A Simple Animal Model for Characterizing Gene Regulatory Control of an Immune Response

A simple animal model for characterizing gene regulatory control of an immune response

Eric Chun Hei Ho

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Medical Biophysics University of Toronto

A simple animal model for characterizing gene regulatory control of an immune response

Eric Chun Hei Ho

Doctorate of Philosophy

Department of Medical Biophysics

University of Toronto

2015 Abstract

A robust but tightly regulated system of innate gut immunity is required for maintaining gut homeostasis. Given the complex nature of the mammalian gut, a simple yet phylogenetically relevant model is useful to decipher the underlying molecular controls that sustain this balance.

Here I use the purple sea urchin larva as a morphologically simple model for these studies. I approach this problem with three interrelated strategies: (1) Characterization of larval immunocytes based on morphology, cell behaviour and gene expression, (2) analysis of gene activity in a model of gut-associated immune response to bacteria and (3) regulatory analysis of selected response genes. I characterize five larval cell types that are active in gut immunity and develop a gut associated infection model using the bacterium Vibrio diazotrophicus. RNA-seq analysis of this model is used to identify immune genes that are differentially expressed over the course of infection. Member of the sea urchin IL-17 multigene family are identified as immune response genes. One subfamily, SpIL-17-I, is expressed early upon exposure to V. diazotrophicus ii

in the mid- and hindgut and is then attenuated. BAC-based GFP transgenes and smaller reporter constructs recapitulate endogenous SpIL-17-I expression. Regulatory regions are defined upstream and downstream of the IL-17 coding sequence. Deletions of critical elements results in a decrease or absence of IL-17 expression but never ectopic expression or expression in the absence of immune stimulus. Downstream function of IL-17 was investigated by perturbing its receptors using Morpholino antisense oligonucleotides. In addition to physiological changes in the larva, expression of several immune effector genes are downregulated in IL-17R perturbed embryos. Collectively this work suggests that a core network of regulatory circuitry is shared with the vertebrates in the gut associated immune response even in this simple larval form. This can then form the basis for more elaborate network analysis of gut immunity in future studies using this model.

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Acknowledgments

First of all, I would like to express my deepest gratitude to my supervisor, Dr. Jonathan

Rast, for providing me with this wonderful opportunity. I am especially thankful for the mentorship and guidance that he has provided over this long journey. He has instilled many invaluable scientific knowledge as well as life lessons that will undoubtedly have a profound effect for the rest of my life.

I would also like to thank my committee members, Dr. Phillip Poussier and Dr. Richard

Wells, for providing invaluable guidance throughout the years. Their ideas and critiques have helped improve the scope and direction of my project.

Members of the Rast lab, both past and present, have been instrumental in the completion of my thesis as well as making my experience more enjoyable. Firstly, I would like to express my deepest appreciation to Dr. Kate Buckley for all her help; without her expertise and advice, I would not have been able to finish my project. Other members of the lab that I would specifically like to thank are Dr. Cynthia Solek for her tutorage during the start of my PhD, Catherine

Schrankel for her friendship and keeping the lab lively, Nick Schuh for bringing a different prospective, and Dr. Casey Wang for being the motherly figure that every labs need (as well as being the in-situ guru).

I have met many friends along the way. I would like to thank Linda for all her help and friendship; Elena and Mei for all their silly moments, Samantha for all her advice, and Shane for our lively conversations.

My family has been a tremendous source of support throughout my PhD studies. I would like to thank my parents for being so helpful and understanding. They were always there, good

or bad, throughout my whole life. I will forever be grateful for all their help. Without them, I would not have been able to achieve the things that I have done in my life. Also, I would like to thank my sister, Cindy. She is always there to help me get out of “interesting” situations that I somehow get myself into. Lastly, I would also like to thank Aunt Catherine for all the caring that she has shown towards me and my wife.

Finally, I would like to thank my wife Loksum. I will always treasure our lively scientific discussion which helped keep the project interesting. I would not have been able to complete this thesis without her love and encouragement.

Table of Contents

Acknowledgments ...... iv

Table of Contents ...... vi

List of Tables ...... xii

List of Figures ...... xiii

List of Appendices ...... xv

List of Abbreviations...... xvi

Contributions from others and publications ...... xix

Chapter 1 Introduction: Host-pathogen interactions and immune diversity across phyla ...... 1

An evolutionary perspective on the immune response...... 1

Cellular, innate, and adaptive immune responses ...... 1

Pathogens, symbionts and the origins of immune complexity ...... 3

Diversity of immune recognition mechanisms across animal phyla ...... 4

Immune cells across animal phylogeny ...... 8

A systems-level approach to studying immunity ...... 9

The gut-associated immune response ...... 13

Gut immunity in vertebrates ...... 13

Crosstalk between the microbiota and the host immune system ...... 16

The role of sequencing data in comparative immunology ...... 18

The sea urchin immune system as a model for immune regulation ...... 19

Sea urchin adult immunity ...... 19

Adult sea urchin immunocytes ...... 19

Immune genes encoded in the sea urchin genome ...... 24

Summary ...... 28

Chapter 2 Cellular and molecular immune responses in the sea urchin larva ...... 31

Introduction ...... 31

Sea urchins have a biphasic lifecycle that includes a feeding larval stage ...... 31

Larvae are characterized by simple morphology ...... 32

Larval immune cells are derived from two precursor populations ...... 34

The sea urchin larva as a simple, systems-level model for immunity ...... 34

Materials and Methods ...... 37

Animals and larval culture ...... 37

Isolation and identification of larval-associated bacterial species ...... 37

Larval bacterial exposure ...... 38

Neutralizing bacteria for larval exposure ...... 39

Intrablastocoelar injection...... 39

Microscopy and time-lapse analysis...... 40

Transcriptome analysis ...... 40

Whole mount in situ hybridization (WMISH) ...... 41

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Results ...... 41

Five classes of immune cells can be identified in the sea urchin larva ...... 41

Larval immunocytes respond to foreign particles ...... 46

Larval immunocytes respond to gut-associated bacterial infection ...... 51

Larvae exhibit similar cellular reaction when exposed to natural pathogens ...... 56

Immune genes are differentially expressed over the course of infection ...... 61

Discussion ...... 65

The purple sea urchin larva as a simple model for immunity ...... 65

The sea urchin larval immune response is a coordinated, system-wide response ...... 68

The larval immune gene repertoire is tightly regulated ...... 69

Larvae mount differential immune responses to distinct pathogens ...... 70

Humoral-like defense systems may have a role in larval immunity ...... 71

Larval immune cells exhibit similarities to adult coelomocytes ...... 72

Conclusions ...... 73

Chapter 3 IL-17 as a primary mediator of sea urchin larval gut immunity ...... 74

Introduction ...... 74

IL-17: a family of pro-inflammatory cytokines ...... 74

IL-17 in vertebrate gut immunity ...... 77

The role of IL-17 outside the mammals ...... 78

Materials and Methods ...... 80

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Preparation and microinjection of reporter constructs ...... 80

Morpholino antisense oligonucleotides (MASO) ...... 80

RNA extraction from transgenic larva ...... 81

Reverse transcription quantitative PCR (RT-qPCR) ...... 81

Confocal imaging ...... 81

Results ...... 82

A small family of SpIL-17 factors are the most strongly upregulated genes in response to bacterial infection ...... 82

The sea urchin genome encodes a multigene family of IL-17 genes ...... 84

SpIL-17(I) is rapidly upregulated in response to infection ...... 85

SpIL-17(I) is expressed in the gut epithelium of infected larvae ...... 85

The sea urchin genome encodes two IL-17 receptors ...... 89

Perturbation of IL-17 signaling causes dysregulation of downstream immune genes ...... 90

Discussion ...... 97

IL-17 is an unusually conserved cytokine ...... 97

The multiplicity of the SpIL-17 family mirrors expansions of sea urchin immune receptors ...... 98

IL-17 plays an ancient role in gut-associated immunity ...... 99

Conclusions ...... 100

Chapter 4 Building a gene regulatory network for the sea urchin larval gut-associated immune response using SpIL-17 as an anchor ...... 102

Introduction ...... 102

Epithelial expression of IL-17 is central to the sea urchin larval immune response ...... 102

The role of IL-17C in epithelial defense in mammals ...... 103

A simple model for investigating regulatory control of IL-17 ...... 103

Materials and Methods ...... 105

Preparation and microinjection of reporter constructs ...... 105

Preparation of truncated and deletion constructs ...... 106

Quantification of GFP transcripts ...... 106

Results ...... 107

A BAC-based reporter construct recapitulates endogenous IL-17(I) expression in response to gut-associated bacteria ...... 107

Deletion analysis identifies upstream and downstream regions of IL-17-07 that contribute to expression regulation ...... 110

Quantification of GFP transcripts indicates that IL-17(I) BAC GFP reporter expression recapitulates endogenous IL-17(I) expression; deletion constructs displayed reduced GFP expression...... 113

Discussion ...... 116

The cis-regulatory modules responsible for IL-17(I) expression are located close to the gene coding sequence ...... 116

IL-17(I) expression is tightly regulated ...... 118

IL-17(I) as an anchor for a GRN describing the gut-associated immune response ...... 120

Conclusions ...... 121

Chapter 5 Discussion...... 124

The sea urchin larva as a simple animal model for investigating fundamental mechanisms that control the gut-associated immune response ...... 124

The sea urchin larva is characterized by a distinct immune system with some similarity to that of the adult ...... 126

An ancient role for epithelial-derived IL-17 in the gut immune response ...... 127

A gene regulatory network approach to mapping the immune response ...... 128

Future directions ...... 129

References ...... 131

List of Tables

Table 2.1: Immune cell types in the sea urchin larva ...... 47

Table 2.2: Bacterial strains isolated from purple sea urchin larvae exposed to macrofiltered Atlantic sea water...... 59

Table 2.3: Larval immune genes expressed after feeding as identified through RNA-seq experiments. SPU, genome reference ID; RPKM, reads per kilobase per million reads mapped...... 62

Table 2.4: Genes expressed in the course of the larval gut infection with homologs in vertebrate immune systems...... 64

Table 2.5: Summary of behaviour of larval immune cells during resting and bacterial exposed state...... 67

Table 3.1: The thirty most differentially expressed genes over the course of the larval gut infection...... 83

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List of Figures

Figure 1.1: Echinoderms are closely related to the chordates and are represented by five extant classes...... 20

Figure 2.1: Schematics of a purple sea urchin larva ...... 33

Figure 2.2: A purple sea urchin larva has a tripartite gut and several subsets of larval immune cells...... 43

Figure 2.3: Larval immunocytes respond dynamically to immunogens in the blastocoel...... 49

Figure 2.4 Exposure to V. diazotrophicus induces cellular responses in the larva...... 52

Figure 2.5: Activation of larval cellular responses requires live bacteria and is reversible...... 54

Figure 2.6: Larval immunocyte migration can be induced by a natural microbiome...... 58

Figure 2.7: Pigment cell migration is dependent on specific species of bacteria...... 60

Figure 2.8: Subsets of activated larval immunocytes express specific immune genes...... 66

Figure 3.1: SpIL-17(I) genes are clustered in tandem within a compact section of the sea urchin genome...... 86

Figure 3.2: Expression of a sea urchin IL-17 homolog peaks over the course of larval gut infection...... 87

Figure 3.3: SpIL-17 receptors exhibit dynamic responses during larval gut infection ...... 91

Figure 3.4: SpIL-17R1 perturbation affects larval physiology and cellular responses...... 93

Figure 3.5: Knockdown of individual IL-17 receptors differentially perturbs larval development ...... 96

Figure 4.1: Three BAC-based fluorescent reporters localize SpIL-17 gene expression to the gut epithelium upon larval immune activation...... 108

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Figure 4.2: BAC-based promoter-deletion assays identify the minimal genomic regions required for IL-17 expression...... 111

Figure 4.3: The expression of the IL-17(I) BAC-GFP reporter mimics endogenous IL-17(I) transcript levels during gut infection...... 114

Figure 4.4: RTqPCR analysis of transgenic IL-17-GFP larvae recapitulates live imaging data in identifying a minimal 5´ region required for IL-17 expression post immune activation...... 115

Figure 4.5: A predicted gene regulatory sub-network controlling larval gut immune responses...... 123

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List of Appendices

Appendix A: Gene expression at the onset of feeding ...... 151

Appendix B: Gene expression in response to bacterial challenge ...... 155

Appendix C: Oligonucleotide sequences used for cloning ...... 164

List of Abbreviations

AMP - anti-microbial peptide

ASW – artificial sea water

BAC - bacteria artificial chromosome bp - base pairs

DNA - deoxyribonucleic acid dpf - days post-fertilization

DSS – dextran sodium sulfate

FREP - fibrinogen related protein

FSW - filtered sea water

GCSF - granulocyte-colony stimulating factor

GFP - green fluorescent protein

GM-CSF - granulocyte-macrophage colony-stimulating factor hoi - hours of infection hpf – hours post-fertilization

IL-17 - interleukin-17

IL-17R – interleukin-17 receptor kb - kilobase

LB - Luria booth

LPS - lipopolysaccharide

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LRR - leucine-rich repeat

LTi - lymphoid tissue inducer

MASO - Morpholino anti-sense oligonucleotide

MB - marine broth

NK - natural killer

NLR - NACHT-like receptor

NOD - nucleotide-binding oligomerization domain nt - nucleotide

PAMP - pathogen-associated molecular pattern

PBS - phosphate-buffered saline

PFA - paraformaldehyde

PRR - pattern recognition receptor qPCR - quantitative polymerase chain reaction

RNA - ribonucleic acid

RNA-Seq - RNA sequencing rpm - rotation per minute

RSS - recombination signal sequence

RT - reverse transcriptase

SRCR - scavenger receptor

TCR - T-cell receptor

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TdT - terminal deoxynucleotidyl transferase

TILL - TIR-like loop

TIR - Toll/interleukin-1 receptor

TLR - Toll-like receptor

V(D)J - variable, diversity, joining

VLR - variable lymphocyte receptor

WMISH - whole mount in-situ hybridization

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Contributions from others and publications

Chapter 1

Sections within the introduction have been published the following:

Ho, Eric C.H. and Rast, Jonathan P. In press. “The immune system of echinoderms”,

Encyclopedia of Immunobiology. Ed, Michael Ratcliffe.

Chapter 2

The work presented in this chapter will be published in two manuscripts. The characterization of the larval cellular immune response and the description of the Vibrio diazotrophicus gut infection model will be published in:

Ho, Eric C.H., Buckley, Katherine M., Wang, Guizhi, and Rast, Jonathan P. in

preparation “Characterization of the cellular and molecular immune response of the

purple sea urchin larva.” Developmental and Comparative Immunology.

The RNA-Seq surveys that were used to characterize the larval molecular response to bacteria will be published in:

Buckley, Katherine M., Ho, Eric C.H., Wang, Guizhi, and Rast, Jonathan P. in

preparation “Gene expression analysis of the system-wide immune response of the purple

sea urchin larva.”

The author of this thesis (E.C.H.H.) characterized the larval immune cells, performed and analyzed the time-lapse microscopy, isolated and identified the bacterial strains from MDIBL and developed the infection model. The RNA-Seq experiments were performed in collaboration with

Dr. Katherine Buckley, who analyzed the sequence data. Dr. Guizhi Wang completed many of the in situ hybridization experiments shown throughout the thesis. xix

Chapter 3

The work presented in this chapter will be included in the following publication:

Buckley, Katherine M.*, Ho, Eric C.H.*, Hibino, Taku, Wang, Guizhi, and Rast,

Jonathan P. in preparation. “An ancient system of IL-17 mediated gut immunity.”

The present author was involved in characterizing the expression of the SpIL-17(I) genes within the context of the larval infection model and for performing the IL-17R1 and IL-17R2 MASO injection experiments. This project was done in collaboration with Dr. Katherine Buckley, who performed the qPCR analysis on the perturbed embryos as well as the genomic analysis. Dr. Taku

Hibino was responsible for the initial identification and characterization of the IL-17 family within the S. purpuratus genome.

Chapter 4

The work described in this chapter will form the foundation of a future publication. All work was performed by E.C.H.H.

Chapter 1 Introduction: Host-pathogen interactions and immune diversity across phyla

An evolutionary perspective on the immune response Cellular, innate, and adaptive immune responses

One of the key advances in immunology occurred in latter part of the 19th century when

Ilya Metchnikoff first observed a cellular immune response in the bipinnaria larva of the sand sifting starfish (Astropectens pentacanthus). Upon injuring the ectoderm, Metchnikoff noted that mesenchymal cells quickly migrated to the site of the injury and formed minute plasmodia

(Metchnikoff 1893). He subsequently experimented on a different species of sea star, Luidia sarsi (then known as Bipinnaria asterigera), which has a larger larval stage and is thus better suited for visual observations. Artificially injuring these larvae with glass needles, rose thorns or sea urchin spines consistently elicited a similar response in which amoeboid cells were recruited to the injury site. Moreover, the phagocytic activities of these amoeboid cells could be induced with the introduction of sheep blood cells and bacteria (Metchnikoff 1893). Later he extended this work to other invertebrates and finally to mammals including humans. This set of experiments illustrated the ubiquity of non-self recognition, and the crucial role of phagocytes in the immune response, laying the foundation for the fields of cellular and innate immunology.

Until relatively recently, the field of immunology has largely focused on the adaptive axis of the jawed vertebrate immune system. This focus has yielded profound advances in the understanding of mechanisms that generate and select useful diversity but has spawned the misconception that immunity within the invertebrates is limited to relatively simple systems that are analogous to vertebrate innate immunity. As outlined below, growing evidence suggests

2 invertebrates share regulatory mechanisms with elements of the vertebrate adaptive system and that the various invertebrate lineages have simultaneously evolved immune recognition and effector complexities, some of which rival those of the vertebrates.

The jawed vertebrate adaptive immune system was built on a foundation of innate systems and as more invertebrates are characterized strict boundaries between innate and adaptive immune systems begin to blur (Litman et al. 2005). A growing number of studies suggest that invertebrate immune systems are far more varied and complex than was once believed. For example, the innate recognition receptor complexity of many invertebrates far exceeds that of mammals

(Buckley & Rast 2015). Although it is not clear how this diversity is used, aspects of recognition selection that are important in the function of the vertebrate adaptive system may play an analogous role in complex innate immune systems (i.e., cellular systems that select useful Ig and

TCR diversity may predate the emergence of V(D)J recombination and have originated from functions to handle large multigene family diversity). Immune systems in both vertebrates and invertebrates have evolved in parallel for hundreds of millions of years in presence of both pathogenic and commensal microbes. This evolutionary pressure drives novelty in immune recognition and effector mechanisms. The fact that the invertebrates dominate in number and diversity among animals and survive in the absence of a vertebrate-type adaptive immune system is a testament to the efficacy and adequacy of these systems.

While it is now clear that V(D)J-mediated adaptive immunity is restricted to jawed vertebrates, several recent studies in invertebrates have identified novel strategies for generating somatic immune diversity in jawless vertebrates and phyla throughout the Bilateria (e.g. (Zhang

& Loker 2003; Ng et al. 2014; Pancer et al. 2004; Brockton et al. 2008; Brites et al. 2008; Ghosh et al. 2011; Dishaw et al. 2012; Hernández Prada et al. 2006; Schmucker & Chen 2009; Buckley

& Rast 2015). A few examples are discussed below. Given that we are just beginning to scratch the surface of understanding metazoan immunity, these findings illustrate the likelihood that many novel systems await discovery. In spite of this diversity, we know that immunity is a property that is needed by all organisms and many of the fundamental mechanisms that underlie even our adaptive system likely evolved in our ancient invertebrate ancestors. By engaging in a comparative approach to immunity research, we can uncover the evolutionarily conserved (and therefore essential) components of the core immune network across bilaterians and at the same time take advantage of experimental opportunities provided by invertebrate models.

Pathogens, symbionts and the origins of immune complexity

An evolutionary “arms race” has emerged in which pathogens adapt to host defense strategies through mutation and subsequent selection. The host in turn counter-adapts by evolving its immune response. Species have to evolve in order to remain extant (or “running” in the same place). This escalation is best described by the Red Queen model in which this type of evolutionary process is a balance of biotic pressures and organisms must constantly adapt in order to survive (Van Valen 1973). The host-pathogen arms race is further complicated by the fact that not all microbes are pathogenic and immune systems must be able to differentiate between friend and foe. The vast majority of microbes in the mammalian gut are non-pathogenic under normal circumstances and these commensal species are essential to host survival (Kamada et al. 2013;

McDermott & Huffnagle 2014). The mechanisms that drive the co-evolution between host- pathogen and host-symbiont are similar in that they are driven by DNA changes that lead to the diversification of immune gene families within populations (Litman et al. 2005). This can

4 subsequently give rise to specialization of recognition and effector systems within a species or phylum (Dheilly et al. 2014; Loker et al. 2004; Herrin & Cooper 2010; Schmucker & Chen 2009).

Diversity of immune recognition mechanisms across animal phyla

Examples of immune receptor and effector diversification are evident across phyla, The most well characterized of which are the mechanisms that drive adaptive immunity within the gnathostomes (jawed vertebrates). The large, diversified repertoires of antigen receptors are generated somatically through V(D)J recombination and expressed in subsets of T cells (T cell receptors [TCR]) and B cells (Immunoglobulin [Ig]) (Janeway et al. 2004). As lymphocytes mature, two recombinases (recombination activating genes 1 and 2; RAG1 and RAG2) recognize and cleave DNA at specific sequences known as recombination signal sequences (RSS) that flank variable (V), diversity (D) and joining (J) gene segments (Janeway et al. 2004). After cleaving the intervening DNA segments, the gene segments are joined and repaired through double strand break repair mechanisms. Germline DNA contains many copies of each of these segments in tandem arrays, and combinatorial assembly can generate up to 106 unique receptors (Janeway et al. 2004). This repertoire is further increased as a consequence of junctional diversity. During the assembly process, additional nucleotides are added by the enzyme terminal deoxynucleotidyl transferase (TdT), which further increases the potential repertoire to 1014 unique molecules

(Janeway et al. 2004). Mature antibodies that have encountered antigen are further refined to increase affinity through somatic hypermutation. This process is generally conserved throughout the jawed vertebrates, although some variation is apparent in specific lineages, particularly with respect to the arrangement of the gene segments and specific isotypes (Janeway et al. 2004).

The V(D)J system stands as the gold standard for a complex diversifying system of immune receptors and until recently was viewed as a complexity that is unique to jawed vertebrates. In recent years several new systems of diversified receptors have been identified and characterized to greater or lesser extents in agnathan vertebrates and invertebrates. These suggest that the jawed vertebrates may not be unique with respect to their need for complex immunity. A few examples of these systems are briefly outlined below.

Example 1: The VLR system in jawless vertebrates – A radically different diversifying gene system expressed in a cell context in common with jawed vertebrates.

The variable lymphocyte receptors (VLRs) of the jawless vertebrates (agnathostomes) illustrate the innovation of adaptive immune mechanisms even within vertebrates (Boehm et al.

2012; Alder et al. 2008; Pancer et al. 2004; Herrin & Cooper 2010). The mechanism that underlies this immune receptor diversification is different yet, in many ways, analogous to that of the gnathostomes. First discovered in the sea lamprey (Petromyzon marinus) and also identified later in hagfish, mature VLRs are either membrane bound or secreted proteins with ectodomains composed of a series of leucine-rich repeat (LRR) domains capped on each end by specialized

LRR-NT and LRR-CT domains. A threonine/proline-rich stalk and a hydrophobic tail region are

C-terminal to the LRR-CT. (Li et al. 2013; Pancer et al. 2004). Three VLR loci (VLRA, VLRB, and VLRC) have been identified and orthologs of each can be found in representative lamprey and hagfish species (Li et al. 2013; Pancer et al. 2004). The germline VLR gene structure consists of 5´ and 3´ invariant regions flanking a non-coding region (Boehm et al. 2012). Multiple LRR coding modules are located in tandem arrays adjacent to this immature VLR gene. During lymphocyte development, the non-coding region in the immature VLR is replaced by the flanking

LRR modules in a step-wise gene conversion-like manner (Boehm et al. 2012). RAG1 and 2 have

6 not been identified in the lamprey and the LRR cassettes are not flanked by RSSs. Instead, this process is likely mediated by cytidine deaminases (Rogozin et al. 2007). Using this mechanism, more than 1014 different VLR receptor sequences can be produced (Kvell et al. 2007).

In spite of the completely different nature of the receptors, the VLR system exhibits striking similarities (and possible homology) to adaptive immunity in jawed vertebrates at the cellular level. VLRA and VLRC are expressed as cell surface proteins on cells with molecular characteristics of αβ and γδ T cells, respectively. VLRB is expressed both as a surface receptor and as a secreted molecule in lymphocytes that are morphologically and functionally similar to B cells (Herrin & Cooper 2010). The understanding of VLR function in the agnathans is that these diversifying receptors act as antibodies and cell receptors much in the way that immunoglobulin and TCR functions in jawed vertebrates. Overall the VLR system stands as an example of a completely unanticipated system of diversified immune receptors that is likely based on a common system of cells and regulatory biology with the jawed vertebrates.

Example 2: Diverse repertoires of pattern recognition receptors in invertebrate deuterostomes

Divergence in immune receptors is also apparent in innate immune systems within the gene families that encode pattern recognition receptors (PRRs), including NOD-like receptors

(NLRs), scavenger receptor cysteine-rich (SRCR) domain proteins, peptidoglycan recognition proteins (PGRPs), and Toll-like receptors (TLRs). Of these, TLRs are the most well-documented receptors in terms of their distribution across phyla, although the more precise nature of their functions is known in only a few instances in insects and vertebrates. Recent analyses of genome sequences have identified TLRs in phyla across Metazoa (Buckley & Rast 2015). Expansion of the gene families that encode TLRs, relative to the vertebrate, is observed in both the invertebrate

7 deuterostomes and within some phyla of the protostomes (Buckley & Rast 2015). Analysis of the sea urchin genome has identified over 200 TLR genes, which may suggest an alternate mode of pathogen recognition and immune activation (Hibino et al. 2006; Buckley & Rast 2012). This expansion of immune receptors is not restricted to TLRs but is also evident in the SRCR domain containing proteins and NLRs within the deuterostomes, lophotrochozoans, cnidarians and sponges (Buckley & Rast 2015). Although the exact function and ligands of the expanded TLR repertoire is currently unknown, evolutionary diversification patterns and expression on immune cells is consistent with the hypothesis that these receptors recognize quickly evolving non-self ligands in a manner that differs from vertebrate TLR usage. These diverse innate immune receptors may reveal further novelties as their function is investigated but they do suggest the potential that the requirement for a regulatory and cellular system capable of handling immune diversity was already part of the immune system of the common deuterostome ancestor.

Example 3: The FREP system in snails - A diversifying system in a protostome

The freshwater snail Biomphalaria glabrata has been used as model to study host:parasite interactions. As the intermediate host of the parasitic flatworm Schistosoma mansoni, which is the causative agent of the tropical disease schistosomiasis (King 2010; Zhang et al. 2008; Moné et al. 2010), understanding the immune system in B. glabrata may uncover unique strategies for disrupting the transmission of the pathogenic trematodes. The interaction between B. glabrata and the parasitic trematode involves a complex molecular crosstalk between several antigens, immune receptors and effector systems that are highly diverse structurally and variable in expression between and within host and parasite population. An important mediator of the snail immune response is the fibrinogen-related proteins (FREPs), which play a direct role in

8 maintaining trematode resistance. FREPs are lectin-like hemolymph proteins that bind and precipitate soluble antigens derived from S. mansoni (Adema et al. 1997). FREPs are highly diverse as a result of somatic diversification that generates unique repertories in individual snails.

For example, the BgFREP3 locus encodes three to five genes, but as many as 34 unique sequences have been obtained from an individual snail (Zhang et al. 2004). The mechanisms that generate this complex FREP repertoire are still unknown. However, preliminary evidence suggest that alternative splicing, nucleotide point mutation and gene conversion events might be involved

(Moné et al. 2010; Dheilly et al. 2015; Hanington et al. 2010; Zhang & Loker 2003). The FREPs illustrate that diversifying receptors exist outside of the vertebrates. By expanding our scope in investigating other non-mammalian models, we can have a better understanding of the mechanisms and evolutionary forces that shape animal immunity.

Immune cells across animal phylogeny

Most multicellular animals have free-migrating cells with properties that are similar to vertebrate blood cells. Invertebrates lack cells that can be clearly defined as lymphocytes.

Instead, their immune cell types have traditionally been classified on the basis of morphology and behavior (Smith 2010). Morphology and function varies among taxa; however, most invertebrates examined have both phagocytic and granular cell subsets (Hartenstein 2006). Blood cells from acoelomate invertebrates are termed amebocytes, interstitial cells or neoblasts. Motile amebocytes carry out multiple functions. For example, amebocytes in sponges function in both the digestion and transport of food particles. Subtypes of amebocytes can also function as stem cells (e.g., archaeocytes in sponges; interstitial cells in Cnidaria (Miller et al. 2000)). In coelomate animals, the blood cells that populate the vascular lumen and coelomic cavity are generally known

9 as hemocytes or coelomocytes. Hemocytes vary among animal groups but can broadly be categorized into four classes (Hartenstein 2006): prohemocytes, hyaline hemocytes

(plasmatocytes or monocytes), granular hemocytes (granulocytes), and eleocytes (hemocytes with inclusions). Prohemocytes are blood cell precursors that are small, round cells with relatively large nuclei and scant cytoplasm (Shrivastava & Richards 1965; Hartenstein 2006). The majority of differentiated blood cells are hyaline hemocytes, which are functionally analogous to the vertebrate monocytes/macrophages. Hyaline hemocytes are generally phagocytic, and function in ingesting apoptotic cells and pathogens. Granular hemocytes are densely packed with granules and are involved in wound healing, blood clotting, phagocytosis and pathogen encapsulation.

Lastly, eleocytes contain lipid or crystalline inclusions that are irregularly sized and shaped.

Eleocytes are also known as chloragogen cells, vacuolated cells, and spherulocytes. Granular and spherule cells also tend to be involved in the release of anti-microbial peptides (AMPs) (Smith

2010). Overall, while cells vary from taxa to taxa, some common morphological and functional features are apparent. Determining the extent to which these represent a common ancestry will require more detailed molecular analysis.

A systems-level approach to studying immunity

Although vertebrate models have allowed us to investigate pathways and systems that are closely associated with human biology, the complexity of these models imposes limitations on some mechanistic studies. This is the case also with immunological research. Given the systemic nature of the immune response, investigating immunity within the context of an intact organism can provide invaluable insight. Some invertebrate models with simpler morphology offer distinct advantages in this type of research. Although traditional genetic invertebrate models have played

10 a significant role in advancing our knowledge in immunity, the field of comparative immunology has been hindered by the lack of sequence data, which complicates identification of rapidly evolving immune gene homologs. Recent advances in massively parallel sequencing overcome this obstacle and pave the way for using non-traditional models in comparative studies, allowing us to identify conserved immune pathways. Some recent and historical immunological findings based on invertebrate models will be reviewed in the following section.

Invertebrate genetic models

Drosophila melanogaster is a powerful genetic model that has revolutionized many fields in biology including development and immunity. The prototypic innate immune receptor, Toll, was first discovered and characterized in Drosophila (Lemaitre et al. 1996). The Drosophila gut has been well-characterized and shares some features with the mammalian gut: it is divided into three compartments and its cells renew constantly during throughout adult life (Davis & Engström

2012; Broderick & Lemaitre 2012). As a consequence of its feeding habits, Drosophila is exposed to a diverse array of microbes, but the diversity of its gut microbiota is relatively low (Broderick

& Lemaitre 2012; Gaboriau-Routhiau et al. 2009). D. melanogaster has evolved mechanisms for recognizing and maintaining commensal bacteria. These include two complementary inducible defense mechanisms: AMP production, which is downstream of the Imd/NF-κB pathway, and reactive oxygen species (ROS) produced by the dual oxidase system. Toll itself does not appear to be involved in microbial recognition in the midgut (Broderick & Lemaitre 2012). Commensal bacteria appear to be able to activate intestine specific transcriptional regulator Caudal, which in turn contributes in the suppression of NF-κB target genes which contains Caudal binding sites

(Bischoff et al. 2006; Lee & Brey 2013). In addition, the compartmentalization of AMP

11 expression (Ryu et al. 2006) ensures the survival of the microbiota. Studies in gnotobiotic animals and indicate that the gut microbiota affect larval growth and adult gut cell renewal. These similarities between D. melanogaster and mammals (e.g. utilization of AMP, the role of commensal species in development) suggest that they may share some analogous mechanisms.

Although there are clear differences in the receptors and effectors used by vertebrates and insects elements of immune pathways may be conserved.

The nematode Caenorhabditis elegans has a simple and transparent body structure and is a powerful model for developmental biology, neurobiology and cell biology (Brenner 1974;

Kosinski & Zaremba 2007). As such, a wealth of genetic and genomic tools have been established for C. elegans and research has been extended into the understanding its immune mechanisms.

Although C. elegans lacks dedicated immune cells, it has emerged as a model to investigate some aspects of host-microbe interactions (Kurz & Ewbank 2003; Balla & Troemel 2013). C. elegans lacks some hallmark immune genes that exist in other metazoans. Key factors such as NF-κB and

MyD88 are absent and its sole TLR homolog does not seem to play a role in immune response

(Fuchs & Mylonakis 2006; Schulenburg et al. 2008; Balla & Troemel 2013). However, transcriptional profiling experiments indicate that C. elegans can recognize different pathogens and mount pathogen-specific responses (Peleg et al. 2008; Pukkila-Worley & Ausubel 2012), although the recognition mechanisms remain undefined.

Immunity in animals that lack mesoderm

Hydra vulgaris is a hydrozoan polyp that belongs to the phylum Cnidaria, a sister phylum to Bilateria. This animal has a relatively simple anatomical structure consisting of two single cell thick epithelial layers: ectoderm on the outside and endoderm lining the inner gut cavity.

Although divergent from vertebrates, it is used as a model for studying gut immunity, in particular the fundamental components of the innate epithelial immune response that are conserved throughout Metazoa (Bosch 2014). Hydra has a transparent body and lacks specialized immune cells, although some cells display allorecognition abilities (Bosch & David 1986; Hildemann et al. 1980). In this system, defense is primary mediated by the epithelium. Receptors structurally related to Toll are activated by contact with certain microbes, which triggers the secretion of AMP

(Bosch et al. 2009; Yang et al. 1998; Kirschning et al. 1998). The immune response of Hydra is evolutionarily geared towards controlling the commensal population rather than defense again invasive pathogens (Bosch 2014).

Sponges (phylum Porifera) have been used as model system for understanding cell- microbe interactions. Sea sponges are ancient sessile, filter-feeding metazoans that interact with a very complex bacterial community. As filter feeders, sponges host dense and diverse communities of symbiotic bacteria, archaea and unicellular eukaryotes (Taylor et al. 2007). Up to 35% of sponge biomass can be attributed to microbes. Stable microbial communities that are distinct from those of the surrounding seawater exist within different compartments of the sponge

(Taylor et al. 2007). Sponges have complex immune systems that include LPS-activated pathways and an interferon-related system (Müller & Müller 2003). They produce a wide range of AMPs in response to microbes (Blunt et al. 2011). Distant homologs of TLRs, expanded families of NLRs, and the presence of immune related regulatory genes such as Nf-κB were identified in analyses of sponge genomes (Srivastava et al. 2010). Sponges are used as a model for investigating the basic mechanism that maintains an active and specific commensal population

(Thomas et al. 2010; Schmitt et al. 2012).

Many animal groups provide models with experimental advantages or “natural experiments” that can be used to elucidate immune mechanisms. Each of these investigations offers opportunities to uncover new areas of immunity that for one reason or another are not yet understood in vertebrates. As the basis of animal immunity is better defined, the comparative information gleaned from these models will become increasingly meaningful.

The gut-associated immune response Gut immunity in vertebrates

The gut immune system is fundamental for survival in all eumetazoans. The gut epithelium is responsible for absorbing nutrients, repelling pathogens and maintaining symbionts.

Commensal microorganisms are present in all known eumetazoans (Purchiaroni et al. 2013;

Sommer & Bäckhed 2013). More than 1013 to 1014 microorganisms of over 1,000 species have been identified in the human gut, the vast majority of which are non-pathogenic (Qin et al. 2010;

Human Microbiome Project Consortium 2012). The primary function of commensal bacteria was thought to be facilitating the metabolism of certain types of polysaccharides as well as producing essential vitamins (Kamada et al. 2013). However, recent studies using germ-free mice indicate that the gut associated microbiota acts in a variety of biological roles including the promotion of lymphoid structure and structural development of the gut (Neish 2009), protection from pathogens

(Ivanov et al. 2009; Kamada et al. 2013) and maintenance of overall gut homeostasis (Ryu et al.

2008; Sommer & Bäckhed 2013). The presence and composition of this microbiota can influence intestinal inflammation and obesity as well as other pathophysiological conditions (Honda &

Littman 2012; McDermott & Huffnagle 2014). The composition of the commensal microbiota is shaped by diet and other factors. This results in a dynamic composition of the microbiota across

14 populations and even throughout the lifetime of the host. Thus, the human gut immune response must be tightly regulated to strike a balance between tolerance and defense, as dysregulation can lead to the development of a disease state (e.g. inflammatory bowel disease)(Sun et al. 2015) .

The immune system of the vertebrate gut consists of several components that work together to properly modulate the microbiota. The gut-associated lymphoid tissues (GALT) include all lymphoid structures and aggregates associated with the intestinal mucosa. The gut immune system is divided into three compartments: the gut lumen, the epithelial layer and the lamina propria. The gut lumen is the site of the mucosal layer and commensal microbiota, which reside in the top layer of the gut mucosa that is facing the lumen (Johansson et al. 2008). The intestinal epithelium consists of a monolayer of columnar epithelia cells interspersed with specialized immune cells such as M cells, goblet cells and Paneth cells located in the crypts. Small intestinal crypts contain small reservoirs of stem cells that support the rapid turnover of the epithelial cells (Ritsma et al. 2014). The majority of gut epithelial cells are enterocytes, which primarily function in nutrient absorption. However, some enterocytes expresses TLRs that trigger the release of pro-inflammatory chemokines and cytokines upon pathogen detection. The apical side of enterocytes are characterized by dense microvilli, which are covered by a glycocalyx that consists of mucin-like polysaccharides and glycoproteins and embedded hydrolytic enzymes that contribute to defense. Enterocytes provide a physical barrier and are closely joined together around their apical end by tight junctions that prevent the invasion of pathogens and intrusion of gut contents through the epithelial layer. Mucus that contains AMPs is produced by the Paneth cells and Goblet cells. Paneth cells secrete a suite of AMPs such as defensin-like molecules, C- type lectin molecules (e.g. RegIII-γ) and glycoproteins (e.g. CRP-ductin) (Muller et al. 2008;

Purchiaroni et al. 2013). Along with AMPs, IgA is also secreted by the B cells in the lamina

15 propria and is transported by the enterocytes for release into the mucosal layer. Dimeric IgA complexes are bound by polymeric immunoglobulin receptor on the basolateral membrane of intestinal epithelial cells and are actively transcytosed into the lumen (Johansen & Kaetzel 2011).

Intestinal intraepithelial lymphocytes (iIEL) which include Natural Killer (NK) cells and both γδ and αβ T cells are interspersed throughout the gut epithelial layer and act as sentinels for both invasive pathogens and damaged epithelial cells (Qiu, Yang, and Yang 2014). This combination of physical and biological properties of the mucosal and gut epithelium layer limits penetration of microbiota.

The lamina propria, which is located on the basolateral surface of the gut epithelium, is made up of loose connective tissues that bridge the gut epithelium and the muscle layer. Various types of immunocytes are present in the lamina propria, including large numbers of macrophages, neutrophils, mature αβ T cells and B cells as well as NKT cells, mast cells and immature dendritic cells (Mak & Saunders 2008). The lamina propria is also characterized by sections of aggregated lymphoid nodules called Peyer’s patches, which extend from the lamina propria layer into the submucosa of the small intestine. Peyer’s patches primarily function in immune surveillance of the intestinal lumen. Specialized sections of Peyer’s patches, known as the follicle-associated epithelium (FAE), sample lumenal contents (Mak & Saunders 2008; Janeway et al. 2004). Within the FAE, specialized epithelial cells known as M cells carry out antigen transcytosis from the gut lumen into the lamina propria to APCs. The mesenteric lymph nodes (MLN) lie at the base of the mesentery and collect lymph draining from the intestinal mucosa for antigen presentation to CD4+

T cells. The MLN, along with the Peyer’s patches, are responsible for the activation of the adaptive immune response (e.g. IgA secretion) in instances where the pathogens are able to penetrate the outer gut mucosal and epithelial defense layers (Mak & Saunders 2008). Vertebrates

16 have developed a robust barrier at the gut epithelium both from a physical and immune standpoint.

Recent studies indicate that in addition to this host barrier, symbiotic microbiota also play an essential role in maintaining immune homeostasis.

Crosstalk between the microbiota and the host immune system

The gut microbiota plays both direct and indirect roles in gut immunity. The normal development and maturation of a gut associated immune system requires the presence of microbiota. In comparison with conventional mice, the gut immune structures and components of germ-free mice tend to be underdeveloped (Round & Mazmanian 2009). The prenatal initiation of the gut associated lymphatic system (e.g., Peyer’s patches and mesenteric lymph nodes) occurs in a sterile environment in response to lymphoid tissue inducer (LTi) cells (Mebius 2003).

Postnatal microbial exposure completes the maturation of these lymphatic tissues (Round &

Mazmanian 2009). In germ-free mice, this results in fewer and smaller Peyer’s patches (Moreau

& Corthier 1988) that are deficient in various pattern recognition receptors (Yi & Li 2012;

Gaboriau-Routhiau et al. 2009), and have relatively low numbers of Th17 cells (Ivanov et al.

2009), Treg cells (Hall et al. 2008) and IgA producing plasma cells (Hapfelmeier et al. 2010). In addition to gut-specific immune structures, the gut microbiota also influences overall host physiology. Changes in the gut microbiota composition can cause the delayed maturation of the gastrointestinal tract and affect the overall development of the organism (Sekirov & Russell

2010). In germ-free mice, the caecum is enlarged, intestinal surface area is reduced and the gut epithelium has a longer regeneration time, which results in reduced microvillus thickness and definition (Lee & Brey 2013). In Drosophila, symbiotic interaction between the host and one of its gut bacterial strains, Acetobacter pomorum, is required to achieve normal development and

17 metabolic rate (Shin et al. 2011). These and other findings indicate that the normal gut microbiota plays a significant role in the development and homeostasis of the host organism in a wide phylogenetic range of organisms.

This idea is reinforced by the observation that germ-free animals are more susceptible to certain pathogens, indicating that the gut microbiota plays a role in host protection. One mechanism by which commensals contribute to defense against the colonization of opportunistic pathogen is through direct competition for nutrients. Studies indicate that pathogens that are metabolically related to commensal bacteria have a more difficult time becoming established and can be more effectively outcompeted (Hooper & Gordon 2001; Kurashima et al. 2013).

Commensal microbiota can also promote the function of the mucosal barrier through indirect stimulation of epithelial immune response. Commensal bacteria induce the production of antimicrobial peptides (e.g., REGIIIγ) through activation of gut epithelial TLRs (Zoumpopoulou et al. 2009). Impairment of AMP production correlates with increased susceptibility of infection

(Ivanov et al. 2008). Aside from inducing AMP production, production of metabolites by commensals can also elicit an immune response by acting as a signal for the gut epithelium to inhibit the translocation of pathogens (Zoumpopoulou et al. 2009). The production of pro-IL-1β can be up-regulated in gut-resident mononuclear phagocytes by the presence of microbiota (Niess et al. 2008). As maintenance of pro-IL-1β is necessary for the rapid recruitment of neutrophils, commensals assist in priming the gut in response to pathogenic bacteria. These highlight the important roles of commensals in direct and indirect maintenance of a healthy and robust gut immune response.

The role of sequencing data in comparative immunology

Host-pathogen co-evolution has likely driven the diversity of immune receptor and effector gene families across Metazoa. As such, immune genes often evolve at a rapid pace

(Murphy 1993; Hughes 1997), which makes it difficult to identify homologs across phyla using traditional molecular biology strategies (e.g., library screening with heterologous probes and degenerate PCR). Genomic and mRNA sequence analysis lend power to these searches. Due to the recent advancement in both genome and whole transcriptome sequencing, in-depth gene discovery and expression analysis can be applied to once neglected non-traditional models. As such, new insights have been gained into invertebrate immune responses. For example, genome studies suggest that sponges and cnidarians possess most of the vertebrate gene families required for a functional immune response (Putnam et al. 2007; Chapman et al. 2010; Shinzato et al. 2011), illustrating the common heritage of immune response mechanisms across animals (Putnam et al.

2007; Chapman et al. 2010; Shinzato et al. 2011; Miller et al. 2007; Srivastava et al. 2010).

Another example of the utility of genome data is identifying the expansion of immune recognition receptor families, such as TLR, NLR and multi-SRCR-domain proteins (described briefly above)

(Buckley & Rast 2015; Hibino et al. 2006). Novel immune factors were also identified using these techniques including sea urchin 185/333 factors (described in detail below) (Buckley et al.

2008; Ghosh et al. 2010), FREPs in molluscs (Zhang et al. 2008; Zhang & Loker 2003) and

DSCAM in insects (Watson et al. 2005). These advances illustrate the complexity of invertebrate immune systems that were once perceived as composed of relatively simple innate responses.

Advancement of next generation sequenceing (NGS) technology allows for cost effective analysis of additional genomes and transcriptomes. Gene expression profiling over the course of infection enables identification of novel immune factors in addition to global measurements of transcript

19 prevalence. The incorporation of these new tools enhances the utility of existing genome data and provides a starting point to characterize the novel immune systems.

The sea urchin immune system as a model for immune regulation

Sea urchin adult immunity

Sea urchins belong to the phylum Echinodermata, one of four phyla that form the deuterostomes (Figure 1.1). As invertebrate deuterostomes, echinoderms are closely related to vertebrates. There are five extant classes of echinoderms: Echinoidea (sea urchins and sand dollars), Holothuroidea (sea cucumbers), Ophiuroidea (brittle stars), Asteroidea (sea stars), and

Crinoidea (feather stars and sea lilies) (Figure 1.1). Echinoderms play a key role in many benthic ecosystems serving as both omnivores and decomposers. Early research into allograft rejection

(Gross et al. 1999) and clot formation (Smith et al. 1996) illustrates the robustness of the sea urchin immune system in the adult.

Adult sea urchin immunocytes

Sea urchin coelomic fluid contains immunocytes as well as many secreted proteins involved in the immune response, including AMPs and complement factors. Coelomic fluid can mediate immune responses via coagulation, encapsulation, opsonization and phagocytosis. The functions and morphologies of various echinoderm coelomocytes have been examined in many reports over the past century (Smith et al. 2010). Eight types of coelomocytes are present in the

Figure 1.1: Echinoderms are closely related to the chordates and are represented by five extant classes.

(A) The evolutionary relationships of several important phyla are shown. Together with the vertebrates, hemichordates, and the recently described Xenacoelomorpha, echinoderms are members of the deuterostome lineage. (B) The phylum Echinodermata consists of five extant classes. Note: the exact phylogeny of the Eleutherozoa within Echinodermata is still under debate.

The tree displayed is reconstructed from the Asterozoa-Echinozoa hypothesis that is based on the adult body form (Mooi & David 2000; Janies et al. 2011). A second model, based shared adult features of enclosed radial elements of the water-vascular system implies that Ophiuroidea are

21 sister to Echinozoa (Holothuroid and Echinoids) with Asteroidea being the outlying group (Smith

1984). The latter model is also supported by analysis of rRNAs and metabolism-associated genes

(Pisani et al. 2012).

22 purple sea urchin that is the subject of this thesis. These can be divided among three main groups based on general morphology and behavior: phagocytes, spherule cells and vibratile cells (Smith et al. 2006; Smith et al. 2010; Gross et al. 1999). Phagocytes are further categorized into three subtypes: discoidal, polygonal and small; and spherule cells are categorized as red and colorless

(Smith et al. 2010).

Phagocytes are the most abundant class of coelomocytes. Three phagocytes subtypes are defined based on cytoskeletal morphology, actin-based motility pattern and gene expression profiles: discoidal, polygonal, and small (Edds 1993; Henson & Kolnik 2003; Henson et al. 1992).

Both discoidal and polygonal cells are classified as large phagocytes due to their size and are involved in a number of immune related functions including bacterial and foreign particles clearance (Kaneshiro & Karp 1980; Wardlaw & Unkles 1978; Yui & Christopher 1983), encapsulation, cytotoxic reactions, graft rejection (Coffaro & Hinegardner 1977; Bertheussen

1979), wound healing and clotting responses (Boolootian & Giese 1959). Discoidal cells have radially arranged actin filaments whereas the actin filaments of polygonal cells are arranged laterally along the length of the cell membrane (Edds 1993). are stationary in vitro whereas polygonal cells are motile (Henson et al. 1992). Small phagocytes, the third type of phagocyte, are smaller than both discoidal and polygonal cells. They are less numerous and their cellular functions are currently unknown, although they express immune-related genes such as Sp185/333

(Brockton et al. 2008).

Red spherule cells are granular cells that contain large pigment granules. Due to their granularity, they also have been called granulocytes or eleocytes. These granules contain echinochrome A (Service & Wardlaw 1984), a naphthoquinone with the capacity to generate peroxide when exposed to extracellular levels of calcium (Perry & Epel 1985; Perry & Epel 1981).

In the presence of bacteria, red spherule cells have been shown to migrate, encapsulate and degranulate pigment (Johnson 1969a; Gerardi et al. 1990; Service & Wardlaw 1984). Red spherule cells display amoeboid-like motility and migrate towards wounds opening to form a rim around the wound edges (Heatfield & Travis 1975; Coffaro & Hinegardner 1977). Colorless spherule cells, the second type of spherule cells, are also highly granular. The function of these cells has not been characterized. These cells can shift to a highly motile amoeboid form and have potent cytolytic activity in the presence of phagocytes (Arizza et al. 2007). Vibratile cells, the third major coelomocyte subtype, are flagellated, highly motile secretory granular cells that are involved clotting reactions (Bookhout & Greenburg 1940; Wardlaw & Unkles 1978).

A major function of coelomic fluid is clotting, which is a defensive strategy against punctures and can ensnare foreign particles. Within echinoderms, clotting is a result of cell-cell adhesion of the phagocytes (Johnson 1969b). The other cell types act as passive players that are trapped within the clot or adhered to the clot surface. Rearrangement of actin cytoskeleton of the phagocytes is required for clot formation (Edds 1977), which also requires calcium (Donnellon

1938; Davidson 1953; Bookhout & Greenburg 1940) and the presence of disulfide bonds

(Bertheussen & Seijelid 1978; Boolootian & Giese 1959). The clotting mechanism of sea urchin coelomocytes differs from that of mammals in so far as the protein content of sea urchin coelomic fluid is relatively low (Holland et al. 1967). A 74 kDa olfactomedin domain containing protein,

SpAmassin-1, is involved in forming large disulfide bond aggregates that adhere coelomocytes to each other (Hillier & Vacquier 2003). Other molecules involved in clotting include Annexin V and von Willebrand factors (Ramírez-Gómez et al. 2008).

Immune genes encoded in the sea urchin genome

The sequencing of the sea urchin genome as well as earlier EST projects provide information to identify immune-related genes (Smith et al. 1996; Hibino et al. 2006). A series of

EST/cDNA or differential display experiments were done to identify differentially expressed genes in LPS-activated coelomocytes (Pancer et al. 1999; Nair et al. 2005; Smith et al. 1996).

From these studies, several homologs of genes involved in vertebrate immunity were identified, including transcription factors SpNFκB and SpRUNT (Pancer et al. 2004), and effector genes such as a C-type lectin gene (Multerer & Smith 2004). The studies were also identified genes not induced by immune challenge such as the complement factor B (SpBf; Terwilliger et al. 2004).

Two of the most important discoveries from the series of EST experiments were complement factors and the sea urchin effector gene family 185/333.

Complement in the sea urchin

The complement system acts as an initiator for activating an adaptive immune response in vertebrates or as an opsonin for recognition and phagocytosis of microbes. Initial investigation of opsonisation and phagocytosis in the green sea urchin, S. droebachiensis, indicated that the presence of mammalian C3 enhanced coelomocyte response (Kaplan & Bertheussen 1977;

Bertheussen 1981; Bertheussen 1982). Screening EST libraries from immune activated sea urchin coelomocytes identified two complement-like proteins (SpC3 and SpBf) (Smith et al. 1998). This was the first evidence of a complement system in an invertebrate. Subsequent genome sequence analysis identified additional thioester proteins (Hibino et al. 2006). Phylogenetic analysis indicates that SpC3 is homologous to vertebrate C3, C4 and C5 paralogs. SpC3 expression is dramatically increased in coelomic fluid in response to LPS and is localized in small vesicles

25 within subpopulations of large phagocytes (Clow et al. 2004). SpC3 is also upregulated in embryos following challenged with heat-killed bacteria (Shah et al. 2003).

A second sea urchin C3 homolog, known as SpC3-2, is expressed at higher levels in the gastrulating embryo and larvae although its function is not well characterized (Smith et al. 2006).

SpC3 contains a conserved thioester site, a histidine region, a putative C3-convertase site and other conserved regions (Al-Sharif et al. 1998). SpC3 has been shown to be involved in opsonisation (Clow et al. 2004). In addition to the C3 homologs, the purple sea urchin also has a single homolog of the vertebrate Bf/C2 family, known as Sp Bf. This gene is expressed in the large phagocytes in several alternatively spliced transcripts. Expression is constitutive and is unaffected by immune challenge with LPS.

The Sp185/333 gene family

Another set of genes identified via EST library screens is known as Sp185/333 (Nair et al.

2005; Smith et al. 1996; Pancer et al. 1999). The Sp185/333 proteins are expressed in polygonal and small phagocytes, are strongly upregulated when challenged with PAMPs such as LPS, dsRNA and heat-killed marine bacteria (Majeske et al. 2013; Brockton et al. 2008; Nair et al.

2005), and have been shown to bind to bacteria and some PAMPs with relatively high affinity

(Majeske et al. 2014). The diversity of the 185/333 transcripts is striking. The 185/333 gene loci contain two exons: the first exon encodes a leader sequence while the second exon is composed of up of different blocks of sequence termed elements. The elements are defined based on sequence comparison amongst the gene family. There are 25 to 27 different elements that range from 12 to 357 bp in size. The appearance of the elements varies in different genes. The diversity of the gene family was examined through sequencing of 171 genes from three individual sea

26 urchins. Although the sequences are more than 88% similar, none of them are exactly identical to each other (Buckley & Smith 2007). In total, based on qPCR, statistical analysis and BAC library screening, there may be up to 60 alleles within the genome. However, more than 100 different transcripts can be obtained from a single individual suggesting of the existence some type of diversifying mechanism (Dheilly et al. 2009; Buckley & Smith 2007). It has been recently shown that individual phagocytes only express one variant of Sp185/333 in response to bacterial challenge (Majeske et al. 2014). Although there are complications since 185/333 proteins are encoded by a polymorphic multigene family, there are strong indications that some form of diversification is responsible for the large number of transcribed sequences found after immune challenge. Although the function of 185/333 is unknown, its expression pattern and its ability to bind bacteria strongly suggest that it acts as a diversified non-self recognition effector.

Expanded multigene families of pattern recognition receptors

Analysis of the sea urchin genome sequence has not only allowed for the identification of novel immune genes but also the characterization of divergent systems of previously known gene families. Highly expanded gene families that encode homologs of TLRs (253 genes), NLRs (>200 genes), and SRCR domain-containing scavenger receptors (218 genes with 1095 SRCR domains) have been identified in the purple sea urchin genome (Hibino et al. 2006). In addition, over 100 small C-type lectins have been identified (Smith et al. 2010).

The purple sea urchin TLRs are divided into eleven subfamilies based on phylogenetic analysis. In contrast to the vertebrates, the TLR subfamilies in echinoderms appear to evolve quickly (Buckley & Rast 2012). Expansions of TLR gene families are also apparent in three additional echinoid species (S. fragilis encodes ~280 TLR genes; Mesocentrotus franciscanus

27 encodes ~230 TLRs; Lytechinus variegatus encodes 68 TLRs; (Buckley & Rast 2012)) as well as other invertebrate deuterostomes, such as amphioxus, which has at least 72 TLR sequences

(Satake & Sekiguchi 2012). TLR expansions have also been described in some lophotrochozoan genomes, including, the polychaete annelid Capitella teleta, which encodes 105 TLR genes

(Davidson et al. 2008). Phylogenetic analysis of the Metazoan TLRs indicates that the echinoderm TLRs form a strongly supported clade that is in independent of TLRs from other species. Although the ligands for the sea urchin TLRs have yet to be identified, given the rapid evolving nature of the receptors and the fact that the TLR are expressed in immune organs and cells, an immune function is likely.

Like the TLRs, expansions of the gene families encoding NLRs and SRCRs are also present throughout Eumetazoa. Large families of NLRs are present within teleost fishes, invertebrate deuterostomes, the protostome C. teleta, the cnidarian Nematostella vectensis and the poriferan Amphimedon queenslandica. Similarly, expansions of the gene families encoding

SRCR domain-containing proteins are evident in the invertebrate deuterostomes, mollusc Lottia gigantea, and basal metazoans ((reviewed in Buckley & Rast 2015)). It is notable that the three families are not always expanded in specific lineages. For example, in N. vectensis, expansions have occurred within the in NLR (42 genes) and SRCR gene families (128 domains in 66 genes), but not TLRs, for which a single gene is present in the genome. These data indicate the immune recognition receptors, which evolved very early in Metazoa, have evolved differently among animal lineages, which may reflect differences in life history environment, or specific immune pressures.

Antimicrobial peptides

AMPs are another important component of the sea urchin immune system (Li et al. 2008;

Li, Blencke, et al. 2010; C. Li et al. 2014; Li, Haug, et al. 2010). AMPs are typically short polypeptides (<100 amino acids) with net positive charge or amphipathic structure that can be active against a wide range of pathogens (e.g. bacteria, fungi and viruses) (Reddy et al. 2004;

Hancock & Sahl 2006). These peptides tend to be divergent among species and require functional analysis for identification. Antimicrobial activity was first detected in coelomocyte extracts of the green sea urchin S. droebachiensis (Haug et al. 2002). Stronglyocin-1 and -2, were subsequently purified by identifying protein fractions that exhibited growth-inhibiting activity against bacteria. The AMPs were then purified by HPLC and partial sequences were obtained that were used to design degenerate primers to isolate the transcript sequence. Two other AMPs,

Centrocin-1 and -2, were subsequently isolated using a similar strategy (Li, Haug, et al. 2010).

The S. purpuratus homologs of these genes were identified computationally using BLAST searches against the genome sequence (Li, Blencke, et al. 2010; Li, Haug, et al. 2010; Li et al.

2008). Structurally, these AMPs are cysteine-rich with six cysteines that form three disulfide bonds (Li et al. 2008; Li, Haug, et al. 2010). Each of these AMPs, including the recombinant S. purpuratus orthologs, exhibits broad bactericidal activity against both Gram-negative and Gram- positive bacteria (Li, Haug, et al. 2010; Li, Blencke, et al. 2010; Li et al. 2008).

Summary

Due to rapid immune gene diversification, the identification of immune-related homologs in invertebrates has been difficult. However, recent advances in genomics and next-generation sequencing allow us to identify homologs of genes known to be involved in vertebrate immunity

29 in increasingly divergent taxa. Consequently, we are now able to exploit the experimental advantages and simple morphology of invertebrate animal models to unravel the complexity of the immune system. This is particularly valuable in the study of the regulation of the gut immune response, given both the morphological complexity of the vertebrate gut and its associated immune system, as well as the diversity of the resident microbiota. The role of the immune system in maintaining a balanced microbiota, however, is likely among the most ancient aspects of immune response. The purple sea urchin larva, which is closely related to chordates as a member of the deuterostome superphylum, is well positioned as a research model. With well-established molecular and experimental techniques, genomic tools, available transcriptomes and a transparent, simple morphology, the purple sea urchin larva provides the opportunity to study the gut-associated immune response within the context of an intact organism.

With this goal, the work presented here is divided into three parts as follows: (1) as a baseline for further investigations, I have characterized the cellular immune system of the sea urchin larva. In Chapter 2, I describe morphologies and behaviours of the larval mesenchymal immune cells as well as the cellular immune response following a gut-associated bacterial infection. (2) To understand the molecular mechanisms that underlie this cellular immune response, I have identified key genes involved in this immune response and performed genome- wide screens of gene activity at several time points of bacterial infection. I have identified a small set of IL-17 factors as key mediators of this response. In Chapter 3, I describe the genomics of

IL-17 in the purple sea urchin, the spatial and temporal expression of IL-17 in the larva and the role of IL-17 in mediating the expression of downstream effector genes. (3) I am interested in developing a gene regulatory network model of the sea urchin larval immune response using IL-

17 as an anchor gene. In Chapter 4, I describe the ongoing efforts to generate the cis-regulatory

30 control of one member of the sea urchin IL-17 family. Finally I conclude with some thoughts on how these findings relate to immunity in other animals and some future prospects for this work.

Chapter 2 Cellular and molecular immune responses in the sea urchin larva

Introduction Sea urchins have a biphasic lifecycle that includes a feeding larval stage

Echinodermata is a diverse phylum composed of about 7,000 extant species that are distributed among five classes (see Figure 1.1). Almost all of these species are dioecious and develop through a biphasic life cycle in which a juvenile adult metamorphosizes from a feeding larval stage. A few species undergo direct development in which large eggs are fertilized and the embryo develops into a juvenile stage directly by bypassing the larval stage entirely (Wray & Raff

1989). We focus here on the indirect development of the purple sea urchin; although the process is similar in other echinoids, the developmental times described below pertain to S. purpuratus.

The early development of the sea urchin embryo has been well-characterized and can be broadly divided into five stages prior to metamorphosis: cleavage, blastula, gastrula, prism and larva (McClay 2011). Gametes are spawned into seawater from the five adult gonapores.

Fertilized eggs initiate radial holoblastic cleavage at around 1 hour post fertilization (hpf). By the seventh cleavage, a 128 cell hollow blastula is formed that surrounds a central cavity known as the blastocoel. The cells, which are roughly the same size, continue to divide although the blastula remains one cell layer thick. By ~20 hpf, the blastula consists of ~1,000 cells. Gastrulation begins at 24 hpf as the first wave of primary skeletogenic mesenchyme cells ingresses into the blastocoel to form the larval skeleton. At about 30 hpf the archenteron begins to invaginate to form the larval gut. Two waves of secondary mesenchymal cells ingress into the blastocoel as the archenteron elongates: the first is a set of differentiated pigment cells, followed by a second wave of cells that

32 differentiate into several types of blastocoelar cells and other mesodermal structures. The embryo enters prism stage by ~72 hpf as the skeleton elongates to define the distinctive pyramidal shape.

The archenteron elongates and fuses with the oral ectoderm at the stomodeum to form the larval mouth. The gut further differentiates into three compartments and the onset of feeding occurs

~96 hpf. The animal remains in the larval stage for up to 10 weeks when it metamorphoses into a juvenile form in response to environmental cues.

Larvae are characterized by simple morphology

Unlike their adult counterparts that exhibit pentaradial symmetry, sea urchin larvae are bilaterally symmetrical. Larvae are transparent and range in length from 100 to 400 µm as they grow. At the onset of feeding, larvae consist of ~4,000 cells. During early larval stage (4 to 14 days post fertilization; dpi) (Figure 2.1), four arms protrude from the anterior (oral) end of the larva. Larvae swim in the direction in which the arms are pointed and are usually observed to be pointed upwards. The blastocoelar cavity contains extracellular matrix and is populated with several cell types (collectively known as blastocoelar cells). With the exception of reproductive systems, the free-swimming, feeding larvae carry out most of the biological functions of an adult animal.

The gut is composed of a monolayer epithelium. Two sphincters define three compartments: the foregut, midgut and hindgut (also referred to as the esophagus, stomach and complete gut structure consists of ~ 430 cells at the onset of feeding, with the majority of the structure consists of ~ 430 cells at the onset of feeding, with the majority of the cells comprising feeding by sweeping food particles into the mouth. Larvae are planktotrophic, but also feed on

Figure 2.1: Schematics of a purple sea urchin larva

Illustration of the front and lateral view of a four-arm purple sea urchin larva as adopted from

Smith et al 2008. The abbreviations are as follows: a, arms; an, anus; b, blastocoel; e, ectoderm; fg, fore gut; hg, hind gut; m, mouth; mg, mid gut.

34 bacteria and multicellular eukaryotes. The rich microbial environment of the sea water, as well as their relatively long-lived state, underscores the fact that a robust immune system is critical for survival.

Larval immune cells are derived from two precursor populations

Sea urchin larval immune cells are derived from two populations of secondary mesenchyme cells that differentiate into two primary classes: the pigment cells and blastocoelar cells. Pigment cells emerge in early gastrulation and quickly differentiate into a uniform cell type

(Gibson & Burke 1985; Ransick & Davidson 2006). About ten hours after pigment cells delaminate, blastocoelar cells ingress from the tip of the archenteron and differentiate into several heterogeneous cell types with both immune and non-immune functions. The regulatory events that lead to the differentiation of the larval immune cells have been well studied. Recent studies have shown that homologs of important vertebrate hematopoietic transcription factors, including

Gata-1/2/3 and Scl/Tal-1/Lyl-1 homologs, are important in this specification process and contribute to the development of blastocoelar cell subtypes (Solek et al. 2013).

The sea urchin larva as a simple, systems-level model for immunity

Echinoderms have been used extensively as models for investigating early embryonic development (reviewed in (McClay 2011)). Sea urchins offer many advantages for studying embryology: development is synchronous and millions of gametes can be cultured from a single mate pair and embryos are highly amenable to experimental manipulation. Consequently, several techniques have been developed for transgenesis and gene perturbation that are essential for

35 constructing gene regulatory networks. Those favorable attributes can also be extended to the larval stage. With the recently available well-annotated genome and transcriptome sequences, the experimental advantages of this model system can now be applied to questions of immunity.

Other species of echinoderm larvae have been used to study immune response. A thorough characterization of immunocyte behavior has been undertaken in the larva of the starfish Asterina pectinifera (Furukawa et al. 2009). In 4 dpf larvae, there are 150 to 190 mesenchyme cells that are mostly located beneath the ectodermal wall with some distributed in the blastocoel. Starfish larval mesenchyme cells exhibit migratory behaviours and have the ability to phagocytose non- cellular particles (e.g. polystyrene beads and oil droplets), bacterial cells, cells from other echinoderms (e.g. sea urchin cells) and fixed allogeneic material (e.g. PFA-fixed starfish sperm).

However, live allogeneic material (e.g. live sperm and mesenchyme cells) was ignored, which may indicate that the larva possess specific recognition mechanisms (Furukawa et al. 2009). In response to bacterial cells injected into the larval blastocoel, multiple mesenchyme cells migrate towards the site, form an aggregate, and then clear the bacteria through phagocytosis. For large foreign particles, multinucleated cell bodies are formed by mesenchyme cells that have undergone cell-fusion to encapsulate the immunogen (Furukawa et al. 2009). This phagocytosis is partially mediated by a scavenger receptor like protein, ApSRCR1, that is expressed in mesenchyme cells and opsonizes bacteria to facilitate phagocytosis and aggregate formation (Furukawa, Matsumoto, et al. 2012). These experiments illustrate the complex immune systems that function within a morphologically simple echinoderm larva.

A successful immune response requires interactions among different cellular compartments. Consequently, the underlying mechanisms that drive this response are difficult to understand without examining the organism as a whole. As such, a systems level model is useful

36 to decipher the tightly regulated molecular mechanisms that control the immune response. Due to both the morphological and molecular complexities of vertebrate immune systems, a phylogenetically proximal yet structurally simple model organism is of great use to gain insights into fundamental mechanisms of immune gene regulation. Core genetic mechanisms are conserved throughout bilaterian organisms. As members of the deuterostome superphylum, the purple sea urchin genome contains many orthologs of many genes that are involved in the vertebrate immune response. These include homologs of immune regulators, receptors and effector genes (Hibino et al. 2006). Virtually all of the hematopoietic transcription factor gene subfamilies that contain two to four members in the vertebrates are present and represented by a single member in S. purpuratus (Hibino et al. 2006).

Here, we present the larval stage of the purple sea urchin as a model for studying the fundamental mechanisms that control the immune response at the gut epithelium. The morphologically simple sea urchin larvae consist of only a few thousand total cells at feeding stages, which is orders of magnitude fewer than other common deuterostome models. Larvae are optically transparent, which allows for detailed imaging of the cellular immune response within the context of an intact organism. Here, we define five types of larval mesenchymal immune cells on the basis of cell behavior, morphology and gene expression. Using time lapse microscopy, we characterize the responses of these cell types to intrablastocoelar injection of foreign particles as well as gut-associated bacterial infection. We have established a robust, reproducible infection model using the marine bacterium Vibrio diazotrophicus, which elicits larval immune responses at the gut epithelium. In addition, we have identified immune genes that are expressed during the course of this infection using RNA-seq and qPCR and localized their expression using in situ

37 hybridization. These findings establish a simple research model in which to characterize fundamental properties of the deuterostome gut immune response.

Materials and Methods Animals and larval culture

Adult S. purpuratus animals were obtained from the Point Loma Marine Invertebrate Lab,

Lakeside, CA. Adults were maintained in aquaria in artificial sea water (ASW; Instant Ocean) at

12°C. Spawning was induced either by gentle shaking or by intracoelomic injection of 0.5 M KCl.

Embryos were cultured at 15°C in 0.45 µm filtered ASW (FSW). At 4 dpf, larvae were transferred to a glass stirring vessels (Corning or CELLine) at a density of one larva per ml and stirred at 30 rpm. Larvae were fed Rhodomonas lens (3000 algae/ml) every other day beginning at 5 dpf.

Isolation and identification of larval-associated bacterial species

At 5 dpf, larvae were transferred to 25 µm filtered sea water collected at the Mount Desert

Island Biological Laboratory (MDIBL; Maine, USA). Sea water was changed daily to facilitate feeding. At 10 dpf, larvae were isolated from other contaminants and nonadherent bacteria by transferring individually through three washes of 0.2 µm FSW with a drawn Pasteur pipet. Larvae were homogenized using a micropestle in 100 µl of FSW and plated onto Marine Broth (MB,

Difco) agar plates. The MB plates were incubated at 15oC for up to 2 days (depending on the bacterial species).

In order to identify the bacterial isolates, PCR with was performed on individual colonies using primers to amplify 16S rRNA (see Appendix C for primer sequences; (Shinzato et al.

2005)). Resulting PCR products were purified with the QIAQuick PCR Purification Kit (Qiagen) and sequenced. BLAST searches were used to classify bacterial species.

Larval bacterial exposure

Wild-type Vibrio diazotrophicus (ATCC 33466) was used to generate a rifampicin (Rif) resistant mutant strain through selection on LB agar (Sigma-Aldrich) containing Rif (100 µg/ml)

(Tupin et al. 2010). A GFP expressing V. diazotrophicus isolate was developed through bacterial conjugations as described (Neiman et al., 2011).

For larval infection, the Rif-resistant V. diazotrophicus were cultured with LB (Sigma-

Aldrich) with Rif (100 µg/ml) at 15°C with shaking at 250 rpm overnight. E. coli were cultured at

37°C in LB. Bacterial species isolated at MDIBL were grown on MB agar plates at 15°C. Bacteria were pelleted or scraped off agar plates and washed three times with FSW. Bacterial concentrations were determined with a Petroff-Hausser counting chamber. Bacteria were resuspended in FSW and added directly into 10 to 14 dpf larva culture at the desired concentration.

Larvae were collected using 100 µm Nitex filters and washed with FSW to remove as much residual bacteria as possible. Additional bacterial residue was removed through transfer in FSW washes under a dissecting microscope.

For the re-infection experiment, larvae were exposed to V. diazotrophicus for 24 hr after which individual larva was washed by sequential transfers through three separate petri dishes each containing 0.20 µm FSW. Larvae were maintained in clean FSW for 24 hr and then transferred

39 to either seawater containing V. diazotrophicus or FSW and inspected under an Axioplan 2 microscope (Zeiss) after 24 hr of treatment.

Neutralizing bacteria for larval exposure

V. diazotrophicus was cultured as described above, washed three times with phosphate- buffer saline (PBS; 130 mM NaCl, 5 mM Na2HPO4, 1.5 mM KH2PO4). Three separate methods were used to kill the bacteria. (1) Heat Killed: Bacteria were incubated at 60oC for 30 minutes

(Shah et al., 2003). The heat-killed bacteria were washed three times with FSW at 3000 x g for

10 minutes at 4oC. (2) Formalin Killed: Bacteria were formalin-killed with 0.8% formalin for 24 hr at room temperature and then 24 hr at 4oC (Nakhamchik et al., 2007). The formalin-treated bacteria were washed twice with PBS and three times with FSW. (3) Acid Killed: Bacteria were treated with 0.1% peracetic acid for 30 min at room temperature, washed twice with PBS and three times with FSW. The efficiency of killing was assessed by plating the neutralized bacteria on LB/Rif agar plates.

Intrablastocoelar injection

Live bacteria or fluorescent tagged Zymosan A particles (Life Technologies) were washed in FSW three times and resuspended at approximately two particles per picolitre. Sea urchin larvae were mounted against a glass slide in a protamine-sulfate treated Petri dish in FSW. The injection apparatus and chilled stage is the same one used for embryo injection (Rast, 2000, Solek et al. 2013). Injected larvae were transferred to a glass bottom petri dish (MatTek) for imaging with Axio Observer.Z1 microscope (Zeiss) using Axiovision software (Zeiss).

Microscopy and time-lapse analysis

Imaging of sea urchin larvae was performed using a Zeiss Axioplan 2 or Observer Z.1 with either Zeiss HrM (black and white) or MrC5 (color) cameras. For time-lapse microscopy, a chiller platform (20/20 Technology, Inc) maintained larvae at 15 oC. Larvae were imaged in a glass bottomed petri dish (MatTek) and gently held in place by a coverslip anchored with double sided tape (0.1 mm thick, 3M 665). Images for time lapse analysis were obtained using both optical sectioning and temporal acquisition settings in the Axiovision software (Zeiss). Post- acquisition image processing and analysis was done using FIJI Software with the Manual

Tracking module (NIH).

Transcriptome analysis

Larvae were cultured at four larvae per ml to obtain higher amounts of RNA. To counteract the possible negative effects attributed to the denser population, FSW was changed every day from three days onward. At 4 dpf, half of the larva culture was collected by filtering through a 40 µM Nitex filter. The remaining larvae were fed with Rhodomonas lens (3,000/ml) at 5 dpf and collected at 6 dpf as above. For larvae infected with V. diazotrophicus, larva at 10 dpf were collected at 0, 6, 12 and 24 hours of infection. Collected larvae were spun down at 3000 x g for 5 min at 4oC and resuspended in TRIZOL. Total RNA was extracted following manufacturer’s protocol.

For the pre/post-feeding larval RNA-seq experiment, mRNA was extracted and sequenced

(single-end) on the Applied Biosystems SOLiD platform at the University of Kiel by Philip

Rosenstiel. For the larval infection RNA-seq experiment, mRNA was purified with Poly(A)

Purist kit (Ambion) and sequenced on the Applied Biosystems SOLiD4 platform at the

Sunnybrook Genomics Facility (paired end). Sequences were mapped to the genome as described in (Buckley & Rast 2012).

Whole mount in situ hybridization (WMISH)

The protocol for WMISH was adapted from (Minokawa et al. 2004) and (Ransick et al.

2002). S. purpuratus larvae were fixed with 4% paraformaldehyde overnight at 4oC.

Hybridization was carried out overnight at 65 oC. Fluorescent WMISH was performed according to published protocols (Croce & McClay 2009). Primers used for making the probes are listed in

Appendix C.

Results Five classes of immune cells can be identified in the sea urchin larva

To begin to characterize the larval cellular immune response, we used time-lapse microscopy to analyze carefully the number, morphology, and behaviour of the mesenchymal cells that populate the blastocoel in 7-14 dpf larvae. These cells emerge from two well-defined precursor populations (Solek et al. 2013; Ruffins & Ettensohn 1996; Materna et al. 2013), and are broadly categorized as either “pigment cells” or “blastocoelar cells”. Under resting conditions, pigment cells are typically apposed to the outer ectoderm (Figure 2.2A; Gibson et al., 1987;

Hibino et al., 2006; Smith et al., 2008). At 10 dpf, purple sea urchin larvae have ~50 pigment cells. Pigment cells are typically stellate, with the red granules spread out around the nucleus and in two to four pseudopodial projections (Figure 2.2B). These highly granular cells contain

42 echinochrome A, a naphthoquinone with antibacterial properties that gives the cells their distinctive red color (Perry & Epel 1981; Service & Wardlaw 1984). In the presence of extracellular concentrations of calcium, echinochrome A produces reactive oxygen species (Perry

& Epel 1981). Pigment cells are morphologically and genetically similar to the red spherule cells of adult sea urchins, which participate in wound healing (Coffaro & Hinegardner 1977). In the larva, these cells have been shown to phagocytose bacteria injected into the blastocoel (E. coli;

(Hibino et al. 2006)).

We have further defined four distinct types of blastocoelar cells on the basis of cell morphology and behaviour: filopodial cells, ovoid cells, globular cells, and amoeboid cells

(Figure 2.2 E-H). Each of these cell types exhibits behaviour and gene expression patterns that are consistent with a role in the immune response.

(1) The first class of cells with immune function include a subset of the filopodial cells. These cells have a have a stellate morphology and form a syncytial networks throughout the blastocoel

(Figure 2.2A,G). The network of filopodial cells can also be found in close apposition to the outer ectodermal epithelium as well as in apposition to the gut epithelium. Functionally, filopodial cells transport vesicles and assist in maintaining the structure of the larva. Similar structural supporting functions are apparent in the larva of a related sea urchin species, Hemicentrotus pulcherriums, in which filopodial cells are involved in skeletogenesis (Yajima & Kiyomoto 2006). These cells also have phagocytic functions when challenged with foreign particles such as bacteria or

Zymosan and express immune effector genes (discussed below). This phagocytic behavior is similar to that of some sea urchin adult coelomocytes (Smith et al. 2010). Because many of the filopodial cells are situated along the inter surface of the epithelium, it is difficult to distinguish

Figure 2.2: A purple sea urchin larva has a tripartite gut and several subsets of larval immune cells.

(A) The lateral view of a 2 week old larva showing the mouth (Mo), foregut (F), midgut (M) and hindgut (H). Under typical laboratory conditions, pigment cells (p) are present near the outer ectoderm. The blastocoel contains ~100 blastocoelar cells that are further categorized into four cell types (see E-H). Scale bar represents 50 μm. (B-H) Immune cells identified in the sea urchin larva. (B-C) Red pigment cells are stellate in their resting state (B), and become rounded and migratory upon immune activation (C). (D) During immune challenges, larval immune cells interact with one another. Panel D depicts interactions between a pigment cell and a blastocoelar cell subtype (filopodial cell; see also panel G). (E-H) Blastocoelar immune cell subtypes include globular cells (E), amoeboid cells (F), filopodial cells (G), and ovoid cells (H). The scale bar represents 20 µm in (B-H).

45 them from the epithelial cells using morphology. Consequently, it is difficult to accurately count the filopodial cells. Identification of molecular markers will help to clarify this question.

(2) The second type of immune blastocoelar cell, termed ovoid cells, is only observed under conditions of immune challenge. These cells are approximately 10-15 μm on their long axis, and are granular, motile and independent of the blastocoelar syncytia. This cell type may be derived from a morphological transformation of filopodial cells shortly after exposure to foreign particles

(Figure 2.2H). They are highly phagocytic upon immune challenge (described below) and aggregate with other immune cells at the location of injected particles. Usually only a few of these cells are present within the larva.

(3) The third type of larval immune cell, the globular cell, (Figure 2.2E) has both motile and sessile forms (Table 2.1). These cells are relatively large (10-15 μm), are full of distinctive, large vesicles and the motile form displays dynamic filopodial projections that make contact with other epithelial cells in the larva in a surveillance-like manner. Their motility is relatively slow compared to that of pigment, ovoid and amoeboid (see below) cells. The number of globular cells decreases in immune challenged larva. Generally two to five of these cells are present in the blastocoel while sessile forms are located at the apex and at the tips of the larval arms, as visualized by a MacpfA perforin-like marker gene that they constitutively express. Their function is unknown, but their behavior and gene expression suggests that they serve in the role of immune surveillance.

(4) The final class of blastocoelar cells with immune activity are amoeboid cells. These cells are comma shaped and about 10 μm by 5 μm in size. They are the fastest of the motile larval immune cells, and are often observed interacting with other cells types, especially pigment cells (Figure

2.2F). They are morphologically similar to the amoeboid form of the adult colorless spherule

46 cells. Their high motility rate and behavior are indicative of a surveillance role. They are recruited to sites of injury and immune challenge. Their intimate interactions with other amoeboid cells and pigment cells after interaction with sites of immune activation suggest that they play role in coordinating immune response.

Given the variation in morphology and behaviours, each of these cell types contributes in distinct ways to the larval immune response. This suggests that an intricate and sophisticated cellular immune system exists in the simple larvae.

Larval immunocytes respond to foreign particles

To further characterize the roles of these cell types in the larval immune response, we investigated the cellular response to foreign particles injected directly into the blastocoel and performed time-lapse imaging. These experiments mimic those first performed by Ilya

Metchnikoff but use much smaller particles and higher-resolution microscopy. To investigate the larval immune response, we used three distinct antigens, two of which were live bacterial strains:

E. coli (DH5α), the marine bacterium V. diazotrophicus, and the yeast cell wall preparation

Zymosan A. V. diazotrophicus was first isolated from the gut of a an adult sea urchin (S. droebachiensis), which is a congener of the purple sea urchin (Guerinot et al. 1982), and we have shown that the larva responds strongly to this strain (see below). In contrast, sea urchins are not likely to have evolved strong immune responses against E. coli, which is not a marine bacterium.

Notably, larvae exhibited distinct responses to each of these immune challenges. The E. coli did not elicit a robust cellular response although previous experiments have shown instances of phagocytosis when this bacteria is injected into the blastocoel (Hibino et al. 2006). However,

Table 2.1: Immune cell types in the sea urchin larva

Cell Types Number per 10 dpf* Function

larva

Pigment cells 50-70 Motile, Surveillance like behavior, ROS production

Ovoid cells variable Motile, Phagocytic

Filopodial cells many Phagocytic

Globular cells ~20 Motile, Surveillance like behavior

Amoeboid cells 2-5 Highly motile, Surveillance like behavior

Dpf, days post-fertilization. ROS, reactive oxygen species

48 upon injection of either Zymosan A or V. diazotrophicus, the immunogens were consistently quickly phagocytosed (Figure 2.3A). When Zymosan A is injected into the blastocoel, filopodial and/or ovoid cells quickly envelope the particles (Figure 2.3A′ and A′′). V. diazotrophicus induces slightly different cellular responses in filopodial cells although both responses occur within ten minutes of bacterial exposure (Figure 2.3 B′ and B′′). When V. diazotrophicus is injected into the blastocoel, filopodial cells form a mesh that surrounds the bacteria within five minutes (Figure

2.3B). Although the mesh tends to stay in one place, it also sometimes migrates through the blastocoel (Figure 2.3B′ and B′′). The rapid movement of the cell cluster suggests that the cluster has an active role in limiting bacterial movement. Other cell types also react quickly to V. diazotrophicus injection. Amoeboid cells quickly migrate to the bacteria (Figure 2.3C), and appear to recruit additional amoeboid cells that form an aggregate around the bacteria (Figure

2.3C′ and C′′).

In addition to blastocoelar cell subtypes, pigment cells also react to the presence of V. diazotrophicus in the blastocoel. Pigment cells migrate from their normal position near the ectoderm towards injected bacteria within the blastocoel. This migration occurs after that of the amoeboid and the filopodial cells. Coincident with the migration, the pigment cells exhibit a change in shape from their typically stellate appearance (Figure 2.1B) to a round shape (Figure

2.1C). These data illustrate a strong cellular response to foreign particles within the blastocoel that varies among the different cell types. However, because bacteria are very rarely observed within the blastocoel under normal conditions, we were interested in investigating the animal’s response to high levels of bacteria in the seawater.

Figure 2.3: Larval immunocytes respond dynamically to immunogens in the blastocoel.

Larvae were injected with immunogenic compounds and imaged by time-lapse microscopy.

Representative images from time-lapse Z-stacks are shown; the yellow stamp indicates time post- injection. (A) Zymosan A (labelled ‘Z’, several particles are circled in Red and pointed with yellow arrows) injected into the blastocoel is partially engulfed by an ovoid cell (white arrow) at around 10 min post injection (A′), and becomes fully engulfed by 30 min (A′′). (B) Filopodial cells form a cluster around bacteria (black circle), un-engaged patch of bacteria to the top of the cell cluster (white circle). This syncytium of cells appears to disperse (B′) and then re-form in another part of the larva to interact with a different patch of bacteria (B′′). The black arrow

50 indicates the direction of movement of the syncytium of cells. (C) A fast moving amoeboid cell

(black circle, labelled as “1”) quickly migrates to the gut and interacts with surrounding bacteria

(C′) as well as another amoeboid cell (“2”). Additional amoeboid cells subsequently arrive at the site, interact, and form a cellular aggregate (C′′, cell labelled “3”)). The scale bar represents 20

µm

Larval immunocytes respond to gut-associated bacterial infection

To mimic environmental exposure to microbes, larvae were exposed to bacteria in the sea water and subjected to time-lapse imaging. Exposure to V. diazotrophicus induced a striking cellular immune response in which cell migration and behaviour were affected (Figure 2.4). The most notable change was a significant migration of pigment cells from the ectoderm through the blastocoel to the gut epithelium (Figure 2.4A, B). This migration was dependent on bacterial dose

(Figure 2.4C). We find that, for a robust response within 24 hr, a bacterial concentration of 100 x 106 bacteria/ml is required, although similar pigment cell migrations are observed with slower kinetics using lower doses (Figure 2.4C).

Exposure to bacteria also causes an increase in the number of cell:cell interactions.

Clusters of interacting ovoid cells, amoeboid cells, and pigment cells are present throughout the blastocoel as well as near the ectoderm. Under normal laboratory conditions in which larvae are not exposed to bacteria, pigment cells and blastocoelar cells are generally segregated in different parts of the larva (e.g. pigment cells are located near the ectoderm whereas blastocoelar cells are dispersed in the blastocoel; Figure 2.2A). One of the most evident interactions is between pigment cells and filopodial cells in which these cells extend pseudopods to interact.

In addition to the activation and mobilization of larval immune cells, the gut morphology changes in response to microbial infection. The gut epithelium begins to thicken at around 12 hours of infection and stabilizes between 12 to 24 hr of infection. The size and the diameter of the gut decrease dramatically (Figure 2.4B), suggesting that the bacterial infection interferes with digestion. The thickening of the gut wall reverses when the larva is removed from the microbial rich environment.

Figure 2.4 Exposure to V. diazotrophicus induces cellular responses in the larva.

7-day old larvae were exposed to the marine bacterium V. diazotrophicus, imaged, and larval cellular responses recorded. (A-B) Exposing the sea urchin larvae to V. diazotrophicus elicts pigment cell migration from the epithelium (A, pre-infection) into the blastocoel and localizing at the gut (B, 24 hrs post-infection). (C) The pigment cell migration is dependent on bacterial concentration. Higher concentrations of V. diazotrophicus elicit faster and more potent cellular responses.

We next investigated the contribution of the bacteria to this response by exposing larvae to killed V. diazotrophicus. For these experiments, three methods were used to kill the bacterial cells: (1) heat-killed, (2) formalin-killed or (3) peracetic acid-killed Rif-resistant V. diazotrophicus were added to the larvae (100 x 106 cell/ml equivalent) and cell migration was examined over the course of 24 hr (Figure 2.5A). The efficacy of these killing assays was assessed by plating treated bacteria on LB/Rif plates. In each case, fewer than 10 colonies were recovered per 106 treated bacteria plated. Pigment cell migration was significantly reduced in response to the killed V. diazotrophicus (Figure 2.5A). This is particularly true in the early stages of infection (6 to 12 hours of exposure). By 24 hr of exposure, pigment cell migration increases slightly in response to the killed bacteria, but remains at significantly lower levels than the live bacteria controls. This delay may result from low levels of surviving bacteria. These data indicate that live bacteria are required to induce a robust larval immune response, which may point to interactions between the bacteria and the gut epithelium during the larval immune response.

Furthermore, we find that the pigment cell migration response to bacteria is reversible.

Larvae were exposed to live V. diazotrophicus for 24 hr, returned to bacteria-free seawater for an additional 24 hr, and pigment cell migration was monitored. At 24 hr of infection, larvae exhibited a robust pigment cell response. However, after exposure to clean seawater, a significant number of pigment cells migrated back to the ectoderm (Figure 2.5B). Re-infection with V. diazotrophicus once again induces pigment cell migration at similar levels. Together, these data indicate that this larval model for gut-associated bacterial infection is inducible, reversible, and requires live bacteria.

Figure 2.5: Activation of larval cellular responses requires live bacteria and is reversible.

Larvae were exposed to Vibrio diazotrophicus under a variety of conditions, and immune responses were measured by counting the number of pigment cells (PC) that migrated away from the outer epithelium. (A) Live bacteria (control, white bars) and bacteria neutralized by three different methods (formalin, acid, and heat-killed; grey to black bars) were introduced to seven day-old larvae as described previously. Pigment cell migration was observed over a period of 24 hrs. The sample size for each time point ranges from 20 to 52 larvae; error bars indicate standard deviation. One-way ANOVA indicates significant (p < 0.001) differences in the larval reaction to live vs. killed bacteria by 24 hrs of exposure. (B) Removing the bacteria reverses the pigment cell migration and the larvae return to a pre-exposure state. The left-most box plot illustrates the average number of pigment cells per larva that have migrated to the gut after 24 hrs of exposure to V. diazotrophicus. Larvae were transferred from the Vibrio rich environment to clean seawater for 24 hrs, at which point the number of gut-associated pigment cells decreased (48 hr clean box plot). Larvae were then re-exposed to Vibrio for 24 hrs, and pigment cells migration was re- assessed (72 hr exposure box plot). The 72 hr Clean Control box plot indicates that larvae that were not re-exposed to Vibrio did not repeat significant pigment cell migration events. Error bars

55 indicate the maximum and minimum number of pigment cells migrated. Significant differences were analyzed using a one-tailed Student’s t-test.

Larvae exhibit similar cellular reaction when exposed to natural pathogens

To validate and expand our infection model, we were interested in characterizing the larval cellular response following exposure to (1) natural microbiota and (2) specific strains of marine bacteria isolated from the natural environment. To examine the interactions between sea urchin larvae and the broad spectrum of both pathogenic and non-pathogenic bacterial and viral species in their natural environment, feeding larvae were cultured in macrofiltered seawater collected from the Atlantic Ocean. In this filtration step, large parasites are removed, but smaller phytoplankton (the primary larval food source in these experiments), bacteria, and viruses remain.

The exact bacterial concentration was not determined but the typical bacterial concentration along coast of Maine is approximately 106 cells/ml (Li et al. 2011). The composition of the bacteria was not determined. Larvae were grown in artificial seawater until the onset of feeding, and then exposed to the macrofiltered natural seawater from 6 to 10 dpf. Pigment cell migration was used to monitor the state of larval immune activation (Figure 2.6). Within 24 hr of exposure to the natural sea water, 20% of larvae exhibited some pigment cell migration, and the proportion of exposed larvae increased gradually over the course of the exposure regiment. By 96 hr of exposure, 80% of larvae were activated (Figure 2.6). These data indicate that, outside of clean laboratory settings, the pigment cells in larvae respond similarly as in our controlled V. diazotrophicus based infection model. Stellate pigment cells near the ectoderm that typify larvae under laboratory conditions may not be an accurate reflection of larvae in their natural microbe rich environment.

To identify additional bacterial species that may induce an immune response in sea urchin larvae, we isolated bacteria from larvae exposed to natural sea water. Larvae were cultured in macrofiltered Atlantic Ocean seawater as described above and, at 7 dpf, washed several times in

57 artificial sea water. This washing procedure eliminates bacteria that are not closely associated with the larvae with the aim of culturing bacteria that form close relationships with the larval gut.

Washed larvae were homogenized, plated onto marine broth agar plates, and grown at 15°C.

Individual colonies were isolated and identified by sequencing the 16S rRNA region. In total, we isolated eight bacterial strains that were closely associated with sea urchin larvae (Table 2.2).

These species were predominantly gram-negative bacteria belonging to the Proteobacteria phylum.

Six of these strains were used to assess the larval cellular immune response in the same manner as our V. diazotrophicus model (Figure 2.7). Larvae were exposed to increasing concentrations of bacteria (0 – 100 x 106 bacteria/mL) and pigment cell migration was assessed at 24 hr. Of the bacteria species tested, two elicited similar pigment cell migration responses to

V. diazotrophicus (V. cyclitrophicus, and a Pseudoalteromonas sp.; Figure 2.7), although the response to these bacteria was less robust. One species (Marinomonas sp.) failed to elicit a cellular response entirely, which recapitulates previous experiments using E. coli. Two additional species (Algicola bacteriolytica and Marine bacterium Tw-2; Figure 2.7) induced pigment cell migration at lower concentrations (10 x 106/ml), but resulted in complete larval death at higher concentrations. Furthermore, an additional strain, Vibrio splendidus, was lethal to larvae even at very low concentrations. When exposed to V. splendidus at 10 x 104/ml, the majority of larvae died within 24 hr. These data indicate that the sea urchin larval immune response is dependent on bacterial species, and may point to an underlying recognition system to discriminate among potential pathogens.

Figure 2.6: Larval immunocyte migration can be induced by a natural microbiome.

Larvae were exposed to macrofiltered Atlantic seawater which contains a natural environmental microbiome, and scored for larval activation (pigment cells have migrated away from the outer ectoderm). (A) The percentage of activated larvae increases during 96 hours of bacterial exposure.

Non-exposed control larvae displayed normal morphology and pigment cell localization. (B) A representative image of an infected larva at 96 hrs post-exposure. The gut is shrunken (black arrow) and pigment cells (red arrows) can be seen in the blastocoel and localizing around the gut epithelium.

Table 2.2: Bacterial strains isolated from purple sea urchin larvae exposed to macrofiltered Atlantic sea water.

Phylum Class Sub-category Proteobacteria Gammaproteobacteria Vibrio cyclitrophicus Proteobacteria Gammaproteobacteria Pseudoalteromonas sp. Proteobacteria Gammaproteobacteria Marinomonas blandensis Proteobacteria Gammaproteobacteria Algicola bacteriolytica Proteobacteria Gammaproteobacteria Uncultured gamma proteobacterium/marine bacterium Tw-2 Proteobacteria Gammaproteobacteria Psychrosphaera saromensis Proteobacteria Gammaproteobacteria Vibrio (Undefined species) Proteobacteria Gammaproteobacteria Vibrio splendidus Proteobacteria Gammaproteobacteria Marinomonas ushuaiensis

Figure 2.7: Pigment cell migration is dependent on specific species of bacteria.

Lab-reared larvae were exposed to increasing concentrations of various species of bacteria that were isolated from natural seawater (see Table 2.2) and scored for pigment cell (PC) migration.

Some species of bacteria formed aggregates at the highest concentration tested, and as such, the larva could not be properly infected due to the settling of the bacteria (indicated by N/A in the graph). One species of Marinomonas bacteria (last panel, bottom right) did not elicit any pigment cell migration, indicating that specific microbial-host interactions may be required for certain cellular responses in the larva. Lines indicated the median number of migrated pigment cells.

Immune genes are differentially expressed over the course of infection

In addition to characterizing the larval cellular immune system, we were interested in understanding the underlying genetic mechanisms that control this response. The immune gene repertoire is well-characterized from the S. purpuratus genome sequence (Buckley & Rast 2011;

Sodergren et al. 2006; Buckley & Rast 2015; Hibino et al. 2006; Buckley & Rast 2012).

Expression of genes in immune-challenged adult coelomocytes has been analyzed in several studies (Smith et al. 1996; Nair et al. 2005; Pancer et al. 1999; Hibino et al. 2006). However, immune gene expression in feeding larvae has been largely unexplored.

To decipher the molecular mechanisms that control the larval immune response, transcriptome analysis was performed to investigate immune genes that are expressed in larva.

We first performed an RNA-seq experiment to identify immune genes that are expressed when the larva is first exposed to the microbe rich environment at the onset of feeding (4 and 6 dpf) as well as basal immune genes that are aleady expressed and acting as sentinels. Gene expression is expressed in units of reads per kilobase per million reads (RPKM). This method of quantifying gene expression from RNA-seq data normalizes for both transcript length and the number of sequencing reads (Mortazavi et al. 2008). Genes with RPKM values of greater than 3 are considered expressed. Of the 1,046 immune genes annotated from the genome (Hibino et al.

2006), a total of 116 genes were expressed in either pre-feeding and/or post-feeding larvae

(Appendix A). Immune-related genes across different functional categories are expressed in both larval stages, including receptors (e.g. NLR), transcription factors (e.g. Rel) and effectors

(185/333). Of the 116 genes, 99 genes are expressed in feeding larvae, of which 53 are upregulated in response to feeding; 16 genes were expressed exclusively in feeding larva (Table

2.3). Within this group of genes, 10 are part of immune effector gene families, Sp185/333 and

Table 2.3: Larval immune genes expressed after feeding as identified through RNA-seq experiments. SPU, genome reference ID; RPKM, reads per kilobase per million reads mapped.

Pre-feeding Post-feeding SPU_ID Name Annotation RPKM RPKM SPU_022179 Sp-185/333D1 185/333 0.8 11.3 SPU_030264 Sp-185/333/D1 185/333 0.5 8.6 SPU_019327 Sp-185/333B3d 185/333 0.6 7.7 SPU_030262 Sp-185/333/E2 185/333 0.3 5.0 MACPF/Perforin-like SPU_028756 Sp-MacpfE.2 protein 1.3 8.1 Sp-Coagulation factor SPU_021228 Sp-Cf5/8L1 5/8-like1 1.7 3.9 SPU_020124 Sp-ElfA E74 1.7 3.9 MACPF/Perforin-like SPU_002548 Sp-MacpfB.0_1 protein 2.8 6.9 Whey associated SPU_004610 Sp-Wap/Wap protein 1.4 4.8 thioester containing SPU_005193 Sp-Tcp2 protein 2 1.7 4.3 MACPF/Perforin-like SPU_014984 Sp-MacpfA.3 protein 2.2 3.4 SPU_012667 Sp-Saa-a Sp-Serum amyloid A-a 1.5 4.7 MACPF/Perforin-like SPU_015144 Sp-MacpfB.2 protein 1.8 4.2 MACPF/Perforin-like SPU_005223 Sp-MacpfA1 protein 0.8 3.5 MACPF/Perforin-like SPU_017952 Sp-MacpfA4 protein 1.0 5.5 SPU_027235 Sp-Blimp1 Krox1a 2.3 5.5

SpMacpfE. The expression of those two gene families of genes suggests the existence of an active larval immune response to the exposure to environmental microbes.

After establishing the larval gut infection model, we identified genes involved in regulating the cellular response. RNA-seq analysis was performed with larvae exposed to V. diazotrophicus for 0, 6, 12 and 24 hr. In total, 317 immune genes are expressed over the duration of the infection (Appendix A). In addition, many of the immune genes that exhibit expression changes have homologs in vertebrate immune systems (Table 2.4). Expressed genes displayed various types of expression pattern over the course of bacterial exposure. For example, members of the immune recognition receptors families, TLR, NLR and SRCR, tend to follow a trend of up regulation after the introduction of bacteria (Table 2.4, Appendix B). Other genes are downregulated, such as the transcription factor SpEgr (Table 2.4, Appendix B). Lastly, some immune genes experience spikes in expression and are then quickly attenuated (e.g. IL-17(I), see Chapter

3). This suggests the presence of complex immune regulatory systems that dynamically regulate gene expression over the course of a gut infection.

To localize gene expression to specific tissues, in situ hybridization experiments were performed.

Several genes are expressed in subsets of larval blastocoelar and pigment cells as well as the gut epithelial cells (Figure 2.8). Some information regarding the gene function can be inferred from the in situ experiments. For example, within the perforin-like Macpf family, two members,

MacpfA.1 and MacpfE.2, are expressed in two distinct cellular compartments (Figure 2.8A). This indicates although these genes have similar sequence and domain structure, they may play different roles within the immune response. Localization data also illustrate the presence of immune systems.

Table 2.4: Genes expressed in the course of the larval gut infection with homologs in vertebrate

RPKM 0 6 12 24 SPU_ID Name Annotation HOI HOI HOI HOI

SPU_030060 Sp-Pu1 SpiB, SpiC 7.0 18.2 33.1 99.5 CCAAT/enhancer binding protein alpha, SPU_001657 Sp-Cebpa C/EBP alpha 146.4 413.3 689.9 317.6 CCAAT/enhancer binding protein (C/EBP), SPU_011002 Sp-Cebpg gamma 13.9 19.3 56.5 24.0 SPU_011197 Sp-IkB Sp-IkappaB, Sp-NF-kappaB Inhibitor 15.6 37.2 37.4 45.0 SPU_015358 Sp-Egr z60 71.7 9.3 8.2 11.4

Transcriptiion Factors Transcriptiion SPU_008177 Sp-Nfkb Nf-kb 10.0 27.6 25.8 37.3 SPU_000816 Sp-Nlr81 NACHT and LRR containing protein 8.1 11.8 15.1 15.7 SPU_000852 Sp-Nlr71 NACHT and LRR containing protein 6.2 7.8 9.2 11.1

SPU_018429 Sp-Srcr142 Scavenger receptor 20.1 45.8 58.2 52.9 SPU_018430 Sp-Srcr143 Scavenger receptor 89.1 193.5 245.4 219.5

receptors SPU_003882 Sp-Pgrp4 Peptidoglycan recognition protein 15.8 17.3 23.4 55.6 SPU_013470 Sp-Tlr071 Toll-like receptor 2.7 3.8 4.9 5.7

Patter recognition recognition Patter SPU_007343 Sp-Myd88 MyD88 8.2 7.8 10.7 20.1 Sp- SPU_017952 MacpfA4 MACPF/Perforin-like protein 37.5 40.8 38.0 53.2 Sp- SPU_028756 MacpfE.2 MACPF/Perforin-like protein 5.3 5.0 2.2 1.7 Sp-Cathepsin10, Sp-CtsF-l1, Sp-CathepsinF-

Effectors SPU_014914 Sp-Cts10 like1 31.0 35.0 35.1 50.2 Sp- SPU_021228 Cf5/8L1 Sp-Coagulation factor 5/8-like1 20.7 25.9 37.8 46.0

SPU_000997 Sp-C3-2 Complement factor 19.2 19.2 18.3 19.1 SPU_005182 Sp-064 Sp-C3 24.5 29.3 43.6 56.1 SPU_012439 Sp-064_1 Sp-C3 13.9 18.2 30.9 30.3 SPU_017239 Sp-064_3 Sp-C3 30.4 33.5 43.8 59.0

Complements SPU_005871 Sp-Il1r1 IL1RA; CD121A; IL1R-alpha 5.2 8.6 9.3 8.4 Sp- SPU_009527 TnfsfL2 tumor necrosis factor superfamily like2 0.2 1.2 3.6 3.2 Sp-

Cytokines SPU_009528 TnfsfL1 tumor necrosis factor superfamily like1 16.2 23.8 62.1 118.0 SPU_001152 Sp-Mif7 Macrophage migration inhibitory factor 62.8 50.9 29.4 26.7 SPU, genome reference ID; RPKM, reads per kilobase per million reads mapped; HOI, hours of infection.

65 different blastocoelar subtypes that have distinct gene expression profiles (Figure 2.8 A,B). The in situ hybridization results indicate that the expression of immune genes is not uniform across all blastocoelar cells. For example, MacpfA.1 is expressed in the globular cells, whereas Sp185/333 appears to be localized to a filopodial cells. The pigment cells also express a distinct suite of immune effector genes. For example, the effector genes Sp185/333 and SpSRCR143 exhibit distinct expression patterns in the blastocoelar and pigment cells, respectively (Figure 2.8B).

Discussion

The purple sea urchin larva as a simple model for immunity

Although the foundations of the field of cellular immunity lie in Metchnikoff’s observations of phagocytosis in echinoderm larvae, the modern use of these larvae as model systems in immunology remains limited to a few recent studies in sea urchin (Silva 2000) and sea star

(Furukawa, Funabashi, et al. 2012; Furukawa et al. 2009; Furukawa, Matsumoto, et al. 2012).

Echinoderms are closely allied with the vertebrates (within the same deuterostome superphylum) and have a well-established suite of experimental advantages, including a well-annotated genome sequence (Rast et al. 2006; McClay 2011; Hibino et al. 2006) and a variety of techniques for transgenesis and gene perturbation (Smith 2008; McClay 2011). Here, we present the sea urchin larva as novel model system for studying the underpinnings of the vertebrate innate immune response. We have characterized several types of larval mesenchymal immune cells on the basis of cell morphology, behaviour and gene expression (Table 2.5). We have additionally defined suites of genes expressed at the onset of feeding when the animal gains immune competency. We have developed a robust, reproducible model for gut-associated bacterial describe the larval

Figure 2.8: Subsets of activated larval immunocytes express specific immune genes.

Larvae infected with V. diazotrophicus were fixed and whole mount in situ hybridization was performed to localize mRNAs of interest. (A) Two MACPF genes (A.1, red and E.2, green) are localized to different subsets of larval blastocoelar immune cells. MACPF-E2 cells were found exclusively around the gut while MACPFA.1 is found in globular cells. (B) Immune genes Sp-

SRCR143 and Sp185/333 are expressed in two different cellular compartments: SpSRCR143 is found in what appears to be pigment cells (based on previous experiments) whereas Sp185/333 is localized to a subset of blastocoelar immune cells.

Table 2.5: Summary of behaviour of larval immune cells during resting and bacterial exposed state.

Cells Resting State Exposed State Motile; Morphological change; Pigment Cells Motile; Intercellular interaction Intercellular interaction Ovoid Cells Absent Motile; Intercellular interaction Filopodial Cells Phagocytic Phagocytic Globular Cells Motile Motile Amoeboid Cells Motile Motile; Intercellular interaction

68 response to bacterial infection model at both the cellular and molecular level. Finally, we find that the larvae differentially respond to varying strains of bacteria, which suggests the presence of complex recognition mechanisms as well as differences in the interactions between bacterial species and the larval immune system. Together, these data indicate that, despite the morphological simplicity and small size of the sea urchin larva, this organism has evolved complex cellular and molecular responses to pathogens.

The sea urchin larval immune response is a coordinated, system-wide response

Sea urchin larvae are transparent, which allows for cell-level imaging of immune activity on an organismal scale. We find that, in response to either particles injected directly into the blastocoel or in the presence specific microbes in the environment, larvae initiate immune responses that involve cells in many compartments of the animal. These include changes in cell behaviour in peripheral immune cells (both the pigment cells and various types of blastocoelar cells) as well as changes in the morphology of the gut epithelium. There are two potential mechanisms for regulating this type of system-wide response: direct cell-cell interactions or signaling molecules that distribute information throughout the animal.

In response to bacteria, we find that the number of interactions between cells increases dramatically. Notably, this is evident both within and among cell populations. The clearest example of this is the pigment cells, which are observed in close contact for long periods of time with both amoeboid cells as well as the gut epithelium. The mechanisms that mediate this potential communication as well as the downstream consequences remain unknown, but given their frequency, it is likely that this type of cell-cell interaction plays a central role in the sea

69 urchin larval immune response. Similarly, in response to bacteria introduced directly into the blastocoel, several types of immune cells are involved in a rapid phagocytosis response. This is particularly apparent among the amoeboid and filopodial cells. These cell interactions mirror those in the vertebrate innate immune system, in which a heterogeneous group of cells each play unique roles (e.g., some cells serve as sensors while others act effector cells). For example, in human cells, dendritic cells activate resting Natural killer cells through either direct (cell contact) or indirect (cytokine) interactions (Cooper et al. 2004; Rescigno et al. 2008). This coordination serves as the basis for generating a robust innate immune response while minimizing autoreactivity (Crozat et al. 2009).

In addition to direct interactions among cells, it is likely that sea urchin larvae express cytokines and chemokines that regulate the immune response. Cytokines are often small, quickly evolving molecules that are subject to strong evolutionary pressure from pathogens.

Consequently, homologs of vertebrate cytokines are often difficult to find, even across vertebrates classes. To identify potential homologs of known cytokines and also to identify novel signaling molecules, we present here a RNA-Seq strategy. We find that several signaling molecules are expressed and tightly regulated during the larval immune response, including homologs of TNF superfamily members and Macrophage inhibitory factors (Mif), as well as a small family of IL-

17 members (discussed in Chapter 3). It is also likely that additional signaling molecules are involved in this response that have divergent sequence and therefore remain unidentified.

The larval immune gene repertoire is tightly regulated

Our RNA-Seq surveys indicate that larvae express a large suite of genes involved in the immune response under baseline (not immune-challenged) conditions. These genes may be

70 involved in early pathogen recognition or in shaping the resident larval microbiota. Their basal expression levels suggest that larvae possess active immune systems that are competent even in the microbe-rich environment of sea water. In response to immune challenge, however, changes in transcription are evident across the genome. Differential kinetics of gene expression suggests the presence of a complex immune gene regulatory network that tightly regulates the immune response. This is evident in both the rapid upregulation of immune signaling and effector genes as well as the attenuation of the response that indicates the presence of negative feedback circuitry.

This type of downregulation is a necessary element of the immune response to avoid autoimmune responses and has been observed in other immune gene regulatory networks (Litvak et al. 2009).

Together, these data indicate that the morphologically simple sea urchin larva is characterized by an intricately regulated, complex molecular immune system. A gene regulatory network framework will be used to disentangle the causal linkages among the many transcription factors expressed in the sea urchin larva in the course of an immune response (see Chapter 4).

Larvae mount differential immune responses to distinct pathogens

We have presented data here that indicate that the larval immune response is dependent upon the presence of live bacteria, and differs based on the strain of bacteria. For example, in response to E. coli, which is not a marine bacterium, or a Marinomnas sp. that was were isolated from sea water, larvae do not exhibit cellular immune responses. These data suggest that the larvae are capable of recognizing pathogenic bacteria and responding appropriately or that active bacterial interactions with the larva play a large role in response. The observation that injected V. diazatrophicus elicits a rapid and much stronger phagocytic response than is stimulated by similar injection of E. coli suggests that aspects of specific recognition may be invoved. This recognition

71 may be mediated through the complex suites of pattern recognition receptors that are encoded in the sea urchin genome, including the TLRs or NLRs. Preliminary data further suggest that distinct bacterial species can also induce pigment cell migration to either the foregut or hindgut (as opposed to the V. diazotrophicus-mediated migration to the midgut; data not shown). The functional consequences of this remain unknown. Additionally, we find that killed V. diazotrophicus do not elicit robust cellular immune responses in the larva when introduced into surrounding sea water. Although it is possible that certain PAMPs are degraded in the killing process, this is unlikely to be the cause of the reduced immune response, as the bacteria were killed in three different ways. An alternate possibility is that bacteria are actively triggering the immune response and that these mechanisms vary among strains.

Humoral-like defense systems may have a role in larval immunity

Consistent with Metchnikoff’s observations, phagocytosis and encapsulation clearly play a central role in the larval immune response. Rapid phagocytosis is apparent in response to direction injection of immunogens into the blastocoel. Evidence also suggests that during infection with V. diazotrophicus, some bacteria are able to penetrate the gut epithelium to enter the blastocoel after 24 hr of exposure (data not shown). Several systems of humoral immunity may be functioning within the sea urchin larval immune response. These include complement factors and two strongylocin antimicrobial peptides. The sea urchin genome encodes several members of the complement pathway, including C3 and Bf. And although the classical terminal pathway is absent, there is a family of perforin-like proteins that may have may have a similar role. In the adult sea urchin, C3 has been shown to opsonize bacteria (Clow et al. 2004). Within approximately 10 minutes after intrablastcoelar injection, individual bacterial cells are agglutinated into small clumps suggesting the function of some soluble factors in the blastocoel.

The fundamental nature of these reactions suggests that these mechanisms may be evolutionarily conserved across Metazoa and may provide insight into the vertebrate cellular innate immune response.

Larval immune cells exhibit similarities to adult coelomocytes

In adult sea urchins, coelomocytes are the primary mediators of the immune response and have been well-characterized (reviewed in Chapter 1, Ghosh et al. 2011; Smith et al. 2010).

Despite the differences in anatomical structure and lifestyle between the larvae and the adults, their immune cell repertoires share many similarities with respect to morphology, gene expression, and function. The clearest example of this affinity is between the larval pigment cells and the adult red spherule cells. Each of these highly granular cells each contains echinochrome

A, which give the cells their distinctive red color. The circulating adult red spherule cells are morphologically similar to the immune activated larval pigment cells (round), and both cells types have been shown to participate in the immune response (adult red spherule cells are involved in clotting and wound healing; larval pigment cells migrate in response to bacterial challenge). Both forms are also highly motile. The two cell types also exhibit similar gene expression patterns

(including PKS, which is involved in the pigment production, as well as the transcription factor gcm) (Calestani et al. 2003).

Additional parallels are evident between the adult colorless spherule cells and the larval amoeboid cells; these cells have similar morphologies. Finally, some of the adult phagocyte population may have affinity with the larval filopodial cells, as both appear to be highly phagocytic. The expression of the immune effector protein Sp185/333 has been localized to a

73 subset of adult phagocytes (e.g., small and polygonal) (Brockton et al. 2008). Similarly, the

Sp185/333 transcripts are found within larval blastocoelar cells. This question will be further resolved as additional genetic markers for larval blastocoelar cells are identified and compared with gene expression in the adult coelomocytes. It is possible that morphologically identical cell populations in both that larva and adult can be further classified on the basis of gene expression.

Conclusions

Together, these data establish the sea urchin larval stage as a novel model for investigating fundamental mechanisms that regulate the deuterostome epithelial immune response. We find that in addition to a complex cellular immune response, the larva is capable of mounting an intricately regulated, complex molecular immune response that is specifically tailored to the pathogen. Many parallels are already evident between the vertebrate gut innate immune response and the sea urchin larval response to bacteria. Future studies will continue to investigate these similarities and establish a gene regulatory network model for the larval immune response. It is likely that the many of the causal linkages that regulate this response are ancient and conserved among phyla and that these findings can be extended to studies in other animal lineages, including vertebrates.

Chapter 3 IL-17 as a primary mediator of sea urchin larval gut immunity

Introduction IL-17: a family of pro-inflammatory cytokines

The pro-inflammatory cytokine IL-17 was discovered in a subtracted cDNA library generated from immune challenged mouse lymphoid cells (Rouvier et al. 1993). Five additional members of the IL-17 family (IL-17B to F) were later identified (Lee et al. 2001; Leon-Reyes et al. 2009; Li et al. 2000; Starnes et al. 2002; Starnes et al. 2001). The most similar of these sequences are IL-17A and F, although the percent amino acid identity within the family ranges from 16% to 50% (Iwakura et al. 2011). The IL-17 ligands are all characterized by four or five conserved cysteine residues in the C-terminus that form a distinctive cysteine knot structure

(Gerhardt et al. 2009). The IL-17 ligands homodimerize to signal through their receptors (except for IL-17A and F which can also heterodimerize). Five IL-17 receptors have been identified in vertebrates (IL-17RA to RE) which contain two extracellular fibronectin III-like domain and an intracellular SEF/IL-17R (SEFIR) domain that mediates signaling (Gaffen 2009; Yao et al. 1995).

The IL-17 receptors heterodimerize with IL-17RA acting as the anchor (Ely et al. 2009; Gaffen

2009). Biological functions of several of the ligands (i.e. IL-17B, IL-17D and IL-17E) have not yet been identified (Song & Qian 2013). Instead, the majority of the research has focused on IL-

17A and IL-17F (Jin & Dong 2013).

IL-17A and IL-17F share the highest sequence similarity, are located closely together on the same chromosome (in both mouse and human), and exhibit similar expression patterns (Wang

75 et al. 2012). These cytokines are predominantly produced by the distinct CD4+ T cell lineage known as Th17 cells. Differentiation of Th17 cells from naïve CD4+ T cells requires TGFβ, IL-

6, IL-21, IL-23 and IL-1β (Korn et al. 2009). The presence of those cytokines, along with T cell receptor signaling, activates the transcription factors STAT3, IRF4, RORγt, RUNX1, BATF and

IκBζ. RORγt is known as a master regulator of Th17 cells and is both necessary and sufficient for maintaining IL-17 expression through STAT3 binding directly to the IL-17 promoter (Korn et al. 2009). IL-17A and IL-17F are also produced by other cell types. These include other T cells lineages such as cytotoxic CD8+ T cells, NKT cells, and γδ T cells (Korn et al. 2009); innate immune cells such as monocytes, neutrophils, LTi cells (Cua & Tato 2010; Korn et al. 2009); innate lymphoid cells (Spits & Cupedo 2012); and barrier cells such as Paneth cells (Takahashi et al. 2008; Cua & Tato 2010). The mechanisms by which IL-17 expression is activated differ among T cell lineages. For example, IL-17 production in NKT and γδ T cells does not require

IL-6 or TCR signaling (Rachitskaya et al. 2008; Shibata & Yamada 2008). IL-17A/F expression is rapidly induced in innate immune cells in the presence of appropriate environmental stimuli, which suggests that this cytokine may serve different functions within the adaptive and innate immune responses (Hirota et al. 2012).

IL-17A and F are potent proinflammatory cytokines. In response to bacterial or fungal infection, these genes are quickly upregulated in innate lymphocytes and later by Th17 cells (Korn et al. 2009). IL-17A and F signal through the heterodimeric receptor complex IL-17RA/RC, which is widely expressed in both non-haematopoietic tissues as well as immune cells. This triggers the expression of a battery of pro-inflammatory cytokines, chemokines and antibacterial peptides (Johnston et al. 2013). Some of the chemokines that are upregulated by IL-17A are

CXCL1, CCL2 and MMP3 (Park et al. 2005); IL-17F signaling activates of IL-6, GCSF and GM-

CSF expression (Jin & Dong 2013). These signaling molecules are involved in recruiting neutrophils and macrophages to the site of inflammation. Although IL-17A and IL-17F serve somewhat overlapping roles in the immune response, in certain cases these proteins have distinct or opposite functions. For example, in a mouse asthma model, IL-17A promotes inflammation whereas IL-17F exhibits the opposite behaviour (Yang, Chang, et al. 2008). Furthermore, IL-17A plays a protective role in dextran sulfate sodium-induced acute colitis whereas IL-17F is pathogenic (Jin & Dong 2013).

Binding of IL-17A (or IL-17A/F heterodimer) through IL-17RA/RC activates the NF-κB,

MAPK and C/EBP cascades to initiate immune responses. Upon ligand binding, the signaling adaptor molecule Act1 is recruited to IL-17RA and initiates signaling through homotypic SEFIR domain interactions. TRAF6 (tumor necrosis factor receptor associated factor) is then recruited to Act1, which functions as an U-box like E3 ligase to ubiquitinate and activate TRAF6 (Gaffen

2009). This in turn activates TAK1 and initiates the NF- κB pathway. TRAF can also initiate

MAPK pathway independently of TAK1 which results in the activation of AP1. Furthermore,

Act1 can activate C/EBP directly, although the mechanism remains unknown.

The functions and ligands of some of the five known IL-17 receptors remain elusive. For example, although it is known that IL-17B binds to IL-17RB, the receptor’s heterodimeric partner as well as the downstream signals are unknown (Gaffen 2009; Ely et al. 2009). Both the ligand and downstream effects of IL-17RD are also unknown (Song & Qian 2013). The majority of research focuses on IL-17RA as it serves as a common heterodimeric partner for IL-17RC, RB and RE (Gaffen 2009) and is the most widely expressed member of the IL-17 receptor family (Ely et al. 2009). Upon binding of IL-17A ligand to the IL-17RA receptor, IL-17RA recruits IL-17RC resulting in a IL-17RA/RC heterodimer (Gaffen 2009).

IL-17 in vertebrate gut immunity

IL-17A and F play a role in maintaining gut homeostasis and pathogen clearance. Innate

IL-17A/F producing cells are enriched in host mucosal tissues such as lung and intestinal barrier and act as sentinels against infection. The gut mucosal tissues contain the highest numbers and types of innate IL-17 producing cells (Ivanov et al. 2009). In mucosal host defense, IL-17 plays a proinflammatory role against microbial infection and has been implicated in colitis in mice

(Buonocore et al. 2010) and inflammatory bowel disease in humans (Geremia et al. 2011).

Deficiency in IL-17 production is linked with increased susceptibility to fungal and bacterial infections (Puel et al. 2011; Hirota et al. 2011) and Th17 gut colonization confers resistance to intestinal pathogens such as Citrobacter and Salmonella (Geddes et al. 2011).

Recent studies have identified a biological function for IL-17C within gut epithelial cells.

Previous studies indicated that TLR5 agonists induced IL-17C expression in the gut (Van Maele et al. 2010), while common pathogens (e.g., S. aureus) or PAMPs induced IL-17C expression in keratinocytes (Holland et al. 2009), bronchial epithelial cells (Pfeifer et al. 2013) or intestinal epithelial cells (Song et al. 2011). IL-17C signaling initiated both NF- κB and MAPK signaling in mouse primary intestinal epithelial cells as well a human colonic tumor cell lines (Song et al.

2011). IL-17C signals through the heterodimeric receptor complex composed of IL-17RA and

IL17RE. Although IL-17RA is widely expressed, IL-17RE expression is mostly confined to epithelial or Th17 cells (Song et al. 2011). Act1 is necessary to mediate the IL-17C signal transduction cascade (Song & Qian 2013). IL-17C gene expression was induced in mucosal epithelial cells in response to infection with C. rodentium (Ramirez-Carrozzi et al. 2011; Song et al. 2011). The function of IL-17C was examined by stimulating human primary epidermal

78 keratinocytes which induces immune related gene productions (Ramirez-Carrozzi et al. 2011).

Similarly, gene expression analysis on colon tissue from C. rodentium infected mice indicates that

IL-17C acts synergistically with IL-22 to induce the production of antibacterial peptides (Song et al. 2011). IL-17C was also shown to contain protective properties against dextran sodium sulfate

(DSS) induced colitis in mice (Reynolds et al. 2012). IL-17C deficiency leads to decrease in mouse body weight and enhanced intestinal epithelium disruption as a consequence of the role of this cytokine in maintaining intestinal barrier integrity by regulating the expression of tight junction protein in intestinal epithelial cells (Reynolds et al. 2012). Together, these data indicate that IL-17C plays a protective role in vertebrate gut immunity as well as maintaining gut homeostasis. The expression of IL-17C must be tightly regulated, however, as overexpression of this protein promotes psoriatic skin (Johnston et al. 2013).

The role of IL-17 outside the mammals

Due to evolutionary pressures that drive the divergence of immune factors, homologs of cytokines tend to be difficult to identify in other species. Notably, IL-17 is one of the few cytokines that can be found across different phyla, which may be a consequence of the highly conserved cysteine residues, and homologs of IL-17 have been identified in several non- mammalian species. The numbers of IL-17 ligands vary among taxa. Within chordates, homologs of IL-17A/F (up to four copies), IL-17C (two genes), IL-17D (1 gene) and a novel teleost-specific

IL-17 (i.e. IL-17N) have been identified (Kono et al. 2011). Here, the expression of IL-17A/F, C and N are upregulated in response to LPS stimulation (Kono et al. 2011) whereas the IL-17D homolog is constitutively expressed (Gunimaladevi et al. 2006; Kumari et al. 2009; Korenaga et al. 2010). In lamprey, four IL-17 homologs were identified through genome and cDNA sequence

79 analysis (Tsutsui et al. 2007; Smith et al. 2013). The expression of one of the members, IL-17D, is dependent on LPS stimulation which is unlike that of the teleost IL-17D homolog, (Tsutsui et al. 2007). The lamprey IL-17D cytokine has also been shown to be expressed in lymphocytes that express the VLRA receptors (Boehm et al. 2012). IL-17 homologs have also been identified within the invertebrates. Three IL1-17A/F homologs are expressed in sea squirt, Ciona intestinalis. These genes are expressed in hemocytes near the pharynx and are upregulated in response to LPS (Vizzini et al. 2015; Terajima et al. 2003). In oysters, two IL-17 homologs are expressed in circulating hemocytes following exposure to bacteria or PAMPs (S Roberts et al.

2008; Wu et al. 2013). Finally, in the purple sea urchin, S. purpuratus, 32 IL-17 genes have been identified through genome and transcriptomic analysis (Hibino et al., 2006, current chapter). This relative sequence conservation indicates that IL-17 is an ancient, conserved cytokine that may play an central role in metazoan survival.

Here, we describe the role of this IL-17 family with the sea urchin larval immune response.

We show that, in response to bacterial infection, larvae undergo significant changes in transcription, the most striking of which is the IL-17 genes. We present here a characterization of the spatial and temporal expression of these genes within the context of the larval infection model (described in Chapter 2). Furthermore, we show that perturbation of IL-17 signaling by interfering with the IL-17 receptors affects downstream gene expression, which points to a central role for IL-17 in this immune response. Together, these data highlight a fundamental function of this cytokine family within the context of mucosal immunity.

Materials and Methods

Animal maintenance, larval cultures and infection, RT-qPCR, WMISH and imaging were performed as described in Chapter 2.2

Preparation and microinjection of reporter constructs

Green fluorescent protein (GFP) reporter constructs were generated using BAC vectors that encompass the SpIL-17(I) genes on S. purpuratus genomic Scaffold1105. The BAC insert is about 125 Mb. A GFP/SV40 3′ UTR cassette was introduced into the coding sequence (6 nt) of the first exon of SpIL-17-05 using homologous recombination in SW105 E. coli cells (Warming et al. 2005). BAC reporter DNA was purified with CsCl gradients or NucleoBond BAC 100 purification columns (Macherey-Nagel) and linearized with AscI (New England Biolabs) prior to injection. Linearized BAC DNA was injected at 500 copies/pl as described (Rast 2000).

Morpholino antisense oligonucleotides (MASO)

MASOs were obtained from Gene Tools and resuspended in water prior to use. Injection solutions were prepared according to Rast et al. 2002. The MASO sequence for SpIL-17R1 splicing blocking sequence is as follows: 5′-CCATTGTTCCCAAACACCTACCACT-3′. the

MASO sequence that blocks the SpIL-17R2 translation is as follows: 5′-

ACACGATTGCGACGGTGGTTAACAT-3′. MASOs were injected at a concentration of 200

μM.

RNA extraction from transgenic larva

At 3 dpf, pluteus stage embryos injected with reporter constructs were transferred to 1 L stirring culture vessels. Larvae were fed Rhodomonas lens (5,000/mL) starting at 5 dpf.

Transgenic larvae were infected at 7 dpf with V. diazotrophicus as described in Chapter 2.2. For experiments that involved nucleic acid extraction, larvae were screened for GFP expression.

GFP+ larvae were removed from the slide and deposited in TRIZOL (Life Technologies) at ~1 larva/µl. Samples were then quickly frozen in dry ice/ethanol bath and stored at -80°C.

Reverse transcription quantitative PCR (RT-qPCR)

Sea urchin larvae were collected using 100 µm Nitex mesh. RNA was extracted with

TRIZOL (Life Technologies) following manufacturer’s protocol. An additional purification step was performed using the RNeasy micro kit (QIAGEN). RNA was DNAse-treated using DNAfree

(Ambion). cDNA was synthesized using Superscript III (Invitrogen) with random hexamers.

Oligos used for transcript quantification are in Appendix C. qPCR was performed with Power

SYBR Green (Life technologies) on the ViiA7 platform.

Confocal imaging

To image the GFP expression in larvae that were transgenic for the IL-17(I)-GFP reporter

BAC, 10 dpf larvae were exposed to V. diazotrophicus for 12 hr and fixed in 4% paraformaldehyde for 1 hr at room temperature. Larvae were then washed and resuspended in

PBS. Hoechst staining was performed using a 1:1000 dilution for 1 hr at room temperature.

Larvae were then washed and resuspended in PBS. Imagining was done with a Lecia SP8 confocal microscope.

Results

A small family of SpIL-17 factors are the most strongly upregulated genes in response to bacterial infection

Our RNA-Seq survey of the system-wide transcriptional changes that occur in the larva in response to the bacteria V. diazotrophicus is described in Chapter 2. These data indicate that different types of immune genes are differentially expressed with varying kinetics over the course of the infection model (Table 2.3, Appendix B). The thirty immune genes that displayed the greatest changes in expression over the course of infection are shown in Table 3.1. Most of the differentially expressed immune genes increase in transcript prevalence in response to bacterial challenge. These include effector genes (e.g., the echinoid-specific 185/333, complement factors, and perforin-like genes), subfamilies of immune recognition receptors (e.g., the TLRs, NLRs and peptidoglycan recognition proteins (PGRPs)), and several intercellular signaling molecules (e.g., members of the tumor necrosis factor homologs (SpTNF) family; Table 3.1 and Appendix B).

Signalling molecules such as TRAF homologs also displayed a general increase in gene expression. However, not all immune related genes are upregulated. For example, the transcription factor SpEgr is sharply downregulated. In contrast, the expression level of SpCebpγ, initially increases, and is then downregulated at 24 hr (Table 3.1). Together, these data point to dynamic changes in regulatory state and immune gene expression being finely adjusted over the course of the infection. One of the most striking results from this RNA-Seq screen, however, was

Table 3.1: The thirty most differentially expressed genes over the course of the larval gut infection.

RPKM 0 6 12 24 SPU_ID Name Annotation HOI HOI HOI HOI SPU_012844 Sp-Il17-2 IL-17 0.2 18.1 4.1 4.1 SPU_019349 Sp-Il17-4 IL-17 0.1 14.0 3.5 3.6 SPU_019350 Sp-Il17-5 IL-17 0.2 17.0 4.0 4.0 SPU_019351 Sp-Il17-6 IL-17 0.2 17.3 3.9 3.9 tumor necrosis factor superfamily SPU_009527 Sp-TnfsfL2 like2 0.2 1.2 3.6 3.2 SPU_030060 Sp-Pu1 SpiB, SpiC 7.0 18.2 33.1 99.5 SPU_022178 Sp-185/333E2 185/333 0.4 1.1 1.0 5.0 SPU_015358 Sp-Egr Egr 71.7 9.3 8.2 11.4 SPU_003912 Sp-Il1ap_1 IL-1 associated protein 2.0 10.3 13.6 16.5 SPU_022179 Sp-185/333D1 185/333 14.6 21.2 20.9 115.6 SPU_012713 Sp-Nlr167 NACHT and LRR containing protein 0.5 1.0 1.7 3.6 SPU_030264 Sp-185/333/D1 185/333 11.7 16.7 16.2 88.5 SPU_019327 Sp-185/333B3d 185/333 11.4 16.9 16.5 84.1 tumor necrosis factor superfamily SPU_009528 Sp-TnfsfL1 like1 16.2 23.8 62.1 118.0 tumor necrosis factor receptor SPU_026216 Sp-Tnfrsf_cl2 superfamily classical2 4.0 22.0 22.8 19.2 SPU_030262 Sp-185/333/E2 185/333 4.4 5.4 5.2 24.3 SPU_013950 Sp-Il1r/Rs1 IL-1R 2.7 12.6 14.8 10.4 SPU_022145 Sp-Srcr176 Scavenger receptor 2.8 1.5 3.2 7.7 SPU_000409 Sp-Il1r/Rs1(d) Il-1R 3.0 13.7 15.8 11.5 SPU_028898 Sp-Traf6 Traf6 4.5 7.2 13.5 22.3 SPU_028724 Sp-Pik2 Sp-Pelle/Irak2 5.7 17.7 23.7 27.5 CCAAT/enhancer binding protein SPU_001657 Sp-Cebpa alpha, C/EBP alpha 146.4 413.3 689.9 317.6 SPU_003911 Sp-Il1ap Il-1 associated protein 1.6 6.1 7.1 7.3 SPU_020311 Sp-Klf2/4 z85 141.9 68.8 38.3 33.3 SPU_024709 Sp-Nlr173 NACHT and LRR containing protein 0.9 1.5 2.2 3.9 SPU_000222 Sp-Pgrp3 Peptidoglycan recognition protein 6.0 7.1 10.8 24.8 SPU_000073 Sp-Pik1 Sp-Pelle/Irak1 3.7 10.6 15.3 12.3 CCAAT/enhancer binding protein SPU_011002 Sp-Cebpg (C/EBP), gamma 13.9 19.3 56.5 24.0 SPU_020740 Sp-Eda2rL1 ectodysplasin A2 receptor 3.7 7.8 10.2 14.9 SPU_011298 Sp-Socs6L Socs6 4.1 8.0 13.8 16.2

84 the identification of a small set of genes that encode homologs of IL-17 which was ranked as the most differentially expressed set of immune genes (Table 3.1).

The sea urchin genome encodes a multigene family of IL-17 genes

The S. purpuratus genome contains 32 genes that encode homologs of IL-17 (known as

SpIL-17). Phylogenetic analysis of these sequences defines ten subfamilies (designated Groups I

– X). The largest of these subfamilies, Group I is composed of 12 highly conserved sequences.

At the nucleotide level, the group I genes are 96% identical. In contrast, members in of group IX share only 82% nucleotide identity. At the amino acid level each of the SpIL-17 proteins share key characteristics with IL-17 proteins from other species and have clearly identifiable IL-17 domains (PF06083). The proteins encoded by the SpIL-17 genes share four conserved cysteine residues that are present in IL-17 proteins within the vertebrates. Phylogenetic analysis of the

SpIL-17 genes indicates that these sequences form a cluster that is distinct from vertebrate and protostome IL-17 genes. Consequently, direct comparisons cannot be made between the sea urchin subfamilies and those of mammalian lineages.

The SpIL-17 genes are encoded on nine genomic scaffolds. Several subfamilies are arranged in tandem arrays. The SpIL-17(I) genes are clustered on two scaffolds and are oriented in the same direction, with the exception of one gene (SpIL-17-01) at the end of the cluster.

Except for a 12 kb gap between SpIL-17-05 and SpIL-17-06, genes within this cluster are even- spaced two exons, the first of which consists of a 100 nt untranslated region, and a small (6 nt) coding sequence (Figure 3.1). We were unable to computationally identify the first exon of Spil-

17-01, which may indicate that SpIL-17-01 is a pseudogene or that the first exon of this gene is highly divergent in sequence (Figure 3.1).

SpIL-17(I) is rapidly upregulated in response to infection

The IL-17 homologs identified in the RNA-Seq screen belong to the group I subfamily.

The SpIL-17(I) genes are strongly upregulated early in infection (within 6 to 8 hr) and are quickly attenuated (expression is reduced by 12 hr) although expression levels remain at slightly higher level than unexposed larvae. This expression pattern is extremely robust and reproducible across multiple replicates and has been confirmed through qPCR (Figure 3.2A). Due to the high level of similarity among the SpIL-17(I) nucleotide sequences, it is difficult to determine expression patterns of specific genes within the subfamily.

SpIL-17(I) is expressed in the gut epithelium of infected larvae

Using WMISH, we have localized the expression of the Spil-17(I) genes to the mid- to hindgut of infected larva (Figure 3.2B). Although WMISH is not quantitative, these experiments also confirm the temporal expression kinetics identified from the transcript quantification analyses as a high number of larvae displayed positive staining at the 6 and 12 hr time point (32 and 35% of larvae analyzed showed positive staining respectively) while no expression was seen at the 0 and 24 hr time points. Because much of the detailed larval morphology is lost in the process of WMISH, we generated a BAC-based fluorescent protein reporter to further localize

SpIL-17(I) expression (Figure 3.2C ). BAC R3-17F18 contains 140 kb of genomic sequence on

S. purpuratus genomic Scaffold1105, and encompasses the eight SpIL-17(I) gene cluster. Using homologous recombination, GFP was inserted into the coding sequence of the first exon of the

SpIL-17-07 gene. Due to the large size of the BAC insert, this reporter construct should include

Figure 3.1: SpIL-17(I) genes are clustered in tandem within a compact section of the sea urchin genome.

The sea urchin IL-17 locus was recovered in Scaffold 1105 during genome sequencing (Sodergren et al. 2006). Most genes are within 4 kb of each other, except for the Spil-17-06 and Spil-17-05 genes, which are separated by a distance of 12 kb (double bars). The GFP BAC reporter was constructed by recombining and replacing the first exon of Spil-17-07 with GFP coding sequence

(Discussed in Chapter 4). Kb, kilobases. GFP, green fluorescent protein.

Figure 3.2: Expression of a sea urchin IL-17 homolog peaks over the course of larval gut infection.

Larvae were exposed to Vibrio diazotrophicus and assayed for immune gene responses by RT- qPCR, whole mount in-situ hybridization (WMISH), and BAC-based GFP reporter proteins. (A)

Expression of SpIL-17(I) spikes at 6 hours post-infection but is quickly attenuated by 12 hours of infection. Inset shows the transcript prevalence between 0 and 12 hrs post-infection. RT-qPCR results were normalized to endogenous 18S levels. (B-E) SpIL-17(I) expression localized by

WMISH (B) and BAC-GFP reporter constructs (C-E). (B) SpIL-17(I) is localized to the hindgut post-infection. In contrast, the immune marker MACPF.E is expressed at the mid-gut. (C-D) The expression of the BAC IL-17(I) GFP construct was exclusive to the gut epithelium (C and D),

88 compared to uninfected controls (E). Panels D and E are counter-stained with Hoechst DNA dye

(blue) which binds to the minor groove of DNA.

89 the majority of the regulatory elements that control SpIL-17(I) expression and provides an invaluable tool for subsequent detailed cis-regulatory analyses (details in Chapter 4).

Transgenic larvae were infected at 10 dpf with V. diazotrophicus and GFP expression was observed. Consistent with both transcript prevalence analyses and WMISH data, no GFP expression was observed prior to microbial exposure. However, GFP expression was evident in the epithelial cells of the midgut by 14 to 16 hr of infection (Figure 3.2C). We have further validated the expression of SpIL-17 in the gut epithelial cells using confocal microscopy on larva using Hoechst nuclear stain (Figure 3.2D and E). GFP expression was observed in the gut epithelium and not in gut-associated immune cells. Together, these data indicate that SpIL-17(I) is a tightly regulated gene expressed in the gut epithelium and may play a role in early signaling during the immune response.

The sea urchin genome encodes two IL-17 receptors

The S. purpuratus genome encodes two homologs of IL-17 receptors (SpIL-17R1 and

SpIL-17R2). SpIL-17R1 is a concatenation of gene models SPU_015346 and SPU_030141 while

SpIL-17R2 is most similar to gene model SPU_009256 (www.spbase.org). Each of these transmembrane proteins is characterized by a conserved intracellular Sef/IL-17R (SEFIR) domain

(Figure 3.3A). The SEFIR domain is present in all vertebrate IL-17 receptor sequences and its immediate signaling adaptor, Act1 (Gu et al. 2013; Chang et al. 2006). The intracellular region of SpIL-17R1 also contains a TILL (TIR-like loop) domain that, in vertebrates, is present in IL-

17RA and is essential for signal transduction (Gaffen 2009; Maitra et al. 2007). Extracellularly, the sea urchin IL-17 receptors are characterized by low matches to EGF domains. This is in contrast to the vertebrate IL-17 receptors, which contain two type III (Liu et al. 2013; Gaffen

2009). Despite the difference in domain structure, the extracellular portions of the receptor sequences exhibit some similarity at the amino acid level. The divergent sequence, however, may reflect differences in binding capacity as a consequence of ligand structure. Despite extensive searches for additional SEFIR domains, we were unable to find an Act1 homolog within the S. purpuratus genome, suggesting that the S. purpuratus IL-17 receptors initiate signaling through an alternative adaptor protein. WMISH indicates that SpIL-17R1 is expressed in the larval gut

(Figure 3.3B). The expression levels of both receptors are relatively low under basal conditions but increase slightly in response to V. diazotrophicus infection (Figure 3.3C). Publicly available transcriptome data from early sea urchin development (www.echinobase.org, (Tu et al. 2012; Tu et al. 2014) indicate that SpIL-17R1 expression peaks early (10 hpf), but then remains at low levels through pluteus stage (Figure 3.3D). In contrast, expression of SpIL-17R2 is slightly higher and peaks around 24 hpf.

Perturbation of IL-17 signaling causes dysregulation of downstream immune genes

To determine the role of the IL-17 system in the sea urchin larval immune response, we interfered with IL-17 signaling by perturbing SpIL-17R1. The expansion and diversity of

SpIL-17(I) ligands preclude the use of MASOs to block their function. Therefore, MASOs were designed that interfere with correct splicing of SpIL-17R1 at exon 15 (Figure 3.4). We have confirmed the effect of the MASO by sequencing the region surrounding the target site in transgenic larvae and find that, in the presence of the SpIL-17R1 splice blocking MASO, an alternative donor site is used in the middle of exon 14 to splice to the standard acceptor site in exon 16. This incorrect splicing yields a frameshift and premature stop codon. The encoded

Figure 3.3: SpIL-17 receptors exhibit dynamic responses during larval gut infection

Two sea urchin IL-17 receptors were identified in the genome, and their larval transcript prevalence assessed by deep sequencing and whole-mount in-situ hybridization (WMISH) post

Vibrio diazotrophicus exposure. (A) Sequence analysis and domain searches identified two IL-17 receptors within the sea urchin genome. Compared to mammalian IL-17R A-E homologs (top panel), both SpIL-17 receptors (bottom panel) contain conserved cytosolic SEFIR domains

(orange ovals); SpIL-17R1 also contains a TILL domain. The sea urchin IL-17 receptors lack the extracellular domains found in mammalian IL-17 families (dotted ovals). (B) RNA-Seq data indicate that SpIL-17R1 and SpIL-17R2 are both upregulated by 12 hours of infection. (C)

WMISH analysis shows SpIL-17R1 becomes activated exclusively at the gut. (D) RNA-seq analysis of early sea urchin development indicates that both receptors are expressed at low levels

(echinobase.org).

92 intracellular region. The efficacy of the MASO-injected larva was assessed at 10 dpf using qPCR, which indicated a significantly lower prevalence of SpIL-17R1 transcripts including exons 15 and

16 than in larvae injected with control MASO (Figure 3.4A).

Perturbation of SpIL-17R1 did not result in increased mortality relative to controls in either unchallenged larvae or those exposed to V. diazotrophicus. However, larvae that were injected with the SpIL-17R1 MASO were significantly smaller than those that received the control

MASO (Figure 3.5C). We also investigated the consequence of perturbing IL-17

Figure 3.4: SpIL-17R1 perturbation affects larval physiology and cellular responses.

Morpholino anti-sense oligonucleotides (MASO) were designed to block IL-17R transcript splicing (see text for details). MASO-injected zygotes were reared to 7 days of age, infected with

Vibrio diazotrophicus, and assessed by RT-qPCR and cellular observations. (A) Schema of the

IL-17R1 splice-blocking MASO target site (red bar) resulting in a truncated, non-functional protein. (B-C) The cellular responses and overall physiology of larvae change under SpIL-17R1 knockdown conditions. MASO-perturbed larvae exhibit fewer pigment cells that have migrated from the outer epithelial layer over the course of Vibrio infection (B), and the average size from mouth to apex of 7 days old larvae is reduced under MASO conditions (C). (D) qPCR analysis of

94 larval transcriptional responses to Vibrio infection under IL-17R perturbation (grey bars), compared to control-injected embryos (black bars). Left panel: MASO-injected larvae have significantly less IL-17R transcripts containing the targeted exon 15 (qPCR primers were designed to exon 15), confirming partial knockdown of the gene. Middle and right panels:

Immune genes that are normally upregulated in the larvae during infection are reduced in expression under IL-17R knockdown, suggesting that IL-17 receptor signalling is important for their activation.

95 protein contains a complete extracellular domain and transmembrane sequence, but no signaling within the context of the immune response to V. diazotrophicus. Larvae in which the IL-17R1 splicing was perturbed did not exhibited lower levels of pigment cell migration relative to controls

(Figure 3.4B). However, dysregulation of several potential downstream genes were observed, including SpIL-17-17 (another S. purpuratus IL-17 family member that is also expressed in the gut epithelium), SpTNFAIP3 (tumor necrosis factor alpha-induced protein; SpTNFAIP3), and the transcription factor Cebpα (Figure 3.4C). Together, these data indicate that signaling through

SpIL-17R1 is central in mediating the cellular and molecular immune responses to bacteria and may contribute to shaping the microbiota.

We were similarly interested in characterizing the role of SpIL-17R2 in the larval immune response. However, MASO reagents that targeted the translation of SpIL-17R2 resulted in early developmental defects (Figure 3.5). Embryos injected with this MASO did not undergo proper gastrulation (Figure 3.5C). This may suggest a role for SpIL-17R2 in early sea urchin development. Similar functions occur in the zebrafish, in which the Sef protein (an ortholog of

IL-17RD) is involved in early development. IL-17RD in zebrafish inhibits FGF-mediated RAS-

MAPK and PI3K signalling (Tsang et al. 2002). Notably, the human IL-17RD co- immunoprecipates with FGFR1 and can inhibit FGF-dependent proliferation and ERK activation

(Xiong et al. 2003; Preger et al. 2004). Further studies may shed more light on a potential developmental function for this receptor.

Figure 3.5: Knockdown of individual IL-17 receptors differentially perturbs larval development

(A-C) Zygotes were injected with control (A), SpIL-17R1 (B), or SpIL-17R2 (C) targeting

MASOs and assessed throughout development. Representative images of larvae 72 hours post- fertilization are shown. (B-C) SpIL-17R1 perturbed larvae (B) appear normal compared to the control (A), whereas SpIL-17R2 perturbed larvae (C) become arrested at mid gastrulation, with severe defects in skeletal and gut development. MASO, morpholino antisense oligonucleotide.

Discussion We present here the first description of a family of IL-17 homologs within an echinoderm.

The purple sea urchin genome encodes 32 IL-17 factors that form ten subfamilies and two IL-17 receptors. We show that of these families are strongly upregulated in response to bacterial challenge, and that expression is restricted to the gut epithelium within the larva. Furthermore, perturbation of IL-17 signaling results in dysregulation of downstream effector gene expression, which suggests that this system plays a central role in the larval immune response. Expression of these genes within the gut epithelium reorients our understanding of the evolution of these genes and may suggest a fundamental role for these proteins within the context of mucosal immunity.

IL-17 is an unusually conserved cytokine

The vast majority of our understanding of immune systems stems from studies in just a few species from two phyla (i.e., chordates and arthropods). This results in a remarkably biased perspective with respect to the diversity of immune responses that exists across metazoan species.

In contrast, simple invertebrate model organisms have contributed significantly to other areas of biology (for example, the sea slug Aplysia californica has long been used as a model for neuronal development). Despite the clear experimental advantages of simple model organisms, one reason that they have not been further exploited within immune research is a consequence of the evolutionary pressures that drive rapid sequence diversification across taxa. As such, it is fairly difficult to identify both homologs of molecules known to be involved in the vertebrate immune response as well as novel immune factors (Litman & Cooper 2007). Recently, however, the advances in next-generation sequencing technology have begun to overcome this problem. As genomic sequences become available, computational strategies can be used to identify distant

98 homologs that would not be accessible using traditional molecular biology (Buckley & Rast

2015). This strategy has been particularly fruitful with respect to pattern recognition proteins, which are often large molecules that exhibit conserved domain architectures.

In general, however, homologs of mammalian cytokines remain particularly difficult to find, even within the jawed vertebrates. This is partially due to evolutionary pressures from pathogens, which often exhibit molecular mimicry of cytokines to manipulate the host immune response. Furthermore, cytokines tend to be very small molecules with highly divergent primary sequence. Although these molecules can sometimes be identified using conserved elements of protein structure, they are generally resistant to computation identification. IL-17 is unique, however, in its evolutionary stability. Homologs of this cytokine are present throughout bilaterians, including molluscs (Li et al. 2014; Roberts et al. 2008), the invertebrate deuterostomes, and the jawless vertebrates. This conservation is largely a consequence of the unique cysteine knot fold structure formed by the IL-17 ligands (Hymowitz et al. 2001).

The multiplicity of the SpIL-17 family mirrors expansions of sea urchin immune receptors

Within most vertebrates, the IL-17 ligands are encoded in small gene families (typically less than 10 members). In contrast, within the S. purpuratus genome, this family has been moderately expanded to include 32 genes. We have surveyed additional echinoderm genomes and find similar expansions within this phylum. In the genome of the green sea urchin Lytechinus variegatus, which shares a common ancestor with S. purpuratus ~50 million years ago (Mya), 15 homologs of IL-17 were identified, and each of the S. purpuratus subfamilies are represented.

The IL-17 genes from these two species are also arranged similarly within the genome, such that

99 genes within subfamilies tend to be tandemly arrayed and certain subfamilies are adjacent to one another (data not shown). We have also estimated the numbers of IL-17 homologs from unassembled trace sequences of two additional strongylocentrid sea urchin species: Allocentrotus fragilis, which diverged from S. purpuratus 5-7 Mya, and S. franciscanus, which last shared a common ancestor with S. purpuratus 20-25 Mya. These species have similarly expanded IL-17 gene families consisting of 38 and 40 genes, respectively. A more distantly related sea star species, Patiria miniata encodes 12 IL-17 homologs, with the notable absence of the IL-17(I) genes.

This expansion mirrors other known expansions within gene families that encode proteins involved in the echinoderm immune response. This most striking example of this is the genes encoding pattern recognition receptors. The S. purpuratus genome encodes, 253 TLR genes, 222

NLR genes and 1095 SRCR domains, which is about a 10-fold expansion relative to either vertebrates or Drosophila (Messier-Solek et al. 2010; Buckley & Rast 2015; Hibino et al. 2006).

Similarly, the sea urchin immune genes effector known as 185/333 are encoded in complex gene family consisting of about 50 highly diverse genes (Buckley et al. 2008). The underlying mechanisms that drive these expansions remain unknown, but may point to a unique strategy for generating immune diversity within an invertebrate.

IL-17 plays an ancient role in gut-associated immunity

Although the IL-17 cytokines are most commonly associated with T cell functions within the vertebrates, growing evidence suggests that members of IL-17 family play important roles in regulating the mucosal immune response (R Pappu et al. 2012; Ramírez-Gómez et al. 2008).

100

Here, we present data that IL-17 expression within the sea urchin larva is restricted to the gut epithelium in response to microbial exposure. No evidence of its expression was observed in any other tissues, including the blastocoelar cells or pigment cells (data not shown). This expression pattern mimics that of IL-17C, which is expressed only in intestinal epithelial cells, but not in fibroblasts of peripheral blood cells (Ramirez-Carrozzi et al. 2011; Song et al. 2011). Notably, when human epithelial cells (colon epithelial cells, primary tracheal epithelial cells or epidermal keratinocytes) are exposed to E. coli, IL-17C expression is rapidly induced within hours of exposure and then quickly attenuated, although expression remains slightly above baseline

(Ramirez-Carrozzi et al. 2011), which mirrors our observations in the sea urchin larva. Together, these data suggest that IL-17(I) may play a similar role to mammalian IL-17C within the context of epithelial immunity. Furthermore, these data suggest that this function for IL-17 may be ancient at least within the invertebrate deuterostomes. The specialized IL-17 signaling system in lymphocytes may thus be a feature that was secondarily acquired during vertebrate evolution.

Conclusions

The gut immune response is a fundamental system of metazoan species. To survive, animals must properly regulate their interactions with microbes across the gut epithelium to both shape a productive microbiota and also to defend against pathogens. The data that we present here indicate that the IL-17 family of cytokines plays a central and conserved role in this dialogue.

Further studies will continue to investigate the crosstalk between the larval microbiota and the IL-

17 signaling system and the consequences on the subsequent host immune response. The sea urchin larva thus offers a unique opportunity to investigate these interactions in a morphologically and molecularly simple system within the context of an intact animal. Finally, information

101 obtained from these studies will serve as an important groundwork for further studies in the role of epithelial IL-17 expression in mammals and will continue to shed light on our understanding of the evolution of animal immunity.

102

Chapter 4 Building a gene regulatory network for the sea urchin larval gut- associated immune response using SpIL-17 as an anchor

Introduction

Epithelial expression of IL-17 is central to the sea urchin larval immune response

In response to challenge with the marine bacteria V. diazotrophicus, the purple sea urchin larva undergoes a robust cellular and molecular immune response that we have characterized in detail using microscopy and RNA-Seq strategies (detailed in Chapter 2). Of all the genes in the purple sea urchin genome, however, a small family of IL-17 genes are the most strongly upregulated in this response (Chapter 3). Genes within two IL-17 subfamilies are strongly upregulated in response to exposure to V. diazotrophicus. The IL-17(I) subfamily is comprised of 12 very closely related genes and is expressed early but is quickly attenuated. In contrast, the single gene of the IL-17(IV) subfamily is expressed early in infection, and is then further upregulated in infection, or is downregulated coincident with IL-17(I) (Chapter 3). Both subfamilies are expressed in the larval gut epithelium. Perturbation of IL-17 signaling by interfering with correct IL-17R1 splicing results in decreased expression of downstream genes in response to bacterial challenge. These data suggest that these IL-17 homologs play an important role in initiating the larval gut-associated immune response.

103

The role of IL-17C in epithelial defense in mammals

The induction cytokine expression in epithelial cells is key to a robust and balanced immune response (Kagnoff 2014; Stadnyk 1994; Griffith et al. 2014). Recent studies have revealed that IL-17C expression in intestinal epithelial cells plays an important protective role against gut infection (Song et al. 2011; Ramirez-Carrozzi et al. 2011; Pappu et al. 2012). IL-17C is expressed in colon tissue of mice challenged with Citrobacter rodentium and offers a protective response in dextran sulfate sodium (DSS) induced colitis. Stimulation of primary epithelial cells with either whole bacteria or with agonists to TLR2, TLR4 or TLR5 induces rapid expression of

IL-17C (Song et al. 2011; Ramirez-Carrozzi et al. 2011). IL-17C expression can also be induced by pro-inflammatory cytokines such as TNFα and IL-1β (Song et al. 2011; Ramirez-Carrozzi et al. 2011). Experiments done in cultured human keratinocytes have identified NF-κB as a key transcription factor involved in TNFα induced expression (Johansen et al. 2011). However, little is known about the molecular mechanisms that are involved in the regulation of IL-17C within the context of gut immune responses.

A simple model for investigating regulatory control of IL-17

The sea urchin larva provides a unique model system in which to decipher the regulatory mechanisms that regulate the expression of IL-17 within the context of a gut infection model.

Cytokine expression can serve as an output for building a simple gene regulatory network (GRN).

Although transcriptional regulation of IL-17 has been examined extensively in vertebrate Th17 cells, systematic efforts to have not yet been extended to epithelial sources of IL-17. However, similar strategies have been used to establish a small GRN describing the innate inflammatory response regarding cytokine IL-6 in human macrophages. Through model prediction and cis-

104 regulatory analysis, a regulatory circuit involving the transcription factors C/EBPδ, NF-κB and

ATF3 defines the ability of TLR4 to discriminate between transient and persistent signals (Litvak et al. 2009). In the context of our sea urchin larval gut immune response model, IL-17 will serve as an anchor for building an analogous GRN.

One established, this GRN will also serve as a foundation on which to understand the contribution of commensal microbiota to the host immune response. The animal gut consists of a large and dynamic microbial population of which a majority is non-pathogenic (Hooper &

Gordon 2001). The maintenance of the commensal population, as well as launching a successful gut immune response, requires the involvement of multiple immune cell types (e.g. cross talk between the epithelial cells and active immune cells). Recent research indicates that the role of the microbiota extends beyond the gut and can influence developmental processes in other areas of the host (Round & Mazmanian 2009; Lee & Brey 2013). It is clear that the timing and colonization of gut microbiota is essential to host homeostasis. However, the detailed molecular mechanisms that govern these interactions have yet to be fully deciphered (Edelman & Kasper

2008; Dishaw et al. 2012), in part due to the morphological complexity of mammals. An immune- competent gut is necessary for the survival in even the simplest animals, and fundamental mechanisms and regulatory modules should be evolutionarily “locked in” early and conserved across metazoans (Bosch et al. 2009). As such, conclusions from the study of the regulatory mechanisms in a simple invertebrate gut model can be used as a blueprint for further studies in vertebrates (Dishaw et al. 2012).

A thorough identification of the cis-regulatory elements and the assembly of a complete

GRN is quite extensive and beyond the scope of the current project. Here, our goal is to identify the regulatory logic that is underlies expression of IL-17(I) within the gut immune response.

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Temporal and spatial expression data indicate that these genes are regulated by three distinct inputs: (1) rapid initiation of expression in the presence of bacteria; (2) localization of expression to the gut epithelium; and (3) attenuation. The genomic clustering of the IL-17(I) genes may also contribute to regulating gene expression. By isolating the regulatory regions that dictate those inputs, we can decipher the regulatory logic controlling IL-17(I) expression and use these data as an anchor in compiling a GRN for the sea urchin larval gut-associated immune response.

Materials and Methods Preparation and microinjection of reporter constructs

Green fluorescent protein (GFP) reporter constructs were generated using BAC vectors containing genomic sequences that encompass the IL-17(I) genes on S. purpuratus genomic

Scaffold1105. The BAC (R3-17F18) contains an insert of 125 Mb and eight IL-17(I) genes.

Homologous recombination was used to introduce a GFP cassette (GFP coding sequence and

SV40 3′UTR into the coding sequence (6 nt) of the first exons of genes IL-17(I)-03, IL-17(I)-05 or IL-17(I)-07 in SW105 E. coli cells (Warming et al. 2005). Recombination arms were designed directly surrounding first exons to retain all of the 5´ UTR as well as intronic region. Primers used to generate the arms for homologous recombination are listed in Appendix C. Resulting

BAC reporter DNA was purified with CsCl gradients or NucleoBond BAC 100 purification columns (Macherey-Nagel), linearized with NotI (New England Biolabs) and analyzed through pulsed-field gel electrophoresis. Due to the similarities between the IL-17(I) family members,

GFP insertion sites within the recombinant BACs were sequenced to confirm the location of the

GFP. BAC reporters were linearized with AscI and injected at 500 copies/pl as described (Rast

2000). Transgenic larvae were reared until 10 days post-fertilization (dpf) upon which the larva

106 were infected with V. diazotrophicus as described in Chapter 3. GFP expression was examined with Axioplan 2 with HrM camera using Axiovision software.

Preparation of truncated and deletion constructs

Using the recombinant BAC construct as a template, a GFP reporter that contains a smaller regulatory region (~2.9 kb upstream and ~3.8 kb downstream from the start of transcription) was constructed using Phusion DNA polymerase (Thermo), adenylated with Taq (Invitrogen) and cloned into TOPO PCR4.1 vector (Invitrogen). This construct was then used to generate smaller deletion constructs in which segments of the 5′ and/or 3′ regions as well as the intron were removed using the PCR and cloning strategy described above. The primers used (and their relative positions to the start of transcription) are listed in Appendix C. For injection, constructs were purified with the QIAprep kits (QIAGEN), linearized with PstI and extracted with the

QIAquick gel extraction kit (QIAGEN) following manufacturer’s protocols. Constructs were then purified with ethanol/sodium acetate precipitation and quantified using a Nanodrop. Plasmid reporters were injected at 200 copies/pl and reared and imaged as described above.

Quantification of GFP transcripts

RNA was isolated and reverse transcribed as described in Chapter 3. Genomic DNA

(gDNA) was isolated using TRIZOL following manufacturer’s protocol. qPCR was performed as described in Chapter 3. Due to random concatenation of the linearized transgenes prior to incorporation into the genome, the number of GFP constructs per transgenic larva varies. To normalize the expression of GFP relative to this copy number variation, qPCR was performed on the gDNA using primers to target GFP as well as SpGatac, which is a single-copy gene (Solek et

107 al. 2013). The ratio of GFP/SpGatac amplification was then used to normalize the expression of

GFP transcripts.

Results A BAC-based reporter construct recapitulates endogenous IL-17(I) expression in response to gut-associated bacteria

WMISH data indicated that the IL-17(I) genes are expressed in the gut epithelium in response to challenge with V. diazotrophicus (see Chapter 3). To further characterize the expression of the IL-17(I) genes, fluorescent protein (GPF) reporter constructs were generated using a BAC that encompasses one of the IL-17(I) clusters (the genomic cluster and an example recombination is shown in Figure 4.1A). Reporters were constructed for three IL-17(I) genes:

IL-17(I)-03, IL-17(I)-05 and IL-17(I)-07 (Figure 4.1 B, C and D). Given the high sequence similarities among the IL-17(I) subfamily members, sequencing analysis identified some non- specific recombination around ~1 kb downstream from end of transcription for IL-17(I)-03 and

IL-17(I)-05 (Figure 4.1). However, the 5′ regulatory region appeared to be intact. These constructs were injected into fertilized eggs, grown to 10 dpf, infected with V. diazotrophicus, and assessed for GFP expression 12-16 hr post-exposure.

Analysis of the GFP indicates that the IL-17(I) genes are expressed in the gut epithelium in response to V. diazotrophicus (Figure 4.1). Specifically, all three reporter constructs expressed

GFP exclusively in the mid- and hindgut epithelium (Figure 4.1B to D). Notably, GFP expression was never observed in uninfected larvae. This indicates that the IL-17(I) genes are tightly regulated and that multiple members of the IL-17(I) family are expressed in response to bacteria.

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Figure 4.1: Three BAC-based fluorescent reporters localize SpIL-17 gene expression to the gut epithelium upon larval immune activation.

(A) Schema of a representative IL-17-GFP reporter BAC. Homologous recombination within an

IL-17(I)-containing BAC was used to replace the first exon of IL-17(I)-07 with GFP-SV40 coding sequence. White sections indicate untranslated regions, blue or green sections indicate translated coding sequences. In sum, three GFP reporters based on IL-17(I)-03, IL-17(I)-05, and IL-17I(I)-

07 genes were constructed in this manner. (B-D) Zygotes were injected with recombinant GFP-

BACs for IL-17(I)-03 (B), IL-17(I)-05 (C) and IL-17I(I)-07 (D). Transgenic larvae (10 days old) were exposed to Vibrio diazotrophicus for 10 hours and assessed by fluorescent microscopy. All three variants displayed GFP expression at larval gut epithelium upon bacterial exposure. GFP

109 expression was not observed in non-exposed control larvae (data not shown; see text for details).

BAC, bacterial artificial chromosome; GFP, green fluorescent protein.

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Deletion analysis identifies upstream and downstream regions of IL-17-07 that contribute to expression regulation

A set of smaller GFP expression reporters was constructed from the IL-17(I)-07 recombinant GFP BAC to further refine our analysis of regulatory sequence. The close proximity of the IL-17(I) genes restricts the maximum length of the reporters that can be generated. The largest reporter contained 2.9 kb upstream and 3.8 kb downstream from the start of transcription

(the distance to the UTRs of adjacent genes; Figure 4.2; Construct 1). The constructs are named according to the following convention: the first number indicates the length (in kb) upstream of the 5′ UTR, followed by “G” for GFP. If the construct contains the intron sequence, it is followed by “i”. The last number indicates the length (in kb) of the sequence downstream of the transcription start site. For example, Construct 1, which contains 2.9 kb upstream and 3.8 kb downstream of the start of transcription is known as (-2.9kb)Gi(+3.8kb). Analysis of GFP expression from (-2.9kb)Gi(+3.8kb) indicates that this reporter construct closely recovers the spatial and temporal expression of both the BAC-based GFP reporter for IL-17(I)-07 as well as endogenous IL-17(I). This suggests that (-2.9kb)Gi(+3.8kb) retains all the important regulatory elements necessary for regulating IL-17(I) and that, although the IL-17(I) family is clustered, the sequence surrounding a single gene is sufficient to drive expression.

Deletion constructs were employed to identify regions of regulatory importance. Sections of the potential regulatory regions were systematically deleted from the 5′ and 3′ regions of the gene (Figure 4.2). Each reporter was then scored based on the number of larvae that contain GFP positive cells normalized to the observed GFP expression from the (-2.9kb)Gi(+3.8kb) construct as a control. In the second largest construct tested (Construct 2; (-1.8kb)Gi(+3.8kb)), 1.1 kb was removed from the 5′ region (Figure 4.2). Using this construct, a significantly reduced number of

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Figure 4.2: BAC-based promoter-deletion assays identify the minimal genomic regions required for IL-17 expression.

(A-B) Zygotes were injected with modified IL-17-GFP reporter BACs and grown to 10 days, exposed to Vibrio diazotrophicus, and scored by fluorescent microscopy. (A) Representative image of gut IL-17-GFP expression 10 hours post-Vibrio exposure (Construct 1; (-2.9)Gi(+3.8)).

(B) Summary of the GFP reporters tested. The top schema indicates 5´ and 3´ regions found within the complete reporter BAC (Construct 1), with untranslated (white), coding (blue), and GFP

(green) sequences indicated. Reporter BACs with various deletions are shown below. The left- most column denotes the construct number and the second column summarizes the coding regions that were retained in each GFP reporter. The third column is the percentage of GFP expressing

(GFP+) larva normalized to Construct 1. The fourth column indicates the average number of GFP expressing cells per GFP+ larva. Both the 5´ region and exon 2 are required to activate GFP expression, indicating that important regulatory inputs are found in these areas of the IL-17 locus.

Intronic regions likely play an important role as well.

112 larvae expressed GFP in response to V. diazotrophicus, although the number of GFP positive cells per larva remains constant (Figure 4.2). Similarly, removing 2 kb from the downstream region reduced the number of larvae expressing GFP (Construct 6; (-2.9kb)Gi(+1.8kb); Figure 4.2). This suggests that both the 5′ and 3′ regulatory regions are involved in setting the activation threshold necessary to initiate IL-17 expression.

To deduce the minimal sequence required to drive GFP expression, additional deletion reporters were constructed and analzyed. Deletion of the sequence downstream of the GFP cassette (including the second exon of IL-17(I)-07) completely eliminates GFP expression (see constructs 13 ((-2.9kb)G), 14 ((-1.8kb)G), and 15 ((0)G); Figure 4.2). Similarly, a reporter construct consisting of only the GFP and the IL-17(I)-07 intron sequence did not express GFP

(construct 16 ((0)Gi); Figure 4.2). These data indicate that the upstream region alone is not sufficient to activate GFP expression. A reporter construct that included the entire IL-17(I) coding sequence, as well as the intron and 5′ and 3′ UTRs (Construct 10, ((0)Gi(+0.8)) resulted in a minimal expression of GFP, suggesting that these untranslated regions may play a role in regulating IL-17 expression. Importantly, none of the deletion constructs examined displayed spatial or temporal misregulation (i.e., constitutive expression without the presence of bacteria or expression in any region other than the gut epithelium). This indicates that IL-17(I) expression is subject to very robust negative regulation.

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Quantification of GFP transcripts indicates that IL-17(I) BAC GFP reporter expression recapitulates endogenous IL-17(I) expression; deletion constructs displayed reduced GFP expression.

Quantitative PCR was used to evaluate the transcriptional activity of this BAC reporter as compared to endogenous SpIL-17(I) expression (Figure 4.3). GFP transcripts are activated by 8 hr of exposure to bacteria, and attenuated by 24 hr, which recapitulates that of endogenous IL-

17(I) (Figure 4.3). This suggests that the lag in the detection of fluorescent GFP is likely a consequence of the time required for protein folding and accumulation. To quantify the contribution of the regulatory regions that direct the expression of IL-17(I)-7, qPCR was performed to quantify the GFP transcription by the truncated reporters. Due to concatenation of the linearized plasmid and BAC DNA, multiple copies are incorporated into the genomes of transgenic animals (Arnone et al. 1997). Consequently, GFP transcript levels were normalized to the number of GFP transgenes. Transgene incorporation was quantified by normalizing GFP

DNA measurements to those of single copy nuclear DNA. The of GFP DNA was compared to the

Ct of a SpGatac (a single copy gene). GFP transcript prevalence was examined at three time points: 0, 8 and 24 hours of infection for two deletion constructs: Construct 1, (-2.9kb)Gi(+3.8kb), and Construct 2, (-1.8kb)Gi(+3.8kb). Construct 2 showed a significant decrease in transcript levels (Figure 4.4) at 8 hr of infection, which supports the hypothesis that the -2.9 to -1.8 kb sequence contains important positive cis-regulatory input elements. Construct 1 exhibits similar expression kinetics to that of the BAC reporter, although the transcript levels are slightly lower at eight hours of infection. This suggests that additional cis-regulatory modules that influence IL-

17(I)-07 expression may be beyond the boundary of examination. Attenuation of GFP expression does not appear to be affected indicating that IL-17(I) is tightly regulated.

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BAC GFP Reporter 800

700 GFP IL-17 (I) 600

500

400

300 Relative Expression Relative 200

100

0 0 8 24 Hours of exposure Figure 4.3: The expression of the IL-17(I) BAC-GFP reporter mimics endogenous IL-17(I) transcript levels during gut infection

A BAC-GFP reporter construct recapitulates the endogenous SpIL-17(I) expression in response to bacteria (see Figure 3.2). RT-qPCR data indicate that GFP transcripts (green bars) are present at 8 hours of infection but are downregulated by 24 hours, comparable to the trend of endogenous

IL-17(I) transcript levels during infection (blue bars). Results were normalized to genomic GFP copy number.

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12000

10000 0 8 24

8000

ess

r p

6000

e Ex e

l e R 4000

2000

0 BAC (-2.9)Gi(+3.8) (-1.8)Gi(+3.8)

Figure 4.4: RTqPCR analysis of transgenic IL-17-GFP larvae recapitulates live imaging data in identifying a minimal 5´ region required for IL-17 expression post immune activation.

10-day old larvae transgenic for three different IL-17-GFP BAC constructs were scored for GFP signal and analyzed by RT-qPCR at 0 hrs (blue bars), 8 hrs (orange bars) and 24 hrs (grey bars) post-Vibrio diazotrophicus infection. The number underneath each construct group indicates the percentage of GFP+ larva. The (-2.9)Gi(+3.8) construct (containing approximately 2.9 kb of 5´ sequence) showed slightly reduced expression at 8 hours compared to the complete BAC-GFP reporter (BAC), however, the overall expression kinetics are similar between the two reporters.

In contrast, GFP expression of the (-1.8)Gi(+3.8) construct (containing only 1.8 kb of 5´ sequence) is severely reduced at 8 hours of infection, compared to the complete BAC and the (-2.9)Gi(+3.8) construct, indicating the potential importance of the deleted 5´ region

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Discussion

We present here the first analysis of the transcriptional regulation of IL-17(I) regulation within the context of a sea urchin larval gut infection. We find that a BAC-based GFP reporter construct is sufficient to recapitulate endogenous expression of IL-17(I). Further deletion analysis indicates that the region surrounding a single gene is sufficient to drive GFP expression, and that removing either the 5′ or 3′ sequences or the intron also reduces expression. Together, these data suggest that the IL-17(I) genes may be transcriptionally regulated individually, and that the regulatory elements are located throughout the intergenic regions. Analysis of transcript prevalence indicates that these constructs accurately recapitulate the both the activation and attenuation of endogenous IL-17(I). Notably, ectopic GFP expression was never observed. GFP was not present in either unchallenged larvae or any tissues other than the gut epithelium, which suggests that these genes are subject to strong negative regulation and are expressed only in the presence of an activation signal.

The cis-regulatory modules responsible for IL-17(I) expression are located close to the gene coding sequence

The IL-17(I) genes are arranged in two genomic clusters. These clusters may represent allelic variation, although further analysis is necessary. The genes are tightly clustered, with ~4 kb separating each gene. This spacing is relatively close; on average, genes within the S. purpuratus genome are 23.5 kb apart (Tu et al. 2012). It is therefore possible that these genes share common cis-regulatory elements that are located outside the cluster and are co-regulated as a consequence of their spatial proximity (Di Stefano et al. 2013). Data from the (-2.9)Gi(+3.8) reporter construct, however, suggests that this region is sufficient to drive GFP expression, which

117 supports the hypothesis that genes can be regulated independently within the cluster. The ability for each gene to be individually regulated suggests that, although the members of the IL-17(I) subfamily share 96% amino acid similarities, individual IL-17(I) genes may contain subtle functional differences and have the ability to be differentially activated under immune stimuli.

Similar gene cluster regulation is apparent in other systems. For example, the β-globin gene cluster consists of five genes that encode the beta subunits of hemoglobin and are differentially expressed in a developmental-stage specific manner in erythroid cells. The regulatory sequences required for the correct initiation of the globin genes are located in the upstream proximal region 5′ to each globin gene (Stamatoyannopoulos 2005). Although the promoters of all the globin genes share high sequence similarities, subtle sequence differences drive differential expression over the course of development. However, the globin genes are also under the control of locus control region (LCR) located upstream of the cluster, up to 50 kb from the globin genes (Stamatoyannopoulos 2005). The globin LCR is defined by five DNaseI hypersensitive regions and contains both enhancer and insulator activity (Cao & Moi 2002). In the absence of the LCR, individual expression of the globin genes is severely reduced (Cao &

Moi 2002). LCRs have also been identified that regulate the Th2 cytokine gene cluster, which encodes il2, il3 and il4 (Rowell et al. 2008). Our qPCR data suggest that the (-2.9)Gi(+3.8) reporter yields slightly lower GFP expression compared to the GFP BAC reporter, which contains a much larger genomic region (Figure 4.3). This may suggest that cis-regulatory elements and possibly an LCR are present further upstream to facilitate the activation of genes within the cluster.

An alternative possibility to account for the high copy number of the IL-17(I) genes is to allow for a rapid increase in transcript accumulation in response to bacterial infection. This is

118 consistent with our measurements of IL-17(I) transcript levels indicating that this gene is quickly activated in the presence of V. diazotrophicus. Copy number variation has been shown to affect gene expression and protein production in other animals (Freeman et al. 2006) and an increase in copies of certain genes has been linked to human health and disease (Zhang et al. 2009). However, if this model is the main driver behind for the increased gene copy number, this mechanism evolved relatively recently. The closely related (~50 million years) sea urchin species, L. variegatus, encodes only two IL-17(I) genes (data not shown).

Our qPCR experiments suggest that the GFP expression from the reporter BAC construct was slightly lower than the endogenous IL-17(I) expression. Although this may be an experimental artifact, it is possible that cis-regulatory elements are located at a significant distance from the BAC cluster (the BAC encompasses a total of 125 kb). Although most genes are regulated by cis-regulatory located relatively close to the transcription start site (Materna &

Oliveri 2008), some examples exist in which regulatory regions are far away (Wittkopp & Kalay

2012). This is particularly true for cytokines (Rowell et al. 2008). For example, the interferon gamma gene (ifng) has multiple cis-regulatory elements that are located ~110 kb from the coding sequence (Takaoka & Yanai 2006; Schoenborn et al. 2007). Further analysis is required to explore this possibility in the sea urchin.

IL-17(I) expression is tightly regulated

In the course of our deletion analysis, GFP misexpression was not observed in any of the truncated reporter constructs. This strongly suggests that IL-17(I) is under tight negative regulatory control. In mammals, IL-17 is a highly inflammatory cytokine, and many biological factors are involved in suppressing its expression (Rajita Pappu et al. 2012; Wang et al. 2012).

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Dysregulation of IL-17 expression can lead to autoimmunity and other chronic immunological diseases (Gaffen et al. 2014). These constraints on IL-17 expression are apparent at the cellular level in the regulation of the differentiation of the Th17 cells (the primary source of IL-17A and

F). Differentiation of naïve T cells to Th17 cells requires both TGF-β and IL-6; TGF-β alone induces Treg lineage differentiation, which has an opposing immunological function (Korn et al.

2009). The transcription factor RORɤt is important in both Th17 differentiation and in the subsequent control of IL-17 expression (Yosef et al. 2013; Gaffen 2009). To drive Th17 development, RORɤt interacts with a network of other transcription factors, including STAT3,

IRF4 and BATF. STAT3 is an important regulator of IL-17 production and binds directly to the promoter region of il17a and il17f (Yang et al. 2011; Durant et al. 2010; Chen et al. 2006). In this context, STAT3 is only activated by IL-6 and IL-23 (Yang et al. 2011; Durant et al. 2010).

DNA methylation of the il17 promoter region is also required for STAT3 to activate il17 transcription in these cells (Thomas et al. 2012). Without proper methylation, STAT5 binds to the promoter region and represses IL-17 expression (Pandiyan et al. 2012; Yang et al. 2011). This tight regulation also extends to the gut-associated immune response, in which IL-17A and F expression are almost non-detectable in non-immune challenged epithelial and associated immune cells (Ishigame et al. 2009). Similarly, IL-17C expression is also tightly controlled. In the absence of bacteria and other agonist ligands of TLR2 and TLR5, IL-17C expression is absent in epithelial cells (Song et al. 2014; Song et al. 2011; Ramirez-Carrozzi et al. 2011).

Although a number of trans-acting factors involved in IL-17A/F expression have been established, the cis-regulatory elements that drive IL-17 transcription remain largely undefined. il17 and il17f are encoded at the same chromosomal locus; in mice, they are separated by 43.9 kb and are transcribed in the opposite direction (Wang et al. 2012). Recent studies have identified

120 eight consensus non-coding (CNS) regions that surround the il17-il17f gene locus (Akimzhanov et al. 2007). Of the eight CNS regions, one interacts with ROR factors (Yang, Pappu, et al. 2008) and regulates both il17 and il17f expression (Wang et al. 2012). A second CNS region lies between the il17 and il17f genes and interacts with the transcription factors STAT, RORɤt and

Runx1 to drive transcription of both loci (Thomas et al. 2012). These studies have focused on IL-

17 expression within Th17 cells, and it is likely that the transcriptional program in epithelial cells is different.

S. purpuratus contains a single STAT homolog that shares 48% sequence identity with

STAT5 (Wang & Levy 2012). An RORγt homolog is absent from the genome (Howard-Ashby et al. 2006). This lack of homologs of key transcriptional activators suggests an alternate activation pathway that is dissimilar to that found in vertebrate Th17 cells. The specific cellular inputs and regulatory elements that govern the activation and suppression of IL-17C in epithelial cells have not yet been studied in detail. Studies indicate that the expression kinetics of innate

IL-17 is different than that of Th17 (Cua & Tato 2010) which further suggests the presence of an alternative regulatory mechanism. The work presented here serves as a foundation to identify novel regulatory inputs to IL-17 that can be further investigated within the context of vertebrate epithelial cells.

IL-17(I) as an anchor for a GRN describing the gut-associated immune response

Our cis-regulatory analysis of IL-17(I) epithelial expression in response to bacterial challenge can serve as an anchor point from which to generate a GRN that characterizes the regulatory interactions controlling the dynamic gut-associated immune response, including the communication among cellular compartments. This can be particularly fruitful in the sea urchin

121 larva, in which quantitative measurements of transcriptional changes can be made within the context of an intact organism and gene perturbation and transgenesis strategies are well- established. We have generated a preliminary logic map of this response using IL-17(I) expression as an output in addition to other data (in particular, RNA-Seq and WMISH data; Figure

4.5). In response to bacteria, changes in gene expression or cell behaviour are observed in three distinct compartments: the gut epithelium, blastocoelar cells and pigment cells.

Although the mechanism remains unknown, the larval immune response is initiated when bacteria are detected by the gut epithelium. The most likely candidates that mediate this recognition are the TLRs, which are involved in IL-17C activation in the vertebrates (Song et al.

2011; Ramirez-Carrozzi et al. 2011), or the NLRs, which are expressed in the larval gut (data not shown; Figure 4.5A). IL-17(I) expression holds a unique place in this network as it is among the first genes to be transcriptionally activated. The IL-17(I) protein then likely signals through the

IL-17 receptors expressed in the gut epithelium, either in an autocrine (which may result in its own attenuation) or paracrine manner (which may amplify the signal to nearby cells) (Figure

4.5B). Finally, this IL-17(I) signaling may either directly or indirectly yield changes in gene expression in the peripheral pigment cells (e.g., activation of cell migration genes) and blastocoelar cells (e.g., upregulation of the immune effector genes 185/333 Figure 4.5C). This preliminary network will continue to be refined as the cis-regulatory inputs of IL-17(I) and other signaling connections are confirmed.

Conclusions

The data obtained from these experiment form the groundwork for establishing a GRN that describes the sea urchin larval gut-associated immune response with IL-17(I) as a central

122 mediator in this response. The identification of regulatory regions regulate IL-17(I) during gut infection will allow us to characterize the trans-acting factors in detail and will allow us to understand the fundamental regulatory mechanisms that drive IL-17 expression within an epithelial context.

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Figure 4.5: A predicted gene regulatory sub-network controlling larval gut immune responses.

Distinct spatial regions of the larva are boxed in green (gut epithelium), red (pigment cells), blue

(filopodial or ovoid cells), brown (amoeboid cells) and light green (Globular cells) with related genes and or factors listed within. Lines indicate links between genes; arrowheads indicate positive inputs, bars indicate repression/negative inputs, and open white circles represent putative signal transduction events. The predicted sequence of events post-bacterial infection are as follows: Microbial signaling through recognition receptors which activates IL-17(I) transcription.

IL-17 may act in an autocrine or paracrine manner on gut epithelial cells to trigger the expression of immune genes. The negative regulation of IL-17(I) is currently unknown and is denoted by

“?”. In additional, based on our model, it is very likely that chemokines play a role in immune cells recruitment. However, their identities are currently unknown. IL-17(I) indirectly activates migration and/or immune gene expression in larval immunocytes.

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Chapter 5 Discussion

The sea urchin larva as a simple animal model for investigating fundamental mechanisms that control the gut-associated immune response

The immune response at the gut epithelium is a tightly regulated, dynamic system that mediates the intricate interactions between the host and its environment. In response to pathogens, the immune system must respond appropriately by integrating signals from inter- and intracellular receptors. This response is coordinated at the system level through direct cell:cell interactions as well as signaling molecules that communicate information among cellular compartments. Once pathogens are cleared, the host immune system must be able to return to a resting state.

Furthermore, the immune system is carefully fine-tuned to avoid autoimmunity and also to shape a complex microbiota that is beneficial for the host. The distributed and complex nature of the gut-associated immune response underscore the need for a simple model system in which immunity can be studied within the context of an intact organism.

Traditional research on immunity has relied on a “reductionist” strategy in which biological components are studied individually (e.g., genes and/or signaling pathways). The resulting information is then pieced together in an attempt to build a predictive model that reflects the behaviour of the system as a whole. This approach is largely a consequence of the morphological and molecular complexity of traditional mammalian research models. This has been an extraordinarily successful approach; however, immunological responses are rarely triggered by discrete signalling pathways and instead result from the integration of numerous inputs from many aspects of biology. As such, examining the components of the immune system

125 separately may not accurately identify the molecular mechanisms that regulate an immune response. For example, in vitro experiments have identified some of the core molecular players involved in T cell development (Crotty 2015), although these interactions may not always reflect the real physiological changes that occur in vivo (Batista & Dustin 2013), particularly within the context of a disease state (Ahrens & Bulte 2013). This complexity is particularly apparent in investigations of the gut-associated immune system. In addition to the morphological complexity of the mammalian gut, the immune system is in constant flux to maintain a healthy microbiota while also defending against pathogens.

As an alternative strategy, simple invertebrate model systems have provided invaluable insights into our broader understanding of immunity. One example of this is the discovery of the prototypical pattern recognition receptor Toll within D. melanogaster (Lemaitre et al. 1996).

Since then, studies in D. melanogaster have been extended to investigate fundamental elements of host-microbiota interactions (Buchon et al. 2013; Erkosar & Leulier 2014; Lee & Brey 2013).

Similarly, invertebrate models, including C. elegans and the bobtail squid (Euprymna scolopes) have provided systems in which to investigate mucosal immunity and host-symbiont interactions

(Pukkila-Worley & Ausubel 2012; Nyholm & McFall-Ngai 2004; Koch & Miyashiro 2014; Miller et al. 2007). As protostomes are quickly evolving, these systems are phylogenetically distinct from the vertebrates, and many of the molecular mechanisms are not conserved. For example, in

C. elegans, homologs of key vertebrate immune regulatory genes, including NF-κB, and, in fact, even dedicated immune cells are absent. To more easily extend the findings from invertebrate models to vertebrates, a more phylogenetically relevant model is helpful.

We present here the larval stage of the purple sea urchin as a simple model system in which to investigate the fundamental mechanisms that direct the gut-associated immune response

126 and that integrate with other aspects of immunity throughout the organism. As basal deuterostomes, sea urchins share key features with the vertebrates, including a core set of transcription factors involved in hematopoiesis (Solek et al. 2013) and many genes involved in the immune response (Hibino et al. 2006). These conserved elements are complemented by several novel echinoderm innovations, including the immune effector genes known as 185/333

(Terwilliger et al. 2007; Buckley et al. 2008). Relying on the extensive information from the sea urchin genome project, it is now possible to identify the molecular mechanisms that underlie the larval cellular immune response. We have characterized the larval immune cell diversity and behavior, and developed a model for gut-associated bacterial infection that is robust and synchronous. This novel system thus holds the potential to uncover broadly conserved, core mechanisms of the immunity and also to provide invaluable insight into the evolution of novel metazoan immune response.

The sea urchin larva is characterized by a distinct immune system with some similarity to that of the adult

The free-swimming, feeding sea urchin larva relies on a relatively complex immune system that supports this animal for up to two months before metamorphosis. Adult sea urchins are significantly longer lived, and the two stages are characterized by very different morphologies.

Given these dramatic differences in body structure and life history, many of the cellular and molecular immune mechanisms are likely to differ between the larva and the adult. On the cellular level, both the larva and adult have similar repertoires of immunocytes. Both stages contain highly motile phagocytes as well as granular, pigmented cells. A few differences are apparent, however. For example, there is no direct counterpart of the adult vibratile cells in the larva

127 although some of the gene products (e.g., Sp185/333) produced by these cells are expressed in the larva. In both stages, similar cellular behaviours are observed (e.g. clotting and phagocytosis).

On the molecular level, around 30% of the annotated immune genes are expressed in the larva in response to immune challenge (Chapter 2). This suggests that the remaining immune gene repertoire may play a role exclusively in the adult immune system or may be inducible in the larva by as yet undefined stimuli.

Other biphasic organisms are similarly characterized by differences in their immune systems between the larval and adult stages. For example, in the tadpole form of the amphibian

Xenopus laevis certain adult immune cell types, such as NK cells, appear to be absent (De Jesús

Andino et al. 2012). The tadpole also exhibits a distinct immune gene expression profiles which result increased susceptibility to certain types of viral infection (Grayfer et al. 2015). Further comparative analyses are required to examine fundamental differences in immune response between the two sea urchin life phases.

An ancient role for epithelial-derived IL-17 in the gut immune response

IL-17 plays a variety of key roles in mediating the vertebrate immune response. Although

IL-17 related signal molecules have been identified for more than a decade as a major mediator of inflammation, many of its functions remain unknown. This is particularly true for the vertebrate IL-17B-E family members. However, epithelial expression of IL-17B, -C, and –D has recently been implicated as a central component of the vertebrate gut innate immune response

(Ramirez-Carrozzi et al. 2011; Song et al. 2011). This work is preliminary and many questions remain open, in particular the transcriptional regulation of IL-17 as well as the downstream

128 consequences of its signaling. Given the role that these cytokines play in the vertebrate innate immune response, it is likely that this function is ancient and can be identified in other phyla.

We present here the first discovery of a role for IL-17 in an invertebrate gut-associated immune response. Although other invertebrates also express IL-17 in response to pathogenic stimulation (e.g., the Pacific oyster (S Roberts et al. 2008; Wu et al. 2013)), no other studies have directly linked homologs of this cytokine to gut immunity. Given the similar expression patterns and kinetics between S. purpuratus IL-17(I) and mammalian IL-17C, it is likely that these proteins play comparable roles within the immune system and may be subject to similar regulatory controls. The work described here provides a platform for further studies of the role of epithelial expression of IL-17 in the immune response within the context of our simple sea urchin larval model that can serve as a blueprint for subsequent studies in vertebrates.

A gene regulatory network approach to mapping the immune response

Gene regulatory networks (GRN) are multiscale, hierarchical networks that describe causal linkages in gene regulation as well as protein-protein interactions (Subramanian et al.

2015). Although this approach has largely been applied to questions of developmental biology, we here aim to generate cellular and gene expression information prerequisite to a GRN model that describes the transcriptional control of the sea urchin larval immune response. Using this framework, unexplained “gaps” within the network can identify alternative methods of immune regulation (e.g. post-transcriptional). The complexity of the immune response lends itself well to this type of analysis. Simple GRNs have been developed to describe macrophage responses to

TLR-4 stimulation (Litvak et al. 2009), B cell development (Ochiai & Maienschein-Cline 2012) and differentiation of CD4+ T cells into Th17 cells (Ciofani et al. 2012; Yosef et al. 2013). Those

129 networks are focused on the biological function or developmental subcircuits of a single immune cell and do not capture the complexity of the system-wide host immune response. In addition, data used to assemble these GRNs were obtained through in vitro experiments (Singh et al. 2014), and may not accurately reflect the complexity of information integrated into the transcriptional state in more natural physiological environments.

The morphologically simple, transparent sea urchin larva allows us to characterize and visualize immune gene expression within the context of the intact organism. The coupling of gene expression with in vivo cellular interactions provides an indication of the functional contributions of the gene as well as the network subcircuits. Our efforts to assemble a GRN for the gut larval immune response will provide a system-wide view of transcriptional changes. This analysis is significantly less challenging than in vertebrates due to the reduced numbers of immune-related transcription factor paralogs encoded in the sea urchin genome (Hibino et al.

2006). Similarities between the two phyla are evident in the transcriptional control of hematopoiesis (Solek et al. 2013), suggesting direct relevance of the sea urchin gene regulation circuitry to vertebrate biology.

Future directions

The sea urchin larva as an immune model provides a new platform for investigating the regulation of the immune system. The sea urchin larva offers reduced anatomical complexity while retaining phylogenetic relevance to vertebrates. Future identification of molecular cell markers will allow for more discrete divisions of larval immunocyte subtypes. This will expand in vivo cell tracking abilities as well as identifying additional immunocytes subtypes.

130

Further refinement of the sea urchin larva gut immune model will involve expanding the types of infections agents used to elicit an immune response. The first stage in this process is to define the larval gut microbiota to better understand the immune mechanisms involved in maintaining this microbiota and the role it plays during infection. The second stage will identify additional infectious agents (e.g., other bacterial species or viral vectors), which will allow us to modulate the inputs to examine gene changes in varying disease states. The identification of potentially different gene expression profile and cellular responses based on environmental stimuli will enhance the data available for the assembly of the immune GRN. Given the advancement in RNA-seq sequencing, transcriptomes of larvae under different infection conditions can be readily examined. The resulting data will expand the scope of our understanding of gut immunity.

Mucosal immunity associated with feeding structures are among the most ancient forms of host:microbe interactions. Elucidating these important interactions has clear consequences for improving human health. More importantly, however, this system provides invaluable insight into our basic understanding of the evolution of immune systems across animals and highlights fundamental mechanisms of immune regulation that have been conserved across phyla.

131

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Appendix A: Gene expression at the onset of feeding

Pre- feeding Feeding SPU_ID Name Annotation RPKM RPKM

Coagulation SPU_021228 Sp-Cf5/8L1 Sp-Coagulation factor 5/8-like1 1.7 3.9 SPU_022934 Sp-PlasmgnL1 Sp-Plg-like1, Sp-Plg-l1 4.3 2.2

Effector SPU_019327 Sp-185/333B3d 185/333 0.6 7.7 SPU_022179 Sp-185/333D1 185/333 0.8 11.3 Sp-Cathepsin2, Sp-CtsL-l1, Sp-CathepsinL- SPU_009042 Sp-Cts2 like1 71.3 57.9 Sp-Cathepsin5, Sp-CtsZ-l2, Sp-CathepsinZ- SPU_013893 Sp-Cts5 like2 8.5 11.3 Sp-Cathepsin10, Sp-CtsF-l1, Sp-CathepsinF- SPU_014914 Sp-Cts10 like1 4.3 5.1 Sp-Cathepsin11, Sp-CtsL-l6, Sp-CathepsinL- SPU_015668 Sp-Cts11 like6 7.4 3.9 SPU_005193 Sp-Tcp2 thioester containing protein 2 1.7 4.3 SPU_005511 Sp-Baxi1 Bax inhibitor 1 24.2 31.3 SPU_019422 Sp-Tecp2 Uncharacterized 10.5 7.8 Sp- SPU_030142 Cd59/Sca2L1 Sp-Cd59/Sca2L1 3.1 5.1 SPU_023758 Sp-Nlr188_1 NACHT and LRR containing protein 3.1 1.3 SPU_002548 Sp-MacpfB.0_1 MACPF/Perforin-like protein 2.8 6.9 SPU_002549 Sp-MacpfB.0 MACPF/Perforin-like protein 3.8 8.7 SPU_005223 Sp-MacpfA1 MACPF/Perforin-like protein 0.8 3.5 SPU_014984 Sp-MacpfA.3 Macrophafe migration inhibitory factor 2.2 3.4 SPU_015144 Sp-MacpfB.2 Macrophafe migration inhibitory factor 1.8 4.2 SPU_017952 Sp-MacpfA4 MACPF/Perforin-like protein 1.0 5.5 SPU_022091 Sp-MacpfA2 MACPF/Perforin-like protein 6.0 30.6 SPU_028756 Sp-MacpfE.2 MACPF/Perforin-like protein 1.3 8.1 SPU_012667 Sp-Saa-a Sp-Serum amyloid A-a 1.5 4.7 SPU_012668 Sp-Saa-b Sp-Serum amyloid A-b 10.1 5.8

Intercellular signaling SPU_000409 Sp-Il1r/Rs1(d) Il1R 3.4 3.3 SPU_005871 Sp-Il1r1 IL1RA; CD121A; IL1R-alpha 4.0 2.0 SPU_013950 Sp-Il1r/Rs1 IL-1 receptor 3.2 3.1 SPU_030262 Sp-185/333/E2 185/333 0.3 5.0 SPU_030264 Sp-185/333/D1 185/333 0.5 8.6 SPU_001152 Sp-Mif7 Macrophafe migration inhibitory factor 30.1 18.0 SPU_011299 Sp-Mif1 Macrophafe migration inhibitory factor 3.0 3.6 SPU_012071 Sp-MifL1 Macrophafe migration inhibitory factor 3.2 2.5

152

SPU_016226 Sp-Mif6 Macrophafe migration inhibitory factor 16.8 10.3 SPU_019323 Sp-MifL2 Macrophafe migration inhibitory factor 29.6 21.2 SPU_020035 Sp-Mif4 Macrophafe migration inhibitory factor 44.8 25.4 SPU_020036 Sp-Mif5 Macrophafe migration inhibitory factor 6.1 7.0 SPU_002792 Sp-Socs2/3 Suppressor of cytokine signaling 6.7 7.1 SPU_027840 Sp-Fkbp12 FKBP 40.2 56.2 SPU_028724 Sp-Pik2 Sp-Pelle/Irak2 4.3 3.5 SPU_009528 Sp-TnfsfL1 tumor necrosis factor superfamily like1 5.3 9.2 tumor necrosis factor receptor superfamily SPU_026216 Sp-Tnfrsf_cl2 classical2 3.4 2.9 SPU_024565 Sp-Cd109L Cd1 4.9 8.4 SPU_000742 Sp-Ube2v1/2 Uev1a 3.8 5.2 SPU_011197 Sp-IkB Sp-IkappaB, Sp-NF-kappaB Inhibitor 10.6 7.7 SPU_018598 Sp-Ubc13 UBE2N 3.7 14.4 SPU_006410 Sp-Pan2 Possible adaptor for NLRs 6.2 11.1 SPU_004610 Sp-Wap/Wap Whey acidic protein 1.4 4.8 SPU_012096 Sp-Ecsit Sp-Sitpec 3.2 1.7

Receptor SPU_022412 Sp-Nlr99 NACHT and LRR containing protein 4.6 5.4 SPU_001016 Sp-Nlr113 NACHT and LRR containing protein 5.4 4.5 SPU_002272 Sp-Nlr105 NACHT and LRR containing protein 3.5 4.0 SPU_002372 Sp-Nlr55 NACHT and LRR containing protein 4.0 3.7 SPU_003366 Sp-Nlr118 NACHT and LRR containing protein 3.2 0.6 SPU_003934 Sp-Nlr64 NACHT and LRR containing protein 4.1 3.6 SPU_004043 Sp-Nlr30 NACHT and LRR containing protein 3.7 3.4 SPU_004165 Sp-Nlr84 NACHT and LRR containing protein 5.3 5.1 SPU_005581 Sp-Nlr87 NACHT and LRR containing protein 3.5 0.6 SPU_005993 Sp-Nlr92 NACHT and LRR containing protein 4.4 4.4 SPU_006456 Sp-Nlr161 NACHT and LRR containing protein 5.1 5.0 SPU_008833 Sp-Nlr112 NACHT and LRR containing protein 4.4 3.5 SPU_009488 Sp-Nlr22 NACHT and LRR containing protein 4.5 3.9 SPU_013206 Sp-Nlr45 NACHT and LRR containing protein 4.0 0.8 SPU_014128 Sp-Nlr11 NACHT and LRR containing protein 4.0 3.5 SPU_021844 Sp-Nlr100 NACHT and LRR containing protein 9.7 0.2 SPU_022294 Sp-Nlr39 NACHT and LRR containing protein 3.7 0.9 SPU_026020 Sp-Nlr59 NACHT and LRR containing protein 3.3 2.9 SPU_026189 Sp-Nlr121 NACHT and LRR containing protein 3.5 0.6 SPU_027858 Sp-Nlr31 NACHT and LRR containing protein 4.6 0.4 SPU_028060 Sp-Nlr122 NACHT and LRR containing protein 6.1 5.5 SPU_028483 Sp-Nlr80 NACHT and LRR containing protein 3.4 3.4 SPU_028595 Sp-Nlr115 NACHT and LRR containing protein 3.4 3.2 SPU_000222 Sp-Pgrp3 Peptidoglycan recognition protein 18.0 13.8 SPU_003882 Sp-Pgrp4 Peptidoglycan recognition protein 35.9 29.8 SPU_000006 Sp-RigIL4 Sp-MDA-5 like 12, Sp-LGP2 like 12 4.0 5.2

153

SPU_000580 Sp-Srcr3 Scavenger receptor 5.3 3.0 SPU_008836 Sp-Srcr72 Scavenger receptor 129.2 73.5 SPU_009145 Sp-Srcr74 Scavenger receptor 325.8 197.5 SPU_010227 Sp-Srcr85 Scavenger receptor 60.1 25.5 SPU_010232 Sp-Srcr86 Scavenger receptor 33.1 13.3 SPU_010991 Sp-Srcr95 Scavenger receptor 5.8 5.6 SPU_010992 Sp-Srcr96 Scavenger receptor 5.8 13.4 SPU_010993 Sp-Srcr97 Scavenger receptor 5.1 6.6 SPU_011101 Sp-Srcr99 Scavenger receptor 15.7 19.7 SPU_011106 Sp-Anxn Annexin 37.4 24.1 SPU_011146 Sp-Srcr100 Scavenger receptor 28.0 13.3 SPU_013876 Sp-TlrP81 Toll-likre receptor 3.2 13.2 SPU_014672 Sp-Clect/Cub CUB, CLECT 5.4 6.2 SPU_014844 Sp-Srcr117 Scavenger receptor 18.3 14.3 SPU_015325 Sp-Srcr124 Scavenger receptor 22.6 27.8 SPU_018429 Sp-Srcr142 Scavenger receptor 54.5 23.8 SPU_018430 Sp-Srcr143 Scavenger receptor 218.5 99.7 SPU_019408 Sp-Cklf4 Chemokine-like factor (CKLF) 5.2 3.7 SPU_024010 Sp-C1qL_9 C1q 5.4 2.1 SPU_027503 Sp-Srcr221 Scavenger receptor 17.3 15.4 SPU_025460 Sp-Rnf11 RNF11 3.9 3.5 SPU_015908 Sp-Nfat Nfat 36.5 57.6

Transcription Factor CCAAT/enhancer binding protein alpha, SPU_001657 Sp-Cebpa C/EBP alpha 73.7 88.1 SPU_002603 Sp-SoxC Sox4/11/22/24 10.4 6.4 SPU_003102 Sp-Jun c-Jun like gene 146.5 125.1 SPU_006814 Sp-Hairy2/4 hairy, enhancer of split, e(spl), HES 9.6 9.2 Microphthalmia-Associated Transcription SPU_008175 Sp-Mitf Factor 4.1 4.4 SPU_008703 Sp-Xbp1 X-box binding protein 14.8 24.3 CCAAT/enhancer binding protein (C/EBP), SPU_011002 Sp-Cebpg gamma 3.6 5.8 SPU_011174 Sp-Nfe2 Nuclear factor, Erythroid 2 24.9 40.5 SPU_012203 Sp-Rel Rel-like gene 4.8 3.6 SPU_015108 Sp-Stat signal transducer and activator of transcription 3.8 3.5 SPU_015358 Sp-Egr z60 11.9 17.2 SPU_015374 Sp-Id Id-1 36.0 43.9 SPU_015712 Sp-Hey4 hairy homolog 9.1 11.4 SPU_017642 Sp-Smad2/3 Smad2/4 3.4 4.6 SPU_020124 Sp-ElfA E74 1.7 3.9 SPU_020311 Sp-Klf2/4 z85 29.6 25.3 SPU_021172 Sp-Fra2 Fosl2 18.6 31.8 SPU_021608 Sp-Hes Hes 48.3 54.7 SPU_025601 Sp-Dr_1 Dr1 4.1 1.4

154

SPU_027235 Sp-Blimp1 Krox1a 2.3 5.5 SPU_030060 Sp-Pu1 SpiB, SpiC 10.8 6.6

*SPU, genome reference ID; RPKM, reads per kilobase per million reads mapped

155

Appendix B: Gene expression in response to bacterial challenge

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change

Coagulation SPU_011257 Sp-A2m Sp-alpha-2-macroglobulin 16.3 45.0 37.0 18.9 2.76 SPU_022934 Sp-PlasmgnL1 Sp-Plg-like1, Sp-Plg-l1 71.8 67.9 33.9 30.7 2.34 SPU_021228 Sp-Cf5/8L1 Sp-Coagulation factor 5/8-like1 20.7 25.9 37.8 46.0 2.21 SPU_021229 Sp-Cf5/8L2 Sp-Coagulation factor 5/8-like2 1.6 2.0 2.2 3.4 2.14 SPU_014730 Sp-PlasmgnL2 Sp-Plg-like 2, Sp-Plg-l2 12.8 12.0 6.9 6.9 1.85 SPU_030138 Sp-TfpiL Sp-Tissue factor pathway inhibitor-like 3.9 5.2 6.8 6.0 1.76 SPU_014731 Sp-Unk_35 Unkempt family zinc finger_35 14.1 12.6 8.3 8.7 1.71 serine (or cysteine) peptidase inhibitor, SPU_018632 Sp-SerpL5 clade B (ovalbumin) 4.2 4.1 2.7 3.7 1.53 serine (or cysteine) peptidase inhibitor, SPU_004543 Sp-SerpL10 clade B (ovalbumin) 4.2 3.7 2.8 4.1 1.49 serine (or cysteine) peptidase inhibitor, SPU_024263 Sp-SerpL9 clade B (ovalbumin) 3.0 3.6 2.5 3.1 1.48 serine (or cysteine) peptidase inhibitor, SPU_013378 Sp-SerpL4 clade B (ovalbumin) 6.6 6.1 4.7 6.1 1.42 serine (or cysteine) peptidase inhibitor, SPU_018631 Sp-SerpL6 clade B (ovalbumin) 3.5 3.2 2.6 3.0 1.38 SPU_021526 Sp-Ams amassin 11.1 12.8 11.9 14.3 1.29 serine (or cysteine) peptidase inhibitor, SPU_018196 Sp-SerpL8 clade B (ovalbumin) 5.0 4.7 3.9 4.8 1.28 serine (or cysteine) peptidase inhibitor, SPU_013377 Sp-SerpL3 clade B (ovalbumin) 9.2 9.0 7.5 9.5 1.26

Effector SPU_022178 Sp-185/333E2 185/333 0.4 1.1 1.0 5.0 12.11 SPU_022179 Sp-185/333D1 185/333 14.6 21.2 20.9 115.6 7.93 SPU_019327 Sp-185/333B3d 185/333 11.4 16.9 16.5 84.1 7.36 SPU_028756 Sp-MacpfE.2 MACPF/Perforin-like protein 5.3 5.0 2.2 1.7 3.06 SPU_007159 Sp-MacpfE1 MACPF/Perforin-like protein 2.8 3.1 1.1 1.1 2.90 Sp-Cathepsin2, Sp-CtsL-l1, Sp- SPU_009042 Sp-Cts2 CathepsinL-like1 282.1 574.3 781.6 585.2 2.77 SPU_006406 Sp-Tecp1 none 7.6 18.1 14.5 7.7 2.38 SPU_005182 Sp-064 Sp-C3 24.5 29.3 43.6 56.1 2.29 SPU_012439 Sp-064_1 Sp-C3 13.9 18.2 30.9 30.3 2.23 Sp-Cathepsin13, Sp-CtsL-l8, Sp- SPU_020838 Sp-Cts13 CathepsinL-like8 2.2 3.1 3.2 4.8 2.16 SPU_028187 Sp-Fb2 SpBf-2 4.8 7.4 7.7 10.3 2.15 SPU_028188 Sp-Fb SpBf 9.0 14.4 13.6 18.4 2.03 SPU_023758 Sp-Nlr188_1 NACHT and LRR containing protein 6.1 10.2 11.0 12.3 2.01 SPU_018503 Sp-064_2 Sp-C3 10.4 12.4 16.7 20.4 1.96 SPU_017239 Sp-064_3 Sp-C3 30.4 33.5 43.8 59.0 1.94 SPU_009091 Sp-Fb3 SpBf-3 3.8 5.3 5.0 7.1 1.84 SPU_002548 Sp-MacpfB.0_1 MACPF/Perforin-like protein 2.6 4.5 4.5 4.2 1.71 SPU_023759 Sp-Nlr188 NACHT and LRR containing protein 7.1 8.6 11.4 11.7 1.66

156

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change

CD59 molecule, complement regulatory SPU_030142 Sp-Cd59/Sca2L1 protein 32.8 40.6 53.9 52.4 1.64 Sp-Cathepsin10, Sp-CtsF-l1, Sp- SPU_014914 Sp-Cts10 CathepsinF-like1 31.0 35.0 35.1 50.2 1.62 SPU_005193 Sp-Tcp2 thioester containing protein 2 12.4 11.4 14.1 18.1 1.58 SPU_005511 Sp-Baxi1 Bax inhibitor 1 44.7 53.0 58.5 69.9 1.56 SPU_014984 Sp-MacpfA.3 MACPF/Perforin-like protein 15.4 17.2 15.0 23.2 1.55 SPU_012667 Sp-Saa-a Sp-Serum amyloid A-a 2.8 3.1 4.3 3.1 1.53 SPU_023794 Sp-Wap Whey acidic protein 3.6 3.7 4.5 5.4 1.52 SPU_002549 Sp-MacpfB.0 MACPF/Perforin-like protein 4.0 5.9 4.2 5.5 1.49 SPU_005205 Sp-Psrl phosphatidyl serine receptor 5.5 5.0 3.8 5.6 1.47 SPU_005223 Sp-MacpfA1 MACPF/Perforin-like protein 31.4 35.7 31.7 46.1 1.47 SPU_022091 Sp-MacpfA2 MACPF/Perforin-like protein 125.2 133.7 115.6 168.0 1.45 SPU_017952 Sp-MacpfA4 MACPF/Perforin-like protein 37.5 40.8 38.0 53.2 1.42 SPU_006587 Sp-Nlr164 NACHT and LRR containing protein 4.7 4.9 5.9 6.6 1.42 Sp-Cathepsin4, Sp-CtsZ-l1, Sp- SPU_009601 Sp-Cts4 CathepsinZ-like1 15.7 18.1 18.4 22.2 1.42 Sp-Cathepsin5, Sp-CtsZ-l2, Sp- SPU_013893 Sp-Cts5 CathepsinZ-like2 18.4 25.5 23.3 25.2 1.39 SPU_006601 Sp-PanApple/Clect none 6.0 6.3 6.6 8.3 1.39 SPU_014229 Sp-MacpfF.1 MACPF/Perforin-like protein 2.6 2.6 2.7 3.5 1.37 SPU_019422 Sp-Tecp2 Tecp 41.7 42.6 34.5 31.5 1.35 transforming growth factor, beta receptor SPU_019168 Sp-Tgfbrap1_1 associated protein 1-1 3.5 3.5 3.2 4.0 1.24 SPU_022988 Sp-Tcp1 thioester containing protein 1 21.8 20.2 23.7 24.0 1.19 Sp-Cathepsin11, Sp-CtsL-l6, Sp- SPU_015668 Sp-Cts11 CathepsinL-like6 13.9 13.3 12.0 14.1 1.17 SPU_000997 Sp-C3-2 Complement factor 19.2 19.2 18.3 19.1 1.05

Intercellular signaling SPU_012844 Sp-Il17-2 IL-17 0.2 18.1 4.1 4.1 96.27 SPU_019349 Sp-Il17-4 IL-17 0.1 14.0 3.5 3.6 95.79 SPU_019350 Sp-Il17-5 IL-17 0.2 17.0 4.0 4.0 90.87 SPU_019351 Sp-Il17-6 IL-17 0.2 17.3 3.9 3.9 80.76 SPU_009527 Sp-TnfsfL2 tumor necrosis factor superfamily like2 0.2 1.2 3.6 3.2 17.20 SPU_003912 Sp-Il1ap_1 IL-1 associated protein 2.0 10.3 13.6 16.5 8.19 SPU_030264 Sp-185/333/D1 185/333 11.7 16.7 16.2 88.5 7.55 SPU_009528 Sp-TnfsfL1 tumor necrosis factor superfamily like1 16.2 23.8 62.1 118.0 7.30 tumor necrosis factor receptor superfamily SPU_026216 Sp-Tnfrsf_cl2 classical2 4.0 22.0 22.8 19.2 5.76 SPU_030262 Sp-185/333/E2 185/333 4.4 5.4 5.2 24.3 5.54 SPU_013950 Sp-Il1r/Rs1 IL-1 receptor 2.7 12.6 14.8 10.4 5.50 SPU_000409 Sp-Il1r/Rs1(d) IL-1R 3.0 13.7 15.8 11.5 5.19 SPU_028724 Sp-Pik2 Sp-Pelle/Irak2 5.7 17.7 23.7 27.5 4.81 SPU_003911 Sp-Il1ap IL-1 associated protein 1.6 6.1 7.1 7.3 4.63 SPU_000073 Sp-Pik1 Sp-Pelle/Irak1 3.7 10.6 15.3 12.3 4.09

157

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change

SPU_020740 Sp-Eda2rL1 ectodysplasin A2 receptor 3.7 7.8 10.2 14.9 4.04 SPU_011298 Sp-Socs6L Socs6 4.1 8.0 13.8 16.2 3.94 SPU_030141 Sp-IL17rL Sp-IL17R-like,Sp-IL17RA-like 1.2 1.5 2.3 4.8 3.89 SPU_011299 Sp-Mif1 Macrophafe migration inhibitory factor 20.4 12.7 11.9 6.1 3.37 Spleen tyrosine kinase, zeta-chain (TCR) SPU_006988 Sp-Syk/Zap70 associated protein kinase 70kDa, Srk 4.8 6.1 11.1 13.7 2.87 Non-catalytic region of tyrosoine kinase SPU_014752 Sp-Nck adaptor protein 3.9 9.9 10.9 9.2 2.79 SPU_001152 Sp-Mif7 Macrophafe migration inhibitory factor 62.8 50.9 29.4 26.7 2.35 SPU_016226 Sp-Mif6 Macrophafe migration inhibitory factor 19.0 21.1 14.6 9.2 2.28 SPU_002792 Sp-Socs2/3 Socs2 50.0 106.9 75.3 77.2 2.14 SPU_005871 Sp-Il1r1 IL1RA; CD121A; IL1R-alpha 5.2 8.6 9.3 8.4 1.80 SPU_012071 Sp-MifL1 Macrophafe migration inhibitory factor 5.0 4.4 3.9 6.8 1.75 SPU_010264 Sp-Hyr Hyr 9.6 10.9 10.4 16.3 1.70 SPU_013810 Sp-Ptpn6/11 SHP-2, PTP1D, PTP2C 9.7 11.0 13.2 16.2 1.67 SPU_004517 Sp-Pellino Pellino protein 4.6 7.1 7.1 7.5 1.63 SPU_007964 Sp-Pias1 GBP; DDXBP1; GU/RH-II 3.7 4.4 4.9 5.5 1.50 SPU_019323 Sp-MifL2 Macrophafe migration inhibitory factor 17.9 18.2 12.6 13.1 1.44 SPU_026496 Sp-Socs4/5 Socs4 5.9 8.5 8.2 7.0 1.43 SPU_020036 Sp-Mif5 Macrophafe migration inhibitory factor 17.9 17.7 19.5 24.7 1.40 SPU_027840 Sp-Fkbp12 Peptidyl-prolyl isomerase FKBP12 196.8 190.3 174.0 145.3 1.35 SPU_020035 Sp-Mif4 Macrophafe migration inhibitory factor 22.0 27.5 24.6 21.0 1.31 tumor nectosis factor receptor superfamily SPU_010230 Sp-TnfrsfL1 like1 4.1 3.2 4.0 4.2 1.30 Fas (TNFRSF6)-associated via death SPU_010777 Sp-Fadd domain 6.8 8.0 8.7 8.3 1.28 SPU_020070 Sp-Sos Sp-Son of sevenless 5.4 5.6 6.2 6.8 1.28 SPU_030148 Sp-Vegf3 Vascular endothelial growth factor 3 3.0 2.7 3.2 3.0 1.19 SPU_028898 Sp-Traf6 Traf6 4.5 7.2 13.5 22.3 5.01 SPU_026495 Sp-Traf3 LAP1; CAP-1; CRAF1 2.5 5.6 8.9 8.6 3.56 SPU_011197 Sp-IkB Sp-IkappaB, Sp-NF-kappaB Inhibitor 15.6 37.2 37.4 45.0 2.89 SPU_007342 Sp-Myd88_1 MyD88 5.2 3.9 7.6 11.2 2.85 SPU_020131 Sp-Tirc7 Sp-TIR-containing 7 2.4 3.1 5.2 6.5 2.75 SPU_007343 Sp-Myd88 MyD88 8.2 7.8 10.7 20.1 2.58 SPU_021673 Sp-Tollip Toll interacting protein 6.2 8.1 10.8 13.3 2.14 Toll-interleukine I receptor interacting SPU_026252 Sp-Tollip_1 protein I 6.8 8.8 12.2 14.3 2.10 SPU_014926 Sp-Tirc3 Sp-TIR-containing 3 6.3 7.2 13.1 11.1 2.08 SPU_027640 Sp-Sarmr8 Sp-Sarm-related 8 3.3 3.4 2.7 5.4 2.02 SPU_003955 Sp-Tab2/3 Map3k7ip2/3 4.3 6.3 7.4 8.4 1.95 SPU_007952 Sp-Tirc2_1 Sp-TIR-containing 2 10.8 16.5 17.9 20.9 1.93 SPU_013299 Sp-Tirc2 Sp-TIR-containing 2 10.8 16.1 17.6 20.5 1.89 SPU_006410 Sp-Pan2 Possible adaptor for NLRs 8.8 6.5 8.9 12.1 1.86 SPU_023069 Sp-TrafB none 12.8 11.7 15.1 21.5 1.84

158

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change

SPU_024565 Sp-Cd109L cd109 68.6 115.6 125.0 100.5 1.82 SPU_011042 Sp-Sarm Sarm1 2.1 2.8 2.6 3.7 1.78 SPU_004107 Sp-Sarmr12 Sp-Sarm-related 12 1.9 3.2 3.1 2.8 1.71 SPU_004610 Sp-Wap/Wap Whey acidic protein 8.9 10.8 10.0 15.0 1.68 myeloid differentiation primary response SPU_022707 Sp-Myd88L2 gene (88) 4.9 3.2 3.0 4.3 1.62 SPU_018168 Sp-Sarmr9 Sp-Sarm-related 9 3.4 2.8 2.9 4.5 1.58 SPU_000764 Sp-Sarmr4 Sp-Sarm-related 4 4.7 4.7 4.6 7.2 1.55 SPU_002696 Sp-Tak1 TAK1; Mkkk7 4.8 5.6 6.2 7.3 1.52 SPU_008332 Sp-Traf4 CART1 3.4 3.3 3.5 5.0 1.50 SPU_018598 Sp-Ubc13 UBE2N 98.6 83.6 66.1 73.1 1.49 SPU_005254 Sp-Tab1 Map3k7ip1 7.0 6.8 7.8 10.0 1.47 SPU_021599 Sp-Ptpr1 Sp-PTPR-G/Z 5.7 6.9 7.3 8.3 1.44 SPU_000742 Sp-Ube2v1/2 Uev1a 18.9 19.7 18.0 24.5 1.36 SPU_003495 Sp-Sarmr10 Sp-Sarm-related 10 3.9 3.8 3.6 4.9 1.35 SPU_003608 Sp-Tirc5 Sp-TIR-containing 5 10.0 8.5 9.1 11.0 1.30 SPU_012096 Sp-Ecsit Sp-Sitpec 17.2 16.0 13.8 17.5 1.27 SPU_004557 Sp-Sarmr11 Sp-Sarm-related 11 3.2 3.1 3.3 3.8 1.22 SPU_008302 Sp-Sarmr13 Sp-Sarm-related 13 3.4 3.9 3.3 3.5 1.17

Receptor SPU_012713 Sp-Nlr167 NACHT and LRR containing protein 0.5 1.0 1.7 3.6 7.57 SPU_022145 Sp-Srcr176 Scavenger receptor 2.8 1.5 3.2 7.7 5.31 SPU_024709 Sp-Nlr173 NACHT and LRR containing protein 0.9 1.5 2.2 3.9 4.14 SPU_000222 Sp-Pgrp3 Peptidoglycan recognition protein 6.0 7.1 10.8 24.8 4.11 SPU_010994 Sp-Srcr98 Scavenger receptor 2.6 3.0 3.2 9.6 3.66 SPU_010232 Sp-Srcr86 Scavenger receptor 11.3 24.5 40.0 33.3 3.53 SPU_003882 Sp-Pgrp4 Peptidoglycan recognition protein 15.8 17.3 23.4 55.6 3.52 SPU_018429 Sp-Srcr142 Scavenger receptor 20.1 45.8 58.2 52.9 2.89 SPU_006721 Sp-Lrr1_1 Leucine rich repeat protein 1_1 4.0 5.9 10.2 11.5 2.86 SPU_011146 Sp-Srcr100 Scavenger receptor 9.5 21.7 26.7 25.4 2.81 SPU_010227 Sp-Srcr85 Scavenger receptor 20.8 46.9 58.1 55.8 2.79 SPU_018430 Sp-Srcr143 Scavenger receptor 89.1 193.5 245.4 219.5 2.75 Cbl proto-oncogene, E3 ubiquitin proteoin SPU_007863 Sp-Cbl ligase 6.2 8.5 11.6 16.9 2.72 SPU_028631 Sp-Nlr107_1 NACHT and LRR containing protein 2.3 4.1 4.2 6.3 2.69 SPU_025460 Sp-Rnf11 Ring finger protein 11 11.2 13.6 19.1 29.5 2.64 SPU_018508 Sp-Srcr144 Scavenger receptor 22.0 41.6 57.4 29.7 2.61 SPU_002028 Sp-Srcr21 Scavenger receptor 5.5 4.2 2.1 4.5 2.54 SPU_025922 Sp-A2m_1 alpha-2-macroglobulin-1 24.3 60.4 49.4 25.8 2.49 SPU_015937 Sp-Srcr128 Scavenger receptor 9.2 14.5 21.0 18.9 2.30 SPU_016374 Sp-Srcr134 Scavenger receptor 5.4 8.9 12.0 9.1 2.24 SPU_010409 Sp-Srcr89 Scavenger receptor 7.0 5.4 3.2 6.0 2.22

159

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change SPU_000580 Sp-Srcr3 Scavenger receptor 3.1 4.7 5.6 6.8 2.21 SPU_018211 Sp-Tlr092 Toll-like receptor 2.7 2.9 3.8 6.0 2.19 SPU_010062 Sp-Srcr83 Scavenger receptor 2.0 3.4 4.3 3.1 2.17 SPU_002350 Sp-Srcr23 Scavenger receptor 5.2 4.0 3.6 2.4 2.16 SPU_026020 Sp-Nlr59 NACHT and LRR containing protein 7.4 11.3 12.9 16.0 2.15 SPU_001423 Sp-Nlr200 NACHT and LRR containing protein 3.9 4.7 5.9 8.3 2.14 SPU_016373 Sp-Srcr133 Scavenger receptor 4.7 7.3 9.9 7.1 2.10 SPU_013470 Sp-Tlr071 Toll-like receptor 2.7 3.8 4.9 5.7 2.08 SPU_027037 Sp-Srcr217 Scavenger receptor 2.1 2.9 2.5 4.4 2.07 SPU_028433 Sp-Nlr129 NACHT and LRR containing protein 2.0 3.1 4.2 4.0 2.07 SPU_001863 Sp-Srcr20 Scavenger receptor 5.1 7.7 10.5 7.9 2.05 SPU_022130 Sp-Nlr70 NACHT and LRR containing protein 6.6 9.1 10.1 13.5 2.05 SPU_007862 Sp-Cblr1 Sp-Cbl-related1 3.4 3.8 5.9 6.7 2.01 SPU_019586 Sp-Lmo4 Lmo 7.1 8.1 12.5 14.2 2.01 SPU_000015 Sp-Nlr94 NACHT and LRR containing protein 7.7 10.1 12.4 15.4 2.00 SPU_019696 Sp-Nlr95 NACHT and LRR containing protein 6.9 8.9 10.8 13.7 2.00 SPU_007620 Sp-Nlr187 NACHT and LRR containing protein 7.1 9.0 11.8 14.1 1.98 SPU_014761 Sp-Nlr111 NACHT and LRR containing protein 2.1 2.3 2.9 4.1 1.98 SPU_001016 Sp-Nlr113 NACHT and LRR containing protein 13.1 17.5 21.7 25.7 1.96 SPU_024439 Sp-Sirpb/GL1 Signal regulatory protein beta/glossy 1 2.3 3.5 4.4 4.6 1.96 SPU_004043 Sp-Nlr30 NACHT and LRR containing protein 12.0 16.4 20.3 23.4 1.95 SPU_019408 Sp-Cklf4 Chemokine-like factor (CKLF) 8.6 6.3 12.3 11.3 1.95 SPU_002372 Sp-Nlr55 NACHT and LRR containing protein 10.2 13.9 16.9 19.7 1.94 SPU_028595 Sp-Nlr115 NACHT and LRR containing protein 11.7 16.4 19.0 22.6 1.93 SPU_023628 Sp-Nlr23 NACHT and LRR containing protein 5.0 6.2 7.5 9.7 1.93 SPU_000816 Sp-Nlr81 NACHT and LRR containing protein 8.1 11.8 15.1 15.7 1.93 SPU_007349 Sp-Srcr39 Scavenger receptor 8.1 5.7 4.2 5.9 1.92 SPU_001107 Sp-Igv Immunoglobulin 2.8 3.7 4.6 5.3 1.92 SPU_004872 Sp-Nlr19 NACHT and LRR containing protein 5.3 7.0 10.1 10.1 1.92 SPU_022412 Sp-Nlr99 NACHT and LRR containing protein 15.2 20.3 27.8 29.1 1.92 SPU_013709 Sp-Vc1_1 Vc1, CD57 2.6 3.4 3.3 5.0 1.91 SPU_017445 Sp-ImcvL Sp-IPS-1/MAVS/Cardif/VISA like 2.5 2.9 4.4 4.8 1.91 SPU_015481 Sp-Nlr85 NACHT and LRR containing protein 5.5 7.2 8.6 10.4 1.90 SPU_003934 Sp-Nlr64 NACHT and LRR contaning protein 10.9 15.4 18.2 20.7 1.90 SPU_008833 Sp-Nlr112 NACHT and LRR containing protein 11.9 16.7 20.0 22.2 1.87 SPU_008267 Sp-Tlr044 Toll-like receptor 2.2 2.3 3.4 4.2 1.87 SPU_009488 Sp-Nlr22 NACHT and LRR containing protein 15.3 21.0 24.2 28.6 1.86 SPU_000896 Sp-Nlr28 NACHT and LRR containing protein 7.2 9.3 11.5 13.5 1.86 SPU_020240 Sp-Nlr110 NACHT and LRR containing protein 4.6 5.9 7.1 8.6 1.86 SPU_028060 Sp-Nlr122 NACHT and LRR containing protein 15.9 22.5 27.0 29.5 1.85 SPU_004165 Sp-Nlr84 NACHT and LRR containing protein 16.0 21.9 26.1 29.5 1.85 SPU_005993 Sp-Nlr92 NACHT and LRR containing protein 12.2 17.6 21.5 22.6 1.85 SPU_014128 Sp-Nlr11 NACHT and LRR containing protein 10.9 14.8 17.7 20.0 1.84

160

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change

transforming growth factor beta receptor SPU_027380 Sp-Tgfbr3 III, betaglycan 1.7 2.3 3.0 3.1 1.84 SPU_015789 Sp-TlrP16 Toll-like receptor 21.7 16.5 11.9 19.2 1.83 SPU_009354 Sp-Srcr75 Scavenger receptor 4.6 6.4 8.4 6.6 1.83 SPU_007113 Sp-Nlr98 NACHT and LRR containing protein 4.0 4.8 5.6 7.2 1.82 SPU_019700 Sp-Nlr77 NACHT and LRR containing protein 7.3 8.8 10.8 13.2 1.82 SPU_002272 Sp-Nlr105 NACHT and LRR containing protein 11.6 14.9 18.2 21.0 1.81 SPU_006456 Sp-Nlr161 NACHT and LRR containing protein 15.2 22.0 25.9 27.4 1.80 SPU_000852 Sp-Nlr71 NACHT and LRR containing protein 6.2 7.8 9.2 11.1 1.80 SPU_001630 Sp-Nlr166 NACHT and LRR containing protein 7.7 9.5 11.6 13.8 1.79 SPU_014672 Sp-Clect/Cub CUB, CLECT 9.4 12.2 16.8 13.0 1.79 SPU_028483 Sp-Nlr80 NACHT and LRR containing protein 7.5 10.1 12.1 13.5 1.79 SPU_001444 Sp-Nlr78 NACHT and LRR containing protein 5.4 6.0 7.8 9.7 1.79 SPU_026304 Sp-Nlr180 NACHT and LRR containing protein 8.2 9.8 13.3 14.7 1.78 SPU_004104 Sp-Wap/Ig Whey acidic protein 4.4 4.8 3.3 5.9 1.78 SPU_008382 Sp-Nlr21 NACHT and LRR containing protein 5.9 7.0 8.5 10.4 1.78 SPU_014095 Sp-Srcr114 Scavenger receptor 5.7 8.3 10.1 7.6 1.77 SPU_022023 Sp-Jak_1 Jak 4.1 5.0 7.1 6.0 1.76 SPU_024487 Sp-Srcr206 Scavenger receptor 5.8 7.8 6.2 10.2 1.75 SPU_005581 Sp-Nlr87 NACHT and LRR containing protein 3.7 4.4 4.9 6.4 1.73 SPU_017245 Sp-Nlr73 NACHT and LRR containing protein 4.7 4.9 6.6 8.1 1.73 SPU_023548 Sp-Fic Fic 4.9 4.3 3.4 2.8 1.73 SPU_009161 Sp-Nlr125_1 NACHT and LRR containing protein 4.1 3.0 2.4 3.4 1.72 SPU_006529 Sp-Gnbp1/2/3B Gram negative binding protein 3.2 3.9 2.2 3.2 1.72 SPU_016532 Sp-Nlr178 NACHT and LRR containing protein 7.1 8.2 8.8 12.1 1.71 SPU_028630 Sp-Nlr107 NACHT and LRR containing protein 5.6 7.6 7.8 9.6 1.71 SPU_020082 Sp-Jak Jak 2.9 3.8 4.9 4.6 1.70 SPU_024390 Sp-Srcr203 Scavenger receptor 4.3 3.8 2.6 4.1 1.70 SPU_022294 Sp-Nlr39 NACHT and LRR containing protein 3.3 4.2 4.3 5.5 1.68 SPU_017453 Sp-Srcr139 Scavenger receptor 8.7 10.0 10.5 14.5 1.66 SPU_019699 Sp-Nlr82 NACHT and LRR containing protein 9.9 10.9 13.6 16.3 1.64 SPU_000006 Sp-RigIL4 Sp-MDA-5 like 12, Sp-LGP2 like 12 8.9 6.7 5.4 7.4 1.64 SPU_022373 Sp-Triad FHIT 2.7 2.4 3.3 3.9 1.61 SPU_014503 Sp-Nlr74 NACHT and LRR containing protein 9.5 11.0 13.1 15.1 1.59 SPU_021370 Sp-Nlr186 NACHT and LRR containing protein 3.9 4.5 5.7 6.1 1.59 SPU_026189 Sp-Nlr121 NACHT and LRR containing protein 2.2 2.5 3.0 3.4 1.58 SPU_007946 Sp-Pgrp2 Peptidoglycan recognition protein 2.2 2.1 2.6 3.3 1.58 SPU_011106 Sp-Anxn Annexin 56.4 52.2 40.0 35.6 1.58 SPU_010991 Sp-Srcr95 Scavenger receptor 22.8 25.9 36.1 25.3 1.58 SPU_015938 Sp-Srcr129 Scavenger receptor 6.5 4.1 5.1 6.2 1.56 SPU_004101 Sp-Srcr36 Scavenger receptor 2.0 2.5 3.0 2.5 1.55 SPU_008504 Sp-Srcr68 Scavenger receptor 2.6 4.1 3.3 4.0 1.55

161

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change

SPU_013206 Sp-Nlr45 NACHT and LRR containing protein 3.5 4.1 4.5 5.4 1.53 SPU_009145 Sp-Srcr74 Scavenger receptor 259.1 242.5 169.7 221.2 1.53 SPU_003366 Sp-Nlr118 NACHT and LRR containing protein 2.0 2.5 2.2 3.0 1.51 SPU_010992 Sp-Srcr96 Scavenger receptor 32.3 39.2 48.6 47.0 1.51 SPU_010993 Sp-Srcr97 Scavenger receptor 30.7 35.2 36.5 46.1 1.50 SPU_006530 Sp-Gnbp1/2/3B_1 Gram negative binding protein 4.8 4.4 3.8 3.2 1.50 SPU_000441 Sp-Igcam/Igcam Immunoglobulin 12.5 12.9 10.4 8.7 1.47 SPU_024501 Sp-Tlr217 Toll-like receptor 3.4 3.6 5.0 4.5 1.47 SPU_008836 Sp-Srcr72 Scavenger receptor 160.6 165.4 118.1 112.4 1.47 SPU_022423 Sp-Srcr191 Scavenger receptor 3.4 3.9 3.5 4.9 1.45 similar to Golgi apparatus protein 1 SPU_008505 Sp-Cys_rich_fgfr precursor 13.5 16.5 18.5 19.2 1.42 SPU_022424 Sp-Srcr192 Scavenger receptor 4.3 5.8 5.1 6.0 1.40 SPU_027503 Sp-Srcr221 Scavenger receptor 23.1 24.3 17.4 19.1 1.40 SPU_026241 Sp-Srcr212 Scavenger receptor 10.6 9.8 12.3 13.7 1.39 SPU_018380 Sp-Tlr200 Toll-like receptor 2.4 2.3 3.2 3.2 1.39 SPU_013876 Sp-TlrP81 Toll-like receptor 14.9 12.9 12.4 10.7 1.39 SPU_003296 Sp-Snf7/cytda Sp-SNF7/cyctidine_deaminase 13.0 16.5 17.8 16.7 1.38 similar to Golgi apparatus protein 1 SPU_018130 Sp-Cys_rich_fgfr_2 precursor 9.1 10.4 12.4 12.2 1.35 SPU_015325 Sp-Srcr124 Scavenger receptor 59.8 52.5 45.6 45.1 1.32 SPU_009220 Sp-Srcr184 Scavenger receptor 2.8 3.7 3.7 3.4 1.32 SPU_015123 Sp-Srcr123 Scavenger receptor 2.9 3.3 3.9 3.7 1.32 SPU_009954 Sp-Tsp1 Tumor suppressor region 1 8.0 9.7 8.8 10.5 1.32 SPU_014844 Sp-Srcr117 Scavenger receptor 54.4 57.4 47.3 43.9 1.31 SPU_011101 Sp-Srcr99 Scavenger receptor 37.0 44.4 34.1 35.2 1.30 SPU_014602 Sp-Srcr115 Scavenger receptor 7.8 8.9 10.1 9.5 1.30 similar to Golgi apparatus protein 1 SPU_019782 Sp-Cys_rich_fgfr_3 precursor 9.4 10.2 11.8 12.1 1.29 SPU_005420 Sp-Srcr42 Scavenger receptor 9.2 10.9 11.5 11.3 1.25 SPU_022151 Sp-Srcr182 Scavenger receptor 6.5 7.5 6.3 6.4 1.20 IFN-gamma-inducible-lysosomal thiol SPU_016423 Sp-Giltl reductase 6.8 7.0 7.9 8.0 1.18

Recombination SPU_012247 Sp-Tsp1_1 Tumor suppressor region 1_1 2.7 5.1 5.5 8.0 3.01 SPU_009817 Sp-Wap/Wap_1 Whey acidic protein 5.2 6.6 7.0 8.5 1.63 SPU_015529 Sp-Dna_pkcs DNA activated protein kinase 3.7 3.0 2.5 2.8 1.47 X-ray repair complementing defective SPU_024517 Sp-Xrcc4 repair in Chinese hamster cells4 8.5 10.6 10.7 8.1 1.32 SPU_012239 Sp-Cern Cern 7.0 7.1 7.4 8.3 1.17 terminal deoxyribonucleotidyltransferase- SPU_009980 Sp-TdTl/Pol/Mu like 4.7 4.4 4.2 4.5 1.10

Transcription Factor

162

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change SPU_030060 Sp-Pu1 SpiB, SpiC 7.0 18.2 33.1 99.5 14.20 SPU_015358 Sp-Egr z60 71.7 9.3 8.2 11.4 8.73 CCAAT/enhancer binding protein alpha, SPU_001657 Sp-Cebpa C/EBP alpha 146.4 413.3 689.9 317.6 4.71 SPU_020311 Sp-Klf2/4 z85 141.9 68.8 38.3 33.3 4.26 CCAAT/enhancer binding protein SPU_011002 Sp-Cebpg (C/EBP), gamma 13.9 19.3 56.5 24.0 4.06 SPU_008177 Sp-Nfkb Nf-kb 10.0 27.6 25.8 37.3 3.72 SPU_028479 Sp-Tel Yan 1.5 2.5 2.7 5.2 3.41 SPU_011190 Sp-Stam Sp-Signal transducer adaptor molecule 7.7 16.1 24.4 17.8 3.17 SPU_012203 Sp-Rel Rel 16.5 27.7 50.9 52.1 3.15 SPU_026877 Sp-Irf4 Interferon regulatory factr 4 2.4 5.8 1.9 2.8 3.03 SPU_026905 Sp-Atf2 Activating transcription factor 2 10.4 29.4 30.2 20.3 2.91 SPU_021172 Sp-Fra2 Fra2p 77.1 41.0 44.1 30.2 2.55 SPU_008703 Sp-Xbp1 X-Box binding protein 229.2 428.5 579.8 431.7 2.53 SPU_010404 Sp-Irf Interferon regulatory factor 2.9 5.3 4.4 2.2 2.43 SPU_018366 Sp-Rara Rar-a receptor 1.6 1.7 1.7 3.5 2.19 SPU_025612 Sp-Runt1_1 SpRunt-1, SpRunt 11.5 24.4 21.8 15.1 2.12 SPU_006917 Sp-Runt1 SpRunt-1, SpRunt 10.3 21.5 19.0 13.5 2.08 SPU_015361 Sp-Unk_86 Unkempt family zinc finger_86 3.6 4.0 7.2 6.7 2.02 SPU_020124 Sp-ElfA E74 13.0 9.7 12.0 19.5 2.01 SPU_013178 Sp-Nr1m3 Grin1 26.4 32.0 20.3 16.0 2.00 SPU_027015 Sp-GataC Gata1/2/3 2.9 5.6 3.8 4.4 1.94 Microphthalmia-associated transcription SPU_008175 Sp-Mitf factor 19.0 34.1 32.1 29.9 1.79 SPU_021557 Sp-Gabp Gamma-aminobutyrate transporter 3.4 5.7 5.9 6.1 1.78 SPU_003102 Sp-Jun c-Jun 202.6 255.1 335.7 189.2 1.77 SPU_009262 Sp-Oct1-2 Pou2F 2.0 2.7 3.3 3.6 1.77 SPU_009876 Sp-FoxP Forkhead box P 5.7 7.2 7.2 9.7 1.68 SPU_003704 Sp-Lef1 Tcf 3.2 4.1 4.6 5.2 1.62 SPU_002603 Sp-SoxC Sox4/11/22/24 19.5 29.6 30.4 31.3 1.61 SPU_016343 Sp-E12 Transcription regulator 9.7 11.0 12.3 15.5 1.60 SPU_011174 Sp-Nfe2 Nuclear factor, Erythroid 2 73.4 109.1 109.4 102.6 1.49 SPU_021608 Sp-Hes Hairy and enhancer of split 63.7 67.1 82.9 56.4 1.47 Sp- SPU_009319 Tryp_SPc/LDL/Cub Tryp 8.4 9.1 11.1 12.0 1.43 SPU_005718 Sp-Dri_2 Deadringer-like 2 4.8 5.8 6.0 6.8 1.43 SPU_015908 Sp-Nfat Nfat 162.9 204.7 188.9 227.4 1.40 SPU_020722 Sp-Smad1/5/8_1 Smad1 13.6 18.5 16.5 17.6 1.36 SPU_000861 Sp-Myb SpMyb, Myb-like transcription factor 9.6 12.4 10.4 9.2 1.36 SPU_027235 Sp-Blimp1 Krox1a 5.0 6.3 5.0 6.7 1.35 signal transducer and activator of SPU_015108 Sp-Stat transcription 51.5 67.3 68.4 57.6 1.33 SPU_015712 Sp-Hey4 hairy homolog 27.4 31.3 36.1 34.6 1.32 SPU_017642 Sp-Smad2/3 Smad2 21.5 18.2 20.7 23.9 1.31

163

RPKM 0 6 12 24 Fold SPU_ID Name Annotation HOI HOI HOI HOI Change

similar to Golgi apparatus protein 1 SPU_009922 Sp-Cys_rich_fgfr_1 precursor 16.5 17.1 17.5 21.4 1.29 SPU_028093 Sp-Scl Scl 3.3 3.7 4.2 4.3 1.28 SPU_004287 Sp-Smad4 Smad4 6.7 6.4 6.6 8.2 1.28 SPU_020123 Sp-ElfB E74 10.9 10.6 9.4 12.0 1.27 SPU_015374 Sp-Id Id 38.6 41.0 39.4 48.5 1.26 SPU_018483 Sp-Erg Fli 6.3 7.3 7.3 7.4 1.17 SPU_002874 Sp-Ets1/2 Ets1 7.2 7.2 6.2 6.9 1.16 SPU_014539 Sp-Pax2-5-8 Pax2 5.2 4.8 4.8 4.9 1.09 SPU_006814 Sp-Hairy2/4 hairy, enhancer of split, e(spl), HES 15.2 16.5 15.8 15.3 1.08 SPU_025601 Sp-Dr_1 Drop_1 17.9 16.7 18.0 17.0 1.08

*SPU, genome reference ID; RPKM, reads per kilobase per million reads mapped; Fold change is a measure of gene expression changes based on the ratio of the highest and the lowest RPKM among the four time points of each gene.

164

Appendix C: Oligonucleotide sequences used for cloning

Primer Name Sequence

Chapter 2 In-situ probes SpMACPFA4 Forward CGACAACCTTTCATGGGACT SpMACPFA4 reverse GGTCCATTTTGATTGCTCGT SpMACPFE1 forward CGGTCAACCATGCTCAATTA SpMACPFE1 reverse CTTCCCAGAACTCAACCTTCTC SpSRCR143 forward CTGGAGAAGAATCGGCTCTC SpSRCR143 Revers TCATCTAAACATACCTTCAAGCACTC Sp185/333 forward TGGAGGTGAAAGTGACACTGA Sp185/333 reverse CATTCTCCTTTCCTCGTCGTT

16S bacterial identification Eub11f3mx TGGHTACCTTGTTACGACTT Eub1511r1mx TGRGTTTGATCMTGGCTYAG

Chapter 3 qPCR primers SpIL-17(I) forward CATCAAGCTGCCCATACGAT SpIL-17(I) reverse GCTGATCGACATCGGGATAC PU.1 forward GCAGCAACTTCACAGGGAAA PU.1 reverse GTGGCCCATTTCGTCACTT SpIL-17-17 forward CTCTGTCCCAGGAAGCAATA SpIL-17-17 reverse GGTGGCCAGTGGGTCTTC SpCebpa forward TATAAGCAGAAGCGGGAACG SpCebpa reverse TTGGAGCTCCTTGTTCTTCG SpIL17R1 Exon 16 reverse ATATGATGAGAATCCTCTTCTCGGGT SpIL17R1 Exon 14 forward AACTGCTCCTGTGTTCCTGGCTAC SpIL17R1 Exon forward CAGCATTGCTAGTGGGCTTCAGGTT SpIL17R1 Exon 15 reverse CCTGGTCTTTAGGATGAACCTGAA SpIL17R1 forward 1 TTCTTTGGCATCATCTTGGA SpIL17R1 forward 2 GCTGTTGTAGCCCAGTCCAT SpIL17R1 reverse CCACCTACACCTCCTCCAGA

BAC recombination 5´ recombination arm forward CGCGAGCTCGGATGCTCATAAACGGAAGG 5´ recombination arm reverse GCACTAGTTTGAATCGTCTTTTTGGAAC 3´ recombination arm forward GGCGGTCGACCAAATATGGTTGCAAAGAGCA 3´ recombination arm reverse CGCGGTACCTGTCGCGTCATGAAAATGAT

165

Primer Name Sequence

Chapter 4 Deletion Constructs (number denotes distance from the start of transcription in bp) 500 bp forward (-404) TGTCGGCACAGTAAATACTATCA 500 bp reverse (+1597) TGCTAATTTTGATGGACGTTTTT 1 kb forward (-824) CGATGCCTGATCCAGCTTAT 1 kb reverse (+2175) TCCCTCTTTCGTTTTCTCCTC 200 bp forward (-51) GAAATCGCCACACATTTTCC 2 kb forward (-1963) TGGGTTCGATTGAAACAAAG 2 kb reverse (+3098) GAATAAGAGCAATAACCATGCAC 300 bp forward (-139) AGAAGCGGTAAGACGACAAAA 3 kb forward (-2924) AATTAATTTTTACCCCGCTCAA 3 kb reverse (+4075) TGGGTAATTTGTAATAGCAGTCAA 400 bp forward (-258) CCCAAGATATTGGTGCAAGC Intronless reverse AGAATACTCAAGCTATGCATCAAGCT Intron reverse AAAAGAATTAAATGAATATG SV40 TTCGATCCAGACATGATA