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Deciphering the genomic tool-kit underlying - interactions Insights through the queenslandica

Benedict Yuen Jinghao B.MarSt. (Hons)

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2016 School of Biological Sciences Deciphering the genomic tool-kit underlying animal-bacteria interactions

Abstract All are inhabited by bacteria, and maintaining homeostasis in the multicellular environment of the host involves the complex balancing act of promoting the survival of symbionts while defending against intruders. (Porifera), in addition to housing diverse bacterial symbiont assemblages, also rely on bacteria filtered from the water column for nutrition. My research uses the genome-enabled demosponge, , a member of one of the earliest-diverging animal phyletic lineages, as an experimental platform to investigate the genomic toolkit underpinning animal-bacteria interactions. Using comparative bioinformatics tools, I characterised a surprisingly large and complex repertoire of innate immune receptors from the NLR family of genes encoded in the A. queenslandica genome. I then used a high throughput RNAseq approach to profile the ’s global transcriptomic response to foreign versus its own native bacteria. Conserved metazoan innate immune pathways were activated in response to both foreign and native bacteria. However, only the native bacteria elicited the expression of a more extensive suite of signalling pathways, involving TGF-β signalling and the transcription factors NF-κB and FoxO. Upregulation of the nutrient sensor AMPK in all treatments along with immune signalling genes, which all regulate FoxO activity, further suggests an interplay between metabolic homeostasis and immunity. Finally, I used microscopy to track the cellular-level processing of the different bacteria by the sponge. Consistent with the observed transcriptional response, the native bacteria were ingested by archaeocytes more rapidly than the foreign bacteria. The slower processing of foreign bacteria also correlated with the expression of numerous Nrf2-associated detoxification genes, indicative of cellular stress, only in response to foreign bacteria.

ii Deciphering the genomic tool-kit underlying animal-bacteria interactions

Declaration by author This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

iii Deciphering the genomic tool-kit underlying animal-bacteria interactions

Publications during candidature Peer-reviewed paper Yuen B, Bayes JM, Degnan SM 2015. The characterization of sponge NLRs provides insight into the origin and of this innate immune gene family in animals. Mol. Biol. Evol. 31(1):106.

Conference abstracts Yuen B, Degnan SM 2013. Why does the genome of the sponge, Amphimedon queenslandica encode highly complex and diversified innate immune receptor systems? In: 9th World Sponge Conference: 2013; Fremantle, Western Australia.

Yuen B, Degnan SM 2015. Why does the genome of the humble marine sponge, Amphimedon queenslandica, encode the potential for a complex innate immune response? In: The 2nd Annual EMBL Australia PhD Symposium – Completing the pipeline: from biology to bioinformatics and back again: 2015; Melbourne, Victoria.

Publications included in this thesis Publication citation – incorporated as Chapter 2. Yuen B, Bayes JM, Degnan SM 2015. The characterization of sponge NLRs provides insight into the origin and evolution of this innate immune gene family in animals. Mol. Biol. Evol. 31(1):106.

Contributor Statement of contribution Benedict Yuen (Candidate) Designed experiments (50%)

Data analysis and interpretation (80%)

Wrote the paper (90%) Joanne Marie Bayes Designed experiments (20%)

Data analysis and interpretation (10%) Sandra M Degnan Designed experiments (30%)

Data analysis and interpretation (10%)

Wrote and edited paper (10%)

iv Deciphering the genomic tool-kit underlying animal-bacteria interactions

Contributions by others to the thesis Sandra M. Degnan contributed to the conception and design of this research, advised on methods and analyses, and provided critical comments on the thesis and on the associated publication in Chapter 2

Joanne M. Bayes was also involved in the of Chapter 2 of this thesis. conception and the analysis of 10% of the data – specifically the identification of the clade AqNLRsC .

Selene Fernandez Valverde, William Hatleberg and Shunsuke Sogabe produced several of the datasets analysed across this thesis (these contributions are specifically acknowledged in the relevant chapters).

Transcriptome sequencing was conducted by the Ramaciotti Centre for Genomics, University of New South Wales, NSW, Australia

Statement of parts of the thesis submitted to qualify for the award of another degree None.

v Deciphering the genomic tool-kit underlying animal-bacteria interactions

Acknowledgements I would like to begin by expressing my heartfelt gratitude to my supervisor, Sandie Degnan, for all the guidance you have given me since I first joined the lab as an Honours student in 2011. Thank you for patiently keeping me on track with encouragement and tough love when it was necessary. My career in science is only just beginning and I could not have picked a better mentor to guide me through the transition from an undergraduate to a post-graduate student. I also wish to thank Bernie Degnan, because I believe this journey began when he sparked my interest in evolution as an undergraduate student in his Marine course back in 2010. I am also grateful for having been given the opportunity to tutor in that same course years later. Thank you both for all the wisdom you have imparted in aspects of both work and , and for teaching me how to science with rigour and integrity.

I wish to thank my co-supervisor Andy Barnes for his advice and also for taking me on as a tutor on the MARS2005 field trips. I’ve had so much fun working with you and the other tutors on Heron, and look forward to one final round this September. I also want to thank my panel of readers Sassan Asgari and Kate Stacey. The frank and honest advice you gave me at each milestone is something I’ve come to greatly appreciate.

This project was funded by an Australian Research Council Discovery grant to Sandie Degnan (DP110104601). I also wish to acknowledge the University of Queensland for awarding me the UQ International scholarship that covered my tuition fees and provided me with a stipend so that I would not starve in my quest to further our understanding of the world around us. I would also like to thank Bernie and Sandie for financing my trip to the 9th World Sponge Conference in Fremantle. Thank you also for funding my fieldwork in one of the most magnificently beautiful places on this planet.

Doing fieldwork on a remote island is challenging on a good day and nightmarish when the weather is not in your favour. I wish to acknowledge the staff at the Heron Island Research Station for all the support they have provided over the multiple field trips I have undertaken over the years.

I have been surrounded by truly intelligent and wonderful colleagues who have made my time learning to science in the Degnan lab immensely enjoyable. I must express my appreciation to the lab members

vi Deciphering the genomic tool-kit underlying animal-bacteria interactions who have been so generous with the time they have spent training me and providing technical advice. Special thanks goes out to Kerry, Carmel, Laura, Daniel, Selene, Bec and Will. I wish to thank to wonder woman Kerry for the amazing job that she does keeping the day to day business of the lab running so smoothly. I am grateful to Carmel, the all-knowing oracle, for allowing me to badger her constantly with all manner of science-y questions. Thank you both for helping me get through the CEL-Seq protocol. I am grateful to Laura for her insight and advice over the years, the chats about food and other random things. I’m always going to remember the 90’s dance party on Heron Island. Thank you to Selene and Will for help with the computational stuff and for the precious insight into dealing with large datasets. I am grateful to Daniel who taught me the fundamentals of microscopy and imaging techniques. Thank you Bec for the conversations about microbes. Both of you have helped me put what would otherwise have been abstract data into a meaningful spongy context.

A big thank you goes out to all other Degnan Labsters, past and present: Jo, Jaret, Andrew, Jabin, Felipe, Shun, Katia, Fede, Maely, Yasu, Kevin, Claire, Gemma, Markus, Arun, Tahsha, Simone, Eunice, Jake, Mel, Betty, Aude. At some point during my time in the lab, you have given me advice and ideas, responded to my queries, or critiqued my oral presentations. Heron island doesn’t always feel like paradise, particularly when you’re in the lab at stupid o’clock, staring down a microscope at little white blobs. However, sunset beers on the jetty with you guys went a long way towards making it better. It has been a pleasure to spending time with all of you outside of work as well, and I hope we cross paths again in the future.

I am grateful to Sheridan, Shun and Laura for reading and critiquing various chapters of my thesis. I particularly wish to thank Laura and Jabin for their help with working out the formatting and InDesign.

Science-ing is really tough and I am only just starting to learn to cope with the frequent setbacks that are part and parcel of being a scientist. Whenever life or science gave me a rasteira, capoeira was there to help me back on to my feet. The melody of the berimbau giving me the strength to take things one step at a time. To my Xango Capoeira family, especially meu Mestre, Caracol, I can’t even begin to describe how grateful I am, because capoeira has played such an important role in fortifying my mental health to me get this far.

vii Deciphering the genomic tool-kit underlying animal-bacteria interactions

To my parents, I know I’ve been distant in the past few years and I’m grateful for the space you’ve given me and the patience you’ve shown. Without your help, I would not have made it to Australia to pursue a vague dream of studying marine biology. Thank you for the faith you have shown in me and all the unconditional love.

Finally, I am eternally grateful to my partner in crime, best friend and companion, Sheridan, who has stood beside me throughout this journey. You have enlightened my views inside and outside of science, and I am a better person for having met you. Thank you for helping me up when I hit rock bottom, and for your faith in my ability to accomplish this when I could not see the light at the end of the tunnel. You’re the greatest.

viii Deciphering the genomic tool-kit underlying animal-bacteria interactions

Keywords animal-bacteria interactions, sponge, symbiosis, immunity, transcriptomics, evolution, pattern recognition receptors, bacteria, genomics

Australian and New Zealand Standard Research Classifications (ANZSRC) 060405 Gene Expression (25%)

060408 Genomics (50%)

060409 Molecular Evolution (25%)

Fields of Research (FoR) Classification 0604 Genetics (70%)

0603 Evolutionary Biology (30%)

ix Deciphering the genomic tool-kit underlying animal-bacteria interactions Table of Contents Chapter 1 - General introduction 1 1.1 Animal-bacteria interactions are ubiquitous and vital 1 1.2 Innate immunity is an important mediator of interdomain communication 1 1.3 Invertebrates may deploy multiple strategies for managing the symbiont bacterial community 3 1.4 Marine sponges as models for studying the mechanisms underlying animal-bacteria interactions 5 1.5 Amphimedon queenslandica – a model with the right biological and in silico resources 9 1.6 Aims of this thesis 10 Chapter 2 - The characterization of sponge NLRs provides insight into the origin and evolution of this innate immune gene family in animals 15 2.1 ABSTRACT 15 2.2 INTRODUCTION 15 2.3 RESULTS AND DISCUSSION 19 2.3.1 NLRs are abundant in the Amphimedon genome and likely already existed in the last common ancestor to the Metazoa 19 2.3.2 The NLR genes encoded by the Amphimedon genome have diverse phylogenetic histories and diverse LRRs 22 2.3.3 NLRs appear to be a metazoan-specific invention characterized by two major gene lineages that each contains multiple lineage-specific expansions 27 2.3.4 The N-terminal of metazoan NLRs is highly variable and characterised by convergent evolution 31 2.4 MATERIAL AND METHODS 33 Chapter 3 - The transcriptional response of the demosponge Amphimedon queenslandica in response to native bacteria versus foreign bacteria provides insight into the molecular mechanisms underlying symbiont recognition and response 43 3.1 ABSTRACT 43 3.2 INTRODUCTION 44 3.3 METHODS 48

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3.3.1 Treatment sources - bacterial community enrichment 48 3.3.2 Characterisation of the treatment microbial communities 50 3.3.3 Collection of juvenile sponges 51 3.3.4 Experimental procedure 51 3.3.5 RNA extraction 52 3.3.6 CEL-Seq2 52 3.3.7 CEL-Seq expression analysis pipeline 53 3.3.8 Differential gene expression analyses and annotation 53 3.4 RESULTS 54 3.4.1 The unhealthy Amphimedon bacterial community structure is slightly altered from the native state, while R. globostellata hosts a diverse bacterial community highly distinct to that of Amphimedon 56 3.4.2 Principal component analysis indicates that the native bacteria and unhealthy-Amphimedon bacteria elicited a distinct transcriptional response than the foreign sponge bacteria 58 3.4.3 Native symbiont bacteria induced the largest transcriptional response 2h post-feeding, followed by a more subdued response by 8h post-feeding 59 3.4.4 Both native bacteria and unhealthy-Amphimedon bacterial treatments induced the upregulation of more putative innate immune pattern recognition receptors than did the foreign sponge bacteria 60 3.4.5 The two symbiont bacterial treatments induced more extensive upregulation of innate immune genes than the foreign sponge bacteria 64 3.4.6 The AMPK pathway was upregulated in response to all three treatments 71 3.5 DISCUSSION 71 3.5.1 The scavenger receptor cysteine-rich domain-containing gene family plays an important role in sponge-bacteria interactions 73 3.5.2 TNF and MyD88-dependent signalling pathways are core components of sponge immune surveillance 77 3.5.3 The expression of transcription factors critical to the innate immune response is induced by native bacteria but not by foreign sponge bacteria 80 3.5.4 Upregulation of the immunosuppressive TGF-β pathway suggests that exposure to the native

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bacteria elicits a tolerogenic response 81 3.5.5 The upregulation of AMPK and FOXO, both key regulators of metabolic homeostasis, suggests that sponge-bacteria interactions are regulated by an interplay between metabolism and immunity 82 3.6 CONCLUSIONS 84 Chapter 4 - The selective cellular processing of native bacteria and foreign bacteria by the demosponge Amphimedon queenslandica 87 4.1 ABSTRACT 87 4.2 INTRODUCTION 88 4.3 METHODS 92 4.3.1 Bacteria tracking experiment 92 4.3.2 Feeding experiment transcriptome data analyses 94 4.3.3 Cell-type specific transcriptome analysis 95 4.4 RESULTS 99 4.4.1 Ingested bacteria particles from all three treatments were observed in and amoeboid cells from 30 minutes post-feeding 99 4.4.2 Ingested bacteria were observed in archaeocytes at 2h post-feeding and bacteria derived from A. queenslandica were more frequently observed in archaeocytes than foreign sponge bacteria 101 4.4.3 Genes putatively involved in regulating sponge-bacteria interactions are constitutively more highly expressed in choanocytes and pinacocytes than in archaeocytes 102 4.4.4 The Nrf2 oxidative/xenobiotic stress response pathway is activated only in response to foreign sponge bacteria at 2 and 8 hours post-exposure 109 4.5 DISCUSSION 111 4.5.1 The differential processing of foreign sponge bacteria and theA. queenslandica-derived bacterial treatments at 2h post-feeding is not likely to be due to size 111 4.5.2 The slower acquisition of foreign sponge bacteria by archaeocytes correlates with the activation of the Nrf2 xenobiotic response pathway 114 4.5.3 Bacteria digestion is a multistep process involving communication amongst three cell types

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115 4.5.4 Choanocytes have dual roles in immunity and digestion 120 Chapter 5 - General Discussion 129 5.1 Overview of project objectives 129 5.2 Large and diverse complements of pattern recognition receptors could mediate sponge- bacteria interactions 129 5.3 Distinguishing native and foreign bacteria 131 5.4 Establishment and regulation of symbiosis 133 5.5 What can the sponge tell us about the origin of immunity and digestion in animals? 135 5.6 Outstanding issues 138 5.7 Concluding remarks and recommendations for the future 140 References 145 Appendices 177

xiii Deciphering the genomic tool-kit underlying animal-bacteria interactions List of Figures Figure 1.1 lllustration of the sponge 6 Figure 2.1 Phylogenetic analysis of the A. queenslandica NACHT-domain containing genes. 19 Figure 2.2 Phylogenetic analysis of the A. queenslandica NLR Clade A gene expansion of 122 genes that make up the majority of the A. queenslandica NACHT domain complement. 21 Figure 2.3 Architectures of potential NLR adaptor and signalling/effector proteins encoded by the A. queenslandica (sponge) genome, in comparison with those identified in other animal genomes. 23 Figure 2.4 Phylogenetic analysis of the metazoan NLR genes constructed from an alignment of the NACHT domains (provided in Supplementary File 4). 28 Figure 3.1 Schematic description of experimental procedure 48 Figure 3.2 Relative abundance of microbial 16S reads 55 Figure 3.3 Principal components analyses plot of samples from all treatment groups and Venn diagrams of numbers of differentially expressed genes across the treatment groups 57 Figure 3.4 Expression patterns of putative pattern recognition receptors and Venn diagram of numbers of differentially expressed SRCRs 61 Figure 3.5 Expression patterns of genes involved in immune signalling pathways. 63 Figure 3.6 Expression patterns of genes mapped to endocytosis, phagosome and lyosomal KEGG pathways and transcription factors 67 Figure 3.7 Expression patterns of genes involved in AMPK, PI3K/AKT, and FoxO pathways 70 Figure 3.8 Illustrations of signalling pathway genes that were differentially expressed 72 Figure 3.9 Summary of gene expression trends 75 Figure 4.1 Schematic description of experimental procedure 91 Figure 4.2 Confocal sections tracking the processing of CFDA-SE labelled bacteria treatments by oscula staged juvenile Amphimedon queenslandica 99 Figure 4.3 The mean numbers (± standard error) of archaeocytes, bacteria-containing archaeocytes and chambers found in juveniles from each treatment group 100 Figure 4.4 Expression patterns of SRCR domain containing genes and genes mapped to the lysosomal KEGG pathway across the three cell-types 103 Figure 4.5 Expression patterns of GPCRs in response to the bacterial treatments and across the three cell- types. 106

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Figure 4.6 Expression patterns of putative Nrf2 target genes 110 Figure 4.7 Time course of events following the ingestion and processing of the bacterial treatments by choanocytes, amoeboid cells and archaeocytes, and the expression patterns of genes with roles in immunity across the three cell-types 117

List of Tables Table 2.1 The full complement of 151 predicted NACHT domain-containing genes encoded by the genome of Amphimedon queenslandica. 36 Table 4.1 Expression patterns of Amphimedon queenslandica genes involved in immunity across choanocytes, pinacocytes, and archaeocytes. 123

List of Appendices Appendix 2.1 Excluded NACHT domain-containing gene models 178 Appendix 2.2 Summary of NLRs and NACHT-containing genes of taxa analysed in this study along with source of data 178 Appendix 2.3 Figure 2.1 alignment FASTA file 178 Appendix 2.4 Figure 2.3 alignment FASTA file 178 Appendix 2.5 Metazoan NLR PhyMl tree 178 Appendix 3.1 Chapter three feeding experiment sample specifications and CEL-Seq2 run distribution 179 Appendix 3.2 DE-Seq2 differential expression analysis R Script 181 Appendix 3.3 Chapter three CEL-Seq2 Sample demultiplexing and read mapping statistics 186 Appendix 3.4 Plots of ERCC spike-in concentration vs number of reads 187 Appendix 3.5 Gene expression data 188 Appendix 3.6 Principle components analyses plot of samples from all treatment groups 188 Appendix 3.7 Complete immune signalling reference pathways from KEGG 189 Appendix 3.8 OTU table and alpha rarefaction plots from characterisation of the bacterial communities in the three treatments 191

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Appendix 4.1 Full time course of confocal sections tracking the processing of CFDA-SE labelled bacteria treatments by oscula staged juvenile Amphimedon queenslandica 195 Appendix 4.3 Expression patterns of lysosomal pathway genes across choanocytes, pinacocytes and archaeocytes 200 Appendix 4.4 Feeding experiment GPCR gene expression data 201 Appendix 4.5 Expression patters of the GPCRs across choanocytes, pinacocytes and archaeocytes 201 202 Appendix 4.6 Feeding experiment Nrf2 target gene expression data 202 Appendix 5.1 Spreadsheet containing counts of reads mapped to the A. queenslandica NLR gene models in the Chapter 3 experiment 202

xvi Deciphering the genomic tool-kit underlying animal-bacteria interactions List of abbreviations Abbreviation Definition ABC ATP-binding cassette ADAMs A disintegrin and metalloproteinase AMP Antimicrobial peptide AMPK Adenosine monophosphate-activated protein kinase ANOVA Analysis of variance AqNLRs Amphimedon queenslandica NLRs ATP Adenosine triphosphate BLAST Basic local alignment search tool bZIP Basic leucine zipper domain CAMKK Ca2+/calmodulin-dependent protein kinase kinase CD163 Cluster of Differentiation 163 Cell expression by linear amplification CEL-Seq and sequencing CFDA-SE Carboxyfluorescein diacetate succinimidyl ester CMFSW Calcium-magnesium-free seawater DAMP Danger associated molecular patterns DAPI 4’,6-Diamidino-2-Phenylindole, Dihydrochloride DMBT1 Deleted in malignant brain tumour 1 DNA Deoxyribonucleic acid dNTPs Deoxynucleotides DUSP1 Dual specificity protein phosphatase 1 EGF Epidermal growth factor EGTA Ethylene glycol-bis(β-aminoethyl ether)-N,N,N’,N’-tetraacetic acid ERCC External RNA Controls Consortium F Foreign sponge bacteria FN3 Fibronectin 3 FoxO Forkhead O FSW Filtered seawater GABA gamma-Aminobutyric acid GBR Great barrier GPCR G-protein coupled receptor GST Glutathione S-transferase GUI Graphical user interface h Hours H Native bacteria enriched from healthy conspecific HMA High microbial abundance HMM Hidden markov model HSD Honest significant difference

xvii Deciphering the genomic tool-kit underlying animal-bacteria interactions

Ig Immunoglobulin IRAK Interleukin-1 receptor-associated kinase IRF Interferon regulatory factor Jnk c-Jun N-terminal kinase KEGG Kyoto Encyclopedia of Genes and Genomes LMA Low microbial abundance LPS Lipopolysaccharide LRR Leucine-rich repeat MAMP Microbe-associated molecular patterns MAPK Mitogen-activated protein kinase MAPKAP MAPK-activated protein kinases MPEG1 Macrophage-expressed protein 1 mRNA Messenger RNA MyD88 Myeloid differentiation factor 88 NADPH Nicotinamide adenine dinucleotide phosphate NBD Nucleotide binding domain NF-κB nuclear factor kappa-light-chain-enhancer of activated B cells NLR Nucleotide binding domain and leucine rich repeat containing gene NLRC CARD domain-containing NLR NLRD DEATH domain-containing NLR NLRX NLR with no additional C-terminal domain Nrf2 Nuclear factor (erythroid-derived 2)-like 2 OTU Operational taxonomic unit p Probability PAMP Pathogen-associated molecular patterns PBST Phosphate-buffered saline TWEEN PCA Principal components analysis PCR Polymerase chain reaction PGRP Peptidoglycan recognition protein PRR pattern recognition receptor RIP2 Receptor interacting protein–2 RNA Ribonucleic acid RNA-Seq RNA sequencing RNI-like Ribonuclease inhibitor-like ROS Reactive oxygen species RTK Receptor tyrosine kinase SRCR Scavenger receptor cystein-rich STAT Signal transducer and activator of transcription TAB1 TGF-beta activated kinase 1 TGF-β Transforming growth factor beta TLR Toll-like receptor

xviii Deciphering the genomic tool-kit underlying animal-bacteria interactions

TNF Tumor necrosis factor TNFR Tumor necrosis factor receptor TRAF Tumor necrosis factor receptor-associated factor U Altered bacterial community enriched from unhealthy conspecific XIAP X-linked inhibitor of apoptosis

xix

Chapter 1 - General introduction

1.1 Animal-bacteria interactions are ubiquitous and vital All animals are transiently or constitutively inhabited by bacteria that can participate in mutually beneficial or harmful interactions with their hosts. Historically, our understanding of animal-bacteria interactions has overwhelmingly been framed in the context of pathogenesis. This view has been challenged in recent years as the advent of next-generation sequencing technologies has led to a new appreciation of the actual ubiquity, diversity, and functional roles of bacteria in animals (Gilbert, et al. 2012; McFall-Ngai, et al. 2013). Recent studies have begun to reveal that the bacterial partners of animals are vital to health and development, contributing to nutrition, pathogen control and even normal developmental programing (Fraune and Bosch 2010; McFall-Ngai, et al. 2013; Douglas 2014). The growing realization of the interdependencies between the animal host and all its microbial partners has given rise to such terms as holobiont, metaorganism, and superorganism, to reflect the sum of a and its resident microbiota as a single functional entity, often resulting from millennia of co-evolution (Eberl 2010; Bosch and McFall-Ngai 2011; Gilbert, et al. 2012). Critical to this deeply co-evolved relationship is an effective means of recognition and signalling. Interdomain communication between animals and bacteria is a two-way process relying on multiple different strategies. In this thesis, I focus on the host’s side of the interchange between animals and bacteria, mediated through the immune system, a mode of interdomain communication that has been studied intensively for over a century.

1.2 Innate immunity is an important mediator of interdomain communication The prevailing view of the immune system is framed by a conceptual dichotomy separating innate immunity, which provides non-specific generalised defence without memory, and adaptive immunity, which enables long-term defence against specific pathogens. The latter has generally been regarded as a adaptation exemplified by the antibody-based system in jawed , which is characterised by de novo generation of diversity and specificity through somatic recombination and immunological memory (Boehm 2012). The innate immune system, on the other hand, is an

1 Deciphering the genomic toolkit underlying animal-bacteria interactions evolutionarily ancient mechanism conserved across both vertebrates and invertebrates, which relies on germ-line encoded pattern recognition receptors (PRRs) to distinguish self from non-self. These PRRs play a critical role in the early response to pathogen invasion by recognising and binding molecules that are characteristic of entire classes of microorganisms, but are absent in animals; these are termed Pathogen Associated Molecular Patterns (PAMPs) (Janeway and Medzhitov 2002). Examples of PAMPs include integral cell wall components (lipopolysaccharides (LPS), peptidoglycan, lipoteichoic acid), surface-bound glycoproteins, lipoproteins, double and single stranded RNAs (dsRNA and ssRNA), and DNA containing unmethylated CpG motifs (Tang, et al. 2012). A PRR binding event typically triggers a signalling cascade that results in the transcription of immune response effector genes encoding antimicrobial products or the activation of cellular immune effectors such as phagocytes (Janeway and Medzhitov 2002). Several major classes of PRRs are conserved across divergent animal lineages, both vertebrate and ; these include the membrane-bound Toll-like receptors (TLRs), C-type lectin receptors, and Scavenger receptor cysteine-rich (SRCR) genes, and the cytosolic Nucleotide- binding domain and Leucine-rich repeat containing genes (NLRs), and retinoic acid-inducible (RIG)- like receptors (Sarrias, et al. 2004; Yoneyama and Fujita 2009; Messier-Solek, et al. 2010; Hansen, et al. 2011; Buckley and Rast 2012). Together, these PRR families comprise a diverse repertoire of tools that is critical to coordinating the immune response.

However, the molecules that constitute PAMPs are essential to all microbes and thus not unique to pathogens (Koropatnick, et al. 2004). Indeed, an animal host is constantly exposed to “PAMPs” from its resident microbiota even in the absence of infection. Accordingly, the use of the term microbe- associated molecular patterns (MAMPs) was introduced to more accurately reflect the presence of these molecules in beneficial microbes (Koropatnick, et al. 2004). Multiple studies in vertebrate and invertebrate animal lineages have now found that bacterial symbionts exert their beneficial influence through MAMP-mediated stimulation of PRR signalling (see review by Chu & Mazmanian 2013). In the cnidarian Hydra sp., a member of one of the oldest extant metazoan phyla, PRR signalling mediated by MyD88, a TLR pathway adaptor protein, promotes the reestablishment of the microbiota following a breakdown of bacterial homeostasis (Franzenburg, et al. 2012). Furthermore, TLR signalling in Hydra has been associated with the production of antimicrobial peptides involved in epithelial defence and in shaping bacterial colonization early in development (Bosch, et al. 2009; Fraune and Bosch 2010).

2 Chapter 1

MAMP signalling is also critical to the interactions between the Hawaiian bobtail squid, Euprymna scolopes, and its bioluminescent bacterial symbiont, Vibrio fischeri. The squid horizontally acquires V. fischeri from the environment and houses the bacteria in a light organ that is used as an anti-predatory strategy (Jones and Nishiguchi 2004). V. fischeri MAMPs, namely LPS and a peptidoglycan derivative known as tracheal cytotoxin, synergistically suppress the innate immune system of E. scolopes by attenuating nitric oxide and nitric oxide synthase production, and are also necessary for inducing the apoptosis and morphogenetic changes associated with V. fischeri colonization (Koropatnick, et al. 2004; Altura, et al. 2011). The exact PRRs and signalling pathways involved in processing these bacterial signals remain unknown, but peptidoglycan recognition proteins and LPS-binding proteins have been implicated (Chun, et al. 2008; Krasity, et al. 2011; Collins, et al. 2012). Finally, an example much closer to home is the mammalian gut, which is colonised by complex microbial communities that influence both immune and metabolic functions affecting gut homeostasis (Erturk-Hasdemir and Kasper 2013). The immunomodulatory effects of these symbiotic bacteria are mediated through the signalling of PRRs such as TLRs, NLRs, and PGRPs (Chu and Mazmanian 2013). A series of studies have recently demonstrated that a MAMP derived from the human gut symbiont Bacteroides fragilis, known as Polysaccharide A, not only directs the maturation of the gut immune system, but also promotes its colonization of the host tissue by actively suppressing immune response through the TLR pathway (Mazmanian, et al. 2005; Round, et al. 2011). Furthermore, recent discoveries in the metazoan Hydra suggest that these dual roles in defence and symbiosis are ancient functions of the innate immune system that can be traced back to the early evolution of animals (Bosch 2013). These examples and many others demonstrate that the innate immune system is a key mediator of the molecular dialogue between animals and bacteria, with important implications for normal host development and homeostasis (see review by McFall-Ngai, et al. 2013).

1.3 Invertebrates may deploy multiple strategies for managing the symbiont bacterial community Invertebrates and vertebrates alike thus have to contend with the same challenge of striking a balance between promoting a beneficial relationship with their resident bacterial community, while simultaneously protecting themselves from potentially pathogenic bacteria. While vertebrates rely on both adaptive and innate immunity to accomplish this, invertebrates fight pathogens and foster symbioses in the absence of the specificity and memory conferred by adaptive immunity. Three possible

3 Deciphering the genomic toolkit underlying animal-bacteria interactions strategies have been proposed to explain how invertebrates are able to recognise and promote symbiotic relationships with bacteria (McFall-Ngai 2007). The first two of these involve compartmentalisation to minimise interactions between symbiont bacteria and the host’s immune system. The first strategy involves partitioning symbionts through intracellular symbiosis (McFall-Ngai 2007; Masson, et al. 2016). Intracellular symbiosis has been reported in all major invertebrate phyletic lineages, while only one occurrence has been documented in vertebrates (Wernegreen 2012).

The second strategy involves compartmentalising the symbiont microbiota in the gut, a strategy that is common to both vertebrates and invertebrates (McFall-Ngai 2007; Hooper, et al. 2012). The symbiotic bacterial community living in the guts of animals plays an important metabolic role through aiding digestion and the provision of metabolites that supplement the host’s diet (McFall-Ngai, et al. 2013; Douglas 2014). In addition to this, the gut serves as an important physical barrier, simultaneously promoting the colonisation of beneficial symbionts while limiting their interaction with other host tissues, which serves to prevent activation of a systemic immune response (Hooper, et al. 2012). Indeed, interactions amongst the host’s metabolism, immune system and the resident bacterial community play an important role in the homeostasis of the holobiont (Hooper, et al. 2012; McFall-Ngai, et al. 2013). This is highlighted by the range of inflammation and metabolic immune disorders affecting humans that are characterised by alterations to the resident gut microbiota, leading to aberrant or excessive activation of the innate immune response (Lamkanfi 2011).

A third possible strategy relies on the use of PRRs of the innate immune system (McFall-Ngai 2007). How invertebrates make the critical distinction between friend and foe through the germline- encoded receptors of the innate immunity, in the absence of the fine-tuned specificity associated with the vertebrate adaptive immunity, is still very poorly understood. However, studies demonstrating phenomenological evidence for immune memory in invertebrates, as well as the capacity to generate tremendous PRR diversity at the genomic level or through alternative splicing, are challenging the long-held paradigm that innate immunity is non-specific and lacks memory (Schulenburg, et al. 2007; Netea, et al. 2011; Ziauddin and Schneider 2012; Criscitiello and de Figueiredo 2013). Hence it has been hypothesised that innate immune complexity, achieved through the diversification and expansion of the PRR complements, could provide invertebrates with the capacity to recognise specific ligands

4 Chapter 1 in to manage their symbiotic bacterial communities. Understanding how invertebrate innate immunity achieves opposing responses to pathogens and symbionts could reveal hitherto unrecognised mechanisms conferring high specificity to PRR responses, and provide novel insight into the origin and evolution of PRR roles in defence and symbiosis (Nyholm and Graf 2012; Chu and Mazmanian 2013; Erkosar, et al. 2013).

1.4 Marine sponges as models for studying the mechanisms underlying animal-bacteria interactions Animals diverged from their protistan ancestors 700-800 million years ago, emerging in a sea of bacterial life that had already been established for approximately three billion years (Knoll 2003; Hentschel, et al. 2012). Animal-bacterial interactions would have originated as early metazoans arose, and the transition to multicellularity was accompanied by the evolution of mechanisms for discriminating between self and non-self, i.e. the immune system (Srivastava, et al. 2010). Sponges (Porifera) are one of the earliest-diverging extant animal phyletic lineages (Philippe, et al. 2009). Sponges thus occupy a unique position for elucidating the evolutionary origins of animal innate immunity and its role in animal-bacterial interactions, as traits shared between sponges and other animals likely reflect shared inheritance from their last common ancestor.

Sponges are sessile filter-feeding animals that can contribute a significant component of the biomass in benthic marine and freshwater habitats throughout the world (Bell 2008). The gross morphology of sponges is very simple, and unlike other animals, sponges lack a gut, instead relying on a water canal system – the aquiferous system – for nutrient capture (Fig. 1.1) (Ereskovsky 2010; Maldonado, et al. 2012). Water is drawn into the sponge body through the beating of flagellated collar cells, known as choanocytes, which form spherical chambers lining the aquiferous system (Fig. 1.1) (Ereskovsky 2010; Maldonado, et al. 2012). A microvillous collar at the base of the acts as a high efficiency micro-filter, enabling the capture of particles as small as viruses and bacteria from the environment (Fig. 1.1) (Hadas, et al. 2006; Mah, et al. 2014). Free-living bacteria in the water column comprise the main component of the sponge diet and are known to make a significant contribution to sponge nutrient requirements (Maldonado, et al. 2012). Through their capacity to pump thousands of

5 Deciphering the genomic toolkit underlying animal-bacteria interactions litres of water per kilogram of sponge each day, sponge filter feeding plays an ecologically important role in the nutrient cycle in benthic marine habitats (Bell 2008). A Osculum

Ostium

Choanocyte chamber

Mesohyl

B Pinacocyte

Choanocyte

Pinacocyte-like amoeboid cell

Mesohyl Archaeocyte Spicule Collagen Microorganism

Figure 1.1 lllustration of the sponge body plan

(A) A schematic diagram of a typical demosponge body plan. Seawater is pumped through the ostia on the sur- face of the outer pinacoderm via the beating of flagellated choanocytes. Choanocytes are arranged in spherical choanocyte chambers found throughout the aquiferous system. Food particles are trapped by the choanocytes and water is then expelled through the osculum. (B) A magnified cross-section of the internal structure of a demosponge. Trapped and internalised microorganisms are transferred from choanocytes to cells residing in the mesohyl including pinacocyte-like amoeboid cells and archaeocytes. Siliceous spicules and collagen provide with structural support. Pinacocytes line the canals of the aquiferous system. In demosponges,

6 Chapter 1

communities of symbiotic microorganisms are found inhabiting surfaces within the mesohyl and also the walls of the aquiferous system. Despite relying on bacteria as a direct source of nutrition, sponges have the capacity to harbour bacterial communities that may comprise up to 35% of the total sponge biomass, at densities up to four orders of magnitude greater than the surrounding environment, and of diversity easily comparable to that of the mammalian gut (Webster, et al. 2010; Hentschel, et al. 2012; Webster and Thomas 2016). Furthermore, a recent global-scale study documenting the bacterial diversity associated with marine sponges demonstrates that these bacterial communities have little in common across species or with free-living bacterial communities in the surrounding sea water (Hentschel, et al. 2002; Simister, et al. 2012; Steinert, et al. 2016; Thomas, et al. 2016). The differences in the bacterial communities across sponge species appear to influence sponge morphology, physiology and metabolism (Weisz, et al. 2008; Giles, et al. 2013). High microbial abundance (HMA) sponges tend to have a denser mesohyl, more complex aquiferous systems and slower filtration rates relative to low microbial abundance species (LMA) (Weisz, et al. 2008). In some sponge species, cyanobacterial symbionts can contribute more than half of the host’s energy requirements through the products of photosynthesis (Webster and Taylor 2012; Webster and Thomas 2016). Bacterial symbionts also contribute biologically active metabolites to the sponge chemical defence arsenal, some of which are of pharmacological interest (Webster and Taylor 2012; Mehbub, et al. 2014). These traits suggest that the sponge-bacteria relationship is deeply co-evolved and potentially ancient.

It is intriguing how these morphologically simple animals, which lack specialised immune cells and an adaptive immune system, are able to fight pathogens while simultaneously fostering a symbiotic relationship with their resident bacterial community, and relying on free-living bacteria for nutrition. Despite being viewed as rather indiscriminate feeders that predominantly rely on size-dependent selection (Pile, et al. 1996; Ribes, et al. 1999; Kowalke 2000), it was observed long ago that sponges display differential treatment of food and symbiotic bacteria (Wilkinson, et al. 1984). Furthermore, Maldonado et al. (2010) described differential processing behaviour when sponges were fed with Escherichia coli and Vibrio anguillarum, which are bacteria of roughly similar sizes. These lines of evidence suggest that sponges have some capacity to qualitatively discriminate amongst the bacteria encountered in the environment.

7 Deciphering the genomic toolkit underlying animal-bacteria interactions

The ability to discriminate native symbiont bacteria from non-symbiont bacteria is likely to be critical to maintaining homeostasis of the sponge holobiont, yet the mechanisms that enable sponges to maintain species-specific communities of bacterial symbionts remain poorly understood. To test whether sponges are able to distinguish non-symbiont from symbiont bacteria, sponges were presented with food bacterial isolates, seawater bacteria and symbiont bacterial isolates (Wilkinson, et al. 1984; Wehrl, et al. 2007). In both these studies, it was observed that a significantly greater number of non- symbiont bacteria were removed from the water column by the sponges than the symbiont bacteria (Wilkinson, et al. 1984; Wehrl, et al. 2007). Two mechanisms were thus hypothesised to explain this discriminatory treatment of symbiont and non-symbiont bacteria (Wilkinson, et al. 1984; Wehrl, et al. 2007; Nguyen, et al. 2013). First, the sponge host does not recognise its symbiont bacteria as food and deliberately avoids taking them up for digestion. Alternatively, symbiont bacteria deploy extracellular masking factors to avoid being trapped and phagocytosed. In an innovative experiment, Nguyen, et al. (2013) examined the second hypothesis using E. coli expressing eukaryotic-like proteins – which could function as masking factors – found in the genome of sponge symbiont bacteria. As sponge cells remain uncultivable, the authors fed the E. coli to amoebae, and observed that bacteria expressing the eukaryotic-like proteins were able to modulate phagocytosis, ultimately leading to the accumulation of bacterial cells in the phagosomes of the amoebae (Nguyen, et al. 2013). Although, these findings may not be directly translatable to sponges, eukaryotic like-proteins are abundant in sponge symbiont bacterial genomes, which suggests they could play an important role in sponge-bacterial interactions (Fan, et al. 2012; Gauthier, et al. 2016). This experiment thus provides a potential mechanism through which sponge symbiont bacteria could render themselves “invisible” to the host.

However, the possibility that sponges are able to actively recognise their symbiont bacteria at a molecular level has not been explicitly studied. Given the ubiquity and diversity of animal-bacteria relationships, it is likely that all animals, including sponges, require a sophisticated system that enables the recognition of friend from foe. The evolutionarily ancient innate immune system, which is common to all animal lineages, is therefore an obvious candidate for mediating sponge-bacteria interactions. Moreover, the exceptional bacterial diversity and intimacy of the sponge-bacteria association, combined with its hypothesised antiquity, make sponges an important system for studying the role of the innate immune system in animal-bacteria interactions.

8 Chapter 1

1.5 Amphimedon queenslandica – a model with the right biological and in silico resources Next generation sequencing technologies have catalysed a recent explosion of publications cataloguing the microbial community associated with sponges (Schmitt, et al. 2012; Webster and Taylor 2012; Dupont, et al. 2013; Kennedy, et al. 2014; Reveillaud, et al. 2014; Steinert, et al. 2016; Thomas, et al. 2016; Webster and Thomas 2016). While these studies have provided insight into the true complexity of the sponge-bacteria association, they also leave many important questions unaddressed, as few studies have applied an experimental approach to dissect the host’s role in controlling the composition of its bacterial community (but see Steindler, et al. 2007; Ryu, et al. 2016). The availability of a near- complete sequenced genome (Srivastava, et al. 2010), easy access to biological material (Leys, et al. 2008), and a combination of biological traits (Degnan, et al. 2015) make the haplosclerid demosponge A. queenslandica an ideal system for experimental study that will allow us to address some of the gaps in our understanding of the molecular mechanisms underlying sponge-bacteria crosstalk.

A population of A. queenslandica is present on the intertidal reef flat in Shark Bay, Heron Island Reef, Great Barrier Reef, Australia, and adults are readily distinguished on rubble by their grey- blue colouration and encrusting-lobate morphology (Hooper and Van Soest 2006). A. queenslandica broods embryos throughout the year in large brood chambers (up to 1 cm in diameter) containing up to 200 embryos of all developmental stages at any one time (Leys and Degnan 2001). The larvae are relatively large (~0.6mm) and easy to handle, and methods have been established for non-destructive larvae collection from the same individual over time (Degnan, et al. 2008). Husbandry techniques have also been established for the in vitro cultivation of larvae through to metamorphosis into actively- feeding juvenile sponges (Gauthier and Degnan 2008; Nakanishi, et al. 2014; Sogabe, et al. 2016). These characteristics allow me year-round access to various developmental stages. In addition to having access to the A. queenslandica genome, methods have been established for generating RNA- Seq data from small amounts of starting material at the whole-organismal level (individual embryos, larvae and juveniles) as well as from single cells (Hashimshony, et al. 2012; Hashimshony, et al. 2015; Hashimshony, et al. 2016; Levin, et al. 2016).

I also have access to metagenomic data from 454 pyrosequencing of the A. queenslandica microbiome and electron micrographs sampled at multiple developmental stages. These data indicate

9 Deciphering the genomic toolkit underlying animal-bacteria interactions that A. queenslandica houses a relatively low abundance and diversity of microbes with strong evidence for vertical transmission of five operational taxonomic units (OTUs) present across nearly all life stages (Fieth et al., in review). Alterations in the composition of sponge bacterial communities have been documented in association with health decline under environmental stress (Webster, et al. 2008; Selvin, et al. 2009). Indeed, the bacterial community in unhealthy A. queenslandica individuals is distinct to that of healthy ones, and, notably, this is marked by a decrease in the abundance of primary symbiont OTUs (Bayes 2013; B. Laglbauer, R. Schuster and S. Degnan, unpublished data). The morphological simplicity of A. queenslandica, and its low microbial diversity and abundance, are an advantage for making phenomenological observations that might be more difficult to dissect in the more complex system of an HMA sponge species.

The availability of genomic, transcriptomic and metagenomic resources, easy access to biological material across the lifecycle, and the stable long term association with vertically transmitted bacteria, thus make A. queenslandica an ideal study species for investigating the interactions between the sponge innate immune system and its bacterial community. Finally, traits shared between sponges and eumetazoans could reflect shared inheritance from their last common ancestor. As one of the earliest-diverging metazoan phyletic lineages, sponges are especially useful as a model for studying the origin and evolution of the immune system and its role in the transition to metazoan multicellularity Understanding the mechanisms that underpin the ancient sponge-bacteria relationship could provide insight into the fundamental principles that govern broader animal-bacteria interactions.

1.6 Aims of this thesis The general objective of this thesis was to investigate the mechanisms underlying sponge-bacteria interactions using genomics and transcriptomics. I pursued three research aims to examine sponge- bacteria interactions at the genomic, transcriptomic and cellular levels:

Aim one: Investigate the evolution and origin of the NLR family of genes in A. queenslandica and other animals

10 Chapter 1

In chapter two, I investigated the expansion and diversification of the NLR gene family in A. queenslandica. The availability of the near-complete genome of the demosponge A. queenslandica allowed me to comprehensively identify and characterise the full complement of this innate immune gene family using bioinformatics tools (Srivastava, et al. 2010). I also interrogated publicly available genomes of metazoans and non-metazoan in search of bona fide NLRs to explore the origin and evolution of this ancient gene family. I used standard phylogenetic methods to place the sponge NLRs in a meaningful evolutionary context. The findings of this study have been published and I have incorporated the manuscript in this thesis.

Aim two: Investigate the molecular mechanisms underlying the discrimination and response to native bacteria versus foreign bacteria using a transcriptomics approach

Genes involved in immunity are upregulated as part of widespread transcriptional changes that occur when sponge larvae make contact with the benthos to settle and metamorphose (Conaco, et al. 2012). This upregulation also correlates with the commencement of active filter-feeding associated with the formation of choanocyte chambers and the aquiferous system (Leys and Degnan 2002; Gauthier and Degnan 2008). This is a critical life history stage at which time the sponge is faced with a new influx of free-living bacteria from the environment. The ability to recognise and respond differently to symbiont and non-symbiont bacteria is likely to be critical to maintaining homeostasis of the sponge holobiont. In chapter three, I investigated how the sponge genome regulates the discrimination and response to its native bacteria and foreign sponge bacteria. I presented actively-feeding A. queenslandica juveniles with bacterial treatments from three different sources: foreign bacteria from a different sponge species ( globostellata), native bacteria from a healthy adult conspecific, and the altered bacterial community of an unhealthy adult conspecific. I then collected whole organismal samples and profiled the transcriptomic responses of the sponge to these treatments using a high-throughput RNA-Seq technique known as CEL-Seq (Hashimshony, et al. 2016).

Aim three: An investigation into how A. queenslandica discriminates and processes native bacteria and foreign bacteria at the cellular level

11 Deciphering the genomic toolkit underlying animal-bacteria interactions

The data from chapter three indicates that A. queenslandica is able to distinguish native symbiont bacteria from foreign sponge bacteria at the molecular level. In chapter four, I built on these data by investigating qualitative differences in the way these same three bacterial treatments were processed at the cellular level. I repeated the experiment designed in chapter three, but this time using a fluorescent cell-tracker dye to label the bacterial treatments. I then used confocal microscopy to observe how the bacteria were processed at 30 minutes, 1 hour, 2 hours, and 8 hours post-treatment. To better understand the molecular basis underpinning the role of the cell-types involved in ingesting and processing the bacterial treatments, I identified a subset of the genes that were differentially expressed in chapter three, based on their roles in regulating the detection and response to bacteria. I then analysed the expression patterns of these genes in cell-specific transcriptome data sets – generated by S. Sogabe – from three cell-types that are involved in capturing and digesting bacteria.

In summary, the main goal of this thesis was to study the role of the host genome in regulating the interdomain interactions between the demosponge A. queenslandica and bacteria. In order to do so, I first identified and characterised putative innate immune receptors in the genome ofA. queenslandica that could potentially mediate the detection of symbiont ligands. Second, I applied a high-throughput RNA- Seq approach to investigate the molecular mechanisms underlying the sponge’s ability to discriminate between native bacteria and foreign bacteria. Third, I used microscopy techniques to investigate how native and foreign bacteria are processed at a cellular level. This thesis represents one of the first studies to combine an experimental approach with the use of genomics and microscopy analyses. My findings provide a framework for future studies to test hypotheses about the evolution and roles of particular immune pathways in the establishment and maintenance of sponge-bacteria symbiosis.

12

14 Chapter 2

Chapter 2 - The characterization of sponge NLRs provides insight into the origin and evolution of this innate immune gene family in animals

2.1 ABSTRACT The “Nucleotide-binding domain and Leucine-rich Repeat”-containing genes (NLRs) are a family of intracellular pattern recognition receptors (PRR) that are a critical component of the metazoan innate immune system, involved in both defence against pathogenic microorganisms and in beneficial interactions with symbionts. To investigate the origin and evolution of the NLR gene family, we characterised the full NACHT domain-containing gene complement in the genome of the sponge, Amphimedon queenslandica. As sister group to all animals, sponges are ideally placed to inform our understanding of the early evolution of this ancient PRR family. A. queenslandica has a large NACHT domain-containing gene complement that is dominated by bona fide NLRs (135) with varied phylogenetic histories. Approximately half of these have a tripartite architecture that includes an N-terminal CARD or DEATH domain. The multiplicity of the A. queenslandica NLR genes and the high variability across the N- and C-terminal domains are consistent with involvement in immunity. We also provide new insight into the evolution of NLRs in invertebrates through comparative genomic analysis of multiple metazoan and non-metazoan taxa. Specifically, we demonstrate that the NLR gene family appears to be a metazoan innovation, characterised by two major gene lineages that may have originated with the last common eumetazoan ancestor. Subsequent lineage-specific gene duplication and loss, domain shuffling and loss have played an important role in the highly dynamic evolutionary history of invertebrate NLRs.

2.2 INTRODUCTION All animals have an innate immune system that differentiates self from non-self by using diverse, genome-encoded pattern recognition receptors (PRRs) (Hoffmann, et al. 1999; Kurtz and Armitage 2006; Rosenstiel, et al. 2009). PRRs recognise and bind to characteristic molecules that identify whole

15 Deciphering the genomic toolkit underlying animal-bacteria interactions classes of microorganisms (Janeway and Medzhitov 2002) - termed interchangeably as Microbial- or Pathogen- Associated Molecular Patterns (MAMPs/PAMPs) (Boller and Felix 2009). A PRR binding event typically triggers a signaling cascade that results in the transcription of immune response effector genes encoding products such as antibacterial, antifungal and antiviral proteins (Janeway and Medzhitov 2002). Several classes of PRRs are conserved among divergent animal lineages, both vertebrate and invertebrate (Buckley and Rast 2012; Hansen, et al. 2011; Messier-Solek, et al. 2010; Sarrias, et al. 2004; Yoneyama and Fujita 2009). Notable among these are the Nucleotide-binding domain and Leucine-rich repeat containing genes (NLRs, known also as Nod-like receptors for nucleotide-binding oligomerization domain receptors).

The NLRs are a family of intracellular sentinels, capable of detecting a wide range of MAMPs that includes bacterial and viral RNA, bacterial flagellin and peptidoglycan components (Kaparakis, et al. 2007; Boller and Felix 2009; von Moltke, et al. 2013). The cytosolic localization of NLRs suggests that they could respond to bacteria that escape extracellular detection and manage to invade the cell, and also to bacterial products that are present in the cell following phagocytosis (Martinon, et al. 2009; von Moltke, et al. 2013). In addition to the detection of intracellular MAMPs, NLRs sense endogenous Danger Associated Molecular Patterns (DAMPs) (Sansonetti 2006). These are signals produced by the host following injury or cellular stress, and include uric acid crystals, reactive oxygen species (ROS), and changes in ATP levels or intracellular potassium concentration (Boller and Felix 2009; Stuart, et al. 2013). Most bacteria, however, are not pathogenic and many are in fact beneficial to the host (McFall-Ngai, et al. 2013). How multicellular hosts differentiate between microbial friend and foe remains enigmatic, and it has been suggested that NLRs likely play an important role in mediating these animal-bacterial interactions (Robertson, et al. 2012; Robertson and Girardin 2013).

Metazoan NLRs are defined by the presence of both a central NACHT (NAIP, CIITA, HET-E and TP1) domain and a series of C-terminal Leucine Rich Repeats (LRRs) (Ting, Harton, et al. 2008). The highly conserved central NACHT domain is a STAND P-loop NTPase that mediates self-oligomerization in the presence of ATP, hence it is also known interchangeably as a nucleotide oligomerization domain (NOD) or Nucleotide binding domain (NBD) (Koonin and Aravind 2002; Wilmanski, et al. 2008). The C-terminal LRRs form the ligand sensing region, although it is presently unclear whether the LRRs

16 Chapter 2 interact with MAMPs/PAMPs directly or via an intermediate (Proell, et al. 2008; Istomin and Godzik 2009). The LRRs also appear to play an autoregulatory role in maintaining the NLR in an inactive formation until a specific signal is detected (Martinon, et al. 2002;Ting, Willingham, et al. 2008).

In addition to these two highly conserved domains, the characteristic metazoan tripartite NLR architecture is completed by the presence of an N-terminal effector domain. The vast majority of N-terminal domains identified to date belong to the death-fold superfamily, which includes the Caspase recruitment domain (CARD), Pyrin domain (PYD) and DEATH domain (Laing, et al. 2008; Proell, et al. 2008; Messier-Solek, et al. 2010). The N-terminal domain is responsible for homotypic protein-protein interactions that initiate immune signalling pathways (Kufer 2008; Shaw, et al. 2010). The activation of at least some NLRs results in the formation of a multiprotein complex called an inflammasome and the activation of Caspase-1, which ultimately leads to the production of inflammatory cytokines or the induction of apoptosis or pyroptosis (Schroder and Tschopp 2010; Aachoui, et al. 2013). In addition, NLRs are also capable of activating NF-κB and p38 MAPK-dependent signalling via interactions with receptor interacting protein–2 (RIP2) kinase (Ting, et al. 2010).

Vertebrate NLRs have been the focus of intense research, not least because of the link between dysfunctional NLRs and several human diseases (Franchi, et al. 2009; Davis, et al. 2011; Dunne 2011). Invertebrate NLRs, by contrast, are poorly characterized, probably in part due to the absence of NLRs in the classical invertebrate model organisms, Drosophila melanogaster and Caenorhabditis elegans (Zhang, et al. 2010). Some existing ambiguity in the invertebrate NLR literature arises from the fact that have a similar family of PRRs with a tripartite architecture comprising a central nucleotide- binding domain (NBD), C-terminal LRRs responsible for MAMP binding, and diverse N-terminal domains involved in signaling (Maekawa, et al. 2011; Yue, et al. 2012). However, the central NBD of NLRs is an NB-ARC domain – also a STAND P-loop NTPase – in place of the NACHT domain (Leipe, et al. 2004). Despite the remarkable structural and functional similarities shared by plant and animal NLR families, these properties are thought to represent convergent evolution rather than shared ancestry (Yue, et al. 2012). In addition, many of the C-terminal repeats associated with genes of the NACHT family are common to the NB-ARC family (Leipe, et al. 2004).

17 Deciphering the genomic toolkit underlying animal-bacteria interactions

Previous reports of metazoan NLRs have not necessarily been restricted to bona fide NLRs composed of NACHT and LRR domains, but have more broadly discussed all genes containing a NACHT or an NB-ARC domain (Lange, et al. 2011; Hamada, et al. 2012). However, the shared characteristics of the NACHT and NB-ARC families is a potential source of confusion that has resulted in conflicting numbers of NACHT- and NB-ARC-containing genes being recorded within the same study, thus confounding interpretations of the reported NLR genome complements in the cnidarian Acropora digitifera, and in the demosponge Amphimedon queenslandica (Hamada, et al. 2012). Considering both NACHT and NB-ARC families together makes it difficult to ascertain the phylogenetic distribution of NLRs specifically, and thus to discuss their origin and evolution. In the present study, we thus focus only on bona fide invertebrate NLRs.

Indeed, a better understanding of the origin and evolution of this pivotal PRR gene family in animals awaits data from a greater number of animal lineages. Here we increase the breadth of data by providing comprehensive annotation of the NLR genes in the sponge ( Porifera) Amphimedon queenslandica - a basal metazoan with a fully sequenced genome (Srivastava, et al. 2010). The phylogenetic status of poriferans as sister group to the makes them ideal for elucidating the origin and evolution of animal innate immunity, because traits shared between sponges and other animals likely reflect shared inheritance from the last common animal ancestor (Philippe, et al. 2009; Srivastava, et al. 2010). To reflect more broadly on the origin and evolution of this gene family, we also report the presence or absence of NLRs in several other organisms including non- metazoan eukaryotes and eumetazoans. To avoid confusion with other NBD-containing genes that do not have LRRs, we at all times adhere strictly to the universal nomenclature proposed by Ting, Harton, et al. (2008) and accepted by the HUGO Gene Nomenclature Committee. Specifically, we restrict ourselves to the definition of the acronym NLR as denoting a “Nucleotide-binding domain and Leucine-rich Repeat”-containing gene, as this highlights the two defining evolutionarily conserved domains while reflecting the (non-homologous) similarity of animal NLRs to the plant NLRs (Ting, Harton, et al. 2008).

18 Chapter 2

2.3 RESULTS AND DISCUSSION 2.3.1 NLRs are abundant in the Amphimedon genome and likely already existed in the last

Figure 2.1 Phylogenetic analysis of the A. queenslandica NACHT-domain containing genes.

The tree presented is a midpoint-rooted Bayesian tree, with branch lengths representing the number of substitu- tions per site. Posterior probabilities and ML bootstrap support values greater than 50% are indicated. AqNLRs form three discrete clusters: AqNLR clade A, AqNLR clade B and AqNLR clade C. A small subset of the 122

AqNLR clade A genes were used in the final analysis. Summary domain architectures characteristic of each major clade are shown below the clade name. The intron/exon organizations of individual AqDEATH-NACHT clade, AqNLR clade B and AqNLR clade C genes are depicted to the right of the tree. Exons are represented

19 Deciphering the genomic toolkit underlying animal-bacteria interactions

by boxes and are drawn to scale. Introns are represented as lines between exons; they range from 45bp to

7kbp in size, but are all depicted as the same size (i.e., not to scale). Assembly gaps, represented by the line breaks, range from 386bp to 1.975kbp. Refer to Appendix 2.3 for the alignment used in the analyses.

common ancestor to the Metazoa Searches based upon the Pfam Hidden Markov Model (HMM) of the NACHT domain (PF05729) identified a total of 244 NACHT domain-containing gene models in the genome ofA. queenslandica. A complementary Amphimedon-based HMM generated by us did not reveal any additional NACHT domains, and it is unlikely that any were missed given that the relaxed specificity that we used for the searches retrieved many non-NACHT P-loop NTPases. Of the 244 gene models that contain specifically a NACHT domain, 93 represent either single genes on small contigs with high nucleotide sequence identity (>95%) to other NACHT domain-containing genes models, incomplete NACHT domains, or NACHT-only gene models (Appendix 2.1). This leads us to believe that these 93 models are more likely to represent erroneously assembled allelic variants rather than independent loci, and thus we excluded them from our final predictive count of 151 NACHT domain-containing genes in the A. queenslandica genome (Table 2.1). Not surprisingly then, this number differs from that previously reported for A. queenslandica (Hamada, et al. 2012; Lange, et al. 2011); these differences are further confounded by the lack of clear discrimination between NACHT and other STAND P-loop NTPase domains in one of the prior analyses (Hamada, et al. 2012).

Among the 151 NACHT domain-containing gene models that can confidently be assigned to discrete loci (Table 2.1), we identified 135 bona fide NLR genes as defined by the presence of both a NACHT and an LRR domain (following Ting, Harton et al. 2008). We designate these as AqNLR genes. Of these 135 AqNLRs, 48 have the characteristic tripartite architecture that also includes an N-terminal CARD or DEATH domain. Although the presence of NACHT domain-containing genes in the A. queenslandica genome has previously been recognized, the gene numbers and domain architectures were either not supplied and thus not available for comparison (Lange, et al. 2011), or were confounded by a lack of discrimination between the NACHT and other STAND P-loop NTPase domains (Hamada, et al. 2012). We provide here, therefore, the first complete list of bona fide NLR

20 Chapter 2 genes in the basal metazoan phylum Porifera and also the first confirmed report of NLRs outside the eumetazoan lineage. This identification of NLRs in the genomes of both sponges and eumetazoans

Figure 2.2 Phylogenetic analysis of the A. queenslandica NLR Clade A gene expansion of 122 genes that make up the majority of the A. queenslandica NACHT domain complement.

This unrooted Bayesian tree was constructed from an alignment of the A. queenslandica NACHT domains.

Posterior probabilities for the major clades are indicated. Neither the presence nor the precise identity of an

N-terminal domain - CARD (AqNLRC, shown in blue), DEATH (AqNLRD, shown in red), or absent (AqNLRX, shown in black) – appears to predict phylogenetic position of the gene. The alignment used to generate this is available upon request.

21 Deciphering the genomic toolkit underlying animal-bacteria interactions suggests that at least one ancestral NLR gene was already present in the last common ancestor of all animals.

2.3.2 The NLR genes encoded by the Amphimedon genome have diverse phylogenetic histories and diverse LRRs Three of the 151 A. queenslandica NACHT domain-containing genes comprise a NACHT domain coupled with C-terminal WD40 repeats. The NACHT domains of the NACHT-WD40 genes did not align well with the remaining 148 NACHT domains, and thus were excluded from further analysis. Phylogenetic analyses of the remaining 148 A. queenslandica NACHT domain-containing genes reveal that they group into four discrete clades with high statistical support; the 135 AqNLRs are split amongst three of these clades (Fig. 2.1). We designate these four clades as the DEATH-NACHT clade, and AqNLR clades A, B and C.

AqNLR clade A is a large group of 122 genes that make up the vast majority of the A. queenslandica NACHT domain complement (Fig. 2.1, 2.2). The AqNLRs in this clade have LRRs that are recognized only by HMM profiles in the Superfamily (SSF52047) and Gene3D (G3DSA:3.80.10.10) protein structure libraries. Interestingly, these HMMs are based upon the crystal structure of the LRR domain of the ribonuclease inhibitor-like (RNI-like) superfamily, unlike the sequence-based Pfam HMM models for LRRs. An N-terminal CARD (AqNLRC) or DEATH (AqNLRD) domain is encoded by almost one third of the clade A genes (38 out of 122), but the remaining two thirds lack the tripartite domain architecture typical of human NLRs and instead comprise just the NACHT-LRR domain combination (AqNLRX). In those genes where an N-terminal domain exists, its precise identity does not predict phylogenetic position of the gene; that is, AqNLRCs and AqNLRDs do not form discrete lineages within the clade (Fig. 2.2). While this pattern points to the role of N-terminal domain shuffling, gain, and loss in the evolution of the Clade A AqNLRs, it comes with the caveat that the models for the clade A genes were frequently situated next to assembly gaps or at the edge of assembled scaffolds. Where possible, we interrogated adjacent genomic sequence N-terminal to an AqNLRX; however, the current poor quality of the assembly at many of these loci reduces our confidence in the reliability of models in this clade and suggests that the actual number of tripartite NLRs in the genome may be greater.

22 Chapter 2

Clade A also contains three gene models that are predicted in the current genome version (gene models version Aqu1; Srivistava et al. 2010) to contain ankyrin (ANK) repeats N-terminal to the NACHT domain. Our closer inspection of these gene models indicates that the ANK repeats are more

Figure 2.3 Architectures of potential NLR adaptor and signalling/effector proteins encod- ed by the A. queenslandica (sponge) genome, in comparison with those identified in other animal genomes. Data for mammals, sea urchin and amphioxus have been copied directly from Figure 3 in Messier-Solek et al.

(2010). These proteins could be involved in NLR signalling pathways via homotypic interactions of the death- fold domains. The A. queenslandica (sponge) list is a conservative selection based on structural similarity to the mammalian ASC adaptor protein (Pyrin-CARD) and RIP2 kinase (Protein kinase-CARD). Not all A. queenslandica death-fold domain-containing gene models are depicted. The DEATH-UDP/PNP domain combination is a novel architecture identified in theA. queenslandica genome. Although proteins of this structure are not known to be involved in NLR signaling, they were included because the UDP/PNP domain has also been identified at the

N-terminus of NLRs in A. digitifera (Fig. 2.4) (Hamada, et al. 2012).

23 Deciphering the genomic toolkit underlying animal-bacteria interactions likely to be part of an adjacent gene upstream of the AqNLR, thus we suggest their inclusion in these gene model is erroneous and we exclude the ANK repeats from our characterization of these three AqNLR clade A genes (Fig. 2.2; Table 2.1). Further, the combination of a NACHT domain coupled with C-terminal ANK repeats, which was previously reported as characteristic of the A. queenslandica NBD gene expansion (Hamada, et al. 2012), cannot be confirmed at all in the genome. Instead, it is the bona fide NLRs (NACHT-LRR) that have undergone a major expansion, rather than the NACHT- ANK domain combination as previously concluded by Hamada et al. (2012).

The expansive AqNLR clade A is the sister group to AqNLR clade B (Fig. 2.1). AqNLR clade B consists of a single NLR that lacks a known N-terminal domain, but has LRRs that are recognized readily by the sequenced-based Pfam LRR clan HMMs (CL0022). Quite divergent from AqNLR clades A and B, AqNLR clade C contains 12 genes, nine of which are tripartite NLRs characterized by an N-terminal DEATH domain, and with C-terminal LRRs also readily recognized by the Pfam LRR clan HMMs (CL0022). The NACHT domain and LRRs of the clade C NLRs are all encoded on one exon, while the LRRs of the single clade B NLR span multiple exons (Fig. 2.1). It is worth highlighting that the exon/intron organisation of the clade B gene AqNLRX1 reflects that of the human NLRC1 and NLRC2 genes, and of Capitella NLRC - Capca1|214069. Similarly, the exon/intron organisation of the clade C NLRs reflects that ofLottia NLRD - Lotgi1|152683, Capitella NLRC - Capca1|207210 and Nematostella NLRX – Nemve1|203213. The AqDEATH-NACHT clade – sister to AqNLR clades A and B – contains 13 genes that all share a common DEATH-NACHT domain structure defined by the absence of any detectable LRRs. Despite falling within the AqDEATH-NACHT clade, no DEATH domain was detected in the genomic sequence N-terminal to the NACHT domains of AqDN2 and AqDN6 (Fig. 2.1). Notably, AqDN2 is a gene located on a small contig, and AqDN6 contains a small assembly gap in the exon where the DEATH domain might occur. In contrast with the AqNLR clade A gene models, those of the AqDEATH-NACHT clade, AqNLR clade B and C were mostly complete, with the exception of a few that contained only small assembly gaps (Fig. 2.1). Importantly, there were no issues relating to poor assembly for the AqNLRX genes in AqNLR clades B and C, suggesting that our inability to identify a N-terminal DEATH domain in these particular genes reflects a genuine absence of this domain, rather than limitations of gene models (Fig. 2.1).

24 Chapter 2

The expansion of the AqNLR gene family (relative, for example, to mammalian NLRs) reflects similar reports of large numbers of NLRs - and indeed other PRRs - in other marine organisms including the scleractinian coral Acropora digitifera, the sea urchin Strongylocentrotus purpuratus, and the Branchiostoma floridae(Appendix 2.2; Messier-Solek et al. 2010; Lange et al. 2011; Hamada et al. 2012) . The relatively short branch lengths of the clade A AqNLRs in particular (Fig. 2.1, 2.2) suggests a recent history of rapid expansion and diversification. Further, the facts thatAqNLR gene models have proven difficult to predict (Table 2.1 indicates multiple gene model versions that we have interrogated to identify the full complement of AqNLRs) and that AqNLR RNASeq data is equally difficult to assemble (personal observation) together suggest that there may be extensive intraspecific polymorphism in these genes. This would be consistent with reports of high intraspecific polymorphism in other PRR gene families, such as TLRs and SRCRs, in the sea urchin Strongylocentrotus purpuratus (Pancer 2001; Messier-Solek, et al. 2010). Equally interesting is the observation that the AqNLR genes of clade A have LRRs that cannot be retrieved through searches based only on genomic sequence similarity, but that can only be recognised through conserved structural features, suggesting a high level of divergence from the Pfam LRR clan (CL0022). Furthermore, these LRRs display great within-clade diversity, ranging from close sequence identity to being un-alignable with each other. The variation in the clade A AqNLRs, and their abundance, leads us to hypothesize that their evolution is being driven by a large and dynamic suite of ligand-binding conditions. Similar observations have been made for evolution of the large family of innate immunity Toll-like receptor (TLR) genes that display highly variable LRRs in (Buckley and Rast 2012).

NLRs exert their functions through interactions of the N-terminal effector domain with downstream adaptor proteins, effector kinases and caspases, often leading to inflammatory or apoptotic responses (Kaparakis, et al. 2007; Schroder and Tschopp 2010; Damm, et al. 2013). The N-terminal effector domain variation in AqNLR clade A, which includes both DEATH and CARD domains, provides an added level of complexity to the signaling potential of this large subfamily. It is noteworthy that the A. queenslandica genome contains a corresponding expansion in the variety of death-fold domain combinations that potentially could interact with the AqNLRs as downstream adaptor and effector proteins (Fig. 2.3). Although this intriguing link requires empirical verification, there are certainly a great many death-fold domain-containing genes (~460, excluding those associated with NLRs) in the

25 Deciphering the genomic toolkit underlying animal-bacteria interactions

A. queenslandica genome. This reflects a similar expansion of both NLR genes (118) and death-fold domain-containing genes (541) in the Branchiostoma genome (Fig. 2.3) (Huang, et al. 2008; Messier- Solek, et al. 2010), where it has been proposed that the co-expansion and diversification of NLRs and death-fold domains are suggestive of enhanced signaling potential (Messier-Solek, et al. 2010).

The much smaller sizes of the other AqNLR clades (Fig. 2.1) suggest that genes in these clades might have evolved divergent functional specialisations relative to the genes in AqNLR clade A. Though we cannot predict the precise functions of the AqNLRs based on phylogenetics alone, it has become increasingly evident in other animals that some NLRs have evolved roles that go beyond pattern recognition (see reviews by Kufer and Sansonetti 2011, and Bonardi et al. 2012). For example, some human NLRs, such as CIITA, have no known role as PRRs but act as signaling platforms that activate other facets of the vertebrate immune system (Kufer and Sansonetti 2011). Thus, the presence of LRRs does not necessarily denote a role in MAMP-binding, and this should be taken into consideration in future studies of NLR subfamilies in invertebrates. The absence of LRRs in genes of the AqDEATH- NACHT clade means that these genes are not strictly bona fide NLRs (Fig. 2.1). However, their domain architecture and phylogenetic relationship suggest that their functions may be closely linked to those of the true AqNLRs, perhaps through their capacity for interactions involving oligomerization via the NACHT domain. Indeed, human NLRP10 is the only human NLR protein that similarly lacks LRRs, and it has been proposed to have a role as a regulatory or adaptor protein (see review by Damm, et al. 2013).

The multiplicity and high level of overall variation of the Amphimedon queenslandica NLRs are consistent with an involvement in immunity (Buckley and Rast 2012, Lange, et al. 2011, Messier-solek, et al. 2010, Hibino, et al. 2006). Furthermore, the possibility of roles as regulatory proteins, the effector domain diversity and the expansion of potential downstream components, all lead us to hypothesise that sponges have an immune system with the capacity to recognise a vast array of ligands, coupled with complex regulatory potential (Messier-solek, et al. 2010).

26 Chapter 2

2.3.3 NLRs appear to be a metazoan-specific invention characterized by two major gene lineages that each contains multiple lineage-specific expansions In addition to the AqNLRs, we report here for the first time bona fide NLRs in the genomes of other metazoan taxa: the Capitella teleta (n=55), the molluscs Lottia gigantea (n=1), Crassostrea gigas (n=1) and Pinctada fucata (n=45), and Strigamia maritima (n=2) and Nasonia vitripennis (n=1) (Appendix 2.2). In contrast, although we identified NACHT domains in the genomes of the placozoan adhaerens, the ctenophore Mnemiopsis leidyi, various arthropods (see Appendix 2.2), and the urochordate Oikopleura dioica, these are always in association with ANK, tetratricopeptide (TPR) or WD-40 repeats, and never with LRR domains. Thus we find no evidence for the existence of bona fide NLRs in the genomes of those animals (Appendix 2.2).

A substantial gap in understanding the evolutionary origin of NLRs was not addressed in previously published studies because no non-metazoan genomes were included for comparison (Hamada, et al. 2012; Lange, et al. 2011). We therefore interrogated the genomes of multiple non-metazoan eukaryotes (Appendix 2.2) in search of conserved NLR domain architectures. We identified multiple NACHT domains in the genomes of the holozoans Capsaspora owczarzaki, Salpingoeca rosetta and Monosiga brevicolis, and the non-holozoan eukaryotes Entamoeba histolytica, Thalassiosira pseudonana, Phytophthora ramorum, Toxoplasma gondii, Podospora aserina and Dictyostelium pupureum, but again only in association with ANK, TPR or WD-40 repeats. Thus we conclude that bona fide NLRs appear not to exist outside of the Metazoa, including in the sister group to the metazoans, the choanoflagellates (represented here by S. rosetta and M. brevicolis). As such, we propose that NLRs are likely a metazoan-specific invention. The conservation of this ancient innate immune gene family in multiple animal lineages since the last common ancestor of all animals, combined with the absence of the gene family in choanoflagellates, suggests an important role for NLRs in the origin and evolution of metazoan multicellularity.

Previous studies have proposed that the evolutionary history of this ancient animal immune gene family has been characterized by lineage specific expansions through multiple rounds of tandem gene duplication, as well as by gene losses occurring independently in multiple taxa (Hamada, et al. 2012; Lange, et al. 2011; Zhang, et al. 2010). The more recent phylogenetic analyses (Hamada, et

27 Deciphering the genomic toolkit underlying animal-bacteria interactions al. 2012; Lange, et al. 2011) were not focused exclusively on bona fide NLRs, but instead discussed the broader NBD gene complex; Hamada, et al. (2012), in particular, did not discriminate between NACHT domain- and NB-ARC domain-containing genes. This lack of discrimination complicates discussion on the origin and evolution of the NLR family in Metazoa, because an NB-ARC – LRR gene architecture has thus far only been recorded in plants and, despite their superficially similarities, the NACHT and NB-ARC domains belong to distinct NTPase families (Leipe, et al. 2004; Yue, et al. 2012). By focussing specifically on NACHT-LRR gene architectures (thebona fide NLRs as defined by Ting, Harton et al. (2008), our results reveal novel, interesting patterns that were obscured by the inclusion of other genes of the NBD complex in previous analyses.

Figure 2.4 Phylogenetic analysis of the metazoan NLR genes constructed from an alignment of the NACHT domains (provided in Supplementary File 4).

28 Chapter 2

The tree presented is an unrooted Bayesian tree, with branch lengths representing the number of substitu- tions per site. Posterior probabilities and ML bootstrap support values greater than 50% are indicated for the clades of interest. The two major metazoan NLR clades are circled by a dashed line and are consistent in both

Bayesian and ML trees (Appendix 2.5). N-terminal effector domain types are shown adjacent to the lineage in which they are observed. Amphimedon queenslandica lineage is in red; Cnidarian lineages (Acropora digitif- era and Nematostella vectensis) are in green; Human NLRs are in blue; Capitella teleta NLRs are in orange;

Strongylocentrotus purpuratus NLRs are in purple; Mollusc NLRs are in dark pink; NLRs are in black. For clarity, only a subset of divergent representatives from each taxon was selected for inclusion in the alignment. The numbers to the right of the name of each taxon indicate the size of the NLR complement in that clade. Refer to Appendix 2.5 for the corresponding ML tree. First, our phylogenetic analyses consistently identify two discrete groups of metazoan NLRs (Fig. 2.4; Appendix 2.5). We designate these two groups as MetazoanNLR clades 1 and 2. All of the AqNLRs fall as a single monophyletic group within MetazoanNLR clade 1. This major clade also contains some of the cnidarian (represented by Nematostella vectensis and Acropora digitifera) genes, one of the polychaete Capitella telata genes, all of the human NLRP genes and most of the human NLRC genes (those known as NODs). The other major grouping, MetazoanNLR clade 2, comprises all of the Strongylocentrotus purpuratus genes, the majority of C. telata genes, all genes from three molluscan taxa (Crassostrea gigas, Lottia gigantea and Pinctada fucata), the majority of the cnidarian (A. digitifera and N. vectensis) genes, and the two well-characterised human NLRs, NAIP and IPAF. The phylogenetic positions of the Pinctada and arthropod NLRs are difficult to resolve. ThePinctada NLR cluster is nested within MetazoanNLR clade 2 in the Bayesian tree (Fig. 2.4) but is positioned as sister group to Clade 2 in the maximum likelihood (ML) tree (Appendix 2.5). The arthropod cluster is not clearly associated with clade 1 or clade 2 in either the Bayesian (Fig. 2.4) or the ML tree (Appendix 2.5). It has previously been reported that the human IPAF and NAIP genes cluster with S. purpuratus NLRs, indicating that the origin of at least these two genes likely predates the evolution of vertebrates (Laing, et al. 2008, Zhang, et al. 2010). The presence of two divergent NLR clades in the genomes of very divergent metazoan phyla (cnidarian, annelid and human) strongly suggests that in fact all eumetazoan NLRs originated from at least two genes already present in the last common eumetazoan ancestor, as opposed to the single ancestral gene proposed previously (Hamada, et al. 2012; Zhang, et al. 2010). Indeed, previous metazoan NLR analyses also provide evidence for divergent NLR clades

29 Deciphering the genomic toolkit underlying animal-bacteria interactions in N. vectensis and A. digitifera, although this was not explicitly discussed as evidence for more than one ancestral gene (Hamada, et al. 2012; Lange, et al. 2011). Interestingly, the genome of the cnidarian Hydra magnipapillata does not include any genuine NLR genes, but does include multiple DEATH- NACHT genes that cluster phylogenetically with vertebrate NLRs as represented by human genes in MetazoanNLR clade 1 (Fig. 2.4; and see also Hamada, et al. 2012; Lange, et al. 2011).

Second, reflecting outcomes of studies of NLR diversification in other animals (Hamada, et al. 2012; Lange, et al. 2011), our results strongly suggest that the large number of NLRs present in the Amphimedon genome have originated via a single lineage-specific expansion, all of which fall as a monophyletic group within MetazoanNLR clade 1 in our metazoan-wide phylogenetic analyses (Fig. 2.4; Appendix 2.5). Based on the current data, we cannot determine whether both ancestral genes were present in the common ancestor of all animals and one was lost after the divergence of A. queenslandica from the eumetazoan lineage, or whether the two ancestral genes arose only in the eumetazoan ancestor. This may be clarified as genomic data from other sponges becomes available.

Third, it is clear that the two major metazoan NLR clades have undergone differential expansion across the animal . Invertebrate NLR expansions predominantly occur in MetazoanNLR clade 2, while the vertebrate expansion occurred in MetazoanNLR clade 1 (Fig. 2.4; Appendix 2.5). The subsets of cnidarian and Capitella NLRs in clade 2 contain more genes than the corresponding taxonomic subsets in clade 1 (Fig. 2.4; Appendix 2.5). It is also interesting to note that the genome of the teleost fish, Danio rerio, contains an expanded subfamily of >70 NLRs orthologous to human NLRC3 (which phylogenetically falls in MetazoanNLR clade 1), but does not contain orthologs to human IPAF and NAIP (Laing, et al. 2008). This is consistent with a more taxonomically limited study by Zhang et al. (2010), which found two discrete clades corresponding to invertebrate and vertebrate NLRs (with the exception of IPAF and NAIP, which nested in the invertebrate clade). The functional significance of this dichotomy is hard to infer given the lineage-specific nature of many NLR expansions. This dynamic evolutionary history is well captured by the substantial difference in numbers of NLRs between the pearl oyster Pinctada fucata (45 genes) and the edible oyster Crassostrea gigas (1 gene), two members of the same () of molluscs (Fig. 2.4; Appendix 2.2, 2.5). This disparity suggests the Pinctada NLR expansion may be a unique response to specific selection pressures of

30 Chapter 2 currently unknown origin. This could provide a useful experimental system for future work aimed at investigating the selective pressures that drive NLR evolution. Similarly, it seems likely that at least one NLR gene was present in the ecdysozoan ancestor but has apparently been lost, perhaps multiple times independently, in many ecdysozoan lineages. In the handful of arthropods in which NLRs have been identified (Appendix 2.2), both the small numbers of NLRs and their apparent lack of effector domains together suggest that these genes may not be involved in arthropod immunity.

2.3.4 The N-terminal domain of metazoan NLRs is highly variable and characterised by convergent evolution There is substantial variation in the N-terminal domains of NLRs across the different animal lineages (Fig. 2.4). At one end of the spectrum are the arthropod NLR genes, which occur only in very small numbers relative to other animals, and all of which comprise only the defining NACHT and LRR domains, but no N-terminal domain. Our metazoan-wide phylogenetic analysis (Fig. 2.4) reveals a lack of correlation between N-terminal effector domain type and phylogenetic position, which supports the suggestion of Zhang et al. (2010) that domain shuffling has been an important feature of the evolutionary history of this gene family. On multiple occasions, domain shuffling appears to have resulted in the independent evolution of identical domain combinations, even though it has been suggested that convergent evolution of domain architectures is probably a rare occurrence (Gough 2005). A plausible alternative is that the same domains have been lost multiple times from a common ancestral pool of N-terminal domains. Regardless, it is difficult to avoid the same conclusion of convergence on common gene structures (in this latter case, convergence on the loss of particular domains, rather than on gain). In particular, the presence of death-fold domains (DEATH, CARD and DED) as N-terminal effector domains is prevalent across the different lineages (Fig. 2.4). In contrast, multiple different domain combinations are apparent even within a single monophyletic anthozoan cnidarian clade in MetazoanNLR clade 2 (Fig. 2.4; note Nematostella cf. Acropora N-terminal domains). This apparent plasticity in the combination of NACHT domains with various N-terminal effector domains makes it difficult to hypothesize on ancestral tripartite NLR domain architecture, and equally difficult to infer function based on domain architecture (Istomin and Godzik 2009; Zhang, et al. 2010).

31 Deciphering the genomic toolkit underlying animal-bacteria interactions

Intriguingly, despite the large sizes of the NLR gene families in Amphimedon, Strongylocentrotus and Branchiostoma, N-terminal effector domain types in these three organisms are limited exclusively to the death-fold domains (DEATH, CARD, DED) (Fig 2.4; Messier-solek, et al. 2010, Hibino, et al. 2006, Huang, et al. 2008) that are the most widespread effector domains across multiple independently derived expansions. Zhang et al. (2010) proposed that the apparent convergent evolution on certain domain combinations suggests constraints enforced by structure requirements for proper NACHT domain function. An alternative explanation for the repeated reinvention of the (death-fold)-NLR associations could be the importance of these effector domains to downstream signaling networks. The ability of death-fold domains to recruit other proteins via homotypic interactions facilitates the formation and regulation of multi-protein complexes that are central to cell death and inflammatory signalling pathways (Kersse, et al. 2011). It is noteworthy that divergent human NLRs (particularly IPAF and NLRPs) form inflammasome protein complexes via homotypic interactions of their death- fold domains (Schroder and Tschopp 2010).

Little is known about invertebrate NLR function, including whether or not they form inflammasome- like complexes as their vertebrate counterparts do. However, members of the STAND class of P-loop NTPases, which includes NACHT domain-containing genes, are known to act as scaffolds for the assembly of protein complexes involved in regulatory networks (Leipe, et al. 2004). It is possible that invertebrate NLRs may form multi-protein complexes via death-fold effector domain interactions, either through direct interactions to recruit effector proteins such as caspases, or indirectly through an adaptor protein analogous in function to the vertebrate ASC adaptor (von Moltke, et al. 2013). The co-immunoprecipitation of HyNLR (a Hydra DEATH-NACHT gene, but not a bona fide NLR) with HyDD-caspase is consistent with the formation of such protein complexes. Furthermore, death-fold domains are important components of the apoptosis network (Kersse, et al. 2011). The initiation of pyroptotic and apoptotic pathways of cell death is a vital component of immune defence (Aachoui, et al. 2013). As awareness of the close integration between the innate immunity and apoptosis increases (Zmasek and Godzik 2013), the early-branching position of A. queenslandica and its strikingly complex NLR repertoire make it an important system for providing new insights into the mechanics of cell death in basal metazoans and the evolution of the role of cell death in defense against pathogens.

32 Chapter 2

The acquisition of novel NLR domain architectures in the anthozoan cnidarians Nematostella and Acropora suggests that functional convergence is not the whole story. These cnidarian NLRs display an unusual propensity for acquiring novel effector domains, as seen in both of the major metazoan NLR clades (Fig. 2.4). The cnidarian genes in MetazoanNLR clade 1 are uniquely characterized by an N-terminal region containing three to four transmembrane domains; HMMER HMMscans identify a Gene3D profile match for the “gap junction channel protein cysteine-rich domain” (1.20.1440.80). Its presence in both Nematostella and Acropora suggests that this NLR combination may have been already present in the anthozoan ancestor. To our knowledge, this is the first report of a putative membrane- bound NLR, and its absence in the other eumetazoan taxa investigated herein suggests this may be an anthozoan-specific innovation. Interestingly in this context, a small number of AqNLRs are also predicted to have one or two N-terminal transmembrane domains (Table 2.1), but further investigation is necessary to confirm their presence because the signals are weak and inconclusive.

In the absence of a classical adaptive immunity, it has been proposed that highly specific immune responses could be generated in invertebrate animals through synergistic interactions among components of the innate immune system (Schulenburg, et al. 2007). The multiplicity of the invertebrate NLRs and of their putative downstream signalling components, coupled with the potential for complex protein-protein interactions via the NACHT and death-fold domains, creates the potential for complex synergistic interactions to occur at the receptor, signalling and effector levels of the NLR immune response (Schulenburg, et al. 2007). This potential raises the possibility that invertebrate NLRs, although superficially similar at a structural level to vertebrate NLRs, might have the capacity for generating an innate immune response of greater specialization and diversity than vertebrate NLRs. As we learn more about the functions of the invertebrate NLRs, it is possible that the line that has conventionally separated our views of the metazoan innate and adaptive immune systems will become increasingly blurred.

2.4 MATERIAL AND METHODS A local version of HMMER 3.0 (Finn, et al. 2011), available from (http://hmmer.janelia.org/ software) was used to interrogate the Joint Genome Institute (JGI) A. queenslandica genome database (www.metazome.net/amphimedon) for Death (PF00531), NACHT (PF05729) and LRR (PF12799)

33 Deciphering the genomic toolkit underlying animal-bacteria interactions domains using Pfam Hidden Markov Models (HMMs) available from http://pfam.sanger.ac.uk/. The same genome is also available for interrogation at EnsemblMetazoa (http://metazoa.ensembl.org/ Amphimedon_queenslandica/Info/Annotation/#about ). As the seed sequences used to create the Pfam HMMs are vertebrate biased (particularly for the Death- and NACHT- domain), we also broadened our search space by constructing our own HMM profiles for each of the three domains of interest that incorporated sequences from A. queenslandica NLRs. We subsequently interrogated the sponge genome for potentially more divergent NLRs using these in-house HMMs. To investigate the origin of the bona fide NLRs (as defined by the NACHT-LRR domain combination; Ting, Harton et al. 2008), a number of fungal, plant, protozoan and metazoan genomes were also interrogated (Appendix 2.2). All protein sequences identified by the HMM searches were further verified for the specific domains by scanning the PFAM, Gene3D (CATH), Superfamily (SCOP), SMART, PROSITE databases using the following search tools: Pfam (http://pfam.sanger.ac.uk/search) Hmmer (http://hmmer.janelia.org/ search), InterProScan v1.05 plug-in for GeneiousPro v6.1.5 created by Biomatters (available from​ http://www.geneious.com/​).

Our search for NLRs in the genome of Amphimedon queenslandica was focused on the most current gene models (Aqu1). The list of NACHT domain-containing Aqu1 gene models were annotated as described above to identify other conserved domains. The complexity of NLR loci appears to pose problems for gene prediction algorithms, as has been reported for other PRR gene families in eumetazoans such as Hydra and Strongylocentrotus (Hibino, et al. 2006; Lange, et al. 2011). For Aqu1 gene models in which only a NACHT domain was detected, we expanded our search for N- and C-terminal domains by interrogating several different versions ofAmphimedon gene models in the same location, as well as directly searching upstream and downstream genomic sequences. The alternate JGI gene models that we searched include Aqu0, Augustus, Augustus-PASA, SNAP and GenomeScan (all available on the JGI browser www.metazome.net/amphimedon). Reciprocal BLAST searches using tripartite AqNLRs were also incorporated to help identify NLRs. To retrieve the most accurate complement of NACHT domain-containing genes, we occasionally determined that concatenation of two gene models was warranted. As independent confirmation of these determinations, the genomic sequences spanning these concatenated models were submitted to the Augustus web server (http:// bioinf.uni-greifswald.de/augustus) to predict the gene structure and coding sequence.

34 Chapter 2

We conducted phylogenetic analyses of NLRs using only the highly conserved NACHT domains as identified by the PFAM HMM. All multiple alignments were performed through the Geneious Pro 6.1.5 MUSCLE plug-in and manually refined in Geneious Pro 6.1.5. The final alignments that we used for phylogenetic analysis are included as Supplementary files 3 and 4. For clarity, due to the large number of NACHT domain-containing genes and NLRs present in some genomes, only selected divergent representatives were included in the final trees presented here; full sets of identifiers included in the alignments are presented in Appendix 2.3 and 2.4. Maximum Likelihood (ML) and Bayesian trees were estimated using PhyML3.1 and MrBayes3.2, respectively (Guindon, et al. 2010; Ronquist, et al. 2012). The appropriate models of evolution for each alignment were determined using the Bayesian Information Criterion implemented in ProtTest3.2 (Darriba, et al. 2011). The best fit model of evolution was determined to be CPREV+I+G for the Amphimedon NACHT alignment containing the reduced subset of Clade A AqNLRs (Adachi, et al. 2000), JTT+G for the Amphimedon NACHT alignment containing all the Clade A AqNLRs (Jones, et al. 1992), and WAG+I+G+F for the metazoan NLR alignment (Whelan and Goldman 2001). Statistical support for bipartitions in the ML analyses was estimated by 250 bootstrap replicates. Bayesian analyses were performed on two parallel runs, with distribution posterior probability (pp) of the generated trees estimated using Metropolis- Coupled Markov Chain Monte Carlo (MCMCMC) algorithm with four chains (1 cold, 3 heated) each and a subsampling frequency of 100. Runs were terminated when the average standard deviation of split frequencies of the two parallel runs was < 0.01 (about 5,500,000 generations). LnL plots were assessed to determine the appropriate burn-in length (25%). A 50% majority rule tree was constructed from the remaining trees. The results presented are consistent with tree topologies generated by both phylogenetic reconstruction methods (ML and Bayesian inference). Phylogenetic trees were drawn using FigTree v1.4.0 (http://tree.bio.ed.ac.uk/software/figtree/).

35 Deciphering the genomic toolkit underlying animal-bacteria interactions

Table 2.1 The full complement of 151 predicted NACHT domain-containing genes encoded by the genome of Amphimedon queenslandica. Assigned Clade ID Contig Gene Model Code* Name# AqDEATH-NACHT Contig5503 Aqu1.202261 AqDN1 AqNLR clade A Contig8999 Aqu1.204459 AqNLRX5 AqDEATH-NACHT Contig9585 Aqu1.204933 AqDN2 AqNLR clade A Contig9959 Aqu1.205319 AqNLRX6 AqDEATH-NACHT Contig10075 Aqu1.205456 AqDN3 AqNLR clade A Contig10163 Aqu1.205553-snap.11240 AqNLRC1 AqDEATH-NACHT Contig10378 Aqu1.206247 AqDN5 AqDEATH-NACHT Contig10379 Aqu1.205832 AqDN4 AqNLR clade A Contig10757 Aqu1.206272 AqNLRX7 AqNLR clade C Contig10761 Aqu1.206280 AqNLRD1 AqNLR clade A Contig10879 Aqu1.206401 AqNLRX8 AqNLR clade A Contig10879 hom.g7908.t1 AqNLRC2a AqNLR clade A Contig11119 Aqu1.206703 AqNLRX9 AqNLR clade A Contig11252 Aqu1.206887-hom.g8479.t1 AqNLRD10a AqNLR clade A Contig11309 Aqu1.206954 AqNLRX10 AqNLR clade A Contig11352 Aqu1.207007 AqNLRX11 AqNLR clade A Contig11422 Aqu1.207127 AqNLRX12 AqNLR clade A Contig11546 Aqu1.207322 AqNLRX13 AqNLR clade A Contig11679 Aqu1.207562 AqNLRX14 AqNLR clade A Contig11719 Aqu1.207649 AqNLRX15 AqNLR clade A Contig11725 Aqu1.207664 AqNLRX16 AqNLR clade A Contig11740 Aqu1.207697 AqNLRX17 AqDEATH-NACHT Contig11763 Aqu1.207748 AqDN6 AqNLR clade A Contig11787 Aqu1.207797 AqNLRX18 AqNLR clade A Contig11837 Aqu1.207890-hom.g9670.t1 AqNLRC3a AqNLR clade A Contig11954 Aqu1.208150 AqNLRD11 AqNLR clade A Contig11961 Aqu1.208167 AqNLRX19 AqNLR clade A Contig11972 Aqu1.208189 AqNLRX20 AqNLR clade A Contig12017 Aqu1.208293 AqNLRD12a AqNLR clade A Contig12054 Aqu1.208371 AqNLRX21 AqNLR clade A Contig12055 Aqu1.208372 AqNLRX22 AqNLR clade A Contig12282 Aqu1.208919 AqNLRX23 AqNLR clade A Contig12315 Aqu1.209017 AqNLRC4 AqNLR clade A Contig12346 Aqu1.209113 AqNLRC5a AqNLR clade A Contig12356 hom.g11149.t1-Aqu0.1446184 AqNLRX24a AqNLR clade A Contig12364 Aqu1.209168 AqNLRX25

36 Chapter 2

AqNLR clade A Contig12383 hom.g11247.t1 AqNLRD13 AqNLR clade A Contig12389 Aqu1.209241 AqNLRX26 AqDEATH-NACHT Contig12407 Aqu1.209295 AqDN7 AqNLR clade A Contig12431 Aqu1.209372-hom.g11418.t1 AqNLRD14 AqNLR clade A Contig12433 Aqu1.209379 AqNLRX27 AqNLR clade A Contig12433 hom.g11429.t1 AqNLRD15 AqNLR clade A Contig12489 Aqu1.209557 AqNLRX28 AqNLR clade A Contig12517 Aqu1.209696 AqNLRX29 AqNLR clade A Contig12522 Aqu1.209720 AqNLRX30 AqNLR clade A Contig12541 Aqu1.209792 AqNLRX31 AqNLR clade A Contig12563 Aqu1.209880 AqNLRX32 AqNLR clade A Contig12563 Aqu1.209876 AqNLRX33 AqNLR clade A Contig12595 hom.g12176.t1 AqNLRX34 AqNLR clade A Contig12595 Aqu1.210033 AqNLRD16 AqNLR clade A Contig12612 Aqu1.210112 AqNLRX35 AqNLR clade A Contig12676 Aqu1.210376 AqNLRX36 AqNLR clade A Contig12677 Aqu1.210377 AqNLRX37 AqNLR clade C Contig12691 aq_ka12691x00220-12691x00230 AqNLRD2 AqNLR clade A Contig12692 Aqu1.210459 AqNLRX38 AqNLR clade A Contig12704 Aqu1.210507 AqNLRX39 AqNLR clade A Contig12733 Aqu1.210691 AqNLRX40 AqNLR clade A Contig12734 Aqu1.210692 AqNLRX41 AqNLR clade A Contig12746 Aqu1.210730 AqNLRX42 AqNLR clade A Contig12746 Aqu1.210737 AqNLRD17 AqNLR clade A Contig12749 Aqu1.210748-hom.g12993.t1 AqNLRX43 AqNLR clade A Contig12812 snap.24323-1447583 AqNLRX44 AqNLR clade A Contig12829 Aqu1.211218 AqNLRX45 AqNLR clade A Contig12852 Aqu1.211347 AqNLRX46 AqNLR clade A Contig12852 Aqu1.211348 AqNLRD18 AqNLR clade A Contig12853 Aqu1.211358 AqNLRX47 AqNLR clade A Contig12862 Aqu0.1447787 AqNLRC6 AqNLR clade A Contig12862: Aqu1.211413 AqNLRX48 AqNLR clade A Contig12883 Aqu1.211532 AqNLRX49 AqNLR clade A Contig12887 Aqu0.1447879-Aqu1.211549 AqNLRD19a AqNLR clade A Contig12894 Aqu1.211616-snap.25520 AqNLRX50 AqNLR clade A Contig12897 Aqu1.211634 AqNLRX51 AqNLR clade A Contig12934 Aqu1.211923 AqNLRX52 AqNLR clade A Contig12934 Aqu0.1448139 AqNLRD20 AqNLR clade A Contig12950 Aqu1.212035 AqNLRX53 AqNLR clade A Contig12951 Aqu0.1448222-snap.26675 AqNLRX54

37 Deciphering the genomic toolkit underlying animal-bacteria interactions

AqNLR clade A Contig12955 Aqu1.212081-snap.26736 AqNLRC7 AqNLR clade A Contig12956 Aqu1.212082 AqNLRC8 AqNLR clade A Contig12968 Aqu1.212194-hom.g14686.t1 AqNLRX55 AqNLR clade A Contig12968 Aqu1.212193 AqNLRD21 AqNLR clade A Contig12974 Aqu1.212264 AqNLRX56 AqNLR clade A Contig12983 Aqu1.212346 AqNLRD22 AqNLR clade A Contig12996 Aqu1.212453 AqNLRX57 NACHT-WD40 Contig13075 hom.g15978.t1 AqNWD40ii AqNLR clade A Contig13099 Aqu1.213597 AqNLRX58 AqNLR clade A Contig13103 Aqu1.213662 AqNLRX59 AqNLR clade C Contig13105 aq_ka13105x00240 AqNLRD9 AqNLR clade C Contig13105 Contig13105:47,116-50,520 AqNLRX2 AqNLR clade C Contig13105 Aqu1.213698 AqNLRX3 AqNLR clade C Contig13105 Aqu1.213699-Aqu1.213700 AqNLRX4 AqNLR clade A Contig13113 Aqu1.213834 AqNLRX60 AqNLR clade A Contig13117 hom.g16620.t1 AqNLRD23 AqNLR clade A Contig13133 Aqu1.214051 AqNLRD24 AqNLR clade A Contig13133 hom.g16827.t1 AqNLRD25 AqNLR clade A Contig13134 Aqu1.214053-snap.31711 AqNLRD26 AqNLR clade A Contig13140 Aqu1.214168-snap.31947 AqNLRX61 AqNLR clade A Contig13141 Aqu1.214170 AqNLRX62 AqNLR clade A Contig13142 hom.g16965.t1 AqNLRX63 AqNLR clade A Contig13142 Aqu1.214186 AqNLRX64 AqNLR clade C Contig13153 aq_ka13153x00250 AqNLRD6 AqNLR clade C Contig13153 Aqu0.1449710 AqNLRD7 AqNLR clade C Contig13153 Aqu0.1449711 AqNLRD8 NACHT-WD40 Contig13169 hom.g17436.t1 AqNWD40iii AqNLR clade A Contig13182 hom.g17680.t1 AqNLRC9a AqNLR clade A Contig13206 Aqu1.215215 AqNLRX65 AqNLR clade A Contig13206 Aqu1.215210 AqNLRD27 AqNLR clade A Contig13206 hom.g18193.t1 AqNLRD28 AqNLR clade A Contig13234 hom.g18804.t1 AqNLRX66 AqNLR clade A Contig13234 Aqu1.215789 AqNLRX67 AqNLR clade A Contig13234 Aqu1.215792 AqNLRX68 AqNLR clade A Contig13234 Aqu1.215790 AqNLRX69 AqNLR clade A Contig13234 Aqu1.215785-snap.35932 AqNLRC10 AqNLR clade A Contig13234 Aqu1.215794 AqNLRC11 AqNLR clade A Contig13245 hom.g19060.t1 AqNLRX70 AqDEATH-NACHT Contig13309 Aqu1.217513 AqDN8 AqNLR clade A Contig13332 Aqu1.218194 AqNLRX71

38 Chapter 2

AqNLR clade A Contig13332 Aqu1.218191 AqNLRX72 AqNLR clade A Contig13332 Aqu1.218192 AqNLRX73 AqNLR clade C Contig13337 Aqu1.218328 AqNLRD3 AqNLR clade A Contig13346 hom.g21949.t1 AqNLRX74a AqNLR clade A Contig13346 Aqu0.1452529-snap.42481 AqNLRC12 AqNLR clade A Contig13346 hom.g21925.t1-snap.42422 AqNLRD29 AqNLR clade A Contig13354 Aqu1.218980 AqNLRX75 AqNLR clade A Contig13354 Aqu1.218975 AqNLRX76 AqNLR clade A Contig13354 ab.g20734.t1 AqNLRD30 AqNLR clade A Contig13358 Aqu1.219134 AqNLRX77 AqNLR clade A Contig13377 Aqu1.219760 AqNLRX78 AqNLR clade A Contig13377 Aqu1.219777 AqNLRX79 AqNLR clade A Contig13377 Aqu1.219767-snap.44836 AqNLRD31 AqNLR clade A Contig13379 Aqu1.219814 AqNLRX80 AqNLR clade A Contig13382 Aqu0.1453371-hom.g23298.t1 AqNLRC13a AqNLR clade C Contig13382 aq_ka13382x00520 AqNLRD4 AqNLR clade C Contig13382 Aqu0.1453359 AqNLRD5 NACHT-WD40 Contig13402 hom.g24038.t1 AqNWD40i AqDEATH-NACHT Contig13409 Aqu1.220886 AqDN10 AqDEATH-NACHT Contig13409 Aqu1.220887 AqDN11 AqDEATH-NACHT Contig13409 Aqu1.220885 AqDN9 AqNLR clade A Contig13412 Aqu1.220996 AqNLRX81 AqNLR clade A Contig13430 hom.g25320.t1 AqNLRX82a AqNLR clade A Contig13430 Aqu1.221813 AqNLRX83 AqNLR clade A Contig13430 Aqu1.221810 AqNLRC14a AqNLR clade A Contig13430 hom.g25317.t1 AqNLRC15 AqNLR clade A Contig13430 snap.49468-Aqu1.221803 AqNLRC16a AqNLR clade A Contig13430 snap.49466-Aqu0.1454627 AqNLRD32 AqNLR clade B Contig13467 Aqu1.223871-Aqu0.1455876 AqNLRX1 AqNLR clade A Contig13472 Aqu1.224254 AqNLRX84 AqNLR clade A Contig13473 Aqu1.224303 AqNLRX85 AqNLR clade A Contig13512 Aqu1.228172 AqNLRX86 AqDEATH-NACHT Contig13514 Aqu1.228453 AqDN12 AqDEATH-NACHT Contig13514 Aqu1.228454 AqDN13 AqNLR clade A Contig13518 Aqu1.229088 AqNLRX87 The list comprises 3 genes characterised by a WD40-NACHT domain combination, 13 genes character- ised by a DEATH-NACHT domain combination, and 135 genes characterised by a NACHT-LRR domain combination. These latter 135 genes represent bona fide NLRs, and include 48 characteristic tripartite NLR genes that also contain an N-terminal CARD or DEATH effector domain.

* Gene model codes were obtained from the Joint Genome Institute Amphimedon queenslandica ge- nome browser accessible at www.metazome.net/amphimedon and include gene models derived from

39 Deciphering the genomic toolkit underlying animal-bacteria interactions multiple different gene prediction algorithms and indicated as: ab.g (Augustus ab initio); aq_ka (PASA and Augustus); snap (SNAP ab initio); hom (Augustus homology); Aqu0; Aqu1. Gene models that have been concatenated are separated by a hyphen.

# AqNLR nomenclature is based on the convention proposed by Ting et al. (2008) and accepted by the HUGO Gene Nomenclature Committee. Therefore the name AqNLRD, for example, indicates a tripar- tite architecture of DEATH-NACHT-LRRs. Domain architecture: D – Death domain, C – Card domain, X – No N-terminal domain, N – NACHT domain. a A transmembrane domain signal was detected at the N-terminus of this gene by TMHMM Server v.2.0 – CBS (available at www.cbs.dtu.dk/services/TMHMM/‎)

40 Chapter 2

41

Chapter 3 - The transcriptional response of the demosponge Amphimedon queenslandica in response to native bacteria versus foreign bacteria provides insight into the molecular mechanisms underlying symbiont recognition and response

3.1 ABSTRACT All animals are faced with the complex challenge of striking a balance between fostering the presence of beneficial residents whilst protecting themselves from potential pathogens. Sponges, one of the earliest diverging animal phyletic lineages, host diverse communities of symbiotic bacteria and also rely on bacteria filtered from the surrounding water for nutrition. Therefore, much like the human gut, sponges simultaneously interact with symbionts, food and potentially pathogenic bacteria on a constant basis. Although we are beginning to understand the true diversity and importance of these sponge-associated bacteria to the homeostasis of the sponge holobiont, we still have a poor understanding of the molecular cross-talk underpinning the sponge-bacteria interactions. Using the genome-enabled demosponge, Amphimedon queenslandica, as an experimental platform, I take a high- throughput, experimentally replicated RNA-Seq approach to profile the sponge’s global transcriptional response to bacteria enriched from three different sources: foreign bacteria from a different sponge species (Rhabdastrella globostellata), native bacteria from a healthy adult conspecific, and the altered bacterial community of an unhealthy adult conspecific. Conserved metazoan innate immune pathways were activated in response to all three treatments. However, only the two conspecific-derived bacterial treatments elicited the expression of a more extensive suite of signalling pathways, involving TGF-β signalling and the transcription factors NF-κB and FoxO, suggesting that these genes have ancient roles in regulating interactions with the resident microbiota. Upregulation of the nutrient sensor AMPK in all treatments further suggests an ancient origin for the interplay between the regulation of metabolic

43 Deciphering the genomic toolkit underlying animal-bacteria interactions homeostasis and immunity. This study provides novel insight into the roles of the innate immune system and metazoan metabolic regulatory genes in mediating animal-bacteria interactions.

3.2 INTRODUCTION Animals interact with free-living environmental bacteria and their symbiotic resident bacteria on a constant basis, and are thus faced with the challenge of promoting symbiosis with native bacteria while simultaneously fighting off potential pathogens. This balancing act of animal-bacteria interactions has evolved since the first animals arose in a world dominated by prokaryotic life. Sponges (Phylum Porifera) emerged more than 600 million years ago and are one of the earliest-diverging animal phyletic lineages still in existence today (Knoll 2003). These sessile, filter-feeding animals pump thousands of litres of water per kilogram of sponge each day, in an environment that is densely populated with bacteria (Bell 2008). Filter-feeding brings sponges into contact with high densities of free-living bacteria on a constant basis, which they trap and rely on as a significant source of nutrition (Bell 2008). This constant influx of free-living bacteria filtered from the environment forms a dominant component of the sponge’s diet, providing essential nutrients such as nitrogen for biomass deposition (Hadas, et al. 2009).

Sponges have a simple body plan that has remained relatively unchanged since they first arose (Li, et al. 1998). Unlike most other animals, sponges lack a or even true tissues, essentially comprising a highly porous collection of loosely organised cells (Ereskovsky 2010). This morphological simplicity is thought to provide limited barriers to the external environment, placing most sponge cells in direct contact with the surrounding water and the free-living bacteria contained within (Tyler 2003; Cereijido, et al. 2004). Nonetheless, sponges are renowned for hosting dense and often highly diverse species-specific communities of bacteria that are found only in very low densities or not found at all in the surrounding environment (Simister, et al. 2012; Thomas, et al. 2016). The importance of these symbionts is emphasised by multiple studies documenting their vertical transmission from parent sponge to progeny, indicating an intimately co-evolved relationship that persists throughout the entire sponge life-cycle (Schmitt, et al. 2007; Sharp, et al. 2007; Lee, et al. 2009; and others). These bacteria reside in the internal matrix of the sponge, known as the mesohyl, and metabolic exchange is thought to be an important factor underlying interactions between the host and its resident bacteria (Hentschel, et al. 2012; Webster and Thomas 2016). Possible roles of sponge bacterial symbionts include fixing

44 Chapter 3

CO2, assimilating the waste products of nitrogen metabolism, and the provision of essential amino acids and vitamins that the sponge is unable to synthesise (Hentschel, et al. 2012; Fiore, et al. 2015; Webster and Thomas 2016).

It has also been hypothesised that sponge symbiont bacteria could serve as a direct nutritional source under high symbiont population densities, and that the host could switch to consuming symbionts as food to control the symbiont population size (Wilkinson 1992; Webster and Thomas 2016). Indeed, there have been reports of phagocytosis of resident bacteria by host cells in sponge larvae and in the mesohyl of adult sponges, albeit with the important caveat that it remains unknown if these bacteria were true symbionts (Vacelet and Donadey 1977; Ereskovsky 2010). In light of these observations, it is noteworthy that bacteria matching the morphology of the primary symbiont bacteria of the demosponge, Amphimedon queenslandica, have been observed being digested by a variety of cell-types in the mesohyl of juvenile sponges and in the inner cell mass of larvae (R. Fieth et al., in review). Thus the ability to discriminate native symbiont bacteria from non-symbiont bacteria is likely to be critical in maintaining homeostasis of the sponge holobiont. It is therefore fascinating that sponges have the capacity to defend themselves from pathogens while simultaneously promoting a beneficial symbiotic relationship with the resident bacterial community, all of which is achieved in the absence of specialised immune cells. Moreover, like all invertebrates, sponges also lack an adaptive immune system such as is present in vertebrates, and thus rely solely on the ancient innate immune system.

The innate immune system uses germ-line encoded pattern recognition receptors (PRRs), to recognise molecules known as microbe-associated molecular patterns (MAMPs), which are conserved across microorganisms but are not present in the host (Janeway and Medzhitov 2002). With a near-complete genome assembly available (Srivastava, et al. 2010), the demosponge A. queenslandica allows us to begin teasing apart the genomic toolkit underpinning the ability of sponges to maintain highly specific associations with bacteria. Indeed, the highly conserved innate immune genes encoded in the A. queenslandica genome, including a complete Toll-like receptor signalling pathway, as well as large complements of both cytosolic and extracellular PRRs, provide the first clues suggesting that the ancient innate immune system may play a significant role in mediating sponge- bacteria interactions (Gauthier, et al. 2010; Yuen, et al. 2014). The most multifarious of these PRRs

45 Deciphering the genomic toolkit underlying animal-bacteria interactions include 135 genes encoding bona fide members of the nucleotide-binding domain and leucine-rich containing gene family (NLRs) as well as nearly 300 genes from the scavenger receptor cysteine-rich domain-containing (SRCR) gene family, which are putatively involved in MAMP recognition (Yuen, et al. 2014; Ryu, et al. 2016). These large complements suggest that this morphologically simple animal without a classical adaptive immune system could have the capacity to detect a large and diverse array of microbial ligands, which perhaps allows it to distinguish and subsequently generate specific responses to foreign and symbiotic bacteria (Degnan 2015). Indeed, it has been hypothesised that these large lineage-specific expansions of putative PRRs could have emerged in the sponge as a result of co-evolution with their resident bacteria (Yuen, et al. 2014; Ryu, et al. 2016). It is thus interesting to note that, in addition to the direct exchange of metabolites, resident bacteria could also affect the metabolism of their animal host through modulation of the innate immune response (Hur and Lee 2015; Boulange, et al. 2016). The importance of symbiont-derived signals to the regulation of immune and metabolic homeostasis of the animals is highlighted by the association of inflammatory and metabolic disorders with an altered gut bacterial community in mammals (Nicholson, et al. 2012). Given this, I hypothesise that interactions between the regulation of immunity and metabolism are likely to underpin the dynamics of sponge-bacteria interactions.

Our growing understanding of the potential to detect bacterial ligands encoded in the A. queenslandica genome and the biology of its relationship with its symbiont bacteria makes A. queenslandica a powerful model for investigating the mechanisms underpinning sponge-bacteria interactions. To investigate the molecular mechanisms underlying the sponge’s ability to distinguish native bacteria from foreign bacteria, I presented actively feeding A. queenslandica juveniles with bacteria enriched from three different sources: the foreign bacteria from a different sponge species (Rhabdastrella globostellata, Carter 1883), native bacteria from a healthy adult conspecific, and the altered bacterial community of an unhealthy adult conspecific. For clarity and ease of description, I use the term “foreign sponge bacteria” specifically to describe the bacteria enriched fromR. globostellata, and use the term “free-living” bacteria to describe bacteria found in the external environment, so as to distinguish the foreign sponge bacterial treatment from non-sponge-associated bacteria found in the environment. The term “native bacteria” is used to refer only to the bacteria associated with healthy A. queenslandica individuals, while I use the term “unhealthy-Amphimedon bacteria” to describe the

46 Chapter 3 bacteria consortia enriched from an A. queenslandica individual with a compromised health status. Both R. globostellata and A. queenslandica are demosponges, but they belong to different orders, and , respectively. The R. globostellata resident bacterial community is species-rich, specific to this sponge species, and distinct from the free-living bacterial community in the surrounding water (Steinert, et al. 2016). A. queenslandica hosts a vertically-inherited bacterial community that is low in diversity and stably persists throughout its lifecycle (Bayes 2013; Fieth et al., in review). The two most prevalent phylotypes that together make up over 60% of the adult A. queenslandica bacterial community, are a Gammaproteobacterium from the Chromatiales order and an unknown Betaproteobacterium, which have been named AqS1 and AqS2 respectively (Gauthier, et al. 2016; Fieth, et al. in review). Both AqS1 and AqS2 are vertically transmitted to the embryos in the brood chamber (Fieth, et al. in review). They subsequently dominate the bacterial community throughout the majority of the A. queenslandica lifecycle and are barely or never detected in the surrounding seawater (Fieth, et al. in review). Like other sponges and animals (Webster, et al. 2008; Selvin, et al. 2009; Sunagawa, et al. 2009; Claesson, et al. 2012), a deteriorated state of health in A. queenslandica, is correlated with changes in the structure and composition of its resident bacterial community that is characterised by a marked decrease in the relative abundance of AqS1 (Bayes 2013).

I collected whole-organismal samples of the juvenile sponges at two and eight hours after they were presented with the foreign sponge bacteria, native bacteria and unhealthy-Amphimedon bacterial treatments, and used a high-throughput RNA-Seq approach to profile their global transcriptional responses. I demonstrate that TNF and MyD88 signalling pathways are upregulated in response to all three bacterial treatments. However, only the native and unhealthy-Amphimedon bacterial treatments further elicited the upregulation of the transcription factors IRF, NF-κB and STAT, key regulators of the metazoan innate immune response. Additionally, only the native and unhealthy-Amphimedon bacterial treatments induced the upregulation of genes involved in the tolerogenic TGF-β pathway. These lines of evidence suggest that A. queenslandica is indeed capable of recognising its native bacteria. Finally, the upregulation of components of the AMPK-FoxO pathway, which has dual roles in regulating metabolism and immunity, suggests that this pathway is involved in regulating sponge- bacteria interactions.

47 Deciphering the genomic toolkit underlying animal-bacteria interactions

Figure 3.1 Schematic description of experimental procedure

(A) A specimen each of Rhabdastrella globostellata (foreign bacteria), a healthy and an unhealthy A. queens- landica were collected from the reef. These animals served as the source of the foreign sponge bacteria, the normal symbiont community and the altered symbiont communities respectively. (B) The bacterial communi- ties were enriched from 5cm3 of tissue excised from each specimen. (C) The three bacteria treatments and the calcium-magnesium-free filtered seawater (CMFSW) treatment control were presented to oscula staged juvenile A. queenslandica, and whole-organismal samples were collected for total RNA extraction at 0, 2, and

8 hours post-exposure.

3.3 METHODS 3.3.1 Treatment sources - bacterial community enrichment In February 2015, one specimen each of Rhabdastrella globostellata (Carter 1883), healthy and unhealthy A. queenslandica adults were non-destructively collected at low tide from the reef flat at Shark Bay, Heron Island reef, using a hammer and chisel to remove the sponges from coral

48 Chapter 3 rubble (Fig. 3.1). The state of health of A. queenslandica individuals was assessed visually. Healthy individuals have a distinctive blue-green colouration while unhealthy sponges can be identified by tissue discolouration that exposes the pale brown colour of the underlying collagen and spongin matrix (Fig. 3.1). The sponges were returned to Heron Island Research Station (HIRS) and maintained under flowing seawater at ambient conditions for no longer than three days to minimise the probability of alterations to their bacterial communities while in aquarium conditions. These animals were used as a source of the following three bacterial treatments: foreign sponge bacteria, native bacteria, unhealthy- Amphimedon bacteria (Fig. 3.1).

Based on protocols adapted from previous studies (Schirmer, et al. 2005; Fieseler, et al. 2006), the bacterial communities were enriched as follows: 5cm3 fragments of tissue, measured by volume displacement, were excised from the adult sponges and placed in 15 mL ice cold 0.22 µm filtered calcium-magnesium-free artificial seawater (CMFSW; 26.24g NaCl, 0.671g KCl, 4.687g Na2SO4,

2.15mM NaHCO3, 10mM Tric-Cl pH 8.0, 2.5mM EGTA per 1 litre distilled water) to facilitate cell dissociation (Dunham, et al. 1983). The tissue fragments were cut into smaller pieces with a sterile scalpel and the samples were incubated in 35 mL ice cold CMFSW on an orbital shaker for 5 minutes at 200 rpm, to remove bacteria from the surrounding water that might be attached to the tissue surface. The supernatant was discarded and the process was repeated. To mechanically dissociate the cells from the spongin extracellular matrix, the tissue fragments were then squeezed through a 25 µm mesh, which had previously been washed with 70% ethanol, into a new tube. The tissue dissociation step produces variable volumes of dissociated cells, so the cell suspensions extracted from all three treatments were brought up to an equal volume of 7.5 mL using CMFSW for the subsequent steps in the protocol. The cell suspensions were centrifuged at 100 g for 15 minutes at 4°C. The entire supernatant was transferred to a new tube and centrifuged at 200 g for 15 minutes at 4°C. The entire supernatant was transferred to a new tube and centrifuged at 300 g for 15 minutes at 4°C. This series of centrifugation steps pellets the denser host-derived material while leaving the less dense bacteria cells in the supernatant. The supernatant was divided into 1 mL aliquots and centrifuged at 8,800g for 10 minutes to obtain a pellet of microbial cells. A subset of the enriched microbial cell pellet from all three treatments were flash frozen in liquid nitrogen and stored at - 80°C for microbial community characterisation. The cell pellets to be used in the experiment were then resuspended in 1 mL 0.22 µm filtered CMFSW and kept on

49 Deciphering the genomic toolkit underlying animal-bacteria interactions ice for no longer than 5 hours. Estimates of the quantities of bacteria in each treatment solution were obtained by direct cell-counting using a Petroff-Neubauer chamber (Marienfeld-Superior). An aliquot of each treatment was loaded into the twin chambers of the cell counter, and bacteria were counted in a total of 5 squares from each chamber. This process was repeated once. The final bacterial density estimated to be present in each treatment well was 2.71 × 104, 2.81 × 104, and 3.55 × 104 particles/mL for the foreign sponge, healthy conspecific and unhealthy conspecific treatment groups respectively.

3.3.2 Characterisation of the treatment microbial communities The pellets from each treatment were resuspended to 10x concentration and a 90 µL aliquot of each resuspended pellet was used for subsequent microbial DNA extraction. I extracted the microbial DNA using the Experienced Users Protocol from the PowerSoil DNA Isolation kit (MO BIO Laboratories) following the manufacturer’s instructions, with minor modifications as described in Fieth et al. (in review). The extracted DNA was quantified using the Qubit DNA assay kit on the Qubit fluorometer (Invitrogen-Life Technologies).

PCR amplicons were generated from 20 ng of each sample using broad specificity bacterial and archaeal forward (pyroL803F mix CCT ATC CCC TGT GTG CCT TGG CAG TCc tpp TCA Gtt aga Kac ccB Ngt agtc) and reverse (pyroL1392R CCA TCT CAT CCC TGC GTG TCT CCG Acct ppT CAG ACA GCa cgg gcg gtg tgtRc) primers following the protocol used by The Australian Centre for Ecogenomics (ACE), University of Queensland. The 16s amplicons were purified as described by Fieth et al. (in review) and submitted for sequencing at ACE on the Illumina MiSeq platform using the MiSeq 500 cycles reagent kit v2. The raw 16S rRNA sequence data were demultiplexed by ACE and processed using QIIME v1.8.0 pipeline (Caporaso, et al. 2010). Only the forward reads (R1) were used for subsequent analyses. The first 20 bases of the demultiplexed raw reads were trimmed to remove primer sequences, low quality reads (< Phred-33 of 15) were trimmed using a sliding window of four bases, and only reads of exactly 250 bases were retained using Trimmomatic v0.36 (Bolger et al. 2014).

Sequences with greater than 97% similarity were clustered into Operational Taxonomic Units (OTUs) and assigned against the greengenes database using UCLUST (Edgar 2010) through the open reference picking strategy implemented in the Qiime pipeline. Chimeric sequences were

50 Chapter 3 identified by ChimeraSlayer (Haas, et al. 2011) and removed. Finally, of the remaining OTUs, only those with a minimum of 20 reads across all three samples were retained for further analyses. Rarefaction curves were generated in QIIME to the data sets rarefied to the smallest sampling effort and alpha diversity was estimated using the chao1, simpson and Shannon indices (Appendix 3.6). Heatmaps of OTU abundance were generated using the R package pheatmap.

3.3.3 Collection of juvenile sponges In February 2015, adult A. queenslandica were non-destructively collected from the Heron Island Reef flat by chiselling them off coral rubble at low tide. The sponges were immediately returned to Heron Island Research Station (HIRS) where they were maintained in aquaria supplied with flowing seawater under ambient conditions. Naturally-spawned larvae were collected in larval traps from 1000H to 1600H, and subsequently kept in a glass beaker of 0.22 µm filtered sea water (FSW) under natural light and temperature conditions for 6-8 hours to develop competency for settlement (Degnan and Degnan 2010). All larvae collected were released by a mixed pool of 25 adult sponges. The articulated coralline alga, Amphiroa fragilissima, was used to induce settlement and metamorphosis in 6-well plates (Corning Costar) in the dark, with 10 larvae per well containing 5 mL FSW, as described by Nakanishi et al. (2015). Post-larval A. queenslandica attached to fragments of A. fragilissima were placed in 24-well plates (Corning Costar); one individual per well in 2 ml 0.22 µm FSW. The post-larvae were then allowed to grow for 96 hours into oscula staged juveniles with a fully formed aquiferous system and choanocyte chambers, indicating the ability to commence filter feeding (Leys and Degnan 2002; Gauthier and Degnan 2008).

3.3.4 Experimental procedure Just before introducing the treatments, each well containing a juvenile received a water change with fresh FSW, and the volume of each well was adjusted to 1.5mL to ensure that each juvenile was exposed to the same concentration of bacterial treatment. The A. queenslandica juveniles were exposed to a 40 µL of bacterial suspension enriched from either a healthy A. queenslandica adult, an unhealthy A. queenslandica adult, or R. globostellata (foreign community) (Fig. 3.1). Juveniles in the no-treatment control groups each received 40 µL of filtered sterilised CMFSW. A total of five juveniles were sampled from each treatment group at each time point after 0, 2 and 8 hours of exposure to the

51 Deciphering the genomic toolkit underlying animal-bacteria interactions bacterial suspensions (Fig. 3.1). For the 8h treatment, the wells were washed once with fresh FSW after 2h to minimise the likelihood of stress caused by prolonged exposure to high densities of live bacteria. The juveniles were stored individually in 300 µL of RNAlater (Sigma) first at 4°C overnight and then at -20°C until time of extraction.

3.3.5 RNA extraction Total RNA from each A. queenslandica juvenile was extracted using the EZ Spin Column Total RNA Isolation Kit (BioBasic Inc., Toronto, Canada) according to the manufacturer’s instructions. Quantity and quality of the extracted RNA were assessed using the Qubit® RNA assay kit on the Qubit® fluorometer (Invitrogen-Life Technologies) and the Agilent 2100 Bioanalyzer (Agilent Technologies). The total RNA was stored at -80°C in UltraPure™ DNase/RNase-free water (ThermoFisher) until further processing.

3.3.6 CEL-Seq2 Total RNA from each individual A. queenslandica juvenile was amplified and sequenced using the cell expression by linear amplification and sequencing (CEL-seq2) method described in (Hashimshony, et al. 2016). The CEL-Seq2 method uses barcoded primers to uniquely tag the 3’ PolyA-tail of all the transcripts in each sample, thus allowing the pooling of samples for a highly-multiplexed analysis.

Specifically, 4 µL of total RNA from each sample, irrespective of concentration, was mixed with 1 µL CEL-Seq primer, 0.5 µL 10mM dNTPs, and 0.5 µL of a 1:10,000 dilution of the External RNA Controls Consortium (ERCC) spike-in kit (Lemire, et al. 2011). 1.2 µL of this mixture was used for subsequent amplification viain vitro transcription. The samples in this experiment were processed in two separate CEL-Seq2 runs, resulting in the generation of two CEL-Seq libraries for sequencing. I evenly divided samples from each treatment group and time point between the two sequencing runs to control for technical variation (Appendix 3.1). The CEL-Seq libraries were sequenced at the Ramaciotti Centre, Sydney, on the rapid run mode of an Illumina HiSeq2500 machine, using HiSeq Rapid SBS v2 reagents (Illumina). Each library was loaded at 8pM equally across both lanes of the run. Paired- end sequencing was performed, reading 15bp for read 1 to recover the barcode and 55bp for read 2 to obtain the mRNA transcript (Hashimshony, et al. 2016).

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3.3.7 CEL-Seq expression analysis pipeline I analysed the CEL-Seq reads using a pipeline generated by the Itai Yanai group at Technion – Israel Institute of Technology for demultiplexing, mapping and counting the transcripts (Hashimshony, et al. 2012; Hashimshony, et al. 2016). The pipeline consists of a series of python-based scripts and is publically available on github (https://github.​ ​com/​yanailab/CEL-Seq-pipeline).​ The demultiplexed raw reads were mapped to the A. queenslandica genome and gene-level read counts were generated based on the latest version of A. queenslandica gene models (Aqu2.1; Fernandez-Valverde, et al 2015). As each CEL-Seq library was loaded across the dual lanes of a single Illumina rapid sequencing run, two duplicate sets of paired-end reads were produced at the end of the run. These duplicate sets of reads were individually processed through the CEL-Seq pipeline, resulting in two raw counts tables corresponding to each lane of the sequencing run. The counts tables from each lane were merged using an R script prior to differential expression analyses (Appendix 3.2). This process was repeated for both CEL-Seq libraries.

Approximately 60% of the reads generated for each sample were successfully mapped to the genome. More detailed statistics from the demultiplexing and mapping steps are in available in Appendix 3.3. The raw counts for the ERCC spike-ins were plotted and satisfied the requirement for linear amplification across the range of spike-in concentrations (Hashimshony, et al. 2012; Hashimshony, et al. 2016) (Appendix 3.4).

3.3.8 Differential gene expression analyses and annotation Principal components analysis (PCA) of the (variance stabilizing transformation) vsd-transformed counts was used to examine the sample to sample distances and to identify potential outliers (Fig 3.3A). The PCA was implemented through DESeq2 version 1.10.1 according to instructions in the DESeq2 manual (Love, et al. 2014). The PCA plot was visualised using the “ggplot2” package (Wickham 2009).

DESeq2 was used to identify genes differentially expressed in each treatment group relative to its corresponding control group. Genes that had only one read mapped across all samples were removed at the start of the analysis, as recommended by the DESeq2 manual, to reduce the memory requirements for subsequent data analyses. A 5% False Discovery Rate cut off was used to produce

53 Deciphering the genomic toolkit underlying animal-bacteria interactions the final lists of differentially expressed genes. Venn diagrams were used to visualise and compare the lists of genes differentially expressed in each treatment group, and were generated using the online tool Venny (http://bioinfogp.cnb.csic.es/tools/venny).

The full-length gene models for all differentially expressed genes from each treatment group were KEGG annotated using BlastKOALA, an online web tool for KEGG annotation (http://www. kegg.jp/blastkoala/) (Kanehisa, et al. 2016). The annotated sequences were subsequently mapped to the canonical reference KEGG pathways using the online tool KEGG mapper (www.genome.jp/kegg/ mapper.html) (Kanehisa, et al. 2016). In addition, I extracted the annotations for all the differentially expressed genes from a table containing the BLAST2GO and InterProScan annotations of the entire predicted proteome of A. queenslandica (W. Hatleberg and S. Fernandez-Valverde, unpublished data). This table was generated by BLAST searching all Aqu2.1 predicted peptides against the NCBI nonredundant protein database (current as of June 2014) with an e-value cutoff set at 1e-3, using a local version of BLAST2GO. All peptide sequences were also analysed for conserved motifs using InterProScan 5.0 on default parameters, and the InterProScan annotations were integrated into the final table using the BLAST2GO GUI. This was used as an independent approach to describing the differentially expressed genes, which also allowed me to analyse the differentially expressed genes for the presence of conserved protein domains. Heatmaps were created to visualise the expression patterns of the genes of interest using the vsd transformed raw reads generated by DESeq2, and heatmaps were created using the R package “pheatmap”. All expression patterns were scaled by each gene row, and genes were clustered based on Euclidean distance using the “ward.D2” clustering algorithm.

3.4 RESULTS To investigate the genomic toolkit underpinning the discrimination and subsequent response to foreign bacteria and native symbionts, I presented feeding, oscula stage (96 hours post-settlement) A. queenslandica juveniles with native bacteria enriched from a healthy conspecific adult, the bacterial community from an unhealthy conspecific adult, foreign bacteria enriched from a different sponge species (R. globostellata), or a control treatment consisting of CMFSW. Entire juvenile sponges were sampled at 0h, 2h and 8h post-exposure to the treatments and assayed for transcriptomic changes compared to the matching control group at each time point.

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A , Sva0725 Relative abundance Acidobacteria, Solibacteres Acidobacteria, PAUC37f , Acidimicrobiia > 80% AncK6, unassigned , Rhodothermi , Anaerolineae 60% Chloroflexi, TK17 Chloroflexi, SAR202 40% , Thaumarchaeota , Gemm−2 < 20% Gemmatimonadetes, unassigned Nitrospirae, Nitrospira PAUC34f No data Poribacteria Proteobacteria, Alphaproteobacteria Proteobacteria, Betaproteobacteria Proteobacteria, Deltaproteobacteria Proteobacteria, Gammaproteobacteria SBR1093, EC214 , Spirochaetes Unassigned

F H U B

Unhealthy

Healthy

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

AqS1 - Gammaproteobacteria, Chromatiales, Ectothiorodospiraceae Other Chromatiales OTUs AqS2 - Proteobacteria, Betaproteobacteria, EC94 Other Betaproteobacteria OTUs Actinobacteria, Acidimicrobiia, Acidimicrobiales Unassigned

Figure 3.2 Relative abundance of microbial 16S reads

(A) Heatmap depicting the relative abundance of 16S amplicon sequences, taxonomically assigned to the level of class, in each of the bacterial treatments used in the experiment. F – Foreign (Rhabdastrella globostellata),

H – Healthy A. queenslandica, U – Unhealthy A. queenslandica.(B) Stacked bar graph depicting the relative

55 Deciphering the genomic toolkit underlying animal-bacteria interactions

abundance of the core symbiont OTUs, AqS1 and AqS2, in the healthy and unhealthy adult A. queenslandica specimens. The remaining lower abundance OTUs were collapsed into groups at the class level. See Appendix

3.8 for a full table of the OTUs detected in the three treatments and their assigned taxonomy.

3.4.1 The unhealthy Amphimedon bacterial community structure is slightly altered from the native state, while R. globostellata hosts a diverse bacterial community highly distinct to that of Amphimedon To characterise the compositions of the communities of bacteria in the three treatments, I amplified and sequenced the 16S rRNA gene from each of the treatments using broad specificity primers. The bacterial community of R. globostellata is highly diverse comprising many different phyla, including members of the Gemmatimonadetes, Chloroflexi, and Actinobacteria lineages, which were especially abundant (Fig. 3.2A). Steinert et al. (2016) similarly noted that OTUs from these lineages were highly abundant components of the bacterial community in R. globostellata sampled in Guam. In contrast, the bacterial communities of both the healthy and unhealthy A. queenslandica adults were low in diversity and overwhelmingly dominated by Gammaproteobacteria and Betaproteobacteria (Fig. 3.2A). In particular, an OTU belonging to the Chromatiales order of Gammaproteobacteria and one from an unknown Betaproteobacterial order known as EC94 were the most abundant members, contributing over 70% of the bacterial communities in the healthy and unhealthy A. queenslandica treatments (Fig. 3.2B). These two OTUs, the Chromatiales and the Betaproteobacterium, were recently named AqS1 and AqS2 respectively (Gauthier, et al. 2016). AqS1 is the most abundant member of the healthy A. queenslandica bacterial community making up over 60% of the community as a whole (Fig. 3.2B). AqS1 has been assigned to the Ectothiorhodospiraceae family of purple sulfur-oxidising bacteria (Gauthier, et al. 2016).

There was a slight shift in the community structure of the unhealthy A. queenslandica individual compared to the bacterial community of the healthy individual (Fig. 3.2B). The unhealthy A. queenslandica bacteria community was characterised by an increase in the proportion of AqS2 present to 20% of the unhealthy community compared to 13% in the healthy community (Fig. 3.2B). This was coupled with a concurrent decrease in the relative abundance of AqS1 to about 50% of the unhealthy A. queenslandica bacterial community (Fig. 3.2B).

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A

F016 20

F215 NoT04

F013 NoT02 U812 U014 U211 F817

U015 NoT29 F212 10 H017

U214 U819 U8112 Treatment U811 control U813 foreign

iance U213 healthy

a r H812 H214 H817 unhealthy H216 0 H215 H818 H211 F017 NoT82 U017 NoT28 Time point F213 NoT06 NoT88

PC2: 5% v NoT83 U011 0h H213 F014 H813 F819 U016 NoT86 2h U215 8h F214 H013H014 NoT03 NoT01 H016 F211 F012 NoT24 NoT89 U212 H8112 H015 −10

F812 NoT26

F813

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B C

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Foreign Unhealthy Foreign Unhealthy

252 28 365 535 125 905

283 117 176 445 49 115

917 71

Healthy Healthy Figure 3.3 Principal components analyses plot of samples from all treatment groups and Venn diagrams of numbers of differentially expressed genes across the treatment groups

(A) Principal components analyses plotted using the VSD transformed raw counts of each sample, with PC1 on the horizontal axis and PC2 on the vertical axis. Samples from the control group and treatment groups from the 0h time point form a distinct cluster separated from the 2h and 8h treatment groups. Colours indicate the type of treatment. (B-C) Venn diagrams summarising the numbers of significantly differentially expressed genes

(both up- and downregulated) across the three treatment groups at (B) 2h post-feeding and (C) 8h post-feed- ing. The intensity of the shading in each section is proportional to the number of genes within the section. The percentage contribution of each section to the total the Venn diagram is indicated below the number of genes

57 Deciphering the genomic toolkit underlying animal-bacteria interactions

within each section. The largest transcriptional response was elicited by the native bacteria from the healthy conspecific at 2h, but this trend is reversed at 8h post-feeding.

3.4.2 Principal component analysis indicates that the native bacteria and unhealthy-Amphimedon bacteria elicited a distinct transcriptional response than the foreign sponge bacteria A principal components analysis of global variation in gene expression amongst individuals revealed that juveniles sampled at the start of the experiment (0h) clustered together with all CMFSW control samples, in a group clearly separated from the 2h and 8h bacteria-treated samples along the PC1 axis; this indicates that PC1 captured the transcriptomic variation caused by the effects of the bacterial treatments (Fig. 3.3A). I saw no evidence of a batch effect potentially caused by partitioning the samples across two sequencing runs, indicating that the effects of the treatments are not a result of technical artefacts (Appendix 3.6). By 2h and 8h, notwithstanding small overlap between the clusters, the sponges that were given the native bacteria and unhealthy-Amphimedon bacterial treatments formed a separate cluster from the samples that were given foreign sponge bacteria along PC1 (Fig. 3.3A). The sponges in the native and unhealthy-Amphimedon bacterial treatment groups were separated from each other on the PC2 axis albeit with some overlap, indicating that there is not a lot of difference separating the responses induced by the two treatments (Fig. 3.3A). Additionally, there was no clear separation of the sponges sampled at 2h and 8h within any treatment group (Fig. 3.3A). In summary, the PCA shows that all three bacterial treatments induced detectable transcriptional changes two hours after feeding. Moreover, it shows that, despite small overlap amongst the samples from each treatment group, there are detectable differences in the transcriptional responses elicited in the sponges by each of the three bacterial treatments.

Since the PCA indicated that there was no difference between the three control time points and non-control 0h samples, the 0h bacterial treatment groups were not included in pairwise comparisons with the 0h control to identify differentially expressed genes. A sample from 8h foreign treatment group was excluded from the final analyses because it was an outlier in the initial PCA analysis (sample ID: F818, Appendix 3.3). Its outlier status was explained by the low success in read mapping (15%) for this particular sample (Appendix 3.3). Another outlier was detected in the 0h foreign treatment group,

58 Chapter 3 clustering with 2h and 8h treatment samples (sample ID: F017, Fig. 3.3A). However, the 0h outlier had no impact on the final analyses.

3.4.3 Native symbiont bacteria induced the largest transcriptional response 2h post-feeding, followed by a more subdued response by 8h post-feeding I performed differential expression analyses between each bacterial treatment group against their corresponding CMFSW controls at the 2h and 8h time points using DESeq2, to identify the genes contributing to the transcriptomic differences.

First, I looked for genes that were differentially expressed after two hours of treatment exposure. Despite the similarity between the native bacteria and unhealthy-Amphimedon bacterial treatment groups in the PCA analysis (Fig. 3.3A), the native bacteria elicited a larger transcriptional response, in terms of number of genes differentially expressed, than the other two treatments. A total of 1821 genes were significantly differentially expressed in response to the native bacteria, of which 1158 were upregulated and 663 were downregulated (Fig 3.3B). In contrast, fewer genes were significantly differentially expressed in response to the other two treatments. The unhealthy-Amphimedon bacteria induced the differential expression of a total of 1121 genes (616 upregulated and 505 downregulated) while a total of 739 genes were differentially expressed in response to the foreign sponge bacteria (461 upregulated and 278 downregulated; Fig. 3.3B). In addition, the two treatment groups that received the healthy and unhealthy A. queenslandica-derived bacteria also shared more of the same differentially expressed genes with each other (445 genes), than with the foreign bacterial treatment group, which is consistent with the way the samples cluster together in the PCA (Fig. 3.3A).

Second, I examined the transcriptional changes in response to the bacterial treatments at 8h post- feeding. In marked contrast with the large transcriptional response observed at 2h (1821 genes), only 356 genes (203 upregulated and 149 downregulated) were significantly differentially expressed in response to the native bacteria at 8h (Fig. 3.3C). Unlike the 2h time point, the other two treatments elicited a greater number of differentially expressed genes than the healthy conspecific at 8h post-feeding (Fig. 3.3C). There were 1262 (642 upregulated and 620 downregulated) and 826 (460 upregulated and 366 downregulated) genes significantly differentially expressed in response to the unhealthy-Amphimedon

59 Deciphering the genomic toolkit underlying animal-bacteria interactions bacteria and the foreign sponge bacteria respectively (Fig. 3.3C). This indicates that the responses to the bacterial treatments changed over time, despite the lack of clear separation between the 2h and 8h time points in the PCA. Furthermore, it shows that the transcriptional response induced by the native bacteria at 8h post-feeding was very much subdued in comparison to the response induced by this treatment at 2h.

3.4.4 Both native bacteria and unhealthy-Amphimedon bacterial treatments induced the upregulation of more putative innate immune pattern recognition receptors than did the foreign sponge bacteria To investigate the molecular mechanisms underlying detection by the sponge of the different bacteria, I examined the BLAST2GO and InterProScan annotations for differentially expressed genes for conserved domains characteristic of known PRR families conserved in animal innate immune pathways.

The major group of receptors differentially expressed across the three treatment groups and both time points consisted of 60 different Scavenger Receptor Cysteine-Rich domain containing genes (SRCR) (Appendix 3.5). Of these 60 SRCRs, 11 were upregulated across both 2h and 8h time points in response to all three treatments, while 19 were downregulated in response to all three treatments across both 2h and 8h time points (Fig. 3.4A). Groups of SRCRs were differentially expressed specifically in response to each of the three different treatments (Fig. 3.4A). The majority of theSRCRs that were upregulated did so in response to the native bacteria and unhealthy-Amphimedon bacterial treatments (Fig. 3.4B). Five SRCRs responded only to native bacteria from the healthy conspecific, while another ten SRCRs were upregulated only in the unhealthy-Amphimedon bacterial treatment group (Fig. 3.4B). Seven of the ten SRCRs upregulated only in the unhealthy-Amphimedon bacterial treatment were upregulated at 8h post feeding, but not at 2h (Appendix 3.5). Eight SRCRs were upregulated in both the native bacteria and unhealthy-Amphimedon bacterial treatment groups but not in the foreign sponge bacterial treatment group (Fig. 3.4B). Five SRCRs were upregulated specifically in response to the foreign sponge bacterial treatment.

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A 0h 2h 8h

C F H U C F H U C F H U Row z-score

4

Astacin 2 FN3 0

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EGF

Tyr kinase AqNLRX1 Astacin

AmqIgTIR1

Tyr kinase

GPCR

Astacin

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Foreign Healthy

5 1 5

11 1 8

10

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Figure 3.4 Expression patterns of putative pattern recognition receptors and Venn diagram of numbers of differentially expressed SRCRs

(A) Expression patterns of putative innate immune pattern recognition receptors (rows) differentially expressed across all samples (columns) in response to the three bacteria treatments (control, C; foreign, F; healthy, H and unhealthy, U) at 0h, 2h and 8h post-exposure. Red indicates high levels of expression while blue low indicates

61 Deciphering the genomic toolkit underlying animal-bacteria interactions

low levels of expression. Rows are clustered by similarity of expression patterns. The heatmap was plotted from VSD transformed counts generated by CEL-Seq and rescaled by row. Rows corresponding to AqNLRX1 and AmqIgTIR1 are indicated by the boxes and the identifier to the right. All other rows correspond to SRCR genes. SRCR genes containing other non-SRCR protein domains are indicated to the right with the identities of the additional domains. For more details on the differentially expressed genes, see Appendix 3.5. (B) Venn diagram summarising the numbers of SRCRs upregulated across the entire experiment. The intensity of the shading in each section is proportional to the number of genes within the section. The majority of the SRCRs were upregulated in response to the bacteria treatments derived from the healthy and unhealthy conspecifics.

Thirty eight of the SRCRs differentially expressed in this experiment were composed of one to 22 tandem repeats of only the scavenger receptor cysteine-rich domain. The remainder of the SRCRs were associated with multiple different functional domains (Fig. 3.4A). Five SRCRs containing the Astacin domain (PF01400), characteristic of one class of zinc-dependent metalloendopeptidases (Park, et al. 2010), were differentially expressed in response to both the native bacteria and unhealthy- Amphimedon bacterial treatments (three upregulated, two downregulated) (Fig. 3.4A). Three of the upregulated SRCRs contain a transmembrane domain and tyrosine kinase domain, indicating that they are receptor tyrosine kinases (Suga, et al. 2013) (Fig. 3.4A). Another four differentially expressed SRCRs contain either an EGF, FN3 or Ig domain (Fig. 3.4A). These domains are typically observed in proteins associated with the metazoan extracellular matrix (Hynes 2012).

In addition to the SRCRs, genes from two other classes of PRRs were upregulated in all three treatment groups. A bona fide nucleotide-binding domain and Leucine-rich repeat containing gene (NLR), AqNLRX1, was upregulated at 2h post-feeding (Yuen, et al. 2014). AmqIgTIR1, one of only two TIR domain-containing genes encoded in the A. queenslandica genome (Gauthier, et al. 2010), was also upregulated after 2h post-feeding (Fig. 3.4A). Furthermore, upregulation of AmqIgTIR1 persisted until 8h post-feeding only in the unhealthy-Amphimedon bacterial treatment group (Fig. 3.4A).

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0h 2h 8h C F H U C F H U C F H U Row z-score Jnk MAP3K15 4

E3 −protein ligase Cbl 2 SMAD3 Ribosomal protein s6 kinase alpha 0

−2 TGF-β ligand NF-κB −4 TGF-β receptor

TGF-β ligand Tumor necrosis factor receptor SMAD1

TRAF2 and NCK-interacting protein kinase

MAPK-activated protein kinase 5 TRAF E3 ubiquitin−protein ligase Xiap TNF alpha−induced protein 8 MAP kinase p38 Dual specicity protein phosphatase 1 Apoptosis regulator bcl−2 TNFAIP3−interacting protein 1 TRAF Jun Fos AmqIgTIR Apoptosis regulator bcl−2−like MyD88 Interferon Regulatory Factor MALT1 Nedd4−like e3 ubiquitin−protein ligase Wwp1 Caspase 8 TGF-β-activate kinase 1

Macrophage−expressed gene 1 protein

MAP2K4 IRAK E3 ubiquitin−protein ligase Smurf2 TGF-β ligand

TRAF TGF-β-activate kinase 1 TRAF TNF ligand TRAF TRAF MAPK−interacting serine threonine−protein kinase 1 TRAF

TRAF

Figure 3.5 Expression patterns of genes involved in immune signalling pathways.

Expression patterns of the genes (rows) involved in TNF, Toll-MyD88-dependent, MAP kinase, and TGF-β signalling, that were differentially expressed across all samples (columns), in response to the three bacteria treatments (control, C; foreign, F; healthy, H and unhealthy, U) at 0h, 2h and 8h post-exposure. Red indicates high levels of expression while blue low indicates low levels of expression. Rows are clustered by similarity of expression patterns. The heatmap was plotted from VSD transformed counts generated by CEL-Seq and rescaled by row. The labels to the right of the heatmap contain the descriptions of the genes in the rows. For more details on the differentially expressed genes, see Appendix 3.5.

63 Deciphering the genomic toolkit underlying animal-bacteria interactions

3.4.5 The two symbiont bacterial treatments induced more extensive upregulation of innate immune genes than the foreign sponge bacteria To delve deeper into the biological and molecular processes involved in discriminating native bacteria from foreign bacteria, I used the BLASTKoala tool to annotate the differentially expressed genes with KEGG terms, and subsequently mapped the annotations to the KEGG pathways database. I then performed a focussed analysis of the pathways that were categorised under Environmental Information Processing, Cellular Processes, and Immune System (Kanehisa, et al. 2016), because these processes are likely to be involved in regulating animal-bacteria interactions (Xiang, et al. 2010; Li, et al. 2013). I also retrieved the BLAST2GO and InterProScan annotations for the differentially expressed genes to manually identify genes of interest to fill gaps that were missed by the automated KEGG pathway mapping.

a. TNF and MyD88-dependent signalling Components of the pro-inflammatory cytokine Tumour Necrosis Factor (TNF) signalling pathway were upregulated in response to all three bacterial treatments at 2h post-feeding. Five members of the Metalloproteinase-disintegrins (ADAMs) family were upregulated by all three treatments at 2h. Two members of this family of enzymes are known to cleave and activate TNF ligands in humans (Mezyk- Kopec, et al. 2009; McMahan, et al. 2013). A single gene encoding a TNF receptor (TNFR) and two genes encoding TNF-receptor associated factors (TRAFs), the scaffold proteins linking TNFR to downstream signalling cascades (Aggarwal 2003), also were upregulated at 2h in all treatment groups (Fig. 3.5). In contrast, several other TRAF genes were downregulated in all treatment groups at 2h post-feeding (Fig. 3.5). It is also worth noting that multiple TRAF-type zinc finger (PF02176)-containing genes were also downregulated in response to all three treatments at both 2h and 8h post-feeding (Appendix 3.5). The BLAST descriptions for these genes suggest they are TRAFs, but they lack MATH/TRAF domain characteristic of typical metazoan TRAF genes (Zapata, et al. 2007) (Appendix 3.5). Two other genes activated by TNF signalling, tnfaip3-interacting protein 1 and TNF alpha-induced protein 8, were also upregulated across all treatment groups at 2h (Fig. 3.5) (Coornaert, et al. 2009; Lou and Liu 2011). Interestingly, a gene encoding a TNF ligand was downregulated only in response to the foreign bacteria at 2h and 8h post-feeding (Fig. 3.5).

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All three bacterial treatments induced the upregulation of genes involved in the MyD88-dependent signalling pathway (Kawai and Akira 2007; Gauthier, et al. 2010). Consistent with the upregulation of the TIR domain-containing receptor AmqIgTIR1, the key signalling adaptor MyD88 was also upregulated across all treatment groups at 2h. In addition to mediating TNF signalling, TRAFs are also involved in regulating MyD88-dependent signalling (Hacker, et al. 2006; Kawai and Akira 2007). The multiple differentially expressed TRAFs may thus also regulate signalling downstream of MyD88 (Fig. 3.5). In addition to the TRAFs, a gene encoding TAB1, a MyD88-signalling intermediate that functions downstream of TRAF (Kawai and Akira 2007), was upregulated at 2h in response to only the native bacteria and unhealthy-Amphimedon bacteria (Fig. 3.5). The upregulation of AmqIgTIR1 and its adaptor MyD88 persisted till 8h post-feeding only in the treatment group that was fed unhealthy-Amphimedon bacteria (Fig. 3.5). In addition, an IRAK-like gene, another MyD88-dependent signalling intermediate (Muzio, et al. 1997; Gauthier, et al. 2010), was also upregulated in this same treatment group at 8h post-feeding (Fig. 3.5).

b. TGF-β signalling pathway Genes involved in Transforming Growth Factor β (TGF- β) signalling were upregulated only in response to the native bacteria and unhealthy-Amphimedon bacterial treatments (Fig. 3.5). A member of the TGF-β ligand superfamily and a member of the TGF-β receptor family both were upregulated at 2h in response to native bacteria enriched from healthy A. queenslandica. The MyD88 signalling intermediate, TAB1, also plays an indispensable role linking TGF-β signalling to map kinase p38 activation (Kim, et al. 2009). As previously mentioned, TAB1 was upregulated at 2h only in the Amphimedon bacterial treatments. Additionally, a gene encoding SMAD3, a transcription factor that directly transduces signals from TGF-β receptor to the nucleus (Derynck and Zhang 2003), was upregulated in response to the unhealthy-Amphimedon bacteria at 2h post-feeding. At 8h post-feeding, components of the TGF-β pathway were upregulated only in the unhealthy-Amphimedon bacterial treatment group. This includes three genes encoding TGF-β ligands, a TGF-β receptor, as well as another SMAD (Fig. 3.5). In addition, the E3 ubiquitin-protein ligases Smurf2, Wwp1, which are all negative regulators of TGF-β signalling (Izzi and Attisano 2006), and E3 ubiquitin-protein ligase Cbl, which conversely promotes TGF-β signalling (Izzi and Attisano 2006), were also upregulated at 8h in response to the unhealthy-Amphimedon bacteria (Fig 3.4).

65 Deciphering the genomic toolkit underlying animal-bacteria interactions

c. Mitogen activated protein kinase pathways The TNF, MyD88-dependent, and TGF-β signalling pathways can all lead to the downstream activation of Mitogen-activated protein (MAP) kinase signalling cascades (Ono and Han 2000). Two major MAP kinases, p38 and JNK, were upregulated in this experiment. Map kinase p38 was upregulated in all treatment groups at 2h, while JNK was upregulated only in the native bacterial treatment at 2h (Fig. 3.5). JNK was also upregulated in response to the foreign sponge bacteria at 8h post-feeding. MAPK-activated protein kinase 5 (MAKAP5), a downstream substrate of p38 that regulates cell motility (Dwyer and Gelman 2014) was also upregulated in all treatments at 2h. In addition to this, DUSP1, a negative regulator of p38 and Jnk signalling (Lang, et al. 2006), was upregulated at 2h all treatments (Fig. 3.5). Both MAPKAP5 and DUSP1 continued to be upregulated in the unhealthy-Amphimedon bacterial treatment group 8h post-feeding (Fig. 3.5). MAPK−interacting serine threonine−protein kinase 1, which is activated by the p38 pathway and is responsible for phosphorylating eukaryotic initiation factor 4E (Buxade, et al. 2008), was downregulated in all treatment groups at 8h post-feeding (Fig. 3.5). A gene encoding a MAP3K, which participates in Jnk pathway regulation (Chen, et al. 2002), was upregulated at 8h in response to the foreign sponge bacteria (Fig. 3.5). Finally, a gene encoding ribosomal S6 kinase, which lies downstream of the Ras–extracellular signal-regulated kinase (ERK)/ mitogen-activated protein kinase (MAPK) signalling cascade (Smith, et al. 1999), was upregulated at 2h in response to both native bacteria and unhealthy-Amphimedon bacterial treatments (Fig. 3.5).

d. Cellular transport and catabolism Over a hundred differentially expressed genes were mapped to the endocytosis, phagosome and lysosome pathways. Roughly half of these genes were upregulated, the vast majority of which are involved in endocytosis and phagocytosis (Fig. 3.6). This includes genes encoding two NADPH oxidases and one Dual oxidase (DUOX), from the NOX/DUOX family of genes responsible for generating the reactive oxygen species required for the microbicidal oxidative respiratory burst (Rada and Leto 2008) (Fig. 3.6). Genes encoding NADPH oxidase in particular were upregulated in all treatments at both 2h and 8h post-feeding. Some of the upregulated genes that mapped to the phagosome pathway have roles in intracellular vesicle transport, including genes encoding cytoplasmic dyneins and sec61 subunits (Jutras and Desjardins 2005) (Fig. 3.6, Appendix 3.5).

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A 0h 2h 8h C F H U C F H U C F H U Row z-score 4

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Fox1 2 FoxM 1 FoxJ1 0 FoxD −1 FoxP −2 TCF/LEF −3 FoxK FoxN2/3 XBP1 NFAT PARb CEBP Nrf2a Nrf2b FoxO FoxG Fos Jun Oasis B ATF2 NF−κB IRF STAT FoxJ2 FoxN1/4b CREB

Figure 3.6 Expression patterns of genes mapped to endocytosis, phagosome and lyosomal KEGG pathways and transcription factors

67 Deciphering the genomic toolkit underlying animal-bacteria interactions

Expression patterns of the genes (rows) mapped to (A) the endocytosis, phagosome, and lysosome KEGG pathways, and to (B) transcription factors that were differentially expressed in response to the three bacterial treatments (control, C; foreign, F; healthy, H and unhealthy, U) at 0h, 2h and 8h post-exposure. In the heatmap, red indicates high levels of expression while blue low indicates low levels of expression. Rows are clustered by similarity of expression patterns. The heatmaps were plotted from VSD transformed counts generated by CEL-

Seq and rescaled by row. (A) The colours in the column beside the heatmap indicate the pathway to which a gene belongs. Genes involved in endocytosis and phagocytosis were generally upregulated, while lysosomal genes were mainly downregulated. (B) The labels to the right of the heatmap contain the descriptions of the transcription factors in each row. For more details on the differentially expressed genes, see Appendix 3.5.

Upregulated genes involved in endocytosis included components of the endosomal sorting complexes required for transport III (ESCRT) complex such as charged multivesicular body proteins, ubiquitin ligases and vacuolar protein sorting-associated protein (Fig. 3.6, Appendix 3.5) (Wollert, et al. 2009). Members of the RAS GTPase superfamily, which regulate intracellular actin dynamics, are involved in the process of both phagocytosis and endocytosis (Miaczynska and Stenmark 2008). Several genes annotated as the GTPase RAC1, were differentially expressed in all treatment groups (Fig. 3.6, Appendix 3.5). Genes encoding Arf GTPase activating protein (ARF-GAP), which regulate membrane traffic and actin remodelling (D’Souza-Schorey and Chavrier 2006), were upregulated only in response to the native bacteria and unhealthy-Amphimedon bacterial treatments (Fig. 3.6, Appendix 3.5). The endocytosis pathway is also a key regulator of receptor tyrosine kinase (RTK) signalling (Goh and Sorkin 2013). Six differentially expressedRTKs were mapped to the KEGG endocytosis pathway, including a hepatocyte growth factor-like gene, an epidermal growth factor receptor-like gene and four RET receptor-like genes (Fig. 3.6). Of these six RTKs, four were upregulated in response to the native bacteria and unhealthy-Amphimedon bacteria.

The downregulated genes mainly encoded lysosomal enzymes. A large number of these (11) were sulfatases, including multiple genes encoding iduronate sulfatase and n-acetylglucosamine-6-sulfatase, which are responsible for heparan sulphate degradation (Parenti, et al. 1997). These sulfatases were downregulated in response to all three treatments and at both time points (Fig. 3.6). Additionally, four

68 Chapter 3 cathepsins and two papain cysteine proteases were also amongst the genes downregulated across the three treatment groups.

e. Transcription factors regulating stress and immune response Multiple members of two major transcription factor families, the bZIPs and Forkheads, were differentially expressed in the response to the bacterial treatments. Of the 17 bZIP transcription factors encoded in the A. queenslandica genome (Jindrich and Degnan 2016), 10 were differentially expressed in this experiment, with most upregulated at 2h post-feeding across the three treatments (Fig. 3.7). Of particular note are Jun and Fos, which were upregulated at 2h in response to all three treatments (Fig. 3.7). Jun and Fos heterodimers are an important component of the AP-1 complex, which is activated by the p38 and Jnk MAP kinase signalling cascades (Shaulian and Karin 2002). Indeed, nearly all the differentially expressed bZIPs are involved in immune and stress response (Bailey and O’Hare 2007; Miller 2009), and they included two Nrf2-like genes, XBP1, OASIS, C/EBP, ATF2 and CREB (Fig. 3.7). Eight hours post-feeding, most of the bZIPs were no longer upregulated. However, Jun, Fos and ATF2 were still upregulated in sponges of the treatment group that were given the unhealthy- Amphimedon bacteria.

Sixteen Forkheads are encoded in the A. queenslandica genome (Larroux, Luke, et al. 2008), and ten of these were differentially expressed in this experiment, six of which were downregulated while four were upregulated (Fig. 3.7). While FoxG was upregulated in all three treatment groups, FoxO and FoxJ2, which regulate metabolism and inflammation respectively (Nakae, et al. 2008; Chen, et al. 2012), were upregulated only in response to the native bacteria and unhealthy-Amphimedon bacteria at 2h post-feeding (Fig. 3.7).

Additionally, three key transcriptional regulators of innate immunity, but belonging to neither of the two families mentioned above, were upregulated only in response to the native bacteria and unhealthy- Amphimedon bacterial treatments, but not the foreign sponge bacteria. The single A. queenslandica NF-κB gene, one member of the interferon regulator factor (IRF) family, and one signal transducer and activator of transcription (STAT) gene, were all upregulated at 2h post-feeding (Fig 3.7). Both the A. queenslandica STAT and IRF genes continued to be upregulated at 8h post-feeding in the unhealthy-

69 Deciphering the genomic toolkit underlying animal-bacteria interactions

Amphimedon bacterial treatment group. Interestingly, a direct activator of human STAT5A, the tyrosine kinase erb-B4 (Williams, et al. 2004; Clark, et al. 2005), was also upregulated at 2h only in response to the native bacteria and unhealthy-Amphimedon bacteria (Appendix 3.5).

f. Effectors Two Bcl-2-like genes and one gene encoding X-linked inhibitor of apoptosis (XIAP) were upregulated across all treatments and time points (Fig. 3.5). These anti-apoptotic pro-survival genes are the transcriptional targets of multiple transcription factors including AP-1, NF-κB and STAT (Dumon, et al. 1999; Li, et al. 2003; Resch, et al. 2006; Cao, et al. 2015). Genes from the macrophage expressed gene 1 (MPEG1) family, cytolytic effector proteins and putative NF-κB targets were upregulated in response to the native bacteria and unhealthy-Amphimedon bacterial treatments at 8h post-feeding (four and two MPEG1 genes in the unhealthy-Amphimedon bacteria and native bacterial treatments respectively, Fig. 3.5).

0h 2h 8h Row C F H U C F H U C F H U z-score

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Figure 3.7 Expression patterns of genes involved in AMPK, PI3K/AKT, and FoxO pathways

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Expression patterns of the genes (rows) mapped to AMPK, PI3K/AKT, and FoxO pathways that were differentially expressed across all samples (columns), in response to the three bacteria treatments (control, C; foreign, F; healthy, H and unhealthy, U) at 0h, 2h and 8h post-exposure. Red indicates high levels of expression while blue low indicates low levels of expression. Rows are clustered by similarity of expression patterns. The heatmap was plotted from VSD transformed counts generated by CEL-Seq and rescaled by row. The colours in the column to the right of the heatmap indicate the pathway to which a gene belongs. For more details on the differentially expressed genes, see Appendix 3.5.

3.4.6 The AMPK pathway was upregulated in response to all three treatments AMP-activated protein kinase (AMPK) is a critical sensor of cellular energy homeostasis (Hardie, et al. 2012). All three subunits of the heteromeric protein were upregulated across the three treatment groups (Fig. 3.8). It is worth noting that the catalytic subunit alpha, in particular, was the only subunit upregulated in all treatments and at all time points. Furthermore, a gene encoding Ca2+/calmodulin- dependent protein kinase kinase (CaMKK), an upstream Ca2+-dependent activator of AMPK signalling (Hawley, et al. 2005), was also upregulated only in the native bacteria and unhealthy-Amphimedon bacterial treatment groups at 2h, and persisted until 8h in the unhealthy-Amphimedon bacterial treatment group (Fig. 3.8). AMPK is an important activator of the transcription factor FoxO (Greer, Oskoui, et al. 2007). A gene encoding FoxO was upregulated in response to the native bacteria and unhealthy- Amphimedon bacterial treatments only. Conversely, the PI3k/AKT pathway, which is also involved in nutrient sensing, is a repressor of FoxO (Santo, et al. 2013). It is noteworthy that many key components of the pathway, particularly the AKT genes, were either downregulated or not differentially expressed in any of the treatments (Fig. 3.8).

3.5 DISCUSSION In the present study, I investigate how the sponge distinguishes its native bacteria from foreign bacteria. I generated and analysed transcriptome datasets tracking the responses of A. queenslandica juveniles to three different bacterial treatments: native bacteria enriched from a healthy conspecific, the altered bacterial community of an unhealthy conspecific, and foreign bacteria enriched from a different sponge species (R. globostellata). This study represents one of the first applications of a

71 Deciphering the genomic toolkit underlying animal-bacteria interactions high-throughput RNA-Seq approach to dissecting the role of the sponge genome in mediating the recognition of and response to sponge symbiont bacteria.

A B TGF-β TNF TNF TGF-β ligand ligand ligand IgTIR ligand TGF-βR TNFR ADAM TGF-βR MyD88 TRAF IRAK Cbl

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Figure 3.8 Illustrations of signalling pathway genes that were differentially expressed

(A) Simplified versions of TNF signalling, MyD88-dependent Toll-like receptor signalling and TGF-β signal- ling pathways, leading to the activation of the MAP kinases p38 and JNK, and the subsequent activation of

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transcription factors involved in immunity. Pathways were adapted from the KEGG reference pathways and a complete version of the pathways is available in Appendix 3.7. (B) TGF-β signalling leading to the activation of SMAD proteins. A complete version of the pathway is available in Appendix 3.7. (C) Multiple players are involved in FoxO activation. This figure was adapted from Eijkelenboom and Burgering (2013). Solid line arrows indicate interactions. Dashed line arrows indicate translocation of a protein into the nucleus. Bar-ending arrows indicate inhibitory activity. Components of the pathways that were not upregulated are indicated with a white fill.

3.5.1 The scavenger receptor cysteine-rich domain-containing gene family plays an important role in sponge-bacteria interactions Despite having undergone immense expansions in several different animals phyletic lineages (Buckley and Rast 2015), the Scavenger Receptor Cysteine-Rich domain-containing (SRCR) genes remain a relatively unexplored aspect of innate immunity because members of the SRCR superfamily have diverse functions, united only by the presence of the scavenger receptor cysteine-rich domain and the characteristic of being extracellularly localised (Sarrias, et al. 2004). Nonetheless, several mammalian members of the SRCR gene family are involved in the innate immune response, and representatives such as CD163 and DMBT1 have been classified as true Pattern Recognition Receptors (PRRs) involved in the direct detection of bacteria ligands through the scavenger receptor cysteine-rich domain (Bikker, et al. 2004; Rosenstiel, et al. 2007; Fabriek, et al. 2009; Vera, et al. 2009). CD163, which comprises nine tandem scavenger receptor cysteine-rich domains and is expressed in human macrophages, mediates binding to both gram-negative and –positive bacteria and also enhances cytokine production (Fabriek, et al. 2009; Madsen, et al. 2010). Similarly, 38 of the SRCR genes differentially expressed in this experiment consisted of one to 22 tandem repeats of just the scavenger receptor cysteine-rich domain (Fig 3.4A). It remains unknown if any of these A. queenslandica SRCR genes are direct orthologues of CD163. Nonetheless, their structural similarity to CD163 and differential expression in response to the bacterial treatments suggests that some of them could function as true PRRs enabling the sponge to detect bacteria ligands. Interestingly, it has been hypothesised that the sequence diversity in the SRCR domain could allow these proteins to bind to a vast diversity of microbial ligands (Buckley and Rast 2015). It is thus noteworthy that SRCR genes in vertebrates mediate binding to a large variety of different host and non-host ligands (Sarrias, et al. 2004). Thus I posit that the upregulation of multiple

73 Deciphering the genomic toolkit underlying animal-bacteria interactions diverse SRCR genes by the sponge could be a priming reaction to increase the sponge’s capacity to bind to a greater diversity of ligands.

Although innate immunity is traditionally viewed as having evolved to fight infection through the recognition of conserved Microbe-Associated Molecular Patters (MAMPs), symbiotic and pathogenic bacteria share the same conserved molecules detected by PRRs of the innate immune system (Koropatnick, et al. 2004). Moreover, recent studies have shown that cross-talk between PRRs and symbiotic bacteria-derived MAMPs is necessary for homeostasis, indicating that innate immune recognition of bacteria is important for the promotion of symbiosis (see review by Chu and Mazmanian, 2013). The Hawaiian bobtail squid, Euprymna scolopes, selectively harbours bioluminescent strains of Vibrio fischeri. Colonisation of the squid host and the induction of subsequent morphogenesis are triggered by the V. fischeri MAMPs, tracheal cytotoxin (peptidoglycan fragment) and lipopolysaccharide (Foster, et al. 2000; Koropatnick, et al. 2004). In particular, a squid PRR from the Peptidoglycan recognition receptor protein (PGRP) family has been implicated in the detection and response to tracheal cytotoxin during colonisation (Troll, et al. 2010). Moreover, it has been revealed that a MAMP, known as Polysaccharide A, produced by the human gut symbiont Bacteroides fragilis, promotes host tissue colonisation by actively suppressing the immune response via the TLR pathway (Mazmanian, et al. 2005; Round, et al. 2011). Indeed, bidirectional crosstalk between the host and symbiotic mammalian gut bacteria residing in the mammalian is mediated through PRRs such as TLRs, NLRs, and PGRPs (Chu and Mazmanian 2013).

The genomes of A. queenslandica and other sponge species encode large complements of SRCR genes (B. Yuen, unpublished data; Ryu, et al 2016). A similarly large complement of SRCR genes in the genome of the purple sea urchin, Strongylocentrotus purpuratus, is thought to be a strategy for immune diversification (Pancer 2001; Hibino, et al. 2006; Buckley and Rast 2015). Interestingly, it was also proposed that the expansion of the SRCR gene family in sponges could be due to the needs of managing the resident microbiota (Ryu, et al. 2016). Indeed, the differential expression of anSRCR gene in another sponge, ficiformis, in response to its symbiotic state (Steindler, et al. 2007), suggests SRCR genes are involved in regulating symbioses in sponges. It is also noteworthy that the upregulation of 19 SRCR genes was found to correlate with a shift in the community structure of

74 Chapter 3 the microbiota during A. queenslandica metamorphosis (Fieth, et al., in review). Hence, the greater number of SRCR genes differentially expressed in response to the native and unhealthy-Amphimedon bacterial treatments but not the foreign sponge bacteria (Fig 3.7A), leads me to hypothesise that the sponge SRCR genes could have evolved to mediate the recognition of symbiont-specific ligands as well as non-specific MAMPs found in all bacteria.

A F H F H

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Cytokines receptors Cellular responses Pattern Bactericidal Bacteria recognition Adaptors and Transcription factors e ectors receptors mediators of signalling

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Figure 3.9 Summary of gene expression trends

75 Deciphering the genomic toolkit underlying animal-bacteria interactions

Venn diagrams summarising the numbers of (A) SRCR genes and (B) expression patterns of genes involved in innate immunity upregulated across the three treatment groups at the 2h and 8h time points. Components of the MyD88-dependent and TNF-signalling pathways were upregulated at 2h across all three treatment groups.

Only the native bacteria and the unhealthy-Amphimedon bacteria treatments elicited the upregulation of NF-κB,

IRF, STAT and FoxO. TGF-β ligand and receptor were upregulated only in response to the native bacteria at 2h.

Several of these innate immune genes remained upregulated at 8h post-feeding in response to the unhealthy-Am- phimedon bacteria only. This includes AmqIgTIR1, MyD88, as well as the transcription factors IRF and STAT.

The cytolytic MPEG1 genes were upregulated in response to both native bacteria and unhealthy-Amphimedon bacteria at 8h. Genes involved in the TGF-β pathway were upregulated only in response to the unhealthy-Am- phimedon bacteria at 8h. The catalytic subunit of AMPK was upregulated at across all three treatment groups at both 2h and 8h post-feeding. Treatment groups: F – foreign sponge bacteria, H – native bacteria enriched from the healthy conspecific, U – altered bacteria consortia enriched from unhealthy conspecific. (C) Schematic diagram describing the proposed sequence of events following bacteria detection, beginning with pattern recognition receptor detection and cytokine activation, leading to the activation of transcription factors, which result in the activation of a tolerogenic response, microbicidal activity, or regulation of the cellular response.

The compositions of the unhealthy and healthy A. queenslandica-derived treatments were very similar and dominated by AqS1 and AqS2, which are not only vertically acquired, but also the most prevalent components of the A. queenslandica microbiome throughout its lifecycle (Fig. 3.2B). On the other hand, the composition of the foreign sponge bacterial treatment was highly distinct to that of both healthy and unhealthy conspecific-derived treatments, containing an abundance of bacterial phylotypes that are not present in the A. queenslandica, such as Actinobacteria, Gemmatimonadetes and Chloroflexi (Fig. 3.2A). Therefore, it is likely that the foreign bacterial treatment contained a large diversity of unique foreign ligands that would not typically be encountered by A. queenslandica. In spite of this, the foreign treatment did not induce the upregulation of innate immune genes to the same extent as the A. queenslandica treatments (Fig. 3.9A,B). In future research, it would be interesting to investigate the nature of the A. queenslandica symbiont-specific ligands responsible for inducing the upregulation of the SRCR genes. However, it must also be acknowledged that SRCR genes could have other roles in the immune response beyond the detection of bacterial ligands. A gene containing 14 scavenger receptor cysteine-rich domains, 6 sushi domains and a transmembrane domain was isolated

76 Chapter 3 from the demosponge cydonium (Blumbach, et al. 1998), and has been implicated in sponge allorecognition (Pancer, et al. 1997; Blumbach, et al. 1998).

3.5.2 TNF and MyD88-dependent signalling pathways are core components of sponge immune surveillance The upregulation the Tumour Necrosis Factor (TNF) and Myeloid Differentiation Primary Response gene 88 (MyD88)-dependent signalling pathways across all three treatment groups suggests these evolutionarily conserved pathways are core components of the sponge innate immune response triggered by MAMP detection (Fig 3.9B-C). The role of Toll-like receptors (TLRs) in MAMP detection and subsequent activation of MyD88-dependent signalling is highly conserved across animals (Rakoff- Nahoum, et al. 2004; Franzenburg, et al. 2012). AmqIgTIR1 is one of two TIR domain-containing genes and multiple other genes involved in the MyD88-dependent signalling pathway encoded in the A. queenslandica genome (Gauthier, et al. 2010). Based on the expression patterns in A. queenslandica embryos, Gauthier et al. (2010) proposed that MyD88 signalling plays a role in sponge development, but they did not examine the role of the pathway in sponge immunity. Furthermore, the sponge TIR- domain containing genes, including the one upregulated in this experiment, do not contain Leucine- Rich Repeat (LRR) domains that are responsible for bacterial ligand-binding and characteristic of true TLRs (Gauthier, et al. 2010).

However, the upregulation of AmqIgTIR1, MyD88, TRAFs, and IRAK in response to all three treatments suggests that this pathway is involved in enabling sponges to respond to bacteria detection (Fig 3.8, 3.9C). Although superficially similar to the cytokine receptor IL1R, the TIR domain of AmqIgTIR1 is more similar to that of TLRs (Gauthier, et al. 2010). Furthermore, the demosponge, domuncula, has been shown to respond to LPS with the upregulation of MyD88 (Wiens, et al. 2005; Wiens, et al. 2007). It is possible that AmqIgTIR1 detects MAMPs directly through its immunoglobulin domain. Immunoglobulin domains mediate ligand-binding across a diverse array of metazoan immune receptors, but are arguably best known for their role in antigen-binding in the vertebrate adaptive immune system (Palsson-McDermott and O’Neill 2007; Cannon, et al. 2010). It is also worth noting that the Hydra TIR-containing genes, which also lack attached extracellular LRRs, appear to associate with LRRs encoded by a separate gene to carry out MAMP detection

77 Deciphering the genomic toolkit underlying animal-bacteria interactions

(Bosch, et al. 2009). It is interesting to speculate that the MyD88 signalling pathway could increase the diversity of ligands it detects through the association of AmqIgTIR1 with other proteins. However, in the current experiment, genes containing only LRR domains, like those of Hydra, were not co- expressed with AmqIgTIR1 in response to the bacterial treatments. Thus further research will need to be done to determine how bacteria ligands activate AmqIgTIR1 and MyD88 dependent signalling in A. queenslandica.

The upregulation of Tumour Necrosis Factor receptor (TNFR) and their signalling adaptors, the TNF receptor associated factors (TRAFs), suggests that TNF signalling is a component of the sponge’s response to bacteria (Fig. 3.9B-C). This is supported by recent work in another demosponge demonstrating that genes encoding a TNF ligand and a TNFR are upregulated by exposure to bacteria (Pozzolini, et al. 2016). In fact, the components of the TNF signalling pathway are highly conserved in animals and the pathway is a conserved modulator of the innate immune response in Drosophila (Mabery and Schneider 2010; Wiens and Glenney 2011). The TNF cytokines exert their biological effects by binding to TNFRs, which in turn activate signalling pathways that either induce cell death or the transcription of genes via AP-1 and NF-κB (Fig. 3.8A) (Cabal-Hierro and Lazo 2012; Hayden and Ghosh 2014). In mammals, TNF-induced cell death is mediated by type I TNFRs that have an intracellular Death domain for interaction with caspases (Cabal-Hierro and Lazo 2012). Despite lacking a Death domain, the Drosophila TNFR is able to induce cell death through JNK-dependent signalling (Igaki, et al. 2002). This suggests that cell death is a possible outcome of sponge TNF activation even though, like Drosophila, Death domain-containing TNFRs are not present in the sponge genome (Srivastava, et al. 2010). However, the upregulation of p38 MAP kinase and the AP-1 transcription factors in all three treatment groups leads me to hypothesise that sponge TNF signalling leads to the transcriptional activation of effector genes that regulate the sponge immune response (Fig. 3.8).

Both the TNF and MyD88 pathways activate MAP kinase signalling cascades and similar transcription factors (NF-κB, Jun, Fos), and are thus likely to have complementary, albeit unique, roles in regulating the sponge immune response (Fig. 3.8). The MyD88-dependent pathway has an ancient, highly conserved role regulating humoral aspects of immunity through antimicrobial peptide (AMP) production in animals as diverse as Hydra, Drosophila and humans (Tauszig, et al. 2000; Lee, et al.

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2008; Bosch, et al. 2009). TNF signalling, on the other hand, is important for the coordination of cellular immunity through autocrine or paracrine signalling (Caldwell, et al. 2014). Indeed, the Drosophila TNF ligand, Eiger, is an important modulator of phagocytic activity, and Eiger-mutant flies show decreased phagocytosis and thus reduced clearance of extracellular pathogens (Schneider, et al. 2007). Therefore, even though the sponge lacks specialised immune cells, it appears that A. queenslandica may use this highly conserved molecular machinery to coordinate its response to bacteria.

Additionally, cross-talk and synergy have been demonstrated between TNF and MyD88 signalling in humans (Yang, et al. 2010; Chang, et al. 2014). This further leads me to hypothesise that there may exist an ancient interplay between the two pathways in the sponge, potentially mediated by their shared regulatory adaptor molecules, the TRAFs (Fig. 3.8). The contrasting expression patterns of the different TRAFs in response to all the bacterial treatments hints at the regulatory complexity underlying TNF and MyD88 signalling (Fig. 3.5), and suggests that the A. queenslandica TRAFs have evolved divergent functions. One possible role of the TRAFs is the attenuation of signalling, and TRAF downregulation in response to bacteria detection would therefore be consistent with the intensification of TNF and MyD88 signalling. In fact, some TRAFs are negative regulators of NF-κB activation in humans and Amphioxus (Hauer, et al. 2005; Yuan, et al. 2009). It is also noteworthy that the TRAF gene family has undergone a large expansion (94 genes) in the A. queenslandica genome as well as the genomes of other sponges (B. Yuen unpublished data; Ryu, et al. 2016). It is thus interesting to speculate that, in the absence of a classic adaptive immunity, the multiplicity of the A. queenslandica TRAF repertoire could provide the sponge innate immune system with the capacity to generate distinct responses to symbionts and foreign bacteria through a limited few pathways. It is thus interesting that six TRAFs are also differentially expressed in correlation with a compositional shift in the microbiota that occurs during A. queenslandica metamorphosis (Fieth et al., in review). Certainly the upregulation of the SRCRs, TNF and MyD88 signalling in all three treatment groups indicates that the sponge symbiont bacteria are not invisible to their host (Fig. 3.9A-B).

79 Deciphering the genomic toolkit underlying animal-bacteria interactions

3.5.3 The expression of transcription factors critical to the innate immune response is induced by native bacteria but not by foreign sponge bacteria The three transcription factors, NF-κB, IRF and STAT are critical nodes of the mammalian innate immune system (Kwon, et al. 2009; Pugazhenthi, et al. 2013; Zaslavsky, et al. 2013). The upregulation of NF-κB, IRF and STAT in response to the native bacteria and unhealthy-Amphimedon bacteria suggests that their roles in regulating the immune response are conserved in the sponge (Fig 3.9B-C). More importantly, it also suggests that the sponge does not just detect symbiont bacteria ligands, but further recognises that the symbiont ligands are different from those of the foreign sponge bacteria, and thus mounts a distinct response to them. The mammalian NF-κB and IRF transcription factors are activated through MyD88-dependent signalling (Fig. 3.8A) (Honda and Taniguchi 2006; Kawai and Akira 2007). However, although AmqIgTIR and MyD88 were upregulated across all three treatments, the foreign sponge bacteria did not induce the upregulation of NF-κB or IRF, at least not within the timeframe of the experiment (Fig. 3.9B). These results suggest that there are nuances to the outcomes of MyD88 signalling in A. queenslandica. Indeed, MyD88-dependent signalling has highly conserved duals roles in defence against pathogens and the promotion of animal-bacteria symbiosis (Bosch, et al. 2009; Franzenburg, et al. 2012). Thus, the upregulation of NF-κB and IRF only in response to native bacteria and unhealthy-Amphimedon bacteria suggests that sponge innate immunity is not simply a non- specific form of defence. InDrosophila , the gut microflora are responsible for the constant activation of NF-κB via the immune deficiency (IMD) pathway (Ryu, et al. 2008), indicating that even under normal physiological conditions, symbiont bacteria are interacting with their host through the immune system.

Interestingly, the transcription factor STAT also has dual roles in immune defence and the promotion of animal-bacteria symbiosis. Under normal conditions, in the absence of infection, the indigenous gut bacteria of Drosophila stimulate a basal level of STAT activity, which appears to be important for the stem cell differentiation required for normal gut epithelium renewal (Buchon, Broderick, Chakrabarti, et al. 2009). Although the highly conserved activator of STAT activity, Janus kinase (JAK), is absent from the A. queenslandica genome (Liongue, et al. 2016), a gene similar to receptor tyrosine kinase erb-B4, which also directly phosphorylates and activates human STAT5A (Williams, et al. 2004; Clark, et al. 2005), was upregulated at 2h only in response to the native bacteria and unhealthy-Amphimedon bacteria (Appendix 3.5). It is thus possible that in the absence of JAK, STAT activity in A. queenslandica

80 Chapter 3 is regulated by receptor tyrosine kinase erb-B4. Furthermore, neither STAT nor erb-B4 were upregulated in response to the foreign sponge bacteria, which suggests that STAT activity in A. queenslandica could be regulated by cues from symbiotic bacteria exclusively. These multiple lines of evidence lead me to hypothesise that the upregulation of these crucial regulators of innate immunity represents the host’s side of the bidirectional conversation between host and symbiont, rather than a defensive response.

3.5.4 Upregulation of the immunosuppressive TGF-β pathway suggests that exposure to the native bacteria elicits a tolerogenic response TGF-β is a major immunosuppressive cytokine with a highly conserved role in metazoan immunity (Detournay, et al. 2012; Johnston, et al. 2016). In fact, the role of TGF-β signalling in promoting host- tolerance of symbiont bacteria has ancient origins. Blocking TGF-β signalling in the anemone Aiptasia pallida was found to cause immune stimulation preventing normal colonisation by its symbionts (Detournay, et al. 2012). In addition to regulating the mammalian immune response, TGF-β is also a critical promoter of tolerance for the bacteria residing in the mammalian gut (Zeuthen, et al. 2008). Probiotic bacterial treatments have also been shown to limit inflammation through the induction of TGF-β-bearing regulatory cells in the mammalian gut (Di Giacinto, et al. 2005). In A. queenslandica, the upregulation of gene encoding the TGF-β ligand and a TGF-β receptor in response to the native bacterial treatment within 2 hours, further supports the idea that the innate immune system mediates sponge-bacteria interactions (Fig 3.8B-C, 3.9 B). Furthermore, the much lower number of genes remaining upregulated after exposure to the native bacterial treatment at eight hours compared to two hours post-feeding, suggests that the initial response of the sponges to the native bacterial treatment was eventually subdued. These lines of evidence lead me to hypothesise that these anti-inflammatory regulators of mammalian immunity have a conserved role promoting tolerance and symbiosis in sponge-bacteria interactions.

There were only minor alterations in the community composition of the bacteria enriched from the unhealthy conspecific in this experiment (Fig. 3.2B). However, it is worth noting that these small changes in the relative abundance of the two dominant symbiont bacterial taxa was sufficient to induce detectable changes in the host’s transcriptomic response. The upregulation of the genes involved in TGF-β signalling in response to the unhealthy-Amphimedon bacterial treatment was only detected

81 Deciphering the genomic toolkit underlying animal-bacteria interactions at 8h post-feeding (Fig 3.9B). Alterations to the normally symbiotic microbiota, termed dysbiosis, is associated with inflammatory bowel diseases in humans, and is due to abnormally excessive local immune responses to components of the microbiota (Hansen, et al. 2010; Sartor and Mazmanian 2012). Dysregulation of TGF-β signalling has been implicated in the increase in the production of inflammatory molecules, and thus gut damage, associated with these inflammatory bowel diseases (Monteleone, et al. 2008). While it is unknown whether the altered bacterial community composition is the cause or consequence of the compromised sponge health, it is interesting to note that the upregulation of the genes involved in TGF-β signalling at 8h post-feeding, occurred in conjunction with the sustained upregulation of critical immune genes such as IRF, STAT, MyD88 and AmqIgTIR, all of which were not differentially expressed in response to the healthy conspecific or foreign bacteria at this same time point (Fig 3.9B). Further research will be required to deduce the mechanisms underlying how such a small change in the relative abundance of symbiont taxa is driving this differential transcriptomic response.

3.5.5 The upregulation of AMPK and FOXO, both key regulators of metabolic homeostasis, suggests that sponge-bacteria interactions are regulated by an interplay between metabolism and immunity A combination of physiological experiments and the use of genomics approaches is beginning to reveal the importance of metabolic interactions to maintaining homeostasis of the sponge holobiont (Hentschel, et al. 2012; Webster and Thomas 2016). However, in addition to facilitating the energy requirements for biosynthesis associated with the immune response, metabolism also has an integral role in directly regulating immune responses. In fact, multiple genes upregulated in this experiment have dual roles in regulating immunity and metabolism. Adenosine monophosphate-activated protein kinase (AMPK), a serine threonine kinase highly conserved in eukaryotes, is a crucial sensor and regulator of cellular energy supply and demand (Hardie, et al. 2012). Under conditions of metabolic stress, AMPK is activated when it detects a reduction in the levels of ATP or acceleration in ATP usage (Fig. 3.8C) (Hardie, et al. 2012). The upregulation of three AMPK subunit genes across all three treatment groups suggests that regulation of metabolic homeostasis is key for the sponge response to bacteria (Fig 3.9B).

AMPK can also be activated upstream independent of ATP levels by a calcium calmodulin- dependent protein kinase kinase, CaMKKβ, which responds to changes in intracellular Ca2+ levels (Fig.

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3.8C) (Hawley, et al. 2005). CaMKKβ is required for Ca2+-dependent AMPK activation to promote autophagy to regulate cellular homeostasis (Hoyer-Hansen, et al. 2007). Activation of the AMPK pathway has many other outcomes, one of which is the direct activation of the FoxO transcription factors, a process that is conserved across C. elegans and humans (Greer, Dowlatshahi, et al. 2007; Greer, Oskoui, et al. 2007; Tullet, et al. 2014). Hence the upregulation of both a CaMKK-like gene and a FoxO gene in response to the native bacteria and unhealthy-Amphimedon bacteria leads me to hypothesise that the CaMKKβ-AMPK-FoxO pathway is functionally conserved in the sponge.

The CaMKKβ-AMPK-FoxO axis not only regulates metabolic homeostasis, but it is also directly involved in the immune response. For example, in the mammalian immune response, T cell antigen receptor stimulation of AMPK requires CaMKKβ and occurs independent of ATP levels to prepare the T cells for the initiation of an energy intensive immune response (Tamás, et al. 2006). Moreover, the CaMKKβ-AMPK-FoxO axis was also recently found to promote antibacterial autophagy in mouse macrophages infected with Escherichia coli (Liu, et al. 2016). It is thus clear that AMPK regulation of FoxO transcription factor activity has dual roles in regulating metabolic homeostasis and immune response. FoxO activity also can also affect the immune response directly through regulation of antimicrobial peptide (AMP) expression, a role which is intriguingly conserved across animals from the basal metazoan Hydra to humans (Becker, et al. 2010; Bridge, et al. 2010). Indeed, in C. elegans, the FoxO transcription factor DAF-16 is necessary for the activation of genes that confer pathogen resistance as well as the regulation of metabolism, suggesting that the dual roles of FoxO in regulating immunity and metabolism are conserved across animals (Miyata, et al. 2008; Singh and Aballay 2009). Furthermore, the development of inflammatory bowel disease following deletion of the FoxO1 gene in the T cells in mice (Ouyang, et al. 2009), highlights the interactions between FoxO regulation of immunity and metabolism. In fact, Becker et al. (2010) revealed a mechanism of cross-regulation between metabolism and immunity through FoxO-dependent control over AMP expression in response to changes in cellular energy status associated with starvation. It is thus interesting that the upstream regulation of FoxO activity can involve a myriad of different immune genes includingp38 , Jnk, STAT and SMAD, all of which were upregulated in this experiment (Fig. 3.8C) (Van Der Vos and Coffer 2008; Eijkelenboom and Burgering 2013). These results thus lead me to propose that there is potential

83 Deciphering the genomic toolkit underlying animal-bacteria interactions for interactions to occur between metabolic and immune regulation in the sponge through the AMPK- FoxO axis, and that these interactions could play an important role in sponge-bacteria interactions.

This observation is most intriguing in light of a hypothesis recently proposed by Broderick (2015) that innate immunity and digestion have a common origin and are therefore likely to have been practically indistinguishable physiological processes in the first animals. This is extremely applicable in the context of sponge biology because direct cellular interactions with the surrounding aqueous environment occur through the sponge aquiferous system (Ereskovsky 2010; Mah, et al. 2014). The aquiferous system, like a true gut, is the location where most digestion and nutrient uptake occur in sponges (Willenz and Van de Vyver 1984). Unlike a true gut, however, sponge digestion is a completely intracellular process, and bacteria particles are phagocytosed to be broken down for nutrient extraction (Kunen, et al. 1971; Frost 1981; Willenz and Van de Vyver 1984). Since bacteria taken up from the environment could also potentially be harmful, sponge cells responsible for digestion are also at the frontline of immunity, and are therefore likely to play a significant role in directing the development and coordination of the sponge immune response.

3.6 CONCLUSIONS The use of transgenic lines and the ability to generate gnotobiotic or aposymbiotic hosts in model systems such as Hydra, zebrafish and mice, have led to new insights into the importance of the immune system as a mediator of interactions between animals and their symbiont bacteria (Chu and Mazmanian 2013). However, attempts to generate sterile or aposymbiotic sponges using antibiotics have not been successful (Richardson, et al. 2012), while sponges also remain unamenable to transgenic technologies. This study works around these limitations by using the novel approach of comparing the transcriptomic response of the sponge to symbiont versus non-symbiont bacteria.

The findings of this study suggest that A. queenslandica is able to recognise and respond to its symbiont bacteria through the upregulation of conserved innate immune signalling pathways. This is consistent with the view that the innate immune system has evolved not just for defence but also for mediating symbiotic interactions between animals and bacteria. Finally, upregulation of the AMPK-FoxO pathway in this experiment further suggests that an ancient regulatory interplay between

84 Chapter 3 metabolism and immunity mediates sponge-bacteria interactions, and further lends support to the hypothesis that metazoan immunity and digestion have a common origin.

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86 Chapter 4 Chapter 4 - The selective cellular processing of native bacteria and foreign bacteria by the demosponge Amphimedon queenslandica

4.1 ABSTRACT Previously I have reported that multiple conserved metazoan immune pathways are upregulated when juveniles of the marine demosponge Amphimedon queenslandica are fed bacteria enriched from their conspecifics. These same pathways, however, are not induced by foreign bacteria from a different sponge species, which suggests that sponges distinguish their native bacteria from foreign bacteria at the molecular level. Here I investigate the sponge’s discrimination of native bacteria from foreign bacteria at the cellular level. A. queenslandica juveniles were presented with fluorescently- labelled bacteria enriched from three sources: a healthy A. queenslandica adult, the altered bacterial community of an unhealthy A. queenslandica adult, and the foreign bacteria from a different sponge (Rhabdastrella globostellata). Within thirty minutes following exposure to the treatments, bacteria from all treatments were being taken up by choanocytes, the flagellated cells that trap food and generate water currents through the sponge. Bacteria were also observed densely packed in amoebocytes, the choanocyte-associated amoeboid cells located immediately adjacent to choanocyte chambers. By two hours, bacteria were observed frequently in the archaeocytes of sponges that had been fed the healthy and unhealthy A. queenslandica-derived bacterial treatments. In contrast, bacteria were rarely observed in the archaeocytes of sponges that had been fed the foreign sponge bacteria. This coincided with the upregulation, at two hours post-treatment, of a suite of Nrf2-regulated oxidative and xenobiotic stress response genes, including antioxidants, multidrug transporters and glutathione s-transferases, only in response to the foreign sponge bacteria. By eight hours, archaeocytes containing bacteria were observed in all sponges across all treatments. Throughout the entire time course, from 30 minutes to eight hours post-exposure, bacteria from all treatments continued to be observed in choanocytes and the choanocyte-associated amoeboid cells. Consistent with the role of these two cell-types at the frontline of interactions with the external environment, genes involved in TNF and MAP kinase signalling were constitutively upregulated in both pinacocytes and choanocytes relative to archaeocytes, suggesting these cells are primed to respond to bacteria.

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4.2 INTRODUCTION Filter-feeding sponges (Porifera) have been an ecologically important component of benthic habitats across tropical, temperate and polar waters since they emerged over 600 million years ago (Knoll 2003; Bell 2008). Sponges have an exceptional capacity to process thousands of litres of water in a day, enabling them to trap and ingest large amounts of suspended microorganisms and dissolved nutrients from the water column (Reiswig 1971, 1974, 1975; Maldonado, et al. 2012). To filter-feed, sponges pump water through a network of canals and chambers known as the aquiferous system. The canals are lined with a single epithelial-like layer of cells known as pinacocytes, while the spherical chambers, consisting of monoflagellated collar-cells known as choanocytes, are interspersed throughout the network of canals (Fig. 1.1). Unlike more morphologically complex animals, food digestion in sponges is a completely intracellular process because they do not have a gut (Kunen, et al. 1971; Frost 1981; Willenz and Van de Vyver 1984). Instead, the beating flagella of the choanocytes generate water flow through the sponge, and a collar of microvilli at the base of the flagellum functions as a micro-scale filter that traps suspended particles for phagocytosis by the choanocyte (Mah, et al. 2014). Through this mode of feeding, sponges are able to efficiently retain a large variety of particles, which can be as small as viruses and bacteria (Reiswig 1971; Hadas, et al. 2006), and as large as yeast (Kunen, et al. 1971; Maldonado, et al. 2010).

The capacity to efficiently capture particulate matter through processing large volumes of water, in combination with their often significant contribution to benthic biomass, has led to the suggestion that sponges play an important role in recycling nutrients, particularly in oligotrophic ecosystems such as coral reefs (Bell 2008; de Goeij, et al. 2013). Hence, the feeding behaviour of sponges has been the focus of a considerable body of research for many decades (Schmidt 1970; Reiswig 1971, 1974, 1975; Frost 1981; Turon, et al. 1997; Ribes, et al. 1999; Wehrl, et al. 2007; McMurray, et al. 2016). Size- based selection appears to be an important factor influencing feeding behaviour in sponges. Although capable of capturing a broad size-range of prey (Kunen, et al. 1971; Ribes, et al. 1999; Hadas, et al. 2006), sponges appear to retain picoplankton or bacteria-sized particles (0.2–2 μm) with the most efficiency (Reiswig 1971, 1975; Ribes, et al. 1999; Hadas, et al. 2009). In fact, free-living bacteria in the water column make up the bulk of their diet and constitute a significant component of sponge nutrient requirements (Hadas, et al. 2009; Maldonado, et al. 2012).

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Some are of the view that sponges are non-selective in their uptake of bacteria-sized particles (Pile, et al. 1996; Kowalke 2000). However, recent studies reveal that sponges do in fact have a size- independent preference for bacteria with a high nucleic acid content, which could be due to their greater nutritive value (Yahel, et al. 2006; Hanson, et al. 2009; McMurray, et al. 2016). Furthermore, sponge feeding preference appears to vary with a whole range of factors including, sponge species, food availability, and time of year (Turon, et al. 1997; Yahel, et al. 2006; Hanson, et al. 2009; McMurray, et al. 2016). Yahel et al. (2006) thus proposed that post-capture recognition, sorting and transport play an important role in bacteria selection process. Evidence of differential processing was observed in the demosponge perlevis, as multiple different cell-types and strategies appear to be involved in mediating the uptake and processing of microorganisms from the water column depending on the nature of the microorganism encountered (Maldonado, et al. 2010). These studies thus suggest that the selective uptake and subsequent processing of bacteria by sponges is an active process that involves more than just particle size

Despite relying on free-living bacteria for nutrition, sponges also simultaneously maintain dense and diverse communities of species-specific bacteria symbionts, most of which are not found, or are extremely rare, in the surrounding environment (Thomas, et al. 2016). These bacterial symbionts reside throughout the extracellular matrix of the sponge mesohyl and in some cases can even be found intracellularly (Hentschel, et al. 2012; Webster and Thomas 2016). Thus, in addition to being capable of discriminating different types of food bacteria, sponges also selectively promote symbioses with specific bacterial species to the exclusion others. Indeed, experiments by Wehrl et al. (2007) and Wilkinson et al. (1984), have demonstrated that sponges take up “food” bacteria isolates at much higher rates than cultured isolates of their own symbiont bacteria, indicating that sponges are able to discriminate symbionts and thus selectively ingest non-symbiont bacteria. However, the mechanisms used by sponges to discriminate symbiont from non-symbiont bacteria remain poorly understood.

With high-throughput sequencing technologies now providing access to the genomes and transcriptomes of the sponge holobiont, we are beginning to gain new insights into the components of the sponge genome involved in mediating symbiont recognition and response (Hentschel, et al. 2012; Ryu, et al. 2016). In the previous Chapter, I presented juveniles of the marine demosponge Amphimedon

89 Deciphering the genomic toolkit underlying animal-bacteria interactions queenslandica with three different bacterial treatments: the foreign bacteria associated with a different sponge species (Rhabdastrella globostellata), the bacterial community of a healthy conspecific, and the bacterial community of an unhealthy conspecific. I then assayed the transcriptome-wide responses induced by these different sources of bacteria. For clarity, I refer to the bacteria consortia associated with the different host species, in this caseR. globostellata, as ‘foreign sponge bacteria’. Conversely, when referring to A. queenslandica, I use the term ‘native bacteria’ to describe the bacteria isolated from the healthy conspecifics, while I use the term “unhealthy-Amphimedon bacteria” to describe the bacteria consortia isolated from the unhealthy conspecific. Although both are demosponges, R. globostellata and A. queenslandica belong to the orders Tetractinellida and Haplosclerida respectively (Carter 1883; Hooper and Van Soest 2006). R. globostellata houses a highly species rich bacterial community that consists of a particularly high abundance of Chloroflexi, and that is also distinct to the free-living bacterial community in the environment (Steinert, et al. 2016). The native bacterial community of A. queenslandica, on the other hand, is relatively species poor and dominated by Gammaproteobacteria bacteria that are a stable presence throughout its lifecycle (Bayes 2013; Fieth et al., in review). Like other animals including sponges (Webster, et al. 2008; Selvin, et al. 2009; Sunagawa, et al. 2009; Claesson, et al. 2012), a compromised state of health in A. queenslandica is correlated with changes in the structure and composition of its resident bacterial community, marked by a decrease in the abundance of the primary symbiont Gammaproteobacteria taxon (Bayes 2013). Thus the three treatments have quite distinct bacteria species compositions.

In Chapter three, I showed that bacteria enriched from A. queenslandica elicited the upregulation of multiple conserved immune genes, including transcription factors such as interferon regulator factor and NF-κB, which are key regulators of the metazoan innate immune response (Kwon, et al. 2009; Pugazhenthi, et al. 2013; Zaslavsky, et al. 2013). Additionally, genes involved in TGF-β signalling pathway, which has a conserved role in regulating symbiont tolerance across metazoans, were also upregulated in response to the symbiont bacteria (Zeuthen, et al. 2008; Detournay, et al. 2012). However, these immune genes and TGF-β signalling pathway components were not induced by the foreign bacteria. These findings support the conclusions of previous studies that sponges are able to discriminate their own symbionts from non-symbiont bacteria (Wilkinson, et al. 1984; Wehrl, et al.

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2007), and further suggest that recognition and response to symbiont bacteria is mediated by highly conserved components of the metazoan immune system.

Figure 4.1 Schematic description of experimental procedure

A) The sources of the three bacteria treatments were collected fresh from their native habitat on Heron Island

Reef; Rhabdastrella globostellata (foreign bacteria), healthy and unhealthy A. queenslandica (native bacteria and altered bacteria communities respectively). (B) The bacterial communities were enriched from 5cm3 of tissue excised from each specimen and labelled with the fluorescent cell tracker dye CFDA-SE. (C) The labelled bacteria treatments were presented to oscula staged juvenile A. queenslandica, and samples were collected at 30 minuters, 1, 2, and 8 hours post-exposure for confocal microscopy.

To gain a better understanding of the mechanisms underlying bacteria discrimination in the sponge, the present study builds on the data collected in Chapter three by investigating how foreign bacteria from a different sponge species, native bacteria from a healthy conspecific and the altered bacterial consortia from an unhealthy conspecific, are processed at the cellular level inA. queenslandica juveniles. I also further analysed the genes that were differentially expressed in response to these same

91 Deciphering the genomic toolkit underlying animal-bacteria interactions treatments in Chapter three, specifically to understand their roles in a cellular context. The analysis of high-throughput RNA-Seq data in combination with microscopy adds new depth to our understanding of sponge digestion and immunity, providing insight into the roles of highly conserved metazoan genes in regulating the sponge’s cellular interactions with bacteria.

4.3 METHODS 4.3.1 Bacteria tracking experiment To investigate whether there are differences in the processing of symbiont bacteria and foreign bacteria at the cellular level, I presented feeding, oscula-stage A. queenslandica juveniles with three different CFDA-SE labelled bacterial treatments; the symbiont bacteria of a healthy conspecific, the altered symbiont community of an unhealthy conspecific, and the foreign bacterial community of R. globostellata, a sponge from a different demosponge order (Fig. 4.1).

a. Bacterial community enrichment and CFDA-SE labelling In December 2015, one specimen each of Rhabdastrella globostellata (Carter 1883), healthy and unhealthy A. queenslandica adults were non-destructively chiselled off coral rubble at low tide from the reef flat at Shark Bay, Heron Island reef (Fig. 4.1). These animals were used as a source of the following three bacterial treatments: foreign sponge bacteria, native Amphimedon-associated bacteria, unhealthy Amphimedon-associated bacteria (Fig. 4.1). The sponges were maintained at Heron Island Research Station (HIRS) under flowing seawater at ambient conditions for no longer than three days to reduce the probability of alterations to their bacterial communities while in aquarium conditions. 5 cm3 fragments of tissue, measured by volume displacement, were excised from the adult sponges and placed in 35 mL ice cold 0.22 µm filtered calcium-magnesium-free artificial seawater (CMFSW). The tissues were then placed on an orbital shaker for 5 minutes at 200 rpm, to remove transiently attached bacteria from the surrounding water. The absence of the cations Ca2+ and Mg2+ in combinations with the presence of chelating agent EGTA in CMFSW facilitates cell dissociation (Dunham et al 1983). The bacterial communities of each of these treatment sources was enriched through filtration and a series of centrifugation steps, as previously described in Chapter three, resulting in pellets of bacteria cells. The bacteria cells were then labelled with the CFDA-SE (Vybrant , Thermofisher Scientific) cell tracker dye by resuspending the final pellet in 1 mL 0.22 µm filtered CMFSW containing 5 µM

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CFDA-SE and incubating for 15 minutes at room temperature in the dark to minimise bleaching of the dye. The cell suspension was then centrifuged at 5000x g for 10 minutes and the pellet was washed twice with 1 mL CMFSW (5000x g, 10 minutes) before resuspension in 1 mL CMFSW. The labelled microbial cell suspension was kept on ice in the dark until the experiments commenced.

b. Collection of juvenile sponges In December 2015, Adult A. queenslandica were collected from the Heron Island Shark Bay reef flat, and returned to HIRS, where they were maintained under flowing water pumped from the reef at ambient conditions. The naturally-spawned larvae were collected from a mixed pool of adults and allowed to develop for 6-8 hours as described in Chapter three. Larvae were induced to settle on the articulated coralline alga, Amphiroa fragilissima, by exposure to the alga for 4 hours in 6-well plates (Corning, Costar), 10 larvae per well in 5 mL 0.22 µm filtered sea-water (FSW). The settled post-larvae were subsequently gently detached using a pair of fine forceps (Dumont #5) and a lancet. The detached post-larvae were resettled on to round coverslips placed in 24-well plates (Corning, Costar); one individual per well in 2ml FSW. The post-larvae were resettled upon glass coverslips to facilitate the use of microscopy for visualisation of the internalised bacterial treatments. I considered the process of resettlement upon the coverslip as the beginning of metamorphosis even though the larvae were originally settled on A. fragilissima 4 hours prior. During the resettlement process, the post-larvae initially retract into a ball before subsequently spreading out to attach to the coverslip in a flat mat. The resettled larvae were allowed to grow for 96 hours on the coverslips until they reached the oscula stage as described in Chapter three.

c. Experimental procedure and observation of bacteria processing The procedure used for the feeding experiment described in Chapter three was repeated with the following modifications. TheA. queenslandica juveniles were exposed to 40 µL of CFDA-SE labelled microbial suspension enriched from either a healthy A. queenslandica, unhealthy A. queenslandica adult sponges, or R. globostellata (foreign sponge bacteria; Fig. 4.1). Whole juvenile sponges were fixed at 30 minutes, 1 hour, 2 hours and 8 hours post-exposure as described in Larroux et al. (2008). Each treatment group consisted of three to four juveniles with the exception of the 2h time point, which consisted of five to six individuals per treatment group. The entire experiment from commencement to

93 Deciphering the genomic toolkit underlying animal-bacteria interactions sample fixation was carried out in the dark to prevent bleaching of the cell tracker dye. The dehydrated samples were rehydrated in PBST as described in Larroux et al. (2008). Nuclei were labelled with a 30 minute incubation in 4′,6-diamidino-2-phenylindole (DAPI; 1:1000, Molecular Probes) and washed in PBST for 5 minutes. The samples were whole-mounted in ProLong Gold anti-fade reagent (Molecular Probes). The Zeiss LSM 510 META confocal microscope was used to observe the samples and capture images, and the image analyses were performed using the software Fiji (Schindelin, et al. 2012).

d. Archaeocyte counts By 2h post-feeding, I frequently observed bacteria in the archaeocytes of individuals that were given native bacteria and unhealthy-Amphimedon bacteria, but rarely in individuals that were fed foreign sponge bacteria (see Results). I therefore sought to obtain an empirical estimate of the rate of bacteria uptake by archaeocytes across the different treatments at 2h. I analysed five to six individuals from each treatment at the 2h time point. The numbers of choanocyte chambers, archaeocytes and bacteria-containing archaeocytes were counted in four confocal image sections obtained from each individual at 40x magnification. These confocal sections were imaged through random haphazard sampling targeted at the middle of the juvenile where most of the labelled bacteria were observed. A one-way ANOVA followed by the Tukey’s HSD post-hoc test were used to test for a statistically significant difference in the numbers of bacteria-containing archaeocytes, total archaeocytes, and choanocyte chambers amongst the treatments.

4.3.2 Feeding experiment transcriptome data analyses In order to investigate the molecular mechanisms underlying the differences in the cellular responses to foreign bacteria and A. queenslandica-derived bacterial treatments, I analysed the lists of differentially expressed genes generated from the feeding experiment in Chapter three. The KEGG annotated gene lists from Chapter three were mapped to the KEGG BRITE database using the online tool KEGG mapper (www.genome.jp/kegg/mapper.html) (Kanehisa, et al. 2016). The KEGG BRITE database contains manually curated functional hierarchies of various biological objects, including protein families (Kanehisa, et al. 2016). I interrogated the differentially expressed gene lists to identify genes that had been annotated as encoding G-protein coupled receptors (GPCRs) (ko04030).

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To identify genes that were upregulated in response to foreign sponge bacteria only, I used Venn diagrams constructed from the differentially expressed gene lists generated in Chapter three, implemented through the online tool Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny). I then extracted the BLAST2GO and InterProScan annotations for these genes as described in Chapter three. I hypothesised that the differences in the cellular processing of foreign sponge bacteria and A. queenslandica-derived bacterial treatments could be due to cellular stress. I manually searched the annotated lists for genes with BLAST hit descriptions that matched putative targets of the transcription factor Nrf2, based on lists described in published literature reviews (Hayes and McMahon 2009; Hayes and Dinkova-Kostova 2014; Lacher, et al. 2015).

Heatmaps were created using the R package “pheatmap, in order to visualise the expression patterns of the genes of interest from the vsd transformed raw reads generated in Chapter three. All expression patterns were scaled by each gene row, and genes were clustered based on Euclidean distance using the “ward.D2” clustering algorithm.

4.3.3 Cell-type specific transcriptome analysis To examine how each cell-type carries out its role in processing the bacteria at a molecular level, I identified several categories of differentially expressed genes in the experiment in Chapter three based on their roles in regulating the detection and response to bacteria. I then analysed their expression patterns across the transcriptome profiles of choanocytes, pinacocytes and archaeocytes. The cell-specific transcriptomes datasets were generated by Shunsuke Sogabe and Daniel Stoupin – PhD students from the Degnan Labs – from cells isolated from three A. queenslandica adult individuals. Healthy adult sponges were collected in the field on Heron Island and returned to the aquarium system at the University of Queensland, St Lucia, Brisbane. The cells were isolated from these sponges within three days from the time they were collected in the field to minimise the duration spent in the aquarium. The sponges were not given any bacterial treatments, and thus represent the “untreated” state of gene expression for these cell-types, which is most comparable to the gene expression of these cell-types in juvenile sponges of the control group in the Chapter three experiment.

95 Deciphering the genomic toolkit underlying animal-bacteria interactions

A brief description of the methods used to generate the cell-type specific transcriptome data is as follows: Sponge cells were mechanically dissociated from live adult tissues. The cell-types were visually identified, and isolated using a micromanipulator, then placed immediately on dry ice. The cells were kept at -80 °C until they were processed. The RNA-Seq data were generated using the CEL-Seq protocol for single cell RNA-seq described in Chapter three (Hashimshony, et al. 2016). Differentially expressed genes were identified through pairwise comparisons between all possible combinations of the three cell-types using EdgeR version 3.3 (Robinson and Smyth 2007a, b; Robinson, et al. 2010; McCarthy, et al. 2012). A 5% False Discovery Rate cut off was used to produce the final lists of differentially expressed genes. The lists of upregulated genes for each cell-type were combined and duplicates were removed to form the final list of genes that represented the transcriptome specific to each cell-type.

To determine how these genes are deployed across the three cell types, I constructed Venn diagrams using Venny 2.1 to identify overlaps across the lists of genes involved in regulating bacteria interactions that had been identified from data in Chapter three and the cell-specific transcriptome data. Heatmaps were created using the R package “pheatmap” to visualise the expression patterns observed. The heatmaps were generated from raw counts that were vsd transformed using DESeq2 version 1.10.1 (Love, et al. 2014). The expression patterns in the heatmaps were clustered based on Euclidean distance using the “ward.D2” clustering algorithm.

96 Chapter 4

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97 Deciphering the genomic toolkit underlying animal-bacteria interactions

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Figure 4.2 Confocal sections tracking the processing of CFDA-SE labelled bacteria treat- ments by oscula staged juvenile Amphimedon queenslandica

The bacteria communities enriched from R. globostellata (foreign), healthy, and unhealthy A. queenslandica were labelled using CFDA-SE (green). The juvenile sponges were presented with the bacteria and then sampled after

30 minutes, 1h, 2h and 8h. Nuclei are stained with DAPI (blue). (A) From 30 minutes to 8 hours, the labelled bacteria from all three treatments are present in choanocytes and the amoeboid cells located beside choanocyte chambers (ch). See Appendix 4.1A-B for details on the 30 minutes and 1 hour post-feeding time points. (B-L)

There are three columns of images representing samples from each of the three treatment groups: (A-B, G-H) foreign sponge bacteria (C-D, I-J), native bacteria (E-F, K-L) unhealthy-Amphimedon bacteria. (A-L) High den- sities of labelled bacteria are localised to amoeboid cells, which are distinguished by their location, morphology and the anucleolate nucleus (unfilled white arrows). The archaeocytes are distinguished from amoeboid cells by the presence of a large nucleolate nucleus (solid arrows). (A-F) By 2 hours post-feeding, the native and unhealthy-Amphimedon bacteria, but not the foreign sponge bacteria, were observed in archaeocytes. Solid red arrows indicate the bacteria-containing archaeocytes. Solid white arrows indicate archaeocytes without bacteria. (A, C, E) Two hours post feeding at 63x magnification. B, D, F) Two hours post feeding overlayed on

DIC channel at 63x magnification. (G-L) By 8 hours post-feeding, labelled bacteria are present in the archae- ocytes of samples from all three treatment groups. (G, I, K) Eight hours post feeding at 63x magnification. (H,

J, L) Eight hours post feeding overlayed on DIC channel at 63x magnification.

4.4 RESULTS 4.4.1 Ingested bacteria particles from all three treatments were observed in choanocytes and amoeboid cells from 30 minutes post-feeding The transcriptome data generated in Chapter three suggest that A. queenslandica recognises and responds differently to its symbiont bacteria than to foreign bacteria. To investigate whether there is discrimination in the processing of symbiont bacteria and foreign bacteria at the cellular level, I presented feeding oscula stage A. queenslandica juveniles with three different CFDA-SE labelled bacterial treatments; the symbiont bacteria of a healthy conspecific, the altered symbiont community of an unhealthy conspecific, and the foreign bacterial community of R. globostellata, a sponge from a different demosponge order.

99 Deciphering the genomic toolkit underlying animal-bacteria interactions

The passage of the bacterial treatments was then observed at four time points from 30 minutes through to 8 hours post-feeding using confocal microscopy. At 30 minutes post-feeding, the labelled bacteria were observed in choanocytes, the flagellated cells comprising the choanocyte chambers, which are responsible for generating water flow through the aquiferous system (Appendix 4.1A). High densities of labelled bacteria were also observed in the amoeboid cells situated immediately adjacent to the choanocyte chambers. These amoeboid cells all possess a small anucleolate nucleus measuring roughly 3µm, and were observed in direct in contact with the choanocytes or separated by a gap bridged by filopodial extensions (Fig. 4.2J, L). These amoeboid cells are thought to be a form of pinacocyte based on their association with the pinacoderm layer and are marked by the presence of multiple large phagosomes (R. Fieth and D. Stoupin, unpublished data). Ingested bacteria were observed in choanocytes and in the amoeboid cells adjacent to the choanocyte chambers across all time points from thirty minutes to eight hours post feeding (Fig. 4.2A-L, Appendix 4.1A-H). I did not observe any differences in the cell-types involved in processing the three different bacterial treatments from 30 minutes to one hour post-feeding.

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Each treatment group consisted of five to six juvenile sponges and four confocal sections were analysed per individual. There was no significant difference in the number of archaeocytes and choanocyte chambers

100 Chapter 4

observed across the treatment groups. However, the number of bacteria-containing archaeocytes observed was significantly higher in the two treatment groups that were given native and unhealthy-Amphimedon bacteria.

There was also significantly more bacteria-containing archaeocytes in the sponges that were fed the bacteria from the healthy conspecific than those that were fed bacteria from the unhealthy conspecific. Values that were significantly different in a one-way ANOVA followed by the Tukey’s HSD post-hoc test (P < 0.01) are denoted an asterisk (*).

4.4.2 Ingested bacteria were observed in archaeocytes at 2h post-feeding and bacteria derived from A. queenslandica were more frequently observed in archaeocytes than foreign sponge bacteria Archaeocytes are easily distinguished from the aforementioned amoeboid cells by the presence of a large (4~5 µm) nucleus containing a prominent nucleolus (~1.7 µm). Two hours post-feeding, bacteria enriched from the healthy and unhealthy conspecifics were observed in archaeocytes in the vicinity of choanocyte chambers (solid red arrows in Fig. 4.2C-F). In contrast, ingested foreign sponge bacteria were rarely observed in the archaeocytes at 2h post-feeding (Fig. 4.2A-B). To obtain a quantitative estimate of the rate of foreign bacteria uptake by archaeocytes at the 2h post-feeding time point, I counted the numbers of choanocyte chambers, archaeocytes, and bacteria-containing archaeocytes in images sampled from sponges of each treatment group. In the images sampled from the juveniles sponges that were fed the foreign sponge bacteria, the native bacteria and unhealthy- Amphimedon bacteria, I counted an average of 79, 85, and 79 choanocyte chambers respectively, and 90, 106, and 96 archaeocytes respectively. There was no significant difference in the number of choanocyte chambers or archaeocytes observed amongst the three treatment groups (p>0.05, Fig. 4.3). However, there was a significantly greater number of bacteria-containing archaeocytes in both native and unhealthy-Amphimedon bacterial treatment groups compared to the foreign treatment group (p<0.01, Fig. 4.3). In fact, an average of only three bacteria-containing archaeocytes per individual were observed in the foreign sponge bacterial treatment group, compared to an average of 32 in the treatment group that was fed native bacteria enriched from the healthy conspecific, and an average of 18 bacteria-containing archaeocytes in the sponges that were fed unhealthy-Amphimedon bacteria (Fig. 4.3). Interestingly, there was also a significantly higher number of bacteria-containing archaeocytes in sponges that were fed native bacteria from the healthy conspecific compared to those that were fed unhealthy-Amphimedon bacteria (p<0.01, Fig. 4.3).

101 Deciphering the genomic toolkit underlying animal-bacteria interactions

At 8 hours post-feeding, labelled bacteria were commonly observed within archaeocytes of sponges in all three treatment groups (Fig. 4.2 G-L). These bacteria-containing archaeocytes were situated in close proximity to choanocytes and amoeboid cells, as well as further away from choanocyte chambers in the cell tracks leading to the periphery of the sponge.

4.4.3 Genes putatively involved in regulating sponge-bacteria interactions are constitutively more highly expressed in choanocytes and pinacocytes than in archaeocytes I first identified groups of genes that were differentially expressed in response to the same bacterial treatments in Chapter three. I then analysed the expression patterns of these genes across transcriptome profiles of choanocytes, pinacocytes and archaeocytes that were generated from adult conspecifics that did not receive any bacterial treatments. The involvement of these three cell-types in mediating ingestion and subsequent processing of bacteria has previously been documented in other demosponges species (Imsiecke 1993; Maldonado, et al. 2010). Additionally, ingested bacteria were observed in the choanocytes, choanocyte-associated amoeboid cells and archaeocytes of A. queenslandica in the present study.

a. Scavenger receptor cysteine-rich domain-containing genes I analysed the Scavenger Receptor Cysteine-Rich domain-containing genes (SRCRs) because they could be involved in the detection of bacterial ligands (Steindler, et al. 2007; Ryu, et al. 2016). In Chapter three, 60 members of the SRCR family were differentially expressed across all three bacterial treatment groups. I analysed the expression patterns of these SRCRs across the untreated transcriptome profiles of choanocytes, pinacocytes and archaeocytes. In general, these SRCRs were found to be constitutively more highly expressed in choanocytes and pinacocytes than in archaeocytes (Fig. 4.4A, Appendix 4.3). Twenty seven SRCRs were upregulated in choanocytes compared to the other two cell-types (Fig. 4.4A). A further eight SRCRs were more highly expressed in both choanocytes and pinacocytes than in archaeocytes (Fig. 4.4A). Four SRCRs were upregulated in pinacocytes compared to the other two cell-types (Fig. 4.4A). Finally, only one SRCR was constitutively upregulated in pinacocytes and archaeocytes compared to choanocytes (Fig. 4.4A). I did not observe any clear patterns correlating the presence of other protein domains associated with the SRCRs and the cell-types in which they are more highly expressed (Fig. 4.4A).

102 Chapter 4

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Figure 4.4 Expression patterns of SRCR domain containing genes and genes mapped to the lysosomal KEGG pathway across the three cell-types

Expression patterns of genes (rows) containing the (A) scavenger receptor cysteine-rich (SRCR) domain and genes mapped to the (B) lysosome KEGG pathway, in choanocytes, pinacocytes, archaeocytes (columns).

Red indicates high levels of expression while yellow indicates low levels of expression. Rows (genes) were clustered by similarity of expression patterns. Both heatmaps were plotted from VSD transformed counts gener- ated by CEL-Seq and are unscaled. (A) The SRCR genes are generally more highly expressed in choanocytes compared to pinacocytes and archaeocytes. The labels to the right of the heatmap indicate additional protein

103 Deciphering the genomic toolkit underlying animal-bacteria interactions

domains associated with the SRCR gene in the row beside the label. RTK – receptor tyrosine kinase, GPCR –

G protein-coupled receptor, EGF – epidermal growth factor. FN3 – fibronectin 3. See Appendix 4.1 for details.

(B) These lysosomal pathway genes were differentially expressed in response to the bacteria treatments in chapter 3. The majority of these genes are upregulated in choanocytes compared to the other two cell-types, and includes many genes encoding heparan sulfatases and cathepsins. See Appendix 4.2 for details.

b. Lysosomal pathway Components of the KEGG lysosome pathway were included in the analysis because sponge digestion is a completely intracellular process involving lysosomal breakdown of phagocytosed particles (Willenz and Van de Vyver 1984). In Chapter three, 36 of the genes identified as differentially expressed in the feeding experiment were mapped to the KEGG lysosome pathway. I analysed the expression patterns of these lysosomal pathway genes across the untreated transcriptome profiles of choanocytes, pinacocytes and archaeocytes. Twenty four of these were upregulated in choanocytes compared to the other two cell-types (Fig. 4.4B, Appendix 4.5). This includes 11 genes from sulfatase family of enzymes involved in heparan sulfate breakdown (Parenti, et al. 1997), as well as four genes encoding members of the cathepsin family, a major group of lysosomal proteases (Fig. 4.4B, Appendix 4.5) (Appelqvist, et al. 2013).

c. G-protein coupled receptors I observed that the processing of the internalised bacteria by the sponges involved multiple cell-types, suggesting that the coordination of cellular feeding behaviour involves intercellular communication. G-protein coupled receptors (GPCRs) are responsible for sensing a large array of ligands and are critical regulators of the chemical signalling underlying cell-cell communication (Bockaert, et al. 2010; Granier and Kobilka 2012). I analysed the transcriptome data generated from the feeding experiment in Chapter three to identify differentially expressed genes that had been KEGG annotated as G-protein coupled receptors (GPCRs). Thirty one GPCRs from the glutamate, rhodopsin, adhesion and frizzled GPCR families were differentially expressed across all three treatment groups (Fig. 4.5A, Appendix 4.5). The majority of these differentially expressed GPCRs (17) are from the glutamate family, and they had BLAST hits with the description gamma−aminobutyric acid type b (GABAB) receptor as well as metabotropic glutamate receptor. Eighteen GPCRs were upregulated

104 Chapter 4 across the three treatment groups, 12 of which were glutamate family GPCRs while four were adhesion family GPCRs (Fig. 4.5A, Appendix 4.6). These four adhesion family GPCRs have BLAST hits with the description latrophilin-2 or latrophilin CIRL−like. I then examined the expression patterns of these GPCRs across the untreated transcriptome profiles of choanocytes, pinacocytes and archaeocytes. Ten of the 31 GPCRs were constitutively upregulated in choanocytes relative to the other two cell-types (Fig. 4.5B, Appendix 4.6), while another nine were constitutively more highly expressed in pinacocytes compared to the other cell-types (Fig. 4.5B). I did not observe any association between the expression of genes from particular GPCR families with the cell-types examined.

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105 Deciphering the genomic toolkit underlying animal-bacteria interactions

Figure 4.5 Expression patterns of GPCRs in response to the bacterial treatments and across the three cell-types.

(A) Expression patterns of the genes (rows) encoding G-protein-coupled receptors (GPCRs) differentially expressed across all samples (columns) in response to the three bacteria treatments at 0h, 2h and 8h post-ex- posure. Red indicates high levels of expression while blue indicates low levels of expression. The majority of the GPCRs upregulated in response to the bacteria treatments are from the Glutamate and Adhesion families.

Rows were clustered by similarity of expression patterns. The heatmap was plotted from VSD transformed counts generated by CEL-Seq and rescaled by row. See Appendix 4.3 for details. (B) Expression patterns of the same GPCRs (rows) in choanocytes, pinacocytes and archaeocytes (columns). GPCR expression is variable across the three cell-types and there was no clear trend correlating GPCR family with cell-type. Rows were clustered by similarity of expression patterns. The heatmap was plotted from VSD transformed counts generated by CEL-Seq and are unscaled. See Appendix 4.4 for details.

d. Bactericidal effector genes I analysed the expression patterns of genes involved in microbicidal activity. This included genes encoding enzymes involved in reactive oxygen species (ROS) generation, which is central to the bactericidal respiratory burst following phagocytosis (Wientjes and Segal 1995), and genes encoding cytolytic proteins from the macrophage-expressed protein 1 (MPEG1) family (McCormack and Podack 2015).

Six genes involved in the generation of ROS were differentially expressed across the three treatment groups (Chapter three). I examined the expression patterns of these genes across the untreated transcriptome profiles of choanocytes, pinacocytes and archaeocytes. Three of these genes were more highly expressed in choanocytes and pinacocytes compared to archaeocytes (Table 4.1). This includes one gene encoding NADPH oxidase, a gene encoding neutrophil cytosol factor 2 that is part of NADPH oxidase complex, and a cytochrome b-245 light chain-like gene (Wientjes and Segal 1995). The other three, which comprise one dual oxidase gene and two genes encoding NADPH oxidase, were not differentially expressed across the three cell-types.

106 Chapter 4

Four genes encoding cytolytic effector proteins from the MPEG1 family were identified as upregulated at 8h post-feeding in sponges from the native bacteria and unhealthy-Amphimedon bacterial treatment groups (Chapter three). All four of these genes were upregulated are upregulated in choanocytes compared to the other two cell-types (Table 4.1).

e. TGF-β pathway In Chapter three, I reported that genes involved in the immunosuppressive transforming growth factor-beta (TGF-β) pathway were upregulated only in response to the native bacteria and unhealthy- Amphimedon bacterial treatments. I examined the expression patterns of the TGF-β pathway genes that were identified as differentially expressed in Chapter three across the untreated transcriptome profiles of choanocytes, pinacocytes and archaeocytes. Genes encoding the TGF-β ligand and its putative TGF-β receptor were most highly expressed in pinacocytes (Table 4.1). Genes encoding SMADs, the downstream signalling effectors activated by TGF-β (Derynck and Zhang 2003), were not differentially expressed across the three cell types (Table 4.1,). Finally, a gene encoding the E3 ubiquitin ligase protein Smurf, a negative regulator of TGF-β signalling (Izzi and Attisano 2006), was more highly expressed in pinacocytes compared to the other two cell-types (Table 4.1).

f. TNF and MAP kinase pathways Genes involved in Tumor Necrosis Factor (TNF) and Mitogen-Activated Protein (MAP) kinase signalling were upregulated in response to all three bacterial treatments (Chapter three). I examined the expression patterns of these genes across the untreated transcriptome profiles of choanocytes, pinacocytes and archaeocytes. The gene encoding a TNF ligand was most highly expressed in choanocytes (Table 4.1). However, its putative binding target – the TNF receptor – was more highly expressed in archaeocytes and pinacocytes (Table 4.1). In addition, five genes from the Metalloproteinase-disintegrins (ADAMs) family of metalloproteases, which could potentially cleave and activate the TNF ligands (Mezyk-Kopec, et al. 2009; McMahan, et al. 2013), were also more highly expressed in choanocytes (Table 4.1). Five genes encoding signalling scaffold proteins from the TNF receptor-associated factor (TRAF) family, which were downregulated across the three treatment groups in the feeding experiment, were more highly expressed in the choanocytes (Table 4.1). On the other hand, the two genes encoding TRAFs that were upregulated in all three treatment groups were both constitutively upregulated in pinacocytes relative

107 Deciphering the genomic toolkit underlying animal-bacteria interactions to archaeocytes (Table 4.1). Additionally, two genes encoding proteins activated by TNF signalling, TNFAIP3-interacting protein 1 and TNF alpha-induced protein 8 (Coornaert, et al. 2009; Lou and Liu 2011), were more highly expressed in choanocytes than in the other two cell-types (Table 4.1).

The MAP kinases p38 and Jnk, both of which can be activated by TNF signalling, were upregulated in the feeding experiment (Ono and Han 2000). These two critical MAP kinases were more highly expressed in choanocytes than the other two cell-types (Table 4.1). The gene encoding p38 was also more highly expressed in pinacocytes than archaeocytes. Additionally, a gene encoding DUSP1, a key regulator of MAP kinase signalling (Lang, et al. 2006), is upregulated in both choanocytes and pinacocytes compared to archaeocytes (Table 4.1).

g. Transcription factors In Chapter three, I reported that several genes encoding transcription factors that regulate the stress response, immunity, and metabolism were differentially expressed in response to the bacterial treatments. The bZIP transcription factors Jun and Fos, which comprise the AP-1 complex activated by MAP kinase signalling cascades (Shaulian and Karin 2002), were upregulated in response to all three bacterial treatments (Chapter three). Both of these were more highly expressed in archaeocytes relative to choanocytes (Table 4.1).

The transcription factors NF-κB, signal transducer and activator of transcription (STAT), and interferon regulatory factor (IRF) are key regulators of the metazoan innate immune response (Kwon, et al. 2009; Pugazhenthi, et al. 2013; Zaslavsky, et al. 2013). Genes encoding these transcription factors were upregulated only in response to the native bacteria and unhealthy-Amphimedon treatments (Chapter three). NF-κB was not differentially expressed across the three cell-types. However, STAT was more highly expressed in pinacocytes and archaeocytes, while IRF is upregulated in choanocytes and pinacocytes compared to archaeocytes (Table 4.1).

Forkhead O transcription factors regulate diverse biological processes including immunity, metabolism, and stress (Eijkelenboom and Burgering 2013). A gene encoding FoxO was also upregulated

108 Chapter 4 only in response to native bacteria and unhealthy-Amphimedon treatments (Chapter three). This FoxO gene was more highly expressed in choanocytes than the other two cell-types (Table 4.1).

4.4.4 The Nrf2 oxidative/xenobiotic stress response pathway is activated only in response to foreign sponge bacteria at 2 and 8 hours post-exposure In Chapter three, I reported that amongst the transcription factors upregulated in response to the foreign bacteria are two genes encoding the bZIP transcription factor Nrf2. Both Nrf2 genes were also upregulated in response to native bacteria at 2h post-feeding. Nrf2 is a critical regulator of the response to a wide variety of cellular stress, and the transcriptional targets of Nrf2 are well characterised and summarised in the literature (Hayes and McMahon 2009; Hayes and Dinkova-Kostova 2014; Lacher, et al. 2015). I hypothesised that the slower processing of foreign sponge bacteria was related to cellular stress, and therefore examined the transcriptome data generated in Chapter three to identify differentially expressed Nrf2 target genes.

I found that an extensive suite of known Nrf2 target genes was upregulated only in response to the foreign bacteria at 2h and 8h post feeding (Fig. 4.6, Appendix 4.6). These Nrf2-target genes can be categorised according to their various biochemical roles (Fig. 4.6, Appendix 4.6). Antioxidants genes included a thioredoxin, a thioredoxin reductase, and a glutamate-cysteine ligase regulatory subunit (Hayes and McMahon 2009; Hayes and Dinkova-Kostova 2014). Two prostaglandin reductase genes involved in phase 1 drug reduction were also upregulated only in response to the foreign sponge bacteria (Hayes and McMahon 2009; Hayes and Dinkova-Kostova 2014). Genes involved in phase 2 drug conjugation included five glutathione S-transferase (GST) genes and one gamma-glutamyltranspeptidase gene (Shen and Kong 2009; Hayes and Dinkova-Kostova 2014). A suite of 11 genes from the ATP- binding cassette (ABC) family of transmembrane transporters involved in phase 3 drug expulsion from the cell were also upregulated only in response to the foreign sponge bacteria (Shen and Kong 2009; Hayes and Dinkova-Kostova 2014). A gene encoding proteasome activator complex subunit 4 and a gene encoding P62/sequestosome 1, both of which are involved in proteasomal degradation, were also upregulated in response to only the foreign sponge bacteria (Komatsu, et al. 2010; Lacher, et al. 2015). Finally, six genes encoding members of the Pirin family of transcription factors, as well as the

109 Deciphering the genomic toolkit underlying animal-bacteria interactions two Nrf2 transcription factors themselves, are all targets of Nrf2 (Hayes and McMahon 2009; Liu, et al. 2013; Brzoska, et al. 2014).

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GCLM 4 Thioredoxin Antioxidant Thioredoxin reductase 3 2 OSGIN Apoptosis OSGIN 0 Prostaglandin reductase Phase I drug reduction Prostaglandin reductase −2 GST GST kappa 1 −4 GST GST Phase II drug conjugation GST GST omega−1 GGT1 ABC transporter ABC transporter ABC transporter ABC transporter ABC transporter Phase III drug transport ABC transporter ABC transporter ABC transporter ABC transporter ABC transporter ABC transporter P62/Sequestosome−1 Proteasomal degradation PSME4 Pirin Pirin Pirin Pirin Pirin Transcription factors Pirin Nrf2a Nrf2b

Figure 4.6 Expression patterns of putative Nrf2 target genes

Expression patterns of putative Nrf2-target genes (rows) differentially expressed across all samples (columns) in response to the three bacteria treatments at 0h, 2h and 8h post-exposure. The Nrf2-target genes were upregu- lated in response to only the foreign sponge bacteria at both 2h and 8h post-feeding. Red indicates high levels of expression while blue indicates low levels of expression. Rows were clustered by similarity of expression patterns. The heatmap was plotted from VSD transformed counts generated by CEL-Seq and rescaled by row.

The labels to the right of the heatmap contain the sequence descriptions of the genes in each row as well as their biochemical functions. See Appendix 4.5 for details.

110 Chapter 4

4.5 DISCUSSION The upregulation of genes involved in the TGF-β signalling pathway and other conserved regulators of metazoan innate immunity in response to the A. queenslandica symbiont bacteria but not the foreign sponge bacteria suggest that A. queenslandica is able to distinguish its symbiont bacteria from foreign sponge bacteria (Chapter 3). In this Chapter, I build on this gene expression data by investigating how the same three treatments – the foreign bacteria from a different sponge species, native bacteria from a healthy conspecific, and bacteria from an unhealthy conspecific, are processed at the cellular level. I also carried out further analyses of the genes that were differentially expressed in response to these three treatments in Chapter three. Specifically, to better understand their roles in a cellular context, I identified a subset of these genes and analysed their expression patterns across the transcriptome profiles of three cell-types that are potentially involved in mediating ingestion and subsequent processing of bacteria in A. queenslandica; these are choanocytes, pinacocytes and archaeocytes.

4.5.1 The differential processing of foreign sponge bacteria and the A. queenslandica-derived bacterial treatments at 2h post-feeding is not likely to be due to size In previous feeding experiments performed on other demosponges, Wehrl et al. (2007) and Wilkinson et al. (1984), found that sponge symbiont bacteria were removed from the water column in lower numbers than specific “food” bacteria isolates as well as free-living seawater bacteria. The authors thus concluded that the bacteria symbionts might be ignored by or invisible to their host (Wilkinson, et al. 1984; Wehrl, et al. 2007). The quantification of bacteria removed from the water was not within the scope of the present study. However, the A. queenslandica juveniles clearly did not ignore the native bacterial treatment, as they were ingested by choanocytes, and then passed on to amoeboid cells and archaeocytes for further processing (Fig. 4.7A). Furthermore, I observed that the both the native and unhealthy-Amphimedon bacterial treatments were acquired by archaeocytes at an earlier stage than the foreign sponge bacteria (Fig. 4.7A). This suggests that A. queenslandica is able to discriminate native bacteria from non-symbiont bacteria.

While I did not measure, and thus cannot rule out, size as a factor contributing to the more efficient processing of the symbiont bacteria, several lines of evidence suggest that this is unlikely to be the case. First, multiple studies have found that selective uptake of certain groups of bacteria picoplankton

111 Deciphering the genomic toolkit underlying animal-bacteria interactions by sponges is a size-independent process (Yahel, et al. 2006; Hanson, et al. 2009; McMurray, et al. 2016). Second, Maldonado et al. (2010) found that the demosponge H. perlevis, was able to digest Escherichia coli more efficiently thanVibrio anguillarum, despite the two species being similar in size, suggesting that digestion efficiency in sponges also is influenced by size-independent factors. Finally, only the native and unhealthy-Amphimedon bacteria, but not the foreign sponge bacteria, elicited the upregulation of components of the TGF-β signalling pathway, which is a highly conserved promoter of symbiont tolerance in animals (Zeuthen, et al. 2008; Detournay, et al. 2012). These A. queenslandica- derived bacterial treatments also induced the upregulation of a greater number of putative pattern recognition receptors from the SRCR family than the foreign sponge bacteria. These transcriptional responses suggest that A. queenslandica is indeed not blind to its symbionts, but rather recognises them and thus responds differently to symbiont bacteria than foreign sponge bacteria. These lines of evidence therefore suggests that size-independent factors are responsible for the differences in the way the treatments were processed.

There is an important point of difference between the current study and previously published studies investigating the uptake of symbiont and non-symbiont bacteria that is worth noting (Wilkinson, et al. 1984; Wehrl, et al. 2007). Wehrl et al. (2007) and Wilkinson et al. (1984) compared the uptake of symbiont bacteria to seawater bacterial consortia or isolates, while in the present study, I instead used the bacterial community associated with a different sponge species as the non-symbiont treatment. A. queenslandica is constantly exposed to seawater bacteria throughout its lifecycle, even in early development. In contrast, the symbiont bacteria of R. globostellata are very specific to this sponge species (Steinert, et al. 2016), and therefore unlikely to have ever been previously encountered by the A. queenslandica juveniles. It is possible that the R. globostellata foreign bacteria are so foreign to A. queenslandica that they were not initially recognised as food, while on the other hand, the A. queenslandica symbionts were quickly recognised by the sponge as a source of nutrition, which is consistent with their efficient processing.

It is thus interesting that the findings of the present study contradict the findings of Wilkinson et al. (1984), who previously reported that the ingestion of symbiont bacteria by choanocytes and pinacocytes rarely occurs. It has been hypothesised that when symbiont population density is high,

112 Chapter 4 the host could begin to treat symbiont bacteria as food in order to control the symbiont population size (Wilkinson 1992; Webster and Thomas 2016). It is possible that the high density of bacteria delivered in the treatments could have been interpreted by the sponge as an increase in the size of the symbiont population, which resulted in their consumption as a form of population control. The capacity to sense bacterial population densities has previously been documented in other organisms. At high densities, Vibrio fischeri, the bacterial symbionts of the Hawaiian bobtail squid Euprymna scolopes, produce bioluminescence through quorum sensing pathways (Verma and Miyashiro 2013). The squid host uses this bioluminescence in a form of camouflage known as counter-illumination, and is thought to be able to perceive this light production, and thus symbiont density, through the extra-ocular photoreceptors in the light organ that houses the symbionts (Tong, et al. 2009). Acyl-homoserine lactones (AHLs) are chemical autoinducers produced by gram negative bacteria during density-dependent quorum sensing (Williams 2007). AHLs have been found to modulate the mammalian immune response and influence the settlement of green macro-algal Ulva intestinalis zoospores, however, the mechanisms underlying the detection of these molecules remain elusive (Sanchez-Contreras, et al. 2007; Williams 2007; Hughes and Sperandio 2008).

Alternatively, the ingestion of symbiont bacteria by the A. queenslandica juveniles could be a response related to the condition of the host. Phagocytes isolated from the E. scolopes host that had been cured of its V. fischeri symbionts were found to bind to five times more V. fischeri cells than the phagocytes isolated from normal uncured hosts (Nyholm, et al. 2009). Moreover, the Nyholm et al. (2009) found that once binding occurred, phagocytosis of symbionts proceeded with the same efficiency as phagocytosis of non-symbiont bacteria. During metamorphosis shortly after settlement, the microbial community of A. queenslandica post-larvae undergoes a drastic shift in community structure characterised by a significant reduction in the abundance of the primary symbiont taxa (Fieth et al. in review). I did not investigate the composition of the bacteria community in the juveniles that were the subjects of this experiment. However, the juvenile sponges used in this experiment are only two days older than the post-larval stage in which the bacterial community shift was observed by Fieth et al. (in review). It is interesting to speculate that the sponge host’s response to being fed symbiont bacteria could be modulated the status of its own symbiotic bacterial community. Therefore, it is noteworthy that bacterial particles matching the morphology of the A. queenslandica symbiont bacteria

113 Deciphering the genomic toolkit underlying animal-bacteria interactions are frequently observed in phagosomes of host cells in the mesohyl of post-larval and juvenile sponges but not in adults (Fieth et al., in review). The phagocytosis of bacteria by host cells in the mesohyl has also previously been observed in other sponges (Vacelet and Donadey 1977). This suggests that under the right conditions, A. queenslandica could treat its symbiont bacteria as food.

4.5.2 The slower acquisition of foreign sponge bacteria by archaeocytes correlates with the activation of the Nrf2 xenobiotic response pathway An alternate, but non-mutually exclusive, possibility is that the foreign sponge bacteria are genuinely more unpalatable than the A. queenslandica-derived bacterial treatments. The bacterial community associated with R. globostellata – the source of the foreign sponge bacteria – is very different in nature to the free-living seawater bacteria consortia (Steinert, et al. 2016), as well as to the seawater isolates tested in previous studies (Wilkinson, et al. 1984; Wehrl, et al. 2007). It is thought that symbiont bacteria are responsible for the production of a huge component of the bioactive metabolites in sponges (Mehbub, et al. 2014). These allelochemicals play an important role in allowing benthic sponges to outcompete other invertebrates for space in the marine environment (Pawlik 2011). Hence, the foreign sponge bacteria could be producing an honest signal, in the form of toxic metabolites, of their ability to cooperate, which could be causing cellular stress and thus making the foreign sponge bacteria more difficult to proces (Hillman and Goodrich-Blair 2016). In fact,R. globostellata is a species renowned for producing many bioactive secondary metabolites, many of which have been shown to possess cytotoxic activity (Bourguet-Kondracki, et al. 2000; Tasdemir, et al. 2002; Fouad, et al. 2006).

The transcription factor, Nrf2, is a highly conserved mediator of the cytoprotective response to exogenous (xenobiotics) and endogenous (reactive oxygen species) stressors (Ma 2013). In conditions causing oxidative or xenobiotic stress, the Nrf2 pathway is activated, culminating in the translocation of the Nrf2 transcription factor from the cytosol into the nucleus, where it activates a suite of genes with a variety of biochemical roles (Hayes and McMahon 2009; Hayes and Dinkova-Kostova 2014). I acknowledge that at least one sponge Nrf2 orthologue was also upregulated in response to both the native bacteria and unhealthy-Amphimedon bacteria. However, this might be explained by the need to ameliorate the effects of an increase in host-generated reactive oxygen species (ROS) associated with the microbicidal respiratory burst (Davidson, et al. 2013; Dias, et al. 2013). Indeed, genes encoding

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NADPH oxidases, the main ROS generating enzymes, were upregulated in response to all three bacterial treatments, which further suggests that oxidative stress is not likely the main reason why the suite of Nrf2 target genes was upregulated only in response to the foreign sponge bacteria. A clue to the nature of the stressor that induced the Nrf2 response might then lie in the large proportion of upregulated Nrf2 target genes encoding drug conjugating GSTs and drug efflux ABC transporters, compared to the relative fewer antioxidant encoding genes (Fig. 4.6). These expression patterns are more consistent with a response to exogenous xenobiotics, and thus I hypothesise that the delayed processing of the foreign sponge bacteria could be associated with cellular stress caused by toxic metabolites produced by the bacteria that normally reside within R. globostellata.

Interestingly, many intracellular and extracellular bacteria pathogens are known to produce toxins that cause DNA or protein damage, or interfere with normal cellular processes like protein translation (Troemel 2012). It is thus noteworthy that SKN-1, the Caenorhabditis elegans orthologue of Nrf2, can be activated by inhibition of both translation initiation and elongation (Wang, et al. 2010; Li, et al. 2011; Dunbar, et al. 2012). Furthermore, SKN-1/Nrf2 is involved in protecting C. elegans from the effects of aPseudomonas aeruginosa toxin that inhibits host protein translation (McEwan, et al. 2012). Just like C. elegans, A. queenslandica is constantly bathed in bacteria, and it is thus unlikely to need to mount an energetically expensive immune response to all the bacteria it encounters, particularly given that both these animals feed on bacteria. Therefore, it is interesting to speculate that, like C. elegans, A. queenslandica could utilise a form of host defence termed “surveillance immunity” that is triggered by the disruption of normal cell functions (Vance, et al. 2009; Fontana, et al. 2011; Troemel 2012). A strategy like this could allow A. queenslandica to distinguish truly harmful bacteria from innocuous food.

4.5.3 Bacteria digestion is a multistep process involving communication amongst three cell types Choanocytes play a vital role in trapping and ingesting bacteria (Frost 1981; Willenz and Van de Vyver 1984; Mah, et al. 2014). Choanocytes are also involved in intracellular digestion of trapped particles, as evidenced by the breakdown of ingested E. coli cells in the choanocytes of H. perlevis (Maldonado, et al. 2010). This leads me to propose that non-specific bactericidal activity occurs in choanocytes, perhaps mediated by the genes encoding the cytolytic proteins MPEG and ROS generating

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Native bacteria and A unhealthy-Amphimedon bacteria All treatments All treatments

Archaeocyte

Pinacocyte-like amoeboid cell Foreign sponge bacteria

Choanocyte

Mesohyl Choanocyte chamber

30min - 1h 2h 8h

B

Choanocyte Choanocyte-associated amoeboid cell

SRCR TNF TNFR SRCR ligand SRCR SRCR

SRCR TRAF SRCR Archaeocyte

TNF DUSP1 P38 Bactericidal ligand effectors Lysosomal enzymes ADAM Fos TNFR

STAT

SRCR SRCR SRCR TRAF IRF DUSP1 TRAF

JNK P38

SRCR

SRCR SRCR

SMURF FoxO IRF GPCR

STAT TGF-βR Fos GPCR Jun

GPCR

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Figure 4.7 Time course of events following the ingestion and processing of the bacterial treatments by choanocytes, amoeboid cells and archaeocytes, and the expression patterns of genes with roles in immunity across the three cell-types (A) By 30 minutes post-feeding, the bacteria (depicted in green) from all three treatments were present in individual choanocytes and in anucleolate, choanocyte-associated amoeboid cells located adjacent to the choanocyte chambers. Bacteria from all three treatments were observed in these two cell-types throughout the entire experiment from 30 minutes to 8 hours post-feeding. By 2 hours post-feeding, bacteria enriched from the healthy and unhealthy conspecifics were observed in nucleolate archaeocytes. In contrast, foreign bacteria were only rarely observed in archaeocytes at this same time point. By 8 hours post-feeding, bacteria from all three treatments were frequently observed in archaeocytes. (B) Schematic depiction of the expression patterns of genes involved in immunity and digestion across the choanocyte, choanocyte-associated amoeboid cell, and archaeocyte. Genes with dual roles in immunity and digestion are upregulated in choanocytes relative to the other two cell-types. enzymes, all of which are constitutively more highly expressed in choanocytes compared to archaeocytes and pinacocytes. The upregulation of lysosomal enzymes in the choanocytes compared to the other two cell-types is also consistent with choanocytes having an important role in digestion. This is also supported by a previous studies that have documented the presence of lysosomes and digestive enzyme activity in choanocytes (Agrell 1952; Willenz and Van de Vyver 1984).

In the current study, the amount of time the ingested bacteria particles spent in choanocytes was likely to have been very brief, as suggested by the accumulation of dense aggregates of ingested bacteria particles in the adjacent amoeboid cells at the earliest time point investigated (30 minutes post- feeding; Fig. 4.7A). This leads me to hypothesise that swift bacterial killing and enzymatic digestion involving lysosomal enzymes occurs in choanocytes before the cargo passed is on to the neighbouring amoeboid cells. While I did not observe any transfer of ingested bacteria between cell-types, I did observe filopodial bridges connecting the two cell-types present (Fig. 4.2J, L). The transfer of ingested bacteria from choanocytes to other cell-types for further processing has previously been documented in demosponges (Schmidt 1970; Imsiecke 1993; Maldonado, et al. 2010). In the freshwater demosponge, lacustris, choanocytes were observed transferring phagocytosed to adjacent archaeocytes as early as 45 minutes post-feeding (Imsiecke 1993). Maldonado et al. (2010) similarly observed the

117 Deciphering the genomic toolkit underlying animal-bacteria interactions transfer of digested bacteria from choanocytes to what the authors described as amoebocytes. The nomenclature of these cell-types is potentially quite confusing. In both these previous studies the cell-types receiving bacteria from choanocytes were what I have described as archaeocytes in the present study, as evidenced by the presence of large nuclei containing prominent nucleoli (Imsiecke 1993; Maldonado, et al. 2010). The bacteria-containing amoeboid cells observed adjacent to the choanocyte chambers in the present study are clearly distinguishable from these archaeocytes by the smaller size of their nuclei and absence of nucleoli, and I therefore conclude that the A. queenslandica amoeboid cells are a distinct cell-type (Fig. 4.7A). Furthermore, the morphology and distribution of these amoeboid cells within A. queenslandica is reminiscent of pinacocytes (R. Fieth and D. Stoupin, unpublished data). While it remains to be determined if the choanocyte-associated amoeboid cells are actual pinacocytes, the accumulation of bacteria particles in this cell-type shortly after the treatments were presented certainly indicates they participate in digestion. These choanocyte-associated amoeboid cells of A. queenslandica thus appear functionally analogous to archaeocytes that receive bacteria from choanocytes in other demosponges. Indeed, the upregulation of ROS-generating enzymes in the pinacocytes of A. queenslandica suggests they have bactericidal capacity. However, it must be noted that ROS are also involved in normal physiological signalling processes (Finkel 2011).

The third step in A. queenslandica bacteria processing involves actual nucleolate archaeocytes (Fig. 4.7A). While I did not observe the transfer of digested bacteria cargo from amoeboid cell to archaeocytes, the direct transfer of digested food between different amoeboid cell-types post-choanocyte uptake has been observed in S. lacustris (Saller 1989; Imsiecke 1993). It is also noteworthy that the release of bacteria into the mesohyl is very rarely documented (Maldonado, et al. 2010). In the absence of a circulatory system, mobile archaeocytes appear to be responsible for nutrient translocation between cells within the mesohyl of demosponges (Kilian 1952; Imsiecke 1993; Maldonado, et al. 2012). These observations indicate that there is some diversity in the way sponges process their food, even within the same subclass (). This also highlights that cellular processes underlying bacteria digestion in the sponge can be quite complex, requiring communication and coordination amongst multiple different cell-types.

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Sponges lack a nervous system and thus rely primarily on the transmission of chemical signals to facilitate intercellular communication (Ellwanger, et al. 2007). Nonetheless, several components of the bilaterian nervous system are conserved in sponges (Srivastava, et al. 2010). For example, the GPCRs from the adhesion and glutamate families are known to have neurobiological functions (Bockaert, et al. 2010; Silva, et al. 2011; Boucard, et al. 2012). The majority of the GPCRs that were differentially expressed in response to the bacterial treatments were from the glutamate family. Metabotropic and GABAB receptors from the glutamate family are well known for modulating synaptic transmission and neuronal excitation (Pinheiro and Mulle 2008; Niswender and Conn 2010). The signalling system based upon the use of these GPCRs relies on the chemical messengers, glutamate, glutamine and GABA, all of which have been detected in the demosponge E. mulleri (Elliott and Leys 2010). It is tempting to speculate sponge feeding behaviour involves cell-cell communication mediated through a signalling system involving the glutamate family GPCRs and these chemical messengers. Studies in recent years have also begun to reveal the role of adhesion GPCRs in the nervous system (Silva, et al. 2011; Boucard, et al. 2012; O’Sullivan, et al. 2014). In particular, the adhesion subfamily I latrophilins are involved in the development of the synapse and neurotransmission (Silva, et al. 2011; Boucard, et al. 2012; O’Sullivan, et al. 2014). The adhesion family GPCRs have undergone a large expansion in A. queenslandica, yet scarcely anything is known about their roles in sponges (Krishnan, et al. 2014). However, none of the adhesion GPCRs differentially expressed in the current study was reported to cluster with the vertebrate subfamily I GPCRs in a recent phylogenetic study by Krishnan et al. (2014). Moreover, despite having BLAST hits for latrophilins, the domain structures of all the A. queenslandica adhesion GPCRs – characterised by Krishnan et al. (2014) – are quite distinct from the domain structures of human subfamily I latrophilins (Hamann, et al. 2015). Therefore, future research will be required to explore the functions of these A. queenslandica adhesion GPCRs.

Nonetheless, the differential expression of glutamate and adhesion GPCRs, in combination with the complex cellular processes involved in sponge feeding thus lead me to hypothesise that a chemical signalling system relying on glutamate and adhesion family GPCRs mediates intercellular communication and coordination in sponge feeding behaviour.

119 Deciphering the genomic toolkit underlying animal-bacteria interactions

Another form of intercellular communication, typically deployed in the context of the mammalian immune response, is paracrine signalling through the activity of cytokines (Lacy and Stow 2011; Thurley, et al. 2015). Cytokines like TNF and TGF-β trigger behavioural changes or differentiation of nearby cell- types through paracrine signalling in mammals (Derynck and Akhurst 2007; Caldwell, et al. 2014), and the upregulation of these cytokines in my experiments suggests they too could participate in coordinating the sponge cellular response to bacteria. TNF in particular is upregulated in choanocytes compared to the other two cells types, and it is possible that choanocytes release TNF ligands to communicate with other cell-types in the vicinity of choanocyte chambers. For TNF to successfully communicate its signal to another cell, the receiving cell has to have the appropriate receptors deployed on the cell surface to receive the signal. The higher levels of TNFR expression in pinacocytes and archaeocytes compared to choanocytes suggests that these cells are competent to receive the TNF signals (Table 4.1). Furthermore, the upregulation of genes that lie downstream of TNF signalling, like TRAFs and the MAP kinase p38 in pinacocytes (Ono and Han 2000; Aggarwal 2003), indicates that pinacocytes are expressing the genes necessary to transduce the TNF signal after it is bound to TNFR. Pinacocytes are found lining canals of the entire aquiferous system in sponges (Ereskovsky and Dondua 2006; Ereskovsky 2010), and are generally present in the vicinity of choanocyte chambers (Imsiecke 1993). However, it is unknown whether the amoeboid cells involved in digesting bacteria are identical to the pinacocyte cell-type in the cell-specific transcriptome data set. Nevertheless, the upregulation of the gene encoding the TNF ligand in choanocytes and genes encoding downstream components of TNF signalling in pinacocytes, suggest that choanocytes may communicate with the different cell-types in their vicinity through TNF signalling.

4.5.4 Choanocytes have dual roles in immunity and digestion In addition to generating water currents through the aquiferous system, choanocytes function to trap and ingest bacteria from the water column. Choanocytes are, therefore, also likely to encounter potentially pathogenic organisms. Multiple genes involved in the innate response are upregulated in choanocytes compared to archaeocytes and pinacocytes. This includes genes encoding putative pattern recognition receptors from the SRCR family, MAP kinases p38 and Jnk, the transcription factor IRF, the TNF cytokine, and the TRAF adaptor signalling molecules. The upregulation of these genes in choanocytes suggests that, in addition to digestion, choanocytes also have the capacity to generate an

120 Chapter 4 immune response (Fig. 4.7B). In fact, phagocytosis is not just an essential step in sponge digestion but is also intrinsic to cellular immunity (Gordon 2016). Moreover, the lysosome and phagosomes implicated in breaking down bacteria for digestion in sponge cells are also critical microbicidal and degradative cellular compartments necessary for clearing internalised pathogens in professional phagocytic immune cells of vertebrates (Sarantis and Grinstein 2012). Hence, in the absence of specialised immune cells, phagocytosis by choanocytes is potentially the first step of both digestion and immune responses in sponges.

Interestingly, several genes with dual roles in metabolism and immunity were not only upregulated in juveniles in response to the bacterial treatments, but were also upregulated in choanocytes compared to archaeocytes and pinacocytes (Fig. 4.7B). The FoxO transcription factors are important regulators of metabolism in both invertebrate and vertebrate animals (Lee, et al. 2003; Baker and Thummel 2007; Eijkelenboom and Burgering 2013). In addition to regulating metabolism, FoxO is also a highly conserved regulator of the immune response in animals including cnidarians, ecydysozoans and mammals (Becker, et al. 2010; Bridge, et al. 2010). DAF-16, the C. elegans orthologue of FoxO, confers resistance to fungal pathogens and is also activated in response to oxidative stress and epidermal wounding (Zou, et al. 2013). FoxO activity also has a more direct effect on the immune response through the regulation of antimicrobial peptide expression, a role that is conserved from the basal metazoan Hydra to more morphologically complex animals such as Drosophila and humans (Becker, et al. 2010; Bridge, et al. 2010). Given the critical role of choanocytes in sponge digestion, the upregulation of an A. queenslandica FoxO orthologue in this cell-type suggests that FoxO regulation of metabolism is conserved in the sponge. Interestingly, the stress-activated immune signalling genes, Jnk and p38 MAP kinases, regulate FoxO activity through direct phosphorylation (Eijkelenboom and Burgering 2013). The co-expression of these signalling genes with FoxO in choanocytes suggests that the dual roles of FoxO regulating the immune response and metabolism could be conserved in the sponge (Fig. 4.7B).

Signalling mediated by the proinflammatory cytokine, TNF, is an instrumental regulator of the immune response in both Drosophila and humans (Schneider, et al. 2007; Mabery and Schneider 2010; Wiens and Glenney 2011; Cabal-Hierro and Lazo 2012). In addition to its role in immunity, TNF is also an important regulator of metabolism. Mammalian TNFs are expressed in adipocytes and

121 Deciphering the genomic toolkit underlying animal-bacteria interactions regulate lipid metabolism (Romacho, et al. 2014). The importance of TNF regulation of metabolism is emphasised by studies showing that TNFs are critical mediators of insulin-resistance associated with obesity in mammals (Ruan and Lodish 2003; Hotamisligil 2006). Interestingly, these dual roles in metabolism and immunity appear to be conserved across animals. The Drosophila TNF ligand, eiger, plays a role in immunity against extracellular pathogens (Schneider, et al. 2007). In addition to this, eiger, which is expressed in the fat body of Drosophila, the organ responsible for storing nutrients and monitoring nutrient levels, also acts as a metabolic hormone by targeting insulin-producing cells to mediate adaptation to nutrient stress (Agrawal, et al. 2016). Knocking down eiger expression in the Drosophila fat body affects the generation of a robust immune response as well as alters feeding rates (Mabery and Schneider 2010). Therefore the expression of a gene encoding the TNF ligand in sponge choanocytes (Fig. 4.7B), as well as the upregulation of the TNF receptor after sponges were fed both symbiont and foreign bacteria, leads me to hypothesise that TNF has an evolutionarily conserved role in regulating the interplay between metabolism and immunity in A. queenslandica.

These findings are worth further consideration in view of a recent hypothesis forwarded by Broderick (2015) that origin of immunity and digestion share a common origin. Unicellular eukaryotes rely on phagocytosis as an ancient means of predating upon other microorganisms in order to obtain nutrition (Desjardins, et al. 2005; Cosson and Lima 2014). Although they are metazoans, sponges lack a true gut and have thus retained phagocytosis as the primary mode of obtaining nutrition. The expression of immune genes as well genes with dual roles in metabolism and immunity in choanocytes, the primary feeding cells of the sponge, could thus reflect an ancient common origin for both digestion and immunity (Fig. 4.7B). Future studies investigating sponge-bacteria interactions will undoubtedly provide new insight into the origin and evolution of these two core physiological processes in animals.

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Table 4.1 Expression patterns of Amphimedon queenslandica genes involved in immunity across choanocytes, pinacocytes, and archaeocytes. Model Description Choanocyte Pinacocyte Archaeocyte None Comment

Bactericidal effectors Macrophage-expressed gene 1 Aqu2.1.20229_001 protein ● Macrophage-expressed gene 1 Aqu2.1.20230_001 protein ● Aqu2.1.24563_001 Macrophage-expressed gene 1 ● Macrophage-expressed gene 1 Aqu2.1.24564_001 protein ● Aqu2.1.35277_001 NADPH oxidase ● ROS generation Aqu2.1.18503_001 Neutrophil cytosol factor 2 ● ● ROS generation Cytochrome b-245 light chain- ROS generation Aqu2.1.28940_001 like ● ● Aqu2.1.39906_001 Dual oxidase ● Aqu2.1.17532_001 NADPH oxidase ● Aqu2.1.15528_001 NADPH oxidase ● TGF-β signalling Transforming growth factor Aqu2.1.33667_001 ● ligand beta-3 Aqu2.1.41568_001 TGF-β receptor type-1 ● receptor Mothers against Aqu2.1.41768_001 decapentaplegic homolog ● signaling (SMAD) 1

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Mothers against Aqu2.1.38665_001 decapentaplegic homolog ● signaling (SMAD) 3 TGF-β -activated kinase 1 and TLR and TGF-β Aqu2.1.43317_001 MAP3k7-binding protein 1 ● signalling TGF-β -activated kinase 1 and TLR and TGF-β Aqu2.1.30895_001 MAP3k7-binding protein 1 ● signalling Nedd4-like e3 ubiquitin-protein negative Aqu2.1.18075_001 ● ligase Wwp1 regulator E3 ubiquitin-protein ligase negative Aqu2.1.43338_001 ● Smurf regulator Positive Aqu2.1.33752_001 E3 ubiquitin-protein ligase Cbl ● regulator TNF signalling Tumor necrosis factor (TNF) Aqu2.1.42149_001 ● ligand TNF receptor superfamily Aqu2.1.23777_001 ● ● member Aqu2.1.34934_001 TNFAIP3-interacting protein 1 ● ● Aqu2.1.21263_001 TNF alpha-induced protein 8 ● TRAF upregulated in Chapter three Aqu2.1.41092_001 Tnf receptor-associated factor 3 ● ● Tnf receptor-associated factor Aqu2.1.41960_001 ● 4-like

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TRAF downregulated in Chapter three Tnf receptor-associated factor Aqu2.1.23780_001 ● 4-like Tnf receptor-associated factor Aqu2.1.23792_001 ● 4-like Tnf receptor-associated factor Aqu2.1.32521_001 ● 4-like Tnf receptor-associated factor Aqu2.1.35164_001 ● 4-like Aqu2.1.35201_001 Tnf receptor-associated factor 5 ● TNF cleavage Disintegrin and Aqu2.1.14221_001 metalloproteinase domain- ● ● containing protein 12-like Disintegrin and Aqu2.1.42575_001 metalloproteinase domain- ● containing protein 10-like Disintegrin and Aqu2.1.39254_001 metalloproteinase domain- ● containing protein 12-like Disintegrin and Aqu2.1.24521_001 metalloproteinase domain- ● containing protein 10-like

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Disintegrin and Aqu2.1.22788_001 metalloproteinase domain- ● containing protein 10 Map kinase Aqu2.1.33939_001 p38 ● ● Aqu2.1.39593_001 Jnk ● Dual specificity mitogen- Aqu2.1.38504_001 activated protein kinase kinase ● 4 Dual specificity protein Inhibits p38 and Aqu2.1.42015_001 ● ● phosphatase 1 Jnk Aqu2.1.41088_001 MAKAP5 ● Transcription factors Aqu2.1.21825_001 Jun ● Aqu2.1.20356_001 Fos ● ● Aqu2.1.31574_001 Interferon regulatory factor ● ● Aqu2.1.43775_001 NF-κB ● Aqu2.1.36465_001 STAT ● ● Aqu2.1.27411_001 FoxOa ●

Genes were categorised into functional groups based on functions (bactericidal effectors and transcription factors) or according to annotated KEGG pathways. The presence of a mark indicates that a gene is significantly upregulated in the particular cell-type in its respective column compared to at least one of the other two cell-types. Marks in the “None” column indicate that the gene was not differentially expressed amongst the three cell-types.

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127 128 Chapter 5 Chapter 5 - General Discussion

5.1 Overview of project objectives Bacteria are ubiquitous, and interactions between animals and bacteria are a vitally important aspect of the biology of all animals (Gilbert, et al. 2012; McFall-Ngai, et al. 2013). Having first emerged some 600 million years ago, sponges are one of the earliest-diverging of the extant animal phyletic lineages (Li, et al. 1998; Knoll 2003; Philippe, et al. 2009). Sponge are filter-feeders that pump large volumes of water to capture free living-bacteria, which they rely on as a direct source of nutrition (Bell 2008; Maldonado, et al. 2012). At the same time, sponges are well known for housing often dense and diverse species-specific bacterial communities (Hentschel, et al. 2012; Webster and Thomas 2016). It is thus intriguing how these morphologically simple animals are able to maintain stable symbiotic relationships with their microbiota, while simultaneously fighting pathogens and feeding on bacteria. All of which is achieved in the absence of specialised immune cells and adaptive immunity. In this thesis, I sought to explore the molecular mechanisms underlying the host’s role in regulating sponge- bacteria interactions, a line of investigation made possible through access to the near-complete draft genome of the marine demosponge Amphimedon queenslandica and the capacity to apply RNA-Seq technology to small amounts of biological material.

5.2 Large and diverse complements of pattern recognition receptors could mediate sponge- bacteria interactions The ancient innate immune system utilised by all animals is conventionally viewed as a relative simple form of non-specific defence without memory (Janeway and Medzhitov 2002). It relies on a finite set of germline-encoded pattern recognition receptors (PRRs) that enable the host to detect microbe associated molecular patterns (MAMPs) that are highly conserved across microorganisms but are not present in the host (Janeway and Medzhitov 2002; Medzhitov and Janeway Jr 2002). The adaptive immune system, on the other hand, is characterised by the capacity for de novo generation of diversity and specificity through somatic recombination (Guo, et al. 2009; Boehm 2012). This innovation, which has evolved twice only in vertebrates, is often considered the ultimate form of immunological complexity because it enables long-term memory of specific pathogens (Guo, et al. 2009; Boehm 2012). Traditionally considered primarily as a means of defence, it is thought that the

129 Deciphering the genomic toolkit underlying animal-bacteria interactions evolution of the adaptive immune system has been driven in part by the need to manage a complex microbiota (McFall-Ngai 2007). However, sponges and all other invertebrates contend with the same pressures from symbiont and pathogenic bacteria as vertebrates, but have been able to thrive for over 600 million years relying only on the innate immune system. Multiple studies in vertebrate and invertebrate animal lineages have now found that, in addition to providing protection against pathogens, the innate immune system also regulates interactions between animals and their beneficial symbiont bacteria (see reviews by Chu and Mazmanian, 2013; Nyholm and Graf, 2012).

The receptors of the innate immune system, with their distinctive structures that make homologs easily identifiable, are therefore prime candidates for investigation of their roles in enabling the sponge to discriminate symbiont from non-symbiont bacteria. In chapter two of this thesis, I comprehensively identified and characterised a large suite of genes encoding members of the NLR family of intracellular PRRs in A. queenslandica. Through their ability to sense a wide range of MAMPs, such as flagellin, peptidoglycan and RNA, NLRs in mammals play a critical role in the innate immune response to bacteria (Kaparakis, et al. 2007). In addition, it has been suggested that NLRs could play an important role in mediating interactions with the mammalian gut bacterial community (Robertson, et al. 2012; Robertson and Girardin 2013)(Robertson and Girardin 2013; Robertson, et al. 2012). There are at least 135 bona fide NLRs, defined by the presence of both a NACHT domain and ligand-binding LRRs, encoded in the A. queenslandica genome. In contrast, humans and mice only have 22 and 35 genes encoding NLRs, respectively (Proell, et al. 2008). The sequence diversity in the putative ligand-sensing region of the AqNLRs, and their structural similarity to their counterparts in other animals, suggest they play a role in immunity. I therefore hypothesise that the expansion of the NLR family in the A. queenslandica genome could provide the sponge with the immunological complexity to detect a highly diverse array of specific ligands.

Another suite of innate immune receptors that could play an important role in the recognition of bacterial ligands is the Scavenger-Receptor Cysteine-Rich (SRCR) domain-containing proteins. The A. queenslandica genome encodes an expanded complement of 1238 SRCR domains distributed across 300 genes, while the human genome only encodes 100 SRCR domains distributed across 16 genes (Buckley and Rast 2015; Ryu, et al. 2016). The size of the A. queenslandica SRCR and NLR

130 Chapter 5 repertoires is consistent with similar large expansions of these gene families in the genomes other invertebrate lineages, including the sea urchin and the (Chapter three, Buckley and Rast 2015). The multiplicity and diversity of both these gene families in the A. queenslandica genome compared to the genomes of mammals suggests that they may function quite different from the vertebrates PRRs. It is tempting to speculate that these large PRR complements could confer the sponge with the ability to detect a greater diversity of ligands, perhaps even with more specificity, than the vertebrate PRRs. Indeed, it has been hypothesised that, in the absence of an adaptive immune system, the expansion and diversification of PRR families could be important for providing invertebrates with the capacity to recognise symbiont-specific ligands to manage the microbiota (McFall-Ngai 2007).

5.3 Distinguishing native and foreign bacteria The presence of these large complements of NLRs and SRCR domain-containing genes suggests that the A. queenslandica genome encodes a powerful “tool-kit” that could potentially regulate interactions between sponges and bacteria. The next goal of my thesis was to investigate to what extent these PRRs are involved in mediating the discrimination of, and the response to, native bacteria and foreign sponge bacteria. To address this question, in chapter three, I presented actively-feeding A. queenslandica juveniles with bacteria enriched from three different sources: the foreign bacteria from a different sponge species (Rhabdastrella globostellata, Carter 1883), native bacteria from a healthy adult conspecific, and the altered bacterial community of an unhealthy adult conspecific. I then used an RNA-Seq approach to profile the global transcriptional responses of individual juvenile sponges at two and eight hours after they were fed the bacterial treatments. In chapter four, I repeated this experiment with the same three bacterial treatments, this time to investigate how they are processed at the cellular level.

Several groups of putative PRRs were differentially expressed across all three treatment groups, including just a single NLR, a TIR-domain containing gene, and 60 SRCR-domain containing genes. Although a few of the SRCR-domain containing genes were upregulated in response to all three treatments, the majority were upregulated in response to the bacterial treatments enriched from the healthy and unhealthy conspecifics, but not from the foreign sponge (Fig 3.7A). This leads me to propose that some of these SRCR-domain containing genes could play a role in the detection of symbiont-specific

131 Deciphering the genomic toolkit underlying animal-bacteria interactions ligands. It is interesting to speculate that the upregulation of fewer SRCR-domain containing genes in response to the foreign sponge bacteria could be interpreted as lack of recognition of the bacteria in this treatment. The large number of SRCR-domain containing genes that were differentially expressed in my experiment reflects the size of the repertoire of SRCR-domain containing genes encoded in theA. queenslandica genome. It is interesting that only one NLR was identified as differentially expressed, because this is disproportionate to the large size of the NLR complement present in the A. queenslandica genome. One possibility is that the A. queenslandica NLRs do in fact respond transcriptionally to the bacterial treatments used in my experiment, but these changes were not captured in any of my experimental time points. It is also possible that the sponge is always primed to respond to bacteria through the NLRs, such that these genes are constitutively expressed and there would be no need for increased levels of transcription in response to the bacterial treatments. Indeed, transcripts mapped to the AqNLRs were detected, albeit at a low level, in all the samples in the Chapter 3 experiment. The normalised average numbers of transcripts mapped to each AqNLR gene model, across all samples, ranged from 1.4 to 10.7 (See Appendix 5.1). Alternatively, the lack of greater numbers of differentially expressed NLRs might indicate that the feeding experiment was not the right context for activating the expression of the sponge NLRs. The role of pathogen stress is worth consideration as it is also considered an important driver of immunological innovations such as the vertebrate adaptive immune system (van Niekerk and Engelbrecht 2015). It is interesting to note that the independently evolved plant NLRs mediate a form of defence known as effector-triggered-immunity, through the direct or indirect detection of pathogen-specific effectors (Chiang and Coaker 2015; Cui, et al. 2015). It is possible that the sponge NLRs are specialised at the detection of pathogen-specific ligands through a similar strategy. However, the NLRs in animals have only ever been reported to bind or respond to MAMPs and damage-associated molecular patterns (Sansonetti 2006; Lange, et al. 2011; Stuart, et al. 2013).

A. queenslandica is faced with a constant influx of bacteria from the environment and mounting an immune response to all the bacteria it encounters would be energetically costly. Unlike the conventional use of PRRs to detect MAMPs that are found both in symbionts and pathogens, “surveillance immunity” is a form of host defence activated in response to pathogen-specific factors, such as toxins that disrupt normal cellular processes like protein translation (Troemel 2012). In Chapter four, I observed that only the foreign sponge bacteria induced the upregulation of a suite of cytoprotective genes that are putatively

132 Chapter 5 regulated by the oxidative/xenobiotic-stress response transcription factor Nrf2. The activation of the Nrf2 pathway suggests that the foreign sponge bacteria were causing the A. queenslandica juveniles to experience cellular stress. This correlated with the observation that the bacteria enriched from both the unhealthy and healthy conspecifics were acquired by archaeocytes more efficiently than the foreign sponge bacteria. The source of the foreign sponge bacteria, R. globostellata, is known to produce secondary metabolites with cytotoxic activity (Bourguet-Kondracki, et al. 2000; Tasdemir, et al. 2002; Fouad, et al. 2006). It is possible that the bacteria enriched from R. globostellata were producing toxic compounds that had to be detoxified first, making the foreign sponge bacteria more difficult to digest. I hypothesise that the Nrf2 pathway was activated in response to exogenous xenobiotic stress caused by toxins associated with the foreign sponge bacterial treatment (Chapter four). It is noteworthy that SKN-1, the Caenorhabditis elegans orthologue of Nrf2, is involved in defence against a pathogen- derived toxin that inhibits host protein translation (McEwan, et al. 2012). I posit that “surveillance immunity” could be an alternative strategy that the sponge might use to discriminate harmful bacteria from benign symbiont or food bacteria (Chapter four).

5.4 Establishment and regulation of symbiosis In addition to these PRRs, my thesis also suggests that conserved innate immune pathways and transcriptional regulators are involved in mediating the sponge’s response to bacteria. In chapter three, I report that only the native bacteria and the altered bacterial communities enriched from conspecifics elicited the upregulation of genes involved in the immunosuppressive transforming growth factor beta (TGF-β) pathway, as well as regulators of the innate immune response, such as the transcription factors IRF, STAT and NF-κB. The upregulation of these genes only in response to bacteria derived from conspecifics suggests that A. queenslandica is able to discriminate and respond to its native bacteria using conserved regulators of the metazoan innate immune system. This leads me to hypothesise that the innate immune system could play a role in the establishment and maintenance of sponge-bacteria symbiosis.

The establishment of the symbiont bacterial community in A. queenslandica begins very early in development when the embryos are growing in the brood chambers of the adult sponge. Maternal cells known as trophocytes migrate into cleavage stage embryos to vertically transmit bacteria symbionts

133 Deciphering the genomic toolkit underlying animal-bacteria interactions

(Fieth et al., in review). The vertical acquisition of symbiont bacteria in A. queenslandica correlates with the expression of the TGF-β ligand in embryonic cells called micromeres that are distributed uniformly across the early cleavage staged embryos (Adamska, et al. 2007). It is thought that the TGF-β pathway plays a role in axial patterning in A. queenslandica, based on the radially symmetrical expression patterns of TGF-β along the anterior-posterior axis of sponge embryos and larvae (Adamska, et al. 2007). However, the TGF-β signalling pathway is also an important mediator of symbiont tolerance in cnidarians and humans (Zeuthen, et al. 2008; Detournay, et al. 2012). Whether the TGF-β pathway is also important for promoting the normal colonisation of A. queenslandica by its symbionts during early development is worth future investigation.

This also raises the interesting possibility that symbiont bacteria could influence developmental processes in the sponge. Symbiotic bacteria have an important role influencing normal development in a variety of animals, as demonstrated by the role of Vibrio fischeri in triggering the morphogenetic development of the light organ of its host, the Hawaiian bobtail squid Euprymna scolopes (Koropatnick, et al. 2004; Altura, et al. 2011). The transcription factor signal transducer and activator of transcription (STAT) is a critical regulator of cell proliferation and differentiation in response to chemical-induced damage and infection in humans and Drosophila (Buchon, Broderick, Poidevin, et al. 2009; Jiang, et al. 2009; Vahedi, et al. 2012). In addition to regulating the immune response, STAT is also involved in normal organ development in Drosophila melanogaster, controlling the diversification of heart precursor cells in cardiogenesis (Johnson, et al. 2011), as well as the maintenance and proliferation of neuroepithelial stem cells (Wang, et al. 2011). Furthermore, the indigenous gut bacteria of Drosophila stimulate a basal level of STAT activity, which is required for stem cell proliferation associated with normal gut epithelium renewal (Buchon, Broderick, Chakrabarti, et al. 2009). Similarly, the choanocytes of sponges are renewed on a regular basis, with some sponges exhibiting very high choanocyte turnover rates (De Goeij, et al. 2009; Alexander, et al. 2014). In my Chapter three experiment, a gene encoding STAT was upregulated only in response to the bacterial treatments enriched from conspecifics. Subsequently, I reported that STAT is upregulated in untreated archaeocytes and pinacocytes compared to choanocytes (Chapter four). Archaeocytes are pluripotent and generally considered the stem cells of the sponge (Funayama 2010). In post-larval A. queenslandica, archaeocytes are one of the cell-types that contribute to choanocyte chamber formation (Sogabe, et al. 2016). It is interesting to speculate that cues from the

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A. queenslandica symbiont bacteria could trigger a homeostatic level of STAT activity that regulates proliferation or differentiation of archaeocytes, which in turn contributes to regular choanocyte renewal. However, the cell turnover dynamics in A. queenslandica and the role that archaeocytes play in the process remain unknown.

5.5 What can the sponge tell us about the origin of immunity and digestion in animals? There are several different views on the origin of immunity in animals. The most common of these is that the immune system arose as a means of defence against harmful invaders such as pathogenic bacteria (Lemaitre and Hoffmann 2007). However, our growing understanding of the ubiquity of bacteria, and the importance of these microorganisms to animal homeostasis, has led to a growing view that immunity may actually have arisen to manage symbiont communities (McFall-Ngai 2007; Bosch 2013). A third hypothesis, recently proposed by Broderick (2015), is that digestion and immunity have a common origin and thus a common goal towards achieving “more efficient energy acquisition”. One of two points central to this hypothesis is that the formation of the gut early in the evolution of animals was a major step driving the evolution of immune functionality (Broderick 2015). In line with this, the author remarked that phagocytosis initiates both digestion and immunity in unicellular eukaryotic amoebae, and that the processes are distinguishable from each other only by outcome (Broderick 2015). A second point raised to support this hypothesis is that multiple genes, particularly lysosomal enzymes and immune pathways, have dual roles in both digestion and microbial clearance (Broderick 2015). Indeed, it was first proposed by Ilya Metchnikoff, over 100 years ago, that phagocytes first arose from nutritive cells, and thus the evolution of multicellularity led to specialisation that allowed cells to be devoted to either nutrition or immunity (Tauber 2003). Sponges are one of the earliest-diverging extant animal phyletic lineages and also one of the morphologically simplest of animals (Philippe, et al. 2009; Dunn, et al. 2015). Lacking a true gut or tissues, food digestion in sponges is a completely intracellular process (Ereskovsky 2010; Maldonado, et al. 2012). Sponges are thus an ideal platform for elucidating the origin of immunity and digestion in animals, because traits conserved in the sponge and across eumetazoans animal lineages including humans, likely reflect a shared inheritance from the last common ancestor of sponges and all eumetazoans.

135 Deciphering the genomic toolkit underlying animal-bacteria interactions

In Chapter three, I reported that juvenile sponges presented with the three bacterial treatments responded with upregulation of pathways and transcriptional regulators of the innate immune response. These bacterial treatments also elicited the upregulation of the cellular metabolic sensor AMP-activated protein kinase (AMPK) and transcriptional regulator of metabolism, FoxO. Subsequent microscopy observations indicate that the ingestion and processing of the treatments were consistent with sponge feeding behaviour (Chapter four). Several of the genes that responded to the bacterial treatments have dual roles in regulating metabolism and immunity, including FoxO, AMPK, and the Tumour Necrosis Factor (TNF) pathway (Tamás, et al. 2006; Eijkelenboom and Burgering 2013; Agrawal, et al. 2016; Liu, et al. 2016). I subsequently analysed the cell-specific transcriptomes of untreated choanocytes, pinacocytes and archaeocytes, the cell-types that are known to mediate sponge feeding (Chapter four). I showed that FoxO and components of the TNF pathway were upregulated in choanocytes compared to archaeocytes and pinacocytes (Chapter four). Multiple other genes involved in immunity, such as SRCR- domain containing genes, MPEG-like genes and mitogen-activated protein (MAP) kinases, were also upregulated in choanocytes compared to the other two cell-types (Chapter four). The findings suggest that, in addition to nutrient acquisition, choanocytes have immune functionality. This is particularly interesting considering that the structural similarities between choanocytes and choanoflagellates, the unicellular eukaryotes that are sister group to all animals, are often discussed as a sign of homology (Maldonado 2004; Funayama 2013; Adamska 2016). This raises the interesting possibility that the ancestral continuity between innate immune defence and digestion has been preserved in sponge choanocytes.

Although the nutritional role and gross morphology of choanocytes are similar to that of choanoflagellates, specific aspects of their structure, function and development are markedly different (Mah, et al. 2014). This has led to the view that similarities between choanocytes and choanoflagellates are a result of convergence rather than shared origin (Mah, et al. 2014; Dunn, et al. 2015). If the sponge choanocyte is a derived cell-type, and not representative of a primordial animal feeding cell, this would then suggest that the dual roles of choanocytes in immunity and digestion is a derived feature and not the result of shared ancestry. Indeed, the more conventional view is that digestion and immunity evolved independently. Additionally, Niekerk and Engelbrecht (2015) argue that a more parsimonious explanation for the dual roles of digestive enzymes in immunity and digestion arose

136 Chapter 5 from the opportunistic co-option of pre-existing genes for the common need to breakdown bacteria. Thus it is possible that the immune genes were co-opted for use when choanocytes evolved, in order to deal with the challenges associated with sponge bacterivory.

This hypothesis of a common origin for immunity and digestion has implications for understanding the evolution of resident microbiota and the role of immunity. When the animal gut first evolved, it provided access to new sources of nutrition, thereby reducing the reliance on bacteria as a direct source of nutrition (McFall-Ngai, et al. 2013; Broderick 2015). However, the formation of the gut simultaneously provided a novel niche for bacterial colonisation (McFall-Ngai, et al. 2013; Broderick 2015). Thus, it has been hypothesised that immunological complexity evolved in parallel with the gut, driven by the need to protect it from invasion and also to manage a complex symbiont bacterial community (McFall-Ngai 2007; McFall-Ngai, et al. 2013). By contrast, Broderick (2015) proposed that the reduced reliance on bacteria for direct nutrition that accompanied the evolution of the gut would have permitted both diversification of the microbiota and the development of more specialised immune responses. Interestingly, despite relying on bacteria as a direct source of nutrition, sponges house dense and diverse species-specific bacterial communities that have little in common with the environment (Thomas, et al. 2016). Sponges are therefore an interesting model for investigating the coevolution of microbiota and immunity in an animal lineage that diverged prior to the evolution of the gut.

The gut serves an important purpose by compartmentalising the resident bacterial community, spatially restricting interactions with the symbiotic bacteria in order to avoid pathologies arising from disturbance of physiological functions or aberrant immune activation (Hooper, et al. 2012; Masson, et al. 2016). In the absence of an adaptive immune system, this form of compartmentalisation could be an important strategy used by invertebrates to manage their symbiont bacterial communities (McFall- Ngai 2007). Sponges are an interesting contrast to this strategy, because their symbiont bacteria are not compartmentalised by a gut. Sponge symbiont bacteria can be found throughout both the mesohyl and aquiferous system in constant direct contact with host cells (Hentschel, et al. 2012, Fieth, et al., in review). It has been proposed that sponge bacterial symbionts are able to mask themselves from the host to avoid ingestion (Wilkinson, et al. 1984; Wehrl, et al. 2007; Nguyen, et al. 2013). A second proposed mechanism was that the sponge is able to recognise its symbiont bacteria and thus deliberately

137 Deciphering the genomic toolkit underlying animal-bacteria interactions avoids digesting them because they are not recognised as food (Wilkinson, et al. 1984, Wehrl, et al. 2007). The experiments in this thesis examine the latter hypothesis, and the findings presented suggest that the sponge is able to recognise and interact with its symbiont bacteria through the innate immune system (Chapter 3). Indeed, the presence of a large and complex repertoire of putative immune receptors encoded in the genome of A. queenslandica, indicates that sponge immunological complexity has evolved in the absence of a gut. I thus speculate that the evolution of the immune system and its role in regulating interactions with the microbiota preceded or occurred in parallel with the emergence of choanocytes as feeding cells.

5.6 Outstanding issues There are three important caveats associated with the experiments performed in chapters three and four. First, the sponges were encountering their native symbionts in an unusual context. AqS1 and AqS2, the dominant members of the A. queenslandica bacterial community, are absent or barely detectable in seawater (Fieth et al, in review), and thus would not normally be encountered in the water column by choanocytes. Although the A. queenslandica symbiont bacteria do not appear to be spatially restricted, interactions between host cells and bacteria residing in the mesohyl could differ from interactions with free-living bacteria encountered by sponge cells in the aquiferous system. Indeed, the upregulation of immune genes in choanocytes of an untreated conspecific suggest that the choanocytes are constantly primed to respond to bacteria (Chapter four). Therefore, it would be interesting in future to investigate the interactions between mesohyl cell-types and symbiont bacteria through the use of single-cell transcriptomics. An interesting target for investigation could be the spicule-forming sclerocytes, because they are the main cell-type observed phagocytosing symbiont bacteria in A. queenslandica juveniles and adults (R. Fieth, unpublished data). In spite of this caveat, similar experiments have been reported in previous studies on other demosponges, and these experiments successfully demonstrated that the sponges were able discriminate symbiont from non-symbiont bacterial treatments that were introduced through the water column (Wilkinson, et al. 1984; Wehrl, et al. 2007).

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Second, native symbiont bacteria are already present in the mesohyl of the sponge, and through the treatments, the sponges were subjected to an unusually high load of bacteria. The healthy and unhealthy A. queenslandica bacteria treatments were administered at a density of roughly 2.8 x 104 cells/ml. By comparison, the seawater around Heron Island where the sponges were collected, contains an estimated 0.5-1.0 x 106 cells/ml (Patten, et al. 2008). Given that the core components of the A. queenslandica bacterial community are rarely detected in seawater, the treatments would have exposed the sponges to a much higher load of symbiont bacteria than they would typically encounter. The ingestion of symbiont bacteria by choanocytes, the choanocyte-associated amoeboid cells and archaeocytes observed in Chapter four is consistent with the way “food” bacteria are reported to be treated in other demosponge species (Schmidt 1970; Imsiecke 1993; Maldonado, et al. 2010). It is possible that this increased symbiont load could be a signal that induces alterations in the behaviour of the host to symbiont bacteria, such as triggering a population control response that culminates in the sponge treating symbiont bacteria as food. High densities of dinoflagellate symbionts in hard predispose their hosts to stress associated with bleaching (Cunning and Baker 2012), indicating that an excessively high population of symbionts is not necessarily beneficial to the host. Indeed, host control over the population density of symbionts is a widespread strategy for regulating symbiosis that has been documented in unicellular eukaryotes as well as in both plants and animals (Prell, et al. 2009; Cunning, et al. 2015; Lowe, et al. 2016). However, prior to the experiment, it was unknown if the native bacteria would be recognised, ignored, ingested or any combination of these three possibilities. Furthermore, seawater naturally contains high densities of bacteria and there was no previous data available on the densities of symbiont or non-symbiont bacteria that would be necessary to achieve the activation of an immune response in the sponge. I therefore decided to use a high treatment density, because I wanted to perturb the system in order to trigger a transcriptional response in the sponge.

The third caveat is that my experiments were performed using juvenile sponges that had only just begun to feed and were not yet fully grown adults. The immune response within a host can be temporally and ontogenetically heterogeneous (Tate and Rudolf 2012). It is possible that the innate immune system of the juvenile sponges was still being primed at the life history stage tested in my experiments. Immune priming is a form of enhanced innate immunity provided by previous exposure to the same bacterial species or strain (Tate and Rudolf 2012). For instance, beetles initially exposed

139 Deciphering the genomic toolkit underlying animal-bacteria interactions to heat-killed bacterial pathogen have higher survival rates following secondary exposure to the live bacteria (Roth, et al. 2009). The exposure to pathogens at an early life history stage can also prime the immune system to provide delayed protection that is retained across life history stages, regardless of dramatic morphological and physiological changes such as metamorphosis (Tate and Rudolf 2012). Furthermore, life history trade-offs could also be a factor influencing the host’s investment in the immune response (Contreras-Garduño, et al. 2014). Therefore, it is possible that the same bacterial treatments tested in this experiment could induce a different response in reproductively-mature adult sponges compared to juvenile sponges. While it is possible that the mature adult sponges might respond differently to the native bacteria, the foreign bacteria enriched from R. globostellata are likely to be very distinct to the free-living bacterial community in seawater (Steinert, et al. 2016). Therefore, it is highly unlikely that mature adult sponges would ever encounter high densities of these foreign sponge bacteria in the environment. Furthermore, the juvenile stage was chosen because interactions with the environment and bacteria change dramatically post-metamorphosis. The commencement of active filter-feeding would lead to the influx of free-living bacteria from the environment, thus it is likely that ability to discriminate symbiont from non-symbiont bacteria becomes critically important during this life history stage.

Despite these caveats, there were consistent differences between the responses of the A. queenslandica juveniles to the foreign sponge bacteria and the A. queenslandica-derived bacterial treatments at both the cellular level and transcriptional level. This suggests that the trends I have observed in my experiments reflect genuine differences that can be attributed to the nature of the treatments.

5.7 Concluding remarks and recommendations for the future This thesis represents one of the first studies to combine an experimental approach with the use of genomics and microscopy analyses to investigate the mechanisms underlying regulation of sponge- bacteria interactions. I contend that A. queenslandica is able to distinguish its symbiont bacteria from non-symbiont bacteria, and that highly conserved components of the metazoan innate immune system contribute to mediating interactions between the sponge and its microbiota. The findings of this thesis provide a framework for future studies to test hypotheses about the evolution and roles of particular immune pathways in the establishment and maintenance of sponge-bacteria symbiosis.

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Although there was only a minor shift in the structure of the unhealthy A. queenslandica bacterial community compared to the healthy individual, there were clear detectable differences in the transcriptomic responses to the health and unhealthy conspecific bacterial treatments (Chapter 3). I propose that an informative avenue of future pursuit would be to investigate the mechanisms driving the differences in the transcriptional response to healthy and unhealthy bacterial communities. In particular, it would be worth looking into how changes in the abundance of the primary symbiont taxa – AqS1 (Chromatiales) and AqS2 (Betaproteobacterium) – influence the expression of conserved immune pathways. Is a reduction in just AqS1, the most abundant symbiont, sufficient to induce a differential transcriptional response? Alternatively, could this differential response be explained by the increase in abundance of AqS2 alone? Along this line of questioning, it would also be interesting to investigate whether the juvenile sponge are preferentially ingesting AqS1 vs AqS2, or if these two dominant taxa are being ingested proportional to their abundance in the treatments.

Another important line of inquiry for the future would be an investigation of the molecular mechanisms regulating when and where symbiont bacteria are regarded as food. Phagocytosis of symbiont bacteria in the mesohyl has been observed in A. queenslandica larvae and juveniles but not in the adults (Fieth, et al. in review). In order to better understand why the symbiont bacteria are being ingested in early life history stages, it would worthwhile repeat this experiment in adult sponges to determine if the symbiont bacteria are ingested or if they are ignored as has been reported in studies on other demosponge species (Wilkinson, et al. 1984; Wehrl, et al. 2007). If adults avoid ingesting the native bacteria, then it might suggest that a host-regulated mechanism determines whether or not symbiont bacteria are regarded as food. The capacity to do single cell-transcriptomics is an extremely valuable tool that could be applied to examine how conserved immune signalling pathways are deployed in the cell-types involved in processing the bacteria. The use of labelled bacteria to identify cells that have ingested symbiont bacteria in conjunction with single cell transcriptomics could provide insight into molecular pathways regulating symbiont ingestion.

Given the caveat that the experiments performed in chapters three and four are based on feeding behaviour, it would be beneficial to investigate how the sponge genome responds when alterations are induced in the bacterial community of the mesohyl through the use of antibiotics. Subsequent research

141 Deciphering the genomic toolkit underlying animal-bacteria interactions could also investigate how bacteria interact with the immune system when sponge larvae make contact with biofilms on algae during settlement and metamorphosis. Finally, it would also be interesting for future studies to investigate the role of the immune system during critical early stages of development, such as during the onset of symbiosis when maternal cells vertically transmit symbionts to embryos in the brood chamber.

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References Aachoui Y, Sagulenko V, Miao EA, Stacey KJ. 2013. Inflammasome-mediated pyroptotic and apoptotic cell death, and defense against infection. Current Opinion in Microbiology 16:319-326.

Adamska M. 2016. Sponges as models to study emergence of complex animals. Current Opinion in Genetics & Development 39:21-28.

Adamska M, Degnan SM, Green KM, Adamski M, Craigie A, Larroux C, Degnan BM. 2007. Wnt and TGF-β Expression in the sponge Amphimedon queenslandica and the origin of metazoan embryonic patterning (sponge developmental signals). PLoS ONE 2:e1031.

Aggarwal BB. 2003. Signalling pathways of the TNF superfamily: a double-edged sword. Nature Reviews Immunology 3:745-756.

Agrawal N, Delanoue R, Mauri A, Basco D, Pasco M, Thorens B, Léopold P. 2016. The Drosophila TNF Eiger is an adipokine that acts on insulin-producing cells to mediate nutrient response. Cell Metabolism 23:675-684.

Agrell I. 1952. Enzymes and cell differentiation in sponges. Arkiv För Zoologi 3:325-330. Alexander BE, Liebrand K, Osinga R, van der Geest HG, Admiraal W, Cleutjens JPM, Schutte B, Verheyen F, Ribes M, van Loon E, et al. 2014. Cell turnover and detritus production in marine sponges from tropical and temperate benthic ecosystems. PLoS ONE 9:e109486.

Altura MA, Stabb E, Goldman W, Apicella M, McFall-Ngai MJ. 2011. Attenuation of host NO production by MAMPs potentiates development of the host in the squid-Vibrio symbiosis. Cellular Microbiology 13:527-537.

Appelqvist H, Waster P, Kagedal K, Ollinger K. 2013. The lysosome: from waste bag to potential therapeutic target. Journal of Molecular Cell Biology 5:214-226.

Bailey D, O’Hare P. 2007. Transmembrane bZIP transcription factors in ER stress signaling and the unfolded protein response. Antioxidants and Redox Signaling 9:2305-2321.

Baker KD, Thummel CS. 2007. Diabetic larvae and obese flies—emerging studies of metabolism in Drosophila. Cell Metabolism 6:257-266.

145 Bayes J. 2013. The characterisation of the microbiome of the demosponge Amphimedon queenslandica reveals host immune responses to changes in the symbiotic bacterial community (Masters Thesis). The University of Queensland.

Becker T, Loch G, Beyer M, Zinke I, Aschenbrenner A, C., Carrera P, Inhester T, Schultze J, L., Hoch M. 2010. FOXO-dependent regulation of innate immune homeostasis. Nature 463:369-373.

Bell JJ. 2008. The functional roles of marine sponges. Estuarine, Coastal and Shelf Science 79:341-353. Bikker FJ, Antoon JML, End C, Renner M, Blaich S, Lyer S, Wittig R, Hof Wvt, Enno CIV, Nazmi K, et al. 2004. Bacteria binding by DMBT1/SAG/gp-340 Is confined to the VEVLXXXXW Motif in its Scavenger Receptor Cysteine-rich domains. Journal of Biological Chemistry 279:47699-47703.

Blumbach B, Pancer Z, Diehl-Seifert B, Steffen R, Munkner J, Muller I, Muller W. 1998. The putative sponge aggregation receptor - Isolation and characterization of a molecule composed of scavenger receptor cysteine-rich domains and short consensus repeats. Journal of Cell Science 111:2635-2644.

Bockaert J, Perroy J, Bécamel C, Marin P, Fagni L. 2010. GPCR Interacting Proteins (GIPs) in the nervous system: Roles in physiology and pathologies. Annual Review of Pharmacology and Toxicology 50:89-109.

Boehm T. 2012. Evolution of vertebrate immunity. Current Biology 22:R722-R732. Boller T, Felix G. 2009. A renaissance of elicitors: perception of microbe-associated molecular patterns and danger signals by pattern-recognition receptors. Annual Review of Plant Biology 60:379-406.

Bonardi V, Cherkis K, Nishimura MT, Dangl JL. 2012. A new eye on NLR proteins: focused on clarity or diffused by complexity? Current Opinion in Immunology 24:41-50.

Bosch TCG. 2013. Cnidarian-microbe interactions and the origin of innate immunity in metazoans. Annual Review of Microbiology 67:499-518.

Bosch TCG, Augustin R, Anton-Erxleben F, Fraune S, Hemmrich G, Zill H, Rosenstiel P, Jacobs G, Schreiber S, Leippe M, et al. 2009. Uncovering the evolutionary history of innate immunity: The simple metazoan Hydra uses epithelial cells for host defence. Developmental and Comparative Immunology 33:559-569. Bosch TCG, McFall-Ngai MJ. 2011. Metaorganisms as the new frontier. 114:185-190. Boucard AA, Ko J, Südhof TC. 2012. High affinity neurexin binding to cell adhesion G-protein- coupled receptor CIRL1/latrophilin-1 produces an intercellular adhesion complex. Journal of Biological Chemistry 287:9399-9413.

146 Boulange C, Neves AL, Chilloux J, Nicholson J, Dumas ME. 2016. Impact of the gut microbiota on inflammation, obesity, and metabolic disease. Genome Medicine 8:42.

Bourguet-Kondracki M, Longeon A, Debitus C, Guyot M. 2000. New cytotoxic isomalabaricane-type sesterterpenes from the New Caledonian marine sponge Rhabdastrella globostellata. Tetrahedron Letters 41:3087-3090.

Bridge D, Theofiles AG, Holler RL, Marcinkevicius E, Steele RE, Martínez DE. 2010. FoxO and stress responses in the Cnidarian Hydra vulgaris (FoxO and stress in Hydra). PLoS ONE 5:e11686.

Broderick NA. 2015. A common origin for immunity and digestion. Frontiers in Immunology 6:72. Brzoska K, Stepkowski TM, Kruszewski M. 2014. Basal PIR expression in HeLa cells is driven by NRF2 via evolutionary conserved antioxidant response element. Molecular and Cellular Biochemistry 389:99-111.

Buchon N, Broderick NA, Chakrabarti S, Lemaitre B. 2009. Invasive and indigenous microbiota impact intestinal stem cell activity through multiple pathways in Drosophila. Genes & Development 23:2333-2344.

Buchon N, Broderick NA, Poidevin M, Pradervand S, Lemaitre B. 2009. Drosophila intestinal response to bacterial infection: Activation of host defense and stem cell proliferation. Cell Host & Microbe 5:200-211.

Buckley KM, Rast JP. 2015. Diversity of animal immune receptors and the origins of recognition complexity in the . Developmental and Comparative Immunology 49:179-189.

Buckley KM, Rast JP. 2012. Dynamic evolution of toll-like receptor multigene families in echinoderms. Frontiers in Immunology 3:136.

Buxade M, Parra-Palau JL, Proud CG. 2008. The Mnks: MAP kinase-interacting kinases (MAP kinase signal-integrating kinases). Frontiers in Bioscience 13:5359-5373.

Cabal-Hierro L, Lazo PS. 2012. Signal transduction by tumor necrosis factor receptors. Cell Signal 24:1297-1305.

Caldwell AB, Cheng Z, Vargas JD, Birnbaum HA, Hoffmann A. 2014. Network dynamics determine the autocrine and paracrine signaling functions of TNF. Genes & Development 28:2120-2133.

Cannon JP, Dishaw LJ, Haire RN, Litman RT, Ostrov DA, Litman GW. 2010. Recognition of additional roles for immunoglobulin domains in immune function. Seminars in Immunology 22:17-24.

147 Cao Z-h, Zheng Q-y, Li G-q, Hu X-b, Feng S-l, Xu G-l, Zhang K-q. 2015. STAT1-mediated down- regulation of Bcl-2 expression is involved in IFN-gamma/TNF-alpha-induced apoptosis in NIT-1 cells. PLoS ONE 10:e0120921.

Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, et al. 2010. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7:335-336.

Carter HJ. 1883. Contributions to our knowledge of the Spongida, Pachytragida. Annals and Magazine of Natural History 11:344-369.

Cereijido M, Contreras RG, Shoshani L. 2004. Cell adhesion, polarity, and epithelia in the dawn of metazoans. Physiological Reviews 84:1229-1262.

Chang YL, Chen TH, Wu YH, Chen GA, Weng TH, Tseng PH, Hsieh SL, Fu SL, Lin CH, Chen CJ, et al. 2014. A novel TLR2‐triggered signalling crosstalk synergistically intensifies TNF‐mediated IL‐6 induction. Journal of Cellular and Molecular Medicine 18:1344-1357.

Chen W, White MA, Cobb MH. 2002. Stimulus-specific requirements for MAP3 kinases in activating the JNK pathway. Journal of Biological Chemistry 277:49105-49110.

Chen X, Cao X, Tao G, Cao Z, Wang S, Zhou F, Xie W, Zhao P, Zhang Z, Cui Z. 2012. Foxj2 expression in rat spinal cord after injury and its role in inflammation. Journal of Molecular Neuroscience 47:158-165.

Chiang Y-H, Coaker G. 2015. Effector triggered immunity: NLR immune perception and downstream defense responses. The Arabidopsis Book 13:e0183.

Chu H, Mazmanian SK. 2013. Innate immune recognition of the microbiota promotes host-microbial symbiosis. Nature Immunology 14:668-675.

Chun CK, Casavant TL, Soares MB, Ruby EG, McFall-Ngai MJ, Troll JV, Koroleva I, Brown B, Manzella L, Snir E, et al. 2008. Effects of colonization, luminescence, and autoinducer on host transcription during development of the squid-Vibrio association. Proceedings of the National Academy of Sciences of the United States of America 105:11323-11328. Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, Cusack S, Harris HM, Coakley M, Lakshminarayanan B, O’Sullivan O, et al. 2012. Gut microbiota composition correlates with diet and health in the elderly. Nature 488:178-184.

Clark DE, Williams CC, Duplessis TT, Moring KL, Jones FE, Notwick AR, Long W, Lane WS, Beuvink I, Hynes NE. 2005. ERBB4/HER4 potentiates STAT5A transcriptional activity by regulating novel STAT5A serine phosphorylation events. Journal of Biological Chemistry 280:24175-24180.

148 Collins AJ, Schleicher TR, Rader BA, Nyholm SV. 2012. Understanding the role of host hemocytes in a squid/Vibrio symbiosis using transcriptomics and proteomics. Frontiers in Immunology 3:91.

Conaco C, Neveu P, Zhou H, Arcila ML, Degnan SM, Degnan BM, Kosik KS. 2012. Transcriptome profiling of the demosponge Amphimedon queenslandica reveals genome-wide events that accompany major life cycle transitions. BMC Genomics 13:209.

Contreras-Garduño J, Rodríguez MC, Rodríguez MH, Alvarado-Delgado A, Lanz-Mendoza H. 2014. Cost of immune priming within generations: trade-off between infection and reproduction. Microbes and Infection 16:261-267.

Coornaert B, Carpentier I, Beyaert R. 2009. A20: central gatekeeper in inflammation and immunity. Journal of Biological Chemistry 284:8217-8221.

Cosson P, Lima WC. 2014. Intracellular killing of bacteria: is Dictyostelium a model macrophage or an alien? Cellular Microbiology 16:816-823.

Criscitiello MF, de Figueiredo P. 2013. Fifty shades of immune defense. PLoS Pathogens 9:e1003110. Cui H, Tsuda K, Parker JE. 2015. Effector-triggered immunity: From pathogen perception to robust defense. Annual Review of Plant Biology 66:487-511.

Cunning R, Baker AC. 2012. Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nature Climate Change 3:259-262.

Cunning R, Vaughan N, Gillette P, Capo TR, Maté JL, Baker AC. 2015. Dynamic regulation of partner abundance mediates response of reef coral symbioses to environmental change. Ecology 96:1411-1420.

D’Souza-Schorey C, Chavrier P. 2006. ARF proteins: roles in membrane traffic and beyond. Nature Reviews Molecular Cell Biology 7:347-358.

Damm A, Lautz K, Kufer TA. 2013. Roles of NLRP10 in innate and adaptive immunity. Microbes and Infection 15:516-523.

Darriba D, Taboada GL, Doallo R, Posada D. 2011. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics 27:1164-1165.

Davidson BA, Vethanayagam RR, Grimm MJ, Mullan BA, Raghavendran K, Blackwell TS, Freeman ML, Ayyasamy V, Singh KK, Sporn MB, et al. 2013. NADPH oxidase and Nrf2 regulate gastric aspiration-induced inflammation and acute lung injury. Journal of Immunology 190:1714-1724.

Davis BK, Wen H, Ting JPY. 2011. The inflammasome NLRs in immunity, inflammation, and associated diseases. Annual Review of Immunology 29:707-735.

149 De Goeij JM, De Kluijver A, Van Duyl FC, Vacelet J, Wijffels RH, De Goeij AFPM, Cleutjens JPM, Schutte B. 2009. Cell kinetics of the marine sponge reveal rapid cell turnover and shedding. Journal of Experimental Biology 212:3892-3900. de Goeij JM, van Oevelen D, Vermeij MJA, Osinga R, Middelburg JJ, de Goeij AFPM, Admiraal W. 2013. Surviving in a marine desert: the sponge loop retains resources within coral reefs. Science 342:108-110.

Degnan BM, Adamska M, Craigie A, Degnan SM, Fahey B, Gauthier M, Hooper JN, Larroux C, Leys SP, Lovas E, et al. 2008. The Demosponge Amphimedon queenslandica: reconstructing the ancestral metazoan genome and deciphering the origin of animal multicellularity. Cold Spring Harbor Protocols 2008:pdb.emo108. Degnan BM, Adamska M, Richards GS, Larroux C, Leininger S, Bergum B, Calcino A, Taylor K, Nakanishi N, Degnan SM. 2015. Porifera. In: Wanninger A, editor. Evolutionary of Invertebrates 1: Introduction, Non-, , Xenoturbellida, : Springer-Verlag Wien. p. 65-106. Degnan SM. 2015. The surprisingly complex immune gene repertoire of a simple sponge, exemplified by the NLR genes: a capacity for specificity? Developmental and Comparative Immunology 48:269-274.

Degnan SM, Degnan BM. 2010. The initiation of metamorphosis as an ancient polyphenic trait and its role in metazoan life-cycle evolution. Philosophical Transactions of the Royal Society B 365:641-651.

Derynck R, Akhurst RJ. 2007. Differentiation plasticity regulated by TGF-beta family proteins in development and disease. Nature Cell Biology 9:1000-1004.

Derynck R, Zhang YE. 2003. Smad-dependent and Smad-independent pathways in TGF-β family signalling. Nature 425:577-584.

Desjardins M, Houde M, Gagnon E. 2005. Phagocytosis: the convoluted way from nutrition to adaptive immunity. Immunological Reviews 207:158-165.

Detournay O, Schnitzler CE, Poole A, Weis VM. 2012. Regulation of cnidarian-dinoflagellate mutualisms: Evidence that activation of a host TGF-beta innate immune pathway promotes tolerance of the symbiont. Developmental and Comparative Immunology 38:525-537.

Di Giacinto C, Marinaro M, Boirivant M, Sanchez M, Strober W. 2005. Probiotics ameliorate recurrent Th1-mediated murine colitis by inducing IL-10 and IL-10-dependent TGF-β-bearing regulatory cells. Journal of Immunology 174:3237-3246.

150 Dias IHK, Chapple ILC, Milward M, Grant MM, Hill E, Brown J, Griffiths HR. 2013. Sulforaphane restores cellular glutathione levels and reduces chronic periodontitis neutrophil hyperactivity in vitro. PLoS ONE 8:e66407.

Douglas AE. 2014. The molecular basis of bacterial-insect symbiosis. Journal of Molecular Biology 426:3830-3837.

Dumon S, Santos SCR, Debierre-Grockiego F, Gouilleux-Gruart V, Cocault L, Boucheron C, Mollat P, Gisselbrecht S, Gouilleux F. 1999. IL-3 dependent regulation of Bcl-xL gene expression by STAT5 in a bone marrow derived cell line. Oncogene 18:4191-4199.

Dunbar Tiffany l, Yan Z, Balla Keir m, Smelkinson Margery g, Troemel Emily r. 2012. C. elegans detects pathogen-induced translational inhibition to activate immune signaling. Cell Host & Microbe 11:375-386.

Dunham P, Anderson C, Rich AM, Weissmann G. 1983. Stimulus-response coupling in sponge cell aggregation: evidence for calcium as an intracellular messenger. Proceedings of the National Academy of Sciences of the United States of America 80:4756-4760.

Dunn CW, Leys SP, Haddock SHD. 2015. The hidden biology of sponges and ctenophores. Trends in Ecology & Evolution 30:282-291.

Dunne A. 2011. Inflammasome activation: from inflammatory disease to infection. Biochemical Society Transactions 39:669-673.

Dupont S, Corre E, Li Y, Vacelet J, Bourguet‐kondracki Ml. 2013. First insights into the microbiome of a carnivorous sponge. FEMS Microbiology Ecology 86:520-531.

Dwyer SF, Gelman IH. 2014. Cross-phosphorylation and interaction between Src/FAK and MAPKAP5/ PRAK in early focal adhesions controls cell motility. Journal of Cancer Biology and Research 2:1045.

Eberl G. 2010. A new vision of immunity: homeostasis of the superorganism. Mucosal Immunology 3:450-460.

Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460-2461.

Eijkelenboom A, Burgering BMT. 2013. FOXOs: signalling integrators for homeostasis maintenance. Nature Reviews Molecular Cell Biology 14:83-97.

151 Elliott GRD, Leys SP. 2010. Evidence for glutamate, GABA and NO in coordinating behaviour in the sponge, muelleri (Demospongiae, ). Journal of Experimental Biology 213:2310-2321.

Ellwanger K, Eich A, Nickel M. 2007. GABA and glutamate specifically induce contractions in the sponge wilhelma. Neuroethology, Sensory, Neural, and Behavioral Physiology 193:1-11.

Ereskovsky AV. 2010. The comparative embryology of sponges. Dordrecht: Springer Netherlands: Dordrecht.

Ereskovsky AV, Dondua AK. 2006. The problem of germ layers in sponges (Porifera) and some issues concerning early metazoan evolution. Zoologischer Anzeiger - A Journal of Comparative Zoology 245:65-76.

Erkosar B, Storelli G, Defaye A, Leulier F. 2013. Host-intestinal microbiota mutualism: “learning on the fly”. Cell Host & Microbe 13:8-14.

Erturk-Hasdemir D, Kasper DL. 2013. Resident commensals shaping immunity. Current Opinion in Immunology 25:450-455.

Fabriek BO, van Bruggen R, Deng DM, Ligtenberg AJM, Nazmi K, Schornagel K, Vloet RPM, Dijkstra CD, van den Berg TK. 2009. The macrophage scavenger receptor CD163 functions as an innate immune sensor for bacteria. Blood 113:887-892.

Fan L, Reynolds D, Liu M, Stark M, Kjelleberg S, Webster NS, Thomas T. 2012. Functional equivalence and evolutionary convergence in complex communities of microbial sponge symbionts. Proceedings of the National Academy of Sciences of the United States of America 109:E1878.

Fernandez-Valverde SL, Calcino AD, Degnan BM. 2015. Deep developmental transcriptome sequencing uncovers numerous new devel enhances gene annotation in the sponge Amphimedon queenslandica. BMC Genomics 16:387.

Fieseler L, Quaiser A, Schleper C, Hentschel U. 2006. Analysis of the first genome fragment from the marine sponge‐associated, novel candidate phylum Poribacteria by environmental genomics. Environmental Microbiology 8:612-624.

Finkel T. 2011. Signal transduction by reactive oxygen species. Journal of Cell Biology 194:7-15. Finn RD, Clements J, Eddy SR. 2011. HMMER web server: interactive sequence similarity searching. Nucleic Acids Research 39:W29-W37.

152 Fiore CL, Labrie M, Jarett JK, Lesser MP. 2015. Transcriptional activity of the , muta Holobiont: molecular evidence for metabolic interchange. Frontiers in Microbiology 6:364.

Fontana MF, Banga S, Barry KC, Shen X, Tan Y, Luo Z-Q, Vance RE. 2011. Secreted bacterial effectors that inhibit host protein synthesis are critical for induction of the innate immune response to virulent Legionella pneumophila. PLoS Pathogens 7:e1001289.

Foster JS, Apicella MA, McFall-Ngai MJ. 2000. Vibrio fischeri lipopolysaccharide induces developmental apoptosis, but not complete morphogenesis, of the Euprymna scolopes symbiotic light organ. Developmental Biology 226:242-254.

Fouad M, Edrada RA, Ebel R, Wray V, Muller WEG, Lin WH, Proksch P. 2006. Cytotoxic isomalabaricane triterpenes from the marine sponge Rhabdastrella globostellata. Journal of Natural Products 69:211-218.

Franchi L, Eigenbrod T, Muñoz-Planillo R, Nuñez G. 2009. The inflammasome: a caspase-1-activation platform that regulates immune responses and disease pathogenesis. Nature Immunology 10:241-247.

Franzenburg S, Fraune S, Künzel S, Baines JF, Domazet-Loso T, Bosch TCG. 2012. MyD88-deficient Hydra reveal an ancient function of TLR signaling in sensing bacterial colonizers. Proceedings of the National Academy of Sciences of the United States of America 109:19374-19379.

Fraune S, Bosch TCG. 2010. Why bacteria matter in animal development and evolution. BioEssays 32:571-580.

Frost T. 1981. Analysis of ingested particles within a fresh-water sponge. Transactions of the American Microscopical Society 100:271-277.

Funayama N. 2010. The stem cell system in demosponges: Insights into the origin of somatic stem cells. Development, Growth, Differentiation 52:1-14.

Funayama N. 2013. The stem cell system in demosponges: suggested involvement of two types of cells: archeocytes (active stem cells) and choanocytes (food-entrapping flagellated cells). Development Genes and Evolution 223:23-38.

Gauthier M-EA, Watson JR, Degnan SM. 2016. Draft genomes shed light on the dual bacterial symbiosis that dominates the microbiome of the coral reef sponge Amphimedon queenslandica. Frontiers in Marine Science 3:196.

Gauthier M, Degnan BM. 2008. Partitioning of genetically distinct cell populations in chimeric juveniles of the sponge Amphimedon queenslandica. Developmental and Comparative Immunology 32:1270-1280.

153 Gauthier MEA, Du Pasquier L, Degnan BM. 2010. The genome of the sponge Amphimedon queenslandica provides new perspectives into the origin of Toll-like and interleukin 1 receptor pathways. Evolution and Development 12:519-533.

Gilbert SF, Sapp J, Tauber AI. 2012. A symbiotic view of life: we have never been individuals. The Quarterly Review of Biology 87:325-341.

Giles EC, Kamke J, Moitinho‐silva L, Taylor MW, Hentschel U, Ravasi T, Schmitt S. 2013. Bacterial community profiles in low microbial abundance sponges. FEMS Microbiology Ecology 83:232-241.

Goh LK, Sorkin A. 2013. Endocytosis of receptor tyrosine kinases. Cold Spring Harbor Perspectives in Biology 5:a017459.

Gordon S. 2016. Phagocytosis: an immunobiologic process. Immunity 44:463-475. Gough J. 2005. Convergent evolution of domain architectures (is rare). Bioinformatics 21:1464-1471. Granier S, Kobilka B. 2012. A new era of GPCR structural and chemical biology. Nature Chemical Biology 8:670-673.

Greer EL, Dowlatshahi D, Banko MR, Villen J, Hoang K, Blanchard D, Gygi SP, Brunet A. 2007. An AMPK-FOXO pathway mediates longevity induced by a novel method of dietary restriction in C. elegans. Current Biology 17:1646-1656.

Greer EL, Oskoui PR, Banko MR, Maniar JM, Gygi MP, Gygi SP, Brunet A. 2007. Energy sensor AMP-activated protein kinase directly regulates the mammalian FOXO3 transcription factor. Journal of Biological Chemistry 282:30107-30119.

Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O. 2010. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic Biology 59:307-321.

Guo P, Hirano M, Herrin BR, Li J, Yu C, Sadlonova A, Cooper MD. 2009. Dual nature of the adaptive immune system in lampreys. Nature 459:796-801.

Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DL, Giannaoukos G, Ciulla D, Tabbas D, Highlander SK, Sodergren E, et al. 2011. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Research 21:434-504.

Hacker H, Redecke V, Blagoev B, Kratchmarova I, Hsu LC, Wang GG, Kamps MP, Raz E, Wagner H, Hacker G, et al. 2006. Specificity in Toll-like receptor signalling through distinct effector functions of TRAF3 and TRAF6. Nature 439:204-207.

154 Hadas E, Marie D, Shpigel M, Ilan M. 2006. Virus predation by sponges is a new nutrient‐flow pathway in coral reef food webs. Limnology and Oceanography 51:1548-1550.

Hadas E, Shpigel M, Ilan M. 2009. Particulate organic matter as a food source for a coral reef sponge. Journal of Experimental Biology 212:3643-3650.

Hamada M, Shoguchi E, Shinzato C, Kawashima T, Miller DJ, Satoh N. 2012. The complex NOD-like receptor repertoire of the coral Acropora digitifera includes novel domain combinations. Molecular Biology and Evolution 30:167-176.

Hamann J, Aust G, Arac D, Engel FB, Formstone C, Fredriksson R, Hall RA, Harty BL, Kirchhoff C, Knapp B, et al. 2015. International Union of Basic and Clinical Pharmacology. XCIV. Adhesion G protein-coupled receptors. Pharmacological Reviews 67:338-367.

Hansen JD, Vojtech LN, Laing KJ. 2011. Sensing disease and danger: a survey of vertebrate PRRs and their origins. Developmental and Comparative Immunology 35:886-897.

Hansen R, Thomson J, El-Omar E, Hold G. 2010. The role of infection in the aetiology of inflammatory bowel disease. Journal of Gastroenterology 45:266-276.

Hanson CE, McLaughlin MJ, Hyndes GA, Strzelecki J. 2009. Selective uptake of prokaryotic picoplankton by a marine sponge ( sp.) within an oligotrophic coastal system. Estuarine, Coastal and Shelf Science 84:289-297.

Hardie DG, Ross FA, Hawley SA. 2012. AMPK: a nutrient and energy sensor that maintains energy homeostasis. Nature Reviews Molecular Cell Biology 13:251-262.

Hashimshony T, Feder M, Levin M, Hall BK, Yanai I. 2015. Spatiotemporal transcriptomics reveals the evolutionary history of the endoderm germ layer. Nature 519:219-222.

Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y, Anavy L, Gennert D, Li S, Livak KJ, Rozenblatt-Rosen O, et al. 2016. CEL-Seq2: sensitive highly-multiplexed single-cell RNA- Seq. Genome Biology 17:77.

Hashimshony T, Wagner F, Sher N, Yanai I. 2012. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell reports 2:666-673.

Hauer J, Püschner S, Ramakrishnan P, Simon U, Bongers M, Federle C, Engelmann H, Dinarello CA. 2005. TNF Receptor (TNFR)-Associated Factor (TRAF) 3 serves as an inhibitor of TRAF2/5-mediated activation of the noncanonical NF-κB Pathway by TRAF-Binding TNFRs. Proceedings of the National Academy of Sciences of the United States of America 102:2874-2879.

155 Hawley SA, Pan DA, Mustard KJ, Ross L, Bain J, Edelman AM, Frenguelli BG, Hardie DG. 2005. Calmodulin-dependent protein kinase kinase-β is an alternative upstream kinase for AMP-activated protein kinase. Cell Metabolism 2:9-19.

Hayden MS, Ghosh S. 2014. Regulation of NF-κB by TNF family cytokines. Seminars in Immunology 26:253–266.

Hayes JD, Dinkova-Kostova AT. 2014. The Nrf2 regulatory network provides an interface between redox and intermediary metabolism. Trends in Biochemical Sciences 39:199-218.

Hayes JD, McMahon M. 2009. NRF2 and KEAP1 mutations: permanent activation of an adaptive response in cancer. Trends in Biochemical Sciences 34:176-188.

Hentschel U, Hopke J, Horn M, Friedrich AB, Wagner M, Hacker J, Moore BS. 2002. Molecular evidence for a uniform microbial community in sponges from different . Applied and Environmental Microbiology 68:4431–4440.

Hentschel U, Piel J, Degnan SM, Taylor MW. 2012. Genomic insights into the marine sponge microbiome. Nature Reviews Microbiology 10:641-654.

Hibino T, Loza-Coll M, Messier C, Majeske AJ, Cohen AH, Terwilliger DP, Buckley KM, Brockton V, Nair SV, Berney K, et al. 2006. The immune gene repertoire encoded in the purple sea urchin genome. Developmental Biology 300:349-365.

Hillman K, Goodrich-Blair H. 2016. Are you my symbiont? Microbial polymorphic toxins and antimicrobial compounds as honest signals of beneficial symbiotic defensive traits. Current Opinion in Microbiology 31:184-190.

Hoffmann JA, Kafatos FC, Janeway CA, Ezekowitz RA. 1999. Phylogenetic perspectives in innate immunity. Science 284:1313-1318.

Honda K, Taniguchi T. 2006. Toll-like receptor signaling and IRF transcription factors. IUBMB life 58:290-295.

Hooper J, Van Soest R. 2006. A new species of Amphimedon (Porifera, Demospongiae, Haplosclerida, ) from the Capricorn-Bunker Group of Islands, Great Barrier Reef, Australia: target species for the ‘sponge genome project’. Zootaxa 1314:31-39.

Hooper LV, Littman DR, Macpherson AJ. 2012. Interactions between the microbiota and the immune system. Science 336:1268-1673.

Hotamisligil GS. 2006. Inflammation and metabolic disorders. Nature 444:860-867.

156 Hoyer-Hansen M, Bastholm L, Szyniarowski P, Campanella M, Szabadkai G, Farkas T, Bianchi K, Fehrenbacher N, Elling F, Rizzuto R, et al. 2007. Control of macroautophagy by calcium, calmodulin- dependent kinase kinase-beta, and Bcl-2. Molecular Cell 25:193-205.

Huang S, Liu H, Ge J, Yu Y, Huang H, Dong M, Yu C, Chen S, Xu A, Yuan S, et al. 2008. Genomic analysis of the immune gene repertoire of Amphioxus reveals extraordinary innate complexity and diversity. Genome Research 18:1112-1126.

Hughes D, T., Sperandio V. 2008. Inter-kingdom signalling: communication between bacteria and their hosts. Nature Reviews Microbiology 6:111-120.

Hur KY, Lee M-S. 2015. Gut microbiota and metabolic disorders. Diabetes and Metabolism Journal 39:198-203.

Hynes R. 2012. The evolution of metazoan extracellular matrix. Journal of Cell Biology 196:671-679. Igaki T, Kanda H, Yamamoto‐Goto Y, Kanuka H, Kuranaga E, Aigaki T, Miura M. 2002. Eiger, a TNF superfamily ligand that triggers the Drosophila JNK pathway. EMBO Journal 21:3009-3018.

Imsiecke G. 1993. Ingestion, digestion, and egestion in (Porifera, Spongillidae) after pulse feeding with Chlamydomonas reinhardtii (Volvocales). Evolutionary, Comparative and Functional Morphology 113:233-244.

Istomin AY, Godzik A. 2009. Understanding diversity of human innate immunity receptors: analysis of surface features of leucine-rich repeat domains in NLRs and TLRs. BMC Immunology 10:48-48.

Izzi L, Attisano L. 2006. Ubiquitin-dependent regulation of TGβ signaling in cancer. Neoplasia 8:677- 688.

Janeway JCA, Medzhitov R. 2002. Innate immune recognition. Annual Review of Immunology 20:197-216.

Jiang H, Patel PH, Kohlmaier A, Grenley MO, McEwen DG, Edgar BA. 2009. Cytokine/Jak/Stat signaling mediates regeneration and homeostasis in the Drosophila midgut. Cell 137:1343-1355.

Jindrich K, Degnan BM. 2016. The diversification of the basic leucine zipper family in eukaryotes correlates with the evolution of multicellularity. BMC Evolutionary Biology 16:28.

Johnson AN, Mokalled MH, Haden TN, Olson EN. 2011. JAK/Stat signaling regulates heart precursor diversification inDrosophila . Development 138:4627-4638.

Johnston CJC, Smyth DJ, Dresser DW, Maizels RM. 2016. TGF-β in tolerance, development and regulation of immunity. Cellular Immunology 299:14-22.

157 Jones B, Nishiguchi M. 2004. Counterillumination in the Hawaiian bobtail squid, Euprymna scolopes Berry (: Cephalopoda). International Journal on Life in Oceans and Coastal Waters 144:1151- 1155.

Jones DT, Taylor WR, Thornton JM. 1992. The rapid generation of mutation data matrices from protein sequences. Computer Applications in The Biosciences 8:275-282.

Jutras I, Desjardins M. 2005. Phagocytosis: at the crossroads of innate and adaptive immunity. Annual Review of Cell and Developmental Biology 21:511-527.

Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. 2016. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Research 44:D457-D462.

Kaparakis M, Philpott DJ, Ferrero RL. 2007. Mammalian NLR proteins; discriminating foe from friend. Immunology and Cell Biology 85:495-502.

Kawai T, Akira S. 2007. Signaling to NF-κB by Toll-like receptors. Trends in Molecular Medicine 13:460-469.

Kennedy J, Flemer B, Jackson SA, Morrissey JP, O’Gara F, Dobson ADW, Horn M. 2014. Evidence of a putative deep sea specific microbiome in marine sponges. PLoS ONE 9:e91092.

Kersse K, Verspurten J, Vanden Berghe T, Vandenabeele P. 2011. The death-fold superfamily of homotypic interaction motifs. Trends in Biochemical Sciences 36:541-552.

Kilian E. 1952. Wasserströmung und Nahrungsaufnahme beim Süsswasserschwamm . Zeitschrift für vergleichende Physiologie 34:407-447.

Kim SI, Kwak JH, Na HJ, Kim JK, Ding Y, Choi ME. 2009. Transforming growth factor-beta (TGF- beta1) activates TAK1 via TAB1-mediated autophosphorylation, independent of TGF-beta receptor kinase activity in mesangial cells. Journal of Biological Chemistry 284:22285-22296.

Knoll AH. 2003. Life on a young planet: the first three billion years of evolution on Earth. Princeton, N.J: Princeton University Press.

Komatsu M, Kurokawa H, Waguri S, Taguchi K, Kobayashi A, Ichimura Y, Sou YS, Ueno I, Sakamoto A, Tong KI, et al. 2010. The selective autophagy substrate p62 activates the stress responsive transcription factor Nrf2 through inactivation of Keap1. Nature Cell Biology 12:213-223.

Koonin EV, Aravind L. 2002. Origin and evolution of eukaryotic apoptosis: the bacterial connection. Cell Death and Differentiation 9:394-404.

158 Koropatnick TA, Engle JT, Apicella MA, Stabb EV, Goldman WE, McFall-Ngai MJ. 2004. Microbial factor-mediated development in a host-bacterial mutualism. Science 306:1186-1188.

Kowalke J. 2000. Ecology and energetics of two Antarctic sponges. Journal of Experimental Marine Biology and Ecology 247:85-97.

Krasity BC, Troll JV, Weiss JP, McFall-Ngai MJ. 2011. LBP/BPI proteins and their relatives: conservation over evolution and roles in mutualism. Biochemical Society Transactions 39:1039-1044.

Krishnan A, Dnyansagar R, Alm?n MSl, Williams MJ, Fredriksson R, Manoj N, Schi?th HB. 2014. The GPCR repertoire in the demosponge Amphimedon queenslandica : insights into the GPCR system at the early divergence of animals. BMC Evolutionary Biology 14:270.

Kufer TA. 2008. Signal transduction pathways used by NLR-type innate immune receptors. Molecular bioSystems 4:380-386.

Kufer TA, Sansonetti PJ. 2011. NLR functions beyond pathogen recognition. Nature Immunology 12:121-128.

Kunen S, Claus G, Madri P, Peyser L. 1971. The ingestion and digestion of yeast-like fungi by the sponge, microcionia prolifera. The International Journal of Aquatic Sciences 38:565-576.

Kurtz J, Armitage SAO. 2006. Alternative adaptive immunity in invertebrates. Trends in Immunology 27:493-496.

Kwon H, Thierry-Mieg D, Thierry-Mieg J, Kim H-P, Oh J, Tunyaplin C, Carotta S, Donovan CE, Goldman ML, Tailor P, et al. 2009. Analysis of interleukin-21-induced Prdm1 gene regulation reveals functional cooperation of STAT3 and IRF4 transcription factors. Immunity 31:941-952.

Lacher SE, Lee JS, Wang X, Campbell MR, Bell DA, Slattery M. 2015. Beyond antioxidant genes in the ancient Nrf2 regulatory network. Free Radical Biology and Medicine 88:452-465.

Lacy P, Stow JL. 2011. Cytokine release from innate immune cells: association with diverse membrane trafficking pathways. Blood 118:9-18.

Laing KJ, Purcell MK, Winton JR, Hansen JD. 2008. A genomic view of the NOD-like receptor family in teleost fish: identification of a novel NLR subfamily in zebrafish. BMC Evolutionary Biology 8:42-42.

Lamkanfi M. 2011. Emerging inflammasome effector mechanisms. Nature Reviews Immunology 11:213-220.

Lang R, Hammer M, Mages J. 2006. DUSP meet immunology: dual specificity MAPK phosphatases in control of the inflammatory response. Journal of Immunology 177:7497-7504.

159 Lange C, Hemmrich G, Klostermeier UC, Lopez-Quintero JA, Miller DJ, Rahn T, Weiss Y, Bosch TCG, Rosenstiel P. 2011. Defining the origins of the NOD-like receptor system at the base of animal evolution. Molecular Biology and Evolution 28:1687-1702.

Larroux C, Fahey B, Adamska M, Richards GS, Gauthier M, Green K, Lovas E, Degnan BM. 2008. Whole-mount in situ hybridization in Amphimedon. Cold Spring Harbor Protocols 3:pdb.prot5096.

Larroux C, Luke GN, Koopman P, Rokhsar DS, Shimeld SM, Degnan BM. 2008. Genesis and expansion of metazoan transcription factor gene classes. Molecular Biology and Evolution 25:980-996.

Lee H-Y, Takeshita T, Shimada J, Akopyan A, Woo J-I, Pan H, Moon SK, Andalibi A, Park R-K, Kang S-H, et al. 2008. Induction of beta defensin 2 by NTHi requires TLR2 mediated MyD88 and IRAK-TRAF6-p38MAPK signaling pathway in human middle ear epithelial cells. BMC Infectious Diseases 8:87-87. Lee OO, Chui PY, Wong YH, Pawlik JR, Qian P-Y. 2009. Evidence for vertical transmission of bacterial symbionts from adult to embryo in the caribbean sponge Svenzea zeai. Applied and Environmental Microbiology 75:6147-6156.

Lee SS, Kennedy S, Tolonen AC, Ruvkun G. 2003. DAF-16 target genes that control C. elegans life- span and metabolism. Science 300:644-647.

Leipe DD, Koonin EV, Aravind L. 2004. STAND, a class of P-loop NTPases including animal and plant regulators of programmed cell death: multiple, complex domain architectures, unusual phyletic patterns, and evolution by horizontal gene transfer. Journal of Molecular Biology 343:1-28.

Lemaitre B, Hoffmann J. 2007. The host defense of Drosophila melanogaster. Annual Review of Immunology 25:697-743.

Lemire A, Lea K, Batten D, Jian Gu S, Whitley P, Bramlett K, Qu L. 2011. Development of ERCC RNA spike-in control mixes. Journal of Biomolecular Techniques 22:S46-S46.

Levin M, Anavy L, Cole AG, Winter E, Mostov N, Khair S, Senderovich N, Kovalev E, Silver DH, Feder M, et al. 2016. The mid-developmental transition and the evolution of animal body plans. Nature 531:637-641.

Leys SP, Degnan BM. 2001. Cytological basis of photoresponsive behavior in a sponge larva. Biological Bulletin 201:323-338.

Leys SP, Degnan BM. 2002. Embryogenesis and metamorphosis in a haplosclerid demosponge: gastrulation and transdifferentiation of larval ciliated cells to choanocytes. Invertebrate Biology 121:171- 189.

160 Leys SP, Larroux C, Gauthier M, Adamska M, Fahey B, Richards GS, Degnan SM, Degnan BM. 2008. Isolation of Amphimedon developmental material. Cold Spring Harbor Protocols 3:pdb.prot5095-pdb. prot5095.

Li C-W, Chen J-Y, Hua T-E. 1998. sponges with cellular structures. Science 279:879-882. Li X, Cui Z, Liu Y, Song C, Shi G. 2013. Transcriptome analysis and discovery of genes involved in immune pathways from hepatopancreas of microbial challenged mitten crab Eriocheir sinensis. PLoS ONE 8:e68233.

Li X, Matilainen O, Jin C, Glover-Cutter KM, Holmberg CI, Blackwell TK. 2011. Specific SKN-1/Nrf stress responses to perturbations in translation elongation and proteasome activity (SKN-1 monitors protein synthesis and degradation). PLoS Genetics 7:e1002119.

Li ZL, Abe H, Otsuki K, Ueki K, Kumagai R, Araki Y, Otsuki Y. 2003. Identification of c-Jun as bcl- 2 transcription factor in human uterine endometrium. Journal of Histochemistry and Cytochemistry 51:1601-1609.

Liongue C, Taznin T, Ward AC. 2016. Signaling via the CytoR/JAK/STAT/SOCS pathway: emergence during evolution. Molecular Immunology 71:166-175.

Liu F, Rehmani I, Esaki S, Fu R, Chen L, de Serrano V, Liu A. 2013. Pirin is an iron-dependent redox regulator of NF-kappaB. Proceedings of the National Academy of Sciences of the United States of America 110:9722-9727.

Liu X, Wang N, Zhu Y, Yang Y, Chen X, Chen Q, Zhou H, Zheng J. 2016. Extracellular calcium influx promotes antibacterial autophagy in Escherichia coli infected murine macrophages via CaMKKbeta dependent activation of ERK1/2, AMPK and FoxO1. Biochemical and Biophysical Research Communications 469:639-645. Lou Y, Liu S. 2011. The TIPE (TNFAIP8) family in inflammation, immunity, and cancer. Molecular Immunology 49:4-7.

Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15:550.

Lowe CD, Minter EJ, Cameron DD, Brockhurst MA. 2016. Shining a light on exploitative host control in a photosynthetic endosymbiosis. Current Biology 26:207-211.

Ma Q. 2013. Role of nrf2 in oxidative stress and toxicity. Annual Review of Pharmacology and Toxicology 53:401-426.

161 Mabery EM, Schneider DS. 2010. The Drosophila TNF ortholog Eiger is required in the fat body for a robust immune response. Journal of Innate Immunity 2:371-378.

Madsen J, Mollenhauer J, Holmskov U. 2010. Review: Gp-340/DMBT1 in mucosal innate immunity. Innate Immunity 16:160-167.

Maekawa T, Kufer TA, Schulze-Lefert P. 2011. NLR functions in plant and animal immune systems: so far and yet so close. Nature Immunology 12:817-826.

Mah JL, Christensen‐dalsgaard KK, Leys SP. 2014. Choanoflagellate and choanocyte collar‐flagellar systems and the assumption of homology. Evolution & Development 16:25-37.

Maldonado M. 2004. Choanoflagellates, choanocytes, and animal multicellularity. Invertebrate Biology 123:1-22.

Maldonado M, Ribes M, van Duyl FC. 2012. Nutrient fluxes through sponges: biology, budgets, and ecological implications. Advances in Marine Biology 62:113-182.

Maldonado M, Zhang X, Cao X, Xue L, Cao H, Zhang W. 2010. Selective feeding by sponges on pathogenic microbes: a reassessment of potential for abatement of microbial pollution. Marine Ecology Progress Series 403:75-89.

Martinon F, Burns K, Tschopp J. 2002. The inflammasome: a molecular platform triggering activation of inflammatory caspases and processing of proIL-beta. Molecular Cell 10:417-426.

Martinon F, Mayor A, Tschopp J. 2009. The inflammasomes: guardians of the body. Annual Review of Immunology 27:229-265.

Masson F, Zaidman-Rémy A, Heddi A. 2016. Antimicrobial peptides and cell processes tracking endosymbiont dynamics. Philosophical Transactions of the Royal Society B 371: 20150298.

Mazmanian SK, Liu CH, Tzianabos AO, Kasper DL. 2005. An immunomodulatory molecule of symbiotic bacteria directs maturation of the host immune system. Cell 122:107-118.

McCarthy DJ, Chen Y, Smyth GK. 2012. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40:4288-4297.

McCormack R, Podack E. 2015. Perforin-2/Mpeg1 and other pore-forming proteins throughout evolution. Journal of Leukocyte Biology 98:761-768.

McEwan D, Kirienko N, Ausubel F. 2012. Host Translational Inhibition by Pseudomonas aeruginosa Exotoxin A Triggers an Immune Response in Caenorhabditis elegans. Cell Host & Microbe 11:364-374.

McFall-Ngai M. 2007. Adaptive Immunity: Care for the community. Nature 445:153.

162 McFall-Ngai M, Gilbert SF, Hentschel U, King N, Kjelleberg S, Knoll AH, Kremer N, Mazmanian SK, Metcalf JL, Nealson K, et al. 2013. Animals in a bacterial world, a new imperative for the life sciences. Proceedings of the National Academy of Sciences of the United States of America 110:3229-3236.

McMahan RS, Riehle KJ, Fausto N, Campbell JS. 2013. A disintegrin and metalloproteinase 17 regulates TNF and TNFR1 levels in inflammation and liver regeneration in mice. American Journal of Physiology - Gastrointestinal and Liver Physiology 305:G25-34.

McMurray SE, Johnson ZI, Hunt DE, Pawlik JR, Finelli CM. 2016. Selective feeding by the giant barrel sponge enhances foraging efficiency: Foraging ofXestospongia muta. Limnology and Oceanography 61:1271–1286

Medzhitov R, Janeway Jr CA. 2002. Decoding the patterns of self and nonself by the innate immune system. Science 296:298-300.

Mehbub MF, Lei J, Franco C, Zhang W. 2014. Marine sponge derived natural products between 2001 and 2010: trends and opportunities for discovery of bioactives. Marine Drugs 12:4539-4577.

Messier-Solek C, Buckley KM, Rast JP. 2010. Highly diversified innate receptor systems and new forms of animal immunity. Seminars in Immunology 22:39-47.

Mezyk-Kopec R, Bzowska M, Stalinska K, Chelmicki T, Podkalicki M, Jucha J, Kowalczyk K, Mak P, Bereta J. 2009. Identification of ADAM10 as a major TNF sheddase in ADAM17-deficient fibroblasts. Cytokine 46:309-315.

Miaczynska M, Stenmark H. 2008. Mechanisms and functions of endocytosis. Journal of Cell Biology 180:7-11.

Miller M. 2009. The importance of being flexible: the case of basic region leucine zipper transcriptional regulators. Current Protein and Peptide Science 10:244-269.

Miyata S, Begun J, Troemel ER, Ausubel FM. 2008. DAF-16-dependent suppression of immunity during reproduction in Caenorhabditis elegans. Genetics 178:903-918.

Monteleone G, Boirivant M, Pallone F, Macdonald TT. 2008. TGF-β1 and Smad7 in the regulation of IBD. Mucosal Immunology 1:S50.

Muzio M, Ni J, Feng P, Dixit VM. 1997. IRAK (Pelle) family member IRAK-2 and MyD88 as proximal mediators of IL-1 signaling. Science 278:1612-1615.

Nakae J, Oki M, Cao Y. 2008. The FoxO transcription factors and metabolic regulation. FEBS Letters 582:54-67.

163 Nakanishi N, Sogabe S, Degnan BM. 2014. Evolutionary origin of gastrulation: insights from sponge development. BMC Biology 12:26.

Nakanishi N, Stoupin D, Degnan SM, Degnan BM. 2015. Sensory flask cells in sponge larvae regulate metamorphosis via calcium signaling. Integrative & Comparative Biology 55:1018-1027.

Netea MG, Quintin J, van der Meer JWM. 2011. Trained immunity: a memory for innate host defense. Cell Host & Microbe 9:355-361.

Nguyen MTHD, Liu M, Thomas T. 2013. Ankyrin‐repeat proteins from sponge symbionts modulate amoebal phagocytosis. Molecular Ecology 23:1635-1645.

Nicholson J, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, Pettersson S. 2012. Host-gut microbiota metabolic interactions. Science 336:1262-1267.

Niswender CM, Conn PJ. 2010. Metabotropic glutamate receptors: physiology, pharmacology, and disease. Annual Review of Pharmacology and Toxicology 50:295-322.

Nyholm SV, Graf J. 2012. Knowing your friends: invertebrate innate immunity fosters beneficial bacterial symbioses. Nature Reviews Microbiology 10:815-827.

Nyholm SV, Stewart JJ, Ruby EG, McFall‐ngai MJ. 2009. Recognition between symbiotic Vibrio fischeri and the haemocytes of Euprymna scolopes. Environmental Microbiology 11:483-493.

O’Sullivan ML, Martini F, Von Daake S, Comoletti D, Ghosh A. 2014. LPHN3, a presynaptic adhesion- GPCR implicated in ADHD, regulates the strength of neocortical layer 2/3 synaptic input to layer 5. Neural Development 9:7.

Ono K, Han J. 2000. The p38 signal transduction pathway: activation and function. Cell Signal 12:1-13. Ouyang W, Beckett O, Flavell RA, Li MO. 2009. An essential role of the forkhead-box transcription factor FoxO1 in control of T cell homeostasis and tolerance. Immunity 30:358-371.

Palsson-McDermott EM, O’Neill LA. 2007. Building an immune system from nine domains. Biochemical Society Transactions 35:1437-1444.

Pancer Z. 2001. Individual-specific-repertoires of immune cells SRCR receptors in the Purple Sea Urchin (S. purpuratus). In: Advances in Experimental Medicine and Biology: Kluwer Academic/ Plenum Publ. New York. p. 31-40.

Pancer Z, Munkner J, Muller I, Muller WE. 1997. A novel member of an ancient superfamily: sponge (Geodia cydonium, Porifera) putative protein that features scavenger receptor cysteine-rich repeats. Gene 193:211-218.

164 Parenti G, Meroni G, Ballabio A. 1997. The sulfatase gene family. Current Opinion in Genetics & Development 7:386-391.

Park J-O, Pan J, Möhrlen F, Schupp M-O, Johnsen R, Baillie DL, Zapf R, Moerman DG, Hutter H. 2010. Characterization of the astacin family of metalloproteases in C. elegans. BMC Developmental Biology 10:14.

Patten N, Mitchell J, Middelboe M, Eyre B, Seuront L, Harrison P, Glud R. 2008. Bacterial and viral dynamics during a mass coral spawning period on the Great Barrier Reef. Aquatic Microbial Ecology 50:209-220.

Pawlik JR. 2011. The chemical ecology of sponges on caribbean reefs: natural products shape natural systems. BioScience 61:888-898.

Philippe H, Derelle R, Lopez P, Pick K, Borchiellini C, Boury-Esnault N, Vacelet J, Renard E, Houliston E, Queinnec E, et al. 2009. revives traditional views on deep animal relationships. Current Biology 19:706–712.

Pile A, Patterson M, Witman JD. 1996. In situ grazing on 10 µm by the boreal sponge lingua. Marine Ecology Progress Series 141:95-102.

Pinheiro PS, Mulle C. 2008. Presynaptic glutamate receptors: physiological functions and mechanisms of action. Nature Reviews Neuroscience 9:423–436.

Pozzolini M, Scarfi S, Ghignone S, Mussino F, Vezzulli L, Cerrano C, Giovine M. 2016. Molecular characterization and expression analysis of the first Porifera tumor necrosis factor superfamily member and of its putative receptor in the marine sponge Chondrosia reniformis. Developmental and Comparative Immunology 57:88-98. Prell J, White JP, Bourdes A, Bunnewell S, Bongaerts RJ, Poole PS. 2009. Legumes regulate Rhizobium bacteroid development and persistence by the supply of branched-chain amino acids. Proceedings of the National Academy of Sciences of the United States of America 106:12477–12482.

Proell M, Riedl SJ, Fritz JH, Rojas AM, Schwarzenbacher R. 2008. The Nod-like receptor (NLR) family: a tale of similarities and differences. PLoS ONE 3:e2119.

Pugazhenthi S, Zhang Y, Bouchard R, Mahaffey G. 2013. Induction of an inflammatory loop by interleukin-1β and tumor necrosis factor-α involves NF-kB and STAT-1 in differentiated human neuroprogenitor cells. PLoS ONE 8:e69585.

Rada B, Leto TL. 2008. Oxidative innate immune defenses by Nox/Duox family NADPH oxidases. Contributions to Microbiology 15:164-187.

165 Rakoff-Nahoum S, Paglino J, Eslami-Varzaneh F, Edberg S, Medzhitov R. 2004. Recognition of commensal microflora by toll-like receptors is required for intestinal homeostasis. Cell 118:229-241.

Reiswig HM. 1975. Bacteria as food for temperate-water marine sponges. Canadian Journal of Zoology 53:582-589.

Reiswig HM. 1971. Particle feeding in natural populations of three marine demosponges. Biological Bulletin 141:568-591.

Reiswig HM. 1974. Water transport, respiration and energetics of three tropical marine sponges. Journal of Experimental Marine Biology and Ecology 14:231-249.

Resch U, Winsauer G, Hofer-Warbinek R, de Martin R. 2006. X-linked inhibitor of apoptosis protein regulates human interleukin-6 in umbilical vein endothelial cells via stimulation of the nuclear factor- kappaB and MAP kinase signaling pathways. Pharmacological Reports 58 Suppl:111-117.

Reveillaud J, Maignien L, Murat Eren A, Huber JA, Apprill A, Sogin ML, Vanreusel A. 2014. Host- specificity among abundant and rare taxa in the sponge microbiome. International Society for Microbial Ecology Journal 8:1198-1209.

Ribes M, Coma R, Gili J. 1999. Natural diet and grazing rate of the temperate sponge avara (Demospongiae, ) throughout an annual cycle. Marine Ecology Progress Series 176:179- 190.

Richardson C, Hill M, Marks C, Runyen‐janecky L, Hill A. 2012. Experimental manipulation of sponge/ bacterial symbiont community composition with antibiotics: sponge cell aggregates as a unique tool to study animal/microorganism symbiosis. FEMS Microbiology Ecology 81:407-418.

Robertson SJ, Girardin SE. 2013. Nod-like receptors in intestinal host defense: controlling pathogens, the microbiota, or both? Current Opinion in Gastroenterology 29:15-22.

Robertson SJ, Rubino SJ, Geddes K, Philpott DJ. 2012. Examining host-microbial interactions through the lens of NOD: From plants to mammals. Seminars in Immunology 24:9-16.

Robinson MD, McCarthy DJ, Smyth GK. 2010. edgeR : a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139-140.

Robinson MD, Smyth GK. 2007a. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23:2881-2887.

Robinson MD, Smyth GK. 2007b. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9:321-332.

166 Romacho T, Elsen M, Röhrborn D, Eckel J. 2014. Adipose tissue and its role in organ crosstalk. Acta Physiologica 210:733-753.

Ronquist F, Huelsenbeck JP, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, Larget B, Liu L, Suchard MA. 2012. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic Biology 61:539-542.

Rosenstiel P, Philipp EER, Schreiber S, Bosch TCG. 2009. Evolution and function of innate immune receptors--insights from marine invertebrates. Journal of Innate Immunity 1:291-300.

Rosenstiel P, Sina C, End C, Renner M, Lyer S, Till A, Hellmig S, Nikolaus S, Folsch UR, Helmke B, et al. 2007. Regulation of DMBT1 via NOD2 and TLR4 in intestinal epithelial cells modulates bacterial recognition and invasion. Journal of Immunology 178:8203-8211.

Roth O, Sadd BM, Schmid-Hempel P, Kurtz J. 2009. Strain-specific priming of resistance in the red flour beetle, Tribolium castaneum. Philosophical Transactions of the Royal Society B 276:145-151.

Round JL, Lee SM, Li J, Tran G, Jabri B, Chatila TA, Mazmanian SK. 2011. The Toll-like receptor 2 pathway establishes colonization by a commensal of the human microbiota. Science 332:974-977.

Ruan H, Lodish HF. 2003. Insulin resistance in adipose tissue: direct and indirect effects of tumor necrosis factor-alpha. Cytokine and Growth Factor Reviews 14:447-455.

Ryu J-H, Kim S-H, Lee DG, Lee W-J, Lee H-Y, Bai JY, Nam Y-D, Bae J-W, Shin SC, Ha E-M. 2008. Innate immune homeostasis by the homeobox gene caudal and commensal-gut mutualism in Drosophila. Science 319:777-782.

Ryu T, Seridi L, Moitinho-Silva L, Oates M, Liew YJ, Mavromatis C, Wang X, Haywood A, Lafi FF, Kupresanin M, et al. 2016. Hologenome analysis of two marine sponges with different microbiomes. BMC Genomics 17:158.

Saller U. 1989. Microscopical aspects on symbiosis of Spongilla lacustris (Porifera, Spongillidae) and green algae. Evolutionary, Comparative and Functional Morphology 108:291-296.

Sanchez-Contreras M, Bauer W, Gao M, Robinson J, Downie J. 2007. Quorum-sensing regulation in rhizobia and its role in symbiotic interactions with legumes. Philosophical Transactions of the Royal Society B 362:1149-1163.

Sansonetti PJ. 2006. The innate signaling of dangers and the dangers of innate signaling. Nature Immunology 7:1237-1242.

167 Santo EE, Stroeken P, Sluis PV, Koster J, Versteeg R, Westerhout EM. 2013. FOXO3a is a major target of inactivation by PI3K/AKT signaling in aggressive neuroblastoma. Cancer Research 73:2189-2198.

Sarantis H, Grinstein S. 2012. Subversion of phagocytosis for pathogen survival. Cell Host Microbe 12:419-431.

Sarrias M, Groenlund J, Padilla O, Madsen J, Holmskov U, Lozano F. 2004. The Scavenger Receptor Cysteine-Rich (SRCR) domain: an ancient and highly conserved protein module of the innate immune system. Critical Reviews in Immunology 24:1-38.

Sartor R, Mazmanian S. 2012. Intestinal microbes in inflammatory bowel diseases. American Journal of Gastroenterology Supplements 1:15-21.

Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, et al. 2012. Fiji: an open-source platform for biological-image analysis. Nature Methods 9:676-682.

Schirmer A, Gadkari R, Reeves CD, Ibrahim F, DeLong EF, Hutchinson CR. 2005. Metagenomic analysis reveals diverse polyketide synthase gene clusters in microorganisms associated with the marine sponge dissoluta. Applied and Environmental Microbiology 71:4840-4849.

Schmidt I. 1970. Phagocytosis and pinocytosis in spongillids. Zeitschrift für vergleichende Physiologie 66:398-420.

Schmitt S, Hentschel U, Taylor M. 2012. Deep sequencing reveals diversity and community structure of complex microbiota in five Mediterranean sponges. The International Journal of Aquatic Sciences 687:341-351.

Schneider DS, Ayres JS, Brandt SM, Costa A, Dionne MS, Gordon MD, Mabery EM, Moule MG, Pham LN, Shirasu-Hiza MM. 2007. Drosophila eiger mutants are sensitive to extracellular pathogens. PLoS Pathogens 3:e41.

Schroder K, Tschopp J. 2010. The Inflammasomes. Cell 140:821-832. Schulenburg H, Boehnisch C, Michiels NK. 2007. How do invertebrates generate a highly specific innate immune response? Molecular Immunology 44:3338-3344.

Selvin J, Shanmugha Priya S, Seghal Kiran G, Thangavelu T, Sapna Bai N. 2009. Sponge-associated marine bacteria as indicators of heavy metal pollution. Microbiological Research 164:352-363.

Shaulian E, Karin M. 2002. AP-1 as a regulator of cell life and death. Nature Cell Biology 4:E131-136.

168 Shaw PJ, Lamkanfi M, Kanneganti T-D. 2010. NOD-like receptor (NLR) signaling beyond the inflammasome. European Journal of Immunology 40:624-627.

Shen G, Kong AN. 2009. Nrf2 plays an important role in coordinated regulation of Phase II drug metabolism enzymes and Phase III drug transporters. Biopharmaceutics & Drug Disposition 30:345-355.

Silva J-P, Lelianova VG, Ermolyuk YS, Vysokov N, Hitchen PG, Berninghausen O, Rahman MA, Zangrandi A, Fidalgo S, Tonevitsky AG, et al. 2011. Latrophilin 1 and its endogenous ligand Lasso/ teneurin-2 form a high-affinity transsynaptic receptor pair with signaling capabilities. Proceedings of the National Academy of Sciences of the United States of America 108:12113-12118. Simister RL, Deines P, Botté ES, Webster NS, Taylor MW. 2012. Sponge‐specific clusters revisited: a comprehensive phylogeny of sponge‐associated microorganisms. Environmental Microbiology 14:517-524.

Singh V, Aballay A. 2009. Regulation of DAF-16-mediated innate immunity in Caenorhabditis elegans. Journal of Biological Chemistry 284:35580–35587.

Smith JA, Poteet-Smith CE, Malarkey K, Sturgill TW. 1999. Identification of an extracellular signal- regulated kinase (ERK) docking site in ribosomal S6 kinase, a sequence critical for activation by ERK in vivo. Journal of Biological Chemistry 274:2893-2898.

Sogabe S, Nakanishi N, Degnan BM. 2016. The ontogeny of choanocyte chambers during metamorphosis in the demosponge Amphimedon queenslandica. EvoDevo 7:6.

Srivastava M, Simakov O, Chapman J, Fahey B, Gauthier MEA, Mitros T, Richards GS, Conaco C, Dacre M, Hellsten U, et al. 2010. The Amphimedon queenslandica genome and the evolution of animal complexity. Nature 466:720-726.

Steindler L, Schuster S, Ilan M, Avni A, Cerrano C, Beer S. 2007. Differential gene expression in a marine sponge in relation to its symbiotic state. Marine Biotechnology 9:543-549.

Steinert G, Taylor MW, Deines P, Simister RL, de Voogd NJ, Hoggard M, Schupp PJ, Pawlik J. 2016. In four shallow and mesophotic tropical reef sponges from Guam the microbial community largely depends on host identity. PeerJ 4:e1936.

Stuart LM, Paquette N, Boyer L. 2013. Effector-triggered versus pattern-triggered immunity: how animals sense pathogens. Nature Reviews Immunology 13:199-206.

Suga H, Dacre M, de Mendoza A, Shalchian-Tabrizi K, Manning G, Ruiz-Trillo I. 2013. Genomic survey of premetazoans shows deep conservation of cytoplasmic tyrosine kinases and multiple radiations of receptor tyrosine kinases. Science Signaling 5:ra35-ra35.

169 Sunagawa S, DeSantis TZ, Piceno YM, Brodie EL, DeSalvo MK, Voolstra CR, Weil E, Andersen GL, Medina M. 2009. Bacterial diversity and White Plague Disease-associated community changes in the Caribbean coral Montastraea faveolata. International Society for Microbial Ecology Journal 3:512-521.

Tamás P, Hawley SA, Clarke RG, Mustard KJ, Green K, Hardie DG, Cantrell DA. 2006. Regulation of the energy sensor AMP-activated protein kinase by antigen receptor and Ca 2+ in T lymphocytes. Journal of Experimental Medicine 203:1665-1670.

Tang D, Kang R, Coyne CB, Zeh HJ, Lotze MT. 2012. PAMP s and DAMP s: signal 0s that spur autophagy and immunity. Immunological Reviews 249:158-175.

Tasdemir D, Mangalindan GC, Concepción GP, Verbitski SM, Rabindran S, Miranda M, Greenstein M, Hooper JNA, Harper MK, Ireland CM. 2002. Bioactive isomalabaricane triterpenes from the marine sponge Rhabdastrella globostellata. Journal of Natural Products 65:210-214.

Tate AT, Rudolf VHW. 2012. Impact of life stage specific immune priming on invertebrate disease dynamics. Oikos 121:1083-1092.

Tauber AI. 2003. Metchnikoff and the phagocytosis theory. Nature Reviews Molecular Cell Biology 4:897-901.

Tauszig S, Jouanguy E, Hoffmann JA, Imler JL. 2000. Toll-related receptors and the control of antimicrobial peptide expression in Drosophila. Proceedings of the National Academy of Sciences of the United States of America 97:10520-10525.

Thomas T, Moitinho-Silva L, Lurgi M, Bjork JR, Easson C, Astudillo-Garcia C, Olson JB, Erwin PM, Lopez-Legentil S, Luter H, et al. 2016. Diversity, structure and convergent evolution of the global sponge microbiome. Nature Communications 7:11870.

Thurley K, Gerecht D, Friedmann E, Hofer T. 2015. Three-dimensional gradients of cytokine signaling between T cells. PLoS Computational Biology 11:e1004206.

Ting JPY, Duncan JA, Lei Y. 2010. How the noninflammasome NLRs function in the innate immune system. Science 327:286-290.

Ting JPY, Harton JA, Hoffman HM, Hugot J-P, Inohara N, Mackenzie A, Maltais LJ, Nunez G, Ogura Y, Otten LA, et al. 2008. The NLR gene family: a standard nomenclature. Immunity 28:285-287.

Ting JPY, Willingham SB, Bergstralh DT. 2008. NLRs at the intersection of cell death and immunity. Nature Reviews Immunology 8:372-379.

170 Tong D, Rozas NS, Oakley TH, Mitchell J, Colley NJ, McFall-Ngai MJ. 2009. Evidence for light perception in a bioluminescent organ. Proceedings of the National Academy of Sciences of the United States of America 106:9836-9841.

Troemel E. 2012. Host detection of pathogen-induced translational inhibition: A new pathogen-specific branch of the innate immune system? Future Microbiology 7:1133-1136.

Troll JV, Bent EH, Pacquette N, Wier AM, Goldman WE, Silverman N, McFall‐ngai MJ. 2010. Taming the symbiont for coexistence: a host PGRP neutralizes a bacterial symbiont toxin. Environmental Microbiology 12:2190-2203.

Tullet JMA, Araiz C, Sanders MJ, Au C, Benedetto A, Papatheodorou I, Clark E, Schmeisser K, Jones D, Schuster EF, et al. 2014. DAF-16/FoxO directly regulates an atypical AMP-activated protein kinase gamma isoform to mediate the effects of insulin/IGF-1 signaling on aging inCaenorhabditis elegans. PLoS Genetics 10:e1004109. Turon X, Galera J, Uriz M. 1997. Clearance rates and aquiferous systems in two sponges with contrasting life-history strategies. Journal of Experimental Zoology 278:22-36.

Tyler S. 2003. Epithelium - The primary building block for metazoan complexity. Integrative & Comparative Biology 43:55-63.

Vacelet J, Donadey C. 1977. Electron microscope study of the association between some sponges and bacteria. Journal of Experimental Marine Biology and Ecology 30:301-314.

Vahedi G, Takahashi H, Nakayamada S, Sun H-W, Sartorelli V, Kanno Y, O’shea John j. 2012. STATs shape the active enhancer landscape of T cell populations. Cell 151:981-993.

Van Der Vos KE, Coffer PJ. 2008. FOXO-binding partners: it takes two to tango. Oncogene 27:2289– 2299. van Niekerk G, Engelbrecht A-M. 2015. Commentary on: “A common origin for immunity and digestion”. Frontiers in Microbiology 6:531.

Vance RE, Isberg RR, Portnoy DA. 2009. Patterns of pathogenesis: discrimination of pathogenic and nonpathogenic microbes by the innate immune system. Cell Host & Microbe 6:10-21.

Vera J, Fenutría R, Cañadas O, Figueras M, Mota R, Sarrias M-R, Williams DL, Casals C, Yelamos J, Lozano F. 2009. The CD5 ectodomain interacts with conserved fungal cell wall components and protects from zymosan-induced septic shock-like syndrome. Proceedings of the National Academy of Sciences of the United States of America 106:1506-1511. Verma S, Miyashiro T. 2013. Quorum sensing in the squid-Vibrio symbiosis. International Journal of Molecular Sciences 14:16386-16401.

171 von Moltke J, Ayres JS, Kofoed EM, Chavarría-Smith J, Vance RE. 2013. Recognition of bacteria by inflammasomes. Annual Review of Immunology 31:73-106.

Wang J, Robida-Stubbs S, Tullet JMA, Rual J-F, Vidal M, Blackwell TK. 2010. RNAi screening implicates a SKN-1–dependent transcriptional response in stress resistance and longevity deriving from translation inhibition. PLoS Genetics 6:e1001048.

Wang W, Li Y, Zhou L, Yue H, Luo H. 2011. Role of JAK/STAT signaling in neuroepithelial stem cell maintenance and proliferation in the Drosophila optic lobe. Biochemical and Biophysical Research Communications 410:714-720.

Webster NS, Cobb RE, Negri AP. 2008. Temperature thresholds for bacterial symbiosis with a sponge. International Society for Microbial Ecology Journal 2:830-842.

Webster NS, Taylor MW. 2012. Marine sponges and their microbial symbionts: love and other relationships. Environmental Microbiology 14:335-346.

Webster NS, Taylor MW, Behnam F, Lucker S, Rattei T, Whalan S, Horn M, Wagner M. 2010. Deep sequencing reveals exceptional diversity and modes of transmission for bacterial sponge symbionts. Environmental Microbiology 12:2070-2082.

Webster NS, Thomas T. 2016. The sponge hologenome. mBio 7:e00135-00116. Wehrl M, Steinert M, Hentschel U. 2007. Bacterial uptake by the marine sponge aerophoba. Microbial Ecology 53:355-365.

Weisz JB, Lindquist N, Martens CS. 2008. Do associated microbial abundances impact marine demosponge pumping rates and tissue densities? Oecologia 155:367-376.

Wernegreen JJ. 2012. Endosymbiosis. Current Biology 22:R555-R561. Whelan S, Goldman N. 2001. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular Biology and Evolution 18:691-699.

Wickham H. 2009. ggplot2 Elegant graphics for data analysis. New York: New York : Springer. Wiens GD, Glenney GW. 2011. Origin and evolution of TNF and TNF receptor superfamilies. Developmental and Comparative Immunology 35:1324-1335.

Wiens M, Korzhev M, Krasko A, Thakur NL, Perović-Ottstadt S, Breter HJ, Diehl-Seifert B, Müllert IM, Müller WEG, Ushijima H. 2005. Innate immune defense of the sponge against bacteria involves a MyD88-dependent signaling pathway: Induction of a perforin-like molecule. Journal of Biological Chemistry 280:27949-27959.

172 Wiens M, Korzhev M, Perovic-Ottstadt S, Luthringer B, Brandt D, Klein S, Müller WEG. 2007. Toll- like receptors are part of the innate immune defense system of sponges (demospongiae: Porifera). Molecular Biology and Evolution 24:792-804.

Wientjes FB, Segal AW. 1995. NADPH oxidase and the respiratory burst. Seminars in Cell Biology 6:357-365.

Wilkinson C. 1992. Symbiotic interactions between marine sponges and algae. In: Reisser W, editor. Algae and symbioses: plants, animals, fungi, viruses, interactions explored. Bristol, United Kingdom: Biopress Ltd. p. 112-128.

Wilkinson CR, Garrone R, Vacelet J. 1984. Marine sponges discriminate between food bacteria and bacterial symbionts: electron microscope radioautography and in situ evidence. Philosophical Transactions of the Royal Society B 220:519-528.

Willenz P, Van de Vyver G. 1984. Ultrastructural localization of lysosomal digestion in the fresh water sponge Ephydatia fluviatilis. Journal of Ultrastructure Research 87:13-22.

Williams CC, Allison JG, Vidal GA, Burow ME, Beckman BS, Marrero L, Jones FE. 2004. The ERBB4/HER4 receptor tyrosine kinase regulates gene expression by functioning as a STAT5A nuclear chaperone. Journal of Cell Biology 167:469-478.

Williams P. 2007. Quorum sensing, communication and cross-kingdom signalling in the bacterial world. Microbiology 153:3923-3938.

Wilmanski JM, Petnicki-Ocwieja T, Kobayashi KS. 2008. NLR proteins: integral members of innate immunity and mediators of inflammatory diseases. Journal of Leukocyte Biology 83:13-30.

Wollert T, Yang D, Ren X, Lee HH, Im YJ, Hurley JH. 2009. ESCRT machinery at a glance. Journal of Cell Science 122:2163-2166.

Xiang L-X, He D, Dong W-R, Zhang Y-W, Shao J-Z. 2010. Deep sequencing-based transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus reveals insight into the immune-relevant genes in marine fish. BMC Genomics 11:472.

Yahel G, Eerkes-Medrano D, Leys S. 2006. Size independent selective filtration of ultraplankton by glass sponges. Aquatic Microbial Ecology 45:181-194.

Yang S-K, Wang Y-C, Chao C-C, Chuang Y-J, Lan C-Y, Chen B-S. 2010. Dynamic cross-talk analysis among TNF-R, TLR-4 and IL-1R signalings in TNFa-induced inflammatory responses. BMC Medical Genomics 3:19-19.

173 Yoneyama M, Fujita T. 2009. RNA recognition and signal transduction by RIG‐I‐like receptors. Immunological Reviews 227:54-65.

Yuan S, Liu T, Huang S, Wu T, Huang L, Liu H, Tao X, Yang M, Wu K, Yu Y, et al. 2009. Genomic and functional uniqueness of the TNF receptor-associated factor gene family in Amphioxus, the basal . Journal of Immunology 183:4560-4568.

Yue J-X, Meyers BC, Chen J-Q, Tian D, Yang S. 2012. Tracing the origin and evolutionary history of plant nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes. The New Phytologist 193:1049- 1063.

Yuen B, Bayes JM, Degnan SM. 2014. The characterization of sponge NLRs provides insight into the origin and evolution of this innate immune gene family in animals. Molecular Biology and Evolution 31:106-120.

Zapata JM, Lefebvre S, Martínez-García V. 2007. Phylogeny of the TRAF/MATH domain. Advances in Experimental Medicine and Biology 597:1-24.

Zaslavsky E, Nudelman G, Marquez S, Hershberg U, Hartmann BM, Thakar J, Sealfon SC, Kleinstein SH. 2013. Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection. BMC Bioinformatics 14:S1.

Zeuthen LH, Fink LN, Frokiaer H. 2008. Epithelial cells prime the immune response to an array of gut‐derived commensals towards a tolerogenic phenotype through distinct actions of thymic stromal lymphopoietin and transforming growth factor‐β. Immunology 123:197-208.

Zhang Q, Zmasek CM, Godzik A. 2010. Domain architecture evolution of pattern-recognition receptors. Immunogenetics 62:263-272.

Ziauddin J, Schneider David s. 2012. Where does innate immunity stop and adaptive immunity begin? Cell Host & Microbe 12:394-395.

Zmasek CM, Godzik A. 2013. Evolution of the animal apoptosis network. Cold Spring Harbor Perspectives in Biology 5:a008649.

Zou C-G, Tu Q, Niu J, Ji X-L, Zhang K-Q. 2013. The DAF-16/FOXO transcription factor functions as a regulator of epidermal innate immunity. PLoS Pathogens 9:e1003660.

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Appendices

A note on additional files Several supplementary files described throughout this thesis are impractically large for inclusion in a book-style thesis. These files are available to download via Cloudstor+ a cloud storage and sharing web service run by AARNet (Australian Academic and Research Network). These appended files are referenced in-text, and their titles and descriptions are listed in thisAppendices section, in the order in which they would normally occur. These online-only files are highlighted with an asterisk here and in the List of Appendices.

Download information for these files is as follows: Link: https://cloudstor.aarnet.edu.au/plus/index.php/s/vRobmT3eAmtJdGA Password = amphimedon

177 Deciphering the genomic toolkit underlying animal-bacteria interactions

Chapter two has been published in an open access journal and the links to the appendices are available as follows: Appendix 2.1 Excluded NACHT domain-containing gene models *Available online http://mbe.oxfordjournals.org/content/suppl/2013/10/03/mst174.DC1/Supplementary_File_1. docx

Appendix 2.2 Summary of NLRs and NACHT-containing genes of taxa analysed in this study along with source of data *Available online http://mbe.oxfordjournals.org/content/suppl/2013/10/03/mst174.DC1/Supplementary_File_2. docx

Appendix 2.3 Figure 2.1 alignment FASTA file *Available online http://mbe.oxfordjournals.org/content/suppl/2013/10/03/mst174.DC1/Supplementary_File_3. docx

Appendix 2.4 Figure 2.3 alignment FASTA file *Available online http://mbe.oxfordjournals.org/content/suppl/2013/10/03/mst174.DC1/Supplementary_File_4. docx

Appendix 2.5 Metazoan NLR PhyMl tree *Available online http://mbe.oxfordjournals.org/content/suppl/2013/10/03/mst174.DC1/Supplementary_File_5.tif

178 Appendices

Appendix 3.1 Chapter three feeding experiment sample specifications and CEL-Seq2 run distribution

Sample name Treatment Time CEL-Seq run # point (h) F012 Foreign 0 1 (BYCS02) F013 Foreign 0 1 (BYCS02) F014 Foreign 0 1 (BYCS02) F211 Foreign 2 1 (BYCS02) F212 Foreign 2 1 (BYCS02) F213 Foreign 2 1 (BYCS02) F812 Foreign 8 1 (BYCS02) F813 Foreign 8 1 (BYCS02) F817 Foreign 8 1 (BYCS02) H013 Healthy 0 1 (BYCS02) H014 Healthy 0 1 (BYCS02) H015 Healthy 0 1 (BYCS02) H211 Healthy 2 1 (BYCS02) H213 Healthy 2 1 (BYCS02) H214 Healthy 2 1 (BYCS02) H8112 Healthy 8 1 (BYCS02) H812 Healthy 8 1 (BYCS02) H813 Healthy 8 1 (BYCS02) NoT01 Control 0 1 (BYCS02) NoT02 Control 0 1 (BYCS02) NoT03 Control 0 1 (BYCS02) NoT23 Control 2 1 (BYCS02) NoT24 Control 2 1 (BYCS02) NoT26 Control 2 1 (BYCS02) NoT82 Control 8 1 (BYCS02) NoT83 Control 8 1 (BYCS02) NoT86 Control 8 1 (BYCS02) U011 Unhealthy 0 1 (BYCS02) U014 Unhealthy 0 1 (BYCS02) U015 Unhealthy 0 1 (BYCS02) U211 Unhealthy 2 1 (BYCS02) U212 Unhealthy 2 1 (BYCS02) U213 Unhealthy 2 1 (BYCS02) U811 Unhealthy 8 1 (BYCS02)

179 Deciphering the genomic toolkit underlying animal-bacteria interactions

U812 Unhealthy 8 1 (BYCS02) U8112 Unhealthy 8 1 (BYCS02) F016 Foreign 0 2 (KR018) F017 Foreign 0 2 (KR018) F214 Foreign 2 2 (KR018) F215 Foreign 2 2 (KR018) F818 Foreign 8 2 (KR018) F819 Foreign 8 2 (KR018) H016 Healthy 0 2 (KR018) H017 Healthy 0 2 (KR018) H215 Healthy 2 2 (KR018) H216 Healthy 2 2 (KR018) H817 Healthy 8 2 (KR018) H818 Healthy 8 2 (KR018) NoT04 Control 0 2 (KR018) NoT06 Control 0 2 (KR018) NoT28 Control 2 2 (KR018) NoT29 Control 2 2 (KR018) NoT88 Control 8 2 (KR018) NoT89 Control 8 2 (KR018) U016 Unhealthy 0 2 (KR018) U017 Unhealthy 0 2 (KR018) U214 Unhealthy 2 2 (KR018) U215 Unhealthy 2 2 (KR018) U813 Unhealthy 8 2 (KR018) U819 Unhealthy 8 2 (KR018)

180 Appendices

Appendix 3.2 DE-Seq2 differential expression analysis R Script ##Combining the counts from both lanes of the CEL-Seq2 run ##For dataset 1(lane 1) make countsTable countsTable <- read.delim(file.choose(), header=TRUE, colClasses=c(“character”, rep(“numeric”, 36))) #”numeric,” followed by number refers to number of samples rownames(countsTable) <- countsTable$Sample countsTable <- countsTable[,-1] #says that the first row is names not variables

##For dataset 2(lane 2) make countsTable2 countsTable2 <- read.delim(file.choose(), header=TRUE, colClasses=c(“character”,rep(“numeric”, 36))) rownames(countsTable2) <- countsTable2$Sample countsTable2 <- countsTable2[,-1]

#To combine counts temp <- cbind(countsTable, countsTable2) countsall <- sapply(unique(colnames(temp)), function(x) rowSums(temp[, colnames(temp) == x, drop = FALSE]))

#To merge samples from different matrices challenge <- merge(as.data.frame(countsTable), as.data.frame(countsTable2), by=”row.names”, sort=FALSE)

##To print/write table write.table(countsall, “c:/mydata.tab”, sep=”\t”)

####################################################################################

#Load the packages into the R environment library(DESeq2) library(gplots) library(ggplot2) library(pheatmap) library(RColorBrewer)

##Import final dataset(ERCC rows removed) counts<-read.table(file.choose(),header=TRUE,row.names=1,sep=”\t”)

181 Deciphering the genomic toolkit underlying animal-bacteria interactions

##If gene names are in column 1 (not rownames) - convert to rownames #counts_rename<-data.frame(counts[,-1],row.names=counts[,1]) #counts<-counts_rename #rm(counts_rename)

#Use DESeq to prepare a data.frame of your full experimental design, this goes in as the colData input for creating the DESeq2 object treatment<-c(rep(“foreign”, 9),rep(“healthy”, 9),rep(“control”, 9),rep(“unhealthy”, 9) ,rep(“foreign”, 5),rep(“healthy”, 6),rep(“control”, 6),rep(“unhealthy”, 6)) time<-c(rep(“0h”, 3),rep(“2h”, 3),rep(“8h”, 3),rep(“0h”, 3),rep(“2h”, 3),rep(“8h”, 3),rep(“0h”, 3),rep(“2h”, 3),rep(“8h”, 3) ,rep(“0h”, 3),rep(“2h”, 3),rep(“8h”, 3),rep(“0h”, 2),rep(“2h”, 2),rep(“8h”, 1),rep(“0h”, 2),rep(“2h”, 2),rep(“8h”, 2) ,rep(“0h”, 2),rep(“2h”, 2),rep(“8h”, 2),rep(“0h”, 2),rep(“2h”, 2),rep(“8h”, 2)) design=data.frame( row.names=colnames(counts), condition=treatment, type=time)

##Create a DeSeq2 object, followed by fitting GLM to data #filterstat: the filter statistic, here the average number of counts per gene across all samples, irrespective of sample annoation, #pvalue: the test p-values dds <- DESeqDataSetFromMatrix(countData=counts, colData=design, design=~ type+condition) #creates a DESeq2 object from counts matrix, experimental design and design forumula colData(dds) #view the experimental design component of DESeq2 object, note that colData specifies informa- tion about the samples and experimental design. The first column of colData would line up with the first column of counts matrix (i.e. genes) as.data.frame(colData(dds)) #comprehensive view the experimental design component of DESeq2 object design(dds) # view formula, condition, the variable of interest is at the end of the formula

##################Exploratory analysis and visualization#################

###Pre-filtering the dataset### ##Reduce the size of the object to increase the speed of the analyses by removing rows which little to no information about gene expression. nrow(dds) #lists number of rows

182 Appendices

#overwrites existing DESeq2 object with an object in which genes with less than one read across all samples (rowsum >1) are removed from the counts matrix dds <- dds[ rowSums(counts(dds)) > 1, ] nrow(dds)

##Counts data are transformed to stabilise the variance across the mean. These become approximately homoskedastic, and can be used directly for computing distances between samples and making PCA plots #Sequencing depth correction is done automatically for the rlog and varianceStabilizingTransformation #rlog and VST are meant for raw counts, use log2 transformation for normalised counts (after estimateSizeFactors) vsd <- varianceStabilizingTransformation(dds, blind=TRUE) #export table write.table(as.data.frame(assay(vsd)),file=’challenge-DESeq2-vst-transformed-counts.txt’, sep=’\t’)

###Sample distances### #Transpose the matrix of values using t, because the dist function expects the different samples to be rows of its argument, and genes to be columns. sampleDists <- dist( t( assay(vsd) ) ) sampleDists

#Set up matrix sampleDistMatrix <- as.matrix( sampleDists ) rownames(sampleDistMatrix) <- paste( vsd$condition, vsd$type, sep=”-” ) colnames(sampleDistMatrix) <- NULL

###PCA plot### plotPCA(vsd, intgroup = c(“condition”, “type”)) library(“ggplot2”) data <- plotPCA(vsd, intgroup=c(“condition”, “type”), returnData=TRUE) percentVar <- round(100 * attr(data, “percentVar”)) ggplot(data, aes(PC1, PC2, color=condition, shape=type)) + geom_point(size=3) + geom_text(aes(label=colnames(sampleDistMatrixVSD)), size=2.5, hjust=0.25, vjust=-0.6, show.legend = F) + xlab(paste0(“PC1: “,percentVar[1],”% variance”)) + ylab(paste0(“PC2: “,percentVar[2],”% variance”)) #to change ggplot colour, use: scale_colour_brewer(palette=”Set1”) or change scale_colour_discrete(1 = 40)

###########################Differential gene expression

183 Deciphering the genomic toolkit underlying animal-bacteria interactions analysis############################################### dds$group <- factor(paste0(dds$type, dds$condition))#adds an additional column to colData that groups treat- ments for easier testing design(dds) <- ~ group #Formula

dds <- DESeq(dds) #estimates size factors (control for differences in library size, dispersions, genewise dis- persions, mean-dispersion relationship, final dispersion estimates, fitting GLM resultsNames(dds) ## Plot of dispersion estimates (Diagnostic plot) plotDispEsts(dds)

#export normalised data (DESeq2 normalisation). write.table(counts(dds,normalized=TRUE),file=”challengeUMI_DESeq2normalized_counts.tab”)

############The actual differential expression bit starts here################## ###First comparison with breakdown, subsequent comparisons will just be code###### ## 2h, Foreign vs Control ## # Fold change calculation: numerator followed by denominator res2FC <- results(dds, contrast=c(“group”, “2hforeign”, “2hcontrol”)) mcols(res2FC, use.names=TRUE)

# Merge fold change data with normalised counts res2FCdata <- merge(as.data.frame(res2FC), as.data.frame(counts(dds, normalized=TRUE)), by=”row.names”, sort=FALSE) write.table(res2FCdata, file=”2h_ForeignVsControl_results.tab”, sep = “\t”)

# Genes with adjusted p-value less than 0.1 (10% false positive, padj=0.1) sum(res2FC$padj < 0.1, na.rm=TRUE) res2FCSig <- subset(res2FC, padj < 0.1)

# Sort it by the log2 fold change estimate to get the significant genes with the strongest upregulation res2FCSig_ordered <- res2FCSig[ order( -res2FCSig$log2FoldChange, decreasing=TRUE), ] write.table(res2FCSig_ordered, file=”2h_ForeignVsControl__padj0.1.tab”, sep = “\t”)

#### Subsequent comparisons begin here ####

184 Appendices

### 2h ### ## 2h, Healthy vs Control ## res2HC <- results(dds, contrast=c(“group”, “2hhealthy”, “2hcontrol”)) sum(res2HC$padj < 0.1, na.rm=TRUE) res2HCSig <- subset(res2HC, padj < 0.1) res2HCSig_ordered <- res2HCSig[ order( -res2HCSig$log2FoldChange, decreasing=TRUE ), ] write.table(res2HCSig_ordered, file=”2h_HealthyVsControl__padj0.1.tab”, sep = “\t”)

## 2h, Unhealthy vs Control ## res2UC <- results(dds, contrast=c(“group”, “2hunhealthy”, “2hcontrol”)) sum(res2UC$padj < 0.1, na.rm=TRUE) res2UCSig <- subset(res2UC, padj < 0.1) res2UCSig_ordered <- res2UCSig[ order( -res2UCSig$log2FoldChange, decreasing=TRUE ), ] write.table(res2UCSig_ordered, file=”2h_unHealthyVsControl__padj0.1.tab”, sep = “\t”)

### 8h ###

## 8h, Healthy vs Control ## res8HC <- results(dds, contrast=c(“group”, “8hhealthy”, “8hcontrol”)) sum(res8HC$padj < 0.1, na.rm=TRUE) res8HCSig <- subset(res8HC, padj < 0.1) res8HCSig_ordered <- res8HCSig[ order( -res8HCSig$log2FoldChange, decreasing=TRUE ), ] write.table(res8HCSig_ordered, file=”8h_HealthyVsControl__padj0.1.tab”, sep = “\t”)

## 8h, Unhealthy vs Control ## res8UC <- results(dds, contrast=c(“group”, “8hunhealthy”, “8hcontrol”)) sum(res8UC$padj < 0.1, na.rm=TRUE) res8UCSig <- subset(res8UC, padj < 0.1) res8UCSig_ordered <- res8UCSig[ order( -res8UCSig$log2FoldChange, decreasing=TRUE ), ] write.table(res8UCSig_ordered, file=”8h_unHealthyVsControl__padj0.1.tab”, sep = “\t”)

##To view results without indepenedent filtering #resNoFilt <- results(dds, contrast=c(“group”, “2hforeign”, “2hcontrol”), independentFiltering=FALSE) #addmargins(table(filtering=(res$padj < .1), noFiltering=(resNoFilt$padj < .1))) #sum(resNoFilt$padj < 0.1, na.rm=TRUE)

### Diagnostic plots ###

185 Deciphering the genomic toolkit underlying animal-bacteria interactions

## MA-plot The log2 fold change for a particular comparison is plotted on the y-axis and the average of the counts normalized by size factor is shown on the x-axis. ##(“M” for minus, because a log ratio is equal to log minus log, and “A” for average) plotMA(res2FC, ylim=c(-5,5)) ## Examine independent filtering attr(res2FC, “filterThreshold”) plot(attr(res2FC,”filterNumRej”), type=”b”, xlab=”quantiles of baseMean”, ylab=”number of rejections”)

Appendix 3.3 Chapter three CEL-Seq2 Sample demultiplexing and read mapping statistics

Appendix3A: Demultiplexing statistics for the CEL-Seq2 experiment in Chapter three Appendix3B: Read mapping statistics for the CEL-Seq2 experiment in Chapter three Samples were split across two sequencing runs. Reads were generated from two lanes in each run.

* Available online via cloudStor+ (https://cloudstor.aarnet.edu.au/plus/index.php/s/vRobmT3eAmtJdGA) Password = amphimedon

186 Appendices

Appendix 3.4 Plots of ERCC spike-in concentration vs number of reads BYCS02-Run1, KR018-Run2

187 Deciphering the genomic toolkit underlying animal-bacteria interactions

Appendix 3.5 Gene expression data This is an excel spreadsheet containing the gene model identifiers, expression values, fold change values, adjusted p-values for all genes analysed/described in Chapter three of this thesis. * Available online via cloudStor+ (https://cloudstor.aarnet.edu.au/plus/index.php/s/vRobmT3eAmtJdGA) Password = amphimedon

Appendix 3.6 Principle components analyses plot of samples from all treatment groups Principlal components analyses potted using the VSD transformed raw counts of each sample with PC1 on the horizontal axis and PC2 on the vertical axis. The colours indicate the sequencing run to which each sample belongs. Samples from the same run did not cluster together.

F016 20

F215 NoT04

NoT02 F013 U812 U014 F817 U211

U015 NoT29 10 F212 H017

U214 U819 Batch U8112 U811 Run 1

U813 Run 2

iance U213

a r H214 H817 H812 H216 0 H215 H211 H818 NoT82 F017 Time point U017 NoT28 NoT88 F213 NoT06 0h PC2: 5% v NoT83 U011 H213 F014 2h H813 F819 U016 NoT86 U215 8h F214 H013H014 NoT03 NoT01 H016 F012 F211 NoT24 NoT89 U212 H8112 H015 −10

F812 NoT26

F813

NoT23

−20

−20 −10 0 10 PC1: 10% variance

188 Appendices

Appendix 3.7 Complete immune signalling reference pathways from KEGG These are the complete versions of the immune signalling pathways depicted in figure 3.8 of the thesis. The components of the pathways that are conserved in A. queenslandica are highlighted in green.

189 Deciphering the genomic toolkit underlying animal-bacteria interactions

190 Appendices

Appendix 3.8 OTU table and alpha rarefaction plots from characterisation of the bacterial communities in the three treatments (A) Table of the OTUs detected in the three bacterial treatments

#OTU ID Foreign Healthy Unhealthy taxonomy 379603 (AqS1) 25 4874 2020 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ Chromatiales; f__Ectothiorhodospiraceae; g__; s__ 4436967 (AqS2) 4 1009 831 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__EC94; f__; g__; s__ New.CleanUp. 0 843 467 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria ReferenceOTU112 New.CleanUp. 0 580 344 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__EC94; f__; ReferenceOTU948 g__; s__ New.CleanUp. 0 79 36 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria ReferenceOTU3731 New.CleanUp. 0 76 32 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria ReferenceOTU3818 587391 92 71 28 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ Chromatiales; f__Ectothiorhodospiraceae; g__; s__ New.CleanUp. 0 37 26 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria ReferenceOTU929 New.CleanUp. 0 28 27 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__EC94; f__; ReferenceOTU151 g__; s__ New.CleanUp. 0 27 5 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria ReferenceOTU965 New.CleanUp. 0 24 14 Unassigned ReferenceOTU2465 New.CleanUp. 2 18 4 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ ReferenceOTU515 Alteromonadales; f__Alteromonadaceae; g__Marinobacter; s__ New.CleanUp. 0 16 7 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__EC94; f__; ReferenceOTU195 g__; s__ New.CleanUp. 0 9 13 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__EC94; f__; ReferenceOTU1385 g__; s__ 2706576 379 1 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__wb1_P06; g__; s__ 568970 23 0 0 k__Bacteria; p__Chloroflexi; c__Anaerolineae; o__Caldilineales; f__ Caldilineaceae; g__; s__ 719803 74 0 0 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__ Rhodospirillales; f__Rhodospirillaceae; g__; s__ 550121 42 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ 542919 29 0 0 k__Bacteria; p__Acidobacteria; c__Solibacteres; o__Solibacterales; f__PAUC26f; g__; s__ 1120649 32 0 0 k__Bacteria; p__SBR1093; c__EC214; o__; f__; g__; s__ 1114496 556 0 0 k__Bacteria; p__Bacteroidetes; c__[Rhodothermi]; o__ [Rhodothermales]; f__Rhodothermaceae; g__; s__ 834257 31 0 0 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__ Syntrophobacterales; f__Syntrophobacteraceae; g__; s__ 542428 137 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ 661170 51 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ Thiotrichales; f__Piscirickettsiaceae; g__; s__ 566357 65 0 0 k__Bacteria; p__Acidobacteria; c__Sva0725; o__Sva0725; f__; g__; s__

191 Deciphering the genomic toolkit underlying animal-bacteria interactions

590831 469 0 0 k__Bacteria; p__Chloroflexi; c__Anaerolineae; o__Caldilineales; f__ Caldilineaceae; g__; s__ 1106412 24 0 0 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__ Syntrophobacterales; f__Syntrophobacteraceae; g__; s__ 4294689 21 0 0 k__Bacteria; p__PAUC34f; c__; o__; f__; g__; s__ 4459768 22 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ HTCC2188; f__HTCC2089; g__; s__ 561516 22 0 0 k__Bacteria; p__Chloroflexi; c__SAR202; o__; f__; g__; s__ 3760117 91 0 0 k__Bacteria; p__PAUC34f; c__; o__; f__; g__; s__ 1908097 375 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ Thiotrichales; f__Piscirickettsiaceae; g__; s__ 4443384 94 0 0 k__Bacteria; p__Chloroflexi; c__TK17; o__mle1-48; f__; g__; s__ 4426552 224 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ 557462 603 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ 645813 115 0 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__TK06; g__; s__ 4355097 71 0 0 k__Bacteria; p__Chloroflexi; c__Anaerolineae; o__Caldilineales; f__ Caldilineaceae; g__; s__ 4389958 21 0 0 k__Bacteria; p__Spirochaetes; c__Spirochaetes; o__Spirochaetales; f__Spirochaetaceae; g__; s__ 698647 54 0 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__; g__; s__ 815121 21 0 0 k__Bacteria; p__PAUC34f; c__; o__; f__; g__; s__ 570363 68 0 0 k__Bacteria; p__Nitrospirae; c__Nitrospira; o__Nitrospirales; f__ Nitrospiraceae; g__; s__ 4298986 22 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ 2383902 64 0 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__koll13; g__; s__ 1118253 22 0 0 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__ Syntrophobacterales; f__Syntrophobacteraceae; g__; s__ 571476 145 0 0 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__ Rhodospirillales; f__Rhodospirillaceae; g__; s__ 155526 25 0 0 k__Archaea; p__Crenarchaeota; c__Thaumarchaeota; o__ Cenarchaeales; f__Cenarchaeaceae; g__Nitrosopumilus; s__ 563693 27 0 0 k__Bacteria; p__Acidobacteria; c__PAUC37f; o__; f__; g__; s__ 1967331 55 0 0 k__Bacteria; p__Poribacteria; c__; o__; f__; g__; s__ 611668 20 0 0 k__Bacteria; p__Chloroflexi; c__SAR202; o__; f__; g__; s__ 4406036 83 0 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__; g__; s__ 1118935 28 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ Alteromonadales; f__HTCC2188; g__HTCC; s__ 4359640 32 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ 397618 30 0 0 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__ Rhodobacterales; f__Rhodobacteraceae; g__; s__ 255100 138 0 0 k__Bacteria; p__Chloroflexi; c__TK17; o__TK18; f__; g__; s__ 1116149 39 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ 1114110 117 0 0 k__Bacteria; p__AncK6; c__; o__; f__; g__; s__ 4294683 66 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ Chromatiales; f__; g__; s__ 575921 66 0 0 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__ Rhodospirillales; f__Rhodospirillaceae; g__; s__

192 Appendices

4437259 34 0 0 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__ [Entotheonellales]; f__; g__; s__ 60165 38 0 0 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__ [Entotheonellales]; f__[Entotheonellaceae]; g__; s__ 658089 85 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ Chromatiales; f__; g__; s__ New.CleanUp. 72 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU75 New.CleanUp. 23 0 0 k__Bacteria; p__Chloroflexi; c__Anaerolineae; o__Caldilineales; f__ ReferenceOTU113 Caldilineaceae; g__; s__ New.CleanUp. 20 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU252 New.CleanUp. 22 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU287 New.CleanUp. 21 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU335 New.CleanUp. 21 0 0 Unassigned ReferenceOTU364 New.CleanUp. 21 0 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; ReferenceOTU419 f__TK06; g__; s__ New.CleanUp. 35 0 0 Unassigned ReferenceOTU647 New.CleanUp. 37 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU1185 New.CleanUp. 32 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ ReferenceOTU1225 Chromatiales; f__; g__; s__ New.CleanUp. 33 0 0 k__Bacteria ReferenceOTU1250 New.CleanUp. 45 0 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; ReferenceOTU1294 f__wb1_P06; g__; s__ New.CleanUp. 33 0 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; ReferenceOTU1295 f__TK06; g__; s__ New.CleanUp. 25 0 0 k__Bacteria; p__Chloroflexi; c__Anaerolineae; o__SBR1031; f__A4b; ReferenceOTU1329 g__; s__ New.CleanUp. 30 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ ReferenceOTU1350 Thiotrichales; f__Piscirickettsiaceae; g__; s__ New.CleanUp. 34 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU1572 New.CleanUp. 95 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ ReferenceOTU1579 Chromatiales; f__; g__; s__ New.CleanUp. 29 0 0 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; ReferenceOTU1628 f__wb1_P06; g__; s__ New.CleanUp. 20 0 0 Unassigned ReferenceOTU1709 New.CleanUp. 23 0 0 k__Bacteria; p__Gemmatimonadetes ReferenceOTU1769 New.CleanUp. 35 0 0 k__Bacteria; p__Chloroflexi; c__SAR202; o__; f__; g__; s__ ReferenceOTU1830 New.CleanUp. 38 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU1947 New.CleanUp. 20 0 0 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__ ReferenceOTU2751 Chromatiales; f__Ectothiorhodospiraceae; g__; s__ New.CleanUp. 22 0 0 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__ ReferenceOTU2915 Rhodobacterales; f__Rhodobacteraceae; g__; s__

193 Deciphering the genomic toolkit underlying animal-bacteria interactions

New.CleanUp. 41 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU3186 New.CleanUp. 38 0 0 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__ ReferenceOTU3319 Syntrophobacterales; f__Syntrophobacteraceae; g__; s__ New.CleanUp. 20 0 0 Unassigned ReferenceOTU3435 New.CleanUp. 25 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU3543 New.CleanUp. 24 0 0 k__Bacteria; p__Gemmatimonadetes; c__Gemm-2; o__; f__; g__; s__ ReferenceOTU3585

(B) Alpha rarefaction plots generated using the chao1, shannon and simpsons indices.

194 Appendices

Appendix 4.1 Full time course of confocal sections tracking the processing of CFDA-SE labelled bacteria treatments by oscula staged juvenile Amphimedon queenslandica The bacteria communities enriched from R. globostellata (foreign), healthy, and unhealthy A. queenslandica were labelled using CFDA-SE (green). The juvenile sponges were presented with the bacteria and then sam- pled after 30 minutes, 1h, 2h and 8h. Nuclei are stained with DAPI (blue). There are three columns of images representing samples from each of the three treatment groups: (i) foreign sponge bacteria (ii), native bacteria (iii) unhealthy-Amphimedon bacteria. (A-H) From 30 minutes to 8 hours, the labelled bacteria from all three treatments are present in choanocytes and the amoeboid cells located beside choanocyte chambers (ch). High densities of labelled bacteria are localised to amoeboid cells, which are distinguished by their location, morphology and the anucleolate nucleus (unfilled white arrows). The archaeocytes are distinguished from amoeboid cells by the presence of a large nucleolate nucleus (solid white arrows). (Ai-iii) 30 minutes post-feeding. (Bi-iii) One hour post feeding. (C-E) By 2 hours post-feeding, the native and unhealthy-Amphimedon bacteria, but not the foreign sponge bacteria, were observed in archaeocytes. (Ci-iii) Two hours post feeding at 40x magnification. (Di-iii) Two hours post feeding at 63x magnification. (Ei-iii) Two hours post feeding overlayed on DIC channel at 63x magnification. (F-G) By 8 hours post-feeding, labelled bacteria are present in the archaeocytes of samples from all three treatment groups. (Fi-iii) Eight hours post feeding at 40x magnification. (Gi-iii) Eight hours post feeding at 63x magnification. (Hi-iii) Eight hours post feeding overlayed on DIC channel at 63x magnification.

195 Deciphering the genomic toolkit underlying animal-bacteria interactions

Ai Aii Aiii

Ch

Ch

Ch

Bi Bii Biii

Ch

Ch Ch

Ci Cii Ciii

Ch Ch

Ch

Di Dii Diii

Ch

Ch

Ch

196 Appendices

Ei Eii Eiii

Ch

Ch

Ch

Fi Fii Fiii

Ch

Ch

Gi Gii Giii

Ch

Ch Ch

Ch

Hi Hii Hiii

Ch

Ch

Ch

197 Deciphering the genomic toolkit underlying animal-bacteria interactions

.Appendix 4.2 Expression patterns of SRCR domain-containing genes across choanocytes, pinacocytes and archaeocytes

Model Description Choanocyte Pinacocyte Archaeocyte None Downregulated in Chapter three Aqu2.1.02693_001 ● Aqu2.1.26831_001 ● Aqu2.1.26832_001 ● Aqu2.1.26833_001 ● Aqu2.1.26834_001 ● Aqu2.1.36392_001 ● Aqu2.1.37934_001 ● Aqu2.1.38653_001 ● Aqu2.1.40697_001 ● Aqu2.1.40698_001 ● Aqu2.1.40699_001 ● Aqu2.1.40696_001 ● Aqu2.1.26213_001 FN3 ● ● Aqu2.1.20689_001 ● Aqu2.1.21079_001 ● Aqu2.1.41683_001 ● Aqu2.1.43780_001 Ig ● Aqu2.1.35973_001 ● Aqu2.1.33895_001 Astacin ● Aqu2.1.33896_001 Astacin ● Upregulated in chapter three Aqu2.1.06930_001 ● Aqu2.1.14687_001 ● Aqu2.1.18017_001 ● Aqu2.1.21524_001 FN3 ● Aqu2.1.24531_001 Astacin ● Aqu2.1.26604_001 EGF ● Aqu2.1.29997_001 ● Aqu2.1.30000_001 ● Aqu2.1.33904_001 ● Aqu2.1.36628_001 ● Aqu2.1.36629_001 ●

198 Appendices

Aqu2.1.38650_001 ● Aqu2.1.38651_001 ● Aqu2.1.16465_001 RTK ● Aqu2.1.03834_001 ● Aqu2.1.14864_001 Astacin ● ● Aqu2.1.19530_001 ● ● Aqu2.1.20823_001 GPCR ● ● Aqu2.1.23696_001 ● ● Aqu2.1.23697_001 ● ● Aqu2.1.23698_001 ● ● Aqu2.1.38647_001 ● ● Aqu2.1.11114_001 ● Aqu2.1.37655_001 ● Aqu2.1.26562_001 ● ● Aqu2.1.08710_001 ● Aqu2.1.18015_001 ● Aqu2.1.36632_001 ● Aqu2.1.22301_001 ● Aqu2.1.00957_001 ● Aqu2.1.18013_001 ● Aqu2.1.02965_001 ● Aqu2.1.44037_001 ● Aqu2.1.29238_001 RTK ● Aqu2.1.35776_001 ● Aqu2.1.43780_001 ● Aqu2.1.25750_001 ● Aqu2.1.15859_001 Astacin ● Aqu2.1.14263_001 RTK ● Aqu2.1.25749_001 ●

199 Deciphering the genomic toolkit underlying animal-bacteria interactions

Appendix 4.3 Expression patterns of lysosomal pathway genes across choanocytes, pinacocytes and archaeocytes These genes were identified as differentially expressed in Chapter three.

Model Description Choanocyte Pinacocyte Archaeocyte None

Aqu2.1.41048_001 cathepsin l ●

Aqu2.1.24663_001 sphingomyelin phosphodiesterase- ● ● like Aqu2.1.24509_001 lysosome membrane protein 2-like ●

Aqu2.1.21070_001 proactivator polypeptide ●

Aqu2.1.44031_001 ap-3 complex subunit delta-1 isoform ● x1 Aqu2.1.44032_001 ap-3 complex subunit delta-1 ●

Aqu2.1.27233_001 tripeptidyl-peptidase 1-like ●

Aqu2.1.35988_001 n-acetylglucosamine-1- ● ● phosphotransferase subunits alpha beta Aqu2.1.34334_001 iduronate 2-sulfatase-like ●

Aqu2.1.43732_001 cathepsin l-like ●

Aqu2.1.23412_001 cathepsin b ●

Aqu2.1.08476_001 papain family cysteine protease ●

Aqu2.1.09007_001 papain family cysteine protease ●

Aqu2.1.38344_001 galactocerebrosidase ●

Aqu2.1.43542_001 ap-4 complex subunit epsilon-1-like ●

Aqu2.1.15460_001 iduronate 2-sulfatase-like ●

Aqu2.1.15459_001 iduronate 2-sulfatase-like ●

Aqu2.1.27612_001 iduronate 2-sulfatase-like ●

Aqu2.1.28087_001 lysosomal alpha-glucosidase-like ● isoform x2 Aqu2.1.34914_001 tartrate-resistant acid phosphatase ● type 5-like Aqu2.1.43734_001 cathepsin l1-like ●

Aqu2.1.39727_001 arylsulfatase a-like ●

Aqu2.1.11548_001 iduronate 2-sulfatase-like ●

Aqu2.1.20498_001 iduronate-2-sulfatase ●

Aqu2.1.38542_001 extracellular sulfatase sulf-2-like ●

Aqu2.1.08305_001 extracellular sulfatase sulf-2-like ●

Aqu2.1.35956_001 n-acetylglucosamine-6-sulfatase-like ●

Aqu2.1.41572_001 n-acetylgalactosamine-6-sulfatase ● isoform x1 Aqu2.1.24668_001 sphingomyelin phosphodiesterase- ● like

200 Appendices

Aqu2.1.32207_001 glucosylceramidase ●

Aqu2.1.27231_001 growth-inhibiting protein 1 ●

Aqu2.1.39923_001 n-acetylglucosamine-1- ● phosphodiester alpha-n- acetylglucosaminidase Aqu2.1.28663_001 sialin ●

Aqu2.1.37964_001 group xv phospholipase a2-like ● ●

Aqu2.1.40890_001 phosphatidylcholine-sterol ● ● acyltransferase-like Aqu2.1.41666_001 lysosomal acid phosphatase-like ●

Appendix 4.4 Feeding experiment GPCR gene expression data This is an excel spreadsheet containing the gene model identifiers, expression values, fold change values, adjusted p-values for genes encoding GPCRs that were differentially expressed in response to the foreign sponge bacteria, native bacteria and unhealthy-Amphimedon bacteria. (data set generated in chapter three) * Available online via cloudStor+ (https://cloudstor.aarnet.edu.au/plus/index.php/s/vRobmT3eAmtJdGA) Password = amphimedon

Appendix 4.5 Expression patters of the GPCRs across choanocytes, pinacocytes and archaeocytes These GPCRs were identified as differentially expressed in response to the foreign sponge bacteria, native bacteria and unhealthy-Amphimedon bacteria.

Model Description Family Choanocyte Pinacocyte Archaeocyte None gamma−aminobutyric acid type b receptor ● ● Aqu2.1.23323_001 subunit 1 Glutamate

gamma−aminobutyric acid type b receptor ● Aqu2.1.28011_001 subunit 2−like Glutamate

Aqu2.1.41592_001 frizzled−3 Frizzled ●

g−protein coupled receptor 126−like ● Aqu2.1.22167_001 isoform x2 Adhesion

gamma−aminobutyric acid type b receptor ● Aqu2.1.43899_001 subunit 2 Glutamate

probable g−protein coupled receptor ● Aqu2.1.29932_001 157−like Adhesion

Aqu2.1.35178_001 thyrotropin receptor isoform x1 Rhodopsin ● ●

gamma−aminobutyric acid type b receptor ● Aqu2.1.27571_001 subunit 2−like Glutamate

probable g−protein coupled receptor ● Aqu2.1.29931_001 157−like Rhodopsin

Aqu2.1.29971_001 probable g−protein coupled receptor 157 Probably Rhodopsin ● probable g−protein coupled receptor Probably Rhodopsin, ● Aqu2.1.11626_001 157−like incomplete model

gamma−aminobutyric acid type b receptor ● Aqu2.1.34923_001 subunit 2−like Glutamate

probable g−protein coupled receptor ● Aqu2.1.39166_001 157−like Adhesion

201 Deciphering the genomic toolkit underlying animal-bacteria interactions

gamma−aminobutyric acid type b receptor ● Aqu2.1.34926_001 subunit 2 Glutamate

low quality protein: probable g−protein ● Aqu2.1.36489_001 coupled receptor 112 incomplete model

gamma−aminobutyric acid type b receptor ● ● Aqu2.1.23001_001 subunit 1−like Glutamate

gamma−aminobutyric acid type b receptor ● Aqu2.1.43900_001 subunit 2−like Glutamate

metabotropic glutamate receptor 3−like ● Aqu2.1.34308_001 isoform x1 Glutamate

gamma−aminobutyric acid type b receptor ● Aqu2.1.39154_001 subunit 2−like Glutamate

Aqu2.1.39648_001 latrophilin−2−like isoform x3 Adhesion ●

Aqu2.1.22312_001 5−hydroxytryptamine receptor 7−like Rhodopsin ●

Aqu2.1.34455_001 metabotropic gaba−b receptor Glutamate ● gamma−aminobutyric acid type b receptor ● Aqu2.1.39153_001 subunit 2−like Glutamate

Aqu2.1.43975_001 latrophilin cirl−like isoform 1 Adhesion ●

gamma−aminobutyric acid type b receptor ● Aqu2.1.43908_001 subunit 2−like Glutamate

gamma−aminobutyric acid type b receptor ● Aqu2.1.35060_001 subunit 2−like Glutamate

Aqu2.1.39650_001 latrophilin−2 isoform x1 Adhesion ● ●

gamma−aminobutyric acid type b receptor ● Aqu2.1.22999_001 subunit 1−like Glutamate

Aqu2.1.39653_001 latrophilin cirl−like isoform 1 Adhesion ● gamma−aminobutyric acid type b receptor ● Aqu2.1.23321_001 subunit 2−like Glutamate

gamma−aminobutyric acid type b receptor ● Aqu2.1.26551_001 subunit 2−like Glutamate

Appendix 4.6 Feeding experiment Nrf2 target gene expression data This is an excel spreadsheet containing the gene model identifiers, expression values, fold change values, adjusted p-values for genes encoding Nrf2 target genes that were upregulated only in response to the foreign sponge bacteria. (data set generated in chapter three) * Available online via cloudStor+ (https://cloudstor.aarnet.edu.au/plus/index.php/s/vRobmT3eAmtJdGA) Password = amphimedon

Appendix 5.1 Spreadsheet containing counts of reads mapped to the A. queenslandica NLR gene models in the Chapter 3 experiment This is an excel spreadsheet containing the gene model identifiers and normalised read counts for the A. queenslandica NLRs. (data set generated in chapter three) * Available online via cloudStor+ (https://cloudstor.aarnet.edu.au/plus/index.php/s/vRobmT3eAmtJdGA) Password = amphimedon

202