University of Florida Thesis Or Dissertation Formatting

IMPACT OF MODELED MICROGRAVITY ON THE BENEFICIAL SYMBIOSIS BETWEEN THE HAWAIIAN BOBTAIL SQUID, EUPRYMNA SCOLOPES, AND ITS BIOLUMINESCENT PARTNER, VIBRIO FISCHERI

ALEXANDREA A. DUSCHER

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2019

To my family and friends for their continuous love and support, to the incredible mentors that have helped guide me along the way, and to my partner for continuously encouraging me throughout it all.

ACKNOWLEDGMENTS

There are many individuals who have inspired and guided me along the way.

Specifically, I would like to thank my mentor, Dr. Jamie Foster, for her support and advice throughout my journey. I would also like to thank Dr. Foster for providing me with my first research experience after my undergraduate studies and for guiding me towards pursuing a PhD. I would like to thank my committee members, Dr. Joseph

Larkin III, Dr. Peter Kima, and my external committee member Dr. Anna-Lisa Paul. I would also like to thank the staff at the Department of Microbiology and Cell Science, specifically Jonathan Orsini for his willingness to assist me with whatever questions I had. I would like to thank the individuals at the Space Life Sciences Lab for their insight and friendship throughout my time working there. In addition, I would like to thank all of the previous and current lab members that have been a tremendous support through some of the best of toughest times, specifically I would like to thank Dr. Dyanna

Louyakis, Dr. Jennifer Mobberley, Dr. Giorgio Casaburi, Joany Babilonia, and my fellow squid mate Maddie Vroom.

I cannot thank my family, friends, and partner enough for their unending support throughout this journey, I will forever be grateful for your encouragement.

I would also like to thank my funding sources, who without this dissertation would not be possible. I would like to give a special thank you to all of those that supported our crowdsourcing campaign to raise money for my research. The generosity of such individuals has taught me more about expanding science into the community and gives me hope for science funding in the future.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF OBJECTS ...... 12

LIST OF ABBREVIATIONS ...... 13

ABSTRACT ...... 14

CHAPTER

1 LITERATURE REVIEW ...... 16

Impact of Microgravity on Microbial Physiology ...... 17 Impact of Microgravity on Beneficial Microorganisms ...... 19 Importance of the Global Bacterial Regulator Protein, Hfq, in Response to Microgravity ...... 20 Impact of Microgravity on Animal Immunological Response ...... 22 The Model System: The Mutualistic Symbiosis Between the Hawaiian Bobtail Squid and its Bioluminescent Bacterium ...... 24 Advantages of Using the Squid-Vibrio System for Microgravity Study ...... 26 Modeling Microgravity Conditions ...... 27

2 TRANSCRIPTIONAL PROFILING OF THE MUTUALISTIC BACTERIUM VIBRIO FISCHERI AND A HFQ MUTANT UNDER MODELED MICROGRAVITY ...... 31

Introduction ...... 31 Methods ...... 35 Bacterial Strains and Growth Conditions ...... 35 RNA Extraction, cDNA Synthesis, and Sequencing ...... 36 Bioinformatic Analysis ...... 37 Real-Time Quantitative PCR (qRT-PCR) ...... 37 Results ...... 38 Overview of Transcriptome Analysis of V. fischeri Cultivars Under Gravity and LSMMG Conditions ...... 38 LSMMG-specific Changes in V. fischeri Transcriptome...... 40 Differential Gene Expression Changes in ∆hfq Mutant Under Both Gravity and LSMMG Conditions During Exponential Phase ...... 41 Differential Gene Expression Changes in ∆hfq Mutant Under Both Gravity and LSMMG Conditions During Stationary Phase ...... 42

Discussion ...... 44

3 THE IMMUNE SYSTEM OF EUPRYMNA SCOLOPES IN RESPONSE TO ITS BENEFICIAL BACTERIA ...... 57

Introduction ...... 57 Materials and Methods...... 62 Identification of Innate Immune and NFκB Specific Related Genes ...... 62 Transcriptome Expression Analysis Across Tissues and Symbiotic State ...... 64 Results and Discussion...... 64 General Remarks ...... 64 Pattern Recognition Receptors ...... 66 Peptidoglycan recognition proteins ...... 66 Toll-like receptor ...... 69 Galectins ...... 71 Effector Enzymes ...... 72 Hemocyanin ...... 73 Chitotriosidase and chitinase ...... 75 Superoxide dismutase ...... 77 Signaling ...... 79 Myeloid differentiation primary response 88 ...... 79 Interleukin-1 receptor-associated kinase 4...... 81 Tumor necrosis factor receptor associated factor ...... 82 Transforming growth factor beta-activated kinase 1 and TAK1-binding proteins ...... 85 IκB kinases ...... 87 Nuclear factor kappa-light-chain-enhancer of activated B cells subunits ... 88 NF-kappa-B pathway inhibitors ...... 90 Conclusions ...... 93

4 TARGETED GENE EXPRESSION ANALYSIS OF THE HOST IMMUNE SYSTEM IN A BENEFICIAL SYMBIOSIS UNDER MODELED MICROGRAVITY CONDITIONS ...... 111

Introduction ...... 111 Materials and Methods...... 115 General Procedures ...... 115 Modeled Microgravity Treatments ...... 115 RNAseq Data Analysis ...... 116 NanoString Target Gene Probe Design ...... 116 RNA Extraction and Gene Expression ...... 116 NanoString Data Analysis ...... 117 Results ...... 118 Analysis of Targeted Genes from Transcriptomic Dataset ...... 118 Overview of NanoString Expression Patterns ...... 121 Differential Expression of Genes Driving Principal Component Analysis Over Time ...... 122

Differential Expression of Genes Between Gravity and LSMMG at the Most Significant Timepoint ...... 124 Discussion ...... 124

APPENDIX

A SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 2 ...... 146

B SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 3 ...... 148

C SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 4 ...... 149

LIST OF REFERENCES ...... 151

BIOGRAPHICAL SKETCH ...... 178

LIST OF TABLES

Table page

2-1 Overview of recovered transcriptome sequencing results from V. fischeri wild-type (WT) and ∆hfq mutant exposed to low shear modeled microgravity (LSMMG) and gravity conditions...... 52

3-1 Transcripts data mined from Euprymna scolopes reference transcriptome ...... 95

4-1 Targeted genes for RNAseq and NanoString expression assay...... 132

A-1 Primers designed for qRT-PCR gene verification ...... 146

C-1 Targeted genes for NanoString expression assay probe design ...... 149

LIST OF FIGURES

Figure page

1-1 Comparison of bacterial liquid cultures in microgravity and gravity conditions. .. 28

1-2 Light organ morphology in adult...... 29

1-3 Bacterial induced light organ phenotypes...... 30

1-4 High-aspect-ratio rotating wall vessel bioreactors (HARVs)...... 30

2-1 Overview of the differentially expressed genes associated to each of the eight transcriptomic comparisons in Vibrio fischeri...... 53

2-2 Heat map depicting the clustering patterns of the eight treatments by KEGG pathways associated with the proposed function of the V. fischeri genes at 12 and 24 h...... 54

2-3 Differential gene expression between LSMMG and gravity (G) in a ∆hfq mutant at 12 h...... 55

2-4 Differential gene expression between WT and ∆hfq mutant under LSMMG at 24 h...... 56

3-1 Euprymna scolopes and the tissues used for transcriptomic analysis...... 96

3-2 Peptidoglycan recognition proteins (PGRPs) found in Euprymna scolopes transcriptome...... 97

3-3 Galectins and Toll-like receptors (TLRs) found in Euprymna scolopes transcriptome...... 98

3-4 Hemocyannins (HCYs) found in Euprymna scolopes transcriptome...... 99

3-5 Chitotriosidases (CHITs) and chitinases (CHIA) found in Euprymna scolopes transcriptome...... 100

3-6 Superoxide dismutase (SODs) found in Euprymna scolopes transcriptome. ... 101

3-7 Myeloid differentiation primary response 88 (MYD88) found in Euprymna scolopes transcriptome...... 102

3-8 Interleukin-1 receptor-associated kinase 4 (IRAK4) found in Euprymna scolopes transcriptome...... 103

3-9 Tumor necrosis factor receptor associated factors (TRAFs) found in Euprymna scolopes transcriptome...... 104

3-10 Transforming growth factor beta-activated kinase 1 (TAK1) and TAK1- binding proteins (TABs) found in Euprymna scolopes transcriptome. 105

3-11 Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor (IκB) kinases (IKKs) found in Euprymna scolopes transcriptome...... 106

3-12 Nuclear factor of kappa light polypeptide gene enhancer in B-cells (NFκB) subunits found in Euprymna scolopes transcriptome...... 107

3-13 Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor (IκBs) subunits found in Euprymna scolopes transcriptome...... 108

3-14 The kappaB-ras1 (KBRS1) and NOD-like receptor family caspase recruitment domain containing 3 (NLRC3) NFκB inhibitors found in Euprymna scolopes transcriptome...... 109

3-15 Putative NFκB pathway in Euprymna scolopes. Light blue genes indicate they have not been found in E. scolopes before but were identified in the reference transcriptome and genome...... 110

4-1 Timeline of what is known in the Euprymna scolopes and Vibrio fischeri symbiosis under normal gravity conditions compared to LSMMG (low shear modeled microgravity conditions)...... 134

4-2 Putative NFκB signaling pathway from known genes that exist in Euprymna scolopes...... 135

4-3 Heatmap of log-transformed RNAseq expression values averaged by treatment...... 136

4-4 Principal componenent analysis (PCA) of all NanoString assay samples...... 138

4-5 Heatmap of log-CPM Nanostring sample expression averaged by treatments and clustered by disimilarities...... 139

4-6 Principal componenent analysis (PCA) of all NanoString assay genes...... 140

4-7 Network plot showing Spearman correlation of voom transformed NanoString assay counts between different genes...... 141

4-8 Log2 fold change of selected genes in aposymbiotic Euprymna scolopes compared to symbiotically infected V. fischeri squid across time...... 142

4-9 Log2 fold change of selected genes in gravity condition Euprymna scolopes compared to LSMMG squid across time...... 143

4-10 Log2 fold change of all genes in Euprymna scolopes at 6 h in gravity compared to LSMMG...... 144

A-1 Growth curves of strains used in this study...... 147

B-1 Alignment of translated Euprymna scolopes peptidoglycan recognition receptor proteins (EsPGRP) transcripts...... 148

LIST OF OBJECTS

Object page

Object 2-1. Significant differentially expressed genes in pairwise comparisons at log2-fold change of +/- 1 and padj ≤ 0.05. (.xlsx and 110 kb)...... 56

Object 2-2. Normalized read counts of data associated with Figure 2. (.xlsx and 18 kb) ...... 56

Object 2-3. Significant Differentially expressed genes within treatments at 12 h compared to 24 h (.xlsx and 29 kb)...... 56

Object 3-1. Transcripts identified from the Euprymna scolopes transcriptome and confirmed in E. scolopes genome (.xlsx and 23 kb) ...... 110

Object 4-1. Log2FoldChange of targeted genes from Casaburi et al., 2017 RNAseq dataset using NOISeqBio (.xlsx and 18 kb)...... 145

Object 4-2. Normalized expression of all targeted gene replicates from Casaburi et al., 2017 RNAseq dataset after alignment to Euprymna scolopes reference transcriptome. (.xlsx and 17 kb) ...... 145

Object 4-3. Log2 fold change and significance testing of targeted genes from NanoString assay using voom transformation and LIMMA statistical testing (.xlsx and 95 kb) ...... 145

Object 4-4. Expression of all targeted gene replicates from NanoString assay normalized to housekeeping genes (indicated in Table 1-C) and background threshold described in the methods. (.xlsx and 24 kb) ...... 145

LIST OF ABBREVIATIONS

CEA Ciliated epithelial appendages

CPM Counts per million

DAP-type Diaminopimelic acid type peptidoglycan

IKK IκB kinase

KEGG Kyoto Encyclopedia of Genes and Genomes

KOs Kyoto Encyclopedia of Genes and Genomes Orthologs

LPS Lipopolysaccharide

LSMMG Low shear modeled microgravity

LRRs Leucine-rich repeats

MAPK Mitogen-activated protein kinase

NFκB nuclear factor kappa-light-chain-enhancer of activated B cells

PGN Peptidoglycan

PGRP Peptidoglycan recognition receptor proteins

PRRs pattern recognition receptors

SOD Superoxide dismutase

TAK1 transforming-growth-factor-beta-activated kinase 1

TGF-β transforming growth factor β

TLR Toll-like receptor

TMM Trimmed mean of M-values

TNF Tumor necrosis factor

WT Wild type Vibrio fischeri strain ES114 qRT-PCR Real-time quantitative reverse transcription polymerase chain reaction

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

IMPACT OF MODELED MICROGRAVITY ON THE BENEFICIAL SYMBIOSIS BETWEEN THE HAWAIIAN BOBTAIL SQUID, EUPRYMNA SCOLOPES, AND ITS BIOLUMINESCENT PARTNER, VIBRIO FISCHERI

Alexandrea A. Duscher

May 2019

Chair: Jamie S. Foster Major: Microbiology and Cell Science

Spaceflight imposes numerous physical challenges to the human body. Although considerable progress has been made in delineating the mechanisms associated with this problem, one area of research that has received little attention is the role that beneficial microbes play in maintaining the healthy immune function of astronauts in space. To address this issue, we used the symbiosis between the bioluminescent bacterium, Vibrio fischeri, and the bobtail squid, Euprymna scolopes, as a model system. In this symbiosis V. fischeri colonize a specialized light organ within the mantle of the squid to produce light and aid in camouflage when the squid hunt at night. During colonization, V. fischeri and exogenous peptidoglycan trigger the migration of host hemocytes into the blood sinus of the squid light organ, the site of the symbiosis.

Previous research found that hemocyte migration is delayed in simulated microgravity suggesting a dysregulation of the host immune response in microgravity. In this dissertation, the effect of modeled microgravity was examined on the genetic response of the squid-vibrio symbiosis from both the perspective of the host and its symbiont. To

do this we placed the squid and bacteria in a simulated microgravity environment using rotating bioreactors that mimic a low shear modeled microgravity (LSMMG). The genetic response of V. fischeri was first analyzed separately from E. scolopes. In addition, components of a putative NFκB signaling pathway that may be important in hemocyte regulation and microgravity response were data-mined and subsequently analyzed from the newly sequenced E. scolopes reference transcriptome and genome. The genes from the putative pathway were further targeted for genetic expression analysis from the host light organ in simulated microgravity and gravity conditions in different symbiotic states across important developmental timepoints. This work is the first to investigate the impact of modeled microgravity on a putative NFκB pathway from the host in regard to its beneficial symbiont in situ while expanding on innate immune components in a symbiotic context.

CHAPTER 1 LITERATURE REVIEW

Gravity has influenced the evolution and development of all living organisms on

Earth (Alpatov et al., 1992; Rea et al., 2016; Volkmann and Baluška 2006; Wuest et al.,

2015). As a consequence, continued exposure to an altered gravitational field has required life on Earth to readjust its normal physiological functioning (Morey-Holton

2003). In spaceflight, there is a lack of gravity that affects the normal function of living organisms, for example, astronauts experience many changes to their overall physiology during spaceflight. This altered physiology includes, but is not limited to, muscle atrophy, decreased bone density, and impairment of normal immune system functions (e.g. Gueguinou et al., 2009; Klein-Nulend et al., 2003; Vernikos 1996). Many of these changes are attributed to the condition of microgravity, or near weightlessness that is experienced during space travel (Cervantes and Hong, 2016). Spaceflight has also been found to alter the microbiome of organisms, which can in turn affect their health (Ge et al., 2015; Round & Mazmanian, 2009; Shi et al., 2017). For example, some of the earliest spaceflight studies found that astronaut’s microbiome before they left Earth was altered once they returned (Brown et al., 1976; Decelle & Taylor, 1976).

More specifically it found that there were distinct differences in the consorita of

Lactobacillus in astronaut’s gut microbiome before spaceflight compared to when they returned to Earth and a similar results was also found in murine models (Lencner et al.,

1984; Ritchie et al., 2015). With an altered microbiome animals have experienced increased inflammation in response to spaceflight (Ge et al., 2015). Despite these studies there still remains a gap in knowledge on how the space environment impacts beneficial microbes and their associated hosts.

Impact of Microgravity on Microbial Physiology

Many studies have focused on the impact of microorganisms in space-like environments. One of the most common findings is that bacterial cultures grown in spaceflight and low-shear stress conditions tend to have increased growth rate and denser structured colonies due to expanded log-phase growth (Foster et al., 2013;

Kacena et al., 1999; Nickerson et al., 2004; Taylor 1974; Wang et al., 2016). This elevated growth may reflect an advantage in microgravity that results from increased access to nutrients in a liquid medium, especially for non-motile bacteria where the degree of sedimentation is much higher under the normal gravitational vector (Fig. 1-1)

(Benoit and Klaus 2007; Kacena et al., 1999).

Another physiological change that occurs in microbes under space flight and modeled microgravity is the alteration in biofilm production. For example, several pathogenic bacteria (e.g., E. coli, Pseudomonas aeruginosa) have been shown to produce altered biofilms in microgravity (Lynch et al., 2006), which may be in response to their altered motility in microgravity (Kim et al., 2013). Increased biofilm production can improve a bacterium’s ability to resist environmental stressors that would normally harm the bacterium. For instance, Salmonella enterica serovar Typhimurium has an enhanced biofilm and increased ability to deter oxidative stress, that may be attributed to the upregulation of its catalase genes (e.g., katN and katG) when in microgravity

(Pacello et al., 2012; Wilson et al., 2007). Additionally, the pathogenic bacterium

Klebsiella pneumoniae also showed an increase in biofilm formation that is coupled with elevated cellulose production in modeled microgravity (Wang et al., 2016). In gram- positive bacterium, such as Staphylococcus aureus, some taxa exhibit increased biofilm production with differential morphologies in microgravity (Castro et al., 2011).

Interestingly, S. aureus was found to have decreased resistance towards oxidative, yet the bacterium had elevated resistance to antibiotic stress, which may be correlated to their specific attachment-independent biofilm formation in microgravity (Castro et al.,

2011).

Another relatively common physiological change in microbes under space flight and modeled microgravity is altered virulence, which may be closely tied to specific stress responses. A recent study revealed that there was an increase in overall virulence and antimicrobial resistance related genes detected through metagenomic sequencing on the International Space Station over time (Singh et al., 2018). Other studies have focused on gene expression of spaceflight or modeled microgravity grown microbes compared to Earth and normal gravity grown. For example, S. Typhimurium was shown to have increased expression of both virulent (e.g., virK) and stress response (e.g., dnaK) genes (Chopra et al., 2006; Wilson et al., 2007), which may be partly responsible for their increased ability to resist macrophages in mice spleen and liver under the influence of microgravity (Nickerson et al., 2000). In addition, P. aeruginosa exhibited increased gene expression of virulence factors when grown in spaceflight conditions (Crabbe et al., 2011).

Not all potential pathogens studied in microgravity conditions, however, showed increased virulence. For instance, a study conducted on Yersinia pestis in modeled microgravity did not observe any enhanced virulence (Lawal et al., 2010), indicating that specific pathogenic bacterial species may be differentially altered in microgravity. In addition, E. coli grown in modeled microgravity exhibited an upregulation of stress- induced and membrane transport genes (Arunasri et al., 2013), but when grown for over

1,000 generations in spaceflight did not have altered virulence genes in its genome

(Tirumalai et al., 2017). Conversely, other studies have shown that E. coli was able to adapt to antibiotics when consistently challenged with them in spaceflight conditions through differential gene expression when compared to Earth-grown controls, indicating that how the bacteria are grown may be important in the different responses seen

(Aunins et al., 2018; Tirumalai et al., 2019).

The virulence and overall response of pathogenic bacteria grown in microgravity can also be altered depending on the nutrient composition of the media that they are grown in and has added variability to bacterial studies in microgravity (Huang et al.,

2018; Morrison et al., 2018; Wilson et al., 2008). In the case of S. Typhimurium, the addition of phosphate ions to the growth media reduced the increased virulence (e.g. virulent gene expression, acid tolerance, death of mice) observed during space flight

(Wilson et al., 2002; Wilson et al., 2008). Similarly, E. coli had observed differential gene expression (Tucker et al., 2007; Vukanti and Leff 2012), whereas P. aeruginosa had altered biofilm architecture depending on the culture media used in microgravity

(Kim et al., 2013). In addition, both E. coli and P. aeruginosa had varied growth patterns in microgravity depending on the media used (Kim et al., 2013; 2014). These findings indicate that the pathogenic response of bacteria can potentially be mitigated in spaceflight to improve the health of animals influenced by microgravity.

Impact of Microgravity on Beneficial Microorganisms

Although pathogen physiology under microgravity conditions has been relatively well studied, the impact of microgravity on beneficial microorganisms is an understudied issue. A study conducted on Russian cosmonauts found a relative increase in opportunistic pathogens and a decreased population of beneficial microbes, specifically

Lactobacillus spp., in spaceflight (Ilyin 2005). Several studies have found that the normal gut microbiota of a murine model shifts under modeled microgravity and could be responsible for driving some of the altered host immune functions, such as increased inflammation, indicating that maintenance of beneficial gut microbes may be increasingly important for long term space missions (Li et al., 2015; Shi et al., 2017,

Yang et al., 2017). Lactobacillus spp. and other naturally occurring probiotics important for gut health are often lost in astronaut food due to the sterilization process (Cooper et al., 2011; Urbaniak and Reid 2016). It has been proposed that addition of probiotics to the astronaut food supply could potentially counter some of the negative effects associated with microgravity, such as the dysregulated immune system and bone density loss (Britton et al., 2014; Saei and Barzegari 2012; Sakai et al., 2018; Urbaniak and Reid 2016).

Two recent investigations found that there are relatively few changes that occur in the transcriptome and physiological response of Lactobacillus acidophilus in modeled microgravity conditions; however, these responses were dependent on the growth conditions used (Castro-Wallace et al., 2017; Shao et al., 2016). Despite these studies there is still relatively little known about the genetic underpinnings of commensal and beneficial host-microbe interactions in microgravity. Identifying the physiological response of these beneficial microbial populations in relation to the host will be crucial for maintaining healthy astronauts during long-term spaceflight missions.

Importance of the Global Bacterial Regulator Protein, Hfq, in Response to Microgravity

Although several studies have examined the transcriptome and genome of microbes in modeled microgravity there have been few genes found that were similarly

altered between the different tax. However, one gene that has been found to be similarly changed among several taxa in microgravity is hfq. The global rRNA regulator

Hfq has been identified as a prevalent response molecule of bacteria in microgravity

(Wilson et al., 2007) and has been found in about half of all known bacterial genomes

(Valentin-Hansen et al., 2004). The Hfq protein stabilizes an interaction between small

RNAs (sRNAs) and their target message RNAs (mRNAs) to influence gene expression

(Boudry et al., 2014; Chao and Vogel 2010). This protein has been implicated as an important mechanism involved in stress response, and therefore, may be especially important in the bacterial response to the stress of microgravity (Grant et al., 2014;

Mukhopadhyay et al., 2016; Wilson et al., 2007). Several studies have shown that the

Hfq-encoding hfq gene is downregulated in microgravity conditions, in both gram- negative and gram-positive pathogens, generally resulting in altered virulence and stress responses (Chao and Vogel 2010). One study found that S. Typhimurium many of the differentially expressed genes in microgravity compared to the gravity control were associated with the Hfq regulon (Wilson et al., 2007). In addition to virulence and stress response, Hfq is involved in other regulation, such as the negative response regulator of bioluminescence in the beneficial symbiont V. fischeri (Lupp and Ruby

2005; Miyashiro et al., 2010). Initial studies of V. fischeri have shown that hfq is also downregulated in modeled microgravity, further confirming that Hfq responds to microgravity stress in microbes (Grant et al., 2014). However, the genetic response and control of Hfq in non-pathogenic bacteria, such as V. fischeri, under the influence of microgravity is not well known.

Impact of Microgravity on Animal Immunological Response

In addition to microorganisms, many studies have focused on the impact of microgravity on astronauts and animal systems. In spaceflight, astronauts experience long-term exposure to microgravity, which can impact their normal physiology. For example, animals in microgravity can experience a loss of muscle mass, which in turn affects their bone structure and density (Klein-Nulend et al., 2003; Vernikos, 1996).

Additionally, microgravity has been shown to also disrupt the typical regulation of the immune system (Chang et al., 2012; Crucian et al., 2013; Gueguinou et al., 2009;

Martinez et al., 2015; Mukhopadhyay et al., 2016). Disordered immune function leaves astronauts more susceptible to infections (Pacello et al., 2012; Nickerson et al., 2000;

Taylor 2015). The innate immune system is the first line of defense that provides a fast and mainly non-specific response to defend an organism against invading pathogens

(Akira et al., 2006), yet has received relatively little attention in space-like conditions and requires further study (Spielmann et al., 2018).

However, several studies have shown that the innate immune system is altered when under the influence of microgravity. For instance, space flight reduces the ability of monocytes to engulf and kill the opportunistic pathogen Escherichia coli (Kaur et al.,

2005; Rykova et al., 2008). Other innate immune cells, such as natural killer cells and neutrophils, also have reduced cellular activity in response to space flight (Foster et al.,

2013; Konstantinova et al., 1993; Kaur et al., 2004; Mehta et al., 2001; Pellis et al.,

1997). Monocytes and dendritic cells also have a decreased response to the endotoxin lipopolysaccharides (LPS), an important microbial associated molecular pattern (MAMP) molecule associated with gram-negative bacteria when under modeled microgravity conditions (Crucian et al., 2011; Kaur et al., 2008; Monsalve et al., 2009;

Mukhopadhyay et al., 2016). The signaling molecules produced by these immune cells, such as cytokines, are also differentially produced in microgravity (Chapes et al., 1994;

Crucian et al., 2000; 2011; 2013; Kaur et al., 2008). In addition, studies in spaceflight and modeled microgravity have also shown alterations to normal developmental processes that are largely drive by the innate immune response, such as accelerated apoptosis (Foster et al., 2013; Kumari et al., 2009; Lewis et al., 1998; Liu and Wang,

2008; Monici et al., 2006; Testa et al., 2014).

The atypical innate immune response in microgravity may be in part due to altered activation of downstream transcription factors. Several human space flight studies have shown that there is differential expression among genes of the nuclear factor kappa beta (NF-κB) pathway, a large regulatory pathway of the innate immune system that leads to activation of the NF-κB transcription factor (Boonyaratanakornkit et al., 2005; Crucian et al., 2015; Mukhopadhyay et al., 2016). For example, tumor necrosis factor (TNF)-mediated NF-κB pathways in human cells are downregulated in the microgravity environment (Chang et al., 2012). In contrast, some studies found increased activation of the NF-κB transcription factor and apoptosis events related to the pathway when in microgravity (Kang et al., 2011; Sharma et al., 2008). Taken together, the combined research conducted on animal systems in microgravity has revealed numerous physiological responses, many of which are related to the innate immune system and are important for overall health. Despite these efforts, there are major gaps in our knowledge of the impact of microgravity on animal immune systems in a host-microbiome context.

The Model System: The Mutualistic Symbiosis Between the Hawaiian Bobtail Squid and its Bioluminescent Bacterium

The normal microbiota of healthy animals is extremely important for immune system homeostasis (Kabat et al., 2014; Henao-Mejia et al., 2012; Macpherson et al.,

2012). Additionally, it is becoming clear that beneficial host-microbe interactions may play a significant role in maintaining healthy immune function of astronauts during long term spaceflight (Li et al., 2015; Xu and Gordon 2003). To determine the impact of microgravity on a mutualistic host-microbe association my dissertational research uses the binary model system between the Hawaiian bobtail squid, E. scolopes, and its beneficial bioluminescent bacterium, V. fischeri. The squid-vibrio symbiosis has been studied for more than 30 years, making it a well-established host-microbe model

(McFall-Ngai and Ruby 1991). The symbiosis interaction occurs in a specialized light organ under the mantle of the squid E. scolopes that is colonized only by V. fischeri

(Fig. 1-2A and 1-2B).

Initially, the squid is born aposymbiotically (i.e. without symbiosis-competent bacteria) and must acquire the symbiont from the surrounding environment (Wei and

Young 1989). To facilitate the inoculation process, the squid ventilates seawater through its mantle and uses special ciliated epithelial appendages (CEA) to direct bacteria towards the light organ (Fig. 1-2C). The presence of bacterial peptidoglycan

(PGN) in the surrounding seawater triggers the CEAs to begin to shed mucus that encourages colonization of V. fischeri (Altura et al., 2013; Nyholm et al., 2000; 2002).

The mucus contains antimicrobial peptides, such as EsPGRP2, that are thought to deter bacteria other than V. fischeri from associating with the CEA near the light organ pores

(Altura et al., 2013; Kremer et al., 2014; Troll et al., 2010). V. fischeri are entrained in

this mucus before they attach to the cilia of the CEAs and aggregate near the light organ pores (Altura et al., 2013; Kremer et al., 2013; Nyholm and McFall-Ngai 2003).

This adhesion induces host transcriptional changes and Vibrio fischeri increases its biofilm dominance to create an environment that allows for further specification of the relationship (Kremer et al., 2013; Nyholm and McFall-Ngai, 2003).

During the aggregation process, the PGN derivative tracheal cytotoxin (TCT) triggers the migration of hemocytes, the squid’s only immune cell, into the blood sinus of the CEAs (Fig. 1-3A) (Altura et al., 2013; Koropatnick et al., 2004). In pathogenic bacteria TCT shedding is used to kill ciliated cells of the host (Preston and Kerr 2001), however, in the squid-vibrio symbiosis the TCT molecule also initiates cell death (i.e. apoptosis) of the CEA, that is associated with normal developmental remodeling of the light organ (Fig. 1-3B) (Koropatnick et al., 2004). Apoptosis is also promoted by another

MAMP signal, lipopolysaccharides (LPS). Specifically, the conserved lipid A component of LPS is considered to be the main trigger of apoptosis (Foster et al., 2000). The host is presented with LPS when it is released from the base of the sheath of V. fischeri’s rotating flagellum (Aschtgen et al., 2016; Brennan et al., 2014). LPS, or endotoxins, constitute a large component of gram-negative bacteria outer membranes and high concentrations of this molecule can cause septic shock (Sweet and Hume 1996).

However, LPS has also been shown to be an important MAMP for human gut homeostasis as well as for normal development of the light organ in the squid-vibrio relationship (Foster et al., 2000; Rakoff-Nahoum et al., 2004).

After triggering host hemocyte flooding the V. fischeri aggregate is chemotactically attracted toward the pore openings of the light organ where they

migrate to the epithelial-lined crypt spaces (Kremer et al., 2014; Mandel et al., 2012;

Wang et al., 2010). Once in the crypt spaces of the light organ V. fischeri begin to undergo cell division until they reach a critical density sufficient to induce bioluminescence through a quorum sensing process (Lupp and Ruby 2005; Nyholm et al., 2000). Approximately 12 h after V. fischeri have successfully colonized the light organ crypts the CEA undergo irreversible morphogenesis that results from CEA apoptosis and another colonization phenotype, regression (Fig. 1-2D and Fig. 1-3C)

(McFall-Ngai and Ruby 1991). After about 24 h post V. fischeri inoculation the blood sinus becomes filled with hemocytes, at which time they are speculated to facilitate in the regression of the CEAs and later become important for delivering chitin to V. fischeri colonized crypt spaces throughout the life of the squid (Heath-Heckman and McFall-

Ngai et al., 2011; Koropatnick et al., 2007; McFall-Ngai et al., 2010; Schwartzman et al.,

2015). Both TCT and LPS are necessary for complete morphogenesis of the light organ

(Fig. 1-2D) (Koropatnick et al., 2014).

Advantages of Using the Squid-Vibrio System for Microgravity Study

The binary nature of the squid-vibrio system can provide key insight into the impact of microgravity on host-microbe interactions as both the eukaryotic host and bacterium can be grown and maintained separately in a laboratory. The complete genomes of both partners, as well as the reference transcriptome of E. scolopes, are available making the squid-vibrio model an ideal candidate to study specific genetic responses in reference to the symbiotic state under microgravity conditions (Belcaid et al., 2019; Ruby et al., 2005). Additionally, the juvenile squid can be infected with V. fischeri at any initial desired time point and the light organ development can be monitored in real time in situ. The morphogenesis of the light organ is completed after a

period of only four days, making this model ideal for rapid developmental studies.

Furthermore, the influence of V. fischeri on the innate immune response of E. scolopes has been extensively studied (Castillo et al., 2015; Goodson et al. 2005; Troll et al.

2009), making the squid-vibrio model extremely useful for interpreting the influence of a beneficial symbiont on normal host immune functions in microgravity conditions (Foster et al., 2011).

Modeling Microgravity Conditions

To avoid high costs and added variability many space biologists have resorted to examining the impact of lowered gravity conditions using ground or Earth-based experiments. The condition of microgravity can be modeled in various ways by using clinostats, random positioning machines, and rotating wall vessels (RWVs), and diamagnetic levitation (Huang et al., 2018; Klaus, 2007; Nickerson et al., 2004). This study utilizes high-aspect-ratio rotating wall vessel bioreactors (HARVs) first developed by the NASA Biotechnological group to model the low shear microgravity environment

(Fig. 1-4) (Wolf and Schwarz, 1991). The constant rotation of the HARVs results in a low-turbulence, low-shear environment that maintains the organisms within the HARV in a constant suspension. In the vertical position the force of gravity is offset by the hydrodynamic fluid forces within the HARV (Fig. 1-4A), which results in the organism being in a constant state of ‘free-fall’ or terminal velocity. Conversely, when the vessel is placed in the horizontal position gravity acts on the fluid suspension within the HARV and serves as a control. These HARVs can be used with cell cultures (Fig. 1-4B) or with the juvenile squid in seawater (Fig. 1-4C). This method of modeling microgravity has been used for over 25 years to mimic the microgravity conditions encountered in space

(Nickerson et al., 2004). In addition, this type of growth condition has also been used to

study the effects of low fluid shear on cells that may represent epithelial brush borders within animal hosts, such as the environment shared between E. scolopes and V. fischeri in the light organ (Guo et al., 2000; Nauman et al., 2007). This dissertational work will focus on 1) investigating the transcriptional response of the symbiont V. fischeri in modeled microgravity, 2) identify and transcriptionally analyze important immune related transcripts in E. scolopes using the recently available reference transcriptome and genome, 3) characterize the genetic expression of E. scolopes immune related genes in modeled microgravity in regard to symbiotic state across time.

Figure 1-1. Comparison of bacterial liquid cultures in microgravity and gravity conditions. Under normal gravity (1 x g) the cultures undergo natural sedimentation that allows only the top layer of bacteria to access media nutrients. In microgravity, there is no sedimentation, resulting in all bacteria having increased access to nutrients throughout the culture tube. Reprinted with permission from microbewiki, https://microbewiki.kenyon.edu/index.php/File:Access.jpg (April 11th, 2019).

Figure 1-2. Light organ morphology in adult A) and juvenile B) E. scolopes. C) On either side of the nascent light organ there are fields of ciliated epithelial appendages (CEA). The beating cilia help to entrain bacteria laden water into the vicinity of the pores on the surface of the light organ. D) Once colonized, V. fischeri induces extensive remodeling of the juvenile light organ within the first few days of post-hatching (symbiotic). Photos courtesy of Dr. Jamie Foster.

Figure 1-3. Bacterial induced light organ phenotypes. A) Hemocyte migration into the blood sinus. B) Apoptosis of epithelial cells of the ciliated epithelial appendage. C) Full regression of ciliated epithelial appendage. Modified from Nyholm et al., 2002 and Dr. Foster.

Figure 1-4. High-aspect-ratio rotating wall vessel bioreactors (HARVs). A) in the vertical position the cell suspension is kept in the middle of the HARV, where as in the horizontal position the forces of gravity are acting on the culture in the HARV and causing sedimentation. B) V. fischeri in media cultures within the HARVs. C) E. scolopes in sterile seawater within the HARV. Courtesy of Dr. Nickerson and the author.

CHAPTER 2 TRANSCRIPTIONAL PROFILING OF THE MUTUALISTIC BACTERIUM VIBRIO FISCHERI AND A HFQ MUTANT UNDER MODELED MICROGRAVITY1

Introduction

All animals form beneficial relationships with microbes (McFall-Ngai et al., 2013).

The normal microbiota of animals is extremely important for maintaining almost every aspect of animal fitness including host development, behavior, and immune system homeostasis (Gensollen et al., 2016 and Kabat et al., 2014). Understanding how these beneficial microbes respond to their continually changing environments represents an important area in animal microbiome research. One particular environment that presents numerous physiological challenges to animals and their microbiomes is spaceflight (Barrila et al., 2016; Guéguinou et al., 2009; Mardanov et al., 2013; Terada et al., 2016; Voorhies et al., 2016). During spaceflight, the reduction in gravity, or microgravity, can have widespread health impacts to the host including bone loss, alterations to the genome, neurovestibular, and immune systems (Borches et al., 2002;

Cervantes et al., 2016; Crucian et al., 2015; Selch et al., 2008; Williams et al., 2009). In particular, animal immune systems are highly dysregulated and host-microbe interactions have now been shown to play a significant role in maintaining healthy immune function during spaceflight (Casaburi et al., 2014).

In addition to physiological changes in human and animal hosts, microbes are also impacted by microgravity. Some microbes exhibit altered growth rates and cell densities grown under both natural and analog microgravity conditions (Arunasri et al.,

1 Reprinted with permission from Duscher, A.A., Conesa, A., Bishop, M., Vroom, M.M., Zubizarreta, S.D. and Foster, J.S., 2018. Transcriptional profiling of the mutualistic bacterium Vibrio fischeri and an hfq mutant under modeled microgravity. npj Microgravity, 4, 25.

2013; Ciferri et al., 1986; Fajardo-Cavazos et al., 2016; Klaus et al., 1997; Taylor et al.,

1974; Tucker et al., 2007). Although this is not a universal response as several taxa, including pathogenic Streptococcus mutants and Salmonella enterica Serovar

Typhimurium, exhibit no changes to growth rates under modeled microgravity conditions

(Nickerson et al., 2000 and Orsini et al., 2017). For many taxa, however, there is an increased growth rate under both natural and simulated microgravity conditions (Grant et al., 2014 and Kacena et al., 1999), which can be highly dependent on the growth media used (Tucker et al., 2007). Although the precise mechanisms underlying the increased growth rate in certain taxa have not been fully elucidated, research has indicated that in some bacteria the lag phase of growth is shortened and the exponential growth phase is lengthened (Klaus et al., 1997).

Microbes also respond to changes in the mechanical and physical forces (e.g., low-shear) associated with microgravity by modifying their gene expression (Abshire et al., 2016; Altenburg et al., 2008; Castro et al., 2011; Chopra et al., 2006; Crabbé et al.,

2010, 2011, 2013; Dingemans et al., 2016; Grant et al., 2014; Kim et al., 2013; Lynch et al., 2006; Mastroleo et al., 2013; Orsini et al., 2017; Purevdorj-Gage et al., 2006;

Rosado et al., 2010; Shao et al., 2017; Soni et al., 2014; Tucker et al., 2007; Vukanti et al., 2008, 2012; Wang et al., 2016; Wilson et al., 2002), secondary metabolism (Demain et al., 2001 and Fang et al., 1997), biofilm formation (Castro et al., 2011; Crabbé et al.,

2008; Dingemans et al., 2016; Kim et al., 2013; Lynch et al., 2006; Wang et al., 2016;

Wilson et al., 2007), and pathogenesis (Nickerson et al., 2004 and Taylor et al., 2015).

Many pathogenic microbes under microgravity conditions exhibit altered virulence

(Chopra et al., 2006; Nickerson et al., 2000; Wilson et al., 2007, 2008; Yang et al.,

2016), resistance to environmental stress and antibiotics (Abshire et al., 2016;

Altenburg et al., 2008; Castro et al., 2011; Crabbé et al., 2010; Lynch et al., 2006;

Pacello et al., 2012; Soni et al., 2014; Taylor et al., 2015; Wilson et al., 2002), as well as increased survival in host macrophages (Chopra et al., 2006; Nickerson et al., 2000,

2004; Wilson et al., 2002). Previous studies have shown that these changes in virulence are environment-dependent and in some cases can be attenuated through media supplementation, such as inorganic phosphate (Wilson et al., 2008). These same studies have also determined there are extensive changes in microbial gene expression both at the transcriptional and translational levels.

One key finding is that microgravity alters the expression of the global regulator

Hfq, an RNA-binding protein that stabilizes an interaction between small RNAs (sRNAs) and their target message RNAs (mRNAs) to influence gene expression (Chao et al.,

2010) and has been found in about half of all known bacterial genomes (Valentin-

Hansen et al., 2004). This protein has been implicated as an important mechanism involved in bacterial stress response, and therefore, may be especially important in microgravity conditions (Grant et al., 2014; Mukhopadhyay et al., 2016; Wilson et al.,

2007). Several studies have shown that the hfq gene is down-regulated in bacteria under natural and modeled microgravity conditions, including beneficial microbes (Grant et al., 2014 and Wilson et al., 2007).

Although significant progress has been made in understanding microbial responses to microgravity, most of these studies have focused on pathogenic strains of microbes (Abshire et al., 2016; Lynch et al., 2006; Nickerson et al., 2004; Wilson et al.,

2007). The effects of microgravity and low shear fluid dynamics on mutualistic bacteria

are relatively unknown. Two recent studies on gut-associated Lactobacillus acidophilus revealed relatively few transcriptional and physiological differences when cultures were grown under low-shear modeled microgravity (LSMMG) conditions (Castro-Wallace et al., 2017 and Shao et al., 2017). For example, no transcriptomic or growth changes were observed when the cultivars were grown under anaerobic conditions (Castro-

Wallace et al., 2017), however, some increased acid stress resistance and antimicrobial activity was observed when grown under aerobic conditions (Shao et al., 2017), suggesting more investigations in how mutualistic bacteria respond to the stress of microgravity are needed.

In this chapter, I investigate the impact of LSMMG on the beneficial symbiont,

Vibrio fischeri, which forms a simplified binary relationship with the bobtail squid

Euprymna scolopes. Vibrio fischeri colonizes the epithelial-lined crypt spaces of a specialized light organ in the squid and induces a series of rapid immunological and developmental changes in the host tissues (Foster and McFall-Ngai, 1998; Koropatnick et al., 2004; McFall-Ngai and Ruby, 1991; Montgomery and McFall-Ngai, 1994). This type of colonization of host epithelial tissues represents the most common form of symbioses in animals (McFall-Ngai, 2014). Previous research on the effects of modeled microgravity on the squid-vibrio system has identified several microgravity-induced phenotypes in the host tissue (Casaburi et al., 2017; Foster et al., 2011; 2013; Grant et al., 2014), however, the effects of LSMMG on the V. fischeri transcriptome has not been explored.

To address this issue, we examined the transcriptional response of V. fischeri cultures to LSMMG at both exponential (12 h) and stationary (24 h) growth phases.

Additionally, the transcriptome of a V. fischeri mutant defective in hfq was also compared to determine the role of this transcriptional regulator in V. fischeri physiology under LSMMG conditions. Previous work has shown that the hfq gene is down regulated in V. fischeri during LSMMG and squid infected with ∆hfq mutants exhibited several altered developmental phenotypes (Grant et al., 2014). Together, this work helps elucidate the impact of microgravity and the importance of Hfq in a beneficial microbe. By understanding the effects that spaceflight has on beneficial microbes critical insight can be inferred into maintaining healthy astronaut microbiomes and decrease the potential health risks associated with the exploration of space.

Methods

Bacterial Strains and Growth Conditions

The wild type strain Vibrio fischeri ES114 (WT), which was isolated from an adult host squid E. scolopes (Boettcher and Ruby, 1990) was used as the parent strain for the deletion ∆hfq mutant and complementation (KV7142, ∆hfq; KV148, ∆hfq attTn7::ermR; KV149, ∆hfq attTn7::hfq; courtesy of K. Visick, Loyola University

Chicago). The strains were grown aerobically overnight in seawater tryptone (SWT) agar at 28°C, in which trace elements are at low concentration (e.g. phosphate (0.1 ppm)) (Grant et al., 2014). High aspect ratio rotating vessels (HARVs; Synthecon,

Houston, TX, USA) were used to model the microgravity environment as previously described (Foster et al., 2013 and Nickerson et al., 2004). Briefly, each HARV was filled with 50 ml of SWT broth inoculated with V. fischeri culture at a concentration of 1x105 cells per ml of SWT. The HARVs were either rotated around a horizontal axis to simulate microgravity (LSMMG) or a vertical axis to serve as a normal gravity (1 x g)

control. The cultures were incubated in the HARVs at 12 and 24 h in the vertical

LSMMG and horizontal gravity control positions at 23°C to replicate temperatures cells would experience in the natural environment. The HARVs were rotated at a constant velocity of 13 rpm, which prevented V. fischeri cells from forming sedimentary aggregates and to match rotation speed used in comparable squid-vibrio experiments

(Foster et al., 2013). Experiments were conducted in triplicate for each condition, strain, and time. Growth curves of all strains used in this experiment are visualized in Fig. A-1 and corresponded to previously published results (Grant et al., 2014 and Foster et al.,

2013). At the end of each HARV experiment V. fischeri were flash frozen in liquid nitrogen to halt gene expression and stored at -80°C until RNA extraction.

RNA Extraction, cDNA Synthesis, and Sequencing

Each replicate V. fischeri WT and ∆hfq culture was thawed on ice and pelleted for RNA extraction. Total RNA was extracted in triplicate for each treatment using

PowerSoil® Total RNA Isolation Kit (Qiagen, Germantown, MD) according to manufacturer’s protocol and was treated with TURBO DNase (Thermo Fisher Scientific,

Waltham, MA) to remove potential contaminating DNA. The RiboMinus rRNA removal kit (Thermo Fisher Scientific, Waltham, MA) was used to deplete large rRNAs and samples were processed with the Zymo RNA Clean & Concentrator kit (Zymo

Research, Irvine, CA). The remaining mRNA was pooled between replicates, the concentration was determined by Qubit 2.0 (Thermo Fisher Scientific, Waltham, MA) and quality was evaluated with a 2100 Bioanalyzer (Agilent Technologies, Santa Clara,

CA). High-quality RNA was converted to cDNA using a modified SuperScript Double

Stranded cDNA synthesis kit (Thermo Fisher Scientific, Waltham, MA). A total of three replicate cDNA libraries were generated for each treatment (note: only two libraries

were generated for ∆hfq gravity controls) and sequenced using the Illumina NextSeq500 platform (2 x150 bp paired-end reads; Illumina, San Diego, CA).

Bioinformatic Analysis

Sequences were quality trimmed and filtered with Trimmomatic v0.32 using default parameters (Bolger et al., 2014). The quality of the output files was then analyzed using FastQC v0.10.1 (Andrews, 2014). Reads were then aligned to the V. fischeri ES114 reference genome (GenBank ID: ASM11800v1) using Bowtie 2 v2.2.8

(Langmead et al., 2009). Gene counts were obtained using HTSeq-count v 0.6.1

(Anders et al., 2013). Genes with no expression across all conditions were removed.

Differential expression analysis was conducted in R using the package DESeq2 (Love et al., 2014). Genes were considered significantly differentially expressed at adjusted p- value (padj) < 0.05. UpSetR was used to visualize the intersection of differentially expressed genes (Conway et al., 2017). The most recent KEGG database was accessed through the R package KEGGREST v1.16.1 to determine KEGG functional pathways and higher KEGG level classification (Tenebaum, 2017). The top 103 DEGs among time treatment comparisons with one defined KEGG pathway were visualized in a heatmap. Expression values for heatmap were normalized with TMM (Trimmed Mean of M-values) using the NOISeq package (Tarazona et al., 2015) and scaled by the sum of each row (z-score) using heatmap.2 in the ggplots package in R (Warnes et al.,

2016). Genes associated with multiple pathways at KEGG level 2, or had no specific

KEGG pathway association, were not displayed in the heatmap.

Real-Time Quantitative PCR (qRT-PCR)

Several significantly differentially expressed genes were chosen for targeted qRT-PCR confirmation. Primers are listed in Supplemental Table A-1. The qRT-PCR

reactions were prepared using the iTaq Universal SYBR Green One-Step Kit (Biorad,

Hercules, CA) with 10 ng of RNA per reaction. Amplification and quantification were completed using a Biorad SCX9600 Real Time System (Biorad, Hercules, CA). The amplification conditions were as follows: an initial incubation at 50°C for 10 min then 1 min at 95°C followed by 39 cycles of 95°C for 10 s and 60°C for 15 s. Each comparison was run in triplicate and three technical replicates were run for each biological replicate.

The relative expression of each gene was analyzed using the comparative Cq method

(ΔΔCq) on the Biorad system. The gene rpoD was chosen as the housekeeping reference gene for normalization of transcript abundances as previously described

(Miyashiro et al., 2010).

Results

Overview of Transcriptome Analysis of V. fischeri cultivars under gravity and LSMMG Conditions

RNA-seq was used to evaluate the transcriptional changes of wild type V. fischeri

ES114 (WT) and a ∆hfq deletion mutant (KV7142) at two key time points during bacterial growth. Strains were grown aerobically in a rotary culture system using high aspect ratio vessels (HARVs) in both gravity and LSMMG positions and their transcriptomes were examined during exponential (12 h) and stationary (24 h) growth phases. Growth curves for all strains, including ∆hfq complementation mutants (KV148,

KV149) are shown in Fig. A-1 and correlate with previously published studies (Foster et al., 2013 and Grant et al., 2014). There was a statistically higher number of colony forming units per ml at 12 h in LSMMG conditions, but the growth curves suggest both the LSMMG- and gravity-treated cultures were in log phase growth. Three libraries were generated for each treatment (note: only two libraries were created for the ∆hfq gravity

controls). An average of 11.19 million high quality reads that consistently mapped (>

95%) to Vibrio fischeri ES114 genome were obtained for each treatment (Table 2-1).

This level of sequencing depth in RNA-seq analyses has been shown to be effective in detecting the majority of significant changes to gene expression profiles in bacteria

(Abshire et al., 2016 and Haas et al., 2012).

For control purposes, we first examined the transition between exponential and stationary phase in both WT and ∆hfq to ensure key metabolic transitions were being captured with the RNA-seq analyses in the HARV environment (Fig. 2-1; Fig. 2-2).

During growth under both LSMMG and gravity conditions the V. fischeri strains exhibited several typical responses of bacterial populations during stationary phase, including an overall down-regulation of genes associated with the translational apparatus, such as ribosomal proteins (e.g. rpsB, rpsG, rpsL, rpsM, rplM), tRNA synthases (e.g., tyrS, leuS, lysS), and translation factors (e.g., tufAB, infC, miaA) (Fig.

2-2; Object 2-1 and 2-2). In each treatment during stationary phase there was also an increase in the expression of several genes typically associated with stress responses, such as oxidative (e.g. VF_A0005, VF_A0335) and heat shock chaperones (e.g. dnaK1, dnaK2, htpG, hslO, hslV, ibpA, VF_1466) (Object 2-1, 2-2, and 2-3). These results are consistent with numerous studies indicating that in stationary phase bacteria become resistant to a wide range of environmental stresses (Kolter et al., 1993 and Lazazzera,

2000) and down-regulate their translational apparatus during nutrient limiting conditions

(Paul et al., 2004). Together, the results indicate that the RNA-seq libraries were capturing the major transcriptional changes in V. fischeri during the different treatments.

LSMMG-specific Changes in V. fischeri Transcriptome

Pairwise comparisons between the WT libraries revealed no significant differentially expressed genes (DEGs; adjusted p-value < 0.05) between the LSMMG and gravity treatments for WT at each of the time points tested, suggesting that the modeled microgravity environment had an overall minimal impact on the transcriptome of WT V. fischeri (Fig. 2-1). However, a comparison between the time points revealed five LSMMG-specific upregulated DEGs in both the WT and ∆hfq cultivars at 12 h when compared to 24 h (Fig. 2-1b; Object 2-3). Two of these DEGs were associated with stress responses, including open reading frame (ORF) VF_2561, whose gene product was annotated as a cold shock protein, and yceD, which encodes for a hypothetical protein that has been implicated in oxidative stress resistance in Bacillus subtilis (Hoper et al., 2005).

At 24 h there was an up-regulation of seven LSMMG-specific genes in both V. fischeri WT and ∆hfq strains when compared to 12 h libraries (Fig. 2-1b; Object 2-3), several of which are known to be critical for stress resistance and microbial pathogenesis. For example, there was an up-regulation of yghU, which encodes for glutathione S-transferase and is essential for the detoxification of reactive oxygen species (ROS) in a wide range of taxa (Kanai et al., 2006) including several symbiotic taxa (Heddi et al., 1998 and Pontes et al., 2008). There was also an increase in expression of blc, which encodes for the outer membrane lipoprotein lipocalin that is upregulated under high osmotic stress conditions in Escherichia coli and thought to play a role in antimicrobial resistance in several other bacteria (Bishop, 2000). Additionally, there was an increase in expression of zwf, which encodes for glucose 6-phosphate dehydrogenase (G6PD) and has been shown to be required for virulence in Salmonella

Typhimurium and protects against reactive oxygen and nitrogen species in both S.

Typhimurium and E. coli (Lundberg et al., 1999 and Sandoval et al., 2011). There was also up-regulation of katA, which encodes for the only periplasmic catalase present in the V. fischeri genome and is induced under oxidative stress conditions as well as required for symbiosis competence in V. fischeri (Visick and Ruby, 1998).

Differential Gene Expression Changes in ∆hfq Mutant Under Both Gravity and LSMMG Conditions During Exponential Phase

In gravity conditions, there were few significant DEGs upregulated in the ∆hfq mutant compared to LSMMG at 12 h (Fig. 2-1, Fig. 2-3a; Object 2-1). One DEGs upregulated in gravity conditions was gnd, which encodes for 6-phosphogluconate dehydrogenase (6PGD), a key enzyme in the pentose phosphate pathway. The 6PGD enzyme produces NADPH, which provides the reducing power to several antioxidant proteins (Ezraty et al., 2017). Additionally, in the ∆hfq mutant, there was an increase in the expression of katA in gravity compared to LSMMG. The RNA-seq trends for katA were independently confirmed with qRT-PCR, although different transcript abundances were observed between the two methodologies for katA likely due to the differences in resolution between the approaches (Fig. 2-3b). At 12 h there were also three ORFs with unknown function upregulated in the ∆hfq gravity conditions (VF_2662, VF_A0979, and

VF_A1190) (Object 2-1).

Under LSMMG conditions, however, the ∆hfq mutant exhibited a pronounced change to its transcriptome compared to gravity controls at 12 h (Fig. 2-1a; Fig. 2-3a;

Object 2-1 and 2-2). During exponential phase in the ∆hfq mutant there was an accumulation of transcripts that encode for several components of the tricarboxylic acid

(TCA) cycle including succinate dehydrogenase (sdhAB), aconitate hydratase (acnB),

succinyl-CoA synthetase (sucCD), fumarate hydratase (fumB), and fumarate reductase

(frdA) (Fig. 2-3a; Object 2-1), all of which have been shown to be repressed by the sRNA RyhB in other taxa (Masse et al., 2005 and Desnoyers et al., 2012). Hfq is required for the stability and pairing of the sRNA RhyB to mRNA (Masse et al., 2005).

RhyB has been identified in the V. fischeri genome (VF_2578), however, it was not significantly differentially expressed in this study. Additionally, there was an enrichment of transcripts associated with fatty acid synthesis (e.g. fabDFH), which in Salmonella

Typhimurium is dependent on the sRNA SmpP (Ansong et al., 2009), as well as oligopeptide transport (e.g., oppADF), which is regulated by the Hfq-dependent small

RNA GcvB in a number of taxa including several vibrios (Silveira et al., 2010).

Homologs to SmpP and GcvB have not yet been reported in V. fischeri.

In LSMMG conditions there was also an increase in transcripts associated with flagella synthesis in exponential phase including genes that encode for both structural

(e.g. flaACE), basal body rod (e.g. flgD) and hook-associated (e.g. flgEK) proteins

(Millikan and Ruby 2004) (Fig. 2-3a; Object 2-1 and 2-2). The differential expression of flaA and flgK were confirmed with qRT-PCR in the ∆hfq (Fig. 2-3b). Hfq has been associated with flagellar synthesis in a wide range of taxa, including both pathogenic

(e.g., Salmonella) and mutualistic bacteria (e.g., Sinorhizobium meliloti), however, in most cases mutants defective in hfq exhibit a repression of flagellar synthesis genes and in some cases are non-motile (Sittka et al., 2008 and Torres-Quesada et al., 2014).

Differential Gene Expression Changes in ∆hfq Mutant Under Both Gravity and LSMMG Conditions During Stationary Phase

The ∆hfq mutants exhibited extensive transcriptional changes during stationary phase under both gravity and LSMMG conditions (Figs. 1-2; 4; Object 2-1 and 2-2). One

pronounced characteristic of the ∆hfq transcriptomes was the up-regulation of numerous transcriptional regulators during stationary phase (Fig. 2-4; Object 2-1 and 2-

2). In both gravity and LSMMG conditions there was increased expression of agaR, which encodes for a putative transcriptional repressor of N-acetyl galactosamine

(GalNAc) transport and metabolism in a wide range of bacterial taxa (Leyn et al., 2012); iscR, a transcriptional repressor of genes associated Fe-S cluster assembly proteins

(Schwartz et al., 2001); and yqhC, whose gene product regulates aldehyde reductase

(Lee et al., 2010).

In gravity conditions, there was differential expression of VF_1401, which encodes for a Fis family transcriptional regulator, and cysB, which belongs to the LysR family of regulators and is a global transcriptional activator of cysteine biosynthesis and sulfur metabolism (Maddocks and Oyston, 2008). CysB is also the only known negative regulator of HslJ, a heatshock/heat-inducible outermembrane lipoprotein (Jovanovic et al., 2003). The hslJ gene is upregulated in the ∆hfq mutant under both gravity and

LSMMG in stationary phase (Fig. 2-4a; Object 2-1 and 2-2). Additionally, in gravity conditions, the ∆hfq mutant had increased expression of genes associated with the

Type II section pathway (e.g. gspD, mshQ2) and several transport proteins (e.g. ybhG, argT, hisP) (Object 2-1 and 2-2).

Under LSMMG conditions, the genes of several different transcriptional regulators were upregulated during stationary phase. For example, VF_2037, which shares similarity to Cro/Ci family transcriptional regulators, was upregulated but has unknown function in V. fischeri. Additionally, nrdR was also upregulated and its gene product represses the ribonucleotide reductase production (i.e., nrdHIEF), bacterial

chemotaxis, and more recently has been shown to inhibit cell adhesion to epithelial cells in E. coli (Naveen and Hsiao, 2016 and Torrents et al., 2007). In addition to transcriptional regulators, there was also a differential expression of genes associated with modifications to the outer membrane in ∆hfq under LSMMG (Fig. 2-4a; Object 2-1 and 2-2). For example, there was increased expression of the gene slp, which encodes for an outer membrane lipoprotein associated with stress responses during stationary phase and is typically repressed by the Hfq-dependent sRNA GcvB (Stauffer and

Stauffer, 2013). There was also an increased expression of skp, a periplasmic chaperone protein that is associated with the RpoE regulon and is involved in the folding of intermediates of outer membrane proteins (Schafer et al., 1999). Interestingly, rpoE transcription was down-regulated at 24 h compared to 12 h in the ∆hfq mutants irrespective of the gravity or LSMMG treatment (Object 2-1).

Discussion

To prepare for long-duration space travel it is essential to have a comprehensive understanding of the impact that spaceflight has on the physiology of host-associated microbiomes to promote and maintain astronaut health. There has been an extensive focus on the effects of spaceflight and simulated microgravity environments on bacterial pathogens (Abshire et al., 2016; Castro et al., 2011; Kim et al., 2013; Nickerson et al.,

2000; Orsini et al., 2017; Pacello et al., 2012; Tucker et al., 2007; Vukanti et al., 2012;

Wilson et al., 2002, 2007), however, only a few studies have begun to examine the impact on beneficial microbes that promote the health of the host organism (Casaburi et al., 2017; Castro-Wallace et al., 2017; Foster et al., 2013; Grant et al., 2014; Shao et al.,

2017). In this study, we expand on this recent work and examine the effects of an

LSMMG environment on the transcriptome of the beneficial symbiont Vibrio fischeri,

which forms a mutualistic association with the bobtail squid Euprymna scolopes and is critical for the host’s normal development. The results of this study suggest that there were few transcriptional changes in the WT V. fischeri under LSMMG and that most changes in the bacterium were attributed to the growth phase transition between exponential and stationary phase. Additionally, RNA-seq analyses revealed that mutants defective in the global regulator Hfq exhibited a pronounced change in transcriptional profiles under LSMMG, providing new insight into the role this regulator plays in the symbiotic V. fischeri under analog microgravity conditions.

Previous studies have shown that V. fischeri exhibits an altered growth response in simulated microgravity conditions, with cultures reaching higher cell densities compared to gravity controls (Grant et al., 2014). This altered growth response under

LSMMG has been observed in many, but not all, taxa (Arunasri et al., 2013; Orsini et al., 2017; Vukanti et al., 2012; Wilson et al., 2002) and is thought to reflect the selected growth medium. The nutritional micro-environment of the cells in LSMMG has been shown to significantly impact microbial physiology (Arunasri et al., 2013 and Tucker et al., 2007). For example, under low phosphate conditions some microbes, such as

Salmonella Typhimurium, exhibit increased virulence (Wilson et al., 2008). Despite the change in growth phenotype in V. fischeri under LSMMG conditions, no significant

DEGs were observed when the transcriptomes of 12 h LSMMG-treated WT cells were compared to 12 h gravity controls, even under the low phosphate conditions of SWT media (Fig. 2-1). Similar results were observed when 24 h LSMMG-treated libraries were compared to 24 h gravity-treated libraries, suggesting that modeled microgravity itself does not significantly alter transcription within V. fischeri compared to gravity

controls. These results are comparable to several recent studies on the effects of

LSMMG on the probiotic strain Lactobacillus acidophilus (Castro-Wallace et al., 2017 and Shao et al., 2017). Under the anaerobic conditions, transcriptomes of the L. acidophilus cultivar showed no DEGs in LSMMG when compared to gravity controls at stationary phase (Castro-Wallace et al., 2017). As both L. acidophilus and V. fischeri typically form associations with host epithelium and are regularly exposed to low shear conditions in their natural environments, the modeled microgravity environment does not likely impose a significant stress for these taxa. Additionally, recent studies have shown that under LSMMG conditions V. fischeri exhibits no delay in colonizing host tissues (Foster et al., 2013) and that during spaceflight V. fischeri reached the same colonization densities as under gravity controls (Casaburi et al., 2017). Together, these results suggest that microgravity conditions do not negatively impact V. fischeri.

Although the overall transcriptional response in V. fischeri was typical of the normal transition to stationary phase, there were several stress-associated genes differentially expressed under LSMMG conditions in both the WT and ∆hfq mutant. Of the DEGs differentially upregulated in LSMMG during the transition to stationary phase, three of the observed genes (i.e., yceD, yghU, and katA) are associated with stress responses and have been observed in E. coli K12 under modeled microgravity conditions (Vukanti et al., 2008). In E. coli these genes are associated with both oxidative and osmotic stress responses and may suggest that under LSMMG a small microenvironment of increased stress may occur around the V. fischeri cells. The formation of nutrient-depleted microenvironments has long been postulated under

LSMMG conditions, which may simulate the genomic and physiological responses of

cells as they transition to stationary phase (Klaus et al., 1997; Vukanti et al., 2008).

During exponential phase, V. fischeri cells are flagellated, as opposed to stationary phase when production of flagella is decreased and cells are non-motile (Edward Ruby, personal communication). The flagella during exponential phase may be disrupting the low shear environment thereby minimizing the effects of LSMMG on the cells and resulting in very few stress-associated genes being differentially regulated under the

LSMMG environments at 12 h.

At 24 h, there was also expression of several other stress-associated genes (e.g. blc, zwf, katA), however, only katA has been previously described in V. fischeri. The katA gene encodes for a periplasmic catalase that is essential for the normal colonization of the host squid and is typically induced as cells approach stationary phase (Visick and Ruby, 1998). The higher expression of katA during LSMMG in stationary phase suggests the cells are experiencing a more pronounced oxidative stress environment compared to the gravity controls. Similar results have been observed in several Salmonella spp., where bacteria grown under LSMMG conditions exhibited a higher resistance to hydrogen peroxide and increased catalase activity

(Pacello et al., 2012). Interestingly, although there was up-regulation of blc, which encodes for an outer membrane lipoprotein in E. coli expressed during osmotic stress

(Bishop, 2000), there were no observed significant DEGs associated with lipopolysaccharide biosynthetic genes or other cell membrane modifications, which have been reported to be differentially regulated under LSMMG (Arunasri et al., 2013;

Orsini et al., 2017; Wilson et al., 2007). Together, the results reinforce the interpretation that the low shear environment of modeled microgravity does not significantly alter the

transcriptional response V. fischeri cultivars, but that the few genes that are differentially expressed are primarily associated with environmental stress responses.

Although the transcriptome of WT cells did not display extensive changes in response to LSMMG, mutants defective in the global regulator Hfq exhibited a pronounced transcriptional response to LSMMG conditions (Fig. 2-1; Object 2-1). The

RNA-binding protein Hfq has been identified as an important transcriptional regulator in several pathogenic taxa in response to both spaceflight and microgravity analog environments (Castro et al., 2011; Crabbé et al., 2011; Wilson et al., 2007). Additionally, the gene encoding Hfq has been shown to be down-regulated in several taxa, including

V. fischeri, under LSMMG conditions (Grant et al., 2014; Wilson et al., 2007). One of the major functions of Hfq is to bind to sRNAs, which then together subsequently target various mRNAs, thereby regulating or modulating the stability of the mRNAs (Beisel and

Storz, 2010). The sRNAs can also serve as activators or repressors of mRNA translation (Masse and Gottesman, 2002). During exponential phase, there was a significant accumulation of transcripts associated with the TCA cycle in the ∆hfq mutant.

Up-regulation of genes associated with the TCA cycle has been observed under modeled microgravity (Abshire et al., 2016; Crabbé et al., 2010; Roy et al., 2016) and many TCA cycle genes are typically repressed by the Hfq-dependent sRNA RhyB

(Desnoyers and Masse, 2012), which recruits RNase E and facilitates mRNA degradation (Masse et al., 2005). In the ∆hfq mutant, the increase in recovered TCA cycle transcripts under LSMMG conditions likely reflects an inhibition of mRNA degradation rather than an up-regulation of these genes during LSMMG.

The ∆hfq mutants also exhibited an increase of transcripts associated with flagellar assembly under LSMMG conditions compared to WT cells during exponential phase growth (Fig. 2-3a; Object 2-1 and 2-2). The expression of flagellar assembly genes under microgravity conditions appears to be highly variable based on the taxa and whether the cells were exposed to actual spaceflight or analog conditions (Benoit and Klaus, 2007; Roy et al., 2016; Tucker et al., 2007; Wilson et al., 2008;). The regulation of flagellar synthesis is complex and is not fully delineated in V. fischeri

(Norsworthy and Visick, 2013), although in many taxa it occurs both at the transcriptional and translational levels (De Lay and Gottesman, 2012). In E. coli there are numerous Hfq-dependent sRNAs involved in the positive (e.g. McaS) and negative

(e.g. ArcZ, OmrA, OmrB, SdsR, GadY, and OxyS) regulation of flagella synthesis, however, none of these Hfq-dependent sRNAs have been reported in the V. fischeri genome. The up-regulation of flagellar synthesis transcripts during exponential phase in

LSMMG may suggest the cells are attempting to move out of potential zones of nutritional depletion. Alternatively, as there is a lack of differentially expressed flagellar synthesis transcripts during stationary phase when nutritional depletion is more severe, the results may simply suggest a lack of negative repression of the flagellar synthesis in the ∆hfq mutants during cell growth. A more detailed analysis of transcriptional and translational regulation of flagella synthesis in V. fischeri is needed.

In stationary phase growth, the ∆hfq mutants exhibited a pronounced increase in the expression of transcriptional regulators under both gravity-specific (e.g. Fis-family regulator, VF_1401; cysB), and LSMMG-specific (Cro/Ci family regulator VF_2037; nrdR) conditions. To our knowledge, none of these regulators have been reported to be

differentially expressed during spaceflight or modeled microgravity conditions. For example, nrdR was first shown to positively regulate synthesis of ribonucleotide reductases in response to DNA damage and oxidative stress in Streptococcus pyrogens

(Borovok et al., 2004) and more recently has been shown in E. coli to be involved in responding to iron starvation (Martin and Imlay, 2011) and the host immune system

(Zhang et al., 2015). As the stress responses of several taxa are altered under spaceflight and analog conditions (Roy et al., 2016), the role of these transcriptional regulators under microgravity-like stress conditions needs to be investigated further.

The ∆hfq mutant also exhibited significant DEGs associated with outer membrane proteins that are differentially expressed during stress conditions in a wide range of taxa. For example, slp is a carbon-starvation induced gene that has been shown to be upregulated in V. cholerae ∆hfq mutants (Ding et al., 2004) and is released in outer membrane vesicles (Lee et al., 2007). In E. coli the Slp lipoprotein is essential for acid and metabolic stress and is negatively regulated by the Hfq-dependent GvcB

(Sharma et al., 2011). Although GvcB has not been reported in V. fischeri it has been reported in several environmental vibrios (Silveira et al., 2010). These differential transcriptional responses in genes encoding for outer membrane proteins in the ∆hfq mutants may indicate that the outer membrane of the mutants may have a different composition compared to the WT cells under both gravity and LSMMG conditions.

Recent studies have shown that V. fischeri-derived outer membrane vesicles can induce full developmental remodeling of the host light organ tissues (Aschtgen et al.,

2016), however, mutants defective in Hfq induce an altered phenotype including a decrease in the number of dying apoptotic cells in the host tissues under both LSMMG

and gravity conditions (Grant et al., 2014). The mechanism for these decreased levels of apoptotic cells in ∆hfq mutants is not clear but may be the product of a remodeled outer membrane.

Although the full range of environmental factors that impact a host’s microbiome in the space environment has yet to be fully understood, this study provides insight into the role that microgravity may have on those beneficial microbes that typically associate with animal tissues. The results suggest that under normal growth conditions modeled microgravity does not negatively impact the transcriptional activities of V. fischeri indicating that the beneficial, mutualistic lifestyle of the bacterium is maintained under analog microgravity conditions. The results also deepen our understanding of the mechanisms by which organisms are adapting to changes in their nutritional environment and how the global regulator Hfq impacts the regulatory processes of V. fischeri in both LSMMG and gravity conditions. These results indicate that Hfq serves as an important mechanism by which V. fischeri regulates responses to external stimuli. As many of the mechanisms by which pathogenic and beneficial microbes sense and respond to their ever-changing environment are shared, it will be critical to continue to explore the processes by which microbes form complex communities and interact with their hosts during spaceflight to help mitigate any potential health threats during long- term missions.

Table 2-1. Overview of recovered transcriptome sequencing results from V. fischeri wild-type (WT) and ∆hfq mutant exposed to low shear modeled microgravity (LSMMG) and gravity conditions. Total reads* per treatment Average reads per Mapped reads (% Time point Treatment Strain (million)* library (million) mapped) 12 h Gravity WT 11.18 3.73 96.31 12 h Gravity ∆hfq 7.20 3.60 96.20 12 h LSMMG WT 11.73 3.91 96.17 12 h LSMMG ∆hfq 11.25 3.75 96.08 24 h Gravity WT 13.03 4.34 95.86 24 h Gravity ∆hfq 8.40 4.20 96.49 24 h LSMMG WT 12.66 4.22 96.57 24 h LSMMG ∆hfq 14.04 4.68 97.10 *high-quality reads were filtered using Trimmomatic default parameters

Figure 2-1. Overview of the differentially expressed genes associated to each of the eight transcriptomic comparisons in Vibrio fischeri. A) Matrix of the significant differentially expressed genes (DEGs) in WT and ∆hfq mutants under LSMMG (M) and gravity (G) conditions with the colors reflecting the relative abundance. B) Venn diagrams indicating the intersections of the significant DEGs shared between the different transcriptomic comparisons in this study at 12 and 24 h.

Figure 2-2. Heat map depicting the clustering patterns of the eight treatments by KEGG pathways associated with the proposed function of the V. fischeri genes at 12 and 24 h. Colors represent the differential abundance of individual genes listed by V. fischeri identification number (VF-ID) for both WT and ∆hfq mutants under low-shear modeled microgravity (µG) and gravity (G) conditions.

Figure 2-3. Differential gene expression between LSMMG and gravity (G) in a ∆hfq mutant at 12 h. A) Volcano plot visualizing the global transcriptional changes within the mutant cells. All normalized transcripts were plotted and each point reflects one gene with those in red indicating significance (adjusted p-value < 0.05). Full details of the significant differentially expressed genes are listed in Object 2-1. B) Quantitative real-time PCR of selected genes compared under LSMMG and gravity conditions in WT and ∆hfq mutants. Genes selected were significant in the RNA-seq results and included genes encoding a flagellin structural protein (flaA), flagellar hook-associated protein (flgK) and catalase (katA). Asterisk indicates significance between comparisons and error bars reflect the standard error of the mean.

Figure 2-4. Differential gene expression between WT and ∆hfq mutant under LSMMG at 24 h. A) Volcano plot visualizing the global transcriptional changes within the V. fischeri cells. All normalized transcripts were plotted and each point reflects one gene with those in red indicating significance (adjusted p-value < 0.05). Full details of the significant differentially expressed genes are listed in Object 2-1. B) Quantitative real-time PCR of selected genes compared under low- shear modeled microgravity (LSMMG) and gravity conditions in wild type (WT) and ∆hfq mutants. Genes selected included dnaK, which encodes for a stress-associated chaperone protein and lpxD, which assists in the biosynthesis of lipid A. Asterisk indicates significance between comparisons and error bars reflect the standard error of the mean.

Object 2-1. Significant differentially expressed genes in pairwise comparisons at log2- fold change of +/- 1 and padj ≤ 0.05. (.xlsx and 110 kb).

Object 2-2. Normalized read counts of data associated with Figure 2. ( .xlsx )

Object 2-3. Significant Differentially expressed genes within treatments at 12 h compared to 24 h (.xlsx and 29 kb).

CHAPTER 3 THE IMMUNE SYSTEM OF EUPRYMNA SCOLOPES IN RESPONSE TO ITS BENEFICIAL BACTERIA

Introduction

During spaceflight, one of the major physiological challenges to animals is the dysregulation of their innate immune system (Mukhopadhyay et al., 2016), however, for several decades the majority of innate immune research on this issue has focused on the response of the immune system to pathogenic challenges (Sonnenfeld, 2003;

McFall-Ngai, 2008). Most, if not all, animals maintain relationships with microbial partners that are beneficial and often persistent over the life of the animal host (Hooper et al., 2012; McFall-Ngai et al., 2010; McFall-Ngai et al., 2013). Hosts have evolved molecular mechanisms within their immune system to identify their symbionts and manage the beneficial relationship (Chu and Mazmanian, 2013). The study of model systems, such as the squid-vibrio symbiosis, in modeled microgravity conditions can aid in better understanding of how the innate immune system of a host responds and adapts to persistent symbionts under spaceflight conditions.

To more fully understand how the spaceflight environment impacts the changes to the innate immune response of the squid-vibrio system it is first necessary to have a comprehensive understanding of the key components of the innate immune system, such as receptors, effectors, and signaling mechanisms, used by the host to respond to the V. fischeri as well as to the space environment. There has been extensive work conducted on receptors important in the squid-vibrio symbiosis, however, there is much less known about the putative effectors and signaling mechanisms in the host immune system.

Very early in the squid-vibrio symbiosis the bacteria infect the light organ, which then undergoes several innate immune-related developmental changes that are activated by microbial-associated molecular patterns (MAMPs). The light organ tissues recognize the bacterial-derived MAMPs through pattern recognition receptors (PRRs) molecules, such as peptidoglycan recognition proteins (PGRPs), toll-like receptors

(TLRs), or galectins (Collins et al., 2012; Goodson et al., 2005; Schleicher and Nyholm,

2011; Troll et al., 2009; 2010). Surface receptors are also differentially expressed in simulated microgravity and spaceflight, making these PRRs important targets for future study on receptors response to microgravity in a symbiotic context (Kaur et al., 2008;

Marcu et al., 2011). The recognition of non-self MAMPs is a conserved feature of the innate immune response in all animals (Medzhitov and Janeway, 2002; Hoffmann,

2003).

The release of the MAMPs from V. fischeri activates the trafficking of hemocytes, the squid’s only immune cell, into blood sinus and crypt spaces of the light organ. The precise role of hemocytes in the initiation of the symbiosis is still largely unknown but the hemocytes have macrophage-like functions and phagocytize non-specific bacteria, including V. fischeri at the beginning of the symbiosis (Nyholm et al., 2009). However, as the symbiosis progresses the hemocytes begin to recognize the V. fischeri cells and by adulthood no longer phagocytize V. fischeri, although the mechanisms of this recognition are not fully understood (Nyholm et al., 2009). In addition, hemocytes carry proteins with enzymatic activity that are important in maintaining the symbiosis and can also act as effectors of the immune system response.

One example of the hemocyte’s enzymatic effector function is chitotriosidase, which also aids in providing a nutritional carbon source for V. fischeri in the crypt spaces throughout the symbiosis (Schwartzman et al., 2015). Hemocytes are in direct contact with V. fischeri and present polymeric chitin within these crypt spaces (Heath-Heckman and McFall-Ngai, 2011; Mandel et al., 2012). Chitin derived oligosaccharides (e.g. chitobiose) are also thought to prime V. fischeri cells and act as a chemoattractant signal during the onset of the symbiosis (Kremer et al., 2013; Mandel et al., 2012).

Chitotriosidase has also been found to be important in the innate immune response and often found associated with innate immune cells (Eijk et al., 2005; Tran et al., 2014).

Transcription of several known chitotriosidases in the squid and chitinases from the symbiont are upregulated during the delivery of chitin to the crypts in adult squid (Wier et al., 2010), presumably to breakdown the host-derived chitin for V. fischeri consumption. The breakdown of chitin within the crypts creates an acidic environment that induces hemocyanin proteins from the blood to release oxygen into the crypt spaces, which in turn increases overall bioluminescence (Kremer et al., 2014).

Additionally, hemocyanin plays an important role in the host squid immune system as it harbors antimicrobial phenoloxidase activity to deter pathogenic bacteria in host invertebrates (Decker and Jaenicke, 2004; Kremer et al., 2014).

At this point in the symbiosis, the crypt spaces within the light organ are an acidic and oxidatively stressful environment. To combat the release of reactive oxygen species

(ROS) E. scolopes uses effector enzymes, such as superoxide dismutase (SOD)

(Shleicher and Nyholm, 2011). SOD enzymes are also important in deterring excessive

ROS in the innate immune response to infection and have been localized to hemocytes

several of invertebrates (Goodall et al., 2004; Yu et al., 2011; Zelck et al., 2005). In addition to oxidative stress recent studies have shown that the expression of effector molecules, such as chitotriosidase, hemocyanin, and SOD, are altered in E. scolopes under the other stress conditions, including modeled microgravity (Casaburi et al.,

2017). These effector enzymes can also initiate downstream signaling pathways important in innate immune response (McCubrey et al., 2006).

The response of E. scolopes to specific various innate immunity PRRs and effectors can initiate signaling cascades that lead to the activation of transcription factors (Goodson et al., 2005). One of the most important groups of transcription factors associated with the onset of the squid-vibrio symbiosis is the NFκB pathway (Chun et al., 2008; Goodson et al., 2005; McFall-Ngai et al., 2010). Several individual components of the NFκB pathway have been found in E. scolopes but most of the pathway has not yet been delineated (Castillo et al., 2015; Collins et al., 2012; Goodson et al., 2005; Schleicher et al., 2014)

Typically, NFκB transcription factors contain Rel homology domains that bind to specific DNA sequences in various genes (Gilmore, 2006). These transcription factors can form homo- or heterodimers that can regulate the expression of many genes involved in innate immune response (Oeckinghaus et al., 2011). When the NFκB transcription factors are not stimulated they are sequestered in the cytoplasm by either the ankyrin repeat portion of the NFκB specific transcription factor, or inhibitor proteins known as IκBs, that inhibit the ability of the transcription factors from translocating into the nucleus (Verma et al., 1995). There are other proteins including KBRS1 and NCLR3

that can regulate NFκB transcription by affecting other components in the signaling pathway (Schneider et al., 2012; Matondo et al., 2017; Zhang et al., 2014).

The most common forms of the transcription factors are RelA and NFκB1/p50

(Hayden and Ghosh, 2011). In the canonical pathway, stimulation, which can occur from different PRR recognition, activates the IκB kinase (IKK) complex that generally consists of three proteins: two catalytic components, the IKKβ and IKKα, and a regulatory component, IKKγ (otherwise known as NEMO (NF-kB essential modifier)). The activated IKK complex then phosphorylates the NFκB inhibitor, nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (IκB), which is then polyubiquitinated and targeted for proteasomal degradation. Once the NFκB subunits are free from IκB they can translocate into the nucleus and initiate transcription of target genes.

The activation of the IKK complex can occur through stimulation of PRRs that then recruit adapter proteins, such as myeloid differentiation primary response 88

(MYD88), that then further activate protein kinases such as IRAK (IL-1 receptor- associated kinase) (Kawai and Akira, 2007). These protein kinases can then dissociate from MYD88 and interact with ubiquitin ligases such as TNF receptor-associated factors

(TRAFs) that can polyubiquitinate themselves as well as other proteins including IKKγ.

These ubiquitinated proteins can then recruit protein kinase complex that includes TAK1

(transforming growth factor-b-activated kinase-1) and TABs (TAK1 binding proteins)

(Adhikari et al., 2007). This TAK1/TAB complex then activates the IKK complex for

NFκB driven transcription. Research has shown that many of the genes that are targeted by NFκB are in response to MAMPs and produce immune-related proteins

such as reactive oxygen species, various cytokines, and antimicrobial peptides

(reviewed in Hayden and Ghosh, 2011).

Components of the NFκB pathway have been found in various cell types, such as specific immune cells and epithelial cells (Vitiello et al., 2011). It is believed that E. scolopes contains an NFκB pathway within cells of the light organ (e.g. epithelial cells from the CEA or in hemocytes) that is most likely activated by PRRs and may be driving the bacteria-induced developmental phenotypes (Goodson et al., 2005; Troll et al.,

2009). However, the NFκB pathway and its activation have not thoroughly been investigated in the squid-vibrio symbiosis.

In this chapter, I build on previous work by utilizing the newly sequenced reference transcriptome and genome (Belcaid et al., 2019) to give a full overview of the genetic components of NFκB related innate immune signaling present in an important beneficial symbiotic model system (Belcaid et al., 2019). In addition, this work aims to reveal a more comprehensive immune system response of E. scolopes across different tissues and in relation to the symbiotic state.

Materials and Methods

Identification of Innate Immune and NFκB Specific Related Genes

All of the genes identified in this study were mined from the fully assembled genome and transcriptome (Belcaid et al., 2019). Briefly, genomic DNA was obtained from a single adult male squid and sequenced using Illumina HiSeq2000 and PacBio to construct the E. scolopes genome. For the reference transcriptome, a hybrid approach with long and short reads from 31 different tissues and developmental stage transcriptomes of E. scolopes was used (Fig. 3-1; Belcaid et al., 2019). In addition, a

PacBio IsoSeq library was also created with RNA extracted from several different

tissues of an E. scolopes squid and pooled (Belcaid et al., 2019). All transcriptomic libraries were quality filtered and normalized before de novo assembly using Trinity v2.4.0 (Grabherr et al., 2011; Haas et al., 2013). Transcripts were annotated using

BLASTX against SwissProt database v.2016 according to Trinotate pipeline v2.0.6

(http://trinotate.github.io/). In addition, previously published E. scolopes sequences of interest were obtained from NCBI GenBank (http://www.ncbi.nlm.nih.gov/) and used to compare to new sequences found from the reference transcriptome.

Innate immunity genes of interest were identified according to the SwissProt

(v.2016) annotation and non-redundant database (NCBI) annotation of the E. scolopes transcriptome (Belcaid et al., 2019). All comparisons of genes were done at the amino acid level unless otherwise stated. Each transcript was translated using the ExPASy

Translate tool (http://web.expasy.org/translate/) and BLASTed to the available genome browser of E. scolopes to determine gene location and alternatively spliced isoforms

(Belcaid et al., 2019). Domain structures of the translated transcripts were identified using InterPro to confirm further transcript annotations (Mitchell et al., 2019; v72; https://www.ebi.ac.uk/interpro/). Similarity searches were performed by using the

BLASTp function on translated transcripts and genes obtained from the NCBI GenBank database to determine relatedness to other organisms. Local sequence similarity was performed at the amino acid level and compared using MUSCLE (MUltiple Sequence

Comparison by Log- Expectation; https://www.ebi.ac.uk/Tools/msa/muscle/) (Edgar,

2004; https://www.ebi.ac.uk/Tools/msa/muscle/) alignment and phylogenetic reconstruction was done using FastTree (Price et al., 2010).

Transcriptome Expression Analysis Across Tissues and Symbiotic State

Gene expression patterns were analyzed across different E. scolopes tissues and symbiotic conditions (Table 3-1; Belcaid et al., 2019). Normalized Trinity cluster gene counts (FPKM; fragments per kilobase transcript length per million fragments mapped) were obtained from a previous study that included all datasets used to complete the E. scolopes reference transcriptome (Belcaid et al., 2019; Casaburi et al.,

2017; Collins et al., 2012; Kremer et al., 2018; Moriano-Gutierrez et al., 2019; Pankey et al., 2014). Briefly, reads from these transcriptomes were aligned to the reference transcriptome using Bowtie and ‘gene’ clusters were created to group isoforms with high sequence similarity according to the Trinity pipeline. The gene cluster counts were

Trimmed mean of M-values (TMM) normalized to obtain an expression count table using Trinity v2.4.0. Comparative gene expression heatmaps were created on log transformed expression values and hierarchically clustered, when applicable, using the hclust() function and heatmap.2 function from the gplots package in R (Warnes et al.,

2016).

Results and Discussion

General Remarks

This chapter takes a comprehensive look at the impact of colonization by V. fischeri on the innate immune system of E. scolopes. Specifically, I am targeting the genetic components of the NFκB immune system response in Euprymna scolopes using the newly available reference transcriptome and genome. Although several components for a putative NFκB signaling pathway have been previously identified in the E. scolopes, the activation and expression within the host squid have not been well studied.

In addition to symbiotic animals infected with the wild type V. fischeri, the impact of mutants defective in luminescence on the innate immune response of the host was also examined, specifically mutants defective in the lux operon (luxCDABEG). Light production within the host light organ is an oxygen demanding process that is a critical element of the symbiosis. Mutants defective in light production are unable to be maintained in the light organ due to the inability to draw down and utilize the oxygen in the crypt spaces and are subsequently unable to persist in the oxidatively stressful environment (Koch et al., 2014; Kremer et al., 2018; Visick et al., 2000).

Also, the differential expression of the innate immune response was examined across different developmental ages of the squid in the presence (i.e., symbiotic) and absence (i.e., aposymbiotic) of V. fischeri. Specifically, I focused on animals at 12 h

(i.e., the point at which the symbiosis becomes irreversible), 24 h (i.e., light organ is colonized and producing light), 4 weeks (i.e., light organ maturation is underway) and adult tissues (i.e., capable of reproduction, 2-3 months). Transcriptomes generated to different tissues within each of these time points were also compared including the reproductive accessory nidamental gland (ANG), brain, eyes, gills, hemocytes, and skin

(Fig 3-1). The ANG is a second symbiotic organ in adult female E. scolopes, which harbors a community of commensal bacteria thought to be important in anti-biofouling of egg clutches (Gromek et al., 2015; Kerwin et al., 2017).

Using the recently released reference transcriptome and genome (Fig. 3-15) I have been able to provide confirmation of many pre-existing hypotheses regarding the function of key innate immune responses as well as highlight several new components of the NFκB signaling cascade and their expression within various tissues within the

host under both apo- and symbiotic states. The results are organized based on those receptors, effectors and signaling molecules associated with the NFκB signaling pathway.

Pattern Recognition Receptors

Several pattern recognition receptors (PRRs) were identified in this study that may play a role in initiating the NFκB signaling pathway (Fig. 3-15). Although most of the PRRs investigated in this study have been previously found in E. scolopes, the specific function and action of these PRRs in the squid-vibrio symbiosis has not been fully elucidated.

Peptidoglycan recognition proteins

Peptidoglycan recognition proteins (PGRPs) are highly conserved PRRs that recognize the peptidoglycan component of bacterial cell walls (Royet et al., 2011). Five

PGRPs have been previously reported in E. scolopes (Goodson et al., 2005; Collins et al., 2012) and a search of the reference transcriptome and genome indicated no additional PGRPs within the host squid. Previous studies on the E. scolopes PGRPs

(EsPGRPs) suggest they are involved in NFκB signaling (Goodson et al., 2005; Troll et al., 2009). Examination of the reference transcriptome identified eight transcripts related to PGRPs according to SwissProt annotation (Table 3-1). One of the transcripts,

PGPLE_DROM, was too short for comparison and did not have PGRP-related domains, whereas all other transcripts aligned to the other PGRPs previously found (Object 3-1).

Interestingly, all PGRPs contained signal peptide sequences, except for EsPGRP4, which instead contained two transmembrane helix domains (Fig. 3-2B). This result was rather surprising considering that when EsPGRP1 was first described a signaling peptide was not reported (Fig. 3-2B; Goodson et al., 2005). This discrepancy may be

due to the advancement of domain analysis techniques, such as InterPro, used today compared to when this gene was first found (Goodson et al., 2005). Further bioinformatic analysis of the PGRPs indicates that they are catalytic as they contain a

N-acetyl-muramyl-L-alanine amidase activity domain and the necessary zinc-binding residues involved in breakdown of peptidoglycan (PGN) from bacteria (Fig. 3-2B;

Dziarski et al., 2016) suggesting the PGRPs may initiate an immune response through

NFκB signaling cascade (Fig. 3-15; Charroux et al., 2009; Troll et al., 2009).

Additionally, examination of the PGRP amino sequences revealed the specific type of PGN that several of the E. scolopes PGRPs may be targeting. Bacteria have different types of PGN, for example, Gram-positive bacteria tend to have Lys-type PGN that are interconnected by a peptide bridge, whereas Gram-negative bacteria have

DAP-type peptidoglycan that substitutes a lysine amino acid with meso–diaminopimelic acid (DAP) and is usually directly crosslinked, as reviewed in Wolf and Underhill, 2017.

PGRPs that bind to DAP-type PGN tend to have conserved glycine and tryptophan amino acid residues within the PGN binding pocket, whereas PGRPs that bind to Lys- type PGN have conserved asparagine and phenylalanine in the binding pocket (Lim et al., 2006; Swaminathan et al., 2006; Zhang et al., 2019). Bioinformatic analyses suggest that EsPGRP1, 2, and 3 bind specifically to DAP-type PGN, although the specific binding capacity of EsPGRP4 and 5 remains unclear (Fig. B-1). Understanding the type of PGN that PGRPs bind provides insight into how the squid detects Gram-negative bacteria. Invertebrates, such as E. scolopes, have lipopolysaccharide binding proteins that recognize Gram-negative bacteria (Krasity et al., 2011; 2015), however, the PGRP

receptors may serve as secondary means of Gram-negative detection, suggesting E. scolopes uses a dual strategy.

The expression of EsPGRPs was varied across different tissues in the host animal and was highly dependent on the symbiotic state. For example, all EsPGRPs exhibited relatively low expression in the head compared to other tissues (Fig. 3-2A).

Interestingly, EsPGRP1, 2, and 3 were generally upregulated in the light organ of symbiotic animals compared to aposymbiotic animals, whereas the expression of

EsPGRP4 and 5 was more variable (Fig. 3-2A). These expression results, along with the specific DAP-type PGN binding analysis, indicates that EsPGRP1, 2, and 3 may be more important in responding to Gram-negative V. fischeri than EsPGRP4 and 5.

Additionally, with the exception of EsPGRP5, all EsPGRPs were upregulated in the symbiotic ANG, suggesting it is playing a role breaking down bacterial PGN from other beneficial bacteria in E. scolopes (Fig. 3-2A; Gromek et al., 2015).

The EsPGRP5 gene was also different in that it was the only PGRP to be expressed in host hemocytes (Fig. 3-2A; Collins et al., 2012). Previous research has shown that EsPGR5 is one of the most abundant transcripts in circulating adult hemocytes of E. scolopes (Collins et al., 2012). EsPGRP5 is also very highly expressed in adult brain, eyes, and gills tissue while being consistently upregulated in gill tissue at all time points, regardless of symbiotic state (Fig. 3-2A).

Of the five EsPGRPs, EsPGRP4 exhibited the most variability with regards to symbiotic state (Fig. 3-2A). For example, EsPGRP4 was downregulated in both symbiotic and aposymbiotic squid at 12 h, whereas it was slightly upregulated or unchanged in 24 h light organ tissues regardless of V. fischeri presence (Fig. 3-2A).

EsPGRP4 also exhibited increased expression in the skin suggesting that it may be important in MAMP detection of PGN in the surrounding seawater. As EsPGRP4 was the only true transmembrane PGRP found in E. scolopes, it may be more important in signal transduction for immune response, such as through the NFκB pathway (Fig. 3-

15).

Conversely, the function of EsPGRP2 may be critical for the detection of V. fischeri, as its expression pattern is highly regulated depending on the light organ symbiotic state (Fig. 3-2A). Previous studies have shown that EsPGRP2 is localized in the cytoplasm of ciliated epithelial appendages (CEAs) where it is later secreted into the extracellular mucus that is produced by the CEAs of the light organ, as well as within the crypt spaces once V. fischeri have successfully colonized there (Troll et al., 2010).

These results suggest that EsPGRP2 may be key during the early colonization stages of V. fischeri as well as it may help control potentially harmful excess PGN produced by

V. fischeri by breaking it down using its amidase catalytic domain (Troll et al., 2010).

Toll-like receptor

Toll-like receptors (TLRs) were first found in D. melanogaster and have since been identified in most animals (Morisato and Anderson, 1995). TLRs are involved in detecting various MAMPs and can initiate downstream immune response cascades

(Takeda et al., 2004). Previously, there has been only one TLR (EsTLR1) reported in E. scolopes (Goodson et al., 2005), however, in my bioinformatic analyses I have identified

18 additional TLR-related transcripts within the E. scolopes transcriptome (Table 3-1;

Object 3-1). However, for the purposes of this dissertation, I will only focus on the recovered transcripts associated with EsTLR1.

Analysis of the translated EsTLR1 transcripts revealed all of the components of a canonical TLR, including a leucine-rich repeat domain (LRR) and a Toll/IL-1 receptor

(TIR) domain (Fig. 3-3B; Goodson et al., 2005). One of the transcripts that aligned with

EsTLR1, TOLL8_DROME, had each of these domains as well as a transmembrane domain sequence (Fig. 3-3B). The second transcript, TOLL8_DROME2, was a very short transcript and only had the LRR domain (Fig. 3-3B). Neither transcript coded for the direct signal peptide sequence that was previously reported in EsTLR1, however, both transcripts aligned well with the previously sequenced EsTLR1 and blasted to the same gene in the genome (Object 3-1; Goodson et al., 2005).

Previous work on the squid symbiosis revealed that EsTLR1 is expressed in the hemocytes of juvenile E. scolopes using qRT-PCR (Bethany Rader unpublished), however, there was no differential expression between aposymbiotic and symbiotic animals. Examination of the reference transcriptome across multiple tissues and time points confirms the previous work but found that additional tissues expressed the

EsTLR1. At 12 h the EsTLR1 gene cluster had very low expression regardless of symbiotic state with the brain and white body (i.e. the source of hemocytes) having the highest expression in both apo- and symbiotic animals (Fig. 3-3A). The expression levels were higher at 24 h but still exhibited no significant differences between apo- and symbiotic animals. The expression results contrasted with immunocytochemistry, which localized the EsTLR1 protein to the cytoplasm of the hemocytes and was significantly more abundant in symbiotic animals (Bethany Rader unpublished). Interestingly, in the

4-week-old squid and adult tissues, the brain exhibited the highest level of expression

(Fig. 3-3A). TLRs have been found to be important in brain development in other

organisms and the expression pattern suggests that EsTLR1 that it may be more important in neurogenesis than in regulating and recognizing V. fischeri symbionts (Fig.

3-3A; Reviewed by Barack et al., 2014).

Galectins

Galectins are carbohydrate-binding proteins that are important PRRs in immune response and are typically upregulated in the presence of environmental bacteria, including in various mollusks (Song et al., 2011; Tasumi and Vasta, 2007; Vasta, 2009).

Only one galectin has been previously reported in E. scolopes and was observed in the adult hemocytes (Schleicher et al., 2011; Collins et al., 2012). Analysis of the reference transcriptome confirmed the presence of a previously reported galectin, EsGalectin

(transcript LEG1_HAECO), that included four tandem repeat GALECT domains suggesting that the galactin binds to beta-galactosides lectins (Fig. 3-3B). My analysis also revealed a second galectin-specific transcript (LEG4_MOUS) in E. scolopes, which harbored only two of the GALECT domains (Fig. 3-3B).

The expression of the two galectin gene clusters varied throughout the host developmental timeline. The EsGalectin, previously reported only in the hemocytes, was found throughout the host squid body with the highest expression in those tissues that directly interface with bacteria (i.e., gills and light organ) compared to those tissues that do not (i.e., brain and eyes). Expression levels of EsGalectin were low at 12 h, whereas by 4 weeks EsGalectin was highly expressed (Fig. 3-3A). Animals infected with the lux mutants, however, showed significantly less expression of EsGalectin and resembled the aposymbiotic animals. The highest expression of EsGalectin was also observed in the adult light organs as well as the adult gills (Fig. 3-3A).

As with EsGalectin, the expression of LEG4_MOUSE galectin was also high in the light organ and gills but appeared to be independent of the symbiotic state (Fig. 3-

3A). Both apo- and symbiotic animals exhibited relatively equal expression. However,

LEG4_MOUSE was also highly expressed in the skin and eye tissues of wild-type animals by 24 h (Fig. 3-3A). Conversely, EsGalectin was only highly upregulated in the skin of adult animals (Fig. 3-3A). These results suggest that the PRR galectins are primarily associated with tissues that interface with bacteria, although the expression in the eye needs further investigation. These results also suggest that there does not appear to be a V. fischeri-specific response for either galectin gene cluster, indicating that the E. scolopes galectins found may not be critical for the establishment the squid- vibrio symbiosis (Fig. 3-3A).

Effector Enzymes

In the innate immune system effector molecules can bind to proteins and impact signaling events. This chapter, however, focuses on specific enzymes that may alter signaling cascades rather than those that bind to proteins. Although analysis of the E. scolopes genome does reveal several protein-binding effector molecules this work will focus on specific enzymes in E. scolopes that may be involved in innate immune signaling including hemocyanin, superoxide dismutase (SOD), and chitotriosidase/chitinases. These three molecules were targeted for this study as previous research has shown these molecules to be important in the squid-vibrio symbiosis and that they are differentially expressed under stress conditions, including modeled microgravity conditions (Casaburi et al., 2017; Kremer et al., 2013; 2014;

Schleicher and Nyholm, 2011).

Hemocyanin

Hemocyanin is an important oxygen-carrying blood cell molecule in many invertebrates and is often found floating in blood rather than being bound by cells, as is the case with hemoglobin (Decker et al., 2007). There have been three hemocyanin transcripts found in E. scolopes and previous studies have found that hemocyanin is delivered into the crypt spaces of the light organ where V. fischeri reside to provide them with oxygen, which in turn initiates their luciferase enzyme to produce light for the squid (Kremer et al., 2014). In addition to its role in delivering oxygen, hemocyanin has also been shown to exhibit anti-microbial phenoloxidase enzyme activity in E. scolopes

(Kremer et al., 2014) and is differentially expressed during stress conditions, including modeled microgravity (Casaburi et al., 2017). These previous studies have indicated that the presence of V. fischeri can greatly modulate the response of hemocyanin.

Bioinformatic analysis of the reference transcriptome recovered a total of 38 distinct hemocyanin-related clustered into 18 putatively different genes (Table 3-1;

Object 3-1). Interestingly, many of the hemocyanins found in the transcriptome and genome appeared to be isoforms of each other as they clustered to the same gene cluster, however, several of the genes within each cluster blasted to different genes in the genome, indicating the difficulty of identifying true isoforms with hemocyanin transcripts (Object 3-1).

Typically, molluskan hemocyanins generally contain seven to eight functional units of a tyrosinase copper-binding domain, which provides the phenoloxidase-like activity of hemocyanin, and an IgG-like beta-sandwich domain that is important in recognizing and binding bacteria or blood cells (Fig. 3-4B; Decker and Jaenicke, 2004;

Kremer et al., 2014). Several of the hemocyanin transcripts present in the E. scolopes

transcriptome have these alternating functional units, but not as many as a full-length hemocyanin, suggesting they may be fragments of a full hemocyanin gene (Object 3-1).

Several of the hemocyanin isoforms also included signal peptide sequences that are typical of hemocyanins secreted into hemolymph in response to pathogenic challenge

(Fig. 3-4B; Kusche and Burmester, 2001; Lee et al., 2003). In this work, a representative hemocyanin is presented along with the expression of each hemocyanin gene cluster (Fig. 3-4). Several of the transcripts had no or very low expression (≤ 1

FPKM) across all tissues examined and were subsequently excluded from expression analysis (Object 3-1).

The majority of recovered hemocyanin gene clusters were upregulated in the gill tissue of all squid age groups indicating the importance of this molecule during respiration (Fig. 3-4A). The upregulation of hemocyanin transcripts in the gills also confirms previous studies that have found hemocyanin to be primarily produced in the gill tissue (Kremer et al., 2014; Schipp et al., 1973). Some of the hemocyanin molecules, however, had differential expression that was more dependent on the host tissue and/or symbiosis state.

The previously reported hemocyanins (EsHCYsub1 and EsHCYsub2) were closely related to the gene cluster_2160 (Object 3-1). Cluster_2160 was upregulated in the light organ and eye tissues of 24 h animals, regardless of symbiotic state (Fig. 3-

4A). Alternatively, two different clusters of hemocyanin genes (cluster_15363 and cluster_16631) were highly upregulated in the adult ANG, indicating that hemocyanin may also be important in monitoring bacterial communities of the symbiotic ANG (Fig. 3-

4A; Kerwin et al., 2017).

Interestingly, one hemocyanin gene (cluster_2161) was upregulated in the adult hemocytes (Fig. 3-4A). This result was surprising as hemocyanin had not been previously reported to be associated or localized with the hemocyte cells of E. scolopes

(Kremer et al., 2014). Although hemocyanin is capable of binding to hemocytes indicating that the gene cluster_2161 may be involved in antimicrobial activities associated with hemocytes (Zhu et al., 2014). Together, the differential localization of hemocyanin genes to different host tissues suggests that this effector molecular has a multi-faceted role in the host animal and associated immune response.

Chitotriosidase and chitinase

Chitotriosidase is an innate immunity effector enzyme important in breaking down chitins into smaller molecules, such as chitobiose, and is critical for the initiation of the squid-vibrio symbiosis (Kremer et al., 2013; Mandel et al., 2012; Wier et al., 2010).

Chitotriosidase and along with chitin molecules have been previously found in E. scolopes hemocytes, however, there was no expression of any of the chitotriosidase/chitinase gene clusters in the hemocyte transcriptome used in this study

(Fig. 3-5A; Heath-Heckman and McFall-Ngai, 2011). In addition, chitotriosidase has been shown to be a marker of innate immune response to infection or disease state, including cancer (Kanneganti et al., 2013). Previously, there had been only two chitotriosidases sequences described in E. scolopes (Schwartzman et al., 2015), however, in this analysis a total of 31 transcripts were observed in the reference transcriptome and were annotated as either chitotriosidase or a closely related chitinase

(Table 3-1; Object 3-1). As in the case of hemocyanin, several of the chitotriosidase gene clusters had no or very low expression (≤ 1 FPKM) across all tissues and were

excluded from expression analysis (Object 3-1). A representative chitotriosidase is presented along with the expression of each gene cluster (Fig. 3-5).

The two chitotriosidases that have been found previously in E. scolopes include

EsCHIT and a putative chitotriosidase, EsPutCHIT (Object 3-1; Schwartzman et al.,

2015). Both of these chitotriosidase genes were closely related to one transcript in the transcriptome (CHIA_BOVIN7) belonging to cluster_626 (Object 3-1). The domains of the EsCHIT translation, used as the representative chitinase, included two transglycosidase domains that surrounded a chitinase insertion domain important in the enzymatic breakdown of chitin molecules, two chitin-binding domains, and a signal peptide sequence (Fig. 3-5B). The signal peptide sequence in chitotriosidases has been found to be important in secretion from immune cells in response to infection

(Kanneganti et al., 2013).

The expression of both EsCHITs (cluster_626) was upregulated in the juvenile animal light organ and gill tissue, as well as the head of the wild type infected juveniles, but was downregulated in the 4-week animals and adult tissue (Fig. 3-5A). The gene cluster_579 and cluster_16641 had similar expression to the EsCHIT cluster (Fig. 3-5A).

These results indicate that perhaps that these chitotriosidase transcripts are more important in the onset of the light organ symbiosis rather than in the maintenance of the association.

Although the EsPutCHIT and EsCHIT were downregulated in the adults, some chitotriosidase gene clusters (cluster_7254, 13525, 12881, 9908, and 17940) were upregulated in the 4-week old and adult animals, particularly the ANG, regardless of symbiotic state (Fig. 3-5A). These results suggest that the different chitotriosidases may

be localized to the different symbiotic organs but may be critical for interfacing with bacteria-associated tissues.

Superoxide dismutase

Superoxide dismutase (SOD) is an important effector enzyme that converts superoxide radicals to molecular oxygen or hydrogen peroxide and represents the cell’s first line of defense against oxidative stress (Castillo et al., 2015; Kliebenstein et al.,

1998). Innate immune cells, including hemocytes, have been found to be able to express SOD when activated by bacterial-derived MAMPs (Zelck et al., 2005). There are three predominate families of SOD: copper/zinc binding, manganese/iron, and mitochondrial SOD (Kliebenstein et al., 1998). Invertebrates have been found to have two different types of SODs, copper/zinc binding and manganese/iron binding (Brouwer et al., 2003; Culotta et al., 2006).

In E. scolopes transcriptome seven transcripts were observed clustering to four different genes in the genome, suggesting the presence of several SOD isoforms. Five of the transcripts contained a copper/zinc-binding domain (Fig. 3-6B) with two of the

SOD transcripts (SODM_RAT and SODC_SCHPO) containing a signal peptide sequence at the N-terminus, which may indicate this molecule is capable of being directed towards extracellular spaces, as has been found in other SODs (Zelko et al.,

2002). There were several SOD transcripts that did not have SOD domain clusters associated with them and they were discarded from further expression analysis (Object

3-1).

Expression analysis of the copper/zinc binding SOD gene clusters showed varied expression among the different tissues and was highly dependent on symbiosis state.

For example, at 12 h the SOD_SCHPO gene cluster exhibited very high expression in

the light organs of aposymbiotic animals but was downregulated compared to the symbiotic squid (Fig. 3-6A). By 24 h, the expression of the SOD_SCHPO gene cluster was low in the light organ regardless of symbiosis state but was enriched in the eye and gill tissues. In the adults, upregulated expression of the SOD_SCHPO cluster was also observed only in the gill tissue (Fig. 3-6A). Alternatively, the SODC_MOUSE gene cluster was upregulated in the eye tissues but downregulated in the gills, suggesting distinctive roles for the SODs that are tissue dependent (Fig. 3-6A). Interestingly, the

SODC_MOUSE cluster also appeared to be the only SOD that was highly expressed in the symbiotic ANG organ (Fig. 3-6A). In addition, SODC_SCHPO had the highest expression of any SOD in the hemocytes, although the expression was still low compared to other tissues (Fig. 3-6A). Oysters contain SOD that mediates phagocytosis activity of their innate immune cells towards pathogens, and there may be a similar function with the SODC_SCHPO cluster in E. scolopes although further hemocyte specific analysis will need to be done (Duperthuy et al., 2011).

Two of the recovered transcripts in the reference transcriptome contained a manganese/iron SOD domain at both the N and C terminals (SODM_RAT and

SODM_CHAFE). One of the transcripts, SODM_RAT, had a signal peptide at its N- terminus suggesting that, as in the SODC_SCHPO, it too is important in secretion of

SOD outside of the cell (Fig. 3-6B). The SODM_RAT cluster showed that it was upregulated in all of the juvenile animal tissues at 12 and 24 h, regardless of symbiotic state. In the adults, however, the SODM_RAT transcripts were downregulated in the

ANG and hemocytes and were only observed upregulated in the brain and gill tissues with little expression in the light organ (Fig. 3-6A). These expression results indicate that

SODM in E. scolopes may be more involved in juvenile animal response but that it is not specific to the initiation of the symbiosis with V. fischeri.

Signaling

Previous work in E. scolopes has found several components of the NFκB signaling pathway, however, a complete putative pathway had not yet been identified

(Goodson et al., 2005; Collins et al., 2012). In this chapter, signaling genes relating to the NFκB pathway were investigated in the E. scolopes transcriptome and genome to further complete a putative signaling cascade (Fig. 3-15). This work also discusses how each of the genes discovered may be involved in the signaling cascade in regard to the squid-vibrio symbiosis.

Myeloid differentiation primary response 88

The myeloid differentiation primary response 88 (MYD88) protein is a key adaptor protein for MAMP detection that is recruited upon receptor activation with its

Toll/Il-1 Receptor (TIR) domain (Kenny and O’Neill, 2008) and has been previously reported in E. scolopes (Fig. 3-15; Collins et al., 2012). Although several TLRs are present in E. scolopes, as described earlier, no IL-1 or IL1-R have been found in E. scolopes (Table 3-1; Fig. 3-3B). Once the MYD88 protein is activated it further recruits kinase proteins that interact with its death domain, such as interleukin-1 receptor- associated kinase 4 (IRAK4).

There were three different MYD88 transcripts found through this study and each belonged to a different gene cluster, although the translations of two of the transcripts

(MYD88_PANTR and MYD88_PANTR2) were exactly the same and blasted to the same gene in the genome (Table 3-1; Object 3-1). The MYD88_PANTR translations had a death domain and SEFIR domain (Fig. 3-7B), which is a conserved sequence

related to the TIR domain that was first found in IL-17 receptors in both eukaryotes and bacteria (Novatchkova et al., 2003). The presence of the SEFIR domain indicates that the MYD88_PANTR transcripts may interact with IL-17 receptors rather than directly with TLRs or IL-1 receptors (Novatchkova et al., 2003). IL-17-like transcripts and IL-17 receptors were identified in E. scolopes transcriptome providing further evidence that this type of signaling may exist in E. scolopes. One caveat to the annotation of the

MYD88 is that previous research found that IL-17 receptors do not interact with MYD88 proteins specifically, indicating that the MYD88 homologs with the SEFIR domains may actually be different adapter proteins that were misannotated as MYD88 (Chang et al.,

2006; Maitra et al., 2007). However, SEFIR domain-containing proteins can also activate the NFκB signaling cascade and proteins with this domain remain important targets for future IL-17 receptor-initiated signaling (Novatchkova et al., 2003).

Although both MYD88_PANTR transcripts translated to the same amino acid sequence, they each had different expression in the squid indicating that they had differential transcriptional regulation. Both transcripts were similarly upregulated in all adult tissue, except for the ANG (Fig. 3-7A). In the juvenile tissue, the MYD88_PANTR2 transcript was upregulated in all light organ, head, and eye tissue, whereas the

MYD88_PANTR transcript was only upregulated in the head tissue (Fig. 3-7A). These expression results indicate that MYD88_PANTR2 may be more important in signaling response in the light organ of juvenile squid.

The other MYD88 related transcript, MYD88_HUMAN, also had a death domain but instead of the SEFIR domain, it contained the usual TIR domain present in most homologs of MYD88, indicating that it most likely is involved with the TLR initiated NFκB

signaling pathway (Kenny and O’Neill, 2008). Interestingly, the MYD88_HUMAN transcript was only upregulated in the 12 h juvenile tissue and only slightly upregulated in the 4-week light organs (Fig. 3-7A). In the adult tissues, MYD88_HUMAN was only upregulated in the skin and ANG and was otherwise downregulated in all other adult tissue (Fig. 3-7A). The upregulation in the ANG and skin adult tissue may indicate that

MYD88_HUMAN is important in signaling from recognition of bacteria in the surrounding seawater, or bacteria that associate with the ANG (Gromek et al., 2015).

Interleukin-1 receptor-associated kinase 4

Interleukin-1 receptor-associated kinase 4 (IRAK4) is an important threonine/serine protein kinase involved in signaling transduction pathways that is generally initiated by PRRs, such as TLRs. MYD88 is used to recruit and activate

IRAK4 using its death domain that further triggers signaling cascades, such as NFκB activation (Fig. 3-15; Li et al., 2002). There has been one IRAK4 previously found in E. scolopes (EsIRAK4) that aligned with one of the IRAK4 related transcripts,

IRAK4_HUMAN, found in the transcriptome (Goodson et al., 2005; Fig. 3-8B; Object 3-

1). There were two other IRAK4 related transcripts found, IRAK4_BOVIN and

PBL6_ARATH (Probable serine/threonine-protein kinase) that were determined to be isoforms of each other (Fig. 3-8B; Table 3-1; Object 3-1). The full-length IRAK4 translations had the required IRAK4 death domain that binds to MYD88 and protein kinase domain (Fig. 3-8B). The expression of the IRAK4 transcripts was low at 12 h regardless of symbiotic state and was upregulated by 24 h in the light organ. This expression in the light organ appeared to persist to adulthood as both clusters were expressed in those adult tissues that interface with bacteria including the light organ, gills, and skin (Figure 3-8A).

Tumor necrosis factor receptor associated factor

The tumor necrosis factor receptor-associated factors (TRAFs) are important ubiquitin ligases involved in immune signaling pathways associated with PRR signaling, especially with TLRs. There are many different types of TRAFs that are important in different parts of immune signaling cascades, however, the function of these different

TRAFs is not well known in cephalopods (Fig. 3-15). In general, TRAFs contain a

TRAF/MATH (meprin and TRAF homology) C-terminal domain that can interact with

IRAK4 (Cao et al., 1996). TRAFs may also contain two different types of zinc fingers, a

TRAF-type and RING-type, which are important for efficient IKK complex activation

(Cao et al., 1996). Only one TRAF, EsTRAF6, had been previously reported in E. scolopes, however, analysis of the reference transcriptome identified a total of 14 potentially new TRAF related transcripts that clustered into seven putative genes (Table

3-1; Fig. 3-9A; (Goodson et al., 2005).

TRAF6 is one of the most important TRAFs in NFκB signaling by interacting with upstream IRAK4, which then ubiquitinates itself before ubiquitinating the IKK complex downstream, specifically the IKKγ component of the complex (Lee and Kim, 2007). The polyubiquitinated TRAF6 then acts as a platform for the TAK1 kinase to interact with its target, the IKK complex (Lee and Kim, 2007). There were five transcripts that annotated to TRAF6 in the transcriptome (Table 3-1) including two, TRAF6_BOVIN and

TRAF6_BOVIN2, that aligned well with EsTRAF6 (Object 3-1). The translations of these transcripts contained both the ring-type and TRAF-type zinc fingers as well as the

TRAF/MATH domain (Fig. 3-9B). The EsTRAF6 cluster was upregulated in the adult

ANG, eyes, and skin tissue while being downregulated in the adult brain and hemocytes

(Fig. 3-9A).

Two other TRAF6s, TRAF6_HUMAN and TRAF6_HUMAN2, aligned well together in a different gene cluster and contained TRAF-type zinc fingers and the

TRAF/MATH domain, however, only TRAF6_HUMAN2 contained the ring-type zinc finger (Fig. 3-9B). Interestingly, the TRAF6_HUMAN gene cluster was downregulated in most tissues but was highly expressed in the 12 h aposymbiotic squid, suggesting this

TRAF6 may be important in the onset of the squid-vibrio symbiosis (Fig. 3-9A). There was one more TRAF6 found in the reference transcriptome, TRAF6_BOVIN3, that did not contain any TRAF domains and was excluded from further analysis (Object 3-1).

There were also two transcripts that related to TRAF2, TRAF2_HUMAN and unk_TRAF2, that clustered together and contained the TRAF/MATH domain, ring-type zinc finger, and a transposase (Fig. 3-9B; Object 3-1). TRAF2 has been found to be important in several signaling cascades, including NFκB, and has been shown to be differentially regulated in modeled microgravity conditions (Zhao et al., 2016). The

TRAF2 cluster was upregulated in all gill tissue but was only highly upregulated in juvenile symbiotic light organs infected with V. fischeri. Interestingly, TRAF2 was highly downregulated in adult ANG but had high upregulation in the skin (Fig. 3-9A).

TRAF3 is important in the regulation of non-canonical NFκB signaling pathways

(Lee and Kim, 2007). The only TRAF3 found belonged to a TRAF3_MOUSE transcript that contained the usual TRAF/MATH domain, ring-type zinc finger, TRAF-type zinc finger, but also surprisingly had a SIAH (seven in absentia)-type zinc finger that is important in E3 ubiquitin ligases (Fig. 3-9B). The SIAH-type zinc finger is a mammalian homolog to the SINA-type zinc finger first found in Drosophila and can activate NFκB pathways (Polekhina et al., 2002). The TRAF3 transcript was upregulated in light organ

V. fischeri infected tissues, except for 4-week animals and was upregulated in adult gill, eye, and skin tissues (Fig. 3-9A).

TRAF7 is a relatively newly discovered TRAF that has been debated as an actually TRAF protein since it does not contain a TRAF homology domain (Xie, 2013).

One TRAF7 transcript was found in the transcriptome containing both ring-type and

TRAF-type zinc fingers as well as the characteristic WD40 repeats that are found only in

TRAF7s, however, no TRAF/MATH domain (Fig. 3-9B; Object 3-1). The TRAF7 gene cluster had very similar expression to the TRAF2 in that it was upregulated early in the symbiotic light organ tissues suggesting that they may have similar functions during the onset of symbiosis (Fig. 3-9A).

Lastly, there were two TRAF4 transcripts, TRAF4_MOUSE and

TRAF4_MOUSE2, found in the reference transcriptome, which appeared to be isoforms of each other as they clustered to the same gene (Object 3-1). TRAF4 has been shown to be closely involved with TRAF6 and may actually be an antagonist of TRAF6 activation (Fig. 3-15; Xie, 2013). The TRAF4_MOUSE2 transcript contained both ring and TRAF-type zinc fingers along with the TRAF/MATH domain, whereas the

TRAF4_MOUSE transcript was missing the ring-type zinc finger suggesting a different isoform structure (Fig. 3-9B). TRAF4 was slightly upregulated in the light organ of juvenile animals but was downregulated at 4 weeks and in the adult light organ. TRAF4 was also the only TRAF upregulated in the eyes of juvenile animals but not in the adult eye tissue. In the adult tissue, the TRAF4 gene cluster was only upregulated in the brain and skin (Fig. 3-9A).

Transforming growth factor beta-activated kinase 1 and TAK1-binding proteins

Transforming growth factor beta-activated kinase 1 (TAK1), also known as mitogen-activated protein kinase kinase kinase 7 (M3K7), is a member of the serine/threonine protein kinase family and is very important in immune signaling transduction pathways. TAK1 is an important member of NFκB signaling, as well as

NFκB-independent signaling pathways, and can be activated by a wide range of effectors, including MAMPs and oxidative stress (Hirata et al., 2017). When activated this protein forms a complex with TAK1-binding proteins (TABs), and in some cases

TRAF6, that then phosphorylates TAK1, to further stimulate the IKK complex for NFκB activation (Fig. 3-15; Hirata et al., 2017). TAK1 and TABs have never before been discovered in E. scolopes. In the E. scolopes reference transcriptome, there were four

TAK1 transcripts and four TAB-related transcripts (Table 3-1; Object 3-1).

Three of the TAK1 transcripts, M3K7_BOVIN, M3K7_PONAB, and

M3K7_HUMAN, clustered to the same gene (Object 3-1). The M3K7_MOUSE transcript also aligned well with the other three transcripts but was much shorter (80 amino acids) and placed into its own gene cluster (Object 3-1). However, the M3K7_MOUSE cluster had no expression in any squid tissue and has been discarded from further analysis.

The M3K7_BOVIN and M3K7_PONAB transcripts had a protein kinase domain and

TAB1 binding sites, as well as were annotated as M3K7 proteins in InterPro analysis

(Fig. 3-10B). The M3K7_HUMAN transcript was missing the TAB1 binding site and was not annotated as a M3K7 protein in InterPro, but this may be due to the isoform being a shorter sequence (Object 3-1). Interestingly, none of the TAK1 transcripts found contained TAB2/3 binding sites according to InterPro (Fig. 3-10B). The TAK1 gene cluster was upregulated in all light organ treatments except for 4-week old animals,

where it was downregulated. In addition, TAK1 was always upregulated in the eyes, brain, and skin, as well as slightly upregulated in all gill and head tissue. TAK1 was also downregulated in the adult ANG tissue (Fig. 3-10A). The TAK1 gene in E. scolopes appeared to be important in the 24 h animals as well as the adults but did not appear to be activated at 12 h and 4-week old animals.

TABs are required for TAK1 activation (Hirata et al., 2017). There were two TAB1 transcripts, TAB1_HUMAN and TAB1_HUMAN2, that clustered to the same gene and one TAB3 transcript, TAB3_MOUSE, found in the transcriptome (Table 3-1; Object 3-1).

There was also one unk_TAB3 transcript that was in the same gene cluster as

TAB3_MOUSE, however, the translation of this transcript did not contain any domains and was discarded from further analysis (Object 3-1). Both TAB1 translations contained the phosphatase domain required for negative regulation of TAK1 but surprisingly did not have a separate TAK1 binding domain (Fig. 3-10B). The TAB1 transcripts were also annotated in InterPro as protein phosphatase 2C family (data not shown). The

TAB3_MOUSE translation had the TAB3 denoted RanBP2 (Ran-binding protein)-type zinc finger, that is required for TAK1 autophosphorylation, and a coil domain near the C- terminus that may be involved in TAK1 binding (Fig. 3-10B). The coupling of ubiquitin conjugation to endoplasmic reticulum degradation (CUE) domain was not distinctly present in TAB3 according to InterPro, but this domain was present when the translation was blasted in NCBI (data not shown).

All TAB genes were downregulated at 12 h but had differential expression in 4- week and adult squid, indicating differential transcriptional regulation in older animals

(Fig. 3-10A). The TAB1 gene cluster was upregulated in all 24 h light organ and gill

tissue but was only upregulated in the ANG, gill, and skin in the adult tissue (Fig. 3-

10A). In the TAB3 gene cluster, the light organ was upregulated in all tissues except for the 4-week old Δlux infected squid, where there appeared to be no change in expression (Fig. 3-10A). The eyes and gills were also slightly upregulated in 24 h animals and highly upregulated in the adult tissues (Fig. 3-10A). Post-transcriptional studies on the TAK1-TAB complex, such as binding and phosphorylation molecular assays, will help elucidate their role in E. scolopes in the squid-vibrio symbiosis.

IκB kinases

The IκB kinases (IKKs) form a complex that phosphorylates the inhibitory IκB, which is then ubiquitinated and degraded to allow NFκB subunits to translocate into the nucleus and activate immune-related gene transcription (Fig. 3-15). The IKK complex is generally formed by IKKγ, IKKβ, and IKKα. One IKK, EsIKKγ, has previously been found in E. scolopes but none of the other components of the IKK complex have been identified before (Goodson et al., 2005). In the E. scolopes reference transcriptome, there was one transcript, OPTN_DANRE, that aligned with the previously found EsIKKγ and blasted to the same gene in the genome (Table 3-1; Object 3-1). The IKKγ component is a regulatory protein member of the IKK complex that recruits the kinase subunits, IKKα and IKKβ, but can still interact with other regulatory proteins (May et al.,

2000). The translated EsIKKγ transcript has the NF-kappa-B essential modulator

(NEMO), which is a polyubiquitination binding site, and CC2-Leucine zipper domain

(Fig. 3-11B). The IKKγ translation also has the C2H2 (classical)-type/‘NEMO’ type zinc finger thought to interact with the other IKK subunits (Fig. 3-11B). There were also two newly discovered isoforms that annotated as IKKβ subunits, IKKB_HUMAN and

IKKB_HUMAN2, and both transcripts contained protein kinase domains (Fig. 3-11B;

Object 3-1).

The expression of the EsIKKγ cluster showed that it was downregulated in 12 h aposymbiotic light organs but upregulated in 12 h symbiotic and all 24 h light organs

(Fig. 3-11A). Conversely, the IKKβ gene cluster was upregulated in all juvenile light organs regardless of symbiotic state. However, in adults both IKK gene clusters were downregulated in the light organ and ANG tissue but upregulated in the brain and skin.

Both IKK gene clusters were upregulated in the juvenile gill tissue and the EsIKKγ gene was also upregulated in adult gill and eye tissue (Fig. 3-11A).

Surprisingly, no IKKα subunit could be found in the E. scolopes reference transcriptome although it is generally involved in the canonical NFκB signaling pathway.

Previous studies have shown that IKKα may not be essential for the canonical NFκB pathway (Fontan et al. 2007; Wu and Miyamoto 2007). Therefore, it is highly possible that another complex containing just IKKβ and IKKγ exists in E. scolopes.

Nuclear factor kappa-light-chain-enhancer of activated B cells subunits

There are several nuclear factor kappa-light-chain-enhancer of activated B cells

(NFκB) subunits that are transcription factors important immune signaling. These transcription factors can be induced by the presence of MAMPs as well as reactive oxygen species. The subunits form a protein complex that translocates into the nucleus and alters genetic transcription, often as a first response to stimuli (Fig. 3-15; Kawai and

Akira, 2007).

Only one NFκB subunit, EsRelA, has been previously found in E. scolopes

(Goodson et al., 2005). In searching the E. scolopes reference transcriptome there was a transcript, DORS_DROME, that aligned with the previously found EsRelA and another

unannotated transcript, unk_RelA, that also clustered with the DORS_DROME transcript and all appeared to be isoforms of the same gene (Object 3-1). The subunit

RelA, also known as transcription factor p65, contains a Rel homology domain as well as an Ig-like, plexins, and transcription factors (IPT) domain that contains an immunoglobulin fold that is involved in DNA binding (Fig. 3-12B).

The Rel subunits are considered class II NFκB transcription factors whereas class I transcription factors include IκB-like domains with ankyrin repeats (Goodson et al., 2005; Kawai and Akira, 2007). Only one class I subunit was found in E. scolopes transcriptome, NFΚB1_CHICK, which shares similarity to the p105/p50 protein. One of the most frequently activated forms of the NFκB subunits is a heterodimer of RelA and p50 (Hayden et al., 2006). The NFKB1_CHICK transcript from the E. scolopes transcriptome contained the same Rel and IPT domains as EsRelA, however, it was much larger and also contained ankyrin repeats near the C-terminus that anchors the protein in the cytoplasm along with a death domain (Fig. 3-12B). Before NFκB1/p105 can form a complex with RelA and be translocated into the nucleus it must undergo ubiquitin-dependent proteolytic degradation of the ankyrin repeats and form into the p50 subunit (Fig. 3-15; Kawai and Akira, 2007). Death domains are not usual components of p105 proteins and may be a sequencing error.

Both of the transcript clusters were generally upregulated in the light organs and gills of 24 h animals and adult skin tissue, indicating that they may form the activated heterodimer for transcription in these tissues (Fig. 3-12A). The NFΚB1_CHICK gene cluster was also upregulated in juvenile head tissue and juvenile eye tissue in aposymbiotic and Δlux mutant infected squid (Fig. 3-12A). The RelA gene cluster was

also upregulated in the adult light organ, eyes, and gills. Conversely, NFKB1_CHICK was only upregulated in adult ANG tissue (Fig. 3-12A). Previous studies have shown that RelA subunits may be able to activate transcription on their own, which may explain some of the differential transcriptional regulation between the two subunits found in this study (Shu et al., 1993).

NF-kappa-B pathway inhibitors

There are several different proteins that are used to regulate NFκB signaling.

One of these proteins called nuclear factor of kappa light polypeptide gene enhancer in

B-cells inhibitor alpha (IκBα), which acts as a direct inhibitor by sequestering NFκB subunits in the cytoplasm using ankyrin repeats, similar to the ankyrin repeat domain present in NFκB1/p105 proteins, to prevent the subunits from being able to translocate into the nucleus (Fig. 3-15). Previously, there has been only one IκB gene, EsIκBα, found in E. scolopes (Goodson et al., 2005). There were two transcripts found in the reference transcriptome, IΚBA_CHICK and IΚBA_CHICK2, that had nearly the same translation yet clustered to different genes (Object 3-1). However, both IκBα transcripts, along with the previously found EsIκBα, blasted to the same gene in the genome indicating that there is most likely only one IκBα gene present in E. scolopes and contains the characteristic ankyrin repeat domain (Fig. 3-13B; Object 3-1). Both

IKBA_CHICK transcripts were similarly upregulated in 24 h light organ and gill tissue, regardless of symbiotic state, as well as upregulation in the adult skin tissue (Fig. 3-

13B). However, the IΚBA_CHICK2 gene cluster was upregulated in all V. fischeri infected 4-week light organs and adult gill tissue, while IKBA_CHICK was upregulated in symbiotic 12 h light organs (Fig. 3-13B). The results suggest that the IκBα transcripts

may be differentially regulated through post-transcriptional activities and should be investigated further.

There was also an alternative IκB transcript found, IκBζ (IΚBZ_MOUSE), with the same ankyrin repeat domain but was much larger than the IκBα transcripts (Fig. 3-13B;

Object 3-1). There is not as much known about IκBζ in invertebrates, however, research has shown that it is capable of regulating NFκB transcription within the nucleus rather than in the cytoplasm, which is the case of IκBα proteins (Fig. 3-15; Willems et al.,

2016). One transcript that was originally annotated as NFΚB1_CHICK2 was found to be an isoform of the IκBζ transcript and may have been misannotated due to the presence of the ankyrin repeats (Object 3-1). These results suggest the importance of manual curation from bioinformatically annotated datasets to ensure correct annotation for downstream analyses. Interestingly, when the genome gene associated with the IκBζ transcripts was blastp into NCBI it matched closest with an uncharacterized protein from

Octopus, suggesting that this gene has not been well studied in cephalopods (Object 3-

1). The IκBζ gene cluster was upregulated in all eye tissue, 24 h gill tissue, and 24 h and adult light organ tissue, regardless of symbiotic state, as well as adult brain tissue

(Fig. 3-13B).

There are other proteins that may affect NFκB signaling upstream of the NFκB subunits, such as the NOD-like receptor family caspase recruitment domain containing

3 (NLRC3) and kappaB-ras1 (KBRS1). The NLRC3 protein may affect NFκB signaling by inhibiting the ubiquitination of TRAF6, however, may play a more important role in regulating T cells of vertebrates (Fig. 3-15; Ramadan and Paczesny, 2015). NLRC3 had never before been found in E. scolopes, however, my analysis revealed one transcript

relating to this gene in the reference transcriptome (Object 3-1). The translation of the

NLRC3 showed that it contained the required leucine-rich repeat (LRR) domains, but it was missing the required NACHT domain and instead has a death domain seen in alternative forms of NLRC proteins (Fig. 3-14B; Damiano et al., 2004). Interestingly, when the NLRC3 translation was examined blastp in the NCBI database it appears to be closely related to an Oyster NLRC-related protein (XP_022297662.1; data not shown). The NLRC3 gene was upregulated eyes and head of juvenile animals but was only upregulated in the juvenile light organ of WT infected squid (Fig. 3-14A). This gene was also highly upregulated in the adult light organ, ANG, and skin (Fig. 3-14A). The expression of the NLRC3 gene suggests that it is upregulated in response to V. fischeri and bacteria-rich tissues (Fig. 3-14A).

The KBRS1 protein can be an important regulator in NFκB signaling by preventing the degradation of IκB in the cytoplasm, further stabilizing IκB’s inhibitory function (Fig. 3-15; Matondo et al., 2017). There were three KBRS1 related transcripts found in the reference transcriptome that clustered into the same gene and are thought to be isoforms (Object 3-1). One transcript, KBRS1_MACFA, contained only the P-loop containing nucleoside triphosphate hydrolase domain whereas a second longer transcript, KBRS1_MOUSE, contained the P-loop as well as a DNA/RNA polymerase domain (Fig. 3-14B). Expression of the KBRS1 gene cluster showed that it was upregulated in the 24 h juvenile light organ and head tissues, regardless of symbiotic state, whereas it was upregulated in the 24 h gill tissue of only Δlux infected squid (Fig.

3-14A). Interestingly, the KBRS1 gene cluster is then upregulated in all 4-week and adult tissues, except for in the adult ANG and gills (Fig. 3-14A). There is very little know

about KBRS1 and KBRS2 in invertebrates and this may be an interesting target for future studies to further understand how the NFκB pathway is regulated in the innate immune system, especially in regard to a beneficial symbiont.

Conclusions

All components of a putative NFκB signaling pathway and other putatively related enzyme effectors were found in E. scolopes transcriptome and genome (Fig. 3-15). In addition, this work was the first to utilize the reference transcriptome to data mine for a specific pathway in E. scolopes. The components of the NFκB signaling pathway found included PRRs, effector enzymes that may also be important in inducing signaling cascades, and NFκB related signaling genes. However, in several cases some genes were either misannotated or portions of other genes, suggesting that perhaps the reference transcriptome model could use additional transcriptomic sequences for completion. This work further indicates the importance of manually curating genes and conducting investigative domain analysis for accurate future expression comparison studies and molecular research that will be focused on the genes or proteins of interest.

Further work will need to be conducted on the transcripts found in this study to localize them in E. scolopes tissue and compare to the expression results. In addition, each of the transcripts and gene clusters analyzed in this study should be tested for differential expression analysis across all tissues at earlier time points known to be important in the squid-vibrio symbiosis to help delineate the function of these genes in establishing and maintaining the symbiosis. Protein studies based off of the transcripts utilized in this study will provide further capacity for studying innate immune pathways in the squid-vibrio symbiosis.

Lastly, many of these genes are differentially regulated in spaceflight and modeled microgravity conditions, making them important targets for further expression studies in microgravity conditions. Several of these genes have been targeted for a temporal differential expression analysis to determine the importance of symbionts in activation of innate immune-related genes, especially associated with the NFκB pathway, in modeled microgravity conditions in chapter 4 of this dissertation.

Table 3-1. Transcripts data mined from Euprymna scolopes reference transcriptome # gene Group Gene name # transcripts* Full name clusters PRR PGRP 8 5 Peptidoglycan recognition protein TLR 2 1 Toll/ Toll-like receptor Galectin/ beta-galactoside-binding Galectin 2 2 lectin

Effector HCY 38 18 Hemocyanin enzyme CHIT/CHIA 31 (3) 25 Chitotriosidase/ Chitinase SOD 7 (3) 4 Super oxide dismutase

Myeloid differentiation primary Signaling MYD88 3 3 response protein Interleukin-1 receptor-associated IRAK4 2 (1) 2 kinase 4 TRAF 10 (1) 7 TNF receptor-associated factor Transforming growth factor beta- TAK1/M3K7 4 2 activated kinase 1/ Mitogen-activated protein kinase kinase kinase 7 TGF-beta-activated kinase 1 and TAB 3 (1) 2 MAP3K7-binding protein 1 Inhibitor of nuclear factor kappa-B IKK 2 (1) 2 kinase Transcription factor p65/ Nuclear factor RelA/NFκB 2 (1) 2 NF-kappa-B IκB 3 (2) 3 NF-kappa-B inhibitor NACHT, LRR and CARD domains- NLRC3 1 1 containing protein 3 NF-kappa-B inhibitor-interacting Ras- KBRS1 3 1 like protein *transcripts in () were annotated differently but clustered with genes of interest

Figure 3-1. Euprymna scolopes and the tissues used for transcriptomic analysis. A) Adult E. scolopes. B) Adult tissues used for transcriptomic data analysis. Source: Belcaid et al., 2019.

Figure 3-2. Peptidoglycan recognition proteins (PGRPs) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; Hemo, hemocytes, S, skin; h, hours.

Figure 3-3. Galectins and Toll-like receptors (TLRs) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. LRR, leucine rich repeat; TIR, toll/IL-1 receptor; LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

Figure 3-4. Hemocyannins (HCYs) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; Hemo, hemocytes, S, skin; h, hours.

Figure 3-5. Chitotriosidases (CHITs) and chitinases (CHIA) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. HT, helix-turn-helix; LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; Hemo, hemocytes, S, skin; h, hours.

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Figure 3-6. Superoxide dismutase (SODs) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; Hemo, hemocytes, S, skin; h, hours.

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Figure 3-7. Myeloid differentiation primary response 88 (MYD88) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. SEFIR, Interleukin-17 receptor and Similar expression to FGF genes protein; TIR, Toll/interleukin-1 receptor. LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

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Figure 3-8. Interleukin-1 receptor-associated kinase 4 (IRAK4) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

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Figure 3-9. Tumor necrosis factor receptor associated factors (TRAFs) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. WD, tryptophan-aspartic acid; MATH, meprin and TRAF homology; SIAH, seven in absentia; LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

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Figure 3-10 Transforming growth factor beta-activated kinase 1 (TAK1) and TAK1- binding proteins (TABs) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. RanBP2, Ran binding protein 2; LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

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Figure 3-11. Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor (IκB) kinases (IKKs) found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. CC2 LZ, coil-coil leucine zipper; C2H2-type zinc finger, classical zinc finger; LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

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Figure 3-12. Nuclear factor of kappa light polypeptide gene enhancer in B-cells (NFκB) subunits found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. IPT, Ig-like, plexins, transcription factors; LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

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Figure 3-13. Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor (IκBs) subunits found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

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Figure 3-14. The kappaB-ras1 (KBRS1) and NOD-like receptor family caspase recruitment domain containing 3 (NLRC3) NFκB inhibitors found in Euprymna scolopes transcriptome. A) Expression pattern of the PGRPs across tissues and symbiotic state. B) Domain structures of the PGRPs according to InterPro. P-loop, phosphate-binding loop; LRR, leucine rich repeat; LO, light organ; APO, aposymbiotic; WT; symbiotically infected with wild type V. fischeri strain ES114; E, eyes; G, gills; H, head; Δlux, symbiotically infected with transient mutant; ANG, accessory nidamental gland; S, skin; h, hours.

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Figure 3-15. Putative NFκB pathway in Euprymna scolopes. Light blue genes indicate they have not been found in E. scolopes before but were identified in the reference transcriptome and genome. Dark blue genes indicate that they have been found in E. scolopes previously. Red outline of dark blue genes indicates that more genes beyond those previously found were identified in E. scolopes transcriptome and genome. Name abbreviations explained in Table 3-1.

Object 3-1. Transcripts identified from the Euprymna scolopes transcriptome and confirmed in E. scolopes genome. '*' indicates that the gene cluster was not used for expression analysis due to no or low (≤ 1 FPKM) expression. (.xlsx)

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CHAPTER 4 TARGETED GENE EXPRESSION ANALYSIS OF THE HOST IMMUNE SYSTEM IN A BENEFICIAL SYMBIOSIS UNDER MODELED MICROGRAVITY CONDITIONS

Introduction

Spaceflight impacts the physiology of all animals and by more fully understanding these impacts we can gain insight into maintaining astronaut’s health during long-term space missions. Within the spaceflight environment, microgravity is one of the most influential factors on animal health, as it can negatively impact cell-mediated as well as humoral immunity (Mukhopadhyay et al., 2016; Rea et al., 2016; Sonnenfeld, 2005).

Due to the high cost of spaceflight, many studies rely on simulated microgravity conditions using low shear modeled microgravity (LSMMG) to mimic key aspects of the spaceflight environment (Fig. 1-4). LSMMG has been used extensively to examine the effects of a low-shear microgravity-like environment on the immune system and has been shown to be comparable to spaceflight responses (Higginson et al., 2016; Wilson et al., 2004). One key pathway in the innate immune system that appears to be altered under both LSMMG and spaceflight is the NFκB signaling pathway

(Boonyaratanakornkit et al., 2005; Vincent et al., 2005; Zhang et al., 2017).

The NFκB signaling pathway is found in most, if not all, animals and can be altered in various cell types (Vitello et al., 2011). Briefly, the NFκB signaling pathway is comprised of transcription factors that are held in the cytoplasm of a cell until upstream signaling events triggers translocation of the homo- or heterodimers into the nucleus where they initiate transcription of NFκB controlled innate immune genes (Oeckinghaus et al., 2011). NFκB signaling can be initiated and regulated through the recognition of microbial-associated molecular patterns (MAMPs) as well as by reactive oxygen species (ROS) (reviewed in Hayden and Ghosh, 2011; Morgan and Liu, 2011).

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Alteration of the NFκB signaling pathway can result in disease among animals (Aradhya et a., 2011). In spaceflight specifically, NFκB may also affect bone and muscle loss in vertebrate animals (Kwon et al., 2009; Pacios et al., 2015). Gene expression studies have also found that NFκB can be dysregulated in spaceflight and LSMMG (Allen et al.,

2009; Mangala et al., 2011; Paulsen et al., 2010). A recent review revealed that the up- or downregulation of NFκB signaling in spaceflight and LSMMG is dependent on the cell type studied (Zhang et al., 2017).

In some cases, the decreased expression of NFκB activation could also be related to the reduction of secondary molecules from activated cells, resulting in further modulation of the immune response (Mukhopadhyay et al., 2016; Wiesner et al., 1997).

Innate immune cells can also be differentially regulated in microgravity and alter immune response, such as the production of different effector molecules in response to microbial-associated molecular patterns (MAMPs), that may be influenced by NFκB signaling (Kaur et al., 2008). In addition, innate immune cells have reduced function in microgravity conditions, such as reduced ability to protect against oxidative stress and reduced granulation (Kaur et al., 2004; 2005; Konstantinova et al., 1993; Mehta et al.,

2001; Morukov et al., 2009; Ott and Pierson, 2004; Rykova et al., 2008).

The majority of space-related research on the immune system has focused on the impact of pathogenic bacteria to the health of animals. However, recent research has shown that commensal and beneficial microbes are also extremely important in maintaining immune homeostasis (Dethlefsen et al., 2007; Goodrich et al., 2016). Yet there has been relatively little research focused on the effect of spaceflight or microgravity on symbiotic systems.

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Humans have a complex consortium of symbiotic organisms associated with them making it difficult to narrow the impact of specific taxa on the innate immune response (Cani, 2018). Additionally, it is difficult to study the immune response of humans in spaceflight due to the innate immune response overlapping with their acquired immunity, therefore simplified model systems are needed (Husebye et al.,

2006). In this study, the monospecific model system between the bobtail squid

Euprymna scolopes and its bioluminescent bacterium Vibrio fischeri was used to investigate the impact of LSMMG on the innate immune response in a beneficial symbiosis. Briefly, E. scolopes are initially born aposymbiotic (i.e. without V. fischeri) and must acquire their symbionts horizontally from the surrounding environment. The squid does this with the help of ciliated epithelial appendages (CEA) that bring in the surrounding seawater with V. fischeri towards their light organ, which V. fischeri then colonizes and produces light to assist in camouflaging the squid when it hunts for prey at night. During the initiation of the symbiosis the squid’s only immune cell, hemocytes, migrate into the blood sinus of the CEA. The CEA then undergo an apoptotic cell death event and subsequently regress (Fig. 4-1; McFall-Nagi, 2014). The normal morphological development of the CEA is believed to be largely driven by the innate immune response to microbial-associated molecular patterns (MAMPs) and may be specifically controlled by NFκB signaling (Chun et al., 2008; Goodson et al., 2005;

McFall-Ngai et al., 2010). Recently, additional components of a putative NFκB signaling pathway and other innate immune-related genes were elucidated from E. scolopes reference transcriptome and genome, further making this system a good model for comparative microgravity study (i.e., Chapter 3 of this dissertation).

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Previous research on the squid-vibrio system in LSMMG has shown that there is a delay of hemocyte migration into the blood sinus of the light organ compared to the normal development of the squid (Foster et al., 2013). In fact, the normal concentration of hemocytes in the blood sinus is never reached under modeled microgravity conditions (Fig. 4-1; Foster et al., 2013). There also is an increase in the number of apoptotic cells within the CEA (Foster et al., 2013). Another recent study has shown that immune-related effector enzymes, such as hemocyanin (HCY), chitotriosidase

(CHIT)/chitinase (CHIA), and superoxide dismutase (SOD), were also targeted as they were found to be significantly upregulated in E. scolopes in LSMMG conditions when aposymbiotic, however, this expression was attenuated in the light organ in the presence of the symbiont (Fig. 4-1; Casaburi et al., 2017). Antioxidant enzymes, such as superoxide dismutase (SOD), are also initially increased in LSMMG and spaceflight conditions in rodents (Rizzo et al., 2012; Wang et al., 2009).

This study aims to target specific components of the innate immune response in

E. scolopes that are closely associated with the NFκB signaling pathway. Additionally, I target LSMMG-induced changes in the host innate immune response during the bacteria-induced developmental timeline. All the genes targeted in this study were recently characterized from the E. scolopes reference transcriptome and genome, however, the effects of LSMMG on all of these genes is unknown (Fig. 4-2). To our knowledge, this is the first study to directly characterize the expression of NFκB and innate immune signaling in a host-symbiont context.

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Materials and Methods

General Procedures

Mature E. scolopes squid were maintained in aquaria within an environmental growth chamber maintained at 23°C over a 12 h light/dark cycle. Clutches of eggs were removed from the adult tanks and incubated separately in individual aquaria for their full developmental cycle (~ 21 days). After hatching, the juvenile squid were maintained in filtered-sterilized seawater (FSW) and as either aposymbiotic (i.e., no symbiosis- competent bacteria) or rendered symbiotic. For symbiotic treatments, animals were inoculated with 1 × 105 cells of V. fischeri ES114 per ml of FSW. The concentration of V. fischeri was determined spectrophotometrically (A600nm) as an OD of 1 corresponds to 3

× 108 cells per ml of culture, as previously determined by plate counts (Boettcher and

Ruby, 1990). In all treatments, the onset of symbiosis was monitored using a photometer (GloMax 20/20 Luminometer, Promega, Corps., Madison, WI).

Modeled Microgravity Treatments

To simulate a low-shear modeled microgravity environment (LSMMG), rotary culture systems were used with 50 ml volume high aspect ratio vessels (HARVs;

Synthecon, Houston, TX) at 13 rpm. The HARVs were either rotated around a horizontal axis to simulate microgravity or a vertical axis to serve as a normal gravity (1 x g) control. Juvenile squid were kept aposymbiotic or symbiotic as described above. The animals were added to the HARVs through an opening on the surface of the HARVs and then sealed with zero headspace. A semi-permeable membrane provided aeration for the host and symbiont. A total of four animals were incubated in each HARV for either 2, 6, 8, 10, 12, 16, 18 or 24 h and then immediately flash frozen in liquid nitrogen.

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For comparison purposes, a subset of animals was collected within 5 min of hatching and frozen in liquid nitrogen (i.e. ‘hatchlings’).

RNAseq Data Analysis

A transcriptomic dataset generated from a previous LSMMG exposure study on the squid-vibrio system was used to conduct a targeted next generation sequencing expression analysis (Casaburi et al., 2017). Differential gene expression analysis and significance testing were conducted with NoiseqBio on individual clusters of genes

(Table 4-1; Tarazona et al., 2015; v2.16). Genes were considered significantly differentially expressed at adjusted p-value ≤ 0.05 and log2 fold change ≥ 1.

Comparative expression heatmaps were created in R using the heatmap.2 function from the gplots package (Warnes et al., 2016).

NanoString Target Gene Probe Design

Target transcripts were chosen using the fully assembled E. scolopes genome and transcriptome obtained from a previous study (Belcaid et al., 2019) and from the isoform and gene analysis conducted in the previous E. scolopes data mining study

(Chapter 3 of this dissertation). Optimal fluorescently tagged probes of 100bp length were designed to ensure that each probe was unique within the dataset. Probes were designed to hit whole genes and not specific isoforms. There were 26 genes of interest and two housekeeping genes targeted for NanoString gene expression assay (Table 4-

1; Fig. 4-2; Table C-1).

RNA Extraction and Gene Expression

Total RNA was extracted in triplicate from E. scolopes light organs using mini

RNAeasy and Qiashredder according to the manufacturer’s protocol (Qiagen,

Germantown, MD). A minimum of 3 - 5 light organs was used for each replicate

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extraction (n = 9 -15 light organs per treatment). Each replicate extraction contained a minimum of three different cohorts of HARV or hatchling experiments to increase overall genetic variability and to ensure that gene expression results were not based on individual squid cohorts. RNA quantity was assessed using Qubit 2.0 (Thermo Fisher

Scientific, Waltham, MA) and RNA quality was assessed was evaluated with a 2100

Bioanalyzer (Agilent Technologies, Santa Clara, CA). All samples were normalized to

20 ng per µl of RNA and 100 ng of RNA was run for each sample on the NanoString nCounter MAX system (NanoString Technologies, Seattle, WA).

NanoString Data Analysis

Data were filtered and normalized using nSolver analysis software (v4.0;

NanoString Technologies). Background subtraction was performed using the geometric mean of the negative controls and data was normalized using the geometric mean of positive controls and housekeeping genes (ActB3 and Pyc1). Voom transformation from the LIMMA package (v3.8) in R was applied to the normalized counts. This transformation uses the empirical Bayes method by pooling estimates of sample variance to assess the expression level variance within samples to transform data into log2-counts per million (CPM). This method has been shown to be more useful for smaller sample size studies, such as NanoString (Laar et al., 2018; Ritchie et al., 2015).

The voom transformed data was passed to a linear model in the LIMMA package to remove the mean-variance relationship and asses statistically significant differentially expressed genes (Richie et al., 2015). Adjusted p-values < 0.05 were considered significantly differentially expressed. Several R packages were used to further analyze and visualize the expression results. These included heatmap.2 from gplots, network

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correlation analysis using the package ‘corrr’ from ggplot2 and ggraph, and prcomp() function was for principal component analysis (PCA).

Results

The innate immune genes targeted in this study were mined from the reference transcriptome and genome and are listed in Table 4-1 and their putative pathway is illustrated in Figure 4-2. The expression of these target genes under gravity and

LSMMG conditions was examined using a two-fold approach that included: 1) data mining a previously generated RNASeq dataset generated under gravity and LSMMG conditions at 12 and 24 h post-inoculation (Casaburi et al., 2017); and 2) using a targeted Nanostring approach at additional light organ developmental time points (0

(i.e., hatchling), 2, 6, 8, 10, 12, 16, 18, 24 h) in the presence and absence of V. fischeri

(Fig. 4-1).

Analysis of Targeted Genes from Transcriptomic Dataset

The RNASeq dataset analysis revealed that many of the innate immune genes discovered in the light organ of the host tissues were not significantly differentially expressed in any comparison at 12 and 24 h (Object 4-1). These target genes included

EsTLR, EsGalectin, EsSOD, one of the MYD88 gene clusters (MYD88_PANTR),

EsIRAK4, TAK1, TAB3, IKKβ, NFκB1, and NLRC3. Additionally, there were also no genes significantly differentially expressed between gravity and LSMMG in symbiotic animals at 24 h (Object 4-1). However, there were several innate immune genes that were significantly differentially expressed between treatments and included genes from pattern recognition receptors (PRRs), effector enzymes, and signaling molecules.

The only PRRs that were differentially expressed in the RNAseq dataset were

EsPGRPs. For example, EsPGRP2 was significantly upregulated in all symbiotic verse

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aposymbiotic conditions (Fig. 4-3; Object 4-1). In addition, the EsPGRP2 gene was the only PRR that was significantly upregulated in gravity compared to LSMMG when aposymbiotic at 12 h (Object 4-1). Conversely, EsPGRP1 was the only PRR significantly upregulated in LSMMG in aposymbiotic animals at 24 h and downregulated in LSMMG in symbiotic animals at 12 h (Object 4-1). No other EsPGRPs were significantly differentially expressed between gravity and LSMMG conditions (Object 4-

1). Finally, EsPGRP1 was significantly upregulated in symbiotic animals compared to aposymbiotic animals at both 12 and 24 h but only under gravity conditions (Fig. 4-3;

Object 4-1).

The EsPGRP3 gene was also significantly upregulated in symbiotic animals compared to aposymbiotic squid but only at 12 h time points regardless of gravity condition (Object 4-1). EsPGRP5 was also upregulated in symbiotic animals, however, only at 24 h in LSMMG (Object 4-1). Surprisingly, EsPGRP4 was the only PGRP that was upregulated in aposymbiotic animals under gravity conditions at 24 h (Object 4-1).

EsPGRP1 and EsPGRP4 were expressed at low levels in all conditions compared to the other three PGRPs (Object 4-2).

The gene cluster belonging to the effector molecule hemocyanin (i.e., EsHCY) was relatively highly expressed in all conditions of E. scolopes compared to other gene targets (Object 4-2). EsHCY was significantly upregulated in LSMMG compared to gravity conditions in aposymbiotic animals at both time points (Fig. 4-3; Object 4-1).

Interestingly, EsHCY was significantly upregulated in symbiotic animals compared to aposymbiotic animals at 24 h in gravity conditions and then switched to being significantly upregulated in aposymbiotic animals at 24 h in LSMMG conditions (Object

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4-1). The EsPutCHIT effector enzyme gene cluster had increased expression in symbiotic animals compared to aposymbiotic animals at 12 in both gravity and LSMMG but was only significantly upregulated in symbiotic animals in LSMMG (Fig. 4-3; Object

4-1). The EsPutCHIT was also significantly upregulated in gravity in aposymbiotic animals at 12 h (Fig. 4-3) but shifted to being significantly upregulated in LSMMG in aposymbiotic animals at 24 h (Object 4-1).

There were several NFκB signaling related genes that were significantly differentially expressed between gravity and LSMMG in aposymbiotic conditions in the transcriptomic dataset. The majority of the signaling genes appeared to be highly upregulated in the 24 h symbiotic animals in LSMMG conditions (Fig. 4-3). Interestingly, no signaling genes were significantly differentially expressed between gravity and

LSMMG in symbiotic animals at either timepoint (Object 4-1). However, two of the signaling genes, TRAF2 and EsIKKγ, were significantly upregulated in LSMMG compared to gravity in aposymbiotic animals at 12 h (Object 4-1). Conversely,

IKBA_HUMAN2 was significantly downregulated in LSMMG compared to gravity controls at 12 h, whereas IKBA_HUMAN was significantly downregulated in LSMMG at

24 h, both in aposymbiotic squid (Object 4-1). TRAF3 and the KBRS1 gene clusters were also significantly downregulated in LSMMG at 24 h in aposymbiotic animals

(Object 4-1). Surprisingly, MYD88_PANTR2 was significantly upregulated in LSMMG at

24 h in aposymbiotic animals, however, MYD88_PANTR was not significantly differentially expressed in any LSMMG verse gravity comparison (Object 4-1).

The majority of signaling genes that were significantly differentially expressed between apo- and symbiotic animals were upregulated in symbiotic squid (Object 4-1).

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These genes included TRAF2, TRAF3, TAB1, IKKγ, both IκBα gene clusters, IκBζ, and

EsRelA (Object 4-1). Only one of the genes, IKBA_HUMAN2, was significantly upregulated in aposymbiotic animals at 24 h in gravity conditions (Object 4-1).

Overview of NanoString Expression Patterns

Principal component analysis (PCA) indicated that replicates were well clustered and that the largest driver of expression for the first principal component (51.19%) appeared to be according to symbiotic state (Fig. 4-4A and B). Interestingly, all 2 h time points clustered with the aposymbiotic and hatchling samples (Fig 4-4B). Although there was not clear clustering of all gravity and LSMMG samples in the PCA there was tighter clustering of these treatments at specific time points (Fig. 4-4C and D). For example, some of the greatest separation between gravity and LSMMG samples was seen at the

6 h time point (Fig. 4-4D).

A heatmap of the NanoString gene expression profile further confirmed that some of the most striking expression changes were between aposymbiotic and symbiotic animals, especially at 6, 8, and 10 h (Fig. 4-5). The majority of the genes were upregulated in aposymbiotic animals compared to symbiotic animals starting at 6 h, except for EsIκBα and EsPGRP2 which were conversely upregulated in symbiotic animals (Fig. 4-5). The heatmap results also showed that there were some other interesting trends over time, such as IKKγ, IκBζ, and EsTRAF6 were all relatively downregulated at the early time points compared to later on, regardless of the symbiotic states (Fig. 4-5). There were also several genes that appeared to have been initially upregulated in hatchling and 2 h animals and then began to show a pattern reflecting symbiosis starting at 6 h, similar to the clustering shown in the PCA plot (Fig. 4-4; Fig.

4-5).

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A PCA plot of loadings indicated that most of the genes had similar expression patterns and were not strongly driving overall gene expression (Fig. 4-6). However,

EsPGRP2 appeared to be the most influential gene driving the first principal component of symbiotic state (Fig. 4-6). Spearman correlation similarly showed that EsPGRP2 appeared to be strongly negatively correlated in expression (r ≥ 0.6) to most of the other genes, especially KBRS1, EsRelA, EsSOD, and TAB3 (Fig. 4-7). In addition, EsIκBα and IκBζ also appeared to be slightly driving the first principal component expression after EsPGRP2 (Fig. 4-6). The strong negative correlation expression patterns of

EsPGRP2 and EsIκBα compared to the other genes were clearly seen in the

NanoString expression heatmap, whereas the IκBζ expression was not as clear between aposymbiotic and symbiotic animals (Fig. 4-5; Fig. 4-7). In addition, IκBζ was only strongly positively correlated with EsIKKγ (Fig. 4-7B). EsPutCHIT and EsIκBα appeared to be opposite drivers of the second principal component (18.11%), with IκBζ also slightly separated from the rest of the genes. Surprisingly, EsPutCHIT gene expression was not strongly correlated to any other gene (Fig. 4-7B).

Differential Expression of Genes Driving Principal Component Analysis Over Time

The driving genes from PCA and correlation analysis, EsPGRP2, EsPutCHIT,

EsIκBα, and IκBζ, were further analyzed for significant differential expression across time. EsPGRP2 and EsIκBα were significantly upregulated in symbiotic compared to aposymbiotic animals at most timepoints, regardless of gravity or LSMMG conditions

(Fig. 4-8; Object 4-3). IκBζ also became significantly upregulated in symbiotic conditions at later timepoints, however, the significant upregulation began 6 h early when under

LSMMG conditions (Fig. 4-8; Object 4-3). Conversely, EsPutCHIT was significantly

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upregulated in aposymbiotic compared to symbiotic squid in gravity conditions at 2 and

16 h, however, EsPutCHIT was no longer significantly differentially expressed at any time point in LSMMG (Fig. 4-8; Object 4-3).

Comparisons of gravity to LSMMG conditions showed that the four genes of interest had much more variable expression over time (Fig. 4-9). EsPGRP2 was only significantly differentially expressed at 6 h where it was downregulated in LSMMG conditions in symbiotic squid (Fig. 4-9B). Although EsPGRP2 appeared to be slightly upregulated in gravity controls of aposymbiotic squid it was not significantly differentially expressed at any time point (Fig. 4-9A). In aposymbiotic animals, EsIκBα was significantly upregulated in gravity at 6 and 12 h (Fig. 4-9A). In symbiotic animals

EsIκBα was also upregulated at 6 h and 18 h, with 12 h showing little change (Fig. 4-

9B). The IκBζ gene was upregulated in gravity at 6 h but then shifted to being downregulated in gravity at 24 h in aposymbiotic animals (Fig. 4-9A). However, in symbiotic animals IκBζ was only significantly upregulated in gravity conditions at 6 and

18 h (Fig. 4-9B). In aposymbiotic animals, EsPutChit was significantly upregulated in gravity conditions only at 2 h (Fig. 4-9A). Interestingly, EsPutCHIT becomes increasingly upregulated in LSMMG until 12 and 16 h were it becomes upregulated in gravity conditions again, indicating that it may go through some type of expression cycling, although these comparisons were not significant (Fig. 4-9A). However, when in symbiotic squid EsPutCHIT is generally upregulated in LSMMG conditions compared to gravity but is only significantly so at 18 h (Fig. 4-9B).

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Differential Expression of Genes Between Gravity and LSMMG at the Most Significant Timepoint

The most significantly differentially expressed genes between gravity and

LSMMG at any timepoint was at 6 h (Object 4-3). The separation between the gravity and LSMMG treatments at 6 h can also be seen from the PCA (Fig. 4-4D). The most genes were significant in symbiotic animals and were generally upregulated in LSMMG compared to gravity, except for EsPGRP2 and EsIκBα which were upregulated in gravity conditions (Fig. 4-10B). The majority of the genes upregulated in LSMMG were signaling related, including the putative inhibitors NLRC3 and KBRS1(Fig. 4-10B). In addition, the only NFκB subunit that was successfully assayed in NanoString, EsRelA, was also upregulated in LSMMG conditions in both apo- and symbiotic animals (Fig. 4-

10). The EsSOD transcript was the only effector enzyme significantly upregulated in

LSMMG, although, EsPutCHIT shared a similar pattern but was not significant (Fig. 4-

10B). Interestingly, EsTLR1 was the only PRR significantly upregulated in LSMMG conditions along with the other signaling genes (Fig. 4-10B). In aposymbiotic animals, a similar expression pattern was seen with most genes upregulated in LSMMG conditions, except for EsIκBα (Fig 4-10A). However, there were several genes that were no longer significant, including EsPGRP2, although it still showed the trend of upregulation in gravity conditions (Fig. 4-10A).

Discussion

This chapter focused on specific genes putatively involved in an NFκB signaling pathway and included genes relating to pattern recognition receptors (PRRs), effector enzymes, and signaling cascade (Fig. 4-2). The expression of these genes was analyzed using two different approaches, 1) data mining from a previously sequenced

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global transcriptomic dataset and 2) further analyzing expression at important developmental timepoints in the squid-vibrio symbiosis using a targeted direct count gene expression assay (i.e., NanoString) (Fig. 4-1). Both studies were conducted in

LSMMG and gravity controls in a symbiotic context.

The gene expression patterns appeared to be driven most according to symbiotic state, especially with the NanoString assay data (Fig. 4-3; 4-4B; and 4-5). Further analysis of Nanostring expression data indicated that EsPGRP2 was most likely the driving factor of the symbiotic state expression pattern (Fig. 4-6). EsPGRP2 was significantly upregulated in symbiotic conditions across all time points after 2 h, regardless of gravity or LSMMG condition in the NanoString assay (Fig. 4-8) and this result was verified in the RNAseq transcriptome (Object 4-1). Previous research has found that EsPGRP2 is localized to the cytoplasm of CEA epithelial cells and is then secreted into the extracellular mucus from CEA during the initiation of the symbiosis in response to peptidoglycan, which EsPGRP2 also has the ability to breakdown. In addition, EsPGRP2 is secreted into the bacterial crypts of the light organ once V. fischeri have successfully colonized (Troll et., 2010). The previous work, along with the present analysis of gene expression across time, shows that EsPGRP2 is indeed responding to the presence of V. fischeri, especially once V. fischeri has successfully colonized the light organ (Fig. 4-1).

When comparing expression patterns further it was found that EsPGRP2, along with EsIκBα, were upregulated in symbiotic animals compared to aposymbiotic animals, while most other genes had the opposite expression and were upregulated in aposymbiotic animals, especially in each middle timepoint (e.g. 6 h, 8 h, 10 h, and 12 h)

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(Fig. 4-5). These results were further confirmed with correlation analysis with both

EsPGRP2 and EsIκBα being highly negatively correlated to most other genes (Fig. 4-7).

In addition, the PCA plot by gene loadings showed that EsIκBα may also be slightly driving the first principal component (Fig. 4-6). EsIκBα generally was significantly upregulated in symbiotic squid at most of the time points in gravity, while the EsIκBα gene was upregulated at all early timepoints in LSMMG (Fig. 4-8). The IκBα gene is involved in holding the NFκB subunits in the cytoplasm of cells and inhibiting their nuclear translocation and subsequent transcription of immune-related genes. Therefore, the upregulation of the NFκB inhibitor EsIκBα in symbiotic animals may be what is causing the decreased expression of the majority of the rest of the NFκB signaling components in symbiotic animals (Fig. 4-5).

The gene loading PCA also showed that IκBζ, an atypical member of the IκB proteins, may also be slightly driving the first principal component along with EsIκBα and EsPGRP2 (Fig. 4-6). Interestingly, IκBζ appeared to be partially driving the first principal component in a different manner as EsPGRP2 and EsIκBα because it was not consistently upregulated in symbiotic animals compared to aposymbiotic squid at the earlier time points but was instead following this pattern only at later timepoints (i.e. before 10 h) (Fig. 4-5). In gravity conditions, IκBζ is only significantly upregulated in symbiotic animals compared to aposymbiotic animals at 16 and 18 h, however, in

LSMMG conditions this trend starts as early as 10 h and continues until 18 h (Fig. 4-8).

This analysis shows that IκBζ is indeed partly driving the symbiotic state separation of the gene expression, but not until later time points and appears to be more exasperated by the LSMMG conditions (Fig. 4-8). The IκBζ protein is important in regulating the

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NFκB subunits in transcription within the nucleus, however, in contrast to the singular inhibitory effects of IκBα, IκBζ can either inhibit or induce NFκB subunit directed transcription (Willems et al., 2016). Recent research has found that IκBζ is necessary for initiating transcription of important cytokines involved in immune cell activation, however, there is not as much known about this protein in invertebrate immune cell regulation (Müller et al., 2018). Overall, differential gene regulation of IκBζ has not been well studied and future research investigating the role of IκBζ in E. scolopes will aid in our overall understanding of NFκB regulation in invertebrates.

Both of the IκB transcripts and EsPGRP2 were more important in driving the gene expression according to symbiotic state for the first principal component (51.19%), however, the driving condition of the second principal component was much less clear

(18.11%) (Fig 4-4). The gene loading PCA indicated that EsIκBα may also be slightly affecting the gene expression variation according to the second principal component, although EsPutCHIT was driving the second principal component to a much larger extent in the opposite direction (Fig. 4-6). The expression pattern of EsPutCHIT was not highly correlated with any other gene expression indicating that its expression pattern was unique among the gene targets (Fig. 4-7B). EsPutCHIT was initially significantly upregulated in aposymbiotic animals compared to symbiotic animals in gravity conditions (Fig. 4-9A). However, the expression of EsPutCHIT then decreased until peaking at a significant level again at 16 h (Fig. 4-9A). This cyclical pattern of expression in the squid chitotriosidase may be reflecting the gene’s role in creating a chemotactic gradient of chitobiose molecules for V. fischeri to follow before being able to colonize the light organ. The EsPutCHIT gene has previously been found to be

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expressed in hemocytes and has an important function in breaking down chitin for the chitobiose gradient (Heath-Heckman and McFall-Ngai, 2011; Kremer et al., 2014). The cycle of EsPutCHIT expression in aposymbiotic animals suggests its initial upregulation is involved in chitin breakdown and then is upregulated again to re-create the chitobiose gradient when V. fischeri has still not colonized the light organ. This hypothesis remains to be tested further in the symbiosis. However, in LSMMG the pattern of EsPutCHIT is less clear and the gene is not significantly differentially expressed at any time point, indicating that LSMMG may be dysregulating the expression, putatively function, of chitotriosidase in E. scolopes (Fig. 4-9B). In LSMMG comparisons, EsPutCHIT was initially upregulated in gravity conditions compared to LSMMG in aposymbiotic animals but then switched to being upregulated in LSMMG in symbiotic animals, although it was not significant until 18 h (Fig. 4-10). The patterns of expression were similar to the significant RNAseq results at 12 and 24 h, however, these time points were not significant in the NanoString assay (Object 4-1). These results suggest that LSMMG may be impacting the regulation of chitotriosidase in E. scolopes, although the pattern is not clear. In addition, EsPutCHIT presence in hemocytes suggests that its dysregulation may also be connected to the delay of hemocytes in LSMMG, although further molecular assays will be needed to provide evidence for this hypothesis (Foster et al.,

2013).

Although the second principal component may be slightly driven by dysregulation of EsPutCHIT in LSMMG and gravity conditions the pattern of other genes between

LSMMG and gravity was much less clear. There was no clear clustering of LSMMG and gravity conditions when all treatments were observed together (Fig. 4-4C). However,

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when conducting significant differential expression analysis LSMMG and gravity conditions appeared to be more important at discrete timepoints. For example, the most differentially expressed genes between gravity and LSMMG were found at 6 h and the symbiotic treatments in this comparison appeared to be slightly driving the second principal component (Fig. 4-4D; Object 4-3). When comparing the specific genes at 6 h it was found that the majority of the significantly differentially expressed genes were related to signaling and were upregulated in gravity conditions compared to LSMMG, regardless of symbiotic state (Fig. 4-10). The EsIκBα and EsPGRP2 were both negatively correlated to all other genes in that they were upregulated in LSMMG conditions regardless of symbiont presence, however, EsPGRP2 was not significantly upregulated in LSMMG in aposymbiotic animals (Fig. 4-7 and 4-10). The negative correlation of EsIκBα follows the expected pattern of regulation for NFκB signaling, however, the negative correlation of EsPGRP2 was much more surprising. These results indicate that EsPGRP2 may be suppressed in LSMMG conditions and could be a potential response regulator of the hemocyte’s delay in LSMMG, especially at 6 h.

Further validation of this hypothesis with hemocyte response to EsPGRP2 expression will need to be investigated.

The only other PRR that was significantly differentially expressed in gravity compared to LSMMG conditions at 6 h was EsTLR1 (Fig. 4-10). EsTLR1 was significantly upregulated in gravity conditions regardless of symbiotic state, indicating that EsTLR1 may have an intrinsic response to LSMMG conditions that are not dependent on the presence of V. fischeri. Previous research has also shown that

EsTLR1 is not differentially regulated in the presence of V. fischeri and may be more

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important in responding to MAMPs from other microbial species (Personal communication with Bethany Rader).

Along with EsPutCHIT another effector enzyme, superoxide dismutase (EsSOD), was also significantly upregulated in LSMMG at 6 h (Fig. 4-10). EsSOD had similar expression to most of the other NFκB signaling genes, indicating that it may be closely involved with the signaling cascade. Other studies have shown that SOD can activate

NFκB driven transcription in response to oxidative stress (Kliebenstein et al., 1998;

Zelck et al., 2005). In addition, previous research in the squid-vibrio symbiosis has found that SOD genes are initially upregulated in LSMMG conditions in aposymbiotic animals, however, the expression is attenuated in the presence of V. fischeri (Casaburi et al., 2017). Interestingly, the same pattern was not seen in this study at 6 h where

EsSOD was significantly upregulated in LSMMG in both apo- and symbiotic animals, indicating that the response is time-dependent. The EsSOD gene chosen for target analysis was not significantly differentially expressed between gravity and LSMMG at any time point in the RNAseq data set (Object 4-1). However, there are several other

SOD genes present in E. scolopes that may follow this pattern and should be targeted for future studies (Chapter 3 of this dissertation).

Through this work it was found that a putative E. scolopes NFκB pathway is differentially regulated in LSMMG conditions in regard to its beneficial symbionts, however, the differential expression was more severe at specific timepoints (i.e., 6 h). In addition, this study found that PRRs and effectors enzymes may be important in initiation of innate immune cells that may be dysregulated by LSMMG. Future work will validate the expression findings from the study using localized molecular assays to

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confirm expression and protein abundance in specific symbiont related tissue.

Specifically, EsPutCHIT and EsPGRP2 will be targeted specifically in hemocytes to determine their role in hemocyte dysregulation in LSMMG. Future expression assays should also include more timepoints to investigate a complete timeline throughout the initiation of the symbiosis under LSMMG conditions.

In addition, this study included two different types of expression assays including a global transcriptomic analysis using RNAseq and targeted direct count genetic expression assay using NanoString. There were several differences between the

RNAseq transcriptome dataset and NanoString results that may be due to the high throughput sequencing of RNAseq that can often ignore genes that may be biological relevant but lowly expressed (Conesa et al., 2016; Law et al., 2014; Robles et al., 2012;

Seyednasrollah et al., 2015). This research further suggests that several mechanisms should be included for expression analysis, as well as alternative validation assays, to provide conclusive evidence of alteration in LSMMG and spaceflight conditions.

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Table 4-1. Targeted genes for RNAseq and NanoString expression assay Group Gene name Transcript name Transcript ID Cluster ID PRR EsPGRP1 PGRP_HUMAN2 g32443.t1 cluster_20358 EsPGRP2 PGRP1_HUMAN TR392087|c0_g2_i3|m.10497 cluster_4678 EsPGRP3 PGRP1_BOSIN c90996_f1p3_2225 cluster_12168 EsPGRP4 PGRP1_CAMDR c13944_f1p1_1948 cluster_11708 PGRP1_CAMDR2 g32454.t1 cluster_11708 EsPGRP5 PGSC2_DROS c202596_f11p9_1744 cluster_2407 PGRP2_HOLDI g26249.t1 cluster_2407 EsTLR TOLL8_DROME TR148504|c1_g1_i1|m.29453 cluster_824 TOLL8_DROME2 c72113_f1p0_2169 cluster_824 EsGalectin LEG1_HAECO TR633522|c3_g2_i1|m.28191 cluster_8370 Effector EsHCY HCYG_ENTDO12 g31252.t1 cluster_2160 enzyme HCYG_ENTDO20 TR231567|c3_g1_i8|m.2596 cluster_2160 HCYG_ENTDO22 c170603_f1p2_1003 cluster_2160 HCYG_ENTDO25 TR231567|c3_g1_i5|m.2581 cluster_2160 HCYG_ENTDO5 c59160_f1p1_2827 cluster_2160 HCYG_ENTDO24 c85019_f1p5_2310 cluster_2160 HCYG_ENTDO21 TR863|c2_g6_i1|m.2615 cluster_2160 HCYA_ENTDO TR231567|c3_g1_i2|m.2569 cluster_2160 HCYA_ENTDO3 c11614_f5p67_3899 cluster_2160 HCYG_ENTDO4 TR863|c2_g2_i1|m.2610 cluster_2160 HCYG_ENTDO7 c153588_f1p1_1087 cluster_2160 HCYG_ENTDO11 g3396.t1 cluster_2160 HCYG_ENTDO16 g31254.t1 cluster_2160 HCYG_ENTDO18 g16382.t1 cluster_2160 HCYG_ENTDO2 TR231567|c3_g1_i4|m.2574 cluster_2160 HCYA_ENTDO5 c67393_f1p33_3615 cluster_2160 EsPutCHIT CHIA_BOVIN7 g73273.t1 cluster_626 EsSOD SODC_BOMMO TR132802|c5_g2_i1|m.7128 cluster_586 unk_SOD2 g89551.t1 cluster_586 Signaling MYD88 MYD88_PANTR c98230_f1p1_1673 cluster_16435 MYD88_PANTR2 c141455_f3p1_1351 cluster_530 IRAK4 IRAK4_HUMAN TR101118|c2_g2_i2|m.40331 cluster_23 TRAF2 TRAF2_HUMAN g53219.t1 cluster_5541 TRAF3 TRAF3_MOUSE c82312_f1p0_2491 cluster_15867 EsTRAF6 TRAF6_BOVIN1 g38415.t1 cluster_4580 TRAF6_BOVIN2 TR385091|c1_g1_i2|m.21623 cluster_4580 TAK1/M3K7 M3K7_BOVIN c69272_f1p1_2522 cluster_16222 M3K7_HUMAN c15923_f4p0_2537 cluster_16222 M3K7_PONAB c55278_f2p1_2609 cluster_16222 TAB1 TAB1_HUMAN g13401.t1 cluster_5482

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Table 4-1. Continued Group Gene name Transcript name Transcript ID Cluster ID TAB1_HUMAN c29899_f1p1_2495 cluster_5482 TAB3 TAB3_MOUSE c16307_f6p3_2164 cluster_12203 IKKβ IKKB_HUMAN TR27266|c2_g2_i1|m.38364 cluster_2799 IKKB_HUMAN2 g2252.t1 cluster_2799 EsIKKγ OPTN_DANRE g33516.t1 cluster_9674 EsRelA DORS_DROME TR173412|c6_g1_i1|m.10498 cluster_11565 NFκB1 NFΚB1_CHICK g24418.t1 cluster_1949 EsIκB IΚBA_CHICK TR535539|c10_g1_i1|m.10125 cluster_6796 IΚBA_CHICK2 c14435_f4p4_2544 cluster_11854 IκBζ IΚBZ_MOUSE c33160_f3p7_3236 cluster_5764 NFΚB1_CHICK2 g87925.t1 cluster_5764 NLRC3 NLRC3_MOUSE g90419.t1 cluster_13796 KBRS1 KBRS1_MACFA TR605905|c5_g7_i5|m.14500 cluster_7949 KBRS1_MACFA2 c69577_f1p0_1947 cluster_7949 KBRS1_MOUSE g38460.t1 cluster_7949

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Figure 4-1. Timeline of what is known in the Euprymna scolopes and Vibrio fischeri symbiosis under normal gravity conditions compared to LSMMG (low shear modeled microgravity conditions). A) Hatchling E. scolopes with light organ highlighted in box. B) the cileated epithelial appendages (CEA) of the light organ. C) Hemocyte migration into the blood sinus of the CEA. D) Apoptosis of CEA epithelial cells. D) Regression of the CEA. Apoptosis related events highlighted in green. Hemocyte related events highlighted in blue. HCY, hemocyannin; SOD, superoxide dismutase. Photos courtesy of Dr. Jamie Foster.

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Figure 4-2. Putative NFκB signaling pathway from known genes that exist in Euprymna scolopes. These genes were targeted for NanoString gene expression assay. Genes in dark blue are considered pattern recognition receptors (PRRs); Genes in pink, light green, and orange are considered effector enzymes; Genes in tan are considered signaling cascade genes. PGRP, peptidoglycan recognition receptor proteins; SOD, superoxide dismutase; CHIT, chitotrisidase/chitinas; HCY, hemocyannin; TLR, toll-like receptor; MYD88, Myeloid differentiation primary response 88; TRAFs, tumor necrosis factor receptor associated factors; NLRC3, NACHT, LRR and CARD domains- containing protein 3; TAK1, Transforming growth factor beta-activated kinase 1/ Mitogen-activated protein kinase kinase kinase 7; TABs, TGF-beta- activated kinase 1 and MAP3K7-binding protein 1; IKKs, Inhibitor of nuclear factor kappa-B kinase; KBRS1, NF-kappa-B inhibitor-interacting Ras-like protein; IκBs, NF-kappa-B inhibitor; RelA, Transcription factor p65/ relish; NFκB1, Nuclear factor NF-kappa-B.

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Figure 4-3. Heatmap of log-transformed RNAseq expression values averaged by treatment. Sample row names explained in Table 1. Gene name used only when all transcripts belonged to the same gene cluster. LSMMG, low shear modeled microgravity; APO, aposymbiotic; SYM, symbiotically infected with V. fischeri; h, hour.

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Figure 4-4. Principal component analysis (PCA) of all NanoString assay samples. A) All samples colored by separate treatment. B) Samples colored by symbiotic state. C) Samples colored by gravity and LSMMG state. D) Samples colored by time point. LSMMG, low shear modeled microgravity; M, modeled microgravity; G, gravity; S, symbiotically infected with V. fischeri; A, aposymbiotic; APO, aposymbiotic; SYM, symbiotically infected with V. fischeri; H, hatchling.

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Figure 4-4. Continued.

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Figure 4-5. Heatmap of log-CPM Nanostring sample expression averaged by treatments and clustered by dissimilarities. Sample row names explained in Table 1. H, hatchling; G, gravity; MMG, low shear modeled microgravity; APO, aposymbiotic; SYM, symbiotically infected with V. fischeri; h, hour.

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Figure 4-6. Principal component analysis (PCA) of all NanoString assay genes.

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Figure 4-7. Network plot showing Spearman correlation of voom transformed NanoString assay counts between different genes. A) All correlations. B) Only correlations with minimum correlation r value ≥ 0.6.

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Figure 4-8. Log2 fold change of selected genes in aposymbiotic Euprymna scolopes compared to symbiotically infected V. fischeri squid across time. A) Under gravity conditions. B) Under LSMMG conditions. ‘*’ denotes significant differential expression with an adjusted p-value ≤ 0.05. apo, aposymbiotic; sym, symbiotically infected with V. fischeri

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Figure 4-9. Log2 fold change of selected genes in gravity condition Euprymna scolopes compared to LSMMG squid across time. A) Aposymbiotic squid. B) Symbiotically infected V. fischeri squid. ‘*’ denotes significant differential expression with an adjusted p-value ≤ 0.05.

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Figure 4-10. Log2 fold change of all genes in Euprymna scolopes at 6 h in gravity compared to LSMMG. A) Aposymbiotic squid. B) Symbiotically infected V. fischeri squid. ‘*’ denotes significant differential expression with an adjusted p- value ≤ 0.05.

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Object 4-1. Log2FoldChange of targeted genes from Casaburi et al., 2017 RNAseq dataset using NOISeqBio. ( .xlsx ). Genes were considered significantly differentially expressed at an adjusted p-value ≤ 0.05 and log2 fold change ≥ 1.

Object 4-2. Normalized expression of all targeted gene replicates from Casaburi et al., 2017 RNAseq dataset after alignment to Euprymna scolopes reference transcriptome. ( .xlsx )

Object 4-3. Log2 fold change and significance testing of targeted genes from NanoString assay using voom transformation and LIMMA statistical testing. Genes were considered significantly differentially expressed at an adjusted p- value ≤ 0.05. ( .xlsx )

Object 4-4. Expression of all targeted gene replicates from NanoString assay normalized to housekeeping genes (indicated in Table 1-C) and background threshold described in the methods. ( .xlsx )

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APPENDIX A SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 2

Table A-1. Primers designed for qRT-PCR gene verification Primer Sequence (5'->3') Product size Tm (°C) rpoD_F AGCACGTACGATCCGTATTCC 60.0 121 rpoD_R GCGTTCAGCAAGCTCTTCAG 59.8 katA_F CCAGATAAGATGCTACAAGGTCG 58.7 146 katA_R CCATCAACACGCATAGCACC 59.6 flgK_F GCCGCACAAAGCTATGCAA 59.8 123 flgK_R AGCAAACTGATTTGCAGCAGG 60.0 flaA_F GAACCATCAATCGAAGGTGAGC 59.7 141 flaA_R AACACCGATAGACACTTGTGC 58.6 dnaK1_F GTGCGGTAACAATTCACGTACT 59.3 129 dnaK1_R CGAATGTTACTTCGATTTGTGGC 58.9 lpxD_F CGTGGTGCTATTGATGACACG 59.7 105 lpxD_R ACCAGCCATTGCTGAACCA 59.9

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Figure A-1. Growth curves of strains used in this study. A) WT strain grown under gravity and LSMMG conditions. B) Δhfq mutant grown under gravity and LSMMG conditions. Growth curves of strains used in this study. A comparison of all the strains including Δhfq complementation mutants grown under LSMMG C) and gravity D) conditions.

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APPENDIX B SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 3

EsPGRP5 - V K Y A V I H H S D T P K C H S K M K C I E R V R S I Q E Y H M H H N H W S D I - 82 EsPGRP4 - V S M V F V H H T A M A H C F H F Q N C S H E V K Q V Q D H H M I Q Y K W S D I - 163 EsPGRP3 - V K Y V F I H H T A M S S C T T R D A C I K A V K D V Q D L H M D G R G W S D A - 105 EsPGRP1 - V K M V F I H H T A M D Y C T N L Y A C S E A M R K I Q N L H M D N R G W S D L - 96 EsPGRP2 - V K M V F I H H T A M D Y C T N I S T C S E Q M R K I Q N F H M D D R G W F D I - 90

Figure B-1. Alignment of translated Euprymna scolopes peptidoglycan recognition receptor proteins (EsPGRP) transcripts. Black highlighted amino acids indicate conserved amino acid residues. Grey highlight amino acids indicate slightly conserved amino acid residues. Blue highlighted amino acids indicate putative binding to DAP-type peptidoglycan.

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APPENDIX C SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 4

Table C-1. Targeted genes for NanoString expression assay probe design Group Gene name Probe sequence (100bp; 5’- 3’) TCTATGAAGGTTATGCCCTTCCTCATGCCATTCTCCGTCTGGATTTG House- ACTB3 GCCGGTCGTGACTTGACCGATTACCTCATGAAGATCCTCACGGAG keeping CGTGGATA AAGCCGCCTATGGAGGTGGTGGCCGTGGCATGCGGGTTGTCCGAT PYC1 CCTTGGATGAAGTAGCAGAGAATTTTGAAAGGGCGTATTCGGAAGC ACTTTCTGC TATCCTTTCGTATGACCTCCTTCACGATCCCAACCACGACCTTTGTA PRR EsPGRP1* CACATAACCATCTTCACCAACCAAGTAGTTGTAACCGAGATCCGAC CATCCTC TTCATACACCACACAGCAATGGATTATTGCACCAACATCTCTACATG EsPGRP2 CTCCGAGCAAATGAGAAAAATCCAGAACTTTCATATGGACGACCGA GGATGGT CCCAACTACGAACTGTTTGGACATCGAGACGTTCGCAAAACAGAGT EsPGRP3 GTCCAGGAGAGAAGTTTTATCAATACATCAGAACATGGAAGCACTA TAGCACTA GATTTGTCATTGAAACCTCGGGTATGAGCGCCAACTCTGTCCCATC EsPGRP4* CTCGGCCTTCATAAACCCGGCCATCTTCACCGATGATGAAATTATA CCCAATAT CGATATTGGATATAACTTTCTGATCGGCAGCGACGGTAACGTGTAC EsPGRP5 GAAGGACGTGGATCGGATACAGTTGGAGCCCACACCAAGTTCTAC AATTCTCAG TCCATTGGCCACTTGGCACCGAACGTGTTCTCATCTAACCGTCATC EsTLR TCGAAGTGCTGATTCTAACCAACAATAGCTTGATTCACTTGGGTGA ATACTCCC TCTTCAGTCCGACTCTTCGGATGATGCTGTGATTGCTTTCCATCTGA EsGalectin ACCCTCGATTGGATTCTAATGAAGTTTGCTGCAACACTTATGATGG CGGATGG GACGCTATGGATGTCTGGCTCAAACACGGTGAACGGCAATTTAGTC Effector EsHCY* CGTAAAAATGTCGACACCTTGAGCCATGAAGAAATTATCAGCCTGC enzyme AAGTGGCA GCTATGACTACCATGGTGGAAGTTTTGACAATGTTACTGGTCATAA EsPutCHIT CAGCCCTCTGTATCCAAGAAAAGAAGAGACTGGAGATGAAAGAACA TTCAATGT ACTGTTGTGGTTCATGCAGATGTTGATGACTTGGGAAAAGGTGATC EsSOD ATGAGTTAAGCAAGACAACGGGTAATGCTGGTGGCCGACTTGCAT GTGGAGTTA AACCTTGCCCAGTTGTTGGATGTTCCCTGTAAAGTCTTTGCCTCTG Signaling MYD88 ACTGGACCAGTTTGGGGGCGGAGGTTGGACTGACCTTTGTTGAAA AAATGTCAT ACGTGGTGGACAATCAACATTTTCTCATCAATGTACAGA IRAK4 ACCTACAGAGCAGAGAAGTATTGACTCGTGTGATAGCTGTTGGAAG ATATCTGACTCAAAG TCCGATTCCGCTAAACAATGCGTACAGGTAGTGTGCGTGTCGGCAT TRAF2* TCTCTAAGCAAACTTGACAAAACCGATGACCACATTCTCTTTGCATC GGATCAC TAACACTTTGATTGAACAAATTTTGGAGCAGACCTCTGTGCATGACC TRAF3 GTGAGATTGGTGTCCACGATGTTAGATTTTCTGAGATGGAAGCCCG ATTAGCA

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Table C-1. Continued Group Gene name Probe sequence (100bp; 5’- 3’) AGAAATTCTCTCCATGAAGGTGCGCTGTCCGAATAGAAAGTCTGGA EsTRAF6 TGTAATACTTGTCTGGAGTTGAAACATATTGAGGAACACGCAGAAA GTTGTCCG TCGGAAAAGGTGCCTTTGGAGTTGTCCGCCGTGCTATGTACCATG TAK1 GGCATAGCGTTGCTGTAAAGTTGATTGAGACTGAAACTGAAAAGAA AGCCTTCAT GCTGAGGGAATGGTAGAAATTTTTGCAAGTAGGGTGAAGTAGGAGT TAB1 GCCACAAGTATTGTCAGCATTAGCGACACCAAACAAGTAGTTAAAG TGACGCAC CCCAAGTTCTGCCTCCTACTCGATGTTCAGCACACCACAGCTTTGC TAB3 ATCACTGGGCAGGTTCAACCGACCACATCTTCGCCTCAAACTTTAC ACGTTTCT GCATTTCAGCAATGGCTGAAGACAATGCTAATATGGGATAAAACGC IKKβ TTCGCGGTAACGATCATGAAGAAAAGGGCAAGAAACAAACGTGCTT CACCGAAC TTCAACTATTGAAAAGAAATACCACAGCGAAAAGAACCGAAATGAT EsIKKγ CAGCTGCAGGGTGCAATTGGTGAACATGCGTCATCTGAAAATGCA GTTGAGGTT CTGACCAGGCATGTAGTGAACCAAAAGAGTTTTTGTATCAGCCACA EsRelA AGATCCAGATCCTGATAGAATAGCAATAAAGCGAAAACGCAAAGCT TCAACTC GACAGATTGAAACTTTTGGCGCCACGGATGTAATCATCAGCAATTT NFκB1* GTGCGTGTCCGCGAATAATAGCCAAATGCAATAAACTGGAAAGATA AAAATAAA GTTATAGAAAAAAAAAAAATGCCATCACAACAGAAGAGCTGGCCGG EsIκB ACATGGGAGCAAACACGTCTTCAAACAATGAAGGAGTCTTTTTCCA GAGACACT TCTGCCTTTGATGAGACCCATTTGAAAGACCTCTGTGAGATCATTG IκBζ AGAAAGATCTCGTTAAAGAAAAGCTTGAAAAGGCGCTGTCTGAGGA TTTGCCAC GCATTTTGCCAGGTGCTGCGGAACACAGACTCTTATCAGGATGTGT NLRC3 CTGAAATGCTGGACGCCATGACCAAAATCTCATTCATGATATCGGA GATCAAT CGACAATCTCTTCAAGAACCTTTCGTTTGGCTCACCTCTCGGATAA KBRS1 CCCAACCTCCAAGTAAATCCGCATTTCCCTTGGGAAGAAAGAATAA AGGAAACG * Probes that were unsuccessful in the NanoString assay

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BIOGRAPHICAL SKETCH

Alexandrea A. Duscher received a Bachelor of Science degree in marine science with a concentration in biology and a Bachelor of Arts degree in environmental studies from Eckerd College, St. Petersburg, FL in 2013. Her independent undergraduate thesis research involved characterizing a Caribbean marine sponge disease in Callyspongia vaginalis under the mentorship of Dr. Koty Sharp. Alexandrea presented this work at her first conference where she met her future PhD advisor Dr. Jamie Foster. Alexandrea accepted a post-baccalaureate position to work on characterizing the microbial communities of Bahamian stromatolites under Dr. Foster in 2013 before pursuing her

PhD to study the impact of modeled microgravity on the beneficial squid-vibrio symbiosis in 2014 at the University of Florida. Alexandrea graduated with her PhD in microbiology and cell science from the University of Florida in May 2019. As an undergraduate and PhD student Alexandrea has presented her research at many conferences where she has won several presentation awards. Throughout her time in

Dr. Foster’s lab Alexandrea received several academic honors, including honorable mention for the NSF Graduate Research Fellowship in 2014 and 2016, the University of

Florida Grinter Fellowship in 2014, and the Florida Space Grant Consortium

Dissertation Improvement Fellowship in 2017. Alexandrea also obtained funding for her research through a crowdfunding initiative, with the help of her labmate Maddie Vroom, using the platform Experiment.com. In addition to academics, Alexandrea has been involved in various scientific community organizations including two different mentorship programs targeted at engaging young women and elementary aged girls in STEM programming as well as participating in Skype a Scientist and virtual teaching to promote STEM and research in K-12 classrooms around the world.

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