, 2012, Montalvo et al., 2014, Ringeisen et al., 2015).

A number of studies analysing microbial community shifts associated with disease events on D. pulchra have suggested that multiple opportunistic pathogens exist on the macroalga (Fernandes et al., 2012, Zozaya-Valdes et al., 2015). However, due to the lack of cultured bacterial isolates from D. pulchra the question of multiple pathogens of this macroalga remained unanswered. Data presented in Chapter 2 of this thesis included the generation of a culture collection of bacteria isolated from bleached and adjacent-to-bleached tissues of D. pulchra (refer to Table 2.2, Appendix II). More than 50% of the isolates in the culture collection were found to represent bacteria involved in disease events in one of two macroalgae, D. pulchra (Fig. 2.3) and Ecklonia radiata (Fig. 2.4). The simultaneous occurrence of these bacteria on healthy algal conspecifics (Fig 2.3, Fig. 2.4) suggests that these strains are also part of the commensal microbial community. Such findings reflect opportunism in bacteria, which following host perturbation, such as those caused by wounds (Campbell et al., 2014), immunodeficiency (Case et al., 2011) and aging (Seyedsayamdost et al., 2011b), may drive the commensal interaction towards disease.

Opportunistic pathogens typically possess a suit of virulence traits that can be readily employed against a susceptible host (Casadevall, 2006). Indeed, the findings of Chapter 3 showed that the cultured isolates involved in disease-associated community shifts had a range of virulence- related traits (Table 3.5) that made them good candidates as opportunistic pathogens of macroalgae. Macroalgae are sessile, may or may not have complex defence systems and are nutritional reserves of the marine environment (Chan et al., 2006, Holdt & Kraan, 2011). Thus, virulence-related traits of bacteria that are particularly relevant against macroalgal hosts and 140

were subsequently found to be commonly present amongst isolates in the culture collection included motility (Table 3.1), biofilm-formation (Fig. 3.1), tolerance to host chemical defences, i.e. furanones and oxidative bursts (Fig 3.2, Table 3.2, Table 3.3) and the degradation of biopolymers (Fig. 3.3, Table 3.4). The widespread occurrence of virulence-related traits further suggests a previously underestimated prevalence of bacteria capable of functioning as opportunistic pathogens in the marine environment. Support for this finding was evident in Chapter 5 when the bacterial community associated with juvenile D. pulchra used in infection experiments showed that while 50% of the untreated bleached controls had putative- and known pathogens present, bleaching in the remaining 50% of samples was unaccounted for (Fig. 5.3). An inability to detect both putative- and known pathogens in bleached samples suggests the presence of yet unknown pathogens of D. pulchra. Also interesting was the detection of N. italica R11 and Phaeobacter sp. LSS9 within background communities (i.e. not as a result of inoculation with putative pathogens) (Fig. 5.3), which have been sporadically detected over multiple bleaching events in the past (Fernandes et al., 2012, Zozaya-Valdes et al., 2015). Taken together, the findings presented here indicate that many bacteria existing on macroalgal surfaces are in fact opportunists with virulence-related characteristics that are detrimental to susceptible hosts. However the proportion of the surface community on macroalgae that exist as opportunistic pathogens remains to be investigated. Further studies employing quantitative- PCR techniques will provide helpful insights into the prevalence and distribution trends of opportunistic pathogens on macroalgal surfaces.

7.3 Significance of model systems and the development of a new infection model

Model systems provide means for studying biological interactions between two or more organisms. When investigating specific interactions, model systems permit manipulation of external factors, such as environmental conditions and host immunity, which is otherwise challenging to achieve in natural settings (O’Callaghan & Vergunst, 2010). Historical models that have contributed immensely to the current understanding of disease include mouse models for animal-pathogen interactions (Marchetti et al., 1995), Arabidopsis model for plant-pathogen interactions (Rhee et al., 2003) and the Caenorhabditis elegans model for parasitic interactions (Aballay & Ausubel, 2002). Indeed, major breakthroughs have been made as a result of these models, which have played a crucial role in establishing the fundamentals of molecular interactions associated with virulence (Check, 2002, Bosch & Rosich, 2008, O’Callaghan & Vergunst, 2010). Thus, in order to have a similar understanding of mechanisms behind

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pathogenic interactions in marine ecosystems, there is need for effective model systems for major habitat-forming organisms, such as macroalgae.

Amongst the few algal model systems to-date, one that has been instrumental in providing enhanced understanding of disease in brown algae is the Ectocarpus siliculosus model (Charrier et al., 2008, Coelho et al., 2012). Benefits of the E. siliculosus model system is reflected by a number of publications that highlighted the role of algal physiology during interactions with the oomycete pathogen, Eurychasma dicksonii (Sekimoto et al., 2008, Gachon et al., 2009, Grenville- Briggs et al., 2011, Zambounis et al., 2013). In addition, recent sequencing of the E. siliculosus genome has provided opportunity for further investigation of the genetic determinants involved in the pathophysiology of the alga (Cock et al., 2010, Grenville-Briggs et al., 2011). With respect to red algae, the D. pulchra model system has been useful in studying chemical interactions between macroalgal hosts and pathogens (Harder et al., 2012). In particular, the model has helped gain an increased understanding of the ecology of defence-chemistry in red algae (Dworjanyn et al., 1999, Manefield et al., 2002, Campbell et al., 2011, Campbell et al., 2014). In recent years D. pulchra has also become a model for bacterial pathogenesis, however to date the focus has been on sporelings cultured in the laboratory (Campbell et al., 2011, Case et al., 2011, Gardiner et al., 2015) with little attention paid to the latter stages of the algal life-cycle. Therefore in order to understand the ecology and pathogenesis of D. pulchra bleaching disease, there is a need to modify the current model to better reflect the bleaching symptoms seen in adult algae in the field.

The work presented in Chapter 4 showed that adult D. pulchra cultured in the aquarium for up to a week showed repeated signs of stress (Fig. 4.3). The fact that adult algae were not able to be maintained under healthy conditions suggested that they were not suitable as models for inoculation studies. Another important requirement for a successful model-system is that it is relatively high-throughput and allows for adequate experimental replication. Thus given the size and space requirement of adult algae, a model based on the culture of smaller juvenile D. pulchra was developed. The newly-developed in vivo infection model using juvenile D. pulchra (Fig. 4.7) proved to be successful and was instrumental in testing ten candidate pathogens for their ability to induce bleaching in the macroalgae. Consequently, five new opportunistic pathogens, i.e. Aquimarina sp. AD1 and BL5, Agarivorans sp. BL7 and Alteromonas sp. BL110 and LSS17 were identified (Fig. 4.8).

Interestingly, the data presented in Chapter 4 (Fig. 4.8) also found that the previously identified pathogen, N. italica R11, for which virulence was determined using algal sporelings in vitro (Case 142

et al., 2011) and on adults in situ (Campbell et al., 2014), was not pathogenic against juvenile D. pulchra (Fig. 4.8). It is possible that the combined contribution of pathogen action and host response can have varying outcomes on the patterns of disease between different life-history stages of the host. From the algal perspective, reduced susceptibility towards N. italica R11 in the juvenile life-history stage maybe indicative of increased resilience. In both terrestrial plants (Bazzaz et al., 1987) and kelps (Graham, 2002), ‘trade-offs’ between functions, such as reproduction and growth, has been observed. Due to a ‘cost’ associated with these functions, energy is prioritised for different functions during distinct life-history stages of the organism (Bazzaz et al., 1987). Thus it is hypothesised that similar ‘trade-offs’ are involved between the growth-intensive sporeling life-history stage, juvenile and the reproductive-adult stage in D. pulchra.

In a broader context, the work presented in Chapter 4 extends the scope of the D. pulchra model from being recognised as a model for studying chemical interactions to one that can be used to explore opportunism in microbial-eukaryote interactions. Together with other algal models, the newly-developed system will contribute towards an increased understanding of the biological and mechanistic processes involved in host-pathogen associations.

7.4 Unique patterns of disease in opportunistic pathogens of D. pulchra

A number of factors, including virulence traits of pathogens and host susceptibility determine the overall outcomes of host-microbe interactions (Casadevall & Pirofski, 2001, Casadevall, 2006). Data presented in Chapter 6 showed that the opportunistic pathogens had a number of putative virulence strategies for inducing disease in macroalgae. Mechanisms for virulence that were unique to representatives of Alteromonadaceae (section 6.3.2) included chemotaxis, motility, secretion systems and enhanced strategies for resistance against host chemical defence. Comparatively, por-secretion systems, gliding motility and specific surface appendages, such as lipopolysaccharides and lipoproteins were unique to Aquimarina sp. It is likely that the unique virulence characteristics contribute to difference in overall behavioural patterns in the individual pathogens. For example, in demonstration of the fulfilment of Koch’s postulates, the work presented in Chapter 5 showed that despite the universal bleaching outcome, abundance patterns of the individual pathogens on bleached and unbleached D. pulchra was unique (Fig. 5.2). Here, increased abundance of the pathogens Aquimarina sp. AD1 and BL110 was observed on bleached algae whereas an opposite trend was observed for the aggressive pathogen, Alteromonas sp. LSS17 (Fig. 4.8, Fig. 5.2c). In addition, Aquimarina sp. AD1

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appeared to have a lower ‘infectious dose’ in comparison to Alteromonas sp. BL110 and LSS17, which is likely due to differences in virulence mechanisms (Fig. 5.2).

Reflections on the prevalence of the newly-characterised pathogens on bleached versus healthy D. pulchra in the field suggested complexity in abundance patterns. For example, not all over- represented strains on bleached samples in the field (Fig. 2.3, Fig. 2.4) were identified as pathogens, i.e. Aquimarina sp. AD10 and Winogradskyella sp. BL18 (Fig. 4.8). Similarly, in comparison to the other pathogens characterised in this study, the abundance of Agarivorans sp. BL7 was highly variable (Fig. 2.3). These findings, together with the previously stated inconsistent prevalence of N. italica R11 and Phaeobacter sp. LSS9 over multiple bleaching events (Fernandes et al., 2012, Zozaya-Valdes et al., 2015) highlight that opportunistic pathogens do not adhere to strict patterns and that variations in abundance during disease is likely a combined result of inherent virulence characteristics of the pathogens and host susceptibility. This work supports a paradigm shift in viewing disease by opportunistic pathogens not as having defined characteristics but as having a number of possible outcomes based on the specific host-microbe interaction (Casadevall & Pirofski, 2001, Casadevall & Pirofski, 2002, Lorenzo, 2015).

The existence of unique abundance patterns in opportunistic pathogens on diseased hosts further indicates subsequent challenges in fulfilling Koch’s postulates. In Chapter 5, whilst asymptomatic infections on unbleached samples and alternatively, ‘shedding’ was predicted to be the reason for lower abundance of the pathogen on bleached individuals inoculated with Alteromonas sp. LSS17 (section 5.4.1), co-infection between strain LSS17 and other putative- and known pathogens was also possible (Fig. 5.3). Neither the classical version of Koch’s postulates nor recent molecular amendments currently account for co-infection of hosts by multiple pathogens (Falkow, 2004). Thus, given that bacterial populations within natural settings are dominated by multi-species communities (Riedel et al., 2001, Rao et al., 2005, Hosni et al., 2011, Venturi & da Silva, 2012), an inability to fulfil Koch’s postulates, may result in the dismissal of opportunism, in addition to pathogenic interactions arising from co-infection of the host by multiple pathogens. Therefore the findings of this study highlight a need for constant revisions and re-consideration on how Koch’s postulates are fulfilled for diseases with unique dynamics.

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7.5 Proposed model of virulence in opportunistic pathogens of macroalgal hosts

Through comparative genomics, a number of putative virulence determinants encoded in the genomes of Agarivorans sp. BL7, Alteromonas sp. BL110 and LSS17 and Aquimarina sp. AD1 and BL5 were highlighted. Whilst some virulence strategies are likely to be generic features (e.g. chemotaxis, motility and surface attachment), others may be targeted specifically against other surface colonisers or macroalgal hosts (e.g. secretion systems). Moreover, some virulence determinants are likely to be used in combination with others to enhance pathogenic activity (e.g. biofilm-formation, siderophore production and oxidative stress resistance). Figure 7.1 illustrates a model, which details the putative mechanisms of virulence identified in this thesis used by opportunistic pathogens to induce disease in macroalgal hosts.

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Fig 7.1: Schematic model of virulence traits in opportunistic pathogens of D. pulchra (healthy and diseased algal host shown on left and right, respectively). Figure legend continued on next page.

Fig. 7.1 continued: Features encircled with red ellipses are shared between pathogens and evidence obtained through 1in vitro assays and/or 2genomic data. The healthy host produces defence molecules (yellow hexagons), which are exported by pathogens using efflux pumps (brown double arcs), whereas oxidative bursts are detoxified with enzymes (orange triangles). Guided flagella motility as a result of chemotaxis towards host exudates (green circles) aids subsequent interactions with the host. Cell attachment to host surface occurs via surface proteins (pili, lipopolysaccharides and lipoproteins) under regulation of the c-di-GMP and QS systems (multi-coloured bars). The T2SS (blue parallel lines) and por-secretion systems (green double arcs) are involved in secretion of enzymes, surface organelles proteins that permit gliding motility. The T1SS (blue parallel lines) is involved in toxin secretion. Targeted secretion of virulence factors occurs via the T6SS (blue parallel lines) and the T3SS (blue cylinders), which also secretes flagella proteins. Localised host interaction occurs via the O-antigens (green hexagon at the ends of yellow lines). A range of cell wall degrading enzymes (purple and blue stars) are produced. Iron is acquired through siderophore activity (grey parallel line and green squares). The QS system represents one mechanism of co- ordinating virulence gene expression.

7.6 Conclusions and future perspectives

The majority of host-microbe interactions are perceived either as pathogenic or non-pathogenic (e.g. commensal) and the concept of opportunistic pathogens is still emerging (Cawthorn, 2011, Brown et al., 2012, Burge et al., 2013). As a result, the presence and implication of opportunistic pathogens in the marine environment is likely to be currently underestimated (Egan et al., 2014). The disease-inducing capacity of opportunistic pathogens needs to incorporate both known virulence determinants (Stover et al., 2000, Fernandes et al., 2011) and enhanced fitness traits (Brüssow, 2007, Brown et al., 2012, Erken et al., 2013, Gardiner et al., 2015). A high prevalence of virulence homologues in the marine environment (Rusch et al., 2007, Persson et al., 2009, Sunagawa et al., 2015) suggests evolutionary selection and high transmission rates of virulence genes (Schmidt & Hensel, 2004, Brown et al., 2012). Thus, in light of changing environmental conditions, which is known to induce both physiological stress on host defence systems (Doney et al., 2012, Wernberg et al., 2013) and regulate expression of virulence genes in pathogens (Campbell et al., 2011, Case et al., 2011, Crisafi et al., 2013), an increased understanding of the impact of opportunistic pathogens on habitat-forming organisms is paramount.

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A traditional culturing approach was successfully used in Chapter 2 to target the isolation of bacteria involved in disease-associated community shifts on D. pulchra. Comparative analysis on sequences of isolates in the culture collection to a former culture-independent study (Zozaya- Valdes et al., 2015) identified not only the isolates driving community shifts on bleached D. pulchra but those that were also members of phylogenetic groups implicated in disease of other marine hosts. The presence of unique virulence mechanisms and unique overall patterns of disease, despite induction of the same disease symptoms in the host supports the concept of a spectrum of possibilities where opportunistic pathogens are involved (Casadevall & Pirofski, 2001, Casadevall & Pirofski, 2002, Lorenzo, 2015). Future work involving time-series investigations of disease progression following inoculation with the individual pathogens is required to understand the dynamics behind unique disease patterns. Complemented with gene-knockout studies to characterise specific pathways predicted in this study to have a role in disease induction, meaningful insights will be gained into the bleaching disease of D. pulchra.

In conclusion, the findings of this thesis provides a deeper understanding of the characteristics of opportunistic pathogens on the surfaces of macroalgae. The work presented compliments the existing model of disease in D. pulchra and further shows that multiple opportunistic pathogens of macroalgae exist, which use a combination of known virulence determinants and fitness traits to induce disease in susceptible hosts under conditions of environmental stress. Through the identification of multiple new pathogens, the development of an infection assay and by identifying putative genes and pathways involved in virulence, the study provides a wide scope for future investigations of host-pathogen interactions in macroalgae.

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Appendix I

Primers (5’-3’) F27 AGA GTT TGA TCC TGG CTC AG R1492 ACG GCT ACC TTG TTA CGA CTT

Media and Buffers

Artificial seawater Per 10 L MilliQ water: NaCl 240 g

H3BO3 0.1144 g

CaCl2.2H2O 4.7211 g

C3H7Na2O6P.H2O 1 g EDTA 0.3001 g

MgSO4 39.13 g

MgCl2.6H2O 0.0433 g KCl 7 g

Na2O3Si.9H2O 0.3005 g

Na2MoO4.2H2O 0.0126 g

NaNO3 3 g Tris base 10 g

ZnCl2 0.0105 g

FeCl3 0.0581 g

500 μg/ml CoCl2.6H2O 810 μl

500 μg/ml CuCl2 860 μl Adjust salinity to 35 ppm Adjust pH to 8.3 using HCl Filter sterilize using a 0.22 µm filter Autoclave

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200 mM Sodium Borate Buffer (20X, pH 8.0) Sodium hydroxide 8 g Boric acid 47 g MilliQ water 1L

XS Buffer (per 50 ml) Potassium ethyl xanthogenate 0.5 g 1M Tris-HCl, pH 7.4 5 ml 0.45M EDTA, pH 8 2 ml 20% sodium dodecyl sulfate 2.5 ml 4M ammonium acetate 10 ml

dH2O up to 50 ml

189

Appendix II

Table 2.2: Origin of isolates and their closest phylogenetic relative based on 16S rRNA gene sequence similarity

Representative Other isolates with BLAST sequence Origin of isolates and the number sequenced unique isolate identical sequence homology (97-100% identity) Algae 1 Algae 2 Algae 3 Algae 4 Algae 5 Algae 7 Adjacent Bleached Adjacent Bleached Adjacent Bleached Bleached Bleached Bleached tissue tissue tissue tissue tissue tissue tissue tissue tissue AD1 AD9,AD15,AD23, Aquimarina 8 4 AD26,AD27,AD29, latercula, AD30,BL1,BL5, NR_113824 BL15,BL22 AD2 AD3,AD4,AD11, Ruegeria atlantica, 10 4 5 14 13 3 AD12,AD17,AD21, NR_112615 AD22,AD31,AD33, AD42,AD46,AD51, AD61,AD62,AD66, AD67,AD68,AD70, AD72,AD75,AD76, AD86,AD87,AD89, AD90,AD93,AD94, BL4,BL13,BL24, BL25,BL38, BL39,BL41,BL42, BL43,BL44,BL47, BL48,BL55,BL57, BL64,BL79,BL81, BL91,BL93,BL106, BL4124

Representative Other isolates with BLAST sequence Origin of isolates and the number sequenced unique isolate identical sequence homology (97-100% identity) Algae 1 Algae 2 Algae 3 Algae 4 Algae 5 Algae 7 Adjacent Bleached Adjacent Bleached Adjacent Bleached Bleached Bleached Bleached tissue tissue tissue tissue tissue tissue tissue tissue tissue AD7 Acinetobacter 1 nosocomialis, NR_117931 AD8 AD16 Shimia marina, 2 NR_043300 AD10 Aquimarina longa, 1 NR_118251 AD28 Psychromonas 1 arctica, NR_028821 AD34 Ruegeria atlantica, 1 NR_112615 AD39 AD52,AD57,AD58 Microbulbifer 4 epialgicus, NR_041493

AD40 Vibrio gigantis, 1 NR_044079 AD41 Microbulbifer 1 epialgicus, NR_041493 AD44 Vibrio gigantis, 1 NR_044079 AD47 Vibrio owensii, 1 NR_117424 AD48 AD60,AD92,AD107 Microbulbifer 3 1 epialgicus, NR_041493

AD59 Microbulbifer 1 epialgicus, NR_041493 AD63 Vibrio alginolyticus, 1 NR_112059 AD65 Vibrio agarivorans, 1 NR_028946 AD77 BL16 Ruegeria atlantica, 1 1 NR_112615 AD82 Shewanella 1 pneumatophori, NR_041292 AD88 Maribacter 1 polysiphoniae, NR_042612 AD91 Ruegeria atlantica, 1 NR_112615 AD95 AD100 Vibrio superstes, 1 1 NR_025675 BL7 Agarivorans albus, 1 NR_114160

BL8 Shimia marina, 1 NR_043300

Representative Other isolates with BLAST sequence Origin of isolates and the number sequenced unique isolate identical sequence homology Algae 1 Algae 2 Algae 3 Algae 4 Algae 5 Algae 7 (97-100% identity) Adjacent Bleached Adjacent Bleached Adjacent Bleached Bleached Bleached Bleached tissue tissue tissue tissue tissue tissue tissue tissue tissue BL11 BL33 Dokdonia 2 donghaensis, NR_043455 BLL18 Winogradskyella 3 echinorum, NR_044564 BL21 BL117, BL118 Vibrio jasicida, 3 NR_113182 BL23 BL94, BL98, BL99, Dokdonia 1 6 BL104, BL105, donghaensis, BL108 NR_043455 BL26 Thalassobius 1 mediterraneus, NR_042377 BL28 Ruegeria atlantica, 1 NR_112615 BL29 Dokdonia genika, 1 NR_113943 BL30 AD105,AD108 Vibrio gigantis, 1 1 1 NR_044079 BL40 Ruegeria atlantica, 1 NR_112615 BL51 Ruegeria atlantica, 1 NR_112615 BL54 Ruegeria 1 meonggei, NR_118639 BL56 BL58,BL65,BL68, Vibrio alginolyticus, 7 BL71,BL73,BL75 NR_122059

Representative Other isolates with BLAST sequence Origin of isolates and the number sequenced unique isolate identical sequence homology Algae 1 Algae 2 Algae 3 Algae 4 Algae 5 Algae 7 (97-100% identity) Adjacent Bleached Adjacent Bleached Adjacent Bleached Bleached Bleached Bleached tissue tissue tissue tissue tissue tissue tissue tissue tissue BL59 Vibrio alginolyticus, 1 NR_122059 BL60 Vibrio alginolyticus, 1 NR_122059 BL62 BL74 Vibrio alginolyticus, 2 NR_122059 BL63 Vibrio alginolyticus, 1 NR_122059 BL86 Alteromonas 1 australica, NR_116737 BL88 BL111 Vibrio comitans, 2 NR_114030 BL89 Vibrio neptunius, 1 NR_025476 BL102 Photobacterium 1 aphoticum, NR_108484 BL103 Psychrobacter 1 submarinus, NR_025457 BL107 Dokdonia 1 donghaensis, NR_043455 BL109 PB43 Vibrio jasicida, 1 1 NR_113182 BL110 PB42 Alteromonas 1 1 genovensis, NR_042667

AD098 Vibrio 1 hangzhouensis, NR_044396 AD101 Vibrio jasicida, 1 NR_113182 AD102 Agarivorans albus, 1 NR_024788 AD103 Vibrio pomeroyi, 1 NR_025547

AD104 Vibrio 1 hangzhouensis, NR_044396

BL0114 Vibrio atypicus, 1 NR_116535

Representative Other isolates with BLAST sequence Origin of isolates and the number sequenced unique isolate identical sequence homology Algae 1 Algae 2 Algae 3 Algae 4 Algae 5 Algae 7 (97-100% identity) Adjacent Bleached Adjacent Bleached Adjacent Bleached Bleached Bleached Bleached tissue tissue tissue tissue tissue tissue tissue tissue tissue BL0115 Vibrio 1 hangzhouensis, NR_044396 BL0119 Ruegeria atlantica, 1 NR_112615 BL40121 Vibrio natriegens, 1 NR_113786 BL40122 Maribacter 1 dokdonensis, NR_043294 BL40123 Shewanella 1 waksmanii, NR_025684 BL40125 Vibrio natriegens, 1 NR_113786 PB1 Pseudoalteromonas 1 arabiensis, NR_113220 PB21 Cellulophaga 1 geojensis, NR_118002

Appendix III

Table 5.1: ANOVA table of results showing variability (interaction) in the bleaching outcome of juvenile D. pulchra over time following inoculation with candidate pathogens

Candidate pathogen Factor P value Aquimarina sp. AD1 interaction1 0.8425 time2 0.2284 outcome 0.0068* Aquimarina sp. AD10 interaction 0.3798 time 0.3798 outcome 0.3253 Aquimarina sp. BL5 interaction 0.5317 time 0.162 outcome 0.0537 Agarivorans sp. BL7 interaction 0.4853 time 0.1259 outcome 0.0052* Winogradskyella sp. BL18 interaction > 0.9999 time 0.3798 outcome > 0.9999 Alteromonas sp. BL110 interaction 0.3479 time 0.2608 outcome 0.0036* Alteromonas sp. LSS17 interaction 0.5209 time > 0.9999 outcome 0.0003* Nautella italica sp. R11 interaction 0.3479 time 0.3479 outcome 0.4354 Microbulbifer sp. U156 interaction 0.2205 time 0.147 outcome 0.0661 Microbulbifer sp. D250 interaction 0.8709 time 0.8709 outcome 0.4619 1 variability between the factors ‘time’ and ‘bleaching outcome’ 2 three independent experiments * denotes p-values below significance level of 0.05

197

Supplementary Information_S1

Gene ID Locus Tag Gene Product Name

Flagellar assembly and motility 2619814570 Ga0074152_1022 Flagellar basal body L-ring protein FlgH 2619814977 Ga0074152_103353 Flagellar basal body-associated protein FliL 2619817569 Ga0074152_111236 Flagellar motor component MotA 2619817570 Ga0074152_111237 Flagellar motor protein MotB 2619817902 Ga0074152_111569 flagellar hook-basal body complex protein FliE 2619817903 Ga0074152_111570 flagellar basal-body M-ring protein/flagellar hook-basal body protein (fliF) 2619817904 Ga0074152_111571 flagellar motor switch protein FliG 2619817905 Ga0074152_111572 Flagellar biosynthesis/type III secretory pathway protein FliH 2619817906 Ga0074152_111573 flagellar protein export ATPase FliI 2619817907 Ga0074152_111574 flagellar export protein FliJ 2619817908 Ga0074152_111575 Flagellar hook-length control protein FliK 2619817909 Ga0074152_111576 Flagellar basal body-associated protein FliL 2619817910 Ga0074152_111577 flagellar motor switch protein FliM 2619817911 Ga0074152_111578 flagellar motor switch protein FliN 2619817912 Ga0074152_111579 flagellar biosynthetic protein FliO 2619817913 Ga0074152_111580 flagellar biosynthetic protein FliP 2619817914 Ga0074152_111581 Flagellar biosynthesis protein FliQ 2619817915 Ga0074152_111582 flagellar biosynthetic protein FliR 2619817916 Ga0074152_111583 flagellar biosynthetic protein FlhB 2619817917 Ga0074152_111584 flagellar biosynthesis protein FlhA 2619817918 Ga0074152_111585 flagellar biosynthetic protein FlhF 2619817919 Ga0074152_111586 MinD-like ATPase involved in chromosome partitioning or flagellar assembly 2619817925 Ga0074152_111592 Flagellar motor component MotA 2619817926 Ga0074152_111593 Flagellar motor protein MotB 2619817992 Ga0074152_111659 Flagellar biosynthesis/type III secretory pathway chaperone 2619817994 Ga0074152_111661 flagella basal body P-ring formation protein FlgA 2619817997 Ga0074152_111664 flagellar basal-body rod protein FlgB 2619817998 Ga0074152_111665 flagellar basal-body rod protein FlgC 2619817999 Ga0074152_111666 Flagellar hook assembly protein FlgD 2619818000 Ga0074152_111667 flagellar hook-basal body protein 2619818001 Ga0074152_111668 flagellar basal-body rod protein FlgF 2619818002 Ga0074152_111669 flagellar basal-body rod protein FlgG, Gram-negative bacteria 2619818003 Ga0074152_111670 Flagellar basal body L-ring protein FlgH 2619818004 Ga0074152_111671 Flagellar basal body P-ring protein FlgI 2619818006 Ga0074152_111673 flagellar hook-associated protein FlgK 2619818007 Ga0074152_111674 flagellar hook-associated protein 3 2619818008 Ga0074152_111675 Flagellin and related hook-associated protein FlgL 2619818009 Ga0074152_111676 Flagellin and related hook-associated protein FlgL 2619818011 Ga0074152_111678 Flagellar capping protein FliD 2619818012 Ga0074152_111679 flagellar biosynthetic protein FliS

198

2619818076 Ga0074152_111743 FimV N-terminal domain 2578286985 ALTBL110_00962 proton translocating ATP synthase, F1 alpha subunit 2578289253 ALTBL110_03231 flagellar protein export ATPase FliI 2578286983 ALTBL110_00960 ATP synthase, F1 beta subunit 2578286304 ALTBL110_00280 Flagellin and related hook-associated proteins 2578286369 ALTBL110_00345 Flagellar basal body-associated protein 2578287052 ALTBL110_01029 Flagellar motor protein 2578287053 ALTBL110_01030 Flagellar motor component 2578288353 ALTBL110_02330 Flagellar protein YcgR/PilZ domain 2578289029 ALTBL110_03006 Flagellin and related hook-associated proteins 2578289241 ALTBL110_03219 flagellar biosynthetic protein FlhF 2578289242 ALTBL110_03220 flagellar biosynthesis protein FlhA 2578289243 ALTBL110_03221 flagellar biosynthetic protein FlhB 2578289244 ALTBL110_03222 flagellar biosynthetic protein FliR 2578289245 ALTBL110_03223 flagellar biosynthetic protein FliQ 2578289246 ALTBL110_03224 flagellar biosynthetic protein FliP 2578289247 ALTBL110_03225 flagellar biosynthetic protein FliO 2578289248 ALTBL110_03226 flagellar motor switch protein FliN 2578289249 ALTBL110_03227 flagellar motor switch protein FliM 2578289250 ALTBL110_03228 Flagellar basal body-associated protein 2578289251 ALTBL110_03229 Flagellar hook-length control protein FliK 2578289252 ALTBL110_03230 flagellar export protein FliJ 2578289254 ALTBL110_03232 Flagellar biosynthesis/type III secretory pathway protein 2578289255 ALTBL110_03233 flagellar motor switch protein FliG 2578289256 ALTBL110_03234 flagellar basal-body M-ring protein/flagellar hook-basal body protein (fliF) 2578289257 ALTBL110_03235 flagellar hook-basal body complex protein FliE 2578289261 ALTBL110_03239 Bacterial flagellin N-terminal helical region 2578289273 ALTBL110_03251 flagellar biosynthetic protein FliS 2578289274 ALTBL110_03252 Flagellar capping protein 2578289275 ALTBL110_03253 Uncharacterized flagellar protein FlaG 2578289276 ALTBL110_03254 Bacterial flagellin N-terminal helical region 2578289277 ALTBL110_03255 Bacterial flagellin N-terminal helical region 2578289278 ALTBL110_03256 Bacterial flagellin N-terminal helical region 2578289280 ALTBL110_03258 flagellar hook-associated protein 3 2578289281 ALTBL110_03259 flagellar hook-associated protein FlgK 2578289282 ALTBL110_03260 flagellar rod assembly protein/muramidase FlgJ 2578289283 ALTBL110_03261 Flagellar basal-body P-ring protein 2578289284 ALTBL110_03262 Flagellar basal body L-ring protein 2578289285 ALTBL110_03263 flagellar basal-body rod protein FlgG, Gram-negative bacteria 2578289286 ALTBL110_03264 flagellar hook-basal body protein 2578289287 ALTBL110_03265 flagellar hook-basal body protein 2578289288 ALTBL110_03266 Flagellar hook capping protein 2578289289 ALTBL110_03267 flagellar basal-body rod protein FlgC 2578289290 ALTBL110_03268 flagellar basal-body rod protein FlgB 2578289293 ALTBL110_03271 flagella basal body P-ring formation protein FlgA 2578289294 ALTBL110_03272 anti-sigma-28 factor, FlgM family 199

2578289295 ALTBL110_03273 FlgN protein 2578289304 ALTBL110_03282 Putative flagellar system-associated repeat 2578289375 ALTBL110_03353 Flagellar hook-length control protein FliK 2578289376 ALTBL110_03354 Uncharacterized homolog of the cytoplasmic domain of flagellar protein FhlB 2578289784 ALTBL110_03763 Flagellar basal body L-ring protein 2578277267 ALTLSS17_00314 Uncharacterized homolog of the cytoplasmic domain of flagellar protein FhlB 2578277268 ALTLSS17_00315 Flagellar hook-length control protein FliK 2578277343 ALTLSS17_00390 Bacterial Ig-like domain (group 3)/Putative flagellar system-associated repeat 2578277352 ALTLSS17_00399 Flagellar biosynthesis/type III secretory pathway chaperone 2578277353 ALTLSS17_00400 anti-sigma-28 factor, FlgM family 2578277354 ALTLSS17_00401 flagella basal body P-ring formation protein FlgA 2578277357 ALTLSS17_00404 flagellar basal-body rod protein FlgB 2578277358 ALTLSS17_00405 flagellar basal-body rod protein FlgC 2578277359 ALTLSS17_00406 Flagellar hook capping protein 2578277360 ALTLSS17_00407 flagellar hook-basal body protein 2578277361 ALTLSS17_00408 flagellar hook-basal body protein 2578277362 ALTLSS17_00409 flagellar basal-body rod protein FlgG, Gram-negative bacteria 2578277363 ALTLSS17_00410 Flagellar basal body L-ring protein 2578277364 ALTLSS17_00411 Flagellar basal-body P-ring protein 2578277365 ALTLSS17_00412 flagellar rod assembly protein/muramidase FlgJ 2578277366 ALTLSS17_00413 flagellar hook-associated protein FlgK 2578277367 ALTLSS17_00414 flagellar hook-associated protein 3 2578277369 ALTLSS17_00416 Flagellin and related hook-associated proteins 2578277370 ALTLSS17_00417 Flagellin and related hook-associated proteins 2578277372 ALTLSS17_00419 Flagellin and related hook-associated proteins 2578277373 ALTLSS17_00420 Uncharacterized flagellar protein FlaG 2578277374 ALTLSS17_00421 Flagellar capping protein 2578277375 ALTLSS17_00422 flagellar biosynthetic protein FliS 2578277402 ALTLSS17_00449 Bacterial flagellin N-terminal helical region 2578277406 ALTLSS17_00453 flagellar hook-basal body complex protein FliE 2578277407 ALTLSS17_00454 flagellar basal-body M-ring protein/flagellar hook-basal body protein (fliF) 2578277408 ALTLSS17_00455 flagellar motor switch protein FliG 2578277409 ALTLSS17_00456 Flagellar biosynthesis/type III secretory pathway protein 2578277411 ALTLSS17_00458 flagellar export protein FliJ 2578277412 ALTLSS17_00459 Flagellar hook-length control protein 2578277413 ALTLSS17_00460 Flagellar basal body-associated protein 2578277414 ALTLSS17_00461 flagellar motor switch protein FliM 2578277415 ALTLSS17_00462 flagellar motor switch protein FliN 2578277416 ALTLSS17_00463 flagellar biosynthetic protein FliO 2578277417 ALTLSS17_00464 flagellar biosynthetic protein FliP 2578277418 ALTLSS17_00465 flagellar biosynthetic protein FliQ 2578277419 ALTLSS17_00466 flagellar biosynthetic protein FliR 2578277420 ALTLSS17_00467 flagellar biosynthetic protein FlhB 2578277421 ALTLSS17_00468 flagellar biosynthesis protein FlhA 2578277422 ALTLSS17_00469 flagellar biosynthetic protein FlhF 2578277657 ALTLSS17_00705 Flagellin and related hook-associated proteins 200

2578278983 ALTLSS17_02031 Flagellar motor component 2578278984 ALTLSS17_02032 Flagellar motor protein 2578279094 ALTLSS17_02142 Flagellar basal body L-ring protein 2578279460 ALTLSS17_02509 Flagellar basal body-associated protein 2578280074 ALTLSS17_03123 Flagellar protein YcgR/PilZ domain 2578280160 ALTLSS17_03209 proton translocating ATP synthase, F1 alpha subunit 2578279852 ALTLSS17_02901 proton translocating ATP synthase, F1 alpha subunit 2578277410 ALTLSS17_00457 flagellar protein export ATPase FliI 2578280153 ALTLSS17_03202 ATP synthase, F1 beta subunit 2578292424 AQUAD1_02545 ATP synthase, F1 beta subunit 2578292516 AQUAD1_02637 proton translocating ATP synthase, F1 alpha subunit 2578291779 AQUAD1_01899 Flagellar motor protein 2578293553 AQUAD1_03677 Flagellar motor protein 2578296677 AQUAD10_01987 ATP synthase, F1 beta subunit 2578294718 AQUAD10_00028 proton translocating ATP synthase, F1 alpha subunit 2578295732 AQUAD10_01042 Flagellar motor protein 2578296707 AQUAD10_02017 Flagellar motor protein 2578297616 AQUAD10_02926 Flagellar motor protein 2578298167 AQUAD10_03480 Flagellar motor protein 2578282998 AQUBL5_02091 ATP synthase, F1 beta subunit 2578283998 AQUBL5_03092 proton translocating ATP synthase, F1 alpha subunit 2578282744 AQUBL5_01834 Putative flagellar system-associated repeat 2578284758 AQUBL5_03852 Flagellar motor protein 2578285837 AQUBL5_04931 Flagellar motor protein

Two-component signal transduction systems

Chemotaxis 2619814582 Ga0074152_10214 Methyl-accepting chemotaxis protein 2619814618 Ga0074152_10250 Methyl-accepting chemotaxis protein 2619814684 Ga0074152_10360 Methyl-accepting chemotaxis protein 2619814726 Ga0074152_103102 Methyl-accepting chemotaxis protein 2619815167 Ga0074152_104135 Chemotaxis protein CheX, a CheY~P-specific phosphatase 2619815225 Ga0074152_104193 Methyl-accepting chemotaxis protein 2619815246 Ga0074152_104214 Methyl-accepting chemotaxis protein 2619815383 Ga0074152_105131 Methyl-accepting chemotaxis protein 2619815389 Ga0074152_105137 Methyl-accepting chemotaxis protein 2619815479 Ga0074152_105227 methyl-accepting chemotaxis sensory transducer with Cache sensor 2619815493 Ga0074152_105241 Methyl-accepting chemotaxis protein 2619815662 Ga0074152_105411 Chemotaxis signal transduction protein 2619815762 Ga0074152_105511 methyl-accepting chemotaxis sensory transducer with Cache sensor 2619815913 Ga0074152_10661 methyl-accepting chemotaxis sensory transducer with TarH sensor 2619816124 Ga0074152_107109 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2619816149 Ga0074152_107134 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2619816330 Ga0074152_107316 Chemotaxis signal transduction protein 2619816467 Ga0074152_107453 methyl-accepting chemotaxis sensory transducer with Cache sensor 201

2619816468 Ga0074152_107454 methyl-accepting chemotaxis sensory transducer with Cache sensor 2619816717 Ga0074152_10866 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2619816723 Ga0074152_10872 Methyl-accepting chemotaxis protein 2619816749 Ga0074152_10898 methyl-accepting chemotaxis sensory transducer with Cache sensor 2619817166 Ga0074152_1102 Methyl-accepting chemotaxis protein 2619817212 Ga0074152_11048 methyl-accepting chemotaxis sensory transducer with Cache sensor 2619817379 Ga0074152_11146 Methyl-accepting chemotaxis protein 2619817390 Ga0074152_11157 Methyl-accepting chemotaxis protein 2619817414 Ga0074152_11181 Methyl-accepting chemotaxis protein 2619817530 Ga0074152_111197 Methyl-accepting chemotaxis protein 2619817591 Ga0074152_111258 Methyl-accepting chemotaxis protein 2619817616 Ga0074152_111283 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2619817625 Ga0074152_111292 Methyl-accepting chemotaxis protein 2619817638 Ga0074152_111305 Methyl-accepting chemotaxis protein 2619817853 Ga0074152_111520 Methyl-accepting chemotaxis protein 2619817922 Ga0074152_111589 Chemotaxis regulator CheZ, phosphatase of CheY~P 2619817923 Ga0074152_111590 Chemotaxis protein histidine kinase CheA 2619817924 Ga0074152_111591 Chemotaxis response regulator CheB 2619817928 Ga0074152_111595 Chemotaxis signal transduction protein 2619817929 Ga0074152_111596 Chemotaxis signal transduction protein 2619817995 Ga0074152_111662 Chemotaxis signal transduction protein 2619817996 Ga0074152_111663 Methylase of chemotaxis methyl-accepting proteins 2619818088 Ga0074152_111755 Methyl-accepting chemotaxis protein 2619818151 Ga0074152_111818 Methyl-accepting chemotaxis protein 2619818257 Ga0074152_11253 Methyl-accepting chemotaxis protein 2619818269 Ga0074152_11265 Methyl-accepting chemotaxis protein 2619818305 Ga0074152_112101 Methyl-accepting chemotaxis protein 2619818519 Ga0074152_112315 Methyl-accepting chemotaxis protein 2578286480 ALTBL110_00457 Methyl-accepting chemotaxis protein 2578286583 ALTBL110_00560 Methyl-accepting chemotaxis protein 2578286584 ALTBL110_00561 Response regulator receiver domain 2578286585 ALTBL110_00562 Chemotaxis protein histidine kinase and related kinases 2578286586 ALTBL110_00563 Chemotaxis signal transduction protein 2578286587 ALTBL110_00564 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578286588 ALTBL110_00565 Methylase of chemotaxis methyl-accepting proteins 2578286589 ALTBL110_00566 Chemotaxis protein; stimulates methylation of MCP proteins 2578286590 ALTBL110_00567 Chemotaxis response regulator 2578286591 ALTBL110_00568 Methyl-accepting chemotaxis protein (MCP) signalling domain 2578286592 ALTBL110_00569 Response regulator receiver domain 2578286593 ALTBL110_00570 Chemotaxis protein histidine kinase and related kinases 2578286594 ALTBL110_00571 Chemotaxis signal transduction protein 2578286595 ALTBL110_00572 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578286596 ALTBL110_00573 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578286597 ALTBL110_00574 Methylase of chemotaxis methyl-accepting proteins 2578286598 ALTBL110_00575 Chemotaxis protein; stimulates methylation of MCP proteins 2578286599 ALTBL110_00576 Chemotaxis response regulator 202

2578286686 ALTBL110_00663 Predicted inhibitor of MCP methylation, homolog of CheC 2578286708 ALTBL110_00685 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578287140 ALTBL110_01117 Methyl-accepting chemotaxis protein 2578287194 ALTBL110_01171 Chemotaxis signal transduction protein 2578287540 ALTBL110_01517 methyl-accepting chemotaxis sensory transducer with Cache sensor 2578287685 ALTBL110_01662 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578287722 ALTBL110_01699 Chemotaxis protein CheC, inhibitor of MCP methylation 2578288065 ALTBL110_02042 methyl-accepting chemotaxis sensory transducer with TarH sensor 2578289039 ALTBL110_03016 Response regulator receiver domain 2578289232 ALTBL110_03210 Chemotaxis signal transduction protein 2578289233 ALTBL110_03211 Chemotaxis signal transduction protein 2578289235 ALTBL110_03213 Chemotaxis response regulator 2578289236 ALTBL110_03214 Chemotaxis protein histidine kinase and related kinases 2578289237 ALTBL110_03215 Chemotaxis protein 2578289238 ALTBL110_03216 Response regulator receiver domain 2578289291 ALTBL110_03269 Methylase of chemotaxis methyl-accepting proteins 2578289292 ALTBL110_03270 Chemotaxis signal transduction protein 2578289434 ALTBL110_03412 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578289532 ALTBL110_03510 Methyl-accepting chemotaxis protein 2578289597 ALTBL110_03576 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578289662 ALTBL110_03641 methyl-accepting chemotaxis sensory transducer with Cache sensor 2578289856 ALTBL110_03835 methyl-accepting chemotaxis sensory transducer with Cache sensor 2578276976 ALTLSS17_00023 methyl-accepting chemotaxis sensory transducer with Cache sensor 2578277039 ALTLSS17_00086 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578277106 ALTLSS17_00153 Methyl-accepting chemotaxis protein 2578277206 ALTLSS17_00253 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578277355 ALTLSS17_00402 Chemotaxis signal transduction protein 2578277356 ALTLSS17_00403 Methylase of chemotaxis methyl-accepting proteins 2578277425 ALTLSS17_00472 Response regulator receiver domain 2578277426 ALTLSS17_00473 Chemotaxis protein 2578277427 ALTLSS17_00474 Chemotaxis protein histidine kinase and related kinases 2578277428 ALTLSS17_00475 Chemotaxis response regulator 2578277430 ALTLSS17_00477 Chemotaxis signal transduction protein 2578277431 ALTLSS17_00478 Chemotaxis signal transduction protein 2578277645 ALTLSS17_00693 Methyl-accepting chemotaxis protein 2578277647 ALTLSS17_00695 Response regulator receiver domain 2578278181 ALTLSS17_01229 Methyl-accepting chemotaxis protein 2578278266 ALTLSS17_01314 CheC-like family/Chemotaxis phosphatase CheX 2578278853 ALTLSS17_01901 Chemotaxis signal transduction protein 2578278899 ALTLSS17_01947 Methyl-accepting chemotaxis protein 2578279164 ALTLSS17_02212 methyl-accepting chemotaxis sensory transducer with Cache sensor 2578279207 ALTLSS17_02255 Chemotaxis response regulator domain 2578279208 ALTLSS17_02256 Chemotaxis protein; stimulates methylation of MCP proteins 2578279209 ALTLSS17_02257 Methylase of chemotaxis methyl-accepting proteins 2578279210 ALTLSS17_02258 PAS domain S-box 2578279211 ALTLSS17_02259 Chemotaxis signal transduction protein 203

2578279212 ALTLSS17_02260 Chemotaxis protein histidine kinase and related kinases 2578279213 ALTLSS17_02261 Response regulator receiver domain 2578279214 ALTLSS17_02262 Methyl-accepting chemotaxis protein (MCP) signalling domain 2578279316 ALTLSS17_02364 Methyl-accepting chemotaxis protein 2578279562 ALTLSS17_02611 Predicted inhibitor of MCP methylation, homolog of CheC 2578279578 ALTLSS17_02627 methyl-accepting chemotaxis sensory transducer with Pas/Pac sensor 2578279673 ALTLSS17_02722 Methyl-accepting chemotaxis protein 2578280664 ALTLSS17_03713 Methyl-accepting chemotaxis protein 2578291915 AQUAD1_02036 OmpA family 2578292603 AQUAD1_02724 OmpA family 2578282370 AQUBL5_01460 OmpA family 2578283306 AQUBL5_02400 OmpA family

Quorum sensing 2619814610 Ga0074152_10242 Bacterial regulatory proteins, luxR family 2619816697 Ga0074152_10846 two component transcriptional regulator, LuxR family 2619818509 Ga0074152_112305 two component transcriptional regulator, LuxR family 2619818877 Ga0074152_112673 two component transcriptional regulator, LuxR family 2578286241 ALTBL110_00217 Bacterial regulatory proteins, luxR family 2578286165 ALTBL110_00141 two component transcriptional regulator, LuxR family 2578287678 ALTBL110_01655 two component transcriptional regulator, LuxR family 2578288190 ALTBL110_02167 two component transcriptional regulator, LuxR family 2578287465 ALTBL110_01442 two component transcriptional regulator, LuxR family 2578287683 ALTBL110_01660 two component transcriptional regulator, LuxR family 2578288131 ALTBL110_02108 two component transcriptional regulator, LuxR family 2578287698 ALTBL110_01675 two component transcriptional regulator, LuxR family 2578289153 ALTBL110_03130 two component transcriptional regulator, LuxR family 2578287691 ALTBL110_01668 Bacterial regulatory proteins, luxR family 2578289300 ALTBL110_03278 Bacterial regulatory proteins, luxR family 2578286176 ALTBL110_00152 two component transcriptional regulator, LuxR family 2578288899 ALTBL110_02876 two component transcriptional regulator, LuxR family 2578287499 ALTBL110_01476 two component transcriptional regulator, LuxR family 2578277790 ALTLSS17_00838 two component transcriptional regulator, LuxR family 2578280643 ALTLSS17_03692 Bacterial regulatory proteins, luxR family 2578277347 ALTLSS17_00394 Bacterial regulatory proteins, luxR family 2578278305 ALTLSS17_01353 two component transcriptional regulator, LuxR family 2578278476 ALTLSS17_01524 two component transcriptional regulator, LuxR family 2578280733 ALTLSS17_03782 two component transcriptional regulator, LuxR family 2578280744 ALTLSS17_03793 two component transcriptional regulator, LuxR family 2578278221 ALTLSS17_01269 two component transcriptional regulator, LuxR family 2578278434 ALTLSS17_01482 two component transcriptional regulator, LuxR family 2578278310 ALTLSS17_01358 two component transcriptional regulator, LuxR family 2578279955 ALTLSS17_03004 two component transcriptional regulator, LuxR family 2578294372 AQUAD1_04497 two component transcriptional regulator, LuxR family 2578289991 AQUAD1_00109 Y_Y_Y domain/Bacterial regulatory proteins, luxR family

204

2578290104 AQUAD1_00222 two component transcriptional regulator, LuxR family 2578293202 AQUAD1_03326 two component transcriptional regulator, LuxR family 2578291213 AQUAD1_01332 two component transcriptional regulator, LuxR family 2578294203 AQUAD1_04327 two component transcriptional regulator, LuxR family 2578293661 AQUAD1_03785 Tetratricopeptide repeat/Bacterial regulatory proteins, luxR family 2578290046 AQUAD1_00164 two component transcriptional regulator, LuxR family 2578292656 AQUAD1_02777 two component transcriptional regulator, LuxR family 2578294571 AQUAD1_04696 two component transcriptional regulator, LuxR family 2578294024 AQUAD1_04148 Bacterial regulatory proteins, luxR family 2578290366 AQUAD1_00485 two component transcriptional regulator, LuxR family 2578291552 AQUAD1_01672 Bacterial regulatory proteins, luxR family 2578292964 AQUAD1_03085 Y_Y_Y domain/Bacterial regulatory proteins, luxR family 2578292427 AQUAD1_02548 two component transcriptional regulator, LuxR family 2578290184 AQUAD1_00302 Tetratricopeptide repeat/Bacterial regulatory proteins, luxR family 2578292059 AQUAD1_02180 two component transcriptional regulator, LuxR family 2578295652 AQUAD10_00962 two component transcriptional regulator, LuxR family 2578299698 AQUAD10_05015 Tetratricopeptide repeat/Bacterial regulatory proteins, luxR family 2578299702 AQUAD10_05019 Bacterial regulatory proteins, luxR family 2578295139 AQUAD10_00449 Y_Y_Y domain/Bacterial regulatory proteins, luxR family 2578297786 AQUAD10_03097 Bacterial regulatory proteins, luxR family 2578295707 AQUAD10_01017 two component transcriptional regulator, LuxR family 2578296886 AQUAD10_02196 two component transcriptional regulator, LuxR family 2578299378 AQUAD10_04693 Bacterial regulatory proteins, luxR family 2578297333 AQUAD10_02643 Tetratricopeptide repeat/Bacterial regulatory proteins, luxR family 2578295309 AQUAD10_00619 Bacterial regulatory proteins, luxR family 2578296929 AQUAD10_02239 two component transcriptional regulator, LuxR family 2578298144 AQUAD10_03457 two component transcriptional regulator, LuxR family 2578295469 AQUAD10_00779 Bacterial regulatory proteins, luxR family 2578296144 AQUAD10_01454 Tetratricopeptide repeat/Bacterial regulatory proteins, luxR family 2578295463 AQUAD10_00773 two component transcriptional regulator, LuxR family 2578299697 AQUAD10_05014 Bacterial regulatory proteins, luxR family 2578299835 AQUAD10_05152 Bacterial regulatory proteins, luxR family 2578296265 AQUAD10_01575 two component transcriptional regulator, LuxR family 2578297382 AQUAD10_02692 Bacterial regulatory proteins, luxR family 2578296066 AQUAD10_01376 two component transcriptional regulator, LuxR family 2578295138 AQUAD10_00448 Bacterial regulatory proteins, luxR family 2578294956 AQUAD10_00266 two component transcriptional regulator, LuxR family 2578285068 AQUBL5_04162 two component transcriptional regulator, LuxR family 2578285605 AQUBL5_04699 Bacterial regulatory proteins, luxR family 2578283974 AQUBL5_03068 Bacterial regulatory proteins, luxR family 2578285854 AQUBL5_04948 Tetratricopeptide repeat/Bacterial regulatory proteins, luxR family 2578282055 AQUBL5_01144 Bacterial regulatory proteins, luxR family 2578281318 AQUBL5_00405 two component transcriptional regulator, LuxR family 2578285392 AQUBL5_04486 Two component regulator propeller/Bacterial regulatory proteins, luxR family 2578283952 AQUBL5_03046 two component transcriptional regulator, LuxR family 2578284237 AQUBL5_03331 two component transcriptional regulator, LuxR family 205

2578282529 AQUBL5_01619 two component transcriptional regulator, LuxR family 2578284054 AQUBL5_03148 two component transcriptional regulator, LuxR family 2578281823 AQUBL5_00912 two component transcriptional regulator, LuxR family 2578285782 AQUBL5_04876 two component transcriptional regulator, LuxR family 2578284170 AQUBL5_03264 Y_Y_Y domain/Bacterial regulatory proteins, luxR family 2578282949 AQUBL5_02042 Bacterial regulatory proteins, luxR family 2578285969 AQUBL5_05063 two component transcriptional regulator, LuxR family 2578284484 AQUBL5_03578 two component transcriptional regulator, LuxR family 2578283612 AQUBL5_02706 Bacterial regulatory proteins, luxR family 2578285121 AQUBL5_04215 two component transcriptional regulator, LuxR family 2578285521 AQUBL5_04615 two component transcriptional regulator, LuxR family

Cyclic-di-GMP signalling 2619817173 Ga0074152_1109 diguanylate cyclase (GGDEF) domain 2619815646 Ga0074152_105395 diguanylate cyclase (GGDEF) domain 2619818094 Ga0074152_111761 diguanylate cyclase (GGDEF) domain 2619818478 Ga0074152_112274 diguanylate cyclase (GGDEF) domain 2619815673 Ga0074152_105422 diguanylate cyclase (GGDEF) domain 2619816213 Ga0074152_107198 diguanylate cyclase (GGDEF) domain 2619818251 Ga0074152_11247 diguanylate cyclase (GGDEF) domain 2619816915 Ga0074152_108264 diguanylate cyclase (GGDEF) domain 2619817376 Ga0074152_11143 diguanylate cyclase (GGDEF) domain 2619818698 Ga0074152_112494 diguanylate cyclase (GGDEF) domain 2619817309 Ga0074152_110145 diguanylate cyclase (GGDEF) domain 2619815755 Ga0074152_105504 diguanylate cyclase (GGDEF) domain 2619818323 Ga0074152_112119 diguanylate cyclase (GGDEF) domain 2619818308 Ga0074152_112104 diguanylate cyclase (GGDEF) domain 2619815391 Ga0074152_105139 diguanylate cyclase (GGDEF) domain 2619818925 Ga0074152_112721 diguanylate cyclase (GGDEF) domain 2619816379 Ga0074152_107365 diguanylate cyclase (GGDEF) domain 2619818688 Ga0074152_112484 diguanylate cyclase (GGDEF) domain 2619818638 Ga0074152_112434 diguanylate cyclase (GGDEF) domain 2619818353 Ga0074152_112149 diguanylate cyclase (GGDEF) domain 2619815717 Ga0074152_105466 diguanylate cyclase (GGDEF) domain 2619816617 Ga0074152_107603 diguanylate cyclase (GGDEF) domain 2619816759 Ga0074152_108108 diguanylate cyclase (GGDEF) domain 2619815951 Ga0074152_10699 diguanylate cyclase (GGDEF) domain 2619815775 Ga0074152_105524 diguanylate cyclase (GGDEF) domain 2619816843 Ga0074152_108192 diguanylate cyclase (GGDEF) domain 2619815490 Ga0074152_105238 diguanylate cyclase (GGDEF) domain 2619818311 Ga0074152_112107 diguanylate cyclase (GGDEF) domain 2619817336 Ga0074152_1113 diguanylate cyclase (GGDEF) domain 2619817209 Ga0074152_11045 diguanylate cyclase (GGDEF) domain 2619815439 Ga0074152_105187 diguanylate cyclase (GGDEF) domain 2619818069 Ga0074152_111736 diguanylate cyclase (GGDEF) domain

206

2619814932 Ga0074152_103308 diguanylate cyclase (GGDEF) domain/hemerythrin-like metal-binding domain 2619815209 Ga0074152_104177 EAL domain, c-di-GMP-specific phosphodiesterase class I 2619817901 Ga0074152_111568 EAL domain, c-di-GMP-specific phosphodiesterase class I 2619817513 Ga0074152_111180 EAL domain, c-di-GMP-specific phosphodiesterase class I 2619818367 Ga0074152_112163 Environmental sensor c-di-GMP phosphodiesterase, EAL domain 2619818445 Ga0074152_112241 Environmental sensor c-di-GMP phosphodiesterase, EAL domain 2619817470 Ga0074152_111137 EAL domain, c-di-GMP-specific phosphodiesterase class I 2619815677 Ga0074152_105426 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2619818626 Ga0074152_112422 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2619817708 Ga0074152_111375 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2619817721 Ga0074152_111388 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2619816946 Ga0074152_108295 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2619815515 Ga0074152_105263 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2619815480 Ga0074152_105228 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2619818717 Ga0074152_112513 Two-component response regulator, PleD family 2578286815 ALTBL110_00792 diguanylate cyclase (GGDEF) domain 2578288554 ALTBL110_02531 diguanylate cyclase (GGDEF) domain 2578289475 ALTBL110_03453 diguanylate cyclase (GGDEF) domain 2578289299 ALTBL110_03277 FOG: EAL domain 2578287704 ALTBL110_01681 diguanylate cyclase (GGDEF) domain 2578288738 ALTBL110_02715 diguanylate cyclase (GGDEF) domain 2578287648 ALTBL110_01625 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578286154 ALTBL110_00130 diguanylate cyclase (GGDEF) domain 2578286072 ALTBL110_00048 diguanylate cyclase (GGDEF) domain 2578286602 ALTBL110_00579 diguanylate cyclase (GGDEF) domain 2578287633 ALTBL110_01610 diguanylate cyclase (GGDEF) domain 2578289068 ALTBL110_03045 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578286376 ALTBL110_00352 diguanylate cyclase (GGDEF) domain 2578287739 ALTBL110_01716 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578288677 ALTBL110_02654 diguanylate cyclase (GGDEF) domain 2578289161 ALTBL110_03138 diguanylate cyclase (GGDEF) domain 2578289793 ALTBL110_03772 FOG: EAL domain 2578286426 ALTBL110_00403 diguanylate cyclase (GGDEF) domain 2578288043 ALTBL110_02020 diguanylate cyclase (GGDEF) domain 2578288982 ALTBL110_02959 diguanylate cyclase (GGDEF) domain 2578288790 ALTBL110_02767 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578289315 ALTBL110_03293 diguanylate cyclase (GGDEF) domain 2578287241 ALTBL110_01218 diguanylate cyclase (GGDEF) domain 2578288151 ALTBL110_02128 diguanylate cyclase (GGDEF) domain 2578288774 ALTBL110_02751 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578288662 ALTBL110_02639 Predicted signal transduction protein containing sensor and EAL domains 2578287417 ALTBL110_01394 diguanylate cyclase (GGDEF) domain 2578286773 ALTBL110_00750 diguanylate cyclase (GGDEF) domain 2578288996 ALTBL110_02973 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578286491 ALTBL110_00468 diguanylate cyclase (GGDEF) domain 2578286467 ALTBL110_00444 diguanylate cyclase (GGDEF) domain 207

2578288623 ALTBL110_02600 diguanylate cyclase (GGDEF) domain 2578289082 ALTBL110_03059 diguanylate cyclase (GGDEF) domain 2578288474 ALTBL110_02451 diguanylate cyclase (GGDEF) domain 2578289645 ALTBL110_03624 diguanylate cyclase (GGDEF) domain 2578286049 ALTBL110_00025 diguanylate cyclase (GGDEF) domain 2578287976 ALTBL110_01953 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578289347 ALTBL110_03325 diguanylate cyclase (GGDEF) domain 2578286202 ALTBL110_00178 FOG: EAL domain 2578286484 ALTBL110_00461 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578288685 ALTBL110_02662 diguanylate cyclase (GGDEF) domain 2578288773 ALTBL110_02750 diguanylate cyclase (GGDEF) domain 2578286931 ALTBL110_00908 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578286364 ALTBL110_00340 diguanylate cyclase (GGDEF) domain 2578286100 ALTBL110_00076 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578288802 ALTBL110_02779 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578287508 ALTBL110_01485 diguanylate cyclase (GGDEF) domain 2578289034 ALTBL110_03011 diguanylate cyclase (GGDEF) domain 2578289828 ALTBL110_03807 GGDEF domain/PAS fold/EAL domain 2578287302 ALTBL110_01279 diguanylate cyclase (GGDEF) domain 2578288801 ALTBL110_02778 FOG: EAL domain 2578289749 ALTBL110_03728 diguanylate cyclase (GGDEF) domain 2578288819 ALTBL110_02796 diguanylate cyclase (GGDEF) domain 2578288583 ALTBL110_02560 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578277013 ALTLSS17_00060 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578277296 ALTLSS17_00343 diguanylate cyclase (GGDEF) domain 2578279134 ALTLSS17_02182 diguanylate cyclase (GGDEF) domain 2578277165 ALTLSS17_00212 diguanylate cyclase (GGDEF) domain 2578280838 ALTLSS17_03887 GGDEF domain/Response regulator receiver domain 2578280327 ALTLSS17_03376 diguanylate cyclase (GGDEF) domain 2578278045 ALTLSS17_01093 diguanylate cyclase (GGDEF) domain 2578277982 ALTLSS17_01030 diguanylate cyclase (GGDEF) domain 2578279455 ALTLSS17_02504 diguanylate cyclase (GGDEF) domain 2578279060 ALTLSS17_02108 diguanylate cyclase (GGDEF) domain 2578280755 ALTLSS17_03804 diguanylate cyclase (GGDEF) domain 2578280579 ALTLSS17_03628 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578279103 ALTLSS17_02151 FOG: EAL domain 2578277566 ALTLSS17_00614 diguanylate cyclase (GGDEF) domain 2578280606 ALTLSS17_03655 FOG: EAL domain 2578280223 ALTLSS17_03272 diguanylate cyclase (GGDEF) domain 2578278349 ALTLSS17_01397 diguanylate cyclase (GGDEF) domain 2578277520 ALTLSS17_00568 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578278134 ALTLSS17_01182 diguanylate cyclase (GGDEF) domain 2578279312 ALTLSS17_02360 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578277603 ALTLSS17_00651 diguanylate cyclase (GGDEF) domain 2578280191 ALTLSS17_03240 diguanylate cyclase (GGDEF) domain 2578278287 ALTLSS17_01335 diguanylate cyclase (GGDEF) domain 208

2578279352 ALTLSS17_02400 diguanylate cyclase (GGDEF) domain 2578277209 ALTLSS17_00256 diguanylate cyclase (GGDEF) domain 2578278513 ALTLSS17_01561 diguanylate cyclase (GGDEF) domain 2578278005 ALTLSS17_01053 Predicted signal transduction protein containing sensor and EAL domains 2578279658 ALTLSS17_02707 diguanylate cyclase (GGDEF) domain 2578278328 ALTLSS17_01376 diguanylate cyclase (GGDEF) domain 2578278803 ALTLSS17_01851 diguanylate cyclase (GGDEF) domain 2578277332 ALTLSS17_00379 diguanylate cyclase (GGDEF) domain 2578280791 ALTLSS17_03840 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578280410 ALTLSS17_03459 diguanylate cyclase (GGDEF) domain 2578280483 ALTLSS17_03532 diguanylate cyclase (GGDEF) domain 2578279681 ALTLSS17_02730 diguanylate cyclase (GGDEF) domain 2578277858 ALTLSS17_00906 diguanylate cyclase (GGDEF) domain 2578277105 ALTLSS17_00152 diguanylate cyclase (GGDEF) domain 2578277925 ALTLSS17_00973 diguanylate cyclase (GGDEF) domain 2578277973 ALTLSS17_01021 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578277832 ALTLSS17_00880 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578280196 ALTLSS17_03245 diguanylate cyclase (GGDEF) domain 2578280419 ALTLSS17_03468 diguanylate cyclase (GGDEF) domain 2578279337 ALTLSS17_02385 diguanylate cyclase (GGDEF) domain 2578277652 ALTLSS17_00700 diguanylate cyclase (GGDEF) domain 2578277348 ALTLSS17_00395 FOG: EAL domain 2578277617 ALTLSS17_00665 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578277013 ALTLSS17_00060 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578277891 ALTLSS17_00939 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578278731 ALTLSS17_01779 diguanylate cyclase (GGDEF) domain 2578279329 ALTLSS17_02377 diguanylate cyclase (GGDEF) domain 2578278274 ALTLSS17_01322 diguanylate cyclase (GGDEF) domain 2578279304 ALTLSS17_02352 diguanylate cyclase (GGDEF) domain 2578277892 ALTLSS17_00940 diguanylate cyclase (GGDEF) domain 2578279465 ALTLSS17_02514 diguanylate cyclase (GGDEF) domain 2578277710 ALTLSS17_00758 diguanylate cyclase (GGDEF) domain 2578277990 ALTLSS17_01038 diguanylate cyclase (GGDEF) domain 2578277198 ALTLSS17_00245 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578279697 ALTLSS17_02746 diguanylate cyclase (GGDEF) domain 2578278327 ALTLSS17_01375 diguanylate cyclase (GGDEF) domain 2578280822 ALTLSS17_03871 diguanylate cyclase (GGDEF) domain 2578278427 ALTLSS17_01475 diguanylate cyclase (GGDEF) domain 2578277679 ALTLSS17_00727 diguanylate cyclase (GGDEF) domain 2578277873 ALTLSS17_00921 diguanylate cyclase (GGDEF) domain 2578277877 ALTLSS17_00925 PAS domain S-box/diguanylate cyclase (GGDEF) domain 2578276993 ALTLSS17_00040 diguanylate cyclase (GGDEF) domain

Curli, capsule, pili, surface proteins 2619814947 Ga0074152_103323 type II secretion system protein E

209

2619814948 Ga0074152_103324 type II secretion system protein F 2619814949 Ga0074152_103325 type II secretion system protein G 2619815051 Ga0074152_10419 Type II secretory pathway ATPase GspE/PulE or T4P pilus assembly pathway 2619815052 Ga0074152_10420 Type II secretory pathway, component PulF 2619815055 Ga0074152_10423 prepilin-type N-terminal cleavage/methylation domain 2619815130 Ga0074152_10498 type 4 prepilin peptidase 1 (EC:3.4.23.43). Aspartic peptidase 2619815131 Ga0074152_10499 Type II secretory pathway, component PulF 2619815132 Ga0074152_104100 type IV-A pilus assembly ATPase PilB 2619815133 Ga0074152_104101 prepilin-type N-terminal cleavage/methylation domain 2619815279 Ga0074152_10527 Tfp pilus assembly protein PilP 2619815280 Ga0074152_10528 Tfp pilus assembly protein PilO 2619815281 Ga0074152_10529 Tfp pilus assembly protein PilN 2619815282 Ga0074152_10530 type IV pilus assembly protein PilM 2619815353 Ga0074152_105101 Tfp pilus assembly protein, tip-associated adhesin PilY1 2619815354 Ga0074152_105102 Tfp pilus assembly protein, tip-associated adhesin PilY1 2619815355 Ga0074152_105103 prepilin-type N-terminal cleavage/methylation domain 2619815356 Ga0074152_105104 Tfp pilus assembly protein PilX 2619815359 Ga0074152_105107 prepilin-type N-terminal cleavage/methylation domain 2619815781 Ga0074152_105530 prepilin-type N-terminal cleavage/methylation domain 2619816659 Ga0074152_1088 prepilin-type N-terminal cleavage/methylation domain 2619816661 Ga0074152_10810 prepilin-type N-terminal cleavage/methylation domain 2619816664 Ga0074152_10813 prepilin-type N-terminal cleavage/methylation domain 2619816665 Ga0074152_10814 type IV pilus modification protein PilV 2619816833 Ga0074152_108182 Lipopolysaccharide export LptBFGC system, permease protein LptF 2619816834 Ga0074152_108183 Lipopolysaccharide export LptBFGC system, permease protein LptF 2619817123 Ga0074152_10940 prepilin-type N-terminal cleavage/methylation domain 2619817229 Ga0074152_11065 type IV pilus biogenesis/stability protein PilW 2619817905 Ga0074152_111572 Flagellar biosynthesis/type III secretory pathway protein FliH 2619817992 Ga0074152_111659 Flagellar biosynthesis/type III secretory pathway chaperone 2578286353 ALTBL110_00329 Capsule biosynthesis GfcC 2578286352 ALTBL110_00328 Group 4 capsule polysaccharide formation lipoprotein gfcB 2578289361 ALTBL110_03339 Capsule polysaccharide export protein 2578286031 ALTBL110_00006 pantothenate kinase, type III 2578287810 ALTBL110_01787 HopJ type III effector protein 2578289365 ALTBL110_03343 Surface lipoprotein 2578288177 ALTBL110_02154 Protein required for attachment to host cells 2578287090 ALTBL110_01067 prepilin-type N-terminal cleavage/methylation domain 2578286185 ALTBL110_00161 prepilin-type N-terminal cleavage/methylation domain 2578287092 ALTBL110_01069 prepilin-type N-terminal cleavage/methylation domain 2578287382 ALTBL110_01359 Type II secretory pathway, prepilin signal peptidase PulO and related peptidases 2578286550 ALTBL110_00527 prepilin-type N-terminal cleavage/methylation domain 2578286552 ALTBL110_00529 prepilin-type N-terminal cleavage/methylation domain 2578287099 ALTBL110_01076 prepilin-type N-terminal cleavage/methylation domain 2578287380 ALTBL110_01357 prepilin-type N-terminal cleavage/methylation domain 2578286548 ALTBL110_00525 prepilin-type N-terminal cleavage/methylation domain 2578287096 ALTBL110_01073 prepilin-type N-terminal cleavage/methylation domain 210

2578287091 ALTBL110_01068 prepilin-type N-terminal cleavage/methylation domain 2578286549 ALTBL110_00526 prepilin-type N-terminal cleavage/methylation domain 2578286551 ALTBL110_00528 prepilin-type N-terminal cleavage/methylation domain 2578287231 ALTBL110_01208 Toxin co-regulated pilus biosynthesis protein Q 2578287153 ALTBL110_01130 type IV pilus biogenesis/stability protein PilW 2578289874 ALTBL110_03853 Type II secretory pathway, ATPase PulE/Tfp pilus assembly pathway, ATPase PilB 2578289812 ALTBL110_03791 type IV pilus secretin (or competence protein) PilQ 2578286539 ALTBL110_00516 Tfp pilus assembly protein PilN 2578286542 ALTBL110_00519 pilus (MSHA type) biogenesis protein MshL 2578289811 ALTBL110_03790 Tfp pilus assembly protein PilP 2578289810 ALTBL110_03789 Tfp pilus assembly protein PilO 2578287435 ALTBL110_01412 pilus retraction protein PilT 2578287100 ALTBL110_01077 type IV pilus modification protein PilV 2578289801 ALTBL110_03780 Tfp pilus assembly protein PilF 2578289809 ALTBL110_03788 Tfp pilus assembly protein PilN 2578287434 ALTBL110_01411 pilus retraction protein PilT 2578286545 ALTBL110_00522 Type II secretory pathway, ATPase PulE/Tfp pilus assembly pathway, ATPase PilB 2578289808 ALTBL110_03787 type IV pilus assembly protein PilM 2578287275 ALTBL110_01252 P pilus assembly/Cpx signaling pathway, periplasmic inhibitor 2578289782 ALTBL110_03761 P pilus assembly/Cpx signaling pathway, periplasmic inhibitor 2578287851 ALTBL110_01828 FimV N-terminal domain 2578289361 ALTBL110_03339 Capsule polysaccharide export protein 2578287692 ALTBL110_01669 Uncharacterized protein involved in formation of curli polymers 2578287694 ALTBL110_01671 Curli assembly protein CsgE 2578286724 ALTBL110_00701 Uncharacterized protein involved in formation of curli polymers 2578287487 ALTBL110_01464 Uncharacterized protein involved in formation of curli polymers 2578287695 ALTBL110_01672 Curlin associated repeat 2578278758 ALTLSS17_01806 Zinc carboxypeptidase/Fibronectin type III domain 2578277680 ALTLSS17_00728 HopJ type III effector protein 2578277278 ALTLSS17_00325 Surface lipoprotein 2578279246 ALTLSS17_02294 prepilin-type N-terminal cleavage/methylation domain 2578278547 ALTLSS17_01595 prepilin-type N-terminal cleavage/methylation domain 2578279248 ALTLSS17_02296 prepilin-type N-terminal cleavage/methylation domain 2578278944 ALTLSS17_01992 prepilin-type N-terminal cleavage/methylation domain 2578278936 ALTLSS17_01984 prepilin-type N-terminal cleavage/methylation domain 2578278945 ALTLSS17_01993 prepilin-type N-terminal cleavage/methylation domain 2578278939 ALTLSS17_01987 prepilin-type N-terminal cleavage/methylation domain 2578278545 ALTLSS17_01593 Type II secretory pathway, prepilin signal peptidase PulO and related peptidases 2578279247 ALTLSS17_02295 prepilin-type N-terminal cleavage/methylation domain 2578278940 ALTLSS17_01988 prepilin-type N-terminal cleavage/methylation domain 2578278935 ALTLSS17_01983 prepilin-type N-terminal cleavage/methylation domain 2578279250 ALTLSS17_02298 prepilin-type N-terminal cleavage/methylation domain 2578279249 ALTLSS17_02297 prepilin-type N-terminal cleavage/methylation domain 2578278943 ALTLSS17_01991 prepilin-type N-terminal cleavage/methylation domain 2578280758 ALTLSS17_03807 Tfp pilus assembly protein PilF 2578278887 ALTLSS17_01935 type IV pilus biogenesis/stability protein PilW 211

2578278499 ALTLSS17_01547 pilus retraction protein PilT 2578279117 ALTLSS17_02165 Tfp pilus assembly protein PilP 2578279603 ALTLSS17_02652 P pilus assembly protein, chaperone PapD 2578278812 ALTLSS17_01860 Toxin co-regulated pilus biosynthesis protein Q 2578279253 ALTLSS17_02301 Type II secretory pathway, ATPase PulE/Tfp pilus assembly pathway, ATPase PilB 2578279116 ALTLSS17_02164 Tfp pilus assembly protein PilO 2578279182 ALTLSS17_02230 Type II secretory pathway, ATPase PulE/Tfp pilus assembly pathway, ATPase PilB 2578277497 ALTLSS17_00544 Flp pilus assembly protein TadD, contains TPR repeats 2578280420 ALTLSS17_03469 P pilus assembly/Cpx signaling pathway, periplasmic inhibitor 2578279118 ALTLSS17_02166 type IV pilus secretin (or competence protein) PilQ 2578278498 ALTLSS17_01546 pilus retraction protein PilT 2578279256 ALTLSS17_02304 pilus (MSHA type) biogenesis protein MshL 2578279114 ALTLSS17_02162 type IV pilus assembly protein PilM 2578279115 ALTLSS17_02163 Tfp pilus assembly protein PilN 2578280477 ALTLSS17_03526 FimV N-terminal domain 2578277282 ALTLSS17_00329 Capsule polysaccharide export protein 2578279478 ALTLSS17_02527 Capsule biosynthesis GfcC 2578279479 ALTLSS17_02528 Group 4 capsule polysaccharide formation lipoprotein gfcB 2578277282 ALTLSS17_00329 Capsule polysaccharide export protein 2578280709 ALTLSS17_03758 Capsule polysaccharide biosynthesis protein 2578291676 AQUAD1_01796 Bacterial capsule synthesis protein PGA_cap 2578293065 AQUAD1_03189 Bacterial capsule synthesis protein PGA_cap 2578292134 AQUAD1_02255 Fibronectin type III domain/Peptidase family M28 2578290410 AQUAD1_00529 pantothenate kinase, type III 2578292753 AQUAD1_02874 Fibronectin type III domain 2578290111 AQUAD1_00229 HopJ type III effector protein 2578290365 AQUAD1_00484 Fibronectin type III domain/Galactose oxidase, central domain 2578290571 AQUAD1_00690 Fibronectin type III domain 2578292062 AQUAD1_02183 Surface lipoprotein of Spirochaetales order 2578292426 AQUAD1_02547 Type IV pili methyl-accepting chemotaxis transducer N-term 2578290966 AQUAD1_01085 Type IV pili methyl-accepting chemotaxis transducer N-term 2578292432 AQUAD1_02553 Histidine kinase/Type IV pili methyl-accepting chemotaxis transducer 2578291568 AQUAD1_01688 Tfp pilus assembly protein PilF 2578292832 AQUAD1_02953 Curlin associated repeat 2578292830 AQUAD1_02951 Curli assembly protein CsgE 2578292828 AQUAD1_02949 Uncharacterized protein involved in formation of curli polymers 2578295231 AQUAD10_00541 Bacterial capsule synthesis protein PGA_cap 2578297330 AQUAD10_02640 Bacterial capsule synthesis protein PGA_cap 2578294948 AQUAD10_00258 HopJ type III effector protein 2578297439 AQUAD10_02749 pantothenate kinase, type III 2578294888 AQUAD10_00198 Fibronectin type III domain 2578294750 AQUAD10_00060 Fibronectin type III domain/Carboxypeptidase regulatory-like domain 2578295485 AQUAD10_00795 Helicase conserved C-terminal domain/Type III restriction enzyme, res subunit 2578296221 AQUAD10_01531 Fibronectin type III domain/Bacterial Ig-like domain (group 2) 2578296934 AQUAD10_02244 Histidine kinase/Type IV pili methyl-accepting chemotaxis transducer 2578296928 AQUAD10_02238 Type IV pili methyl-accepting chemotaxis transducer N-term 212

2578295538 AQUAD10_00848 prepilin-type N-terminal cleavage/methylation domain 2578295555 AQUAD10_00865 Type II secretory pathway, ATPase PulE/Tfp pilus assembly pathway, ATPase PilB 2578296994 AQUAD10_02304 Curlin associated repeat 2578297146 AQUAD10_02456 Curlin associated repeat 2578297142 AQUAD10_02452 Uncharacterized protein involved in formation of curli polymers 2578297144 AQUAD10_02454 Curli assembly protein CsgE 2578282932 AQUBL5_02025 Bacterial capsule synthesis protein PGA_cap 2578281068 AQUBL5_00155 Bacterial capsule synthesis protein PGA_cap 2578281706 AQUBL5_00795 Fibronectin type III domain 2578283776 AQUBL5_02870 Fibronectin type III domain/Peptidase family M28 2578281312 AQUBL5_00399 HopJ type III effector protein 2578282724 AQUBL5_01814 Fibronectin type III domain 2578285783 AQUBL5_04877 Fibronectin type III domain/Receptor L domain 2578282114 AQUBL5_01204 pantothenate kinase, type III 2578282459 AQUBL5_01549 Alginate lyase/Fibronectin type III domain/F5/8 type C domain 2578284234 AQUBL5_03328 Surface lipoprotein of Spirochaetales order 2578282739 AQUBL5_01829 Histidine kinase/Type IV pili methyl-accepting chemotaxis transducer 2578284483 AQUBL5_03577 Type IV pili methyl-accepting chemotaxis transducer N-term 2578282734 AQUBL5_01824 Type IV pili methyl-accepting chemotaxis transducer N-term 2578282982 AQUBL5_02075 Type II secretory pathway, ATPase PulE/Tfp pilus assembly pathway, ATPase PilB 2578284987 AQUBL5_04081 Tfp pilus assembly protein PilF 2578283137 AQUBL5_02230 Tfp pilus assembly protein PilF 2578283879 AQUBL5_02973 Curlin associated repeat 2578283875 AQUBL5_02969 Uncharacterized protein involved in formation of curli polymers 2578283877 AQUBL5_02971 Curli assembly protein CsgE

Gliding-motility 2619815341 Ga0074152_10589 SprA-related family 2578286907 ALTBL110_00884 SprA-related family 2578279771 ALTLSS17_02820 SprA-related family 2578290049 AQUAD1_00167 gliding motility-associated protein GldC 2578290647 AQUAD1_00766 cell surface protein SprA 2578291200 AQUAD1_01319 gliding motility-associated C-terminal domain 2578290677 AQUAD1_00796 conserved repeat domain/gliding motility-associated C-terminal domain 2578291824 AQUAD1_01944 gliding motility-associated ABC transporter ATP-binding subunit GldA 2578292524 AQUAD1_02645 protein involved in gliding motility SprE 2578290907 AQUAD1_01026 gliding motility-associated C-terminal domain 2578290826 AQUAD1_00945 SprT-like family 2578290176 AQUAD1_00294 protein involved in gliding motility GldD 2578291092 AQUAD1_01211 gliding motility-associated ABC transporter permease protein GldF 2578291729 AQUAD1_01849 gliding motility-associated C-terminal domain 2578291336 AQUAD1_01456 bacterial surface protein 26-residue repeat/gliding motility-associated 2578291063 AQUAD1_01182 gliding motility-associated C-terminal domain 2578290048 AQUAD1_00166 gliding motility-associated lipoprotein GldB 2578290488 AQUAD1_00607 gliding motility-associated lipoprotein GldH

213

2578290896 AQUAD1_01015 gliding motility-associated C-terminal domain 2578290029 AQUAD1_00147 peptidyl-prolyl isomerase, gliding motility-associated 2578293456 AQUAD1_03580 gliding motility-associated C-terminal domain 2578291201 AQUAD1_01320 gliding motility-associated C-terminal domain 2578291064 AQUAD1_01183 gliding motility-associated C-terminal domain 2578290175 AQUAD1_00293 gliding motility-associated protein GldE 2578298209 AQUAD10_03522 bacterial surface protein 26-residue repeat/gliding motility-associated 2578299617 AQUAD10_04934 protein involved in gliding motility GldE 2578295953 AQUAD10_01263 gliding motility-associated lipoprotein GldH 2578297820 AQUAD10_03131 gliding motility-associated ABC transporter ATP-binding subunit GldA 2578299616 AQUAD10_04933 protein involved in gliding motility GldD 2578294725 AQUAD10_00035 protein involved in gliding motility SprE 2578296595 AQUAD10_01905 gliding motility-associated ABC transporter permease protein GldF 2578298141 AQUAD10_03454 protein involved in gliding motility GldC 2578297658 AQUAD10_02968 peptidyl-prolyl isomerase, gliding motility-associated 2578296397 AQUAD10_01707 gliding motility-associated C-terminal domain 2578298436 AQUAD10_03750 SprT-like family 2578298726 AQUAD10_04040 gliding motility-associated C-terminal domain 2578296108 AQUAD10_01418 gliding motility-associated C-terminal domain 2578294787 AQUAD10_00097 gliding motility-associated C-terminal domain 2578295608 AQUAD10_00918 conserved repeat domain/gliding motility-associated C-terminal domain 2578298142 AQUAD10_03455 gliding motility-associated lipoprotein GldB 2578295057 AQUAD10_00367 cell surface protein SprA 2578297226 AQUAD10_02536 gliding motility-associated C-terminal domain 2578297130 AQUAD10_02440 gliding motility-associated C-terminal domain 2578280963 AQUBL5_00050 gliding motility-associated lipoprotein GldH 2578281564 AQUBL5_00651 protein involved in gliding motility GldD 2578283542 AQUBL5_02636 protein involved in gliding motility SprE 2578283628 AQUBL5_02722 gliding motility-associated C-terminal domain 2578284411 AQUBL5_03505 gliding motility-associated ABC transporter permease protein GldF 2578281825 AQUBL5_00914 gliding motility-associated lipoprotein GldB 2578283629 AQUBL5_02723 gliding motility-associated C-terminal domain 2578282276 AQUBL5_01366 peptidyl-prolyl isomerase, gliding motility-associated 2578282349 AQUBL5_01439 SprT-like family 2578281563 AQUBL5_00650 protein involved in gliding motility GldE 2578282205 AQUBL5_01295 gliding motility-associated C-terminal domain 2578285825 AQUBL5_04919 gliding motility-associated ABC transporter ATP-binding subunit GldA 2578281826 AQUBL5_00915 gliding motility-associated protein GldC 2578284216 AQUBL5_03310 gliding motility-associated C-terminal domain 2578282206 AQUBL5_01296 gliding motility-associated C-terminal domain 2578283593 AQUBL5_02687 gliding motility-associated C-terminal domain 2578281680 AQUBL5_00769 gliding motility-associated C-terminal domain 2578281291 AQUBL5_00378 conserved repeat domain/gliding motility-associated C-terminal domain 2578281382 AQUBL5_00469 cell surface protein SprA 2578285249 AQUBL5_04343 bacterial surface protein 26-residue repeat/gliding motility-associated

214

Supplementary Information_S2

Gene ID Locus Tag Gene Product Name

Toxic materials efflux 2619814641 Ga0074152_10317 transcriptional regulator, TetR family 2619814646 Ga0074152_10322 Na+-driven multidrug efflux pump 2619814650 Ga0074152_10326 transcriptional regulator, TetR family 2619814693 Ga0074152_10369 putative efflux protein, MATE family 2619814704 Ga0074152_10380 Multidrug resistance efflux pump 2619814744 Ga0074152_103120 Threonine/homoserine efflux transporter RhtA 2619814881 Ga0074152_103257 Predicted arabinose efflux permease, MFS family 2619814888 Ga0074152_103264 transcriptional regulator, TetR family 2619814891 Ga0074152_103267 Multidrug efflux pump subunit AcrB 2619814892 Ga0074152_103268 RND family efflux transporter, MFP subunit 2619814893 Ga0074152_103269 efflux transporter, outer membrane factor (OMF) lipoprotein, NodT family 2619814898 Ga0074152_103274 RND family efflux transporter, MFP subunit 2619814899 Ga0074152_103275 Multidrug efflux pump subunit AcrB 2619814910 Ga0074152_103286 Na+-driven multidrug efflux pump 2619814934 Ga0074152_103310 Multidrug resistance efflux pump 2619814985 Ga0074152_103361 Threonine/homoserine/homoserine lactone efflux protein 2619814998 Ga0074152_103374 putative efflux protein, MATE family 2619815024 Ga0074152_103400 transcriptional regulator, TetR family 2619815270 Ga0074152_10518 Predicted Co/Zn/Cd cation transporter, cation efflux family 2619815372 Ga0074152_105120 putative efflux protein, MATE family 2619815494 Ga0074152_105242 Multidrug efflux pump subunit AcrB 2619815495 Ga0074152_105243 RND family efflux transporter, MFP subunit 2619815496 Ga0074152_105244 transcriptional regulator, TetR family 2619815533 Ga0074152_105281 transcriptional regulator, TetR family 2619815687 Ga0074152_105436 transcriptional regulator, TetR family 2619815706 Ga0074152_105455 transcriptional regulator, TetR family 2619815785 Ga0074152_105534 putative efflux protein, MATE family 2619815918 Ga0074152_10666 Threonine/homoserine/homoserine lactone efflux protein 2619815929 Ga0074152_10677 The (Largely Gram-negative Bacterial) Hydrophobe/Amphiphile Efflux-1 2619815930 Ga0074152_10678 RND family efflux transporter, MFP subunit 2619815931 Ga0074152_10679 transcriptional regulator, TetR family 2619815933 Ga0074152_10681 putative efflux protein, MATE family 2619816070 Ga0074152_10755 transcriptional regulator, TetR family 2619816127 Ga0074152_107112 Predicted exporter protein, RND superfamily 2619816131 Ga0074152_107116 Predicted arabinose efflux permease, MFS family 2619816167 Ga0074152_107152 The (Largely Gram-negative Bacterial) Hydrophobe/Amphiphile Efflux-1 2619816168 Ga0074152_107153 RND family efflux transporter, MFP subunit 2619816186 Ga0074152_107171 Threonine/homoserine/homoserine lactone efflux protein 2619816189 Ga0074152_107174 transcriptional regulator, TetR family 2619816214 Ga0074152_107199 Threonine/homoserine/homoserine lactone efflux protein 2619816283 Ga0074152_107269 efflux transporter, outer membrane factor (OMF) lipoprotein, NodT family

215

2619816284 Ga0074152_107270 Multidrug efflux pump subunit AcrB 2619816285 Ga0074152_107271 RND family efflux transporter, MFP subunit 2619816295 Ga0074152_107281 Predicted exporter protein, RND superfamily 2619816296 Ga0074152_107282 transcriptional regulator, TetR family 2619816307 Ga0074152_107293 putative efflux protein, MATE family 2619816360 Ga0074152_107346 RND family efflux transporter, MFP subunit 2619816361 Ga0074152_107347 Multidrug efflux pump subunit AcrB 2619816403 Ga0074152_107389 RND family efflux transporter, MFP subunit 2619816404 Ga0074152_107390 heavy metal efflux pump, CzcA family 2619816622 Ga0074152_107608 Cation efflux family 2619816645 Ga0074152_107631 Predicted arabinose efflux permease, MFS family 2619816756 Ga0074152_108105 putative efflux protein, MATE family 2619816791 Ga0074152_108140 Multidrug resistance efflux pump 2619816823 Ga0074152_108172 transcriptional regulator, TetR family 2619816959 Ga0074152_108308 Multidrug efflux pump subunit AcrB 2619816960 Ga0074152_108309 RND family efflux transporter, MFP subunit 2619816993 Ga0074152_108342 transcriptional regulator, TetR family 2619817072 Ga0074152_108421 transcriptional regulator, TetR family 2619817080 Ga0074152_108429 Predicted arabinose efflux permease, MFS family 2619817217 Ga0074152_11053 Predicted arabinose efflux permease, MFS family 2619817391 Ga0074152_11158 Threonine/homoserine efflux transporter RhtA 2619817411 Ga0074152_11178 Multidrug efflux pump subunit AcrB 2619817412 Ga0074152_11179 RND family efflux transporter, MFP subunit 2619817484 Ga0074152_111151 Threonine/homoserine/homoserine lactone efflux protein 2619817492 Ga0074152_111159 transcriptional regulator, TetR family 2619817496 Ga0074152_111163 Predicted arabinose efflux permease, MFS family 2619817503 Ga0074152_111170 Predicted arabinose efflux permease, MFS family 2619817595 Ga0074152_111262 transcriptional regulator, TetR family 2619817600 Ga0074152_111267 transcriptional regulator, TetR family 2619817750 Ga0074152_111417 Multidrug resistance efflux pump 2619818219 Ga0074152_11215 transcriptional regulator, TetR family 2619818242 Ga0074152_11238 transcriptional regulator, TetR family 2619818254 Ga0074152_11250 Threonine/homoserine/homoserine lactone efflux protein 2619818456 Ga0074152_112252 Threonine/homoserine/homoserine lactone efflux protein 2619818533 Ga0074152_112329 Multidrug efflux pump subunit AcrB 2619818534 Ga0074152_112330 RND family efflux transporter, MFP subunit 2619818550 Ga0074152_112346 Predicted arabinose efflux permease, MFS family 2619818551 Ga0074152_112347 RND family efflux transporter, MFP subunit 2619818552 Ga0074152_112348 The (Largely Gram-negative Bacterial) Hydrophobe/Amphiphile Efflux-1 2619818581 Ga0074152_112377 transcriptional regulator, TetR family 2619818583 Ga0074152_112379 Multidrug efflux pump subunit AcrB 2619818642 Ga0074152_112438 RND family efflux transporter, MFP subunit 2619818643 Ga0074152_112439 The (Largely Gram-negative Bacterial) Hydrophobe/Amphiphile Efflux-1 2619818659 Ga0074152_112455 RND family efflux transporter, MFP subunit 2619818660 Ga0074152_112456 RND family efflux transporter, MFP subunit 2619818661 Ga0074152_112457 Multidrug efflux pump subunit AcrB 216

2619818746 Ga0074152_112542 putative efflux protein, MATE family 2619818818 Ga0074152_112614 Multidrug efflux pump subunit AcrB 2619818819 Ga0074152_112615 RND family efflux transporter, MFP subunit 2619818820 Ga0074152_112616 RND family efflux transporter, MFP subunit 2619818821 Ga0074152_112617 transcriptional regulator, TetR family 2619818882 Ga0074152_112678 Threonine/homoserine/homoserine lactone efflux protein 2619818890 Ga0074152_112686 Multidrug resistance efflux pump 2619818898 Ga0074152_112694 Predicted arabinose efflux permease, MFS family 2619818907 Ga0074152_112703 transcriptional regulator, TetR family 2578289835 ALTBL110_03814 transcriptional regulator, TetR family 2578289054 ALTBL110_03031 Cation/multidrug efflux pump 2578288532 ALTBL110_02509 Transcriptional regulators 2578288799 ALTBL110_02776 Cation/multidrug efflux pump 2578288286 ALTBL110_02263 Na+-driven multidrug efflux pump 2578289220 ALTBL110_03198 transcriptional regulator, TetR family 2578288075 ALTBL110_02052 Cation/multidrug efflux pump 2578286434 ALTBL110_00411 Multidrug resistance efflux pump 2578288984 ALTBL110_02961 Cation/multidrug efflux pump 2578289833 ALTBL110_03812 Cation/multidrug efflux pump 2578286122 ALTBL110_00098 transcriptional regulator, TetR family 2578289658 ALTBL110_03637 Cation/multidrug efflux pump 2578288056 ALTBL110_02033 Cation/multidrug efflux pump 2578286332 ALTBL110_00308 Multidrug resistance efflux pump 2578288055 ALTBL110_02032 Cation/multidrug efflux pump 2578288987 ALTBL110_02964 transcriptional regulator, TetR family 2578289578 ALTBL110_03556 Cation/multidrug efflux pump 2578288989 ALTBL110_02966 Cation/multidrug efflux pump 2578288301 ALTBL110_02278 Predicted exporters of the RND superfamily 2578287237 ALTBL110_01214 Predicted exporters of the RND superfamily 2578277060 ALTLSS17_00107 Cation/multidrug efflux pump 2578277442 ALTLSS17_00489 transcriptional regulator, TetR family 2578277707 ALTLSS17_00755 transcriptional regulator, TetR family 2578279389 ALTLSS17_02438 Multidrug resistance efflux pump 2578276980 ALTLSS17_00027 Cation/multidrug efflux pump 2578280166 ALTLSS17_03215 Transcriptional regulators 2578278191 ALTLSS17_01239 Cation/multidrug efflux pump 2578279140 ALTLSS17_02188 Cation/multidrug efflux pump 2578278663 ALTLSS17_01711 Multidrug resistance efflux pump 2578279572 ALTLSS17_02621 Cation/multidrug efflux pump 2578278169 ALTLSS17_01217 Cation/multidrug efflux pump 2578277630 ALTLSS17_00678 Cation/multidrug efflux pump 2578277708 ALTLSS17_00756 Cation/multidrug efflux pump 2578277536 ALTLSS17_00584 Multidrug resistance efflux pump 2578278168 ALTLSS17_01216 Cation/multidrug efflux pump 2578278662 ALTLSS17_01710 MarR family 2578277705 ALTLSS17_00753 Cation/multidrug efflux pump 217

2578279142 ALTLSS17_02190 transcriptional regulator, TetR family 2578280004 ALTLSS17_03053 Predicted exporters of the RND superfamily 2578278806 ALTLSS17_01854 Predicted exporters of the RND superfamily 2578290152 AQUAD1_00270 Cation/multidrug efflux pump 2578290074 AQUAD1_00192 Cation/multidrug efflux pump 2578291029 AQUAD1_01148 Transcriptional regulators 2578291027 AQUAD1_01146 Cation/multidrug efflux pump 2578293625 AQUAD1_03749 Cation/multidrug efflux pump 2578290514 AQUAD1_00633 Predicted exporters of the RND superfamily 2578291811 AQUAD1_01931 Predicted exporters of the RND superfamily 2578297354 AQUAD10_02664 Transcriptional regulators 2578299061 AQUAD10_04375 Multidrug resistance efflux pump 2578295582 AQUAD10_00892 Cation/multidrug efflux pump 2578299074 AQUAD10_04388 Multidrug resistance efflux pump 2578299629 AQUAD10_04946 Multidrug resistance efflux pump 2578297351 AQUAD10_02661 Cation/multidrug efflux pump 2578299656 AQUAD10_04973 Multidrug resistance efflux pump 2578296949 AQUAD10_02259 Multidrug resistance efflux pump 2578297726 AQUAD10_03036 Cation/multidrug efflux pump 2578299039 AQUAD10_04353 Predicted exporters of the RND superfamily 2578295002 AQUAD10_00312 Predicted exporters of the RND superfamily 2578281897 AQUBL5_00986 Cation/multidrug efflux pump 2578281170 AQUBL5_00257 Cation/multidrug efflux pump 2578284135 AQUBL5_03229 Multidrug resistance efflux pump 2578281172 AQUBL5_00259 Transcriptional regulators 2578282017 AQUBL5_01106 Multidrug resistance efflux pump 2578281423 AQUBL5_00510 Predicted exporters of the RND superfamily

Oxidative stress response enzymes 2619814691 Ga0074152_10367 peroxiredoxin, Ohr subfamily 2619814745 Ga0074152_103121 Glutathione S-transferase 2619815099 Ga0074152_10467 Glutathione S-transferase 2619815166 Ga0074152_104134 Alkyl hydroperoxide reductase subunit AhpC (peroxiredoxin) 2619815664 Ga0074152_105413 Glutathione S-transferase 2619816026 Ga0074152_10711 Organic hydroperoxide reductase OsmC/OhrA 2619816088 Ga0074152_10773 Glutathione S-transferase 2619816188 Ga0074152_107173 Peroxiredoxin 2619816495 Ga0074152_107481 Peroxiredoxin 2619816627 Ga0074152_107613 Glutathione S-transferase 2619816807 Ga0074152_108156 superoxide dismutase, Ni 2619817434 Ga0074152_111101 Glutathione S-transferase 2619817538 Ga0074152_111205 Peroxiredoxin 2619817587 Ga0074152_111254 Cytochrome c peroxidase 2619817601 Ga0074152_111268 Glutathione S-transferase 2619817632 Ga0074152_111299 Glutathione S-transferase

218

2619817832 Ga0074152_111499 catalase/peroxidase HPI 2619818237 Ga0074152_11233 Glutathione S-transferase 2619818437 Ga0074152_112233 Glutathione S-transferase, N-terminal domain 2619818454 Ga0074152_112250 Glutathione S-transferase 2619818918 Ga0074152_112714 Catalase 2619818939 Ga0074152_112735 Superoxide dismutase 2578288616 ALTBL110_02593 3-hydroxyacyl-CoA dehydrogenase 2578286979 ALTBL110_00956 Glutathione S-transferase 2578286683 ALTBL110_00660 NTP pyrophosphohydrolases containing a Zn-finger 2578288575 ALTBL110_02552 peroxiredoxin 2578287908 ALTBL110_01885 Uncharacterized oxidoreductases, Fe-dependent alcohol dehydrogenas 2578287287 ALTBL110_01264 Aerobic-type carbon monoxide dehydrogenase, large subunit CoxL/CutL 2578289651 ALTBL110_03630 NTP pyrophosphohydrolases including oxidative damage repair enzymes 2578286660 ALTBL110_00637 Glutathione S-transferase 2578289072 ALTBL110_03049 fatty oxidation complex, beta subunit FadI 2578288512 ALTBL110_02489 isocitrate dehydrogenase, NADP-dependent, monomeric type 2578287443 ALTBL110_01420 Glutathione S-transferase 2578287538 ALTBL110_01515 Glutathione S-transferase 2578287682 ALTBL110_01659 NAD-dependent aldehyde dehydrogenases 2578289682 ALTBL110_03661 fatty oxidation complex, beta subunit FadA 2578289543 ALTBL110_03521 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2578288694 ALTBL110_02671 HMGL-like 2578288784 ALTBL110_02761 xanthine dehydrogenase, molybdenum binding subunit apoprotein (EC 1.17.1.4) 2578287808 ALTBL110_01785 Enoyl-CoA hydratase/carnithine racemase 2578288145 ALTBL110_02122 Peroxiredoxin 2578286180 ALTBL110_00156 Catalase 2578287783 ALTBL110_01760 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2578287838 ALTBL110_01815 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2578287809 ALTBL110_01786 NADH:flavin oxidoreductases, Old Yellow Enzyme family 2578289025 ALTBL110_03002 NTP pyrophosphohydrolases including oxidative damage repair enzymes 2578289071 ALTBL110_03048 fatty oxidation complex, alpha subunit FadJ 2578289116 ALTBL110_03093 Peroxiredoxin 2578288248 ALTBL110_02225 Peroxiredoxin 2578289800 ALTBL110_03779 Catalase 2578286887 ALTBL110_00864 NTP pyrophosphohydrolases including oxidative damage repair enzymes 2578287254 ALTBL110_01231 Enoyl-CoA hydratase/carnithine racemase 2578288783 ALTBL110_02760 xanthine dehydrogenase, small subunit 2578287286 ALTBL110_01263 Aerobic-type carbon monoxide dehydrogenase, middle subunit 2578289681 ALTBL110_03660 fatty oxidation complex, alpha subunit FadB 2578287866 ALTBL110_01843 Catalase 2578287444 ALTBL110_01421 Cu/Zn superoxide dismutase 2578289765 ALTBL110_03744 ribosome small subunit-dependent GTPase A 2578287440 ALTBL110_01417 NADH:flavin oxidoreductases, Old Yellow Enzyme family 2578287727 ALTBL110_01704 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2578288283 ALTBL110_02260 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2578289123 ALTBL110_03100 Superoxide dismutase 219

2578286486 ALTBL110_00463 Glutathione S-transferase 2578288314 ALTBL110_02291 Serine-pyruvate aminotransferase/archaeal aspartate aminotransferase 2578288649 ALTBL110_02626 Glutathione S-transferase 2578287443 ALTBL110_01420 Glutathione S-transferase 2578287352 ALTBL110_01329 Ubiquinone biosynthesis hydroxylase, UbiH/UbiF/VisC/COQ6 family 2578287538 ALTBL110_01515 Glutathione S-transferase 2578288649 ALTBL110_02626 Glutathione S-transferase 2578286979 ALTBL110_00956 Glutathione S-transferase 2578287464 ALTBL110_01441 S-(hydroxymethyl)glutathione dehydrogenase/class III alcohol dehydrogenase 2578288571 ALTBL110_02548 Ubiquinone biosynthesis protein COQ7 2578288874 ALTBL110_02851 Ubiquinone biosynthesis hydroxylase, UbiH/UbiF/VisC/COQ6 family 2578286660 ALTBL110_00637 Glutathione S-transferase 2578286486 ALTBL110_00463 Glutathione S-transferase 2578277975 ALTLSS17_01023 HMGL-like 2578278018 ALTLSS17_01066 Glutathione S-transferase 2578280463 ALTLSS17_03512 Aerobic-type carbon monoxide dehydrogenase, middle subunit CoxM/CutM 2578279846 ALTLSS17_02895 Glutathione S-transferase 2578279411 ALTLSS17_02460 sarcosine oxidase, beta subunit family, heterotetrameric form 2578280511 ALTLSS17_03560 Enoyl-CoA hydratase/carnithine racemase 2578278236 ALTLSS17_01284 Peroxiredoxin 2578277613 ALTLSS17_00661 fatty oxidation complex, beta subunit FadI 2578278404 ALTLSS17_01452 Glutathione S-transferase 2578280729 ALTLSS17_03778 Catalase 2578279408 ALTLSS17_02457 sarcosine oxidase, gamma subunit family, heterotetrameric form 2578279753 ALTLSS17_02802 NTP pyrophosphohydrolases including oxidative damage repair enzymes 2578277883 ALTLSS17_00931 xanthine dehydrogenase, molybdenum binding subunit apoprotein (EC 1.17.1.4) 2578276956 ALTLSS17_00003 fatty oxidation complex, alpha subunit FadB 2578279410 ALTLSS17_02459 sarcosine oxidase, delta subunit family, heterotetrameric form 2578279559 ALTLSS17_02608 NTP pyrophosphohydrolases containing a Zn-finger 2578280436 ALTLSS17_03485 Catalase 2578278492 ALTLSS17_01540 NADH:flavin oxidoreductases, Old Yellow Enzyme family 2578279536 ALTLSS17_02585 Glutathione S-transferase 2578280418 ALTLSS17_03467 Cu/Zn superoxide dismutase 2578278781 ALTLSS17_01829 Enoyl-CoA hydratase/carnithine racemase 2578278671 ALTLSS17_01719 HMGL-like 2578277169 ALTLSS17_00216 Uncharacterized oxidoreductases, Fe-dependent alcohol dehydrogenase family 2578280510 ALTLSS17_03559 NADH:flavin oxidoreductases, Old Yellow Enzyme family 2578280533 ALTLSS17_03582 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2578277558 ALTLSS17_00606 Peroxiredoxin 2578277884 ALTLSS17_00932 xanthine dehydrogenase, small subunit 2578278488 ALTLSS17_01536 Glutathione S-transferase 2578278095 ALTLSS17_01143 isocitrate dehydrogenase, NADP-dependent, monomeric type 2578277614 ALTLSS17_00662 fatty oxidation complex, alpha subunit FadJ 2578279409 ALTLSS17_02458 sarcosine oxidase, alpha subunit family, heterotetrameric form 2578279397 ALTLSS17_02446 NAD-dependent aldehyde dehydrogenases 2578278306 ALTLSS17_01354 NAD-dependent aldehyde dehydrogenases 220

2578280256 ALTLSS17_03305 3-hydroxyacyl-CoA dehydrogenase 2578280215 ALTLSS17_03264 peroxiredoxin 2578277661 ALTLSS17_00709 NTP pyrophosphohydrolases including oxidative damage repair enzymes 2578280462 ALTLSS17_03511 Aerobic-type carbon monoxide dehydrogenase, large subunit CoxL/CutL 2578276955 ALTLSS17_00002 fatty oxidation complex, beta subunit FadA 2578280017 ALTLSS17_03066 Serine-pyruvate aminotransferase/archaeal aspartate aminotransferase 2578278359 ALTLSS17_01407 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2578279309 ALTLSS17_02357 Glutathione S-transferase 2578279076 ALTLSS17_02124 ribosome small subunit-dependent GTPase A 2578279962 ALTLSS17_03011 Peroxiredoxin 2578277551 ALTLSS17_00599 Superoxide dismutase 2578276987 ALTLSS17_00034 NTP pyrophosphohydrolases including oxidative damage repair enzymes 2578277097 ALTLSS17_00144 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2578278576 ALTLSS17_01624 Ubiquinone biosynthesis hydroxylase, UbiH/UbiF/VisC/COQ6 family 2578278404 ALTLSS17_01452 Glutathione S-transferase 2578278018 ALTLSS17_01066 Glutathione S-transferase 2578279309 ALTLSS17_02357 Glutathione S-transferase 2578277815 ALTLSS17_00863 Ubiquinone biosynthesis hydroxylase, UbiH/UbiF/VisC/COQ6 family 2578279846 ALTLSS17_02895 Glutathione S-transferase 2578278477 ALTLSS17_01525 S-(hydroxymethyl)glutathione dehydrogenase 2578278488 ALTLSS17_01536 Glutathione S-transferase 2578279536 ALTLSS17_02585 Glutathione S-transferase 2578290942 AQUAD1_01061 Enoyl-CoA hydratase/carnithine racemase 2578294122 AQUAD1_04246 Uncharacterized oxidoreductases, Fe-dependent alcohol dehydrogenase family 2578293420 AQUAD1_03544 3-hydroxyacyl-CoA dehydrogenase 2578292788 AQUAD1_02909 Mevalonate kinase 2578290398 AQUAD1_00517 Enoyl-CoA hydratase/carnithine racemase 2578289930 AQUAD1_00048 HMGL-like 2578291998 AQUAD1_02119 NTP pyrophosphohydrolases containing a Zn-finger 2578293421 AQUAD1_03545 acetyl-CoA acetyltransferases 2578291464 AQUAD1_01584 Cu/Zn superoxide dismutase 2578291327 AQUAD1_01447 Superoxide dismutase 2578292031 AQUAD1_02152 Peroxiredoxin 2578292952 AQUAD1_03073 Acyl-coenzyme A oxidase (EC 1.3.3.6) 2578290022 AQUAD1_00140 Serine-pyruvate aminotransferase/archaeal aspartate aminotransferase 2578293418 AQUAD1_03542 Long-chain acyl-CoA synthetases (AMP-forming) 2578292035 AQUAD1_02156 Peroxiredoxin 2578294502 AQUAD1_04627 isocitrate dehydrogenase, NADP-dependent, monomeric type 2578293446 AQUAD1_03570 Peroxiredoxin 2578291887 AQUAD1_02008 ribosome small subunit-dependent GTPase A 2578290735 AQUAD1_00854 Zn-dependent alcohol dehydrogenases, class III 2578290819 AQUAD1_00938 Monoamine oxidase 2578298226 AQUAD10_03539 Acyl-coenzyme A oxidase (EC 1.3.3.6) 2578298490 AQUAD10_03804 Peroxiredoxin 2578296648 AQUAD10_01958 Peroxiredoxin 2578298830 AQUAD10_04144 3-hydroxyacyl-CoA dehydrogenase 221

2578298802 AQUAD10_04116 ribosome small subunit-dependent GTPase A 2578299010 AQUAD10_04324 Catalase 2578295424 AQUAD10_00734 HMGL-like 2578297810 AQUAD10_03121 ribosome small subunit-dependent GTPase A 2578294909 AQUAD10_00219 Superoxide dismutase 2578295806 AQUAD10_01116 Superoxide dismutase 2578296645 AQUAD10_01955 Peroxiredoxin 2578298003 AQUAD10_03316 Catalase 2578297666 AQUAD10_02976 Serine-pyruvate aminotransferase/archaeal aspartate aminotransferase 2578299281 AQUAD10_04596 hypothetical protein 2578295961 AQUAD10_01271 Enoyl-CoA hydratase/carnithine racemase 2578298828 AQUAD10_04142 Long-chain acyl-CoA synthetases (AMP-forming) 2578297581 AQUAD10_02891 NAD-dependent aldehyde dehydrogenases 2578298831 AQUAD10_04145 acetyl-CoA acetyltransferases 2578296450 AQUAD10_01760 1-Cys peroxiredoxin (EC 1.11.1.15) 2578295805 AQUAD10_01115 Cu/Zn superoxide dismutase 2578299164 AQUAD10_04478 isocitrate dehydrogenase, NADP-dependent, monomeric type 2578295488 AQUAD10_00798 Peroxiredoxin 2578295865 AQUAD10_01175 Zn-dependent alcohol dehydrogenases 2578295601 AQUAD10_00911 Flavin containing amine oxidoreductase/NAD(P)-binding Rossmann-like domain 2578297687 AQUAD10_02997 Zn-dependent alcohol dehydrogenases, class III 2578284813 AQUBL5_03907 Peroxiredoxin 2578283491 AQUBL5_02585 Peroxiredoxin 2578285561 AQUBL5_04655 isocitrate dehydrogenase, NADP-dependent, monomeric type 2578281966 AQUBL5_01055 Xanthine dehydrogenase, iron-sulfur cluster and FAD-binding subunit A 2578282020 AQUBL5_01109 isocitrate dehydrogenase 2578285398 AQUBL5_04492 Uncharacterized oxidoreductases, Fe-dependent alcohol dehydrogenase family 2578283470 AQUBL5_02564 acetyl-CoA acetyltransferases 2578284270 AQUBL5_03364 Enoyl-CoA hydratase/carnithine racemase 2578281272 AQUBL5_00359 Superoxide dismutase 2578281806 AQUBL5_00895 ribosome small subunit-dependent GTPase A 2578283393 AQUBL5_02487 HMGL-like 2578284389 AQUBL5_03483 Cu/Zn superoxide dismutase 2578283466 AQUBL5_02560 Long-chain acyl-CoA synthetases (AMP-forming) 2578285586 AQUBL5_04680 Serine-pyruvate aminotransferase/archaeal aspartate aminotransferase 2578283468 AQUBL5_02562 3-hydroxyacyl-CoA dehydrogenase 2578284716 AQUBL5_03810 Acyl-coenzyme A oxidase (EC 1.3.3.6) 2578281965 AQUBL5_01054 Xanthine dehydrogenase, molybdopterin-binding subunit B 2578285414 AQUBL5_04508 Peroxiredoxin 2578285687 AQUBL5_04781 NAD-dependent aldehyde dehydrogenases 2578283494 AQUBL5_02588 Peroxiredoxin 2578282906 AQUBL5_01999 Mevalonate kinase 2578283235 AQUBL5_02329 Enoyl-CoA hydratase/carnithine racemase 2578282542 AQUBL5_01632 Zn-dependent alcohol dehydrogenases, class III 2578282358 AQUBL5_01448 Monoamine oxidase 2578285116 AQUBL5_04210 Monoamine oxidase 2578285448 AQUBL5_04542 Predicted flavoprotein involved in K+ transport

222

Supplementary Information_S3

Gene ID Locus Tag Gene Product Name

Agarase 2578296333 AQUAD10_01643 Por secretion system C-terminal sorting domain

Alginate lyase 2578292110 AQUAD1_02231 Alginate lyase 2578294555 AQUAD1_04680 Alginate lyase/F5/8 type C domain 2578291715 AQUAD1_01835 Alginate lyase/Heparinase II/III-like protein 2578294063 AQUAD1_04187 Ricin-type beta-trefoil lectin domain-like/Alginate lyase 2578295770 AQUAD10_01080 Alginate lyase 2578296864 AQUAD10_02174 Alginate lyase 2578295286 AQUAD10_00596 Alginate lyase 2578296874 AQUAD10_02184 Alginate lyase 2578296865 AQUAD10_02175 Alginate lyase/F5/8 type C domain 2578299683 AQUAD10_05000 Alginate lyase/Fascin domain 2578296868 AQUAD10_02178 Alginate lyase/Heparinase II/III-like protein 2578284858 AQUBL5_03952 Alginate lyase 2578282459 AQUBL5_01549 Alginate lyase/Fibronectin type III domain/F5/8 type C domain 2578283938 AQUBL5_03032 Alginate lyase/Heparinase II/III-like protein 2578282460 AQUBL5_01550 Ricin-type beta-trefoil lectin domain-like/Alginate lyase

Alpha-amylase 2578293740 AQUAD1_03864 Glycosidases 2578289884 AQUAD1_00002 Glycosidases 2578293550 AQUAD1_03674 Glycosidases 2578294091 AQUAD1_04215 Glycosidases 2578291574 AQUAD1_01694 Glycosidases 2578289883 AQUAD1_00001 Alpha amylase, catalytic domain 2578290157 AQUAD1_00275 Por secretion system C-terminal sorting domain 2578291111 AQUAD1_01230 Domain of unknown function (DUF1939)/Alpha amylase, catalytic domain 2578296947 AQUAD10_02257 Glycosidases 2578297385 AQUAD10_02695 Glycosidases 2578296352 AQUAD10_01662 Glycosidases 2578296939 AQUAD10_02249 Glycosidases 2578297970 AQUAD10_03283 Domain of unknown function (DUF1939)/Alpha amylase, catalytic domain 2578296352 AQUAD10_01662 Glycosidases 2578298836 AQUAD10_04150 Alpha amylase, catalytic domain/Alpha-amylase C-terminal beta-sheet domain 2578284755 AQUBL5_03849 Glycosidases 2578282084 AQUBL5_01173 Glycosidases 2578285420 AQUBL5_04514 Glycosidases 2578282747 AQUBL5_01837 Glycosidases

223

2578283175 AQUBL5_02268 Domain of unknown function (DUF1939)/Alpha amylase, catalytic domain 2578281892 AQUBL5_00981 Por secretion system C-terminal sorting domain 2578283474 AQUBL5_02568 Por secretion system C-terminal sorting domain 2578282746 AQUBL5_01836 Alpha amylase, catalytic domain/Beta-galactosidase C-terminal domain

Cellulase 2578292558 AQUAD1_02679 Por secretion system C-terminal sorting domain 2578290141 AQUAD1_00259 Por secretion system C-terminal sorting domain 2578292857 AQUAD1_02978 Cellulase M and related proteins 2578293980 AQUAD1_04104 Por secretion system C-terminal sorting domain 2578290559 AQUAD1_00678 Por secretion system C-terminal sorting domain 2578291768 AQUAD1_01888 Por secretion system C-terminal sorting domain 2578294996 AQUAD10_00306 Por secretion system C-terminal sorting domain 2578299176 AQUAD10_04490 Cellulase M and related proteins 2578297193 AQUAD10_02503 Endoglucanase 2578297135 AQUAD10_02445 Cellulase (glycosyl hydrolase family 5)/Collagen triple helix repeat (20 copies) 2578299322 AQUAD10_04637 Por secretion system C-terminal sorting domain 2578284102 AQUBL5_03196 Por secretion system C-terminal sorting domain 2578284336 AQUBL5_03430 Por secretion system C-terminal sorting domain 2578281971 AQUBL5_01060 Por secretion system C-terminal sorting domain 2578285740 AQUBL5_04834 Por secretion system C-terminal sorting domain 2578284527 AQUBL5_03621 Cellulase M and related proteins 2578283866 AQUBL5_02960 Por secretion system C-terminal sorting domain 2578283992 AQUBL5_03086 Por secretion system C-terminal sorting domain

Xylanase 2578294204 AQUAD1_04328 Leucine Rich repeats (2 copies)/Putative glycoside hydrolase xylanase 2578292490 AQUAD1_02611 Beta-1,4-xylanase 2578290673 AQUAD1_00792 Predicted xylanase/chitin deacetylase 2578294161 AQUAD1_04285 Predicted xylanase/chitin deacetylase 2578299867 AQUAD10_05184 Putative glycoside hydrolase xylanase 2578297569 AQUAD10_02879 Leucine rich repeat (2 copies)/Putative glycoside hydrolase xylanase 2578295019 AQUAD10_00329 Predicted xylanase/chitin deacetylase 2578283348 AQUBL5_02442 Predicted xylanase/chitin deacetylase 2578283146 AQUBL5_02239 Beta-1,4-xylanase 2578282528 AQUBL5_01618 Leucine rich repeat/Putative glycoside hydrolase xylanase 2578281406 AQUBL5_00493 Predicted xylanase/chitin deacetylase

Pectate-lyase 2578290815 AQUAD1_00934 Pectate lyase superfamily protein/Chondroitinase B

224

Supplementary Information_S4

Gene ID Locus Tag Gene Product Name

Bioactives and toxins 2619814639 Ga0074152_10315 Permease of the drug/metabolite transporter (DMT) superfamily 2619814817 Ga0074152_103193 Permease of the drug/metabolite transporter (DMT) superfamily 2619815453 Ga0074152_105201 Permease of the drug/metabolite transporter (DMT) superfamily 2619816904 Ga0074152_108253 Permease of the drug/metabolite transporter (DMT) superfamily 2619817299 Ga0074152_110135 ABC-type bacteriocin/lantibiotic exporters 2619817300 Ga0074152_110136 ABC-type bacteriocin/lantibiotic exporters 2619818081 Ga0074152_111748 Uncharacterized membrane protein, required for colicin V production 2619818309 Ga0074152_112105 Outer membrane receptor for ferrienterochelin and colicins 2619818883 Ga0074152_112679 Polyketide cyclase / dehydrase and lipid transport 2578288697 ALTBL110_02674 Polyketide cyclase / dehydrase and lipid transport 2578289513 ALTBL110_03491 Outer membrane receptor for ferrienterochelin and colicins 2578289767 ALTBL110_03746 Predicted permease, DMT superfamily 2578288175 ALTBL110_02152 Outer membrane receptor for ferrienterochelin and colicins 2578286433 ALTBL110_00410 colicin V processing peptidase. Cysteine peptidase. MEROPS family C39 2578287325 ALTBL110_01302 Polyketide cyclase / dehydrase and lipid transport 2578286948 ALTBL110_00925 Predicted thioesterase 2578288150 ALTBL110_02127 Predicted permease, DMT superfamily 2578289467 ALTBL110_03445 Predicted permease, DMT superfamily 2578286946 ALTBL110_00923 Permeases of the drug/metabolite transporter (DMT) superfamily 2578286242 ALTBL110_00218 ABC-type bacteriocin/lantibiotic exporters 2578287812 ALTBL110_01789 Predicted thioesterase 2578286270 ALTBL110_00246 Antidote-toxin recognition MazE 2578287231 ALTBL110_01208 Toxin co-regulated pilus biosynthesis protein Q 2578286238 ALTBL110_00214 Toxin with endonuclease activity YhaV 2578287841 ALTBL110_01818 toxin-antitoxin system, toxin component, Txe/YoeB family 2578288867 ALTBL110_02844 Outer membrane receptor for ferrienterochelin and colicins 2578288000 ALTBL110_01977 Outer membrane receptor for ferrienterochelin and colicins 2578288033 ALTBL110_02010 Predicted dithiol-disulfide isomerase involved in polyketide biosynthesis 2578286511 ALTBL110_00488 Predicted thioesterase 2578287482 ALTBL110_01459 Polyketide cyclase / dehydrase and lipid transport 2578287991 ALTBL110_01968 Predicted permease, DMT superfamily 2578287856 ALTBL110_01833 Uncharacterized membrane protein, required for colicin V production 2578286816 ALTBL110_00793 Permeases of the drug/metabolite transporter (DMT) superfamily 2578287647 ALTBL110_01624 putative toxin-antitoxin system toxin component, PIN family 2578278666 ALTLSS17_01714 Outer membrane receptor for ferrienterochelin and colicins 2578279682 ALTLSS17_02731 Predicted permease, DMT superfamily 2578280722 ALTLSS17_03771 ABC-type bacteriocin/lantibiotic exporters 2578278812 ALTLSS17_01860 Toxin co-regulated pilus biosynthesis protein Q 2578280472 ALTLSS17_03521 Uncharacterized membrane protein, required for colicin V production 2578278772 ALTLSS17_01820 Outer membrane receptor for ferrienterochelin and colicins

225

2578279285 ALTLSS17_02333 Predicted thioesterase 2578280644 ALTLSS17_03693 ABC-type bacteriocin/lantibiotic exporters 2578277971 ALTLSS17_01019 Polyketide cyclase / dehydrase and lipid transport 2578279815 ALTLSS17_02864 Predicted thioesterase 2578280267 ALTLSS17_03316 Predicted dithiol-disulfide isomerase involved in polyketide biosynthesis 2578280493 ALTLSS17_03542 Predicted thioesterase 2578279151 ALTLSS17_02199 Permeases of the drug/metabolite transporter (DMT) superfamily 2578279834 ALTLSS17_02883 Polyketide cyclase / dehydrase and lipid transport 2578279078 ALTLSS17_02126 Predicted permease, DMT superfamily 2578279008 ALTLSS17_02056 addiction module toxin, RelE/StbE family 2578290382 AQUAD1_00501 SnoaL-like polyketide cyclase 2578290807 AQUAD1_00926 Permeases of the drug/metabolite transporter (DMT) superfamily 2578293994 AQUAD1_04118 Outer membrane receptor for ferrienterochelin and colicins 2578294188 AQUAD1_04312 Outer membrane receptor for ferrienterochelin and colicins 2578292386 AQUAD1_02507 amino acid adenylation domain 2578293812 AQUAD1_03936 putative toxin-antitoxin system antitoxin component, TIGR02293 family 2578290498 AQUAD1_00617 Predicted thioesterase 2578294047 AQUAD1_04171 Outer membrane receptor for ferrienterochelin and colicins 2578294260 AQUAD1_04384 Outer membrane receptor for ferrienterochelin and colicins 2578293222 AQUAD1_03346 amino acid adenylation domain 2578290516 AQUAD1_00635 Polyketide cyclase / dehydrase and lipid transport 2578294158 AQUAD1_04282 Outer membrane receptor for ferrienterochelin and colicins 2578293907 AQUAD1_04031 Outer membrane receptor for ferrienterochelin and colicins 2578291103 AQUAD1_01222 Predicted permease, DMT superfamily 2578294585 AQUAD1_04710 AMP-binding enzyme C-terminal domain/Condensation domain 2578293699 AQUAD1_03823 amino acid adenylation domain 2578293241 AQUAD1_03365 Predicted thioesterase 2578290520 AQUAD1_00639 Polyketide cyclase / dehydrase and lipid transport 2578292389 AQUAD1_02510 amino acid adenylation domain 2578294057 AQUAD1_04181 ABC-type bacteriocin/lantibiotic exporters 2578290519 AQUAD1_00638 Polyketide cyclase / dehydrase and lipid transport 2578293042 AQUAD1_03165 ABC-type bacteriocin/lantibiotic exporters 2578292655 AQUAD1_02776 non-ribosomal peptide synthase domain TIGR01720/adenylation domain 2578294304 AQUAD1_04428 Polyketide cyclase / dehydrase and lipid transport 2578290499 AQUAD1_00618 Predicted permease, DMT superfamily 2578294433 AQUAD1_04558 Colicin immunity protein / pyocin immunity protein 2578290522 AQUAD1_00641 Polyketide cyclase / dehydrase and lipid transport 2578290209 AQUAD1_00328 Predicted thioesterase 2578293700 AQUAD1_03824 amino acid adenylation domain 2578292616 AQUAD1_02737 Outer membrane receptor for ferrienterochelin and colicins 2578289938 AQUAD1_00056 Outer membrane receptor for ferrienterochelin and colicins 2578292085 AQUAD1_02206 Inner membrane protein involved in colicin E2 resistance 2578291353 AQUAD1_01473 Predicted thioesterase 2578290033 AQUAD1_00151 Outer membrane receptor for ferrienterochelin and colicins 2578290177 AQUAD1_00295 Permeases of the drug/metabolite transporter (DMT) superfamily 2578292653 AQUAD1_02774 amino acid adenylation domain 226

2578289896 AQUAD1_00014 ABC-type bacteriocin/lantibiotic exporters 2578293976 AQUAD1_04100 Colicin V production protein 2578296184 AQUAD10_01494 Outer membrane receptor for ferrienterochelin and colicins 2578299040 AQUAD10_04354 Polyketide cyclase / dehydrase and lipid transport 2578298923 AQUAD10_04237 Polyketide cyclase / dehydrase and lipid transport 2578296905 AQUAD10_02215 putative toxin-antitoxin system antitoxin component, TIGR02293 family 2578295924 AQUAD10_01234 Colicin V production protein 2578296047 AQUAD10_01357 Protein involved in biosynthesis of mitomycin antibiotics/polyketide fumonisin 2578295695 AQUAD10_01005 Putative toxin 43/Domain of unknown function (DUF4280) 2578299655 AQUAD10_04972 ABC-type bacteriocin/lantibiotic exporters 2578298152 AQUAD10_03465 Polyketide cyclase / dehydrase and lipid transport 2578297345 AQUAD10_02655 Toxin with a H, D/N and C signature 2578297296 AQUAD10_02606 Outer membrane receptor for ferrienterochelin and colicins 2578298343 AQUAD10_03657 Outer membrane receptor for ferrienterochelin and colicins 2578297792 AQUAD10_03103 ABC-type bacteriocin/lantibiotic exporters 2578296203 AQUAD10_01513 Outer membrane receptor for ferrienterochelin and colicins 2578296508 AQUAD10_01818 Predicted permease, DMT superfamily 2578299073 AQUAD10_04387 colicin V processing peptidase. Cysteine peptidase. MEROPS family C39 2578296948 AQUAD10_02258 ABC-type bacteriocin/lantibiotic exporters 2578299586 AQUAD10_04901 Predicted thioesterase 2578298046 AQUAD10_03359 amino acid adenylation domain 2578299875 AQUAD10_05192 Permeases of the drug/metabolite transporter (DMT) superfamily 2578299062 AQUAD10_04376 ABC-type bacteriocin/lantibiotic exporters 2578299691 AQUAD10_05008 Predicted thioesterase 2578299615 AQUAD10_04932 Permeases of the drug/metabolite transporter (DMT) superfamily 2578298924 AQUAD10_04238 Polyketide cyclase / dehydrase and lipid transport 2578296990 AQUAD10_02300 Predicted thioesterase 2578299041 AQUAD10_04355 amino acid adenylation domain 2578296717 AQUAD10_02027 Outer membrane receptor for ferrienterochelin and colicins 2578298009 AQUAD10_03322 Inner membrane protein involved in colicin E2 resistance 2578297555 AQUAD10_02865 Outer membrane receptor for ferrienterochelin and colicins 2578299876 AQUAD10_05193 Predicted thioesterase 2578298925 AQUAD10_04239 Polyketide cyclase / dehydrase and lipid transport 2578297619 AQUAD10_02929 Outer membrane receptor for ferrienterochelin and colicins 2578299000 AQUAD10_04314 Outer membrane receptor for ferrienterochelin and colicins 2578299800 AQUAD10_05117 Permeases of the drug/metabolite transporter (DMT) superfamily 2578282047 AQUBL5_01136 Predicted thioesterase 2578285505 AQUBL5_04599 Outer membrane receptor for ferrienterochelin and colicins 2578281076 AQUBL5_00163 Metallopeptidase toxin 4 2578285775 AQUBL5_04869 Inner membrane protein involved in colicin E2 resistance 2578284566 AQUBL5_03660 Polyketide cyclase / dehydrase and lipid transport 2578284708 AQUBL5_03802 Outer membrane receptor for ferrienterochelin and colicins 2578283020 AQUBL5_02113 Polyketide cyclase / dehydrase and lipid transport 2578280949 AQUBL5_00036 Predicted thioesterase 2578284047 AQUBL5_03141 amino acid adenylation domain 2578285328 AQUBL5_04422 ABC-type bacteriocin/lantibiotic exporters 227

2578283598 AQUBL5_02692 non-ribosomal peptide synthase domain TIGR01720/adenylation domain 2578285855 AQUBL5_04949 amino acid adenylation domain 2578282018 AQUBL5_01107 ABC-type bacteriocin/lantibiotic exporters 2578283600 AQUBL5_02694 amino acid adenylation domain 2578285335 AQUBL5_04429 Outer membrane receptor for ferrienterochelin and colicins 2578281592 AQUBL5_00681 Predicted thioesterase 2578284565 AQUBL5_03659 Polyketide cyclase / dehydrase and lipid transport 2578282484 AQUBL5_01574 Outer membrane receptor for ferrienterochelin and colicins 2578284048 AQUBL5_03142 amino acid adenylation domain 2578281565 AQUBL5_00652 Permeases of the drug/metabolite transporter (DMT) superfamily 2578284051 AQUBL5_03145 Predicted thioesterase involved in non-ribosomal peptide biosynthesis 2578284046 AQUBL5_03140 amino acid adenylation domain 2578284325 AQUBL5_03419 putative toxin-antitoxin system antitoxin component, TIGR02293 family 2578284568 AQUBL5_03662 Protein involved in biosynthesis of mitomycin antibiotics/polyketide fumonisin 2578285132 AQUBL5_04226 Permeases of the drug/metabolite transporter (DMT) superfamily 2578284136 AQUBL5_03230 ABC-type bacteriocin/lantibiotic exporters 2578282279 AQUBL5_01369 SnoaL-like polyketide cyclase 2578284567 AQUBL5_03661 Polyketide cyclase / dehydrase and lipid transport 2578283599 AQUBL5_02693 amino acid adenylation domain 2578282272 AQUBL5_01362 Outer membrane receptor for ferrienterochelin and colicins 2578280986 AQUBL5_00073 Colicin V production protein 2578281797 AQUBL5_00886 Outer membrane receptor for ferrienterochelin and colicins 2578285738 AQUBL5_04832 Predicted thioesterase 2578280948 AQUBL5_00035 Predicted permease, DMT superfamily 2578283040 AQUBL5_02133 delta endotoxin, N-terminal domain 2578282661 AQUBL5_01751 Predicted permease, DMT superfamily

Biosynthesis of siderophore group nonribosomal peptides 2578289573 ALTBL110_03551 DNA primase, catalytic core 2578286161 ALTBL110_00137 alpha-L-glutamate ligases, RimK family 2578286505 ALTBL110_00482 UDP-N-acetylmuramate:L-alanyl-gamma-D-glutamyl-meso-diaminopimelate ligase 2578289396 ALTBL110_03374 Superfamily II DNA and RNA helicases 2578278760 ALTLSS17_01808 Glutathione synthase (glutaminyl transferase) 2578280748 ALTLSS17_03797 alpha-L-glutamate ligases, RimK family 2578279290 ALTLSS17_02338 UDP-N-acetylmuramate:L-alanyl-gamma-D-glutamyl-meso-diaminopimelate ligase 2578277247 ALTLSS17_00294 Superfamily II DNA and RNA helicases 2578277066 ALTLSS17_00113 DNA primase, catalytic core 2578290237 AQUAD1_00356 UDP-N-acetylmuramate-alanine ligase 2578290045 AQUAD1_00163 DNA primase, catalytic core 2578292772 AQUAD1_02893 isochorismate synthase (EC 5.4.4.2) 2578290583 AQUAD1_00702 SSU ribosomal protein S6P modification protein 2578298145 AQUAD10_03458 DNA primase, catalytic core 2578298404 AQUAD10_03718 UDP-N-acetylmuramate-alanine ligase 2578298232 AQUAD10_03545 SSU ribosomal protein S6P modification protein 2578296537 AQUAD10_01847 isochorismate synthase (EC 5.4.4.2)

228

2578296731 AQUAD10_02041 SSU ribosomal protein S6P modification protein 2578285039 AQUBL5_04133 isochorismate synthase (EC 5.4.4.2) 2578282400 AQUBL5_01490 SSU ribosomal protein S6P modification protein 2578281439 AQUBL5_00526 UDP-N-acetylmuramate-alanine ligase 2578281822 AQUBL5_00911 DNA primase, catalytic core

229

Supplementary Information_S5

Gene ID Locus Tag Gene Product Name

Secretion systems 2619814679 Ga0074152_10355 twin arginine-targeting protein translocase, TatA/E family 2619814680 Ga0074152_10356 twin arginine-targeting protein translocase TatB 2619814681 Ga0074152_10357 Twin arginine targeting (Tat) protein translocase TatC 2619814754 Ga0074152_103130 type VI secretion lipoprotein, VC_A0113 family 2619814756 Ga0074152_103132 type VI secretion system peptidoglycan-associated domain 2619814757 Ga0074152_103133 type VI secretion protein IcmF 2619814768 Ga0074152_103144 type VI secretion ATPase, ClpV1 family 2619814769 Ga0074152_103145 Type VI protein secretion system component Hcp (secreted cytotoxin) 2619814770 Ga0074152_103146 Rhs element Vgr protein 2619814945 Ga0074152_103321 type II secretion system protein C (GspC) 2619814946 Ga0074152_103322 type II secretion system protein D 2619814947 Ga0074152_103323 type II secretion system protein E 2619814948 Ga0074152_103324 type II secretion system protein F 2619814949 Ga0074152_103325 type II secretion system protein G 2619814950 Ga0074152_103326 hypothetical protein 2619814951 Ga0074152_103327 type II secretion system protein I 2619814952 Ga0074152_103328 type II secretion system protein J 2619814953 Ga0074152_103329 Type II secretory pathway, component PulK 2619814954 Ga0074152_103330 type II secretion system protein L 2619814955 Ga0074152_103331 Type II secretory pathway, component PulM 2619815125 Ga0074152_10493 preprotein translocase, SecA subunit 2619815347 Ga0074152_10595 protein translocase subunit secB 2619815376 Ga0074152_105124 signal recognition particle-docking protein FtsY 2619815822 Ga0074152_105571 membrane protein insertase, YidC/Oxa1 family, N-terminal domain 2619816003 Ga0074152_106151 protein translocase subunit secY/sec61 alpha 2619816452 Ga0074152_107438 protein translocase, SecG subunit 2619816849 Ga0074152_108198 type I secretion outer membrane protein, TolC family 2619817246 Ga0074152_11082 protein translocase subunit secF 2619817247 Ga0074152_11083 protein-export membrane protein, SecD/SecF family 2619817248 Ga0074152_11084 preprotein translocase, YajC subunit 2619817704 Ga0074152_111371 signal recognition particle subunit FFH/SRP54 (srp54) 2619818781 Ga0074152_112577 twin arginine-targeting protein translocase, TatA/E family 2578288129 ALTBL110_02106 tRNA (mo5U34)-methyltransferase 2578288318 ALTBL110_02295 Protein kinase domain 2578287382 ALTBL110_01359 Type II secretory pathway, prepilin signal peptidase PulO and related peptidases 2578286983 ALTBL110_00960 ATP synthase, F1 beta subunit 2578286985 ALTBL110_00962 proton translocating ATP synthase, F1 alpha subunit 2578286174 ALTBL110_00150 Protein kinase domain 2578288838 ALTBL110_02815 protein-(glutamine-N5) methyltransferase, release factor-specific 2578286851 ALTBL110_00828 ribosomal protein L11 methyltransferase

230

2578289253 ALTBL110_03231 flagellar protein export ATPase FliI 2578288130 ALTBL110_02107 tRNA (cmo5U34)-methyltransferase 2578278632 ALTLSS17_01680 Serine/threonine protein kinase involved in cell cycle control 2578277410 ALTLSS17_00457 flagellar protein export ATPase FliI 2578278219 ALTLSS17_01267 tRNA (mo5U34)-methyltransferase 2578280021 ALTLSS17_03070 Protein kinase domain 2578280160 ALTLSS17_03209 proton translocating ATP synthase, F1 alpha subunit 2578278545 ALTLSS17_01593 Type II secretory pathway, prepilin signal peptidase PulO and related peptidases 2578279850 ALTLSS17_02899 ATP synthase, F1 beta subunit 2578279852 ALTLSS17_02901 proton translocating ATP synthase, F1 alpha subunit 2578278784 ALTLSS17_01832 Protein kinase domain 2578279721 ALTLSS17_02770 ribosomal protein L11 methyltransferase 2578278220 ALTLSS17_01268 tRNA (cmo5U34)-methyltransferase 2578280153 ALTLSS17_03202 ATP synthase, F1 beta subunit 2578277839 ALTLSS17_00887 protein-(glutamine-N5) methyltransferase, release factor-specific 2578280735 ALTLSS17_03784 Soluble NSF attachment protein, SNAP/Protein kinase domain 2578291514 AQUAD1_01634 putative phosphoesterase 2578294523 AQUAD1_04648 HipA N-terminal domain/HipA-like C-terminal domain 2578292274 AQUAD1_02395 Calcineurin-like phosphoesterase 2578294000 AQUAD1_04124 protein-(glutamine-N5) methyltransferase, release factor-specific 2578293882 AQUAD1_04006 Predicted N6-adenine-specific DNA methylase 2578292516 AQUAD1_02637 proton translocating ATP synthase, F1 alpha subunit 2578292424 AQUAD1_02545 ATP synthase, F1 beta subunit 2578293466 AQUAD1_03590 Ribosomal protein L11 methylase 2578295903 AQUAD10_01213 Calcineurin-like phosphoesterase 2578297868 AQUAD10_03181 Helicase conserved C-terminal domain/SNF2 family N-terminal domain 2578294718 AQUAD10_00028 proton translocating ATP synthase, F1 alpha subunit 2578299130 AQUAD10_04444 Ribosomal protein L11 methylase 2578298963 AQUAD10_04277 putative phosphoesterase 2578298775 AQUAD10_04089 Predicted N6-adenine-specific DNA methylase 2578297030 AQUAD10_02340 protein-(glutamine-N5) methyltransferase, release factor-specific 2578297170 AQUAD10_02480 NLI interacting factor-like phosphatase 2578296677 AQUAD10_01987 ATP synthase, F1 beta subunit 2578281923 AQUBL5_01012 Calcineurin-like phosphoesterase 2578282509 AQUBL5_01599 Ribosomal protein L11 methylase 2578281002 AQUBL5_00089 Calcineurin-like phosphoesterase 2578282998 AQUBL5_02091 ATP synthase, F1 beta subunit 2578283076 AQUBL5_02169 HipA N-terminal domain/HipA-like C-terminal domain 2578283750 AQUBL5_02844 Predicted N6-adenine-specific DNA methylase 2578283998 AQUBL5_03092 proton translocating ATP synthase, F1 alpha subunit 2578283795 AQUBL5_02889 protein-(glutamine-N5) methyltransferase, release factor-specific

231

Supplementary Information_S6

2578289892 AQUAD1_00010 Por secretion system C-terminal sorting domain 2578289893 AQUAD1_00011 hypothetical protein 2578289894 AQUAD1_00012 hypothetical protein 2578289895 AQUAD1_00013 hypothetical protein 2578289909 AQUAD1_00027 hypothetical protein 2578289914 AQUAD1_00032 Nucleoside-diphosphate-sugar epimerases 2578289949 AQUAD1_00067 hypothetical protein 2578289956 AQUAD1_00074 hypothetical protein 2578289958 AQUAD1_00076 hypothetical protein 2578289959 AQUAD1_00077 Uncharacterized protein conserved in bacteria 2578289961 AQUAD1_00079 hypothetical protein 2578289962 AQUAD1_00080 3-oxoacyl-(acyl-carrier-protein) synthase 2578289963 AQUAD1_00081 acyl-CoA thioester hydrolase, YbgC/YbaW family 2578289964 AQUAD1_00082 ABC-2 family transporter protein 2578289966 AQUAD1_00084 NlpC/p60-like transpeptidase 2578289967 AQUAD1_00085 hypothetical protein 2578289968 AQUAD1_00086 hypothetical protein 2578289969 AQUAD1_00087 3-oxoacyl-[acyl-carrier-protein] synthase III 2578289970 AQUAD1_00088 Flavodoxin 2578289971 AQUAD1_00089 Predicted acyltransferase 2578289972 AQUAD1_00090 Acyl carrier protein 2578289975 AQUAD1_00093 WG containing repeat 2578289977 AQUAD1_00095 Dehydrogenases (flavoproteins) 2578289982 AQUAD1_00100 hypothetical protein 2578289984 AQUAD1_00102 Por secretion system C-terminal sorting domain 2578289988 AQUAD1_00106 hypothetical protein 2578290020 AQUAD1_00138 DKNYY family 2578290023 AQUAD1_00141 hypothetical protein 2578290039 AQUAD1_00157 Acyl-CoA hydrolase 2578290050 AQUAD1_00168 hypothetical protein 2578290068 AQUAD1_00186 cAMP-binding proteins 2578290070 AQUAD1_00188 hypothetical protein 2578290071 AQUAD1_00189 Por secretion system C-terminal sorting domain 2578290085 AQUAD1_00203 Beta-lactamase class C and other penicillin binding proteins 2578290097 AQUAD1_00215 Dioxygenase 2578290133 AQUAD1_00251 Nitroreductase 2578290137 AQUAD1_00255 hypothetical protein 2578290141 AQUAD1_00259 Por secretion system C-terminal sorting domain 2578290145 AQUAD1_00263 hypothetical protein 2578290146 AQUAD1_00264 hypothetical protein 2578290147 AQUAD1_00265 hypothetical protein 2578290168 AQUAD1_00286 AraC-type DNA-binding domain-containing proteins 2578290232 AQUAD1_00351 UDP-galactopyranose mutase (EC 5.4.99.9) 2578290253 AQUAD1_00372 hypothetical protein 232

2578290301 AQUAD1_00420 hypothetical protein 2578290303 AQUAD1_00422 Predicted Zn-dependent proteases and their inactivated homologs 2578290304 AQUAD1_00423 Predicted Zn-dependent proteases and their inactivated homologs 2578290305 AQUAD1_00424 Domain of unknown function (DUF4159) 2578290307 AQUAD1_00426 Protein of unknown function DUF58 2578290308 AQUAD1_00427 N-terminal double-transmembrane domain 2578290309 AQUAD1_00428 hypothetical protein 2578290310 AQUAD1_00429 hypothetical protein 2578290319 AQUAD1_00438 hypothetical protein 2578290320 AQUAD1_00439 MBOAT, membrane-bound O-acyltransferase family 2578290321 AQUAD1_00440 Domain of unknown function (DUF4386) 2578290322 AQUAD1_00441 Predicted membrane protein 2578290324 AQUAD1_00443 Transcriptional regulators 2578290325 AQUAD1_00444 hypothetical protein 2578290326 AQUAD1_00445 Acetyltransferases 2578290327 AQUAD1_00446 Transposase and inactivated derivatives 2578290328 AQUAD1_00447 hypothetical protein 2578290339 AQUAD1_00458 Alpha/beta hydrolase family 2578290361 AQUAD1_00480 Outer membrane protein beta-barrel domain 2578290362 AQUAD1_00481 hypothetical protein 2578290363 AQUAD1_00482 Receptor L domain 2578290364 AQUAD1_00483 Receptor L domain 2578290365 AQUAD1_00484 Fibronectin type III domain/Galactose oxidase, central domain 2578290368 AQUAD1_00487 TPR repeat 2578290374 AQUAD1_00493 hypothetical protein 2578290392 AQUAD1_00511 Peptidase_C39 like family 2578290398 AQUAD1_00517 Enoyl-CoA hydratase/carnithine racemase 2578290400 AQUAD1_00519 hypothetical protein 2578290433 AQUAD1_00552 hypothetical protein 2578290434 AQUAD1_00553 Predicted membrane protein 2578290436 AQUAD1_00555 CotH protein 2578290437 AQUAD1_00556 hypothetical protein 2578290444 AQUAD1_00563 Arsenate reductase and related proteins, glutaredoxin family 2578290445 AQUAD1_00564 hypothetical protein 2578290446 AQUAD1_00565 Protein of unknown function (DUF1328) 2578290449 AQUAD1_00568 hypothetical protein 2578290450 AQUAD1_00569 Uncharacterised ACR, COG2135 2578290452 AQUAD1_00571 conserved hypothetical protein 2578290453 AQUAD1_00572 hypothetical protein 2578290454 AQUAD1_00573 hypothetical protein 2578290455 AQUAD1_00574 Uncharacterized homolog of Blt101 2578290457 AQUAD1_00576 Predicted permease 2578290458 AQUAD1_00577 Mechanosensitive ion channel 2578290459 AQUAD1_00578 Topoisomerase IB 2578290460 AQUAD1_00579 hypothetical protein 2578290461 AQUAD1_00580 NADPH-dependent FMN reductase 233

2578290462 AQUAD1_00581 Predicted membrane protein 2578290465 AQUAD1_00584 hypothetical protein 2578290467 AQUAD1_00586 hypothetical protein 2578290468 AQUAD1_00587 hypothetical protein 2578290469 AQUAD1_00588 Gas vesicle protein 2578290470 AQUAD1_00589 NAD-dependent aldehyde dehydrogenases 2578290471 AQUAD1_00590 hypothetical protein 2578290475 AQUAD1_00594 hypothetical protein 2578290493 AQUAD1_00612 Predicted periplasmic protein 2578290505 AQUAD1_00624 Two component regulator propeller 2578290533 AQUAD1_00652 hypothetical protein 2578290537 AQUAD1_00656 Por secretion system C-terminal sorting domain 2578290545 AQUAD1_00664 hypothetical protein 2578290557 AQUAD1_00676 E3 Ubiquitin ligase 2578290561 AQUAD1_00680 Arabinose efflux permease 2578290588 AQUAD1_00707 hypothetical protein 2578290597 AQUAD1_00716 hypothetical protein 2578290618 AQUAD1_00737 hypothetical protein 2578290622 AQUAD1_00741 Rhodanese-like domain 2578290654 AQUAD1_00773 hypothetical protein 2578290657 AQUAD1_00776 hypothetical protein 2578290676 AQUAD1_00795 hypothetical protein 2578290677 AQUAD1_00796 conserved repeat domain/gliding motility-associated 2578290687 AQUAD1_00806 NAD-dependent aldehyde dehydrogenases 2578290688 AQUAD1_00807 Dihydrodipicolinate synthase/N-acetylneuraminate lyase 2578290705 AQUAD1_00824 hypothetical protein 2578290763 AQUAD1_00882 Glycosyl Hydrolase Family 88 2578290765 AQUAD1_00884 D-glucuronate isomerase (EC 5.3.1.12) 2578290770 AQUAD1_00889 hypothetical protein 2578290772 AQUAD1_00891 hypothetical protein 2578290775 AQUAD1_00894 hypothetical protein 2578290777 AQUAD1_00896 hypothetical protein 2578290779 AQUAD1_00898 OmpA family 2578290780 AQUAD1_00899 hypothetical protein 2578290787 AQUAD1_00906 hypothetical protein 2578290793 AQUAD1_00912 AraC-type DNA-binding domain-containing proteins 2578290794 AQUAD1_00913 hypothetical protein 2578290801 AQUAD1_00920 hypothetical protein 2578290805 AQUAD1_00924 Acetyltransferases, including N-acetylases of ribosomal proteins 2578290823 AQUAD1_00942 hypothetical protein 2578290827 AQUAD1_00946 Uncharacterized protein containing a von Willebrand factor type A 2578290830 AQUAD1_00949 hypothetical protein 2578290839 AQUAD1_00958 Por secretion system C-terminal sorting domain 2578290846 AQUAD1_00965 Por secretion system C-terminal sorting domain 2578290856 AQUAD1_00975 Protein of unknown function (DUF3703) 2578290863 AQUAD1_00982 hypothetical protein 234

2578290864 AQUAD1_00983 SusD family/Starch-binding associating with outer membrane 2578290869 AQUAD1_00988 hypothetical protein 2578290872 AQUAD1_00991 Domain of unknown function (DUF4480) 2578290873 AQUAD1_00992 Acetyltransferase (GNAT) family 2578290874 AQUAD1_00993 hydroxylysine kinase/5-phosphonooxy-L-lysine phospho-lyase 2578290887 AQUAD1_01006 Dolichyl-phosphate-mannose-protein mannosyltransferase 2578290894 AQUAD1_01013 hypothetical protein 2578290904 AQUAD1_01023 two component transcriptional regulator, LytTR family 2578290910 AQUAD1_01029 Prolyl oligopeptidase family 2578290919 AQUAD1_01038 hypothetical protein 2578290922 AQUAD1_01041 Thiol-disulfide isomerase and thioredoxins 2578290932 AQUAD1_01051 hypothetical protein 2578290937 AQUAD1_01056 hypothetical protein 2578290941 AQUAD1_01060 Domain of unknown function (DUF4177) 2578290965 AQUAD1_01084 hypothetical protein 2578290974 AQUAD1_01093 Uncharacterized conserved protein 2578290996 AQUAD1_01115 hypothetical protein 2578291000 AQUAD1_01119 hypothetical protein 2578291047 AQUAD1_01166 hypothetical protein 2578291056 AQUAD1_01175 Uncharacterized protein required for cytochrome oxidase assembly 2578291072 AQUAD1_01191 hypothetical protein 2578291073 AQUAD1_01192 Protein of unknown function (DUF1569) 2578291074 AQUAD1_01193 Outer membrane receptor proteins, mostly Fe transport 2578291076 AQUAD1_01195 hypothetical protein 2578291091 AQUAD1_01210 hypothetical protein 2578291097 AQUAD1_01216 Domain of unknown function (DUF4174) 2578291148 AQUAD1_01267 Por secretion system C-terminal sorting domain 2578291155 AQUAD1_01274 Uncharacterized protein conserved in bacteria 2578291156 AQUAD1_01275 hypothetical protein 2578291158 AQUAD1_01277 hypothetical protein 2578291159 AQUAD1_01278 hypothetical protein 2578291193 AQUAD1_01312 hypothetical protein 2578291208 AQUAD1_01327 hypothetical protein 2578291209 AQUAD1_01328 hypothetical protein 2578291232 AQUAD1_01351 Formyl transferase, C-terminal domain/Formyl transferase 2578291234 AQUAD1_01353 Microcystin-dependent protein 2578291251 AQUAD1_01370 hypothetical protein 2578291256 AQUAD1_01375 Predicted ATPases of PP-loop superfamily 2578291259 AQUAD1_01378 hypothetical protein 2578291273 AQUAD1_01393 transcriptional regulator, AraC family 2578291274 AQUAD1_01394 hypothetical protein 2578291275 AQUAD1_01395 hypothetical protein 2578291282 AQUAD1_01402 transcriptional regulator, TetR family 2578291292 AQUAD1_01412 Signal transduction histidine kinase 2578291299 AQUAD1_01419 hypothetical protein 2578291300 AQUAD1_01420 FOG: PKD repeat 235

2578291301 AQUAD1_01421 hypothetical protein 2578291313 AQUAD1_01433 MORN repeat variant 2578291343 AQUAD1_01463 Histidine kinase/Histidine kinase-, DNA gyrase B- 2578291345 AQUAD1_01465 hypothetical protein 2578291350 AQUAD1_01470 hypothetical protein 2578291356 AQUAD1_01476 hypothetical protein 2578291369 AQUAD1_01489 Uncharacterized conserved protein 2578291418 AQUAD1_01538 YARHG domain 2578291420 AQUAD1_01540 Uncharacterized protein conserved in bacteria 2578291424 AQUAD1_01544 NADH(P)-binding 2578291427 AQUAD1_01547 hypothetical protein 2578291444 AQUAD1_01564 hypothetical protein 2578291453 AQUAD1_01573 hypothetical protein 2578291454 AQUAD1_01574 Immunity protein 14 2578291457 AQUAD1_01577 RHS repeat-associated core domain 2578291458 AQUAD1_01578 hypothetical protein 2578291461 AQUAD1_01581 Bacteriocin-protection, YdeI or OmpD-Associated 2578291462 AQUAD1_01582 hypothetical protein 2578291463 AQUAD1_01583 hypothetical protein 2578291467 AQUAD1_01587 hypothetical protein 2578291468 AQUAD1_01588 hypothetical protein 2578291469 AQUAD1_01589 Acetyltransferases, including N-acetylases of ribosomal proteins 2578291499 AQUAD1_01619 Common central domain of tyrosinase 2578291500 AQUAD1_01620 hypothetical protein 2578291501 AQUAD1_01621 hypothetical protein 2578291502 AQUAD1_01622 Dehydrogenases (flavoproteins) 2578291503 AQUAD1_01623 hypothetical protein 2578291504 AQUAD1_01624 hypothetical protein 2578291513 AQUAD1_01633 hypothetical protein 2578291519 AQUAD1_01639 Predicted phosphatase 2578291555 AQUAD1_01675 hypothetical protein 2578291565 AQUAD1_01685 Uncharacterized paraquat-inducible protein B 2578291594 AQUAD1_01714 hypothetical protein 2578291629 AQUAD1_01749 four helix bundle protein 2578291645 AQUAD1_01765 Por secretion system C-terminal sorting domain 2578291680 AQUAD1_01800 hypothetical protein 2578291683 AQUAD1_01803 Predicted ATPases of PP-loop superfamily 2578291687 AQUAD1_01807 Outer membrane protein beta-barrel domain 2578291700 AQUAD1_01820 Phytoene dehydrogenase and related proteins 2578291704 AQUAD1_01824 hypothetical protein 2578291705 AQUAD1_01825 tRNA_anti-like 2578291707 AQUAD1_01827 Polysaccharide lyase 2578291708 AQUAD1_01828 Por secretion system C-terminal sorting domain 2578291709 AQUAD1_01829 Por secretion system C-terminal sorting domain 2578291716 AQUAD1_01836 IPT/TIG domain 2578291718 AQUAD1_01838 SusD family/Starch-binding associating with outer membrane 236

2578291730 AQUAD1_01850 hypothetical protein 2578291731 AQUAD1_01851 Protein of unknown function (DUF3999) 2578291735 AQUAD1_01855 hypothetical protein 2578291743 AQUAD1_01863 DinB superfamily 2578291752 AQUAD1_01872 hypothetical protein 2578291753 AQUAD1_01873 hypothetical protein 2578291757 AQUAD1_01877 hypothetical protein 2578291758 AQUAD1_01878 Phytoene dehydrogenase and related proteins 2578291775 AQUAD1_01895 hypothetical protein 2578291782 AQUAD1_01902 hypothetical protein 2578291795 AQUAD1_01915 hypothetical protein 2578291821 AQUAD1_01941 Lipocalin-like domain 2578291843 AQUAD1_01964 Bacterial transferase hexapeptide (six repeats) 2578291844 AQUAD1_01965 Predicted dehydrogenases and related proteins 2578291854 AQUAD1_01975 Bacterial sugar transferase 2578291928 AQUAD1_02049 hypothetical protein 2578291929 AQUAD1_02050 FecR family protein 2578291938 AQUAD1_02059 Oxidoreductase family 2578291952 AQUAD1_02073 Predicted membrane protein/domain 2578291954 AQUAD1_02075 hypothetical protein 2578291956 AQUAD1_02077 Domain of unknown function (DUF4350) 2578291960 AQUAD1_02081 hypothetical protein 2578291976 AQUAD1_02097 Outer membrane protein 2578291978 AQUAD1_02099 Esterase/lipase 2578291980 AQUAD1_02101 hypothetical protein 2578291981 AQUAD1_02102 Predicted membrane protein 2578291983 AQUAD1_02104 AraC-type DNA-binding domain-containing proteins 2578291986 AQUAD1_02107 hypothetical protein 2578291988 AQUAD1_02109 Glycine zipper 2578291991 AQUAD1_02112 YHYH protein 2578291995 AQUAD1_02116 Glycosyl hydrolases family 2, sugar binding domain 2578291996 AQUAD1_02117 Acetyltransferase (GNAT) family 2578292000 AQUAD1_02121 Acetyltransferases, including N-acetylases of ribosomal proteins 2578292002 AQUAD1_02123 Glyoxalase-like domain 2578292011 AQUAD1_02132 Isopropylmalate/homocitrate/citramalate synthases 2578292020 AQUAD1_02141 STAS domain 2578292032 AQUAD1_02153 Por secretion system C-terminal sorting domain 2578292041 AQUAD1_02162 Por secretion system C-terminal sorting domain 2578292042 AQUAD1_02163 Phospholipase A1 2578292044 AQUAD1_02165 Por secretion system C-terminal sorting domain 2578292053 AQUAD1_02174 hypothetical protein 2578292058 AQUAD1_02179 Signal transduction histidine kinase 2578292060 AQUAD1_02181 hypothetical protein 2578292061 AQUAD1_02182 hypothetical protein 2578292062 AQUAD1_02183 Surface lipoprotein of Spirochaetales order 2578292067 AQUAD1_02188 hypothetical protein 237

2578292071 AQUAD1_02192 Uncharacterized protein conserved in bacteria (DUF2059) 2578292074 AQUAD1_02195 hypothetical protein 2578292087 AQUAD1_02208 hypothetical protein 2578292091 AQUAD1_02212 hypothetical protein 2578292093 AQUAD1_02214 Peptidase family S41 2578292100 AQUAD1_02221 Acetyltransferases 2578292138 AQUAD1_02259 Ankyrin repeats (3 copies) 2578292142 AQUAD1_02263 Glycosyl hydrolase family 26 2578292151 AQUAD1_02272 hypothetical protein 2578292164 AQUAD1_02285 Glycosyltransferases involved in cell wall biogenesis 2578292168 AQUAD1_02289 hypothetical protein 2578292170 AQUAD1_02291 hypothetical protein 2578292181 AQUAD1_02302 Protein of unknown function (DUF3010) 2578292184 AQUAD1_02305 PEP phosphonomutase and related enzymes 2578292185 AQUAD1_02306 Putative esterase 2578292186 AQUAD1_02307 hypothetical protein 2578292187 AQUAD1_02308 hypothetical protein 2578292198 AQUAD1_02319 hypothetical protein 2578292199 AQUAD1_02320 Predicted endonuclease containing a URI domain 2578292200 AQUAD1_02321 hypothetical protein 2578292201 AQUAD1_02322 Protein of unknown function (DUF1266) 2578292202 AQUAD1_02323 hypothetical protein 2578292203 AQUAD1_02324 hypothetical protein 2578292204 AQUAD1_02325 hypothetical protein 2578292210 AQUAD1_02331 hypothetical protein 2578292222 AQUAD1_02343 Small-conductance mechanosensitive channel 2578292237 AQUAD1_02358 hypothetical protein 2578292245 AQUAD1_02366 Predicted Zn-dependent peptidases 2578292266 AQUAD1_02387 hypothetical protein 2578292272 AQUAD1_02393 Protein of unknown function (DUF3108) 2578292273 AQUAD1_02394 Galactose oxidase, central domain 2578292292 AQUAD1_02413 Membrane protein involved in the export of O-antigen and teichoic acid 2578292293 AQUAD1_02414 hypothetical protein 2578292294 AQUAD1_02415 Polysaccharide pyruvyl transferase 2578292295 AQUAD1_02416 Methyltransferase domain 2578292337 AQUAD1_02458 Acetyltransferase (GNAT) domain 2578292338 AQUAD1_02459 Glycosyltransferase 2578292340 AQUAD1_02461 Membrane protein involved in the export of O-antigen and teichoic acid 2578292341 AQUAD1_02462 Glycosyltransferase 2578292342 AQUAD1_02463 WxcM-like, C-terminal 2578292343 AQUAD1_02464 Glycosyl transferase family 2 2578292344 AQUAD1_02465 Glycosyl transferase family 2 2578292345 AQUAD1_02466 Predicted proline hydroxylase 2578292349 AQUAD1_02470 hypothetical protein 2578292353 AQUAD1_02474 hypothetical protein 2578292357 AQUAD1_02478 Domain of unknown function (DUF4377) 238

2578292363 AQUAD1_02484 hypothetical protein 2578292364 AQUAD1_02485 hypothetical protein 2578292371 AQUAD1_02492 hypothetical protein 2578292374 AQUAD1_02495 hypothetical protein 2578292375 AQUAD1_02496 Uncharacterized alpha/beta hydrolase domain (DUF2235) 2578292376 AQUAD1_02497 Protein of unknown function (DUF2931) 2578292401 AQUAD1_02522 hypothetical protein 2578292433 AQUAD1_02554 hypothetical protein 2578292437 AQUAD1_02558 Por secretion system C-terminal sorting domain 2578292443 AQUAD1_02564 Por secretion system C-terminal sorting domain 2578292444 AQUAD1_02565 hypothetical protein 2578292453 AQUAD1_02574 hypothetical protein 2578292466 AQUAD1_02587 hypothetical protein 2578292470 AQUAD1_02591 hypothetical protein 2578292490 AQUAD1_02611 Beta-1,4-xylanase 2578292491 AQUAD1_02612 hypothetical protein 2578292492 AQUAD1_02613 SusD family/Starch-binding associating with outer membrane 2578292507 AQUAD1_02628 hypothetical protein 2578292509 AQUAD1_02630 hypothetical protein 2578292513 AQUAD1_02634 hypothetical protein 2578292514 AQUAD1_02635 hypothetical protein 2578292528 AQUAD1_02649 hypothetical protein 2578292539 AQUAD1_02660 Por secretion system C-terminal sorting domain 2578292552 AQUAD1_02673 hypothetical protein 2578292562 AQUAD1_02683 Periplasmic protease 2578292571 AQUAD1_02692 Domain of unknown function (DUF4126) 2578292576 AQUAD1_02697 Prolyl oligopeptidase family 2578292579 AQUAD1_02700 Phytoene dehydrogenase and related proteins 2578292592 AQUAD1_02713 Putative GTPases (G3E family) 2578292593 AQUAD1_02714 Prolyl oligopeptidase family 2578292621 AQUAD1_02742 Imidazolonepropionase and related amidohydrolases 2578292637 AQUAD1_02758 hypothetical protein 2578292652 AQUAD1_02773 hypothetical protein 2578292656 AQUAD1_02777 two component transcriptional regulator, LuxR family 2578292657 AQUAD1_02778 Por secretion system C-terminal sorting domain 2578292658 AQUAD1_02779 Por secretion system C-terminal sorting domain 2578292671 AQUAD1_02792 Dehydrogenases with different specificities 2578292672 AQUAD1_02793 D-mannonate dehydratase (EC 4.2.1.8) 2578292682 AQUAD1_02803 hypothetical protein 2578292683 AQUAD1_02804 2OG-Fe dioxygenase 2578292685 AQUAD1_02806 AAA domain 2578292686 AQUAD1_02807 ATPases of the AAA+ class 2578292687 AQUAD1_02808 hypothetical protein 2578292689 AQUAD1_02810 ABC-type nitrate/sulfonate/bicarbonate transport system 2578292690 AQUAD1_02811 NMT1-like family/OmpA family 2578292691 AQUAD1_02812 hypothetical protein 239

2578292692 AQUAD1_02813 hypothetical protein 2578292693 AQUAD1_02814 hypothetical protein 2578292698 AQUAD1_02819 hypothetical protein 2578292706 AQUAD1_02827 hypothetical protein 2578292722 AQUAD1_02843 hypothetical protein 2578292730 AQUAD1_02851 hypothetical protein 2578292734 AQUAD1_02855 Flavodoxin reductases (ferredoxin-NADPH reductases) family 1 2578292743 AQUAD1_02864 hypothetical protein 2578292750 AQUAD1_02871 hypothetical protein 2578292758 AQUAD1_02879 Acetyltransferases, including N-acetylases of ribosomal proteins 2578292761 AQUAD1_02882 hypothetical protein 2578292764 AQUAD1_02885 Por secretion system C-terminal sorting domain 2578292767 AQUAD1_02888 hypothetical protein 2578292838 AQUAD1_02959 hypothetical protein 2578292845 AQUAD1_02966 Domain of unknown function (DUF4480) 2578292846 AQUAD1_02967 hypothetical protein 2578292859 AQUAD1_02980 3-hydroxymyristoyl/3-hydroxydecanoyl-(acyl carrier protein) dehydratases 2578292861 AQUAD1_02982 Uncharacterized protein conserved in bacteria 2578292862 AQUAD1_02983 Acyltransferase/MMPL family 2578292863 AQUAD1_02984 Phytoene dehydrogenase and related proteins 2578292864 AQUAD1_02985 Acyl-coenzyme A:6-aminopenicillanic acid acyl-transferase 2578292865 AQUAD1_02986 Coenzyme F390 synthetase 2578292867 AQUAD1_02988 hypothetical protein 2578292869 AQUAD1_02990 hypothetical protein 2578292886 AQUAD1_03007 hypothetical protein 2578292887 AQUAD1_03008 Protein of unknown function (DUF4056) 2578292888 AQUAD1_03009 hypothetical protein 2578292889 AQUAD1_03010 Glycosyltransferases, probably involved in cell wall biogenesis 2578292890 AQUAD1_03011 Acyltransferase 2578292891 AQUAD1_03012 Protein of unknown function (DUF422) 2578292893 AQUAD1_03014 lycopene cyclase domain 2578292895 AQUAD1_03016 amidohydrolase 2578292900 AQUAD1_03021 Predicted acetyltransferase 2578292907 AQUAD1_03028 hypothetical protein 2578292923 AQUAD1_03044 conserved hypothetical integral membrane protein 2578292936 AQUAD1_03057 hypothetical protein 2578292975 AQUAD1_03096 hypothetical protein 2578292994 AQUAD1_03115 hypothetical protein 2578293019 AQUAD1_03140 Peptidase family C25 2578293029 AQUAD1_03152 hypothetical protein 2578293030 AQUAD1_03153 Domain of unknown function (DUF4480) 2578293063 AQUAD1_03187 hypothetical protein 2578293065 AQUAD1_03189 Bacterial capsule synthesis protein PGA_cap 2578293102 AQUAD1_03226 hypothetical protein 2578293110 AQUAD1_03234 hypothetical protein 2578293113 AQUAD1_03237 hypothetical protein 240

2578293114 AQUAD1_03238 hypothetical protein 2578293128 AQUAD1_03252 MORN repeat variant 2578293130 AQUAD1_03254 Por secretion system C-terminal sorting domain 2578293154 AQUAD1_03278 Lysophospholipase L1 and related esterases 2578293163 AQUAD1_03287 hypothetical protein 2578293165 AQUAD1_03289 hypothetical protein 2578293190 AQUAD1_03314 Response regulator receiver domain 2578293192 AQUAD1_03316 Putative peptidoglycan binding domain 2578293216 AQUAD1_03340 Na+/proline symporter 2578293218 AQUAD1_03342 hypothetical protein 2578293223 AQUAD1_03347 Transcription antiterminator 2578293239 AQUAD1_03363 hypothetical protein 2578293243 AQUAD1_03367 Por secretion system C-terminal sorting domain 2578293252 AQUAD1_03376 hypothetical protein 2578293253 AQUAD1_03377 Predicted acyltransferases 2578293267 AQUAD1_03391 Histidinol-phosphate/aromatic aminotransferase 2578293270 AQUAD1_03394 Proteolipid membrane potential modulator 2578293271 AQUAD1_03395 hypothetical protein 2578293272 AQUAD1_03396 hypothetical protein 2578293274 AQUAD1_03398 C1q domain 2578293275 AQUAD1_03399 Por secretion system C-terminal sorting domain 2578293291 AQUAD1_03415 hypothetical protein 2578293294 AQUAD1_03418 hypothetical protein 2578293295 AQUAD1_03419 Galactose mutarotase and related enzymes 2578293296 AQUAD1_03420 hypothetical protein 2578293297 AQUAD1_03421 hypothetical protein 2578293305 AQUAD1_03429 hypothetical protein 2578293322 AQUAD1_03446 hypothetical protein 2578293329 AQUAD1_03453 FecR family protein 2578293331 AQUAD1_03455 Domain of unknown function (DUF4249) 2578293345 AQUAD1_03469 Predicted membrane protein (DUF2306) 2578293364 AQUAD1_03488 hypothetical protein 2578293365 AQUAD1_03489 Predicted flavoprotein involved in K+ transport 2578293370 AQUAD1_03494 hypothetical protein 2578293382 AQUAD1_03506 hypothetical protein 2578293390 AQUAD1_03514 Por secretion system C-terminal sorting domain 2578293391 AQUAD1_03515 hypothetical protein 2578293413 AQUAD1_03537 Predicted permeases 2578293417 AQUAD1_03541 Methylase involved in ubiquinone/menaquinone biosynthesis 2578293450 AQUAD1_03574 hypothetical protein 2578293478 AQUAD1_03602 RHS repeat-associated core domain 2578293491 AQUAD1_03615 hypothetical protein 2578293508 AQUAD1_03632 hypothetical protein 2578293540 AQUAD1_03664 hypothetical protein 2578293542 AQUAD1_03666 hypothetical protein 2578293543 AQUAD1_03667 Uncharacterized protein conserved in bacteria 241

2578293545 AQUAD1_03669 Uncharacterized protein conserved in bacteria 2578293551 AQUAD1_03675 hypothetical protein 2578293552 AQUAD1_03676 Protein of unknown function (DUF3108) 2578293557 AQUAD1_03681 Uncharacterized protein conserved in bacteria 2578293571 AQUAD1_03695 Conserved protein/ associated with flavoprotein oxygenases 2578293574 AQUAD1_03698 hypothetical protein 2578293582 AQUAD1_03706 hypothetical protein 2578293588 AQUAD1_03712 Glyoxalase-like domain 2578293593 AQUAD1_03717 Domain of unknown function (DUF4437) 2578293595 AQUAD1_03719 Nitroreductase 2578293614 AQUAD1_03738 hypothetical protein 2578293615 AQUAD1_03739 hypothetical protein 2578293630 AQUAD1_03754 Beta-lactamase class C and other penicillin binding proteins 2578293652 AQUAD1_03776 Histidine kinase 2578293653 AQUAD1_03777 EF hand 2578293654 AQUAD1_03778 YHYH protein 2578293655 AQUAD1_03779 Uncharacterized protein conserved in bacteria 2578293661 AQUAD1_03785 Tetratricopeptide repeat/Bacterial regulatory proteins, luxR family 2578293676 AQUAD1_03800 RNA polymerase sigma factor, sigma-70 family 2578293678 AQUAD1_03802 hypothetical protein 2578293690 AQUAD1_03814 Acylphosphatases 2578293692 AQUAD1_03816 purine-nucleoside phosphorylase, family 1 (deoD) 2578293695 AQUAD1_03819 Predicted membrane protein 2578293717 AQUAD1_03841 Por secretion system C-terminal sorting domain 2578293719 AQUAD1_03843 Predicted esterase 2578293727 AQUAD1_03851 Protein of unknown function (DUF2752) 2578293728 AQUAD1_03852 Interferon-induced transmembrane protein 2578293769 AQUAD1_03893 Adenine-specific DNA methylase 2578293770 AQUAD1_03894 hypothetical protein 2578293773 AQUAD1_03897 Por secretion system C-terminal sorting domain 2578293777 AQUAD1_03901 hypothetical protein 2578293793 AQUAD1_03917 Transglutaminase-like superfamily 2578293801 AQUAD1_03925 Predicted hydrolases or acyltransferases 2578293813 AQUAD1_03937 hypothetical protein 2578293816 AQUAD1_03940 hypothetical protein 2578293818 AQUAD1_03942 SnoaL-like domain 2578293841 AQUAD1_03965 hypothetical protein 2578293850 AQUAD1_03974 hypothetical protein 2578293865 AQUAD1_03989 CAAX protease self-immunity 2578293873 AQUAD1_03997 UDP-N-acetylglucosamine 2-epimerase 2578293889 AQUAD1_04013 hypothetical protein 2578293897 AQUAD1_04021 hypothetical protein 2578293898 AQUAD1_04022 Predicted phosphohydrolases 2578293899 AQUAD1_04023 hypothetical protein 2578293902 AQUAD1_04026 hypothetical protein 2578293903 AQUAD1_04027 hypothetical protein 242

2578293904 AQUAD1_04028 hypothetical protein 2578293905 AQUAD1_04029 Fatty acid desaturase 2578293906 AQUAD1_04030 Fatty acid desaturase 2578293916 AQUAD1_04040 Glycosyl hydrolases family 11 2578293924 AQUAD1_04048 phage shock protein C (PspC) family protein 2578293926 AQUAD1_04050 YARHG domain 2578293941 AQUAD1_04065 Domain of unknown function (DUF4386) 2578293961 AQUAD1_04085 hypothetical protein 2578293962 AQUAD1_04086 hypothetical protein 2578293963 AQUAD1_04087 hypothetical protein 2578293985 AQUAD1_04109 hypothetical protein 2578293986 AQUAD1_04110 Fe-S oxidoreductase 2578293987 AQUAD1_04111 Methyltransferase domain 2578293988 AQUAD1_04112 Fe-S oxidoreductase 2578293993 AQUAD1_04117 Protein of unknown function (DUF3667) 2578293997 AQUAD1_04121 hypothetical protein 2578294010 AQUAD1_04134 hypothetical protein 2578294014 AQUAD1_04138 Domain of unknown function (DUF1083) 2578294019 AQUAD1_04143 Protein of unknown function (DUF4199) 2578294020 AQUAD1_04144 hypothetical protein 2578294029 AQUAD1_04153 Beta-lactamase enzyme family 2578294037 AQUAD1_04161 Periplasmic protease 2578294046 AQUAD1_04170 SusD family/Starch-binding associating with outer membrane 2578294053 AQUAD1_04177 Protein of unknown function (Porph_ging) 2578294059 AQUAD1_04183 exopolysaccharide biosynthesis polyprenyl glycosylphosphotransferase 2578294068 AQUAD1_04192 Methyltransferase domain 2578294072 AQUAD1_04196 Histidine kinase 2578294081 AQUAD1_04205 TonB-linked outer membrane protein, SusC/RagA family 2578294089 AQUAD1_04213 Protein of unknown function (DUF2807) 2578294098 AQUAD1_04222 hypothetical protein 2578294106 AQUAD1_04230 Beta-lactamase class C and other penicillin binding proteins 2578294110 AQUAD1_04234 Mechanosensitive ion channel 2578294122 AQUAD1_04246 Uncharacterized oxidoreductases 2578294124 AQUAD1_04248 hypothetical protein 2578294126 AQUAD1_04250 hypothetical protein 2578294127 AQUAD1_04251 hypothetical protein 2578294128 AQUAD1_04252 hypothetical protein 2578294145 AQUAD1_04269 Por secretion system C-terminal sorting domain 2578294147 AQUAD1_04271 TIGR02594 family protein 2578294148 AQUAD1_04272 Holin family 2578294153 AQUAD1_04277 hypothetical protein 2578294159 AQUAD1_04283 hypothetical protein 2578294160 AQUAD1_04284 Outer membrane lipoprotein-sorting protein 2578294162 AQUAD1_04286 Beta-ketoacyl synthase, N-terminal domain 2578294164 AQUAD1_04288 Acyl carrier protein 2578294165 AQUAD1_04289 hypothetical protein 243

2578294175 AQUAD1_04299 transcriptional regulator, AsnC family 2578294183 AQUAD1_04307 Glycosyltransferase 2578294191 AQUAD1_04315 hypothetical protein 2578294202 AQUAD1_04326 Signal transduction histidine kinase 2578294203 AQUAD1_04327 two component transcriptional regulator, LuxR family 2578294204 AQUAD1_04328 Leucine Rich repeats (2 copies)/Putative glycoside hydrolase xylanase 2578294227 AQUAD1_04351 Response regulator receiver domain 2578294228 AQUAD1_04352 Signal transduction histidine kinase 2578294230 AQUAD1_04354 hypothetical protein 2578294239 AQUAD1_04363 hypothetical protein 2578294240 AQUAD1_04364 Putative esterase 2578294241 AQUAD1_04365 hypothetical protein 2578294242 AQUAD1_04366 hypothetical protein 2578294256 AQUAD1_04380 Glycosyltransferase 2578294257 AQUAD1_04381 GDP-mannose 4,6-dehydratase 2578294258 AQUAD1_04382 Nucleoside-diphosphate-sugar epimerases 2578294259 AQUAD1_04383 Lipocalin-like domain 2578294293 AQUAD1_04417 hypothetical protein 2578294299 AQUAD1_04423 hypothetical protein 2578294302 AQUAD1_04426 Uncharacterized conserved protein 2578294304 AQUAD1_04428 Polyketide cyclase / dehydrase and lipid transport 2578294309 AQUAD1_04433 hypothetical protein 2578294310 AQUAD1_04434 Por secretion system C-terminal sorting domain 2578294352 AQUAD1_04476 Alpha/beta hydrolase family 2578294353 AQUAD1_04477 transcriptional regulator, LytTR family 2578294384 AQUAD1_04509 hypothetical protein 2578294392 AQUAD1_04517 hypothetical protein 2578294393 AQUAD1_04518 AraC-type DNA-binding domain-containing proteins 2578294402 AQUAD1_04527 Peptidase family S41 2578294408 AQUAD1_04533 hypothetical protein 2578294412 AQUAD1_04537 hypothetical protein 2578294437 AQUAD1_04562 DEAD/DEAH box helicase 2578294448 AQUAD1_04573 Pirin-related protein 2578294449 AQUAD1_04574 AraC-type DNA-binding domain-containing proteins 2578294462 AQUAD1_04587 hypothetical protein 2578294464 AQUAD1_04589 Protein of unknown function (DUF1572) 2578294481 AQUAD1_04606 hypothetical protein 2578294492 AQUAD1_04617 hypothetical protein 2578294493 AQUAD1_04618 hypothetical protein 2578294497 AQUAD1_04622 Acetyltransferase (GNAT) domain 2578294501 AQUAD1_04626 hypothetical protein 2578294517 AQUAD1_04642 hypothetical protein 2578294518 AQUAD1_04643 hypothetical protein 2578294522 AQUAD1_04647 Helix-turn-helix 2578294523 AQUAD1_04648 HipA N-terminal domain/HipA-like C-terminal domain 2578294525 AQUAD1_04650 O-antigen ligase like membrane protein 244

2578294527 AQUAD1_04652 hypothetical protein 2578294528 AQUAD1_04653 LytTr DNA-binding domain 2578294529 AQUAD1_04654 hypothetical protein 2578294530 AQUAD1_04655 Conjugative transposon protein TraO 2578294533 AQUAD1_04658 Beta-lactamase class C and other penicillin binding proteins 2578294535 AQUAD1_04660 Por secretion system C-terminal sorting domain 2578294538 AQUAD1_04663 hypothetical protein 2578294539 AQUAD1_04664 Helix-turn-helix domain 2578294541 AQUAD1_04666 hypothetical protein 2578294550 AQUAD1_04675 hypothetical protein 2578294551 AQUAD1_04676 hypothetical protein 2578294552 AQUAD1_04677 hypothetical protein 2578294565 AQUAD1_04690 CpeT/CpcT family (DUF1001) 2578294578 AQUAD1_04703 hypothetical protein 2578294584 AQUAD1_04709 conserved hypothetical protein 2578294586 AQUAD1_04711 Secreted and surface protein containing fasciclin-like repeats 2578294589 AQUAD1_04714 hypothetical protein 2578294591 AQUAD1_04716 Protein of unknown function (DUF4199) 2578294595 AQUAD1_04720 DNA-methyltransferase (dcm) 2578294596 AQUAD1_04721 Histidine kinase-, DNA gyrase B-, and HSP90-like ATPase 2578294597 AQUAD1_04722 Repeat domain in Vibrio, Colwellia, Bradyrhizobium and Shewanella 2578294614 AQUAD1_04739 Uncharacterized protein containing a von Willebrand factor type A 2578294622 AQUAD1_04747 hypothetical protein 2578294623 AQUAD1_04748 hypothetical protein 2578294626 AQUAD1_04751 hypothetical protein 2578294629 AQUAD1_04754 hypothetical protein 2578294630 AQUAD1_04755 hypothetical protein 2578294637 AQUAD1_04762 hypothetical protein 2578294644 AQUAD1_04769 hypothetical protein 2578294645 AQUAD1_04770 FKBP-type peptidyl-prolyl cis-trans isomerase/YceI-like domain 2578294647 AQUAD1_04772 Lipocalin-like domain 2578294656 AQUAD1_04781 Predicted RNA methylase 2578294657 AQUAD1_04782 hypothetical protein 2578294672 AQUAD1_04797 hypothetical protein 2578294673 AQUAD1_04798 Gas vesicle protein 2578294674 AQUAD1_04799 hypothetical protein 2578294686 AQUAD1_04811 PKD domain 2578294688 AQUAD1_04813 hypothetical protein 2578294689 AQUAD1_04814 hypothetical protein 2578294690 AQUAD1_04815 hypothetical protein

245