Transcriptome Response Associated with Protective Immunity in T and B Cell Deficient Ebrz Afish

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Theses and Dissertations Theses and Dissertations

1-1-2013

Transcriptome Response Associated with Protective Immunity in T and B Cell Deficient ebrZ afish

Aparna Krishnavajhala

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Transcriptome response associated with protective immunity in T and B cell deficient

zebrafish

Aparna Krishnavajhala

A Dissertation Submitted to the Faculty of Mississippi State University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Immunology in the College of Veterinary Medicine

Mississippi State, Mississippi

August 2013

Aparna Krishnavajhala

2013

Transcriptome response associated with protective immunity in T and B cell deficient

zebrafish

Aparna Krishnavajhala

Approved:

______Lora Petrie-Hanson Stephen B. Pruett Associate Professor Professor Basic Sciences Basic Sciences (Major Professor) (Committee Member)

______Xiu-Feng (Henry) Wan Larry Hanson Associate Professor Professor and Graduate Coordinator Basic Sciences Basic Sciences (Committee Member) (Committee Member)

______Kent H. Hoblet Dean & Professor College of Veterinary Medicine

Name: Aparna Krishnavajhala

Date of Degree: August 17, 2013

Institution: Mississippi State University

Major Field: Immunology

Major Professor: Lora Petrie-Hanson

Title of Study: Transcriptome response associated with protective immunity in T and B cell deficient zebrafish

Pages in Study: 124.

Candidate for Degree of Doctor of Philosophy

RAG1-/- mutant zebrafish lack T and B lymphocytes. However, when re-exposed to homologous bacteria, these fish mount a response that provides specific protection. To further define this response, we utilized microarray analyses to determine the mechanisms underlying innate immune system memory in zebrafish. We also analyzed interferon (IFN) gamma by qRT-PCR. It is produced by activated NK cells and could indicate if this cell mediates the protective response seen in lymphocyte deficient zebrafish. Pathological studies and in situ hybridizations were performed to observe tissue changes and location of the cells that produced IFN gamma. Following bacterial re- exposure, zebrafish transcripts in cell receptor activation, cell proliferation and cytotoxic function categories were differentially expressed. We found high expression of IFN gamma in the lymphocyte like cell population after bacterial exposure and this was induced to a higher level in fish that had been vaccinated. The phagocytic cell population showed no induction of INF gamma. Over-all, the pathological response was much less severe in the vaccinated (48 hps) fish. Our microarray and pathological findings indicate that the primary immune response of mutant zebrafish is not impaired, and they

demonstrate an enhanced innate immune response following secondary bacteria exposure. Following homologous secondary exposure, mutant zebrafish have a cell population that is undergoing upregulated cell receptor activation, cell cytotoxic functions and cell proliferation. This cell population expresses INF gamma. Activated T cells, NK-T cells and NK cells express INF gamma. Since RAG1 deficient zebrafish do not have T or NK-T cells, this cell population is most likely NK cells.

DEDICATION

This work is dedicated to my beloved father and mother Krishnavajhala

Mallikharjuna Vara Prasad Sarma,and Sumithra Devi without whose support and encouragement it would not have been possible.

ACKNOWLEDGEMENTS

First and foremost I would like to thank Dr. Lora Petrie- Hanson for her invaluable support during these past five years of my life. Dr. Petrie is the most adorable person whom you will never forget once you meet her. As my major advisor, she gave me complete freedom in pursuing my objectives and we always had insightful discussions in answering the scientific questions. She taught me both consciously and unconsciously. I sincerely appreciate Dr. Petrie for all her contributions in terms of time, intellectual ideas, funding, advice, patience, friendship, and motivation that kept my PhD experience as productive and stimulating. I also remain indebted for her understanding and support during the times when I was really down and depressed due to personal problems. She contributed immensely to my personal and professional life at MSU. I am also thankful for the excellent example she has provided as a successful woman and professor.

I would like to extend special thanks to my committee members Dr. Henry Wan ,

Dr. Larry Hanson, and Dr. Stephen Pruett who provided me encouraging and constructive feedback. Without great help from Dr. Hanson, I could not have finished quantitative real time PCR analysis. Dr. Wan immensely helped me in analyzing microarray data and providing me critical and constructive feedback. My sincere gratitude is reserved for Dr.

Pruett for being a member in my advisory committee, for being a mentor in teaching me research ethics and extending financial assistantship in my hour of need.

iii

I would also like to take this opportunity to thank Assistant Professors Dr. Todd

Pharr and Dr. Fiona McCarthy for their valuable comments and suggestions. Dr. Fiona guided me with my data analysis to find Gene Ontology in spite of her busy schedule. Dr.

Pharr was kind and generous in providing me with his expertise or study materials.

I received valuable guidance and support from my lab mate Dr. Claudia Hohn from the very beginning of my Ph.D. She was constant source of inspiration, feedback and support in and out of my academic process. Amongst my fellow graduate students in the department of Veterinary medicine, I thank Kamalakar Chatla and Ahmed Omar-Ali who helped me in all possible ways they could in performing my lab work. Ahmed shared his expertise in reading pathology slides. I have appreciated the expertise of Lori

Ford for her help in zebrafish sampling and her advice in trouble shooting the RNA isolation to get better yields. I am grateful to our departmental administrative assistant

Tony Roberson for her help and support in every single time I approached her. I personally thank Suzette Johnson for her relentless and timely supply of resources that I required in my work. The group has been a source of friendship as well as good advice and collaboration.

I thank my friends Dr. Krishna Kumari yalavarthi, Dr. Gail Moraru and Dr. Mais

Ammari for their moral support and being there to listen and comfort me during my tough times.

Without the support of Dr. Phyllis Benjamin I would not have successfully overcame some obstacles in my life. She gave me enough energy and spirit to move forward when I was really in need of it. I am greatly indebted to Karin Lee for

recognizing and providing me with opportunities to participate in all possible community service through Starkville Multicultural Lions Club.

Finally, I would like to acknowledge my family and friends for their invaluable support. They extended all their support to fulfill my dream. Words cannot express the feelings that I have for my family members. Dr. Bindu Nanduri was more a family member rather a faculty to me. I will never forget her moral support.

TABLE OF CONTENTS

DEDICATION ...... ii

ACKNOWLEDGEMENTS ...... iii

LIST OF TABLES ...... ix

LIST OF FIGURES ...... x

CHAPTER

I. INTRODUCTION ...... 1

Zebrafish: Immunological model ...... 1 Generation of RAG1-/- mutant Zebrafish: Targeting Induced Local Lesions in Genomes (TILLING) a reverse genetic mechanism ...... 4 Role of RAG protein in VDJ recombination ...... 5 Pathogen ...... 8 Microarray...... 10 Why zebrafish, Edwardsiella ictaluri and microarray technology were used in the present study ...... 12 References ...... 14

II. REVIEW OF LITERATURE ...... 19

References ...... 34

III. TRANSCRIPTOME ANALYSIS OF GENES ASSOCIATED WITH INNATE IMMUNE SYSTEM MEMORY IN T AND B LYMPHOCYTE DEFICIENT ZEBRAFISH ...... 37

Introduction ...... 37 Methods...... 39 Animal and Source ...... 39 Experimental Design ...... 40 RNA preparation ...... 41 Microarray...... 41 Data Analysis ...... 42 Primer Design and Nucleotide Sequences for quantitative RT-PCR (qRT-PCR) ...... 43 vi

Quantitative real-time PCR analysis ...... 43 Flow Cytometry ...... 45 Results ...... 46 Microarray Analysis...... 46 Primary exposure ...... 46 Vaccinated fish...... 50 ID mapping ...... 54 Functional analysis using GO ...... 55 Agbase analysis ...... 55 AgriGO: GO enrichment analysis ...... 56 Quantitative RT-PCR ...... 64 Discussion ...... 65 Conserved acute phase response, immune response related transcripts in zebrafish afterprimary infection with E. ictaluri ...... 66 Protein degradation, processing, localization and folding ...... 68 Expression profile demonstrating a unique secondary response ...... 69 Transcription factors ...... 69 Conclusions ...... 79 Primary response ...... 79 Secondary Response ...... 79 References ...... 81

IV. CELL AND TISSUE CHANGES ASSOCIATED WITH PRIMARY AND SECONDARY BACTERIAL EXPOSURES IN RAG1-/- MUTANT ZEBRAFISH ...... 95

Introduction ...... 95 Methods...... 96 Zebrafish maintenance ...... 96 Bacteria culture and preparation ...... 97 Bacterial injections and sampling ...... 97 In situ hybridization (ISH) ...... 98 Results ...... 99 Discussion ...... 103 References ...... 107

V. CONCLUSIONS...... 109

References ...... 113

vii

APPENDIX

A. A LIST OF ZEBRAFISH TRANSCRIPTS THAT WERE UPREGULATED IN THE KIDNEY FOLLOWING PRIMARY INFECTION WITH E. ICTALURI VACCINE STRAIN AND THE FOLD CHANGE RELATIVE TO SHAM INFECTED CONTROLS ...... 114

viii

LIST OF TABLES

3.1 A list of primer and probe sequences and annealing temperatures that were used for quantitative RT-PCR...... 43

3.2 A list of Zebrafish transcripts that were upregulated following primary infection compared to sham infected controls...... 47

3.3 A list of transcripts that were differentially expressed (p< 0.05) between EE and SE...... 52

3.4 A comparison of expression analyses of selected genes by microarray and qRT-PCR...... 65

3.5 Relative expression of IFN γ in sorted kidney leukocytes...... 65

4.1 IFNγ sense and anti sense Probes for In situ Hybridization ...... 99

4.2 In situ treatments to identify IFNγ expressing cells in SS, SE, and EE exposed mutant zebrafish...... 99

LIST OF FIGURES

1.1 Overview of target-selected mutagenesis in zebrafish...... 5

3.1 Bar graph of differentially expressed transcripts between EE and SE...... 51

3.2 Hierarchical tree graph of GO terms from EE and SE for biological function category...... 59

3.3 Hierarchical tree graph of GO terms from EE and SE for molecular function category...... 60

3.4 Hierarchical tree graph of GO terms from EE and SE for cellular component category...... 61

3.5 A flash bar chart of over represented terms from the GO functional analysis...... 62

4.1 Mutant zebrafish liver. A) Non-exposed healthy liver. B) 48 h post primary injection ...... 101

4.2 Mutant zebrafish renal hematopoietic tissue. A) 48 h post primary. B) 48 h post secondary ...... 102

CHAPTER I

INTRODUCTION

Zebrafish: Immunological model

Zebrafish (Danio rerio) belong to the family cyprinidae and are a widely used non-mammalian animal model. Zebrafish embryos develop ex uteri and are optically transparent allowing easy observation of the fertilized egg (Van Der Sar, Appelmelk et al.

2004), and host pathogen interactions using fluorescently labeled immune cells and microbes can be analyzed in the living organisms (Stockhammer, Zakrzewska et al.

2009). Zebrafish are used as an infection model because they act as a bridge between lower animal models such as the fruit fly and mammalian models such as mice and rats.

Zebrafish share several orthologous genes with mammals and extensive contiguous blocks of synteny between the zebrafish and human genomes have been revealed.

Therefore, immunological findings in zebrafish can often be translated to other higher vertebrates and mammals (Wienholds, Schulte-Merker et al. 2002) . Zebrafish are becoming a favorable model for epidemiological studies because of several advantages such as small size (<4cm), which is suitable to monitor the progression of disease, because the entire fish can be mounted on a single slide for histological examination

(Phelps and Neely 2005). Other advantages of the zebrafish model are easy handling, rapid development, high fecundity, minimal space requirements, optical transparency of larvae, year-round spawning and the genetic similarity to humans and other vertebrates 1

(Herbomel, Thisse et al. 2001, Phelps and Neely 2005). Fruit flies do not have the components of the adaptive immune system and do have many features of the innate immune system of mammals, the zebrafish on the other hand, has both innate and adaptive immune systems thus proves useful in study of infectious diseases (Yoder,

Nielsen et al. 2002, Van Der Sar, Appelmelk et al. 2004, Mathavan, Lee et al. 2005,

Hohn and Petrie-Hanson 2007, Lieschke and Currie 2007).

So far, zebrafish have been a powerful animal model to study vertebrate genomics and development and it is now developing into a highly valuable model to study the interplay between host - pathogen interactions. Zebrafish have been used to study infectious diseases caused by Edwardsiella tarda (Pressley, Phelan et al. 2005),

Salmonella typhimurium (van der Sar, Musters et al. 2003, Stockhammer, Zakrzewska et al. 2009), Mycobacterium marinum, Vibrio anguillarum (Meijer, Verbeek et al. 2005,

Phelps and Neely 2005, van der Sar, Spaink et al. 2009), Streptococcus iniae (Miller and

Neely 2004) and to develop vaccinations against viral hemorrhagic septicemia virus

(VHSV) (Novoa, Romero et al. 2006) and to study human developmental disorders

(Ekker, Stemple et al. 2007). Pathogens that are not documented to infect fish such as

Bacillus subtilis, Escherichia coli, Salmonella typhimurium, and multiple species of

Listeria have also been successfully used to infect zebrafish (Trede, Langenau et al.

2004). Studies about immune responses and infectious diseases in the zebrafish seem to provide pertinent information on host-pathogen interactions that can be directly used to understand human diseases (Trede, Langenau et al. 2004, Tobin and Ramakrishnan

2008). For example, infection of zebrafish with Mycobacterium marinum results in characteristic granulomas similar to those found in humans due to tuberculosis (TB)

caused by M. tuberculosis infection, whereas in mouse model, TB pathology follows a completely different progression (Phelps and Neely 2005, Tobin and Ramakrishnan

2008).

Earlier studies with larval channel catfish (Ictalurus punctatus) showed, that when exposed to Edwardsiella ictaluri (E. ictaluri) well before the development of the acquired immune system, protection upon secondary exposure to the same pathogen was consistently observed (Mackey 2002). This demonstrates that the innate immune system of fish can provide enhanced protection upon re-exposure to the same pathogen in the absence of functional T and B lymphocytes and acquired immune responses. This phenomenon has been observed in some invertebrates such as bumble bees and fruit flies

(Kurtz 2005, Sadd and Schmid-Hempel 2006, Pham, Dionne et al. 2007), and jawless vertebrates such a lamprey (Pancer, Amemiya et al. 2004). This indicates that the innate immune system is more adaptable than expected, which may be an important element of the vertebrate immune system. Several studies have focused on finding the mechanisms of the innate immune system that performs the memory function. Hapten based contact hypersensitivity studies (CHS) have demonstrated that NK cells are necessary and sufficient to mediate memory responses in T and B lymphocyte deficient (Rag-2 mutant) mice (O'Leary, Goodarzi et al. 2006, Paust, Senman et al. 2010). In the mouse cytomegalovirus (MCMV) infection model, C57BL/6 (B6) mice, that had NK cells with dominant expression of the Ly49H receptor, were utilized to demonstrate the expansion of NK cells bearing Ly49H+ activating receptors upon recognition of the viral m157 protein, and they reached maximum numbers by 7 days post infection (dpi) and the elevated numbers continued to be present at 15 dpi and 28 dpi. It was also shown that the

Ly49H+ NK cells recognize the viral ligand m157 only. A contraction phase was reached at 37 dpi with Ly49H+ NK cell numbers returning to the pre-infection levels after viral clearance (Sun, Beilke et al. 2009, Biron 2010, Sun, Beilke et al. 2010).

Generation of RAG1-/- mutant Zebrafish: Targeting Induced Local Lesions in Genomes (TILLING) a reverse genetic mechanism

Targeting Induced Local Lesions in Genomes has been a cost effective high- throughput screening method (Wienholds, Schulte-Merker et al. 2002, Wienholds and

Plasterk 2004, Sood, English et al. 2006). Mutagens such as N-ethyl-N-nitrosourea induce point mutations in male F0 zebrafish. Direct sequencing is most often used to screen for missense and nonsense mutations in genomic DNA for the gene of interest

(Fig. 1). Wienholds et al. (2002) sequenced five fragments of the Rag1 gene in 2600 F1 zebrafish and identified Rag1 null alleles. In this manner 15 Rag1 mutations were identified using target selected mutagenesis in zebrafish. One among them is a single nucleotide polymorphism (SNP) that results in premature stop codon in the Rag1 catalytic domain (Wienholds, Schulte-Merker et al. 2002). VDJ recombination is carried out by functional RAG1 protein to generate the T and B lymphocyte antigen binding repertoire (Schatz, Oettinger et al. 1989). Mutation at this locus in Rag1 results in loss of function thus leading to lymphocyte deficiency and ultimately results in loss of acquired immune responses (Wienholds, Schulte-Merker et al. 2002).

Figure 1.1 Overview of target-selected mutagenesis in zebrafish.

Adult male zebrafish were mutagenized by ENU. The mutagenized zebrafish were crossed with wild-type female zebrafish to create the F1 generation. Sperm was isolated and cryopreserved from the fertile F1 male zebrafish. Genomic DNA isolated and screened for mutations by nested PCR amplification of the target gene and followed by DNA sequence analysis. After a particular mutation is identified, in vitro fertilization (IVF) is performed to recover the F2 line bearing the mutation. Finally, mutations can be bred to homozygosity and explored for phenotypes. The picture has taken from Wienholds et al. 2002 (Wienholds, Schulte-Merker et al. 2002) .

Role of RAG protein in VDJ recombination

Recombination activating genes (RAG1 and RAG2) encode an enzyme known as recombinase. Recombinase plays an important role in genomic rearrangements of

immunoglobulin and T cell receptor molecules during VDJ recombination. RAG1 and

RAG2 proteins are observed in developing lymphocytes only when they are involved in the antigen receptor assembly (Janeway, Murphy et al. 2008). RAG1 and RAG2 are essential to generate mature T and B lymphocytes. Coordinated activity of RAG1 and

RAG2 is required for VDJ recombination, which is a multi-step enzymatic process. The enzyme complex creates single strand DNA breaks in the double stranded DNA molecule between flanking recombination signal sequence (RSS) and the coding sequence segment of the antigen receptor. This process is carried out in two steps. A nick at the 5’ end of the conserved RSS heptamer that is contiguous with the coding sequence is introduced, thus leaving 3’-hydroxyl (OH) group at one end of the broken DNA fragment and at the other (RSS) end, a 5’-phosphate (PO4) group. The next step joins the nick containing two different chemical groups, 3’-OH and 5’- PO4 present between the RSS and the gene segment located on the opposite strand. This results in the RSS to have a 5’- phosphorylated double-stranded nick and the coding region to have a covalently closed hairpin structure. Until the DNA breaks are enzymatically repaired, RAG proteins remain associated with DNA to initiate V DJ recombination to produce mature T and B lymphocytes from pre-T and B cells respectively (Hohn 2008, Janeway, Murphy et al.

2008).

VDJ recombination is observed in jawed fish and higher vertebrates. T cell receptors (TCR) and immunoglobulins (Ig) that recognize diverse microbial antigens and tumor cells are generated though this site-specific recombination process (Hohn 2008,

Janeway, Murphy et al. 2008). The first recombination event occurs between one D gene segment and a J gene segment during the development of a B cell. DNA located between

these two gene segments is deleted. The V gene will join the D-J recombined gene segments from the upstream region thus forming a VDJ gene, removing the genes between V and D-J gene segments from the genome of the cell. RNA with exons and introns is produced containing the VDJ region of the Ig heavy chain and the constant chains (Cμ and Cδ). The molecular composition of the primary transcript is V-D-J-Cμ-Cδ.

This is further processed by adding polyadenylation (poly-A) tail after Cμ chain and the removal of DNA between VDJ segment and Cδ gene segment. This results into the translation of the Ig μ heavy chain protein. The heavy chain locus consists of an array of constant (C) regions instead of a single C chain, each representing a different and unique isotype. Immunoglobulin light chain loci (kappa and lamda) also undergo similar rearrangements in a similar manner with the exception that light chains do not contain a

D segment. Upon translation kappa or lamda chains produce Ig k or Ig λ light chain proteins. Lastly, the Ig μ heavy chain and Ig k or Ig λ light chain assembly results in the creation of the immunoglobulin which is membrane bound and is expressed on the immature B cell surface (Hohn 2008, Janeway, Murphy et al. 2008) .

T cell receptors (TCR) are generated by same way as B cell receptors; therefore it is not surprising that the TCRs resemble immunoglobulins structurally. The organization of V, D, J gene segments is essentially similar to that of immunoglobulin gene segments.

The TCRα locus is analogous to the light chain of immunoglobulins and consists of a V and J gene segment. The TCRβ locus is analogous to the heavy chain of immunoglobulins and consists of V and J gene segments and additionally contains a D gene segment. The TCR will have D-to-J recombination taking place first in the β chain.

The V region will join the recombined D-J gene the incorporation of a constant domain

gene finally results in V- D- J - C. Introns will be spliced out from the mRNA and translated into a TCR C protein. Next, the TCRα chain rearrangements that include V-to-J recombination analogous to immunoglobulin light chain gene rearrangements will take place. Finally, the α β-TCR is formed up on assembly of β- and α- chains which will be expressed on majority of the T cells (Janeway, Murphy et al. 2008).

Defects in RAG genes that control VDJ recombination will affect both T and B cells equally, and ultimately leads to the loss of functional T and B lymphocytes

(Janeway, Murphy et al. 2008).

Pathogen

Edwardsiella ictaluri (E. ictaluri) belongs to the family Enterobacteriaceae. It is a gram-negative, motile, rod shaped, 0.5- by 1.25-µm, obligate pathogen with peritrichous flagella. It is an oxidase negative and fermentative bacterium. Small, opaque, circular colony formation usually takes about 48 hrs on sheep blood agar plates (Hawke, Brenner et al. 1981, Francis-Floyd, Beleau et al. 1987). It was first described in 1976 as an etiological agent that causes enteric septicemia of catfish (ESC) (Hawke 1979; Hawke et al 1981). Transmission of the disease occurs predominantly through shedding of bacteria from diseased fish or carriers and cannibalism of dead fish. This disease is a main threat during spring and fall when water temperatures are in the range of 22-28°C. The acute form of ESC is associated with high mortalities and the chronic form attacks the central nervous system (Shotts, Blazer et al. 1986). Outbreaks were reported from 60.6% of farm operations and ESC accounts for nearly 30% of annual production loss to the commercially raised channel catfish farming industry worldwide (Klesius 1992,

Shoemaker, Klesius et al. 1997, Booth, Elkamel et al. 2006, Russo, Shoemaker et al. 8

2009). Recent reports have established that E. ictaluri is an intracellular pathogen that is detected in the internal organs. It enters the brain first and systemic infection disseminates from there (Klesius 1992, Booth, Beekman et al. 2009). During the early stages of infection E. ictaluri is contained within macrophages associated with the vascular system, suggesting that bacteria carried systemically via blood by phagocytes

(Baldwin and Newton 1993). The route of E. ictaluri entry and method of exposure plays an important role in the dissemination of bacteria in various organs of fish. Entry of E. ictaluri can take place through various routes such as oral, nasal and gills (Shotts, Blazer et al. 1986, Baldwin and Newton 1993, Morrison and Plumb 1994). Fish can be challenged with E. ictaluri orally, by immersion, or intra-peritoneal and intra-muscular injection. Orally challenged fish show E. ictaluri in the head kidney first and then throughout the body within an hour post infection (Baldwin and Newton 1993).

Immersion challenged fish show E. ictaluri first in the gills and also in the liver

(Nusbaum and Morrison 1996). It was also demonstrated that intracellular replication of

E. ictaluri occurs within neutrophils and macrophages (Booth, Elkamel et al. 2006). The reason for using field isolates of E. ictaluri in our study is because of its extensive study as a catfish pathogen. Catfish and zebrafish share similarities with respect to development and maturation of the immune system and also the optimal temperature for zebrafish is the same as for E. ictaluri (Petrie-Hanson and Ainsworth 1999, Petrie-Hanson, Romano et al. 2007). It has been shown that E. ictaluri results in the same pathological manifestations in zebrafish and catfish (Petrie-Hanson, Romano et al. 2007). Presence of

E. ictaluri in zebrafish brain results in tail chasing behavior, and hanging in the water with head up and tail down. Intense petechiation on the ventral side of the body,

abdominal ascites and exophthalmia (pop-eye) were observed. Tissue necrosis or a common spotted red and white appearance was observed in the liver. Muscle, intestine and fat have manifested petechial hemorrhages and the intestine was frequently filled with bloody fluid.

Two strains of E. ictaluri were used in this study, the attenuated strain RE-33 and a wild- type (field isolate) strain. For vaccination (primary) exposures RE-33 the attenuated strain of E. ictaluri (Klesius and Shoemaker 1999) was used and for protection

(secondary) exposures the field isolate was used.

Microarray

Microarray technology enables the screening of transcripts and simultaneous determination of tens of thousands of transcripts within an intricate mixture on a glass microscopic slide (Peatman, Terhune et al. 2008, Li, Olohan et al. 2009). This is a powerful tool to study the molecular basis of host pathogen interactions (Meijer, Verbeek et al. 2005, McMahon, Hiew et al. 2007). This is also becoming a standard tool in genetic research. High quality microarray chips are available ready-made, unlike a few years ago, when chips were custom made by the researcher who used them and also analyzed the results (Gautier, Cope et al. 2004). The primary example of the commercial microarray products are Affymetrix oligo nucleotide chips (http://www.affymetrix.com). Each gene is represented on the Affymetrix gene chip in the form of oligonucleotide probes (oligos) designed from the available cDNA sequence (Li, Olohan et al. 2009). Each oligo is 25 bases long, and the number of oligos per gene can range from 11-20 (Gautier, Cope et al.

2004).The number of probes can range from 10,000 to 20,000 per slide and rarely they go up to 40,000 (per genome/tissue) (Li, Olohan et al. 2009). The oligos can be short (25- 10

30mer) or long (65-70mer), depending on the manufacturer and the detection criterion.

Some of the examples of the customized Affymetrix zebrafish genome array are explained here. In one study the array was designed for the detection of 14,900 transcripts that account for nearly 33% of the predicted genome. Each transcript was detected using

16 different oligonucleotide probes. Predicted protein sequence from all the probes of the transcript is confirmed by determining the similarity between known mammalian protein sequences with that of full length mRNA sequence of zebrafish probe sets. Each zebrafish Affymetrix probe is BLASTed against the open data bases such as GenBank and Sanger etc. (Andreasen, Mathew et al. 2006). High density in situ oligonucleotide microarray with nearly 19,000 catfish unique sequences was produced to demonstrate the differences in LPS treated catfish spleen global gene expression. To better understand

ESC caused by infection with E. ictaluri, the 19K array is enriched to 28,000 by adding

7159 acute phase response related transcripts from blue catfish (Ictalurus furcatus) a close relative of channel catfish along with some extra immune and non-immune catfish transcripts. There is more than 98% nucleotide similarity within cDNA transcripts of channel catfish and blue catfish. The modified 28K microarray design should help capture a major part of the catfish transcriptome to find out APR profiling of channel catfish after infection with E. ictaluri (McMahon, Hiew et al. 2007, Peatman,

Baoprasertkul et al. 2007). This array can also be used for transcriptome profiling of blue catfish and channel catfish livers after infection with E. ictaluri and compare their expression patterns (Peatman, Terhune et al. 2008). In another study microarray slides were custom designed with 43,371 probes of 60 oligonucleotide lengths each by Agilent

Technologies (http://www.home.agilent.com). Commercially available slides from

Agilent contained 21,496 identical probes compared to the above 43,371 probes. This custom made slide was used to study the immune responses of time resolved transcriptome profiling of zebrafish embryo to wild-type and LPS-mutant Salmonella typhimurium infection (Stockhammer, Zakrzewska et al. 2009). In the other study expression profiles of zebrafish after infection with Mycobacterium marinum were performed using different microarrays, namely MWG 14k zebrafish oligo set, the sigma-

Genosys 16k zebrafish oligo library and the Affymetrix Genome array and the expressions were compared with controls. The three microarray platforms comprise 4138

UniGene clusters. This dataset was further analyzed to find the 2 or 3 fold up and down regulated transcript expression compared to controls. A reference set of 63 (2 fold) upregulated and 93 (2 fold) down-regulated transcripts for transcriptome analysis of

Zebrafish and M. marinum interactions was used (Meijer, Verbeek et al. 2005).

Why zebrafish, Edwardsiella ictaluri and microarray technology were used in the present study

One of the most important diseases affecting farm-raised channel catfish is enteric septicemia of catfish (ESC). This disease is caused by a gram negative intracellular pathogen Edwardsiella ictaluri. ESC was responsible for 52% of losses reported in channel catfish fry in 2003 (USDA 2003). Using microarrays, host (zebrafish) pathogen

(E. ictaluri) interactions can be studied at molecular level. Small quantities of starting

RNA sample is enough to get information about thousands of transcripts simultaneously

(Meijer, Verbeek et al. 2005).

Specific Aims:

1. Evaluation of differentially expressed genes in RAG1 -/- mutant zebrafish

following primary exposure to Edwardsiella ictaluri

2. Evaluation of differentially expressed genes in RAG1 -/- mutant zebrafish

following secondary exposure to Edwardsiella ictaluri

3. Cell and Tissue Changes Associated with Primary and Secondary

-/- Bacterial Exposures in RAG1 Mutant Zebrafish and Tissue Localization

of IFNγ expressing cells

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CHAPTER II

REVIEW OF LITERATURE

Vertebrates can perform both anticipatory and non-anticipatory immune responses whereas invertebrates are known to perform only non-anticipatory responses (Klein

1989). It could either be that, invertebrates never had the anticipatory immune response or that they have a different mechanism than vertebrates. It is reasonable to try to find the roots of anticipatory immune responses in invertebrates. Lymphoid tissue is absent in the most primitive chordates whereas cyclostomes have only a diffused lymphoid tissue and it is proposed that even more primitive animals survived without any lymphoid tissue at all (Klein 1989).

It has been proven that Drosophila melanogaster is a powerful model to study the innate immune system. When Drosophila was primed with a sublethal dose of

Streptococcus pneumonia, they were protected upon exposure to a lethal dose of the same bacteria administered one week later. The protection was detectable within 24 hours of priming and persisted for the life of the insect (Pham, Dionne et al. 2007).

In cockroaches, vaccination with killed Pseudomonas aeuriginosa resulted in protection as long as 28 days post immunization (Faulhaber and Karp 1992). Bombus terrestris, commonly known as the bumblebee have been able to survive and clear a secondary homologous bacterial challenge compared to heterologous secondary

infection. This demonstrates that its immune system can respond specifically to a pathogen that had been encountered previously (Sadd and Schmid-Hempel 2006).

Copepods, Macrocyclops albidus, have shown a lessened secondary infection with parasites, when exposed primarily to a related parasite than to an unrelated parasite.

The copepod’s defense system reacts efficiently to parasites that are antigenically similar to previously exposed parasites. If primary and secondary challenges involve closely related parasites, the intensity of infection during secondary challenge is relatively low

(Kurtz and Franz 2003, Little, Hultmark et al. 2005).

The mechanisms to perform specific memory would be an evolutionary advantage if inherited. Immunological memory might have developed in long-lived invertebrates first and then in their relatives who are shorter- lived. In colonial animals like cockroaches and earthworms, graft rejection is relatively quick when skin grafts are used from genetically dissimilar donors for the second time compared to the first time. Thus, immunological memory does exist in invertebrates (Kurtz 2005, Little, Hultmark et al.

2005).

Pathological microorganisms are a threat to metazoans. Thus an ideal immune system has efficient mechanisms to recognize and counter attack the invading/dangerous pathogenic microorganisms and leave self-cells unharmed. Also, the immune system should have flexibility and diversity. It should be economical enough to use the minimal cells needed and not use up a large genome space (Hoffmann and Reichhart 2002, Du

Pasquier, Zucchetti et al. 2004). Metazoans rely on innate immune responses to protect themselves from invading microbes. Classic adaptive immune responses first appeared in cartilaginous fish. Innate immune responses are retained in Gnathostomes. Due to the

limitations of their adaptive immune responses, all metazoans rely on innate immunity for initial infections (Hoffmann and Reichhart 2002, Plouffe, Hanington et al. 2005). For that matter, defense ability of all invertebrates depend on innate immune response and vertebrates on the other hand have adaptive immune responses as well (Matsunaga and

Rahman 1998). The innate immune system can be considered a universal host defense mechanism (Takashi Aoki 2008). Invertebrates and agnatha (the jaw-less vertebrates) do not have the hallmark components of adaptive immune responses such as immunoglobulins, T-cell receptors and major histocompatibility complex. Thus it can be concluded that in early vertebrates the adaptive immune system evolved unexpectedly

(Matsunaga and Rahman 1998). Jaw-less vertebrates have two surviving taxons, the lamprey and hagfish; these are early vertebrates and are considered the primitive extant vertebrates (Matsunaga and Rahman 1998, Rogozin, Iyer et al. 2007). They possess variable lymphocyte receptors (VLRs) instead of immunoglobulins. VLRs are composed of extremely diverse leucine-rich repeats (LRRs). Two types of VLRs have been identified, VLRA and VLRB (Pancer, Amemiya et al. 2004). Both agnathan and gnathostome lymphocyte receptor diversity is generated by somatic rearrangements to anticipate any encountered pathogenic/microbial epitopes. Recombination of immunoglobulin (Ig) and T-cell receptor VD J gene segments in gnathostomes are dependent on recombination-activating gene (RAG) encoded protein RAG1 and RAG2 to generate the receptor repertoire. RAG1 and 2 are expressed only when they are engaged in their antigen receptor assembly in developing lymphocytes (Janeway, Murphy et al.

2008). Further, diversity is generated by somatic hypermutation and/or gene conversions with the help of the enzyme activation-induced cytosine deaminase (AID) (Pancer,

Amemiya et al. 2004, Rogozin, Iyer et al. 2007). T-cell receptor and Ig loci are extremely intricate even in primitive gnathostomes such as sharks and rays (Pancer, Amemiya et al.

2004). VDJ recombination is guided by noncoding and conserved DNA sequences found right next to recombination signal sequences (RSSs) (Janeway, Murphy et al. 2008). In agnatha, VLR genes of lymphocytes are assembled by somatic DNA rearrangement that leads to mature VLR genes, but the mechanism of assembly is still not clear. Each lymphocyte will have a mature VLR gene and a unique diversity region. Highly variable sets of LRRs are present in the diversity region. The predicted scheme for the large repertoire is combinational assembly of diverse LRR cassette arrays that flank the germline VLR genes. Each cassette encodes one to three different types of LRRs.

Assembly of each mature VLR consists of numerous genomic cassettes through a gene conversion-like process and DNA cytosine-deaminase an activation induced deaminase

AID/APOBEC family member is also involved in VLR assembly (Rogozin, Iyer et al.

2007). It can be concluded from the jaw-hypothesis that the predatory life style of jawed vertebrates is causing injuries to the gastrointestinal tract, thus leading to infections. In order to protect the local regions from these infections, the adaptive responses were evolved in primitive gnathostomes (Matsunaga and Rahman 1998).

The earliest class of vertebrates includes the fish, and they possess both arms of the immune system, innate and adaptive immune system. To protect themselves from potential pathogens that are constantly present in the environment, fish have developed several constitutive as well as inducible immune responses. There are a lot of similarities between mammalian and fish cells of innate immune response (Plouffe, Hanington et al.

2005). Evolutionarily, zebrafish are more closely related to mammals than to the fruit fly

and nematodes. Fish also possess unique cell populations and receptors (Plouffe,

Hanington et al. 2005). Novel natural killer (NK) cell receptors were identified in teleost species. Initially NK cells were described as large granular lymphocytes with distinctive cytoplasmic granules. Later, additional NK cell populations were described. These were described as innate immune cells with a distinct lymphocyte lineage and are considered as minor population of total lymphocytes. They have cytokine producing abilities in addition to cytotoxicity (Vivier, Tomasello et al. 2008). NK cells use pattern recognition receptors to screen somatic cells so as to distinguish healthy from abnormal cells (Paust,

Senman et al. 2010). A novel receptor was first identified in puffer fish and later in multiple bony fish species including zebrafish (Yoder, Turner et al. 2010) are novel immune-type receptors (NITRs) which are unique to fish, no known homologues are found in mammals and are considered to be functionally orthologous to mammalian NK cell receptors (Plouffe, Hanington et al. 2005). The zebrafish genome contains nearly 40 different NITR genes encoded on chromosomes 7 and 14 that are classified into 14 families (Plouffe, Hanington et al. 2005). Chromosome 7 consists of 36 NITR genes classified into 12 families that have been found to exist as a single gene cluster.

Chromosome 14 consists of 3 NITR genes classified into the remaining two families of

NITRs that are encoded in a gene cluster (Yoder, Turner et al. 2010). All NITRs are immunoglobulin (Ig) super family members (Plouffe, Hanington et al. 2005). NITRs contain two extracellular Ig domains, a transmembrane region and a cytoplasmic tail that is connected to the immunoreceptor tyrosine based inhibitory motifs (ITIMs), which help to deactivate the cells on which ITIMs are expressed (Yoder, Mueller et al. 2001, Plouffe,

Hanington et al. 2005). One Ig domain is variable (V)-type and the second Ig domain is

intermediate (I)-type in all NITRs (Yoder, Litman et al. 2004, Yoder, Turner et al. 2010).

Innate and adaptive characteristics are associated with NITRs (Yoder, Mueller et al.

2001). NITR expression is abundant in spleen, kidney and intestine (mainly in hematopoietic tissues) of zebrafish and catfish. Their expression is less abundant in muscle and liver. This suggests the significant role of these receptors is controlling cell mediated (cellular) immune responses (Plouffe, Hanington et al. 2005, Yoder, Turner et al. 2010). The role of NITR genes as NK cell receptors is supported by their expression in the lymphoid lineage. Expression of NITRs throughout the development of zebrafish suggests that they might play certain alternate functions but their chief role is as NK receptors. The size of the NITR transcripts is between 1.4 to 2.0 kB (Yoder, Turner et al.

2010). NITRs can either activate or inhibit cytotoxicity and/or cytokine production of NK cells (Yoder 2009). Detection of target cells by NK cells involves various germline- encoded activating and inhibitory cell surface receptors, which regulate activities of NK cells (Vivier, Tomasello et al. 2008, Orr and Lanier 2010). Based on function, NITRs are classified into two groups 1) inhibitory and 2) activating receptors. Inhibitory receptors contain cytoplasmic ITIM (Jeffrey 2009, Yoder 2009). The activating receptors are non- covalently associated via a positively charged residue in their transmembrane domain

(TM), thus partnering with an adaptor protein which has an activating motif such as cytoplasmic immunoreceptor tyrosine-based activation motif (ITAM) (Jeffrey 2009,

Yoder 2009, Orr and Lanier 2010) . One example of an adaptor molecule is DAP-12 that contains a negatively charged residue in its TM and a cytoplasmic ITAM. Most of the

NITRs are found to be inhibitory as with mammalian NK cell receptors. In zebrafish only one of 39 NITRs are have been found to have activating ability (Jeffrey 2009, Yoder

2009). Inhibitory signals from ITIMs dictate the NK cells to kill infected cells and not kill the healthy ones. The ligands for all the ITIMs expressed by NK cells are well defined on the target cell (Long 2008). When these ITIMs on natural killer cell receptors (NKR) are bound to their ligands, phosphorylation of tyrosine residue occurs. This triggers SHIP-1, the lipid phosphatase for the degradation of phosphatidylinositol-3,4,5-triphosphate, to form phosphatidylinositol-3,4-bisphosphates, (SHP-1) or SHP-2 (tyrosine phosphatase).

These phosphatases are recruited by inhibitory NKRs to suppress NK cell responses by dephosphorylating the tyrosine kinases associated with activating NKRs. Thus these phosphatases stop the Ca2+ influx, production of cytokines, degranulation and NK cell proliferation (Lanier 2008). The NITR family of receptors recognizes wide ranges of ligands on the target cells. The NITR V domain and the T cell receptor (TCR) V domain share some structural similarities and suggest that ligand recognition by NITRs may follow a similar method (Yoder 2009). The prerequisites for an NK cell to kill a target cell selectively are a lack of major histocompatibility complex (MHC) class I expression on the target cell, and activating ligand recognition on the target cell. If these conditions are met, the activating receptors on the NK cells are signaled to instantaneously lyse the target cell; effector differentiation is not needed (Long 2008, Paust, Senman et al. 2010).

NK cells can also be activated by certain cytokines such as type I interferon (IFN) and interleukin 12, 15 and 18 (Paust, Senman et al. 2010). Lysis of infected cell does not require gene transcription, indicating that mature NK cells are primed constitutively, and will have pre-synthesized effector molecules such as IFN γ, granzyme and perforin. Upon activation of NK cells, the effector molecules stored in cytoplasmic granules are released to lyse the target cell. NK cells have the ability to acquire nearly 20 % of the MHC-I

complexes from the surrounding cells (Paust, Senman et al. 2010). Human killer immune-type receptor (KIR) and the murine LY49 family of activating receptors appeared due to gene duplication events and sometimes the conversion products from inhibitory receptors. Therefore the ligand binding extracellular domains of these activating receptors are the homologous counter parts of their inhibitory receptors. These activating receptors have the ability to bind with MHC class I ligands with low affinity

(Orr and Lanier 2010). It is still unclear if one receptor is enough for the activation of NK cells. Activating NK cell receptors can be categorized into three groups: receptors that use ITAMs for signaling, those that are DAP-10/12 associated (DNAX activating protein of 10 and 12 kDa), and others which use several other signaling mechanisms (Bryceson,

March et al. 2006). Unlike what happens in T and B cells, effector functions are not initiated by a single dominant receptor in NK cells, but result when paired in different combinations of huge numbers of receptors. It has been demonstrated that activating receptors on freshly isolated human NK cells tried to cross-link using agonist antibodies except for CD16, none of the NK cell receptors were able to show cytokine production or cytolytic activity. The effector functions were elicited when different pairs of receptors cross-linked simultaneously (Lanier 2008). NK cells might have evolved together with T cells (Sun, Beilke et al. 2009). It has been found that NK cells possess properties previously attributed to T and B cells (Ugolini and Vivier 2009). Being a bridge between both immune systems, NK cells possess the properties of both innate and adaptive immune cells. It has also been demonstrated that NK cells experience all four phases of adaptive immune response against a viral pathogen (Sun, Beilke et al. 2009). These are antigen specific activation, expansion, contraction and memory phases. These responses

can be attributed to only some subsets of NK cells (Butler and Harty 2009, Sun, Beilke et al. 2009, Paust, Senman et al. 2010). As innate responders, NK cells have germline coded receptors, react to peripheral infections, rapidly traffic and mediate effector mechanisms without prior sensitization (Sun, Beilke et al. 2010). In the murine cytomegalo virus

(MCMV) infection model, C57BL/6 (B6) mice were utilized to demonstrate the expansion of NK cells bearing Ly49H+ activating receptors upon recognition of viral m157 protein, and reaching maximum numbers by 7 days post infection (dpi). Elevated numbers continued to be present at 15 dpi and 28 dpi compared to uninfected mice (Sun,

Beilke et al. 2009). The Ly49H+ NK cells recognized the viral ligand m157 only (Sun,

Beilke et al. 2009, Biron 2010). A contraction phase was reached at 37dpi with LY49H+

NK cell numbers returning to the pre-infection levels after viral clearance. Adoptive transfer of mature wild-type NK cells into DAP-12 deficient mice has demonstrated secondary expansion in mice upon MCMV infection of the recipient mice. It has also been demonstrated that the memory pool of antigen experienced Ly49H+ NK cells can persist in lymphoid (spleen and lymph node) and non-lymphoid tissues (liver) of mice post infection with MCMV (Sun, Beilke et al. 2010). The amplitude and kinetics of NK cell responses specific to MCMV mimic primary T cell responses (Sun, Beilke et al.

2009). Similar to most of the T-cell mediated responses, maximal NK cell expansion takes place at 7-8 days post infection (PI), which is independent of antigen specific NK cell precursor frequency (Sun, Beilke et al. 2010). The Ly49H+ NK cell expansion, contraction, persistence and secondary expansion mirror the attributes of T cells responses (Sun, Beilke et al. 2009). Adoptive transfer of memory Ly49H+ NK cells into naïve mice have shown heightened response compared to the naïve NK cell responses

following MCMV infection. Memory NK cells also had higher levels of Ly49H (Sun,

Beilke et al. 2009). It was difficult to detect memory NK cells after 4-5 months PI, but there was a robust expansion upon re-infection with MCMV (Sun, Beilke et al. 2010).

The NK cells are still present but may not persist the entire life span of the host. This principle can be applied when designing vaccines that can provide protective immunity through specific NK cell induction.

Molecular mechanisms and cell to cell interactions responsible for promoting and sustaining memory T and B cells need to be investigated for memory NK cells (Sun,

Beilke et al. 2010). Diversity in NK cell lineage has been due to the variability in the germline-encoded surface receptor repertoire and their activating and dampening properties, which accounts for adaptive characteristics of NK cells displayed in response to MCMV infection. In response to specific pathogen antigen, NK cells are activated and proliferate, which in turn is recognized by the activating receptors to clear the pathogen.

Upon pathogen clearance, NK cells will contract and results in a memory pool. Re- encounter results in heightened immunity, thus it can be demonstrated that as such there exists a lot of adaptive immune properties in NK cells (Sun, Beilke et al. 2010). It still need to be determined if NK cells respond similarly to antigens, other than MCMV.

Moreover, only C57BL/6 has the LY49H expression upon MCMV infection, and also

NK cell receptors that have the ability to recognize MCMV antigens directly are not expressed in 90% of the out bred and some of the inbred mouse strains other than

C57BL/6. On an average, a distinct NK cell from mice can randomly express 2-3 members from the LY49 family. The frequency of expression of any given LY49 subsets varies from tissue to tissue (Paust, Senman et al. 2010).

Hapten based contact hypersensitivity studies (CHS) have demonstrated that NK cell are required and sufficient to mediate memory responses in T and B lymphocyte deficient (Rag-2 mutant) mice (O'Leary, Goodarzi et al. 2006). The mice had functional

NK cells. The authors have examined the cellular infiltration of the urinary bladder following primary and secondary epicutaneous exposure to 2,4-dintrofluorobenzene

(DNFB). After the secondary exposure, recruitment of inflammatory cells was the same in mutant and wild-type mice, indicating T cells were not involved in the response. Both mutant and wild-type sensitized mice had cell counts twice that of naïve mutant and wild- types, indicating a true adaptive, or memory response had occurred. Natural killer cells were the predominant infiltrating cell type. This type of exposure was repeated on the skin, with similar results. Several other related compounds such as 4-ethoxy methylene-2 phenyl-3-oxalin-5-one and picryl chloride were used to test the specificity of the secondary response, and it was demonstrated to be inducible and hapten specific, or a true adaptive secondary response (in mutant mice). Contact hypersensitivity responses were mounted only when the mice were exposed to the same hapten they encountered during sensitization, but these mice did not respond when challenged with a different hapten.

NK cells from these mice have recalled the hapten used for sensitization for several months. Thus T and B lymphocyte deficient mice have the capability to learn, remember, and discriminate between specific antigens that are the characteristic features of the adaptive immune response (O'Leary, Goodarzi et al. 2006).

Adoptive transfer studies indicated that NK cells from liver and spleen were the specific leukocytes involved in providing hapten specific memory transfer from DNFB sensitized Rag2-/- to naive recipient mice. There are multiple subsets of NK cells, based

on anatomic location, surface phenotype and immunological activity. Hapten-specific

CHS response was mediated by a distinct population of NK1.1+ cells which expressed

CD11b, CD90 (Thy1.2), Mac-1, Ly49C/I and CD186 (CXCR6) (O'Leary, Goodarzi et al.

2006, Paust, Senman et al. 2010, Paust and von Andrian 2011) in the sensitized mice liver, whereas sensitized or naïve NK cells from the spleen have not mediated the adaptive immune responses upon hapten re-exposure. This indicates that NK memory cells were located in the liver. Recognition of self MHC-I molecules is necessary for NK cell licensing during their development so these NK cells can exert effector functions against certain antigens. Licensing is required for optimal hapten based recall responses.

But all the markers involved in recall responses in hepatic NK cells were found on splenic NK cells, which do not confer memory. These findings indicate that these markers by themselves or in combination are insufficient to provide or acquire antigen- specific memory (Paust and von Andrian 2011). RAG-independent memory responses are also independent of MHC haplotype (O'Leary, Goodarzi et al. 2006, Paust, Senman et al. 2010, Paust and von Andrian 2011). Authors have also concluded that the NK cells that are hapten specific are different from memory NK cells from MCMV infected mice, and are found both in the liver and spleen (Paust, Senman et al. 2010). It can be assumed that the hapten specific memory NK cells in sensitized mice are first primed by dendritic cell or any other antigen presenting cells in peripheral lymph nodes followed by their entry into blood before entering into the liver and live as long-lived memory cells and persist for very long time in the liver (O'Leary, Goodarzi et al. 2006). When the mouse ear was challenged with hapten, the memory NK cells located in the liver migrated to the ear, which is independent of L-selectin but rely on β2 integrins and P and E selectins

from the endothelial cells. Upon re-exposure to hapten, NK cells are activated and recruit neutrophils, macrophages and dendritic cells. Hepatic and splenic NK cells utilize different mechanisms to mediate memory responses in an organ specific manner for the viral clearance against MCMV infection. Perforins are used for viral clearance in the spleen and protection is mediated by IFNγ in the liver. The molecular mechanism of how

Rag1/2 deficient mice mediate antigen specific responses is still unclear. RAG- independent generation of lymphocyte repertoire was evidenced in hagfish and lamprey, using recombinational assembly of LRR gene segments to get protection from a variety of antigens (Paust, Senman et al. 2010). Actually, there does not seem to exist any pressure for immune system memory to boost antimicrobial effects, however, there should have been a pressure to self-regulate by protecting the host from the infection and the other is protection from the immune responses when there is a persistent infection. So it is assumed that subset immune responses have evolved to activate antimicrobial defenses (Biron 2010).

NK cells and CD8 T cells each represent nearly 10% of the white blood cells in the peripheral compartments. NK cells have germline coded activating and inhibitory receptors as NK gene complex products. Nearly 20-100% of the total NK cells express particular activating receptors. When specific genes are present, the basal frequencies of

NK cell activating receptor product expression are considerably higher compared to antigen specific T cell expression. NK cells also express inhibitory receptor signals. In response to virus infections the basal NK cell numbers that respond to the infection are significantly higher than those of antigen specific CD8 T cells. Following different viral infections, specific activating receptors expressed by NK cell subsets increase. It has been

observed in mice infected with MCMV, m157 is recognized and the expansion of Ly49H subset was observed. Expansion of NKp30 subsets was observed during chronic HCV infections. However, there are certain viral infections in humans such as HIV, HCV, and varicella zoster, where NK cell functions are reduced dramatically. NK cell responses during MCMV infection in mice without Ly49H gene have demonstrated the crucial role of Ly49H receptor during NK cell subsets proliferation, and also for NK cell maintenance during sustained infections. This is similar to maintenance of antigen specific T cells during sustained infections. In the absence of subsets with activating receptors for the ligand, overall NK cell numbers drop to below pre-infection levels. The Ly49H molecule also has the capability to develop memory which is in parallel with TCR role in stimulated T cells (Biron 2010).

NK cells can mediate positive feed-forward effects on T cells in response to infections. TH1 cells are the products of CD4 T cell differentiation promoted by IFN γ produced by NK cells. NK cells also help reduce type I interferon (IFN-αβ) levels by regulating viral replication thus avoiding the consequential negative effects of dendritic cell availability to promote T cell responses. NK cells can be activated to regulate T cell responses negatively by producing IL-10. During HCV infection and when Ly49H supports the maintenance of NK cells during un-controlled MCMV infection, NK cells produce IL-10. In the latter case, IL-10 limits the extent of the CD8 T cell responses and provides protection against diseases mediated by T cells. Regulation will be lost if the

NK cell maintenance was absent and the consequences would have been dangerous.

During LCMV infections, T cell responses will interfere with clearing the virus rather than immunopathology thus IL-10 limits T cell responses. IL-10 can be synthesized by

CD8 T cells to get protection from inflammatory disease during influenza virus infection.

From the above observations it is demonstrated that there are pathways that enhance production and or maintenance of a number of NK and T cells will regulate IL-10 production. Cytokine production either by NK cells or T cells can result in auto- or cross- regulation thus antimicrobial defenses can be accessed and protected from immune mediated diseases by keeping the magnitude of responses under control (Biron 2010).

References

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CHAPTER III

TRANSCRIPTOME ANALYSIS OF GENES ASSOCIATED WITH INNATE

IMMUNE SYSTEM MEMORY IN T AND B LYMPHOCYTE

DEFICIENT ZEBRAFISH

Introduction

The zebrafish is a primary model for studying vertebrate development (Langenau,

Ferrando et al. 2004, Mathavan, Lee et al. 2005). Also, because zebrafish and human immune systems and the response to infections are similar, it has been increasingly used as a model for studying basic immunology (Meeker and Trede 2008). Transparency during early development, small size (5 cm), high fecundity, and rapid embryonic development, make zebrafish the one of the best models for studying developmental immunology (Trede, Langenau et al. 2004, Meeker and Trede 2008, Petrie-Hanson, Hohn et al. 2009). Furthermore, Because zebrafish are more dependent on innate immune system than are most mammals, they are an excellent model for studying mechanisms of innate immunity.

Fish are extremely diverse; they have adapted to extreme and often highly variable aquatic environments and they are constantly exposed to a wide variety of pathogens. Thus they have developed elaborate constitutive and inducible innate immune responses. These responses are often triggered by germline-encoded receptors that recognize the pathogen associated molecular patterns associated with tissue injury and 37

infection (Plouffe, Hanington et al. 2005 , O'Leary, Goodarzi et al. 2006). Fish rely heavily on the innate immune response for protection from infectious agents. It provides the host with critical first line of defense. As a first responder, it also controls the initial stages of the acquired immune system response (Plouffe, Hanington et al. 2005).

Traditionally it has been thought that repeated exposure to the same pathogen will not modify the nature of the innate response, whereas the adaptive immune system has enhanced secondary responses (O'Leary, Goodarzi et al. 2006). However, hapten based hypersensitivity studies demonstrated natural killer (NK) cell mediated memory responses in Rag-2 deficient mice, that lack T and B lymphocytes (O'Leary, Goodarzi et al. 2006). NK cells share many characteristics with CD8+ T cells. During an infection, the T cell’s adaptive immune response is comprised of expansion, contraction, memory, and recall phases. NK cells were shown to undergo the same four phases after mouse are infected with cytomegalovirus (Murid Herpesvirus 1). This process had previously only been ascribed to cells of adaptive immune system (Sun, Beilke et al. 2009). One important tool for dissecting components of the immune system in the whole animal model is the experimental production of mutants which lack expression of specific genes.

The studies on SCID (Severe Combined Immune Deficiency) mice and rag 1 and rag 2 mutant mice have provided much information about the role of the innate immune response to infectious diseases. Hapten-based hypersensitivity studies have demonstrated that both the mutants have normal levels of functional macrophages, polymorphonuclear leukocytes (neutrophils) and Natural Killer (NK) cells but do not contain T and B lymphocytes (Mombaerts, Iacomini et al. 1992, O'Leary, Goodarzi et al. 2006).

Earlier studies demonstrated that channel catfish (Ictalurus punctatus) with minimally organized lymphoid tissue could mount a specific antibody at 21 day post hatch against E. ictaluri (Petrie-Hanson and Ainsworth 2001). The immune system of fish at this life stage appeared similar to lymphocyte deficient mutant mice. Vaccination with E. ictaluri had similar effects in rag1-/- mutant and wild type zebrafish. Challenge with the same species of bacteria as the vaccination provided specific protection but exposure to a different species of bacterium did not, demonstrating a specific acquired immune response in rag1-/- mutant zebrafish (Hohn and Petrie-Hanson 2012). When the leukocyte component of kidney of rag1-/- mutant zebrafish were compared to wild types, the percentage of lymphocyte like cells was nearly 50% less and lacked T or B lymphocytes; the percentage of monocytes was similar but significantly more granulocytes were present in the mutants (Petrie-Hanson, Hohn et al. 2009). These findings suggest that there is an adaptive characteristic in the cells of innate immune system that is providing specific protection in fish.

The objective of this project is to identify the cells of innate immune system that are the primary effectors in the adaptive immune response and to evaluate the changed in the gene expression patterns associated with this response.

Methods

Animal and Source

Adult zebrafish used were produced and reared in the specific pathogen free fish hatchery in the College of Veterinary Medicine following standard operating procedures,

2006, CVM-MSU http://www.cvm.msstate.edu/. Zebrafish/index.html. During all experiments, fish were maintained in 15 L aerated flow-through tanks with charcoal 39

filtered de-chlorinated municipal water at 26o C with a water flow rate of 0.5 L/Min in a single pass flow through system. Fish were fed twice daily with Zeigler™ Adult

Zebrafish Diet (Aquatic Habitats™, Apopka, FL).

Experimental Design

The transcriptome study consisted of three treatments that involved bacterial inoculation and one control group that received no bacterium. Two groups received a primary inoculation of 104 CFU/fish RE33, a commercial, live, attenuated Edwardsiella ictaluri vaccine strain (AQUAVAC-ESC® Intervet, Inc.) and two groups received a secondary exposure to the wild type strain. These groups were designated SS, SE, ES and

EE for sham primary-sham secondary, Sham primary-bacterial exposed secondary, vaccine exposed primary-sham secondary and vaccine exposed primary-bacterial exposed secondary, respectively.

Adult Zebrafish were infected by intraperitoneal inoculation with E. ictaluri. The time gap between primary and secondary inoculations was 4 weeks. The fish were sacrificed 48 hours after the secondary inoculation. Control fish were injected with phosphate-buffer saline and sacrificed at the same time as the infected fish. Sampled zebrafish were euthanized by immersion in 340 mg/L Tricaine Methane Sulfonate (MS-

222) (Argent chemical laboratories, WA). The kidneys from groups of three fish were collected and pooled, flash frozen in liquid nitrogen and stored at -80oC until RNA extraction.

RNA preparation

Total RNA was isolated from three replicates of pooled kidneys (n=3) from each experimental group by homogenizing the tissue in liquid nitrogen (LN) and TRIZOL reagent extraction (Invitrogen) according to the manufacturer’s protocol. Then the RNA samples were purified using RNeasy columns (Qiagen) according to the RNA cleanup protocol in the RNeasy Mini Handbook (Qiagen), including the on column DNase treatment step. The RNA concentrations were determined spectroscopically. Each extraction resulted in approximately 10 µg of total RNA and the A260/280 ratio was greater than 1.8 for all samples. Quality f RNA sample was assessed by measuring RNA integration number (RIN) using Lab-on-a-chip analysis (total RNA nano biosizing assay,

Agilent) with the Agilent 2100 Bioanalyzer (Raman, O'Connor et al. 2009). The RNA samples used in this experiment had RIN values ranging from 7.3 -9.4, with most being greater than 9.0.

Microarray

The RNA was processed and the transcriptome of each sample was evaluated using the Affymetrix Zebrafish Array (15,617 probe sets) according to the manufacturer’s protocols. In short, total RNA concentrations of 10 µg were used to synthesize double stranded cDNA followed by its clean up using the GeneChip One-Cycle cDNA synthesis kit and Clean Up Module respectively. The resulting cDNA was used in a 16 hr in vitro transcription reaction to produce Biotin labeled cRNA using IVT Labeling kit and

GeneChip clean up module respectively. NanoDrop spectrophotometric analysis was used to measure the final yield of the biotin-labeled cRNA and 20 µg of biotin-labeled cRNA was fragmented and then hybridized to the chip and labeled with Streptavidin- 41

phycoerythrin using the Affymetrix Fluidic station. Chips were scanned using the

Affymetrix scanner and image data for zebrafish Genome array was processed using the

Affymetrix Microarray Suite version 5.0 software. All gene expression data were evaluated for perfect match and mismatch values and normalized to the median measurement for the genes across all of the arrays in the dataset.

Data Analysis

Statistical analysis (Student's t test) was performed to identify differentially expressed transcripts (DET) between EE and SE treatment groups. The EE treatment group was first compared to SS. Only genes that were significantly different from SS were evaluated in pair wise comparison of EE to SS. DETs were mapped to UniprotKB and Genbank refseq protein accessions. Functional analysis of the DETs was performed with the help of their protein accessions using (pre-existing) GO annotation identification, GO enrichment and pathways and networks. Pre-existing GO annotations were identified using Agbase-GOretriever, GO enrichment analysis was performed using singular enrichment analysis (SEA) that computes statistically significant GO term enrichment using Fisher’s exact test for DET compared to their background. Pathways and networks analysis is performed using ingenuity pathway analysis (IPA) tool, to visualize significant networks and their assigned biological functions from scientific literature. Functional modeling was done using pre-existing GO annotations of DETs and whole array by submitting the data to the agbase GOSlim viewer and were summarized using generic GOSlim set. This study enabled us to identify GO annotations that were grouped into three functional categories that provide information about vaccination effect in zebrafish. The percentages of GO terms between DETs and the array were compared. 42

GO annotations for array were obtained from the Affymetrix annotation files which were last updated on the 06/10/11.

Primer Design and Nucleotide Sequences for quantitative RT-PCR (qRT-PCR)

Primers and probes for qRT-PCR were either published sets or were designed using NCBI Primer BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). Primers, probes, accession numbers, and references are given in Table 3.1.

Table 3.1 A list of primer and probe sequences and annealing temperatures that were used for quantitative RT-PCR.

Gene Accession Anne Forward Primer Reverse Primer aling temp ARP1 NM_131580 61OC CTGCAAAGATGCTCAGGGA TTGAGCCGACATTG Probe:FAM- TCTG TTCTGAAAATCATCCAACTGCTGGATG ACTACC-BHQ INFγ1 NM_212864 61OC CTTTCCAGGCAAGAGTGCAGA TCAGCTCAAACAAA Probe:FAM- GCCTTTCG ACCGCTATGGGCGATCAAGGAAAACG AC-BHQ STAT1b BC044185.1 56OC TCTCTAGCCATCGTTCGCTTCC GATCTCTTTTGGCAT CGGGTCA

SAA BI883568 58OC GCAGTGGTATCGCTTCCCAGGAG AGCTTCATAGTTCC CGCGTGCAT

IRF11 BE556864 57OC GTGGGCCATTCACACAGGTA TTCTGCAGACGTGT CCTCAC LOC795 AW420565 57OC TGGGAAACGCACCATCTGAA AGTGCCTCCACATG 887 AGTCAACC

1Vojtech LN, Sanders GE, Conway C, Ostland V, Hansen JD (2009) Host immune response and acute disease in a zebrafish model of Francisella pathogenesis. Infect Immun 77:914-925

Quantitative real-time PCR analysis

The RNA samples used for microarray analysis were also used for quantitative real time polymerase chain reaction (qRT-PCR). Each RNA sample was treated with

RQ1 RNase-free DNase (Promega, Madison, WI) to degrade contaminating DNA associated with the total RNA sample. Each DNase treatment reaction consisted of 19 µl

of nuclease free water, 1 µl RNasin plus RNase inhibitor (Promega, Madison, WI), 5 µl of 10X reaction buffer, 5 µl DNase and 20 µl total RNA. The contents of the reaction were mixed well and DNA digestion of total RNA samples was carried out by incubating the samples at 37°C for 1h. 5 µl of DNase stop solution was added to inactivate the

DNase followed by incubation at 65°C for 10 minutes. The samples were further purified by RNA clean & concentrator kit-25 (Zymo Research corp.) according to the manufacturer’s instructions. Total RNA was eluted into 23 µl of DNase-RNase free water and was stored at -80°C. The resulting RNA quality and quantity was assessed using a

Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE) and an Agilent 2100 Bioanalyzer (Agilent technologies, Palo, CA).

Four genes were tested with at least three biological replicates per treatment and control each, representing three fish each. A negative control (no template) was utilized for each primer set to rule out the sample contamination. The RNA (2 µg) samples were reverse transcribed using super script VILO cDNA synthesis kit (Invitrogen) according to the manufacturer’s protocol to generate first strand cDNA. Then qPCR was performed using hydrolysis probe assays (ARP and INFγ) or SYBR green assays using Stratagene

Mx3000P instrument (Agilent Technologies). All hydrolysis probe qPCR reactions were carried out in 20 µl volume which contained cDNA template (5 ng), nuclease free water

(8.8 µl), 10x buffer (2.5 µl), 5mM magnesium sulfate (1.5 µl), dNTPs (0.5 µl), forward primer (0.5ul), reverse primer (0.5ul), probe (0.5 µl) and hot start Taq polymerase (0.2

µl). The cycling parameters consisted of 10 min at 95oC then 45 cycles of 30s at 95oC, and 1 min at 61oC. SYBR green assays used EXPRESS SYBR GreenER qPCR supermix kit (Invitrogen) following manufacturer’s instructions. All qPCR reactions were carried

out in 20ul volume which contained 20ng cDNA template (5 µl), supermix universal (10

µl), primers (0.4 µl each), reference dye ROX (0.04 µl) and water (4.16 µl). Supermix in turn is composed of Taq DNA polymerase, SYBR GreenER fluorescence dye, MgCl2 and dNTPs. The manufacturer’s amplification program was optimized to the primer/template systems. The cycling parameters consisted of 10 min at 95oC then 40 cycles of 30s at

95oC, 1 min at respective annealing temperatures, and 15s at 72oC. Melting curve analysis was performed on all SYBR green assays to confirm that signal was due to the specific amplified product. Pearson correlations of qPCR data with microarray data were performed using SAS 9.2 software (SAS Institute Inc.,Cary, NC, USA).

Flow Cytometry

The kidneys from SS, SE, and EE treated mutant zebrafish were excised and processed separately. Each kidney was collected in a microfuge tube with 300 µl of 1x L-

15 with 10% FCS. Cells were freed from the tissue by repeated pipetting using a 1 mL pipettor. The cell suspension was strained through a 40 µm Falcon® nylon cell strainer

(BD Biosciences, San Jose, California) using an additional 200 µl PBS and 1% FBS. The kidney interstitial cells were maintained at 4oC until they were analyzed on flow cytometry. Kidney interstitial cells were examined and sorted by flow cytometry (FACS

Aria, Becton Dickinson) forward scatter (FSC) and side scatter (SSC) analyses. Optimal separation of two different cell populations present in kidney interstitial cells was achieved by adjusting the instrument settings. Gate was set up for the two identified populations, event rate and total population was measured and analyzed using BD

FACSDivaTM software (Becton Dickinson). Leukocyte type were confirmed by re-sorting

(100um nozzle, 20psi) 2000 cells from each of the identified populations. Sorted cells 45

were centrifuged using swing-bucket centrifuge at 1200 x g for 10 minutes at 4oC.

Supernatant was removed using a 1 mL pipettor and the pellet was lysed directly in TRI-

Reagent at the rate 1mL of the TRI-Reagent for up to 107 animal cells using the Direct- zol RNA MiniPrep kit (Zymo Research) according to the manufacturer's protocol and then processed as described above for qRT-PCR.

Results

To elucidate the process involved in the protective immunity in lymphocyte deficient rag1-/- zebrafish, we compared expression levels of transcripts from the vaccinated and infected (EE) fish to that of non-vaccinated and infected fish (SE). The differentially expressed genes were evaluated by GO functional analysis and GO enrichment analysis was performed. Results from the above studies are explained in detail in the subsequent sections.

Microarray Analysis

A total of 15617 transcripts were detected on the SS, SE, ES and EE treatments in the microarray experiment. Each treatment was designed to get different outcomes. SS was the no treatment control. ES represents fish that were vaccinated but then received a sham challenge (Table A.1). From the SE treatment group, it is possible to find out just the effects of bacterial injection, whereas EE enables us to observe the effect of vaccination which was followed by bacterial injection.

Primary exposure

Transcriptional profiling of zebrafish hematopoietic kidney after primary exposure with Edwardsiella ictaluri had up-regulated expression of 129 transcripts 46

represented the microarray at 95% confidence (Table 3.2). The observed differences in increased transcript expression in primary exposed compared to non exposed mutant fish are in the range of 5-1.0 fold.

The majority of the upregulated transcripts were grouped into acute phase response, complement activation, immune response, response to stimulus, proteasomes, protein degradation, processing and heat shock protein categories.

Table 3.2 A list of Zebrafish transcripts that were upregulated following primary infection compared to sham infected controls.

Functional Fold classification Accession Putative ID difference Protein degradation NM_131375.1 proteasome activator subunit 1 1.456107519 AW420599 proteasome (prosome, macropain) subunit, alpha type, 2 1.537063194 AI878703 proteasome (prosome, macropain) 26S subunit, non-ATPase, 12 1.882078969 NM_131678.1 proteasome (prosome, macropain) subunit, beta type, 9b 1.547871834 NM_131795.1 proteasome (prosome, macropain) subunit, alpha type, 6b 1.465710587 BM776726 proteasome (prosome, macropain) subunit, alpha type,5 1.46080581 NM_153655.1 proteasome (prosome, macropain) subunit, alpha type, 6a 1.289222347 BM037579 proteasome (prosome, macropain) subunit, beta type, 1 1.200739227 BI534099 proteasome (prosome, macropain) subunit, beta type, 2 1.209112514 AI477254 proteasome (prosome, macropain) 26S subunit, ATPase, 3 1.167589576 AA658796 proteasome (prosome, macropain) subunit, alpha type, 8 1.157853387 BI867867 proteasome (prosome, macropain) assembly chaperone 1 1.155346818 BM859971 proteasome (prosome, macropain) subunit, beta type, 4 1.111095754 BG305906 proteasome (prosome, macropain) 26S subunit, ATPase, 1b 1.110038362 BC049471.1 proteasome (prosome, macropain) 26S subunit, ATPase, 1a 1.083532561 BC044358.1 proteasome (prosome, macropain) 26S subunit, non-ATPase, 7 1.117427693 AI943154 proteasome (prosome, macropain) 26S subunit, ATPase, 6 1.036215752 BM102205 proteasome (prosome, macropain) 26S subunit, non-ATPase, 3 1.03255884 BI867479 proteasome (prosome, macropain) 26S subunit, ATPase, 4 1.022923298 BC045970.1 proteasome (prosome, macropain) subunit, alpha type, 4 1.024666414 BC042325.1 proteasome (prosome, macropain) 26S subunit, non-ATPase, 12 1.015459136 BC049010.1 proteasome (prosome, macropain) subunit, beta type, 3 1.361401934 Acute phase response AA497156 complement component 7 2.711235993 CD014253 complement component 1, q subcomponent-like 4 like 2.543084809 47

Table 3.2 (continued)

BI878414 complement component c3b /// si:dkey-76b14.4 1.891921911 NM_131338.1 complement factor B /// zgc:153240 1.941949535

BQ284848 complement component 9 1.941177194 BM778002 complement component 9 1.896008966 BI845861 CXC chemokine 46 1.585600317 BI883568 SAA 4.945338295 BC048037.1 ceruloplasmin 2.426394913 BI845737 C1q and tumor necrosis factor related protein 4 1.379582099 Immune Response BG985449 calreticulin like 1.54697111 BG302583 calreticulin, like 2 1.83177963 BG985448 calreticulin like 2.059551719 BC046906.1 calreticulin like 2.268209135 BG985448 calreticulin like 2.179232528 BI983290 calreticulin, like 2 1.377430169 NM_131047.1 calreticulin 1.262957707 CD606274 signal transducer and activator of transcription 1b 2.220123148 BC044185.1 signal transducer and activator of transcription 1b 4.124193546 BI845861 CXC chemokine 46 1.585600317 CA474845 Tnf receptor-associated factor 2a 1.328678832 AW232141 lipopolysaccharide-induced TNF factor 1.318596853 Z46776.1 major histocompatibility complex class I gene 1.453299417 BM082447 TNF receptor-associated factor 7 1.451537876 BM775009 tumor necrosis factor (ligand) superfamily, member 10 like 4 1.444936285 AF515275.1 coagulation factor V 1.328445833 AW232141 lipopolysaccharide-induced TNF factor 1.318596853 NM_131672.1 colony stimulating factor 1 receptor, a 1.482921894 BM095893 interferon regulatory factor 9 1.81895559 BC049424.1 interferon regulatory factor 7 1.863780175 BQ479755 si:ch211-89f7.4 :chemokine CCL-C5a 4.326214098 BE556864 interferon regulatory factor 11 3.94700698 AW019258 Similar to chemokine (C-X-C motif) receptor 3.1 2.108884501 BM102177 similar to CC chemokine SCYA103 1.063116385 Response to Stimulus AF533769.1 myxovirus (influenza) resistance A 3.826118133 AF510108.1 heat shock protein 90, beta (grp94), member 1 1.839962141 NM_153657.1 prostaglandin-endoperoxide synthase 2a 1.829305794 AW232570 glutathione peroxidase 1b 1.753479161 BI474294 ras homolog gene family, memberGb 1.548966166

Table 3.2 (continued)

NM_131157.1 crystallin, alpha B, a 1.779110338 nuclear factor of kappa light polypeptide gene enhancer in B- AW019105 cells inhibitor, alpha a 2.250052292 Miscellaneous SWI/SNF related, matrix associated, actin dependent regulator AW171078 of chromatin, subfamily a, member 5 1.247599938 AL925726 fatty acid binding protein 1b-like 1.243418738 BQ450267 IMP4, U3 small nucleolar ribonucleoprotein, homolog (yeast) 1.242436608 CDP-diacylglycerol--inositol 3-phosphatidyltransferase BI865765 (phosphatidylinositol synthase) 1.242263428 BM186551 protein O-fucosyltransferase 2 1.236996416 BC053310.1 iroquois homeobox protein 4a 1.235529787 CD605135 NHa-ras Harvey rat sarcoma viral oncogene homolog b 1.233934081 Novel protein similar to vertebrate cyclic nucleotide gated BG305942 channel protein family 1.231240895 CA472784 ubiquitin carboxyl-terminal hydrolase L5 1.22927282 translocase of inner mitochondrial membrane 8 homolog A BI672243 (yeast) 1.228679678 AI965054 NSFL1 (p97) cofactor (p47) 1.225614645 AW171596 centrosomal protein 55 like 1.219244028 AW777876 tyrosyl-tRNA synthetase 1.210944877 BC049319.1 vaccinia related kinase 2 1.208703546 nuclear factor of kappa light polypeptide gene enhancer in B- BI881888 cells 2, p49/p100 1.20805041 CD283149 asparagine synthetase 1.20485812 BI983167 calcineurin-like phosphoesterase domain containing 1 1.201518532 SWI/SNF related, matrix associated, actin dependent regulator AW171078 of chromatin, subfamily a, member 5 1.247599938 AL925726 fatty acid binding protein 1b-like 1.243418738 BQ450267 IMP4, U3 small nucleolar ribonucleoprotein, homolog (yeast) 1.242436608 CDP-diacylglycerol--inositol 3-phosphatidyltransferase BI865765 (phosphatidylinositol synthase) 1.242263428 BM186551 protein O-fucosyltransferase 2 1.236996416 BC053310.1 iroquois homeobox protein 4a 1.235529787 CD605135 NHa-ras Harvey rat sarcoma viral oncogene homolog b 1.233934081 Novel protein similar to vertebrate cyclic nucleotide gated BG305942 channel protein family 1.231240895 CA472784 ubiquitin carboxyl-terminal hydrolase L5 1.22927282 translocase of inner mitochondrial membrane 8 homolog A BI672243 (yeast) 1.228679678 AI965054 NSFL1 (p97) cofactor (p47) 1.225614645 AW171596 centrosomal protein 55 like 1.219244028 AW777876 tyrosyl-tRNA synthetase 1.210944877 BC049319.1 vaccinia related kinase 2 1.208703546 nuclear factor of kappa light polypeptide gene enhancer in B- BI881888 cells 2, p49/p100 1.20805041 CD283149 asparagine synthetase 1.20485812

Table 3.2 (continued)

BI983167 calcineurin-like phosphoesterase domain containing 1 1.201518532 Unannotated BQ616817 --- 3.619344146 AI496754 --- 4.373735771 AL725462 --- 3.039173573 BI878415 --- 2.601745091

BI672165 --- 2.697690393 CD605001 --- 2.466878713 BI865858 --- 2.438501226 AI617721 --- 2.530152336 BI864002 zgc:92903 2.377660001 CD015330 zgc:152809 2.046307942 BI864822 zgc:158271 2.516612853 AW174559 wu:fj05f05 4.730217037 BM186508 zgc:152945 2.830896542 AI496738 wu:fb64b08 3.866031518 AI384591 wu:fb10g08 2.122730272 AI331661 wu:fa99f01 2.180037946 AI584672 wu:fb82a05 2.174633072 AI397316 wu:fb09h07 2.167623349 AI477673 zgc:103710 2.057605963 BI878750 si:dkey-53p21.1 2.185374681 AI974163 si:ch1073-126c3.2 2.185000536 BQ075086 si:rp71-1c23.2 2.482899493 BM777312 si:ch211-20b12.2 2.430396283 BM277076 si:dkey-27i16.2 2.050685063 AW232318 wu:fj17f10 2.040389333 BM777295 Zgc:172136 2.020805737

Vaccinated fish

We compared the gene expression of EE with that of SE in order to evaluate gene that encode products that have a role in the protective immunity seen in the vaccinated

Rag1 mutant zebrafish. For this reason, first EE was compared with SS. Genes that showed significant changes in expression (DETs) were selected and were further compared with SE. This process identified 98 statistically significant, and up- and down-

regulated transcripts (Table 3.3). Of these, 46 (46.9%) were up-regulated and 52 (53%) were down-regulated in EE compared to SE (Fig. 3.1).

Figure 3.1 Bar graph of differentially expressed transcripts between EE and SE.

Bar graph demonstrating the differentially expressed transcripts between vaccinated (EE) and non-vaccinated (SE) zebrafish and the numbers with GO annotation.

Table 3.3 A list of transcripts that were differentially expressed (p< 0.05) between EE and SE.

Functional Fold Classification Accession Putative ID difference Signal Transduction AY245546.1 pancreas specific transcription factor, 1a 1.4248564 Cell proliferation AY269790.1 fibroblast growth factor 2 1.2784336 D49972.1 bone morphogenetic protein 4 2.2686536 NM_131635.1 fibroblast growth factor 4 -1.9751336 BG985627 BMP binding endothelial regulator -1.0152304 Receptor Binding AF318394.1 novel immune-type receptor 1k* 1.533402 Intracellular NM_131008.1 spondin 2b, extracellular matrix protein 1.2243558 Cellular Metabolic process AL715408 High-mobility group protein isoforms I and Y 1.0482427 NM_131256.1 nuclear receptor subfamily 6, group A, member 1a -1.9057999 Structural Morphogenesis AL723844 myosin-10-like -2.8897076 AL922076 collagen triple helix repeat containing 1b -2.1751793 AI331605 collagen, type I, alpha 2 -1.0141198

AL672176 collagen type XI alpha-2 -1.0306758 Immune Response BI476419 19 (chemokine (C-C motif)-like) 3.1044014 NM_131385.1 recombination activating gene 2 1.5715873 BQ450131 Myeloid/lymphoid or mixed-lineage leukemia 3a -1.325213 BI865907 relaxin 3a 2.2479468 Miscellaneous BI982955 myomegalin-like 2.7361798 BC051151.1 similar to mucin 1.491553 resistance to inhibitors of cholinesterase 8 BG305271 homolog A 1.4264007 NM_131669.1 ATPase, Na+/K+ transporting, beta 2a polypeptide 1.2700753 AY161857.1 melanin-concentrating hormone receptor 1a 1.2491393 AB097825.1 trophoblast glycoprotein-like 1.2355278 BG884560 Zinc finger protein 347-like 1.2162185 AL724232 LSM14 homolog A (SCD6, S. cerevisiae) 1.0996213 BQ132362 similar to MGC107856 protein 1.0679448 BM777899 similar to MGC107856 protein -1.0957169 BI842004 synaptotagmin IV -1.1001618 AJ286843 hypothetical protein LOC100331174 -1.1235053 AI397227 envoplakin -1.1430245 BQ078258 similar to CG14142-PA -1.1469466 AF495875.1 estrogen-related receptor gamma a -1.3828564

Table 3.3 (continued)

BI845673 protein kinase (cAMP-dependent) inhibitor beta -1.5030164 NM_181497.2 Neuropilin 1a-like /// neuropilin 1a -1.578668 AI331287 TLC domain containing 1 -1.6717343 NM_131287.1 SRY-box containing gene 17 -1.584243 BG303134 DEAD (Asp-Glu-Ala-Asp) box polypeptide 51 -2.0876298 BQ074821 doublesex and mab-3 related transcription factor -2.3731684 CD606487 SET domain, bifurcated 2 -2.8538423 BI983629 mCG142610-like -1.8298359 Unannotated AI793605 wu:fc49d07 2.6504161 BM187461 zgc:92035 2.2996033 AW059176 2.1737237 BI867354 si:ch211-147m20.1 1.9192022 BG303757 si:dkey-4c15.6 1.5619177 BI709723 zgc:165508 1.4714972 BI891762 zgc:158366 1.4638643 BM005167 --- 1.3862775 AI544667 wu:fb77d09 1.3832316 AL731009 --- 1.3729446 AL722543 --- 2.0075505 AW281782 --- 2.2427615 AL719663 Wu:fc11a05 1.2914202 BI877608 zgc:152863 1.1424723 AW233702 wu:fj40e09 1.1144797 BG728511 --- 1.0407045 AA497170 --- 1.8085751 BI883324 --- 2.1613121 BQ419619 --- 2.0832168 AW018957 --- 2.2219393 BI710320 --- 2.3408558 BG883314 --- 2.2250069 CD606304 --- 1.4665004 BQ092536 --- 1.4309018 BM186516 --- 1.3577334 BI864110 --- 1.3536182 AL913138 --- 1.2142156 AFFX-Dr- pAsRed2 --- 1.0867881 CD283215 wu:fb81c07 -1.0332313 BI318519 --- -1.0924965 BI847022 --- -1.5061064 BM186526 --- -1.5448244 53

Table 3.3 (continued)

BM571195 si:ch211-266a5.1 -1.6606248 BE605275 wu:fb15e04 -1.7141458

BG305300 --- -1.8103463 AL719266 zgc:110283 -1.8852786 BE201957 zgc:194138 -1.9698771 BI673395 --- -1.9842097 BI671488 --- -2.0579275 AL722000 --- -2.4008245 AI794137 hypothetical protein LOC100332904 -2.4113868 BM154625 wu:fb12c09 -2.439278 BM025943 Si:ch211-261c8.5 -2.5580451 BI882036 zgc:64003 -3.3085005 BI982878 --- -1.9922919 AI959658 wu:fd12e04 -1.6830505 BI845653 --- -1.1600338 AI444465 wu:fb39e08 -1.9695472 BI981210 --- -1.1832179 AW279902 si:ch73-46j18.5 -1.3152468 BM005010 --- -1.7118405 AW280155 wu:fj51e11 -2.1208847 AL927596 --- -2.2341451 AI721504 wu:fc44h05 -2.3347106 AI878410 wu:fc57f08 -2.4814408 BI979237 --- -2.5911717 AI667492 --- -2.6889276 AL724042 --- -2.8792889

*Mammalian ortholog

ID mapping

To perform the functional analysis of the DETs it is necessary to map them to the protein identifiers/accessions of their putative products and categorize the function of the gene products. Out of 98 proteins, 64% code for predicted proteins which have uniprotKB and genbank refseq protein IDs. Of these genes, 46% were up-regulated and

53% of them were down regulated. Of the unannotated genes, 26% of the total were 54

expressed sequence tags (ESTs) that do not have connections to predicted or known zebrafish genes, and 1% were not listed in NCBI. The annotation of the remaining 7% of the genes have been removed from the NCBI database, but they possess the uniprotKB and GenBank refseq protein accession IDs, so were included in the analysis along with the 64% predicted proteins, thus bringing the number of protein identifiers used in the analysis to 71.

Functional analysis using GO

Comparing functions of DETs EE between SE allows us to identify processes that are different in the response of vaccinated in Rag1-/- mutant zebrafish compared to that of non-vaccinated fish. This in turn allows us to identify likely mechanisms involved in protection.

Agbase analysis

GO annotations were available for 59% of the gene products (Fig. 3.2). We tried to assign annotations to the remaining 14% of the gene products that had protein identifiers using the Goanna tool (McCarthy, Bridges et al. 2007), but we were not successful.

When we compared the functional percentages of the DET and the total array transcipts, we found that molecular functions such as actin binding, receptor binding, lipid binding and nucleic acid binding were over represented in DET 4.75, 4.42, 2.34, and

2,14 fold, respectively. On the other hand protein binding, protein kinase activity and catalytic activity were underrepresented by 0.13, 0.25 and 0.48 fold, respectively.

Proteinaceous extracellular matrix, extracellular space, cytoplasmic membrane bound

vesicles, nucleolus, cytoskeleton and chromosome components were over represented in

DET by 15.65, 5.6, 3.5, 2.7, 2.38 and 1.92 respectively, whereas various organelles and cytoplasm sub-category are underrepresented by 0.65, and 0.33 respectively. In the biological process category, response to endogenous stimulus, cell-cell signaling and cell proliferation are overrepresented in DET by 4.63, 1.98, and 1.90 respectively, whereas protein metabolic process, cellular component organization and transport were underrepresented by 0.46, 0.26, and 0.212 fold, respectively.

AgriGO: GO enrichment analysis

Out of 71 differentially expressed transcripts that had uniprot IDs, 32 had GO annotations and 21 GO terms associated with these were significant (p<0.05). Results are in the form of hierarchical tree graphs (Fig 3.2-3.4) and a flash bar chart (fig 3.5). The hierarchical tree graphs (also called directed acyclic graphs) are comprised of boxes, which are nodes, and connecting lines. The boxes in the graph contain GO ID, GO term and p-value. The color of the box indicates the abundance of the GO term and the boxes containing statistically non-significant GO terms appear in white. The colors are represented from level 1 to 9 from light yellow to deep red. Level 9 represents highly significant terms and level 1 represents less significant terms (p≤0.05). The connecting lines can be of different types, solid, dashed and dotted lines. Always, a solid arrow connects two statistically significant GO terms, a dashed arrow connects one statistically significant and one statistically non-significant GO term, and a dotted arrow, connects two statistically non-significant terms. Colors of the lines convey their relationship between the boxes that they connect. The rank direction of the graph can be from top to

bottom and from a more generalized term to a specific term or vice versa (Du, Zhou et al.

2010) .

When the ontologies of overrepresented GO terms in the hierarchical tree graph created by SEA were visualized, the biological process category had 10 highly enriched

GO (child/secondary) terms. The hierarchical tree graph of biological process was constructed from a general GO term to a specific GO term. The general GO term is cellular rocess and the specific GO term is transcription DNA dependent with term enrichment (several transcripts performing a single function) 15 and 5 respectively (Fig.

3.2).

The hierarchical tree graph for biological process category is explained from top

(specific term) to bottom (general term) with the following example in Fig 3.2. The Go terms are represented in double quotations (""). "Transcription DNA-dependent" is part of "transcription", on the other hand, "transcription" positively regulates "regulation of transcription". Further, "transcription" and "RNA metabolic process" are part of

"nucleobase nucleoside nucleotide and nucleic acid metabolic process", in turn are part of

"nitrogen compound metabolic process" and "cellular metabolic process". "Nitrogen compound metabolic process", "cellular metabolic process" and "biosynthetic process" are part of "metabolic process". Finally, "cellular metabolic process" is also a part of

"cellular process".

The molecular function category had 8 enriched GO (child/secondary) terms. The flash bar chart was constructed from a general GO term binding to a specific GO term transition metal ion binding (Fig. 3.3).

There are three significantly enriched GO terms in the cellular component category. None of them were directly connected with each other (Fig. 3.4).

Figure 3.2 Hierarchical tree graph of GO terms from EE and SE for biological function category.

Boxes in the graph represent their GO ID, term identification and statistical information. The significant terms (p<0.05) are marked in color, while non-significant terms shown as white boxes. The degree of color saturation of a box is positively correlated with the enrichment level of that term. The color of the highly enriched term looks red and that of very low enriched terms looks yellow. Solid, dotted, and dashed lines indicate two, one and zero enriched terms at both ends connected by the line respectively. The rank direction of the graphs are set to from bottom to top.

Figure 3.3 Hierarchical tree graph of GO terms from EE and SE for molecular function category.

Figure 3.4 Hierarchical tree graph of GO terms from EE and SE for cellular component category.

Figure 3.5 A flash bar chart of over represented terms from the GO functional analysis.

GO functional analysis of differentially expressed proteins between EE and SE (p<0.05) compared to the entire GO on the chip from three categories. The Y- axis represents the percentage of genes mapped by the terms and abundance of GO terms. The x-axis represents the definition of the GO terms.

Gene products that appear to be performing the important functions are pancreas specific transcription factor 1a, fibroblast growth factor 2, bone morphogenetic protein 4, fibroblast growth factor 4, BMP binding endothelial regulator, spondin 2b, extracellular matrix protein, High-mobility group protein isoforms I and Y, nuclear receptor subfamily

6, group A, member, myosin-10-like, collagen triple helix repeat containing 1b, collagen, type I, alpha 2, collagen type XI alpha-2, 19 (chemokine (C-C motif)-like), and novel immune-type receptor 1k.Based on the studies from mutant mice infected with cytomegalovirus, it can be translated to mutant zebrafish infected with E.ictaluri by

looking at the over represented categories and the transcripts believed to be performing those functions.

When Rag1-/- mutant zebrafish were infected with the intracellular bacterium E. ictaluri, spondin 2 must have activated the NK cell receptor NITR1k. Spondin 2 was believed to be involved in identifying the intracellular bacteria. NITR1k and spondin 2 transcripts are upregulated in this study. The activated NK cell receptor must have recognized the bacterial antigen. In order for receptor activation to happen, there should be some communication between the receptor and the downstream signaling molecules, which is evidenced by the over representation of cell communication, signal transduction and receptor binding categories. PTF1a is believed to be involved in performing signal transduction process, which is upregulated in our study. Upon ligation proinflammatory cytokines were released, which is supported by the upregulated expression of 19

(chemokine (C-C motif)-like) that helps in cytokine secretion by leukocytes and provides proadhesive and migratory signals to the leukocytes. The activated NK cells must have secreted the effector cytokine IFNγ, which is evidenced by the upregulated expression of

IFNγ in the peripheral blood leukocyte and lymphocyte-like cell populations. Later, NK cells must have clonally proliferated, which is further supported by the over representation of the category cell proliferation and transcripts such as FGF-2, 4, BMP-4,

BMPER, HMGI(Y), and PTF1a which regulate proliferation. In order for the NK cell to perform cytotoxic functions, it should undergo cytoskeletal remodeling, which is demonstrated by the over representation of the structural morphogenesis category and differential expression of the transcripts performing structural morphogenesis function such as myosin 10, envoplakin, collagen triple helix repeat containing 1b, collagen, type

I, alpha 2, collagen type XI alpha-2 and Ric-8. Migration of NK cells is supported by the differential expression of spondin 2, BMP-4 and FGF-2, that are involved in cellular migration.

Quantitative RT-PCR

Four DETs microarray analysis were selected for further confirmation of expression patterns using qRT-PCR. The fold difference range was from 16-fold to 30- fold for the four genes selected from the microarray analysis. Specific primers were designed (Table 3.4) for qRT-PCR analysis. The qRT-PCR results (Table 3.2) in confirmed the results from microarray, with all tested genes showing a statistically significant (p<0.05) correlation with the microarray results. Fold difference values obtained by Microarray analysis were not essentially predictive of the relative expression levels obtained by qRT-PCR.

Because INF was not present on the microarray and is induced in activated NK cells, we evaluated expression of this gene in the whole kidney and in sorted kidney leukocytes of fish from EE, to SE and SS treatments. We found high expression in the lymphocyte like population after bacterial exposure and this was induced to a higher level in fish that had been vaccinated (Table 3.5). In comparison, the phagocytic cell population showed no induction of INF.

Table 3.4 A comparison of expression analyses of selected genes by microarray and qRT-PCR.

Gene Accession Treatment Microarray qRT-PCR Correlat relative relative ion expression expression (r)

STAT1b BC044185.1 SE 6.7 1.722083Aa 0.9846 EE 6.7 1.642959A ES 2.4 0.31148B SS 3.3 0.74136AB SAA BI883568 SE 10.8 1.73966A 0.9839 EE 11.1 2.39436A ES 6.2 0.01564A SS 6 0.00806A IRF1b BE556864 SE 12.2 13.6003A 0.9563 EE 11.9 8.19998A ES 8.2 0.35582B SS 8.3 0.69379B LOC795887 AW420565 SE 11 7.41946A 0.9511 EE 11.2 12.1675A ES 6.3 0.37062B SS 6.6 0.60434AB IFNγ NM_212864 SE N/A 0.3216 N/A EE 2.8164 ES 0.3328 SS 0.2479 aValues with different letter designations are significantly different (P<0.05)

Table 3.5 Relative expression of IFN γ in sorted kidney leukocytes.

Cell population Treatment Mean Expression Lymphocyte-like cells SS -0.01556 SE 0.986082 EE 2.065564 Phagocytic cells SS -0.95721 SE 0.751878 EE -0.47684 Whole kidney hematopoietic tissue had lymphocyte-like cells, phagocytic cells and hematopoietic precursors.

Discussion

Microarray analysis is used to measure differential expression of thousands of genes from a single RNA sample and host pathogen interactions can be observed at molecular level. It is a powerful tool to identify genes and their expression patterns

(Harvey, Heguy et al. 2007). This allowed fish researchers to perform expression analysis on tens of thousands of genes in organisms or animals exposed to pathogenic

microorganisms and changes in the environmental conditions (Peatman, Baoprasertkul et al. 2007). A primary example for commercial microarray products are Affymetrix oligonucleotide chips consisting of a set of 11-20 oligonucleotide probes of 25 bases each to represent a (single) gene. The perfect match (PM) probe and a mismatch (MM) probe differ only at the 13th base and the PM will only hybridize with transcripts from the intended gene. The level of gene expression is a combination of the data from the entire probe set. MM probes are used to quantify non-specific and cross-hybridization (Raman,

O'Connor et al. 2009).

Conserved acute phase response, immune response related transcripts in zebrafish afterprimary infection with E. ictaluri

The products of the majority of the upregulated transcripts were grouped into acute phase response, complement activation, immune response, response to stimulus, proteasomes, protein degradation, processing and heatshock proteins categories. The genes encoding acute phase proteins that were up-regulated in response to primary infection were ceruloplasmin and major acute phase reactant apolipoprotein of the HDL complex. Ceruloplasmin is involved in iron binding, homeostasis and transport. One important innate defense is the sequestering of iron to limit the availability of this critical nutrient to the invading bacteria.

At least 6 of the upregulated transcripts encoded complement components including C1q like gene, C3b, factor B, C7 and C9, which indicating the involvement of the complement systems in response to infection. The teleost fish complement system exhibits conserved roles such as sensing and clearing the invading pathogens (Boshra, Li et al.). The expression of complement system components have been shown to be

responsive to infection in other fish. Analysis of complement protein C7 in healthy and

Aeromonas hydrophila infected tissues indicated the key involvement of the C7 gene in tissue specificity and pathogen responses (Shen, Zhang et al. 2012). The C7 responses in grass carp were sensitive and rapid in response to a pathogenic bacterial infection, indicates the involvement of C7 in innate immune responses to bacteria in teleost fish

(Shen, Zhang et al. 2012). Complement component C1q like gene, which is a bona fide

C1q polypeptide from the heterotrimeric molecule involved in classical pathway (Hu, Pan et al. 2010).

Another large functional category with upregulated transcripts is the immune response category. Some of these included most highly upregulated transcripts such as chemokine CCL-C5a, similar to CC chemokine SCYA103, signal transducer and activator of transcription 1b (STAT1b), interferon regulatory factor 11, colony stimulating factor 1 receptor alpha, TNF receptor-associated factor, TNF (ligand) superfamily, member 10, TNF receptor-associated factor 2a, coagulation factor V, lipopolysaccharide-induced TNF factor, interleukin enhancer binding factor 2, and nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha a.

E. ictaluri is an intracellular bacterium that was demonstrated to survive and replicate within phagocytic cells (Baldwin and Newton 1993). The MHC I expression is also an important signal that suppresses NK mediated killing of healthy cells (Kumar and

McNerney 2005). This observation indicated the enhanced innate immune response in the fish that were exposed to EE compared SE.

Expression of pro-inflammatory signaling molecule heat shock protein (Hsp) 60

(up regulated in primary exposed fish compared to non exposed fish) in humans is

associated with functional TLR-4. Hsp 60 is involved in ATP-dependent protein folding.

Elevated amounts of Hsp60 in circulation indicate various disease states such as diabetes

(Yuan, Dunn et al. 2011). Hsp 90 act as a chaperone and is involved in housekeeping functions such as protein folding by preventing aggregation of unfolded protein and it also prevents the folded proteins from unfolding and aggregation which is an ATP independent process (Picard 2002).

Among the immune response related transcripts, suppressor of cytokine signaling

1 which is present in multiple forms in fish is upregulated in response to infection. This is a negative feedback mechanism that attenuated cytokine actions including INF.

Protein degradation, processing, localization and folding

Nearly 35 transcripts were upregulated which were associated with proteasomes, protein degradation and processing. Proteasomes are involved in nonlysosomal intracellular protein degradation (Coux, Tanaka et al. 1996). They regulate cell cycle, various cellular processes such as proliferation, differentiation, apoptosis and response to external stimuli (Ciechanover and Schwartz 1998). Some of the up-regulated transcripts have roles in protein processing and folding such as dolichyl-diphosphooligosaccharide- protein glycosyltransferase, glycosyltransferase-like domain containing 1 and Dnajb 11 protein. The antigenic peptides presented on MHC I molecules are produced by proteolytic degradation in the cytosol by proteasome, transported to endoplasmic reticulum, and loaded onto MHC I molecules with the help of several other proteins. The upregulation of the ER chaperone calreticulin which is present in various forms further support the MHC I mediated immune response. Calreticulin is unique in its ability to bind to peptides that are suitable to be loaded on MHC I molecules (Nicchitta and Reed 2000). 68

Expression profile demonstrating a unique secondary response

Transcription factors

In depth evaluation of transcription factors can enhance the analysis of gene expression patterns (Normanno, Dahan et al. 2012). This approach was incorporated in our evaluation of differential expression of transcripts related to RNA metabolic process

(GO:0016070), transcription (GO:0006350), and regulation of transcription

(GO:0045449). Efficient control over metabolic process is the key for the survival of cells (Bauer and Metzler 2012). For proper control of metabolism and other cellular processes, transcription factors may need to occur in a specific sequence (Simon, Barnett et al. 2001). Some of our up-regulated genes were in the nitrogen compound metabolic process (GO:0006807), cellular metabolic process (GO:0044237), and biosynthetic process (GO:0009058) sub-categories, and may be occurring in a sequential manner.

It is speculated that high mobility group (HMG) I(Y) proteins may act as modulators of the function of several transcription factors (Shannon, Himes et al. 1998).

Increased expression of HMGI(Y) occurs during rapid proliferation of certain cells from rat embryos and from undifferentiated cells of young rat thymus (Goodwin, Cockerill et al. 1985). HMG I(Y) proteins may have a role in controlling the immune response

(Shannon, Himes et al. 1998). HMGI (Y) binds specifically to A-T rich regions on the double stranded (ds) DNA, affecting chromatin conformation to regulate gene expression.

This facilitates binding of transcription factors to dsDNA (Solomon, Strauss et al. 1986,

Russnak, Candido et al. 1988). In our experiment, HMGI(Y) expression may be associated with rapid expansion of the memory cell population following secondary exposure. The rate of transcription of large proportion of immune response related genes

such as interferon β, E-selectin, TNF-β, IL-2 and granulocyte macrophage colony stimulating factor (GM-CSF) and certain chemokines are correlated to the presence of

HMGI(Y) protein (Bustin and Reeves 1996). This protein binds with transcription factors in the absence of DNA and affects transcription factors binding to DNA by introducing bends into DNA (Bustin and Reeves 1996). In our study HMGI(Y) up-regulation correlates with the over representation of the parent GO category binding (GO:0005488), and certain GO terms such as sequence-specific DNA binding transcription factor activity

(GO:0003700).

Another up regulated functional group the GO sub-category Proteinaceous extracellular matrix (GO:0005578), is up-regulated 15 fold in our data compared to the array. This observation is further supported by up-regulated expression of spondin 2 by

2.3 fold in vaccinated fish compared to the non-vaccinated fish. Spondin 2 (mindin) is an extracellular matrix (ECM) protein that plays essential roles in innate immunity (He, Li et al. 2004). Spondin 2 may act as a unique pattern-recognition moiety (Maki, Tomohito et al. 2011) for macrophages by direct interaction with pathogenic microbes (He, Li et al.

2004). It also interacts directly with receptors on neutroplils (Jia, Li et al. 2005). Direct interaction is mediated by carbohydrate groups of microbial lipopolysaccharide (LPS).

Spondin 2 functions like a lectin, directly binding to pathogenic microbes. Spondin 2 activates a general phagocytosis receptor on macrophages. It is also demonstrated that spondin 2 may have the ability to recognize intracellular pathogens (He, Li et al. 2004).

In mammals, it plays an important role in leukocyte recruitment during viral infection, and helps in leukocyte migration after bacterial infection in the lungs. It provides adhesion sites for migration of leukocytes by binding to the receptors expressed on

macrophages and neutrophils. Neutrophil migration is facilitated by spondin 2 only though adhesion not by regulating its cellular function. Spondin 2 acts as a ligand for integrins α4, M and β2 for the recruitment of neutrophils (Jia, Li et al. 2005). E. ictaluri is a facultative intracellular pathogen, and spondin 2 may be playing an important role in recognizing E. ictaluri when they are localized in intracellular compartments.

In our study Spondin 2 may also enhance macrophage phagocytosis of E. ictaluri when they are located in extracellular compartments. The extracellular space sub- category that was up regulated is 5 fold. The genes included in extracellular space are bone morphogenic protein (BMP)-4, collagen (Coll)-2, fibroblast growth factor (FGF)-4, myosin heavy chain14 (MYH), and spondin2.

Leukocytes such as granulocytes, monocytes and lymphocytes receive signals such as proadhesive, promigratory, and activation from chemokines. Chemokines are expressed by various cell types in response to inflammatory stimuli. Chemokines also induce various biological activities such as effects on degranulation, cell division, cell activation, and secretion of cytokines in both leukocytic and non-leukocytic cell types

(Taub 1996). We found substantial up regulation of 19 (chemokine (C-C motif)-like). It has been demonstrated that CC chemokines promote chemotaxis of anti-tumor NK cells

(Maghazachi, al-Aoukaty et al. 1994). In mammals, birds and fish, different types of chemokines perform similar cellular migration mechanisms. A primitive vertebrate, the lamprey, has a chemokine belonging to the CXC family. Regulation of cell trafficking by chemokines appeared during the evolution of vertebrates with their complex cell system

(Laing and Secombes 2004). Zebrafish have increased number of chemokines due to duplication events after its divergence from other teleosts. Subfamilies such as CXC, CC,

XC and CX were found in zebrafish. CX is a novel subfamily found only in zebrafish. It is also speculated that these novel chemokine genes are involved specifically in zebrafish development. To cope with their environmental challenges, each species acquired/has species-specific chemokines during their evolution (Nomiyama, Hieshima et al. 2008).

Zebrafish not only has an extensive chemokine system but also has a well established CC chemokine family (DeVries, Kelvin et al. 2006). To understand this complex network of molecules research needs be carried out to in zebrafish (Alejo and Tafalla 2011).

Summarization using the GOSlim Viewer resulted in three categories of GO annotations: cellular components, molecular functions and biological process. The over represented sub-categories from the cellular component category are cell part, cell organelle, intracellular, plasma membrane, cellular component in general and protein complex. In our study the sub-category cytoplasmic membrane bound vesicles

(GO:0016023) (parent GO term: cell part) is over represented . These gene products are involved in transportation of macromolecules to their cellular destinations.

Macromolecules are exchanged between endoplasmic reticulum, golgi apparatus, lysosomes and plasma membrane through vesicular transport (Cooper and Hausman

2004) . The vesicles that bud from plasma membrane using endocytosis are also destined to lysosomes where lysosomes break down the macromolecules and/or potentially pathogenic microorganisms into their constituent particles.

In addition sub-category, intracellular is also overrepresented which may be due to the efforts to eliminate the E. ictaluri an intracellular pathogen. This idea is further substantiated by the over representation of the sub-category transporter activity

(GO:0005215) from molecular function category and transport (GO:0006810) from biological process category.

Transforming growth factor-beta (TGF-β) family is one of the major regulators of transcription factors. The TGF- β are involved in cell division, differentiation, migration, adhesion, organization, and death. (Massagué, Seoane et al. 2005). Bone morphogenic proteins (BMP) are signaling cytokines belonging to the superfamily of TGF-βs and are involved in the regulation of cell proliferation, differentiation, apoptosis and morphogenesis (Kingsley 1994, Hogan 1996). Function and development of specific hematopoietic lineages are mediated by individual BMPs (Detmer, Steele et al. 1999).

They are also involved in blood vessel formation (ten Dijke and Arthur 2007). BMP4 is involved in embryonic hematopoiesis (Johansson and Wiles 1995). BMP endothelial cell precursor derived regulator (BMPER), is an extracellular BMP modulator that plays an important role in BMP4 function in endothelial cells (Heinke, Wehofsits et al. 2008,

Kelley, Ren et al. 2009). Both BMP and BMPER are necessary for endothelial cells to deliver pro-BMP signals (Heinke, Wehofsits et al. 2008). Low concentrations of

BMPER enhance endothelial cell sprouting and at high concentrations sprouting is inhibited. BMPER is also involved in endothelial cell migration (Heinke, Wehofsits et al.

2008) by modulating the expression of adhesion molecules (Helbing, Rothweiler et al.

2010).

Mutations in BMP4 in humans and zebrafish have resulted in kidney malformations such as defective general structural formation and defective final differentiation . BMP4 expression is associated with the developing pronephric

mesoderm in normal zebrafish. Zebrafish BMP4 shares 68% identity and 80% similarity to that of human, mouse and frog BMP4 (Weber, Taylor et al. 2008).

Fibroblast growth factor-2 is a pleiotropic component (Bikfalvi, Klein et al.

1997). FGF-2 is involved in cytokine interaction networks for positive regulation of hematopoiesis. FGF-2 is involved in the regulation of pathological and physiological hematopoiesis, granulopoiesis, and megakryocytopoiesis. Granulopoiesis is mediated by

FGF-2 though secondary cytokine production, stimulation of granulocytic progenitor growth and differentiation. FGF-2 stimulates proliferation, enhances cytokine secretion, and prevents apoptosis. It is also involved in proliferation and/or survival of hematopoietic progenitors (Allouche and Bikfalvi 1995).

In mamals, the insulin superfamily includes the relaxin (RLN) family of peptides molecules such as relaxin (RLN) and relaxin3a/b (Good-Avila, Yegorov et al. 2009).

RLN is a pleotropic hormone with wide range of biological activities on various tissues and organs in various physiological and pathological conditions (Bani 1997). Teleost

RLN3a and RLN3b paralogues display similarities in evolution and expression compared to mammals (Good-Avila, Yegorov et al. 2009). Relaxins are involed in wound healing, fibrosis, allergic responses (Sherwood 2004) regulation of appetite, feeding in rats

(McGowan, Stanley et al. 2005). Neurons that had RLN3 in them are involved in stress response. RLN3 acts a s a neurotransmitter, and is involved in behavioural regulation thus making the individuals to adapt to environmental stress (Tanaka, Kodama et al.

2010). Relaxin acts on inactivation of contractile machinary of a cell thus leading to cell relaxation. It is also involved in vasodilation in several organs and tissues (Bani 1997).

Dilation of the blood vessels is a result of the movement of tissue macrophage derived

cytokines to the site of injection and/or bacterial presence, which inturn will lead to the movement of leucocytes such as neutrophils and monocytes to the site of bacterial infection (Janeway, Murphy et al. 2008).

Up-regulated expression of relaxin 3 in immunized fish compared to the non- immunized fish may be related to increase or more rapid expression of cytokines by leukocytes involved in the memory response.

Immunological implications of melanain concentrating hormone (MCH) are still in its infancy. MCH expressing cells occur in both the spleen and thymus. (Lakaye,

Coumans et al. 2009). Alpha- Melanin concentrating hormone plays an important role in host defense. Alpha-MCH is an ancient anti-inflammatory peptide produced by phagocytes and keratinocytes. Increased expression of alpha-MCH in the blood indicates infectious and inflammatory disorders. Elevated levels of α-MCH in human plasma have antimicrobial functions (Anna, Lorena et al. 2000). Under inflammatory conditions,

MCH receptor (MCHR1) expression was upregulated on human colonic epithelial cells

(Kokkotou, Moss et al. 2008). In our study fish kidney must have been inflammed due to the injection of E.ictaluri thus resulted in upregulated MCHR1 expression in kidney epithelial cells.

In the present study MCH receptor 1 was upregulated in immunized fish, suggesting that the innate immune system is providing enhanced protection for the immunized fish compared to the non-immunized fish. It is still to be estoblished if there is any association between MCHR1 and the memory associated with the cells of innate immune system.

Fibroblast growth factor (FGF) signaling system has been identified in multicelluar organisms ranging from invertebrate, nematode, Caenorhabditis elegans to vertebrate, the human, Homosapiens. (Nobuyuki and David 2004). FGF and FGF receptor (FGFR) gene families in the human and mouse comprise 22 and 4 members, respectively. In zebrafish on the other hand the FGF gene family comprise 27 members, which includes several parologs. The co-evolution of FGF and FGFR gene families enabled the FGF signaling system to acquire functional diversity. This has allowed the involvement of FGF signalling in many physiological and developmental processes. FGF knockout and mutation studies in mice and zebrafish respectively indicated the crucial role of FGFs in various developmental process (Itoh 2007).

Fibroblast growth factor (FGF) is an angiogenic growth factor with pleiotropic functions such as cell proliferation and differentiation of various cell types. Myelopoiesis stimulation is greatly enhanced by FGF2 (Allouche and Bikfalvi 1995). Differentiation of skeletal muscle (Bikfalvi, Klein et al. 1997), granulopoietic progenitors or stem cells and granulopoiesis is stimulated by FGF2 through a secondary cytokine.It also supressess the functions of growth inhibitors including interferons or TGFβ. FGF2 can aid in the stimulation of early and late hematopoiesis performed by hematopoietic cytokines. This suggests that FGF-receptors (FGF-Rs) are present on both lineage committed and pluripotent progenitor cells including bone marrow (including stromal cells), megakarycytes, macrophages, and granulocytes. On the other hand FGF-2 was documented to be expressed in stromal cells, macrophages and leukemic cell lines and is involved in physioligical and pathological hematopoiesis (Allouche and Bikfalvi 1995).

FGF4 important for development of visceral organs and is transcriptionally regulated by lymphoid enhancer factor (LEF)-1 belonging to subfamily of HMG proteins

(Yamauchi, Miyakawa et al. 2009). In our study FGF4 was down regulated in immunized fish.

Up-regulated expression of FGF2 and bone morphogenetic protein BMP4 suggesting increased hematopoiesis. BMP binding endothelial regulator (BMPER) is a secreted protein that interacts with, and inhibits BMP function. BMPER down regulationstrengthens the evidence that BMP4 has a role in this response. FGF2 was involved in granulopoiesis in response to bacterial infection. As mentioned above in this article, relaxins were involve in vasodilation, and movement of macrophage derived cytokines to the site of infection, followed by movement of granulocytes.

Neuropilin (NP) 1 is a receptor expressed on the endothelial cells that selectively binds to vascular endothelial growth factor (VEGF) (Neufeld, Cohen et al. 1999). NP-1 supports the protective mechanisms of VEGF on glomerular endothelial cells, preventing damage and apoptosis. NP-1 expression in glomeruli is correlated with damage (Vadasz,

Ben-Izhak et al. 2011). It was also reported that NP-1 is involved in the initiation of primary immune response (Tordjman, Lepelletier et al. 2002). Expression of NP1 in the immunized fish was low compared to non-immunized fish, suggesting that the immune system protected the kidney from damage by the bacteria.

Myeloid/lymphoid mixed-lineage leukemia protein (MLL) which is a Drosophila trithorax (trx) G homolog, plays an important role in hematopoietic stem cell (HSC) development in embryos (Jude, Climer et al. 2007). Embryonic stem cells deficient in

MLL failed to differentiate into any of HSC types in fetal liver or in adult animals (Ernst,

Fisher et al. 2004). Germline loss of function studies have demonstrated (Ernst, Fisher et al. 2004) that MLL is essential for both development and maintenance of HSC (Jude,

Climer et al. 2007). MLL is maternally supplied, expressed in the adults and is an important transcriptional regulator during the entire lifespan of zebrafish (Robinson,

Germano et al. 2011).

MLL , FGF2 and BMP4 were differentially expressed (MLL down and FGF2 and

BMP4 up) in immunized fish compared to the non-immunized fish after bacterial chellenge, suggesting dynamic regulation of hematopoiesis in the vaccinated fish.

Resistance to inhbitors of cholinesterase-8 (RIC-8) or synembryn was isolated originally from C.elegans using genetic analysis (Miller, Emerson et al. 2000). RIC-8 is a unique non-receptor (Tall, Krumins et al. 2003) guanine nucleotide exchange factor that enhances the exchange of GDP-GTP in the absence of receptor binding to the membrane

(Willard, Kimple et al. 2004). It was also observed that Ric-8A down regulation resulted in defective platelet derived growth factor (PGDF)- induced dorsal ruffle turnover and reduced PGDF-initiated cell migration. The RIC-8A activity in cells is enhanced by

PGDF treatment. These observations indicate that RIC-8A is involved in PGDFR mediated actin cytoskeletal rearrangements (Wang, Guo et al. 2011). Upregulation of

RIC-8A in the immunized fish suggests involvement in cell differentiation.

Hormomonals and neurological factors work in orchestra to regulate reproductive, develpomental, repair and immunological processes. The signals that are involved in the induction of immune responses often suppress other processes. The immune zebrafish had incresaed expression of cytokines and INF induced genes, dynamic regulation of factors that control hematopoeisis other factors that are more vegitative in nature were

significantly down regulated. The down regulated gene products included Nuclear receptor subfamily 6, groupA, member 1 (NR6A1), envoplakin, myomegalin, Collagen triple helix containing-1, collagen I and collagen XI, myosin binding protein C, Myosin

10, A-kinase anchoring proteins, synaptotagmin, , Pancreatic transcription factor 1a, ceramide synthases proteins (TLC domain containing 1 and Na(+), K(+)-ATPase), and genes involved in gonadal development (Doublesex- and Mab-3-related transcription facor 1).

Conclusions

Primary response

The categories over represented in the primary response compared to the control were acute phase response, immune response, response to stimulus, proteasome, protein processing and degradation and chaperones. These are normal components of the innate response and cellular injury and indicate activation that the innate immune system.

Secondary Response

Different categories and GO terms that were over represented in the secondary compared to the primary are consistent with NK cells providing protection to vaccinated

Rag1-/- mutant zebrafish.

In order for NK cells to show their effector functions, NK cell receptors should be activated. This is evidenced by the over representation of cell communication, signal transduction and receptor binding and binding categories.

Activated NK cells were believed to be involved in secreting pro-inflammatory, effector cytokines and undergoing clonal proliferation, was evidenced by, upregulated

expression of IFNγ and C-C chemokine, and over representation of the cell proliferation category respectively in EE compared to SE.

Activation of leukocytes is a cell differentiation process. Cell differentiation process is suggested by the over representation of transport, structural morphogenesis, intracellular, intracellular membrane bound organelles and cellular metabolic process categories.

Functional analysis of differentially expressed transcripts between EE and SE associated with specific secondary immune responses corroborate potential heightened and more rapid secondary responses of memory NK cells.

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CHAPTER IV

CELL AND TISSUE CHANGES ASSOCIATED WITH PRIMARY AND

SECONDARY BACTERIAL EXPOSURES IN RAG1-/- MUTANT

ZEBRAFISH

Introduction

Danio rerio is a widely used pathogenesis model. This utility has been demonstrated with the human pathogens Listeria monocytogenes (Menudier, Rougier et al. 1996), zoonotics Streptococcus (Neely, Pfeifer et al. 2002) Mycobacterium (Davis,

Clay et al. 2002), and Edwardsiella tarda (Pressley, Phelan et al. 2005) as well as the fish pathogen Edwardsiella ictaluri (Petrie-Hanson, Romano et al. 2007).

Zebrafish are an excellent model for investigating immunological processes; their innate and adaptive immune responses are very similar to mammalian immunity. The best use of the zebrafish model is for understanding the evolution of innate immunity and the early interactions of innate and acquired immune responses. Fish are not immunologically mature when they hatch. Depending on the specie, T and B cells may not develop until 6 weeks post-hatch. The development of acquired immunity in fish can be viewed as a ‘time-line’ of the evolution of acquired immunity.

Recombination activation gene RAG1-/- mutant zebrafish are comparable to

Severe Combined Immunodeficient (SCID) and RAG 1 and 2 deficient mutant mice.

These fish have a significantly reduced lymphocyte-like cell population that lacks mature 95

T and B cells, but includes Non-specific cytotoxic cells (NCC) and Natural Killer (NK) cells (Petrie-Hanson, Hohn et al. 2009). Compared to wild-types, these fish have a comparable monocyte/macrophage population and significantly increased neutrophil numbers. Investigating immune responses in RAG1-/- mutant zebrafish may reveal the first functions of innate immune cells as they occurred in early vertebrates.

RAG1-/- mutant zebrafish can mount protective adaptive immune responses following repeated homologous bacterial exposure (Hohn and Petrie-Hanson 2012). The cells involved have not been identified. In SCID mice, NK cells have demonstrated adaptive responses to chemical haptens (O'Leary, Goodarzi et al. 2006) and murid cytomegalo virus (Sun and Lanier 2009). RAG1-/- mutant zebrafish were the first T and B cell deficient vertebrate to mount an adaptive immune response to a bacterial agent, specifically an intra-cellular bacteria, Edwardsiella ictaluri (Hohn and Petrie-Hanson

2012). Natural killer cells are believed to be mediating this response. qRT-PCR results demonstrated a significant increase of INF in the lymphocyte-like cell population of mutant zebrafish following secondary homologous E. ictaluri exposure. The aims of this part of the study were to determine the tissue location of the INFexpressing cells, and describe the pathogenesis of the primary and secondary responses to intra-muscular injections of E. ictaluri in RAG1-/- mutant zebrafish.

Methods

Zebrafish maintenance

Zebrafish were propagated and reared in the Specific Pathogen Free Hatchery in the College of Veterinary Medicine at Mississippi State University following standard operating procedures. In the experimental challenge rooms, adult mutant zebrafish were 96

held in 15-L tanks with charcoal filtered dechlorinated municiple water at 26˚C supplied to each tank at a rate of 0.5 L/min in a flow-through single –pass system. Air stones were provided to each tank to provide aeration. Adult zebrafish were fed TetraMin Tropical

Flake Feed twice daily. The Institutional Animal Care and Use Committee at Mississippi

State University approved all experimental animal protocols.

Bacteria culture and preparation

Edwardsiella ictaluri was a case isolate from fish submitted to the Fish

Diagnostic Lab at CVM-MSU. Culture identifications were confirmed by biochemical analysis using the bioMerieux api20E strip (BioMerieux, 69280 Marcy l’Etoile, France).

Aliquots (0.5 ml) were stored in 20% glycerol at -80˚C until needed for trials, at which time one aliquot was thawed and added into Brain Heart Infusion broth and incubated in a shaker incubator at 30˚C overnight. Logarithmic phase cultures were obtained by dilution of the overnight culture 1:10 and grown until the optical density was 0.4 at 540 nm which corresponds to 108 colony forming units (CFU) per ml. Culture purities were assessed and bacterial concentrations determined by plating serial dilutions on 5% sheep blood plates.

Bacterial injections and sampling

Adult (6-9 month old) RAG1-/- mutant zebrafish were anesthetized in 110 mg/L buffered tricaine methane sulfonate (MS222). Each fish was IM injected on the lateral line above the anal fin using an insulin syringe. All primary vaccinations were 104

CFU/fish RE33, a commercial, live, attenuated Edwardsiella ictaluri vaccine strain

(AQUAVAC-ESC® Intervet, Inc.) (Phillip and Craig 1999). The secondary exposure

was delivered at 1 month (Trial 1) post-vaccination and consisted of 104 CFU/fish

Edwardsiella ictaluri. This secondary injection is referred to as protection exposure. All injections were delivered in a total volume of 10µl phosphate buffered saline (PBS).

Sham injected fish received 10 µl PBS. After recovery from anesthesia, fish were moved to tanks in a flow–through water system and maintained at 26˚C ± 1˚. All fish were held under the same conditions during all experiments and were observed 3 times a day for clinical signs of disease. Moribund fish were euthanatized in 340 mg/L MS222.

Fish were sampled at 24 and 48 hours post injection. After euthanasia in 340mg/L

MS222, an incision was made in the abdominal body wall and the fish fixed in 10% phosphate buffered formalin to preserve tissues in their natural state and fix all components for 24-48 hours. Routine paraffin embedding was performed, tissues were sectioned at 4-6μ, and stained with Hematoxylin and Eosin staining (H &E). All slides were viewed and examined under a light microscope.

In situ hybridization (ISH)

This procedure involves the hybridization of labeled nucleic acid probes with target mRNA, and identifies the location of the cells that produce IFN. Zebrafish were sampled and prepared as described above. Sections were deparaffinized following standard methods. Hybridizations were performed with oligonucleotide probes (Table

4.1) according to standard systems (Vector Labs). Treatments specific for IFNγ expressing cells and controls for non-specific probe binding, non-specific binding of primary antibody and non-specific binding of primary or secondary antibodies were performed in each treatment group (Table 4.2).

Table 4.1 IFNγ sense and anti sense Probes for In situ Hybridization

Sequence name Sequence 5' to 3' IFNγ antisense probe 355 [AminoC6+Dig]CGTCCTGAGCTCTGCACTCTTGCCTGGAAA

IFNγ antisense probe 396 [AminoC6+Dig]ATCGGGTCGTTTTCCTTGATCGCCCATAGC

IFNγ sense probe 355 [AminoC6+Dig]TTTCCAGGCAAGAGTGCAGAGCTCAGGACG

IFNγ sense probe 396 [AminoC6+Dig]GCTATGGGCGATCAAGGAAAACGACCCGAT

Table 4.2 In situ treatments to identify IFNγ expressing cells in SS, SE, and EE exposed mutant zebrafish.

Probe Detection Evaluates for

L anti IFNγ sense probe 355 and 396 1˚, 2˚ IFNγ expressing cells

L anti IFNγ sense probe 355 and 396 1˚, 2˚ Non-specific binding of secondary antibody

U IFNγ sense probe 355 and 396 1˚, 2˚ Non-specific probe binding

No probe 1˚, 2˚ Non-specific binding of primary or antibody

antibody

Results

In 48 hours post primary (48 hpp) fish the liver demonstrated foci where sinusoidal macrophages had phagocytosed large numbers of rod shaped bacteria (Fig. 4.1). The hepatic parenchyma demonstrated diffuse intracellular edema, foci of necrosis, and an overall mild grade of necrosis. Granular lymphocytes were present in the sinusoids. No bacteria were seen in sinusoids where granular lymphocytes were located.

In the 48 hours post the primary (48hpp) kidney, an intermediate grade of multi- focal necrosis of the renal interstitial tissues was seen, renal corpuscles demonstrated increased Bowman’s space, and there was some necrosis of renal tubules.

The liver demonstrated foci where sinusoidal macrophages had phagocytosed large numbers of rod shaped bacteria. The hepatic parenchyma demonstrated diffuse intracellular edema, foci of necrosis, and an overall mild grade of necrosis. Granular lymphocytes were present in the sinusoids. No bacteria were seen in sinusoids where granular lymphocytes were located.

The gut demonstrated foci of hemorrhagic and necrotizing enteritis and there were increased numbers of eosinophils in the lamina propria compared to control. Granular lymphocytes were also present in the lamina propria.

In the 48 hours post the secondary (48hps) kidney, renal interstitial tissue appeared normal, but multi-focal tubular necrosis and ‘lymphocyte-like cell’ infiltration of the renal interstitial tissue were seen. The hepatic parenchyma demonstrated mild intra-cellular edema and the gut appeared normal.

In situ hybridizations demonstrated positive staining cells in hepatic sinusoids and gut of primary exposed and vaccinated/secondary exposed fish. However, remarkable differences between treatment groups could not be discerned. No staining was observed in the no probe control treatment, while the sense probe control and the anti-sense probe without primary antibody demonstrated non-target background staining in kidney tubular epithelial cells and gut lamina propria and gill cartilage.

100

Figure 4.1 Mutant zebrafish liver. A) Non-exposed healthy liver. B) 48 h post primary injection

A) Non-exposed healthy liver with sinusoid (S), hepatocyte (h), fat storage cell (f) and hepatic NK cell or pit cell (arrow head). B) 48 h post primary injection (1x104 cfu/fish). Note the liver sinusoidal macrophages phagocytosed large numbers of rod shaped bacteria (arrows) (H&E staining,100x).

101

Figure 4.2 Mutant zebrafish renal hematopoietic tissue. A) 48 h post primary. B) 48 h post secondary

A) 48 h post primary injection (1x104 cfu/fish). Note the necrosis of interstitial tissue (arrow). Some tubules are necrotic (arrow head). B) 48 h post secondary injection (1x104 cfu/fish) of previously vaccinated fish. Note the marked reduction of necrotic interstitial tissue (arrow), yet more necrotic tubules (arrow head) than in A (H&E staining, 20x).

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Discussion

In wild-type zebrafish, the pathology of IM injected E. ictaluri is splenic, hepatic, cardiac and renal necrosis (Petrie-Hanson, Romano et al. 2007), and the 48 hours post the primary (48hpp) response in mutant zebrafish was very similar. Renal interstitial tissue was targeted, and in microarray analyses, a large proportion of genes associated with kidney disease were upregulated. The gills were not markedly affected. In control fish, there were not aggregations of small lymphocytes in the lamina propria; eosinophils and large granular lymphocytes were present. The primary exposed gut was markedly affected, and large granular lymphocytes and eosinophils were present in the lamina propria; the number of eosinophils was greater than in the control fish.

In the zebrafish E. ictaluri pathogenesis model (Petrie-Hanson, Romano et al.

2007), a marked reduction of neutrophils was observed in the renal interstitial tissues at

48 hours post injection (hpi), with further neutrophil reduction from 48 to 96 hpi. Much less neutrophilic infiltration of the liver (compared to other tissues) was reported in channel catfish and zebrafish (Newton, Wolfe et al. 1989, Petrie-Hanson, Romano et al.

2007) , respectively.

Cellular changes in the hepatic parenchyma in 48hpp exposed mutant zebrafish were suggestive of liver tissue undergoing a primary acute phase response. Microarray analysis also demonstrated up-regulation of transcripts associated with acute phase response proteins.

The 48hpp mutant zebrafish liver did not demonstrate marked inflammatory infiltration. Similar findings were observed in other studies. In channel catfish, macrophages were the first hepatic inflammatory infiltrate, at 5 days post exposure

103

(Newton, Wolfe et al. 1989). Macrophages and neutrophils infiltrated the channel catfish liver, but significantly less than the spleen(Elibol-Flemming, Boyle et al. 2009). In the wild type E. ictaluri pathogenesis model, the liver demonstrated mild patchy necrosis with rod-shaped bacteria associated with necrosis (Petrie-Hanson, Romano et al. 2007), and marked inflammatory infiltrate was not described.

Macrophages and occasional NK cells were observed in the margins of sinusoids in mutant zebrafish. Hepatic NK cells constitute a tissue specific NK cell population, and are called pit cells (Wisse, Luo et al. 2000). These cells adhere to endothelial cells and their numbers increase following exposure to biological response modifiers (Bouwens

1990).

The tissue changes associated with the 48hps treatment compared to the 48hpp, were very interesting. When the same dose was administered to vaccinated versus naïve fish, minimal necrosis of the renal interstitial tissue was seen, yet necrosis of the renal tubules was comparable to the post-primary response. There was also an infiltration of

‘lymphocyte-like’ cells into the kidney interstitial tissue. In both the renal interstitial and liver, much less bacteria was seen. The liver demonstrated subtle edema changes, and the gut demonstrated less severe changes as well. Over-all, the pathological response was much less severe in the vaccinated (48hps) fish.

Our In situ hybridizations performed on the 48 hours post the primary and 48 hours post the secondary (48hps) exposed mutant zebrafish demonstrated INF expressing cells in the liver and intestinal lamina propria. In the liver, these cells were localized near vessels and along sinusoids. Positive staining cells were not observed in the renal interstitial tissue. Our findings suggest that the cells up-regulating INFare

104

present in the liver and the intestinal lamina propria. It is likely that the INFexpressing

NK cells are resident in the liver and intestinal lamina propria. Non target staining observed in the sense probe control and the anti-sense probe with out primary antibody treatments resulted from endogenous peroxidase and non-specific binding against the primary or secondary antibodies.

Quantitative RT-PCR performed on kidney tissue demonstrated the 48 hps treatment had significantly greater INF expression than the 48hpp treatment. When qRT-PCR were performed on sorted peripheral blood leukocytes, the lymphocyte-like cell population from fish that received a secondary exposure had greater INF expression than the lymphocyte like cells from the 48 hpp treatment. In the 48 hpp and 48 hps treatments, the granulocyte population did not demonstrate increased INF expression.

Kidney interstitial leukocytes from vaccinated mutants conferred protective immunity to naive mutants in adoptive transfer experiments (Hohn and Petrie-Hanson 2012).

However, no postivie staining cells were observed in kidney interstitial tissue in the in situ hybridization study. In the mutant mouse models of NK cell mediated specific memory, tissue associated NK cell subsets were described, and the NK cells that conferred protective immunity were from the liver (O'Leary, Goodarzi et al. 2006, Sun and Lanier 2009). In our zebrafish study, if hepatic NK cells were mobilized to mediate protective immunity, these cells would traffic through the kidney, and these trafficking may explain our findings. The classic description of an NK cell is a large, granular lymphocyte. Large lymphocytes with a ‘more granular cytoplasm’ were previously described in RAG1-/- mutant zebrafish, and were part of a ‘lymphocyte-like’ cell population that comprised 34.7% of peripheral leukocytes (Petrie-Hanson, Hohn et al. 105

2009). Natural killer cells are currently regarded as innate immune cells and the genes that encode pathogen recognition receptors do not undergo gene rearrangement

(Bryceson and Ljunggren 2008). However, these cells are capable of mediating adaptive immunity in RAG1-/- mutant zebrafish (Hohn and Petrie-Hanson 2012) and mouse models (O'Leary, Goodarzi et al. 2006, Sun and Lanier 2009).

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Neely, M. N., J. D. Pfeifer and M. Caparon (2002). "Streptococcus-zebrafish model of bacterial pathogenesis." Infect Immun 70(7): 3904-3914.

Newton, J. C., L. G. Wolfe, J. M. Grizzle and J. A. Plumb (1989). "Pathology of experimental enteric septicaemia in channel catfish, Ictalurus punctatus (Rafinesque), following immersion-exposure to Edwardsiella ictaluri." Journal of Fish Diseases 12(4): 335-347.

O'Leary, J. G., M. Goodarzi, D. L. Drayton and U. H. von Andrian (2006). "T cell- and B cell-independent adaptive immunity mediated by natural killer cells." Nat Immunol 7(5): 507-516.

Petrie-Hanson, L., C. Hohn and L. Hanson (2009). "Characterization of rag1 mutant zebrafish leukocytes." BMC Immunology.

Petrie-Hanson, L., C. L. Romano, R. B. Mackey, P. Khosravi, C. M. Hohn and C. R. Boyle (2007). "Evaluation of zebrafish Danio rerio as a model for enteric septicemia of catfish (ESC)." J Aquat Anim Health 19(3): 151-158. 107

Phillip, H. K. and A. S. Craig (1999). "Development and use of modified live Edwardsiella ictaluri vaccine against enteric septicemia of catfish." Advances in Veterinary Medicine 41: 523-537.

Pressley, M. E., P. E. Phelan, 3rd, P. E. Witten, M. T. Mellon and C. H. Kim (2005). "Pathogenesis and inflammatory response to Edwardsiella tarda infection in the zebrafish." Dev Comp Immunol 29(6): 501-513.

Sun, J. C. and L. L. Lanier (2009). "Natural killer cells remember: an evolutionary bridge between innate and adaptive immunity?" Eur J Immunol 39(8): 2059-2064.

Wisse, E., D. Luo, D. Vermijlen, B. Ahishali, V. Triantis, G. Plakoutsi, F. Braet and K. Vanderkerken (2000). On the cell biology of pit cells, the liver-specific NK cells, 2000-02.

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CHAPTER V

CONCLUSIONS

The zebrafish is a primary model for studying vertebrate development (Langenau,

Ferrando et al. 2004, Mathavan, Lee et al. 2005). Also, because zebrafish and human immune systems and the response to infections are similar, it has been increasingly used as a model for studying basic immunology (Meeker and Trede 2008). Transparency during early development, small size (5 cm), high fecundity, and rapid embryonic development, make the zebrafish, the one of the best models for studying developmental immunology (Trede, Langenau et al. 2004, Meeker and Trede 2008, Petrie-Hanson, Hohn et al. 2009). Furthermore, because zebrafish are more dependent on innate immune mechanisms than are most mammals, they are an excellent model for studying mechanisms of innate immunity.

To elucidate the process involved in protective immunity in lymphocyte deficient rag1-/- zebrafish (Hohn and Petrie-Hanson 2012), we compared expression levels of transcripts from the vaccinated and infected (EE) fish to that of non-vaccinated and infected fish (SE). The differentially expressed genes were evaluated by GO functional analysis and GO enrichment analysis was performed. Results from the above studies are explained in detail in the subsequent sections.

Comparison of EE with SE would provide insights into the role of vaccination in

Rag1-/- mutant zebrafish. For this reason, first EE was compared with non-exposed (SS), 109

genes that showed significant changes in expression were selected and were further compared with SE. This process identified 98 statistically significant, and up and down- regulated genes.

From the above data it can be said that the gene products that appear to be performing the important functions are pancreas specific transcription factor 1a, fibroblast growth factor 2, bone morphogenetic protein 4, fibroblast growth factor 4,

BMP binding endothelial regulator, spondin 2b, extracellular matrix protein, High- mobility group protein isoforms I and Y, nuclear receptor subfamily 6, group A, member, myosin-10-like, collagen triple helix repeat containing 1b, collagen, type I, alpha 2, collagen type XI alpha-2, 19 (chemokine (C-C motif)-like), novel immune-type receptor

1k, and melanin-concentrating hormone receptor 1a.

Microarray analysis identified 129 transcripts exhibiting altered transcript expression between SE and SS. The observed differences in increased transcript expression are in the range of 5-1.0 fold.

The majority of the upregulated transcripts between SE and SS were grouped into the following categories: acute phase response, complement activation, immune response, response to stimulus, carbohydrate metabolism, proteasomes, protein degradation processing and heatshock proteins.

The acute phase proteins up-regulated in this study were ceruloplasmin and major acute phase reactant apolipoprotein of the HDL complex., belongs to the SAA family.

Ceruloplasmin is also involved in iron binding and transport. At least 6 of the annotated transcripts represented complement components including C1q like gene, C3b, factor b, 7 and 9 were up-regulated in response to primary bacterial challenge, which indicated the

110

involvement complement response to infection with the expression of most of the alternate complement pathway molecules.

Because INF was not present on the microarray and this is induced in activated

NK cells which may be important in the protection seen in lymphocyte deficient zebrafish, we evaluated expression of this gene in the whole kidney and in sorted kidney leukocytes of fish from EE, to SE and SS treatments. We found high expression in the lymphocyte like population after bacterial exposure and this was induced to a higher level in fish that had been vaccinated. In comparison the phagocytic cell population showed no induction of INF.

Infection with E.ictaluri in the 48 hours post the primary (48hpp) kidney, results in an intermediate grade of multi-focal necrosis of the renal interstitial tissues, and some necrosis of renal tubules. The sinusoidal macrophages in liver were performing phagocytosis of large numbers of rod shaped bacteria. The hepatic parenchyma demonstrated diffuse intracellular edema, foci of necrosis, and an overall mild grade of necrosis due to infection with E.ictaluri. Granular lymphocytes were present in the sinusoids. No bacteria were seen in sinusoids where granular lymphocytes were located.

E.ictaluri infection also demonstrated foci of hemorrhagic and necrotizing enteritis and there were increased numbers of eosinophils in the lamina propria compared to control in the gut. Granular lymphocytes were also present in the lamina propria.

In the 48 hours post the secondary (48hps) kidney, renal interstitial tissue appeared normal. It can be concluded from this observation that 48hps the immune system had responded quicker compared to 48hpp and cleared the bacteria before they could cause much damage to the tissues. But there is multi-focal tubular necrosis and 111

‘lymphocyte-like cell’ infiltration of the renal interstitial tissue were seen. The hepatic parenchyma demonstrated mild intra-cellular edema and the gut appeared normal. In situ hybridizations were difficult to interpret. Positive staining cells were seen in some liver sinusoids, in the gill and lamina propria of the gut. Our observations suggest that Natural

Killer cells are involved in specific protection to E. ictaluri in T and B lymphocyte deficient zebrafish.

112

References

Hohn, C. and L. Petrie-Hanson (2012). "Rag1 -/- Mutant Zebrafish Demonstrate Specific Protection following Bacterial Re-Exposure." PLoS ONE 7(9).

Meeker, N. D. and N. S. Trede (2008). "Immunology and zebrafish: spawning new models of human disease." Dev Comp Immunol 32(7): 745-757.

Petrie-Hanson, L., C. Hohn and L. Hanson (2009). "Characterization of rag1 mutant zebrafish leukocytes." BMC Immunol 10: 8.

Trede, N. S., D. M. Langenau, D. Traver, A. T. Look and L. I. Zon (2004). "The use of zebrafish to understand immunity." Immunity 20(4): 367-379.

113

APPENDIX A

A LIST OF ZEBRAFISH TRANSCRIPTS THAT WERE UPREGULATED IN THE

KIDNEY FOLLOWING PRIMARY INFECTION WITH E. ICTALURI VACCINE

STRAIN AND THE FOLD CHANGE RELATIVE TO SHAM

INFECTED CONTROLS

114

Accession Putative ID Fold Difference BM095864 MpV17 transgene, murine homolog, glomerulosclerosis 1.359278 AI882824 acid phosphatase 5a, tartrate resistant 1.356978 AW019321 urate oxidase 1.356844 BQ284686 novel galactose binding lectin domain containing protein 1.355023 NM_180964.2 claudin d 1.346409 BG728739 TAF5-like RNA polymerase II, p300/CBP-associated factor (PCAF)-associated 1.344563 BI877907 dopey family member 2 /// hypothetical protein LOC100332551 zgc:173770 1.343743 NM_131427.1 Orb/CPEB-related RNA-binding protein 1.34307 AI667206 spectrin alpha 2 1.338113 BM101494 syntabulin (syntaxin-interacting) 1.337356 AY283178.1 zygote arrest 1 1.336137 BM266118 Sorting nexin 18a 1.331521 AW116246 cell division cycle 20 homolog 1.329484 BQ264030 zona pellucida glycoprotein 2, like 2 1.329063 BI673771 vimentin-type intermediate filament-associated coiled-coil protein-like 1.325316 AI815336 WEE1 homolog 2 (S. pombe) 1.324453 BM025856 WD repeat domain 92 1.322808 BI979948 latrophilin-2-like 1.318779 BM095156 synaptotagmin XIII 1.311106 BM036896 GINS complex subunit 2 1.30894 AY326458.1 zic family member 5 (odd-paired homolog, Drosophila) 1.30829 BM316545 LSM11, U7 small nuclear RNA associated 1.298908 BM096299 Zp2.2 protein-like /// zgc:111913 /// zona pellucida glycoprotein 2 1.291448 BC044513.1 poly(A) binding protein, cytoplasmic 1-like 1.288855 AW826223 zgc:66455 1.284986 BC044528.1 DEAD (Asp-Glu-Ala-Asp) box polypeptide 41 1.283672 BC044146.1 lipase, endothelial 1.279445 BI885857 ariadne homolog, ubiquitin-conjugating enzyme E2 binding protein, 1 like 1.276824 BM957986 carbonic anhydrase XV b 1.274901 CB353911 SRY-box containing gene 19b 1.272833 AI522712 Cytochrome P450, family 51 1.268124 AW019689 complement component 8, gamma polypeptide 1.268085 BI980112 nucleophosmin/nucleoplasmin, 4 1.26633 BM775009 tumor necrosis factor (ligand) superfamily, member 10 like 4 1.265988 NM_180965.2 claudin g 1.261789 BM402113 SRY-box containing gene 19b 1.256749 NM_178306.2 tryptophan hydroxylase 1 (tryptophan 5-monooxygenase) a 1.255164 AW343652 SUMO1/sentrin specific peptidase 6b 1.254325 CA474865 dynein, light chain, LC8-type 2a 1.241339 BM533986 ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 7.1 1.239306

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phosphate regulating gene with homologues to endopeptidases on the X CA475091 chromosome 1.232324 BM533848 Novel rhamnose binding lectin 1.230684 BM141572 methylphosphate capping enzyme 1.223082 NM_131533.1 homeo box A9b 1.221844 AY279108.1 hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 1 1.212993 BM103855 zinc finger protein 111-like 1.210018 phosphate regulating gene with homologues to endopeptidases on the X AW116573 chromosome 1.209651 BM534266 retinol binding protein 1b, cellular 1.209412 BM082881 snail homolog 3 1.205742 BC044148.1 thymidine kinase 1, soluble 1.202026 BG303935 lysophosphatidylcholine acyltransferase 4 1.185386 BI709704 protein arginine methyltransferase 6 1.183484 BI891363 non-SMC condensin I complex, subunit H 1.17979 AW058746 AT rich interactive domain 3B (Bright like) 1.178481 BI878221 Fanconi anemia, complementation group G 1.17297 BQ479973 Phosphorylase, glycogen; liver (Hers disease, glycogen storage disease type VI) 1.171126 AB074154.1 chromobox homolog 2 (Drosophila Pc class) 1.169749 BI891588 mitochondrial topoisomerase I 1.159907 AY281362.1 cytochrome P450, family 17, subfamily A, polypeptide 1 1.145857 AI721448 nuclear autoantigenic sperm protein (histone-binding) 1.144427 BM102667 interleukin 13 receptor, alpha 2 1.137686 AL721341 WD repeat domain 74 1.136348 CD015028 sex hormone binding globulin 1.135406 BI867183 NAD(P) dependent steroid dehydrogenase-like 1.12398 BG307558 chromatin licensing and DNA replication factor 1 1.121587 NM_131282.1 forkhead box A sequence 1.115046 BC048053.1 hydroxysteroid (17-beta) dehydrogenase 12a 1.112993 AI444261 insulin-degrading enzyme 1.104982 BQ262563 similar to N-acetylgalactosaminyltransferase 1.104274 BM859647 Zp2.2 protein-like /// zgc:111913 /// zona pellucida glycoprotein 2 1.10201 BM530311 CUE domain containing 2 1.101062 BM572096 similar to gemin 7 1.100291 AW116296 zinc finger protein 326 1.086471 BI709547 novel galactose binding lectin domain containing protein 1.086208 AI957736 aurora kinase B 1.086075 BC045441.1 myoneurin 1.082998 D26172.1 orthodenticle homolog 1b /// wu:fc92e03 1.082609 BG303857 coiled-coil domain containing 138-like 1.076361 BC050502.1 dopey family member 2 /// hypothetical protein LOC100332551 1.073549 NM_131457.1 bone morphogenetic protein receptor, type 1ba 1.070469 BG737465 fatty acid amide hydrolase 2b 1.06816 116

AL925336 pogo transposable element with ZNF domain a 1.067508 NM_131331.1 zona pellucida glycoprotein 3 1.06258 BQ263377 dynein, light chain, LC8-type 2a 1.061668 BQ262828 geminin, DNA replication inhibitor 1.060398 BC045864.1 progestin and adipoQ receptor family member Va 1.059763 NM_153667.2 triosephosphate isomerase 1a 1.059685 BC045912.1 uracil-DNA glycosylase 1.055151 BG727351 Fukutin related protein 1.041236 NM_131810.1 connexin 44.2 1.040628 BM533561 similar to mCG8648 1.034333 AF024535.1 neurogenin 1 1.030006 CD606355 hypothetical LOC793997 1.029716 AF483203.1 DNA (cytosine-5-)-methyltransferase 1 1.024406 BI474952 LIM domain only 7b 1.023633 BI879288 Similar to CUB domain-containing protein 1 1.021548 BM533848 novel rhamnose binding lectin-like 1.020894 BI672501 MUS81 endonuclease homolog (yeast) 1.020181 BQ264365 inhibitor of growth family, member 2 1.019643 U02544.1 wingless-type MMTV integration site family, member 10a 1.779856 BQ284334 ubiquitin protein ligase E3 component n-recognin 3 1.777128 NM_131226.1 retinal homeobox gene 2 1.917922 BM342706 crystallin, gamma MX, like 2 1.859191 BQ077745 Actin filament associated protein 1-like 1 1.834793 BQ262831 MpV17 transgene, murine homolog, glomerulosclerosis 1.859171 NM_131161.1 POU class 3 homeobox 1 1.823569 NM_130948.1 decapentaplegic and Vg-related 1 1.795531 AL729646 titin a 1.783442 AW116727 A kinase (PRKA) anchor protein 1b-like 1.789815 BI982036 copine II 1.794056 NM_131805.1 ephrin B1 1.977871 BI867650 marapsin-like 3.868995 BQ479880 CG14619-like 2.550846 NM_131208.1 LIM homeobox 3 2.056671 NM_152985.1 odorant receptor, family D, subfamily 111, member 8 2.93092 NM_131056.1 preproinsulin 2.687295 BI980704 UPF0308 protein C9orf21 homolog 1.95893 NM_131878.1 nanos homolog 1.766929 BC048135.1 transmembrane protease, serine 4b 1.757351 BM859664 nucleoplasmin 2 1.744284 BQ616045 alveolin-like 1.737759 BI867996 general transcription factor IIIA, b 1.733566 AW422834 Cytochrome P450, family 2, subfamily J, polypeptide 23 1.732212 117

AI584313 SIN3 homolog B, transcription regulator (yeast) 1.730238 BM095864 MpV17 transgene, murine homolog, glomerulosclerosis 1.727819 AL719376 zic family member 3 heterotaxy 1 (odd-paired homolog, Drosophila) 1.726775 NM_145195.1 baculoviral IAP repeat-containing 5B 1.716888 BI867089 general transcription factor IIIA, b 1.714334 AL919330 similar to avidin 1.701503 AW154503 murinoglobulin-1-like /// si:dkey-46g23.3 /// zgc:171446 1.701168 AW117128 ventral expressed homeobox 1.689807 BQ479973 phosphorylase, glycogen; liver (Hers disease, glycogen storage disease type VI) 1.683107 AI667622 elongin A-like 1.682518 AW154503 murinoglobulin-1-like /// murinoglobulin-1-like /// si:dkey-46g23.2 1.643986 NM_131454.1 eukaryotic translation initiation factor 4e 1b 1.639985 BI980590 similar to Unc119b 1.637687 BC044421.1 protein phosphatase 1, regulatory (inhibitor) subunit 3B 1.632683 BC049403.1 orthodenticle homolog 1b /// wu:fc92e03 1.631172 BC044528.1 DEAD (Asp-Glu-Ala-Asp) box polypeptide 41 1.612813 NM_131830.1 neural cell adhesion molecule 2 1.605826 BQ617063 dopey family member 2-like 1.628691 BM082885 solute carrier family 2 (facilitated glucose transporter), member 15b 1.575411 BM402130 lin-28 homolog A (C. elegans) 1.569772 BQ262324 Polyadenylate-binding protein 2-like 1.567057 BQ078419 zona pellucida glycoprotein 3c 1.56199 AW171609 bucky ball 1.55639 AI964120 zona pellucida glycoprotein 3a.2 1.552707 BG305657 phosphorylase, glycogen; liver (Hers disease, glycogen storage disease type VI) 1.548549 BM036885 cell division cycle associated 9 1.53885 BM141117 zona pellucida glycoprotein 3a.1 1.528667 BI843232 latrophilin-2-like 1.525364 BI878956 Polymerase (DNA directed), lambda 1.523946 AL907236 H2A histone family, member X-like 1.523415 BQ616036 zona pellucida sperm-binding protein 2-like /// si:dkeyp-50f7.2 1.522527 NM_131441.1 notch homolog 1a 1.514563 BC053242.1 B-cell translocation gene 4 1.514291 NM_130949.1 forkhead box A2 1.506907 phosphate regulating gene with homologues to endopeptidases on the X BM095403 chromosome 1.503217 AW116507 zona pellucida glycoprotein 3b 1.502774 BC048055.1 NIMA (never in mitosis gene a)-related kinase 2 1.496042 BC046901.1 ELOVL family member 6, elongation of long chain fatty acids like 1.488413 NM_131827.1 zona pellucida glycoprotein 2.2 /// zona pellucida glycoprotein 2.3 1.486979 AB032727.1 v-mos Moloney murine sarcoma viral oncogene homolog 1.484612 BM071714 similar to novel galactose binding lectin domain containing protein 1.484298

118

BI983985 WEE1 homolog 2 (S. pombe) 1.481267 AF499607.1 linker histone H1M 1.479992 BM101590 zona pellucida protein C 1.468967 BQ262819 zona pellucida glycoprotein 2, like 1 1.473579 BM181708 caprin family member 2 1.457593 BI979059 Tryptophan hydroxylase 1 (tryptophan 5-monooxygenase) a 1.467653 BC047173.1 phosphoribosyl transferase domain containing 1 1.457332 BC044516.1 scavenger receptor class B, member 1 1.454748 BM036780 S100 calcium binding protein A1 1.447355 BM183795 transmembrane protein 144b 1.44587 CD602606 novel NACHT domain containing protein 1.445415 AF268045.1 cyclin A1 1.471079 BI983434 fatty acid amide hydrolase 2b 1.440123 BM140763 quinoid dihydropteridine reductase b2 1.43916 phosphate regulating gene with homologues to endopeptidases on the X BM185229 chromosome 1.437343 BI878195 retinol saturase (all-trans-retinol 13,14-reductase) like 1.421647 BG883862 LSM11, U7 small nuclear RNA associated 1.418543 BM140763 quinoid dihydropteridine reductase b2 1.418387 BQ262988 ST6 beta-galactosamide alpha-2,6-sialyltranferase 1 1.417506 AW232391 SET domain containing 4 1.414043 BQ617017 SH2 domain containing 5 1.413886 BM777971 oogenesis-related gene 1.411744 AW116206 conserved hypothetical protein-like 1.411313 BE693149 Zic family member 2 (odd-paired homolog, Drosophila) b 1.409511 NM_130939.1 cth1 1.384692 NM_131112.1 POU domain, class 5, transcription factor 1 1.366587 BC045423.1 LSM14B, SCD6 homolog B (S. cerevisiae) 1.382052 BI983877 scavenger receptor class B, member 1 1.400854 BM104322 Similar to protein associated with topoisomerase II homolog 2 1.397714 BC046889.1 tubulin, alpha 4 like 1.373639 AL920522 LIM homeobox 8a 1.372949 BM036733 similar to novel zinc finger protein 1.371901 BQ131188 zinc finger protein 300-like 1.370612 BQ616029 DEAD (Asp-Glu-Ala-Asp) box polypeptide 41 1.40613 BI428243 WD repeat domain 92 1.405397 BM080888 si:ch211-239e6.4 1.403346 BC047798.1 zgc:55983 1.398407 BM101604 zgc:103482 1.394107 BI672582 cyclin B2 1.392808 BI709648 si:dkey-167i21.2 1.390406 BM571460 zgc:171750 1.389226

119

BM775389 --- 1.381387 BC049064.1 zgc:56699 1.38041 BM101541 im:6902407 1.368807 BM777856 zgc:136338 1.361314 AL721480 Si:ch211-61f14.1 1.359672 BM034951 zgc:110249 /// zgc:174288 1.312303 BQ617060 si:dkey-229d11.5 1.316483 BQ078248 Zgc:152968 1.33878 BQ132828 si:ch211-132b12.8 1.300235 AI964104 zgc:171750 1.328803 AA605725 zgc:101565 1.290294 BI888620 --- 1.289972 BG892035 zgc:114123 1.31343 AI545152 zgc:101663 1.310673 BQ260754 zgc:152977 1.309913 BE201567 wu:fb12c06 1.291495 BQ617061 hypothetical protein LOC100148225 1.275593 AW116196 hypothetical protein LOC100332822 1.267467 BQ616894 zgc:122991 /// zgc:63694 1.258559 BM095187 zgc:66479 1.263324 BM956831 zgc:158856 1.257556 BQ260474 wu:fi34e01 1.261781 BI709943 zgc:153675 1.221182 CD583414 zgc:113159 1.230634 AW466524 zgc:152652 1.247439 BI864451 zgc:154054 1.243523 BM778173 zgc:158494 1.196953 BM777122 wu:fj80h11 1.235569 CB923507 yippee-like 3 1.196192 BM156751 si:dkey-229d11.5 /// zgc:55557 1.190925 BI709981 zgc:174637 1.121406 BM023811 zgc:153251 1.119137 BQ284849 zgc:162344 1.114335 BM081091 hypothetical protein LOC100331917 1.457325 BM184021 hypothetical protein LOC100148225 /// zona pellucida glycoprotein 3 1.081772 AW116196 hypothetical protein LOC100332822 1.248714 NM_131331.1 hypothetical protein LOC100148225 /// zona pellucida glycoprotein 3.1 1.085869 BM156918 wu:fi34b01 1.176317 BM104058 wu:fi04f09 1.172815 BQ284774 wu:fc26g07 1.148132 BI867772 zgc:92204 1.094035 AI476926 si:ch211-102c2.7 1.125082 120

BQ617060 si:dkey-229d11.5 /// zgc:55557 1.09132 BI705586 zgc:173544 1.169503 BM531146 --- 1.165412 BI866891 --- 1.110983 BM571671 --- 1.087704 BQ285167 zgc:73273 1.084681 AI943054 wu:fc83f10 1.048612 BE016868 si:ch211-152c12.2 1.061904 BC050178.1 zgc:56698 1.07814 BM185184 zgc:111913 1.064264 BM890553 si:ch73-191k20.4 1.045062 CB358407 zgc:110717 1.040058 BI673583 zgc:152938 1.038969 AW115821 si:ch73-250d21.1 1.038477 AL719632 si:dkey-170l10.1 1.035526 BM571816 zgc:173548 1.023893 BM185371 zgc:77294 1.024581 BI890296 zgc:92313 1.032298 AI415900 zgc:123008 1.023226 AL924397 zgc:86839 1.018489 AW344046 zgc:175195 1.017147 BM082460 zgc:101748 1.014697 BM071786 zgc:174310 1.013578 BQ261237 wu:fd44f01 1.002966 BE200619 Zgc:101810 3.951754 BQ450176 si:ch211-237a6.7 2.758252 BQ078290 --- 2.708568 BI709723 zgc:165508 2.583506 BI710290 --- 2.477294 AI657578 zgc:92566 2.427623 BI980141 zgc:109983 2.371976 BM777861 zgc:171977 2.286093 AL926536 --- 2.225724 AFFX-Dr-M10961-4 --- 2.131834 BM183236 --- 2.126304 CD596570 si:dkeyp-117h8.4 2.072969 BM530349 --- 2.041755 BM776641 --- 1.95541 BG306133 zgc:171672 1.908963 BM181894 --- 1.899158 BI673509 zgc:110788 1.844783 AI974190 wu:fd36a11 1.841774 121

BQ264147 zgc:171517 1.830931 BM775300 si:ch211-173p18.2 1.791271 AW115581 zgc:66432 1.788409 BM025209 hypothetical protein LOC100332062 1.756133 BM777078 --- 1.760388 BM533916 --- 1.770673 BM533791 hypothetical protein LOC100331622 1.731512 BM957882 --- 1.731265 BM096011 zgc:111868 1.732529 BI878882 --- 1.754938 BM096083 si:dkey-179j5.6 1.745786 BQ131690 --- 1.723644 BQ260855 si:dkey-208k4.2 1.717701 BC049512.1 si:dkey-37o8.1 1.681351 BM071900 zgc:165453 /// zgc:165518 1.710961 BM534028 --- 1.687775 BQ263462 si:dkey-42i9.8 1.728654 BQ480688 si:ch211-125e6.12 /// si:ch211-125e6.13 1.669949 BI979582 hypothetical LOC564755 1.664381 BI702605 Zgc:171551 1.66353 BQ284688 hypothetical protein LOC100332466 1.655485 AW154503 zgc:171446 1.655468 CD596570 si:dkeyp-117h8.4 1.652721 BI843257 zgc:162879 1.649987 BM095897 si:ch73-90k17.1 1.647249 BM777970 si:ch211-145c1.1 1.62711 BQ260849 --- 1.624629 AL727128 Si:ch211-173n20.1 1.643634 AW019018 --- 1.634174 BI980436 --- 1.641448 BI867164 zgc:55413 1.638183 BI983354 hypothetical protein LOC554876 1.620335 BG304084 zgc:171426 1.611911 BG728961 si:dkey-88l16.3 1.611084 BC049317.1 hypothetical LOC795535 1.609941 BM777292 zgc:114123 1.607395 BM776600 zgc:165551 /// zgc:173545 1.604322 BG304084 zgc:171426 1.603297 BI980436 --- 1.602288 BQ616022 si:ch211-51n14.2 1.601446 BQ617023 hypothetical LOC100002310 1.600076 BC049465.1 zgc:171779 1.591399 122

AW343538 zgc:55877 1.585969 BQ284585 --- 1.58433 AW344251 zgc:153595 1.538504 AW171358 zgc:114123 1.53545 AW077844 Zgc:153681 1.53514 BQ078507 zgc:92287 1.534641 BI983550 si:ch211-250e5.16 1.528949 BQ285285 zgc:193933 1.548315 BQ262524 zgc:153499 1.547735 BM777878 wu:fd14c10 1.556955 BG303513 --- 1.543055 BQ262955 im:7158796 1.552062 BM104000 si:ch211-14a17.7 1.56874 BC050507.1 zgc:56231 1.542574 BQ284688 hypothetical protein LOC100332466 1.56325 BG883244 --- 1.52231 BM101522 zgc:173545 1.516431 BM141054 wu:fd14g04 1.516247 BM534465 zgc:174165 1.473416 BM156869 wu:fi42e03 1.471428 BM956864 --- 1.501537 BM095404 zgc:174165 1.475112 BM185180 zgc:171474 1.487682 BI878842 zgc:55888 1.494108 BM861523 zgc:162945 1.484585 BM103888 zgc:195245 1.480176 BE016414 sb:cb98 1.503449 BI880880 wu:fj82b07 1.501427 BM183345 im:7158925 1.499365 BM104389 Wu:fi19d08 1.499293 BM316066 zgc:91918 1.497915 BQ262405 zgc:92591 1.497842 BQ616902 Zgc:171750 1.497587 AW344204 zgc:152652 /// zgc:171750 1.496116 BM957522 --- 1.468163 BM861474 zgc:165551 1.434919 BI881901 si:dkey-89b17.4 1.429623 BC045475.1 zgc:55843 1.428429 AW115955 --- 1.423606 BQ264147 zgc:171517 1.423487 BQ285274 zgc:165539 1.423355 BQ264131 --- 1.421854 123

BM571093 zgc:110183 1.466603 BQ480645 zgc:109753 /// zgc:173837 1.44232 AW154284 zgc:173770 1.466363 AW115973 wu:fd16d01 1.417656 BI979232 zgc:56565 1.460768 BM104070 zgc:63792 1.438149 BM140635 wu:fi38e01 1.449972 BM082789 hypothetical LOC100003370 1.445014 BQ284815 hypothetical LOC100006609 1.443031 BG883314 --- 1.410209

124