GENOMIC APPROACHES TO CHARACTERIZATION OF THE INNATE IMMUNE RESPONSE OF CATFISH TO BACTERIAL INFECTION Except where reference is made to the work of others, the work described in this dissertation is my own or was done in collaboration with my advisory committee. This dissertation does not include proprietary or classified information. _____________________________ Eric James Peatman Certificate of Approval: _____________________________ _____________________________ Jeffery Terhune Zhanjiang Liu, Chair Assistant Professor Alumni Professor Fisheries and Allied Aquacultures Fisheries and Allied Aquacultures _____________________________ _____________________________ Nannan Liu Covadonga Arias Associate Professor Assistant Professor Plant Pathology and Entomology Fisheries and Allied Aquacultures _____________________________ George T. Flowers Interim Dean Graduate School GENOMIC APPROACHES TO CHARACTERIZATION OF THE INNATE IMMUNE RESPONSE OF CATFISH TO BACTERIAL INFECTION Eric James Peatman A Dissertation Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Auburn, Alabama May 10, 2007 iii GENOMIC APPROACHES TO CHARACTERIZATION OF THE INNATE IMMUNE RESPONSE OF CATFISH TO BACTERIAL INFECTION Eric James Peatman Permission is granted to Auburn University to make copies of this dissertation at its discretion, upon request of individuals or institutions and at their expense. The author reserves all publication rights. _________________________ Signature of Author _________________________ Date of Graduation iv VITA Eric James Peatman, son of James Bruce and Susan Elaine Peatman, was born August 21, 1981, in Panorama City, California. He graduated from William S. Hart High School, Newhall, California, in 1999. He attended Auburn University, Auburn, Alabama from September 1999 to December 2002, graduating summa cum laude with a Bachelor of Science degree in Fisheries and Allied Aquacultures. In August 2004, he obtained a Masters of Science degree in Fisheries and Allied Aquacultures. He continued his studies, entering the Cell and Molecular Biosciences/Fisheries and Allied Aquacultures program to pursue a Doctor of Philosophy degree in August 2004. v DISSERTATION ABSTRACT GENOMIC APPROACHES TO CHARACTERIZATION OF THE INNATE IMMUNE RESPONSE OF CATFISH TO BACTERIAL INFECTION Eric James Peatman Doctor of Philosophy, May 10, 2007 (M. Sc., Auburn University 2004) (B. Sc., Auburn University 2002) 177 Typed Pages Directed by Zhanjiang (John) Liu Genetic selection for disease resistance encoded in the genomes of blue catfish and channel catfish continues to hold the greatest potential for long-term solutions to aquaculture-based disease outbreaks. Progress towards this goal requires the development of genomic resources for catfish, including expressed sequence tags (ESTs). In the context of catfish immune research, ESTs provide a foundation for both research on individual immune-related genes and microarray-based transcriptome analysis following infection. Both approaches are needed to advance our knowledge of teleost immunity and move closer to identification of genetic sources of disease resistance. My research, as presented here, encompasses these two complementary approaches to EST research with in-depth studies of the catfish CC chemokine family and development and vi utilization of a high-density oligonucleotide microarray for expression analysis following E. ictaluri infection. Twenty-six CC chemokines from catfish were mapped to BAC clones. Through a combination of hybridization and fluorescent fingerprinting, 18 fingerprinted contigs were assembled from BACs containing catfish CC chemokine genes. The catfish CC chemokine genes were found to be not only highly clustered in the catfish genome, but also extensively duplicated at various levels. The catfish CC chemokine family is the largest characterized CC chemokine family to-date, and it serves as a reference for chemokine studies in teleost fish as well as for studies of gene duplication patterns in catfish. ESTs were also utilized in the development of a 28K in situ oligonucleotide microarray composed of blue catfish (Ictalurus furcatus) and channel catfish (Ictalurus punctatus) transcripts. Initial microarray analyses in channel catfish and blue catfish liver following an infection with E. ictaluri captured a high number of unique, differentially expressed transcripts and indicated the strong upregulation of several pathways involved in the inflammatory immune response. The construction and utilization of high-density oligonucleotide microarrays from channel catfish and blue catfish ESTs represent a strong foundation for future, widespread use of microarrays in catfish research. vii ACKNOWLEDGEMENTS The author would like to thank Dr. John Liu for his invaluable advice and assistance in all stages of this work. He is grateful for time and expertise offered to him by his committee members, Dr. Jeffery Terhune, Dr. Cova Arias, and Dr. Nannan Liu. Furthermore, the author greatly appreciates the technical assistance and support of all those in the Fish Molecular Genetics and Biotechnology Laboratory, especially Dr. Huseyin Kucuktas, Ping Li, Chongbo He, Dr. Puttharat Baoprasertkul, and Dr. Baolong Bao. Above all, he thanks his wife, Allison, for her encouragement and her tireless support of his efforts. viii Style manual or journal used Immunogenetics Computer software used Microsoft Word, Microsoft Excel, Adobe Photoshop 6.0, DNASTAR, PAUP, MEGA 3.0, RMA, SAM, OvergoMaker, Vector NTI, FPC, ClustalW, PriFi, Fast PCR, Blast2GO, Spidey, UniProt, and REST v. 384b ix TABLE OF CONTENTS LIST OF TABLES????????????????????????... x LIST OF FIGURES...?...????..????????????.????? xi I. INTRODUCTION??????????????????????.. 1 II. CATFISH CC CHEMOKINES: GENOMIC CLUSTERING, DUPLICATIONS, AND EXPRESSION AFTER BACTERIAL INFECTION WITH EDWARDSIELLA ICTALURI.????????...... 14 Abstract?????????????????????????. 15 Introduction???????????????????????... 16 Materials and methods???????????????????.. 18 Results?????????????????????????... 27 Discussion????????????????????????. 40 III. MICROARRAY-BASED GENE PROFILING OF THE ACUTE PHASE RESPONSE IN CHANNEL CATFISH (ICTALURUS PUNCTATUS) AFTER INFECTION WITH A GRAM NEGATIVE BACTERIUM............ 48 Abstract?????????????????????????. 49 Introduction???????????????????????... 50 Materials and methods???????????????????.. 52 Results?????????????????????????... 61 Discussion????????????????????????. 68 IV. TRANSCRIPTOMIC PROFILING OF THE LIVERS OF BLUE CATFISH (ICTALURUS FURCATUS) FOLLOWING INFECTION WITH EDWARDSIELLA ICTALURI......................................................................... 78 Abstract?????????????????????????. 79 Introduction???????????????????????... 80 Materials and methods???????????????????.. 83 Results?????????????????????????... 92 Discussion????????????????????????. 102 V. CONCLUSIONS???????????????????????. 113 CUMULATIVE BIBLIOGRAPHY???????????????............ 119 APPENDICES??????????????????????????. 147 x LIST OF TABLES 1. Previously reported fish CC chemokines ????????????...... 8 2. A comparison of in situ and spotted array platforms ????????? 10 3. Microarray studies in aquaculture species and their pathogens?????.. 12 4. Overgo probes for BAC hybridization??????????????... 20 5. Primers for CC chemokine PCR and RT-PCR???????????... 26 6. Mapping of catfish CC chemokine genes to BACs ?????????... 29 7. Contigs and singletons produced by fluorescent fingerprinting?????. 31 8. Up-regulated CC chemokines ?.................................................................... 38 9. Down-regulated CC chemokines????????????????? 38 10. Primers used for qRT-PCR confirmation (5?-3?)??????????? 61 11. Profile of significant, differentially-expressed genes in channel catfish?? 63 12. Channel catfish genes upregulated 5-fold or greater in the liver????? 64 13. Unique, significantly downregulated channel catfish transcripts????... 67 14. Confirmation of microarray results by qRT-PCR??????????... 68 15. Primers used for real-time RT-PCR validation (5?-3?)????????... 92 16. Catfish transcripts upregulated in the blue catfish liver????????. 95 17. Unique, significantly downregulated catfish transcripts in blue catfish liver. 101 18. Validation of microarray results by QRT-PCR???????????... 102 xi LIST OF FIGURES 1. Example of fingerprinted BAC contigs .??????????????... 32 2. Phylogenetic tree of catfish CC chemokines ????????????? 34 3. Expression analysis of the 26 catfish CC chemokines ?????????. 37 4. Human-catfish CC chemokine comparative analysis?????????? 44 5. APP genes upregulated two-fold or greater in channel catfish??????.. 70 6. Iron homeostasis diagram????????????????????.. 74 7. Analysis and Gene Ontology (GO) annotation of 98 unique, significantly upregulated transcripts in blue catfish???????????????... 97 8. Significantly upregulated transcripts in blue catfish assigned to lower level (>6) GO biological process categories ???????????????. 98 1 I. INTRODUCTION Overview Channel catfish (Ictalurus punctatus) is the most important freshwater aquacultured species in the United States. In 2003, 660 million pounds of pond-reared catfish, representing two billion dollars in economic value, were harvested in the U.S. (USDA, 2003). Aquaculture has grown rapidly in the last three decades to become an important alternative food source to collapsing natural fisheries. Catfish producers, like those of more traditional livestock, desire improvement in important production and performance traits such as disease resistance, growth rate, feed conversion efficiency, body conformation and fillet yield. Of those traits, disease resistance remains the most important and the most elusive. Fast-acting bacterial pathogens (predominantly Edwardsiella ictaluri and Flavobacterium columnare) continue to cause widespread losses to the industry. While several vaccines have been recently developed, implementation and rigorous field testing have been slow, and their long term efficacy is still in question. Antibiotic use is limited by regulation, cost, and growing bacterial resistance. Direct selection for disease resistance has proven to be very difficult and is beyond the capacity of most catfish breeding companies (Dunham et al. 1993; Dunham and Liu 2003). 2 While channel catfish has been the species traditionally cultured in the U.S., small numbers of the closely-related (~98% nucleotide similarity) species blue catfish (Ictalurus furcatus) have been raised. Blue catfish exhibit markedly higher resistance to enteric septicemia of catfish (ESC, caused by E. ictaluri), but are inferior to channel catfish in resistance to columnaris disease (caused by F. columnare, Dunham et al. 1993; Wolters et al. 1994; 1996). Under artificial challenge, heavy mortalities occur as early as four days after onset of infection with either disease, before the adaptive immune response can be established (Wolters et al. 1994). Genetic selection for disease resistance encoded in the genomes of blue catfish and channel catfish continues to hold the greatest potential for long-term solutions to aquaculture-based disease outbreaks. Efficient identification of genes contributing to resistance and susceptibility to disease, however, is a multi-stage process necessitating the implemenation of a genome-based research program. Genome research requires the development of a number of resources that facilitate the organization of large amounts of genetic information into units that can be easily captured, mapped, and characterized. These resources include linkage maps, physical maps, bacterial artificial chromosome (BAC) libraries, and expressed sequence tags (ESTs). The integration of these tools produces a genome framework upon which the researcher can pinpoint the genotypic origins of phenotypic trait differences. Expressed sequence tags, as a component of genome research, serve several functions. They are effective tools for gene discovery, rich sources of molecular markers for mapping, and the raw material for the development of microarrays (Liu 2006). In the context of catfish immune research, ESTs provide a foundation for both 3 research on individual immune-related genes and microarray-based transcriptome analysis following infection. Both approaches are needed to advance our knowledge of teleost immunity and move closer to identification of genetic sources of disease resistance. The two approaches are complementary, in that gene-based research makes microarray results more informative while microarray research provides a broader context for the study of immune genes. Historically, studies of the catfish immune system have focused on antibody- based defenses (see Bengten et al. 2006 for a summary). More recently, a consensus has grown that the teleost innate immune response is crucial in determining survival to acute pathogen infections (Ellis 2001). However, few of the innate immune components known from mammalian systems had been identified in fish until recently. Utilizing new catfish EST resources produced in part from the RNA of catfish infected with E. ictaluri (Liu et al. 2007) we have, therefore, isolated and characterized several components of the innate immune response in blue and channel catfish to search for the molecular underpinnings of these differences in disease resistance (He et al. 2004; Xu et al. 2005; Bao et al. 2005; 2006a; Wang et al. 2006a; 2006b; 2006c; Peatman et al. 2005; Peatman et al. 2006; Bao et al. 2006b). One objective of my research focused particularly on CC chemokines, a superfamily of chemotactic cytokines with over 26 members in catfish (He et al. 2004; Peatman et al. 2005; Peatman et al. 2006; Bao et al. 2006a). Expressed sequence tags can also be utilized for microarray-based research. Microarray experiments, when properly conducted, offer an accurate, global assessment of gene expression under a given treatment condition. The high-density capacity of 4 microarrays allows the analysis of expression patterns under infection of a large set of all existing, unique blue catfish and channel catfish ESTs, rather than only the handful of genes previously identified as being involved in the teleost innate immune response. Transcriptome analysis is essential to identify catfish genes involved in immune responses which may not play similar roles in mammalian systems. Additionally, microarray technology allows faster functional screening of large EST datasets than is possible by traditional methods. This allows the researcher to narrow his focus from a large set of genes to a manageable subset of expression candidates for mapping and further functional characterization. My research, as presented here, encompasses the two complementary approaches to EST research described above with in-depth studies of the catfish CC chemokine family and development and utilization of a high-density oligonucleotide microarray for expression analysis following E. ictaluri infection. Separate literature reviews of CC chemokines and microarray technology, below, are followed by a publication-based presentation of my findings. Following the three chapters of results, an overall conclusion serves to summarize the work, suggests implications of the research, and provides directions for future research in the field. Chemokines Inflammation, the attempt to localize cellular injury caused by an infectious agent, is an important part of inducible innate immunity and can be seen within 1-2 days of infection. Chemokines are a family of structurally related chemotactic cytokines that regulate the migration of leukocytes, under both physiological and inflammatory 5 conditions (Neville et al. 1997; Laing and Secombes 2004a; Liu and Peatman 2006). They are structurally-related small peptides, with the majority containing four conserved cysteine residues. Based on the arrangement of these conserved cysteine residues (Ahuja and Murphy 1996; Murphy et al. 2000), chemokines were divided into four subfamilies: CXC (?), CC (?), C, and CX3C. Corresponding to these subfamilies of chemokine proteins, their encoding genes were designated by SCY (for small inducible cytokines) followed by a letter A, B, C, or D (for CC, CXC, C, and CX3C, respectively). CXC and CC are the two major subfamilies. To date, 16 CXC chemokines, 28 CC chemokines, two C chemokines, and one CX3C chemokine have been identified from mammalian species (Bacon et al. 2003). The identification of members of the largest family of chemokines, CC chemokines, in non-mammalian species has been slow, due in part to the rapid divergence rate of CC chemokines. In addition, most CC chemokines are small in size with fewer than 100 amino acids. As a result, molecular cloning based on hybridization or PCR using heterologous probes or primers designed from sequences of different species are not very effective. Similarly, low sequence conservation hinders sequence analysis using bioinformatic approaches at the nucleotide level. Nevertheless, a number of important single gene discoveries have aided our understanding of fish CC chemokines (Table 1; He et al. 2004). The CK1 gene of rainbow trout (Oncorhynchus mykiss) was the first fish CC chemokine described (Dixon et al. 1998). It contains six cysteine residues and has the highest similarity to mammalian CCL20. The second fish CC chemokine, CC chemokine 1, was identified from common carp (Cyprinus carpio) (Fujiki et al. 1999). 6 Carp CC chemokine 1 shares highest similarity to the allergenic/MCP subgroup of mammalian CC chemokines. The third fish CC chemokine, CK2, was also identified from trout (Liu et al. 2002). These early efforts provided the first indication of the difficulty of establishing orthologies with mammalian sequences. All these proteins share only low levels of identity (<40%) with any potential mammalian counterparts. The early scarcity of fish CC chemokine discoveries led to speculation that fish may have few orthologues for the chemokines existing in mammals (Laing and Secombes 2004a; Huising et al. 2003a). With a foundation of small discoveries, the expectation was that only a handful of distinct CC chemokines would be found in a given species. The high numbers of mammalian CC chemokines appeared unique in the phylogenetic spectrum. Therefore, the recent rapid discovery of fish chemokines using genomic approaches has come as a surprise to some. Seven new chemokines from two cichlid fish, Paralabidochromis chilotes and Melanochromis auratus, and catshark Scyliorhinus canicula were reported recently (Kuroda et al. 2003), followed by sequencing and analysis of 14 CC chemokines from catfish (He et al. 2004), and 15 trout CC chemokines (Laing and Secombes 2004b) (Table 1). The in silico identification of these last two large sets of CC chemokines, in particular, challenged notions of the fish immune system and prompted the further investigation of the catfish CC chemokine family. Analysis and further sequencing of new EST resources from catfish identified 12 additional CC chemokine cDNAs (Peatman et al. 2005), representing the largest CC chemokine family identified to-date. This work was followed up with isolation and sequencing of 23 of the 26 catfish CC chemokine genes corresponding to the identified transcripts (Bao et al. 2006a). Tissue expression 7 analysis was also carried out at this time, in an attempt to classify the chemokines by expression patterns. However, both gene structure and tissue expression provided few clues as the orthologies between catfish CC chemokines and their mammalian counterparts. It was known that chemokines were tightly clustered within the genomes of mammals and chicken (Nomiyama et al. 2001; Wang et al. 2005) and that correlations existed between their genomic architecture and the inducibility of their expression. Therefore, analysis of genomic clustering of the CC chemokines in catfish could reveal important information regarding their identities and help to explain the modes of duplication and divergence that resulted in the present repertoire of vertebrate CC chemokines. Chapter II describes the methodology used and results obtained in research on catfish CC chemokine genomic clustering, duplications, and expression after bacterial infection. 8 Table 1 Previously reported fish CC chemokines Species CC chemokine Accession Number References CK1 AF093802 Dixon et al. 1998 CK2 AF418561 Liu et al. 2002 CK3 AJ315149 Sangrador-Vegas et al. unpublished CK4A CA371157 CK4B CA352593 CK5A CA383670 CK5B CA374135 CK6 CA355962 CK7A CA355962 CK7B CA346976 CK8A CB494647 CK8B CA353159 CK9 CA378686 CK10 CA361535 CK11 BX072681 CK12A CA358073 Rainbow trout CK12B CA346383 Laing and Secombes, 2004b Carp CC chemokine 1 AB010469 Fujiki et al. 1999 CC chemokine AU090535 Kono et al. 2003 Japanese flounder Paol-SCYA104 AB117523 Khattiya et al. 2004 Meau-SCYA101 AY178962 Meau-SCYA102 AY178963 Pach-SCYA101 AY178964 Pach-SCYA103 AY178965 Pach-SCYA104 AY178966 Pach-SCYA105 AY178967 Cichlids Pach-SCYA106 AY178968 Kuroda et al. 2003 Trsc-SCYA107 AB174767 MIP3?1 AB174768 Dogfish MIP3?2 AB174766 Inoue et al. 2005 Catshark Scca-SCYA107 AY178970 Kuroda et al. 2003 Icfu-SCYA101 AY555498 Icpu-SCYA102 AY555499 Icfu-SCYA103 AY555500 Icfu-SCYA104 AY555501 Icpu-SCYA105 AY555502 Icfu-SCYA106 AY555503 Icfu-SCYA107 AY555504 Icpu-SCYA108 AY555505 Icfu-SCYA109 AY555506 Icfu-SCYA110 AY555507 Icpu-SCYA111 AY555508 Icpu-SCYA112 AY555509 Icpu-SCYA113 AY555510 Catfish Icfu-SCYA114 AY555511 He et al. 2004 9 Microarrays Researchers have long harnessed the basic molecular principle of nucleic acid hybridization to study the expression patterns of cell transcripts. Transcript studies allow a valuable assessment of the genetic response to environmental changes (i.e. infection, temperature, feeding rates). Incremental progress over the last two decades has been made from radioactively-labeled probing of one gene to tens of genes to nylon-filter-based macro-arrays containing hundreds of genes. In early years, progress in transcript detection techniques largely corresponded to strides in gene sequencing and discovery. However, as gene sequencing grew exponentially in the early 1990s and genomic approaches such as PCR revolutionized molecular biology, a similarly radical leap forward was needed to bring transcript studies into the ??omics? era. This leap was provided by microarrays. While microarrays utilize several recent technological innovations, they are, at their core, simply a high density dot blot. In both cases, DNA is anchored or spotted onto a surface and then probed with labeled molecules. Hybridization and subsequent signal detection depends on the presence of complementary nucleotide sequences between the probes and the spotted sample. Microarrays achieve higher gene feature densities and, therefore, greater power for expression analysis by applying new tools to this old process. High-density spotting robots and photolithography allow each feature to be placed accurately within nanometers of the next feature on a glass slide, clearly an impossible task with the human hand. Furthermore, fluorescence-based probe labeling provides a cleaner and clearer signal than the radiation traditionally used in blotting. Finally, laser scanners 10 facilitate the resolution of such tremendous feature densities and provide accurate fluorescent signal quantification (Peatman and Liu 2007). There are two primary approaches to microarrays, differing in both their construction and their sample labeling. Spotted arrays are constructed by spotting long oligos or cDNAs using a printing robot (Schena et al. 1995), whereas in situ arrays are constructed by synthesizing short or long oligos directly onto the slide by photolithography (Fodor et al. 1991; see Table 2 for a comparison of the two platforms). A decade of refinements of both spotted and in situ microarray technologies have resulted in further capacity increases and widened array applications without altering the fundamentals of either approach. Microarray technology is now widely accessible in biomedical and agricultural genetics research. Table 2 A comparison of several important aspects of in situ and spotted array platforms. *Cost/slide can vary significantly from these figures depending on design, quantities ordered, core facility discounts, etc. In situ arrays Spotted arrays Starting material DNA sequences DNA sequences or cDNA Array fabrication In situ synthesis by photolithography Robotic spotting Features >400,000 <50,000 Spot quality High Variable Oligo length 23-25mer, 60-70mer Usually 70mer Labeling Single dye label- e.g., biotin- streptavidin- phycoerithryn Two dye label-Cy3, Cy5 Cost/slide >$500* <$100* Probe/slide One Two Dye swapping? No Yes Controls PM/MM, +/- Duplicates, +/- Providers Affymetrix, Nimblegen, etc. Species groups, core facilities, biotech Only within the last several years, however, have researchers in aquaculture species generated sufficient expressed sequence tags (EST) to justify using transcriptomic approaches for expression analysis. The field is still in its infancy and distribution of resources remains uneven. Concerted effort by researchers working on salmonid 11 species has resulted in the generation of several arrays that are now available to the general research community. These arrays have been rapidly integrated into salmonid research, as seen in Table 3. The largest salmonid microarray generated to-date contains 16,006 cDNAs with 13,421 coming from Atlantic salmon and 2,576 from rainbow trout (von Schalburg et al. 2005a). Table 3 lists additional microarray studies conducted on aquaculture species or aquaculture-associated pathogens. With the exception of salmonids, other microarray studies have, for the most part, been small- scale, non-collaborative efforts. A forthcoming microarray from oyster should also be widely distributed. To-date, the vast majority of published microarray studies has used PCR-amplified spotted cDNA clones to fabricate the array. However, as microarray research typically takes several years from its inception to reach publication, the recent trends toward spotted oligos and in situ microarrays may not be reflected in the aquaculture literature for several years. A well-designed microarray can be a valuable asset to an aquaculture species group, especially if the cost per slide can be minimized to the extent that researchers can integrate transcriptomic approaches into their already established research. Microarray studies are most successful when they are just one of several approaches used to answer biological questions. For example, salmonid researchers have implemented array technology in their study of reproductive development, toxicology, physiology, and repeat structures (von Schalburg et al. 2006; Tilton et al. 2005; Vornanen et al. 2005; Krasnov et al. 2005a). While species with completed genome sequences have expanded microarray research into such fields as comparative genomic hybridization (CGH), SNP analysis, methylation analysis, 12 proteomics, and metabolomics in recent years, research on aquaculture species has been confined to expression analysis. Table 3 Microarray studies in aquaculture species and their pathogens Species Common name References Cyprinus carpio Common carp Gracey et al. 2004 Ictalurus punctatus Channel catfish Ju et al. 2002 Li and Waldbieser 2006 Ameiurus catus White catfish Kocabas et al. 2004 Paralicthys olivaceous Japanese flounder Kurobe et al. 2005; Byon et al. 2005; 2006 Platichthys flesus European flounder Williams et al. 2003 Salmo salar Atlantic salmon Morrison et al. 2006; Martin et al. 2006 ; Jordal et al. 2005; von Schalburg et al. 2005a; Aubin-Horth et al. 2005; Ewart et al. 2005; Rise et al. 2004a; 2004b Oncorhynchus mykiss Rainbow trout Purcell et al. 2006; MacKenzie et al. 2006; von Schalburg et al. 2006;2005b Tilton et al. 2005; Krasnov et al. 2005a;2005b;2005c Vornanen et al. 2005; Koskinen et al. 2004a;2004b Oncorhynchus keta Chum salmon Moriya et al. 2004 Astatotilapia burtoni African cichlid Renn et al. 2004 WSSV and Penaeus sp. White spot syndrome virus and shrimp species Lan et al. 2006; Marks et al. 2005; Tsai et al. 2004; Dhar et al. 2003; Khadijah et al. 2003 Sparus auratus Gilthead seabream Sarropoulou et al. 2005 Aeromonas salmonicida Furunculosis Nash et al. 2006 Crassostrea sp. Oyster Submitted In catfish, a priority was placed on establishing a high quality EST resource with a large number of unique genes before constructing microarrays. An initial in situ oligonucleotide microarray was constructed utilizing only ESTs from channel catfish and validated with LPS-injected fish (Li and Waldbieser 2006). However, this array did 13 not include ESTs from blue catfish, important for additional, unique genes contained in their sequences as well as for analysis of differential expression between the two species. Additionally, the original array design did not contain several hundred immune-related genes recently generated in our lab from both blue and channel catfish. Construction of a more comprehensive catfish oligonucleotide microarray and its validation for capturing the expression profiles of channel catfish and blue catfish after E. ictaluri infection are described in Chapters III and IV of the dissertation. 14 II. CATFISH CC CHEMOKINES: GENOMIC CLUSTERING, DUPLICATIONS, AND EXPRESSION AFTER BACTERIAL INFECTION WITH EDWARDSIELLA ICTALURI 15 Abstract Chemokines are a family of structurally related chemotactic cytokines that regulate the migration of leukocytes under both physiological and inflammatory conditions. CC chemokines represent the largest subfamily of chemokines with 28 genes in mammals. Sequence conservation of chemokines between teleost fish and higher vertebrates is low and duplication and divergence may have occurred at a significantly faster rate than in other genes. One feature of CC chemokine genes known to be conserved is genomic clustering. CC chemokines are highly clustered within the genomes of human, mouse, and chicken. To exploit knowledge from comparative genome analysis between catfish and higher vertebrates, here we mapped to BAC clones 26 previously identified catfish (Ictalurus sp.) chemokine cDNAs. Through a combination of hybridization and fluorescent fingerprinting, 18 fingerprinted contigs were assembled from BACs containing catfish CC chemokine genes. The catfish CC chemokine genes were found to be not only highly clustered in the catfish genome, but also extensively duplicated at various levels. Comparisons of the syntenic relationships of CC chemokines may help to explain the modes of duplication and divergence that resulted in the present repertoire of vertebrate CC chemokines. Here we have also analyzed the expression of the transcripts of the 26 catfish CC chemokines in head kidney and spleen in response to bacterial infection of Edwardsiella ictaluri, an economically devastating catfish pathogen. Such information should pinpoint research efforts on the CC chemokines most likely involved in inflammatory responses. 16 Introduction Chemokines are a superfamily of chemotactic cytokines in mammals and a crucial part of the innate immune response of higher vertebrates. They play roles in immunosurveillance under homeostasis as well as stimulating the recruitment, activation, and adhesion of cells to sites of infection or injury (Neville et al.1997; Moser and Loetscher 2001; Laing and Secombes 2004a). Recent research has found that some chemokine genes have important roles during normal development and growth (e.g., David et al. 2002; Molyneaux et al. 2003; Baoprasertkul et al. 2005). Chemokines are structurally related small peptides, with the majority containing four conserved cysteine residues. Based on the arrangement of these conserved cysteine residues (Murphy et al. 2000), chemokines were divided into four subfamilies:CXC (?), CC (?), C, and CX3C. CC chemokines constitute the largest subfamily of chemokines with 28 CC chemokines identified from mammalian species (Bacon et al. 2003). The largest number of CC chemokines found in a single species is 24 from humans, missing orthologues to the murine CCL6, CCL9/CCL10, and CCL12. The majority of human, murine, and chicken CC chemokine genes are organized in gene clusters within their genomes. The largest clusters are found on human chromosome 17, mouse chromosome 11, and chicken chromosome 19 (Nomiyama et al. 2001; Wang et al. 2005). There are correlations between genomic architecture and the inducibility of their expression, with inflammatory CC chemokines constituting the large clusters, and a few homeostatic CC chemokines distributed among several chromosomes. Additionally, orthologies across species are relatively high between the 17 non-clustered CC chemokines, but low when comparing the clustered CC chemokines of several species (Wang et al. 2005; Peatman et al. 2005). Establishing orthology between fish and mammalian CC chemokines has been problematic. Sequence conservation of chemokines is low and duplication and divergence may have occurred at a significantly faster rate than in other genes. Concrete orthologues cannot be identified for the majority of CC chemokine transcripts found from catfish or trout based on either sequence identities or phylogenetics (Laing and Secombes 2004b; He et al. 2004; Peatman et al. 2005). Even gene organization (exon/intron) has been found to differ between evident orthologous chemokines in human, chicken and catfish (Wang et al. 2005; Bao et al. 2006a). Genomic location of CC chemokines is important, therefore, in attempting to trace the origins of CC chemokines in teleosts and higher vertebrates. Comparisons of syntenic relationships of CC chemokines may help to explain the modes of duplication and divergence that resulted in the present repertoire of vertebrate CC chemokines. Progress on identifying immune molecules in teleost fish has not traditionally come from the genome-enabled model species (Danio rerio, Takifugu rubripes). Rather it has been generated more slowly in several aquaculture species (catfish, salmonids, carps, flounders) where disease problems are a serious economic issue. The lack of even a draft genomic sequence in catfish makes cross-species comparisons of genomic neighborhoods much more difficult. We have used, therefore, a novel approach of overgo and cDNA hybridizations and bacterial artificial chromosome (BAC) fingerprinting and clustering to determine the architecture of the catfish CC chemokines without a draft genome sequence. Here we report the genomic architecture of 18 previously sequenced catfish CC chemokine genes as well as their expression patterns after bacterial infection. Comparisons of CC chemokine arrangements and duplication between catfish, chickens, and humans reveal rapid multiplication of some chemokine genes. . Materials and methods BAC library screening and BAC isolation High-density filters of the channel catfish BAC library were purchased from Children?s Hospital of the Oakland Research Institute (CHORI, Oakland, CA), and screened using overgo hybridization probes (Cai et al.1998; Bao et al. 2005; Xu et al. 2005). Each set of filters contained a 10X genome coverage of the channel catfish BAC clones from BAC library CHORI 212 (http://bacpac.chori.org/library.php?id=103). The catfish BAC library was screened using a two-step procedure. First, pooled overgo probes of catfish CC chemokines were used to identify BAC clones with inserts likely containing chemokine genes. These positive BACs were then manually re-arrayed onto nylon filters and screened individually using labeled cDNA probes. Overgo primers were designed based on the coding sequence of the 26 chemokine cDNAs (Table 1). The overgo hybridization method was adapted from a web protocol (http://www.tree.caltech.edu/) with modifications (Bao et al. 2005; Xu et al. 2005). Briefly, overgos were selected following a BLAST search against GenBank to screen out repeated sequences and then purchased from Sigma Genosys (Woodlands, 19 Texas). Twenty-six overgos were pooled together, initially. Overgos were labeled with 32P-dATP and 32P-dCTP (Amersham, Piscataway, NJ) in overgo labeling buffer (Ross et al. 1999) at room temperature for 1 h. After removal of unincorporated nucleotides using a Sephadex G50 spin column, probes were denatured at 95?C for 10 min and added to the hybridization tubes. Hybridization was performed at 54?C for 18 hr in hybridization solution (50 ml of 1% BSA, 1 mM EDTA at pH 8.0, 7% SDS, 0.5 mM sodium phosphate, pH 7.2). Filters were washed and exposed to X-ray film at -80?C for two days. 20 Table 1 Overgo probes for BAC hybridization Gene Upper primer (5?-3?) Lower primer (5?-3?) SCYA101 GCGTTGCTATTTCGCTGGCAAATC CACACAGTCTCTCTCTGATTTGCC SCYA102 GTGCTGCTTGCACTTTTTGGATGC CAGGTGCAGTAGTGATGCATCCAA SCYA103 GTCCTCTGTTTTCTCCTGCTTCTG TTGGGTACATGCATGCCAGAAGCA SCYA104 CCTGTCTTCAGTCCTTCACAATGG CCGTTTGCATTCTGTGCCATTGTG SCYA105 ACAAACGTCGTGTGTGTGCAAACC ACCCACTCATCCTTGGGGTTTGCA SCYA106 AACAGCGGCATCTGATATTGGCAC CACACGTCCTGTTTCTGTGCCAAT SCYA107 AGGCTTCCACCAAAGAAATCACCG AATCCTGTGATGGGCACGGTGATT SCYA108 GTAAACACCAGTGTGGAAACGCTG AGAGGAAAGACCTGAGCAGCGTTT SCYA109 CAACCGTAATGGCAAGAGCAAAGG GGTCTTTCACTGAGCTCCTTTGCT SCYA110 GAAACAGCACTGTGTGGATCCAAC GTTGACCCAAACAGCTGTTGGATC SCYA111 GCTCATGTTGTTCCTCCTACTTCC GGGGGAATTTTCCCATGGAAGTAG SCYA112 CCTCCACAAATGTGTGAACACCTC GACATAGCCACGTGAAGAGGTGTT SCYA113 CAAAGCCTGGTGGAATCCTACTAC TCTCTGGAGTCTGAACGTAGTAGG SCYA114 CCATCTGGACTGTAACAGATGCAG CAGGGGCTCACTTTTTCTGCATCT SCYA115 TTCACTGAAGGGATGCGTTTCACG AGACGTTTTTGGTGCCCGTGAAAC SCYA116 CATGGCCTTTTTGGACCACAGAGG ATTCCCTGGTGGCATGCCTCTGTT SCYA117 TCTACTCAGACGCTCAGCCTTTTG TCAGGATGTGCAGGAGCAAAAGGC SCYA118 TCCTAAGCAAGTCCGTGTGACAAG CCAGTAGCTCACAATGCTTGTCAC SCYA119 CTGCTCTATCCACTCTTCTTCTGC AGAGGCAGAACACCATGCAGAAGA SCYA120 cDNA probe cDNA probe SCYA121 AGATGAATCGTGTGGTTTTGGTCC ATCAGGAAGAAGCCCAGGACCAAA SCYA122 CAGCAAGGCTTCATTGTTACGACG GGTTAGGGAACTTAGGCGTCGTAA SCYA123 AACGTAGTGTGTGTGCAAACCCCA TGCACCCACTTATCCTTGGGGTTT SCYA124 CTCGACCTAACCTCAAACGTGTGT TGGCCAGAGGATTTAAACACACGT SCYA125 TTGACTCAGAGAGACCTCACCTTG ACTGAATCGCATGGCTCAAGGTGA SCYA126 CTCGTGCTGCTTATTCGTGGAAAG TTGGTGCGCACAATCTCTTTCCAC Positive clones were identified according to the clone distribution instructions from CHORI and picked from the channel catfish BAC library. Approximately 200 positive BAC clones were identified through the hybridization of overgos for the 26 catfish CC chemokines. These 200 BAC clones were picked, cultured in 2X YT media overnight, and manually arrayed on Immobilon nylon membranes (Millipore, Bedford, MA). Briefly, 4 ?L of each overnight culture well was spotted in duplicate on the 21 membrane and allowed to dry. The membranes were placed in a dish containing 3M Whatman paper saturated with 10% SDS for 3 min, transferred to a second tray containing 0.5 N NaOH, 1.5 M NaCl for eight minutes without agitation, before being transferred to another dish containing 1.5 M NaCl, 0.5 M Tris-HCl, pH 7.2, 1 mM EDTA and immersed for 3 minutes with agitation. This second wash was repeated in a new dish with fresh solution. The membrane was air-dried at room temperature and DNA was fixed to the membrane by UV cross linking using a UV Stratalinker 2400 (Stratagene, La Jolla, CA) with the auto crosslink function. Probes based on catfish CC chemokine cDNAs were prepared from previously cloned plasmids. Probes were prepared using the random primer labeling method (Sambrook et al. 1989) with a labeling kit from Roche Diagnostics (Indianapolis, Indiana). The membranes were pre-hybridized in 50% formamide, 5X SSC, 0.1% SDS (w/v), 5X Denhardt?s and 100 ?g/ml sonicated and denatured Atlantic salmon sperm DNA for 2 h. Hybridization was conducted for at least 16 h at 42?C in 50% formamide, 5X SSC, 0.1% SDS (w/v), and 100 ?g/ml sonicated and denatured Atlantic salmon sperm DNA with probes added. The nylon membranes were washed first in 500 ml of 2X SSC for 10 min, followed by three washes in 0.2X SSC with SDS at 0.2% (w/v) at 45?C for 15 min each. The membranes were then wrapped in Saran wrap and exposed to Kodak BioMax MS film for autoradiography. Positive BAC clones were identified for each catfish CC chemokine and BAC DNA was isolated with the Qiagen R.E.A.L Prep 96 BAC DNA isolation kit (Qiagen, Valencia, CA) following the manufacturer?s protocol. 22 Fluorescent fingerprinting and BAC contig construction Positive BAC clones were fingerprinted and assembled, where possible, using the protocol described by Luo et al. (2003) with modifications. Briefly, BAC DNA was simultaneously digested with four 6-base pair (bp) recognizing restriction endonucleases (EcoRI, BamHI, XbaI, XhoI) generating 3' recessed ends and one 4-bp recognizing restriction endonuclease (HaeIII) generating a blunt end. Each of the four recessed 3' ends of restriction fragments was filled in using DNA polymerase with different fluorescent dyes using the SNaPshot kit (Applied Biosystems). Such labeling reactions allowed labeling of four sets of restriction fragments, providing a high level of confidence for contig assembly. Restriction fragments ranging from 50 bp to 500 bp were sized by an ABI PRISM 3130 XL automated sequencer producing *.fsa files. Genoprofiler (You et al. 2003) converts *.fsa files to *.sizes files which can be utilized by FPC (Soderlund, et al. 1997; 2000) for contig assembly. A 0.2 to 0.4 bp tolerance range was used in FPC, keeping the probability of coincidence (Sulston score, Sulston et al. 1988; 1989) low to avoid false assembly. A p-value of 10 -10 was used for contig assembly. The results of BAC fingerprinting from FPC are image files of each BAC contig. Data from contig assembly were used with previous hybridization data to obtain the patterns of CC chemokine genes. Fingerprinting does not allow the user to discern the order of genes within each contig, therefore the order of genes was arbitrarily assigned. Each fingerprint contig or singleton should represent a different genomic region based on its restriction pattern. In order to assess the reliability of the BAC contigs, we conducted two types of analyses. First, overgo hybridization and cDNA 23 hybridization data was carefully analyzed to match the contigs assembled from fingerprinting; second, cut off p-values were varied, using a range of p-values from 10 - 10 , 10 -8 , 10 -6 , and 10 -2 , in order to see how that would affect the contig assembly. Only by increasing the p-value for assembly to 10 -2 , an unacceptably low standard for assembly, are any of the contigs or singletons merged together. In cases where the combination of contig assembly and hybridization suggested the presence of multiple gene copies, the letters of A-F were assigned to differentiate between distinct genomic copies of the catfish CC chemokines. Fish rearing, bacterial challenge and sampling Channel catfish were reared at the hatchery of the Auburn University Fish Genetics Research Unit. Challenge experiments were conducted as previously described (Dunham et al. 1993) with modifications. Briefly, catfish were challenged in a rectangular tank by immersion exposure for 2 h with freshly prepared culture of ESC bacteria, E. ictaluri. One single colony of E. ictaluri was isolated from a natural outbreak in Alabama (outbreak number ALG-02-414) and inoculated into brain heart infusion (BHI) medium and incubated in a shaker incubator at 28?C overnight. The bacterial concentration was determined using colony forming unit (CFU) per ml by plating 10 ?l of 10-fold serial dilutions onto BHI agar plates. At the time of challenge, the bacterial culture was added to the tank to a concentration of 3 x10 7 CFU/ml. During challenge, an oxygen tank was used to ensure a dissolved oxygen concentration above 5 mg/L. After 2 h of immersion exposure, 15 fish were randomly taken and placed into a 24 rectangular trough containing pond water with constant water flow through. Replicates of troughs were used to provide one trough for each sampling time point. For the control fish, 15 fish were incubated in a separate rectangular tank with the same fish density as the challenge tanks. The only difference was that ESC bacteria were not added. After 2 h, these control fish were incubated in a separate trough at the same density as the challenged fish. After challenge, head kidney and spleen samples were collected at 4 h, 24 h, and 3 days. At each time point, 10 fish were sacrificed for sampling. The fish were euthanized with tricaine methanesulfonate (MS 222) at 300 mg/L before tissues were collected. Tissues were kept in a ?80 ?C ultra-low freezer until preparation of RNA. Samples of each tissue from 10 fish were pooled. The pooled tissues were rapidly frozen with liquid nitrogen. In order to obtain samples representing the average of the 10 fish, the pooled tissue samples were ground with a mortar/pestle to fine powders and were thoroughly mixed. A fraction of the mixed tissue samples was used for RNA isolation. RNA isolation and RT-PCR RNA was isolated following the guanidium thiocyanate method (Chomczynski and Sacchi 1987) using the Trizol reagents kit from Invitrogen (Carlsbad, CA) following manufacturer?s instructions. Extracted RNA was stored in a -70?C freezer until used as template for reverse transcriptase PCR (RT-PCR). The RT-PCR reaction was conducted using a two-step approach with M-MuLV reverse transcriptase (New 25 England Biolabs, Ipswich, MA) with the primers listed in Table 2. Detailed procedures followed the manufacturer?s instructions (Wang et al. 2006a). Briefly, 1 ?g of total RNA was used in each first-strand reaction. PCR reactions were carried out as described above with two modifications. The primers of ?-actin (Table 2) were added to serve as an internal control. Challenge tissue RNA samples were amplified for 32 cycles. RT- PCR reactions were conducted for one gene at a time, and the images of agarose gels were compiled together into a single figure and, therefore, expression levels can only be analyzed separately for each gene. 26 Table 2 Primers for PCR and RT-PCR Gene Upper primer (5? to 3?) Lower primer (5? to 3?) SCYA101 TGTGTGCTGTAAGGAGGTTTCC TTCTGTGGCACGATTGTGGTCG SCYA102 CTGCACCTGGTAACTACCGTCG GTTTCTTTGGGATCCAGCGTGC SCYA103 TGCATGTACCCAAGTTTGGCAC TTCATCAGTTCTTGCACCCAGG SCYA104 TCTCTCCTGCTGGTTCTGCTGG TAATTTGTCGCCGGAGTCTTGG SCYA105 AGATACCAGACACAACCGATCC GCTGATCAGTTGTTTGCTTGCT SCYA106 GTCTCTTGGAGAGCAAGCACTG CATCAGCTCTCTGACCCAGTCG SCYA107 CAGCCAGAAGATCCGAAGCCTC TGGAAGTGGAGCCGGTTGTCTG SCYA108 TGCAAACGAACCAGAACCATGC TCGGTTGAGGTTGGATCACGTC SCYA109 ACCAGCGACACTTTCGTTCCAC GCTCTTGCCATTACGGTTGTCC SCYA110 ATGAGGAACCTGACGGCTCTGC AGCTGTTGGATCCACACAGTGC SCYA111 AGACGCTACCTATCAAGCGCTC CAGTTGCGTGAAAGCTGCAGTG SCYA112 TCGCTGGATGCTGGCTTCTGTG TGACCTTGTTATGAGGTTGCTG SCYA113 TCCACAAAGCCTGGTGGAATCC AGTTGTTCTTTGTCGCACGAGG SCYA114 ATGAGGAGCCTGGCTGCCATAG GATGCAGGGAGGCAGTGGTTGG SCYA115 TGGTGCTGCTGAGTGCAGTCAC ACCCAGGCGTCAGTGGGTTTGG SCYA116 ACTCCACTTCTCAGCTGCCCTG CAAGGTGAGGACGGGTCCAAGC SCYA117 TCCTGCACATCCTGAGGATTGC TCTCAGTAGCCGGGACTTCACG SCYA118 CACCACTGCAGTGTTCTCCAGC TCTCCTTTGGAGCATCTGGTGC SCYA119 TGGTGTTCTGCCTCTGTGCCAG TGTTCTGTGGAATGGTCACCTC SCYA120 CTGCTGGTTCTGCTGGGTCTCG TGCGGTCTGCACACGCCTTACG SCYA121 TCTGCATCCATCTGCTGAGAAC GTGCGTACGTGTTGCGTCTCAG SCYA122 TGAGCTTCACACACCTGCTGAG AGCCTTGCTGTTCACACTGTGC SCYA123 TCCTTCACAGCGGCTCAGAGTG TGGGGTTTGCACACACACTACG SCYA124 GCCTTCAGTCCTTCACAACAGC TGACATCAGGGTCTGCACACAC SCYA125 CTTCAGCCTGGCACAAGGTTCG CTAGCGCAAATGAGCCGACCTC SCYA126 TTCTACAGCGCCACTGAGTCGA AGTTAGGTCTCAGAAACGTTGC Actin AGAGAGAAATTGTCCGTGACATC CTCCGATCCAGACAGAGTATTTG Phylogenetic analyses and comparative genomics Phylogenetic trees were drawn from ClustalW (Thompson et al. 1994) generated multiple sequence alignments of amino acid sequences using the neighbor-joining method (Saitou and Nei 1987) within the Molecular Evolutionary Genetics Analysis 27 [MEGA (3.0)] package (Kumar et al. 2004). Data were analyzed using Poisson correction and gaps were removed by complete deletion. The topological stability of the neighbor joining trees was evaluated by 1000 bootstrapping replications. Comparisons of genomic organization and architecture of the CC chemokines among catfish, chicken, and humans were made with the aid of BLAST searches, phylogenetic sequence comparisons, and searches against the Ensembl genome browsers for human, and chicken. Results Mapping catfish CC chemokine genes to BACs We previously identified a total of 26 CC chemokines in catfish through the analysis of ESTs (He et al. 2004; Peatman et al. 2005) named SCYA101-SCYA126. In order to map these chemokine genes to BACs, overgo probes were designed based on the cDNA sequences and used to screen high-density BAC filters. Initially, pooled overgo probes for the 26 CC chemokines were used in the first screening that resulted in the identification of a pool of potential BACs positive to CC chemokine probes. The positive pool of BAC clones was picked from the arrayed BAC library and re-arrayed to nylon membranes for confirmation using individual cDNA probes. cDNA probes for each CC chemokine were used to screen the positive BACs. As shown in Table 3, use of 26 cDNA probes in separate hybridizations resulted in 232 cumulative positive BAC hits for the catfish chemokine genes. The 28 hybridization pattern, however, indicated that many of the chemokine probes had positive results on the same BAC clones. Considering these overlaps, only 92 distinct BAC clones were represented in the positive set. This pattern of distinct cDNA probes hybridizing to the same BAC clones strongly suggested the presence of clusters of catfish CC chemokine genes in the genome context. 29 Table 3 Mapping of catfish CC chemokine genes to BACs through cDNA hybridization. A total of 92 unique BACs are represented in a cumulative total of 232 positive clones Genes Positive BAC clones SCYA101 105_D15, 006_I13, 028_G4, 025_A20, 067_J3, 026_K13, 050_J5, 051_D5, 115_I22, 167_G22, 090_M4, 007_C11, 003_N13 SCYA102 104_A3,088_M10, 164_N20, 044_A24, 069_N2,108_I9, 126_K10, 117_D24, 149_M19, 011_N1, 125_O17 SCYA103 039_K13 SCYA104 152_F2, 122_C9, 147_M12, 062_A9, 141_G12, 015_J14, 069_A16, 066_B19, 082_A13, 091_H12, 159_B7 SCYA105 50_J5 SCYA106 029_L5, 080_O10, 143_I8, 097_I13, 161_K1, 103_L4, 189_G23, 129_N10, 098_H1 SCYA107 006_I13, 067_J3, 050_J5, 090_M4, 007_C11, 003_N13, 184_M14, 071_C6 SCYA108 042_A8, 052_C23, 149_I8 SCYA109 029_L5, 080_O10, 143_I8, 097_I13, 161_K1, 103_L4, 189_G23, 129_N10, 098_H1 SCYA110 030_D8, 099_C4, 149_D11, 003_P23, 041_O13, 142_A8, 045_L17, 105_B8, 144_H14, 119_E18, 179_H22 SCYA111 030_D8, 099_C4, 149_D11, 003_P23, 041_O13, 142_A8, 045_L17, 105_B8, 144_H14, 121_I22, 129_P14, 119_E18 SCYA112 105_D15, 006_I13, 067_J3, 026_K13, 167_G22, 090_M4, 007_C11 SCYA113 037_D15, 052_F9 SCYA114 090_M4, 164_N20, 044_A24, 069_N2, 126_K10, 117_D24, 009_P8 SCYA115 105_D15, 099_C4, 179_H22, 006_I13, 028_G4, 025_A20, 067_J3, 026_K13, 050_J5, 051_D5, 115_I22, 167_G22, 090_M4, 007_C11, 003_N13 SCYA116 006_I13, 067_J3, 050_J5, 003_N13, 184_M14 SCYA117 105_D15, 030_D8, 099_C4, 149_D11, 003_P23, 041_O13, 045_L17, 105_B8, 144_H14, 107_K11, 102_J7, 153_I24, 154_F9, 163_F4, 072_K10, 125_D4, 073_P7, 121_I22, 065_H1, 061_G20, 129_P14, 059_H18, 119_E18, 062_I3, 167_E6, 045_09 SCYA118 067_J3, 090_M4, 003_N13, 184_M14 SCYA119 104_A3, 088_M10, 164_N20, 044_A24, 069_N2, 108_I9, 126_K10, 117_D24, 149_M19, 009_P8, 049_P12 SCYA120 152_F2, 122_C9 SCYA121 105_D15, 030_D8, 099_C4, 149_D11, 003_P23, 041_O13, 142_A8, 045_L17, 105_B8, 144_H14, 107_K11, 102_J7, 153_I24, 154_F9, 163_F4, 072_K10, 125_D4, 073_P7, 121_I22, 065_H1, 061_G20, 129_P14, 059_H18 SCYA122 030_D8, 099_C4, 149_D11, 003_P23, 041_O13, 142_A8, 045_L17, 105_B8, 144_H14, 102_J7, 129_P14, 119_E18, 062_I3 SCYA123 042_A8, 152_F2, 122_C9, 029_N24, 147_M12, 052_C23, 149_I8, 143_P9, 139_D5 SCYA124 152_F2 SCYA125 031_N17, 042_B8, 188_D18, 192_L24, 163_G22, 158_L9 SCYA126 099_C4, 142_A8, 045_L17, 105_B8, 144_H14, 102_J7, 121_I22, 129_P14, 119_E18, 062_I3 047_K12 30 Genomic clustering and duplication of catfish CC chemokine genes Given the likelihood of genomic clustering of CC chemokine genes within the channel catfish genome, we utilized our pool of positive BAC clones for analysis using fluorescent fingerprinting to determine genomic copy numbers and cluster membership. The fingerprinted contigs and singletons (those BAC clones that did not assemble with others) are listed in Table 4. A total of 18 contigs were constructed after BAC fingerprinting, and an example of the contigs is shown in Fig. 1. Eight BAC clones for which we had hybridization data were not assembled into contigs and are listed as singletons at the bottom of the table. A pattern of gene duplication and clustering was immediately obvious from the merged data from fingerprinting and hybridization. Only five CC chemokines, SCYA103, SCYA105, SCYA108, SCYA113, and SCYA124, were present in a single copy. Five CC chemokines have at least two copies in the catfish genome?SCYA110, SCYA111, SCYA116, SCYA118, and SCYA125. Three genomic copies were found for eight of the catfish CC chemokines including SCYA102, SCYA104, SCYA106, SCYA109, SCYA119, SCYA120, SCYA122, and SCYA126. Four copies were found for five of the catfish CC chemokines including SCYA101, SCYA112, SCYA114, SCYA115, and SCYA126. Two CC chemokines, SCYA121 and SCYA123, had five genomic copies. Lastly, six distinct genomic copies were found for SCYA117 (Table 4). 31 Table 4 Contigs and singletons produced by fluorescent fingerprinting of catfish BAC clones. BAC contigs were constructed using fluorescent fingerprinting with a cut off p- value of 10 -10 . BAC clones containing CC chemokine genes were initially selected for fingerprinting by pooled overgo probes, and, in most cases, also confirmed by using individual cDNA probes. Assignment of letters A-F to chemokine genes was arbitrary to differentiate between distinct copies of chemokines in different genomic regions. ?*? indicates two distinct copies as determined by direct sequencing Contigs Chemokines together based on fingerprinting and/or cDNA hybridization BAC clones in each contig/singleton 1 125A 163_G22, 055_F15, 135_J15, 045_I14, 003_B23, 015_L09, 013_M08, 190_O24, 186_O14, 103_I22, 163_G22, 136_B14, 080_J22, 139_A20, 082_G20, 176_O19 2 103 039_K13, 183_K11, 151_B13, 064_C5, 068_K21, 127_B21, 182_I19, I82_K19, 120_O3, 071_O4, 110_J16, 176_K21 3 113 037_D15, 052_F9, 146_N23, 093_C8, 025_C1 4 102A-114C-119A 104_A3, 088_M10, 009_P8 5 102B-114A,B*-119B-107A 164_N20, 044_A24, 069_N2,108_I9, 126_K10, 117_D24, 149_M19, 011_N1, 071_C6 6 107B-101A-112A-115A-116A-118A 006_I13, 028_G4, 067_J3, 003_N13, 184_M14 7 107C-101B-112B-115B-116B-118B-114D- 105 050_J5, 090_M4 8 101C-112C-115C 025_A20, 026_K13, 051_D5, 115_I22, 167_G22, 007_C11 9 101D-112D-115D-117A-121A 105_D15, 154_F9, 073_P7, 065_H1 10 117B-121B 153_I24, 072_K10, 125_D4, 061_G20, 059_H18, 167_E6, 045_09 11 117C-121C-110A-111A-122A-126A 030_D8, 003_P23, 041_O13, 142_A8, 121_I22, 129_P14, 102_J7, 163_F4 12 117D-121D-110B-111B-122B-126B-115E 099_C4, 149_D11, 045_L17, 105_B8, 144_H14, 119_E18, 179_H22 13 126C 047_K12, 108_F9, 103_G10 14 106A-109A 097_I13, 161_K1, 129_N10 15 106B-109B 029_L5, 080_O10, 143_I8, 103_L4 16 106C-109C 189_G23, 182_C23, 050_E23 17 108-123A 042_A8, 052_C23, 149_I8, 029_N24, 143_P9, 139_C13 18 104A-123B 147_M12, 062_A9, 141_G12, 015_J14, 069_A16, 066_B19, 082_A13, 091_H12, 159_B7 Singletons 1 104B-120A,B*-123C 122_C9 2 104C-120C-123D-124 152_F2 3 123E 139_D5 4 117E-121E 107_K11 5 117F-122C-126D 062_I3 6 119C 049_P12 7 125B 158_L9 8 102C 125_O17 Fig. 1 Example of fingerprinted BAC contigs--contig 12 containing 117D-121D-110B- 111B-122B-126B-115E. Identifiers on each line are BAC clone names. Note that the contigs do not allow the determination of CC chemokine gene arrangement order. Eighteen of the fingerprinted BAC contigs or singletons contained more than one catfish CC chemokine gene, illustrating extensive and repetitive genomic clustering. Clusters of genes ranged in size from containing eight genes (Contig 7) to containing only two (numerous contigs). Membership within the different contigs was often highly similar or identical, suggesting that segmental gene duplication was likely responsible for the genesis of many of these clusters. For example, there are three contigs containing SCYA106 and SCYA109, each in a different contig. Likewise, contigs 11 and 12 share identical members with the exception of SCYA115. Several of the catfish CC chemokines, such as SCYA117 and SCYA121, are present in both the smaller contigs (i.e. Contig 10) and the larger clusters (i.e. Contigs 9,11), indicating possible genomic rearrangements. 32 33 Genomic architecture and phylogenetic analysis Genomic sequencing allowed us to previously obtain the encoding genes for 23 of the 26 catfish CC chemokine cDNAs (Bao et al. 2006a). When the deduced amino acid sequences of the coding regions of the 23 genes and the three cDNAs were subjected to phylogenetic analysis, and sequence similarity compared with genomic location, several interesting patterns emerged (Fig. 2). In several instances there was a high correlation between sequence similarity and genomic architecture. For instance, SCYA123, SCYA108, SCYA124, SCYA120A, and SCYA120B are located together in the genome, and they also share a branch of the phylogenetic tree as analyzed by sequence similarities. A very strong clade containing SCYA111, SCYA121, SCYA117, and SCYA122 is present on the tree, and the four CC chemokines also are found together in Contigs 11-12. Two additional and similar correlations between tree position and genomic architecture can be seen for SCYA106-SCYA109 and for SCYA116- SCYA118-SCYA102-SCYA101-SCYA107-SCYA114A-SCYA114B (Fig. 2). Such correlations provide additional support for the theory that tandem and/or segmental gene duplications were involved in the evolution of the catfish CC chemokine genes. SCYA105 SCYA123 SCYA108 SCYA124 SCYA120A SCYA120B SCYA111 SCYA121 SCYA117 SCYA122 34 SCYA104 SCYA115 SCYA103 SCYA125 SCYA126 SCYA106 SCYA109 SCYA113 SCYA112 SCYA119 SCYA110 SCYA116 SCYA118 SCYA102 SCYA101 SCYA107 SCYA114A SCYA114B99 97 75 87 99 87 63 65 65 57 76 72 55 87 60 50 35 20 32 41 35 27 12 8 18 0.2 Fig. 2 The phylogenetic tree was drawn from ClustalW generated multiple sequence alignment of amino acid sequences using the neighbor-joining method within the MEGA 35 (3.0) package. Data were analyzed using Poisson correction and gaps were removed by complete deletion. The topological stability of the neighbor joining trees was evaluated by 1000 bootstrapping replications, and the bootstrapping values are indicated by numbers at the nodes. Circles indicate chemokines sharing both sequence similarity and genomic architecture as described in the text. GenBank accession numbers of the sequences used are: DQ173276 (SCYA101), DQ173277 (SCYA102), DQ173278(SCYA103), DQ173279(SCYA104), AY555502(SCYA105), DQ173280(SCYA106), DQ173281(SCYA107), DQ173282(SCYA108), DQ173283(SCYA109), DQ173284(SCYA110), DQ173285(SCYA111), DQ173286(SCYA112), DQ173287(SCYA113), DQ173288(SCYA114A), DQ173289(SCYA115), DQ173290(SCYA116), DQ173291(SCYA117), DQ173292(SCYA118), DQ173293(SCYA119), DQ173294(SCYA120A), DQ173295(SCYA121), DQ173296(SCYA122), CB937548(SCYA123), DQ173297(SCYA124), BM028237(SCYA125), and DQ173298(SCYA126). Expression analysis of catfish CC chemokine genes We previously reported the expression of 12 catfish CC chemokine (SCYA115- SCYA126) after challenge with Edwardsiella ictaluri (Peatman et al. 2005). To determine expression patterns of all known catfish CC chemokines, here we conducted expression analysis of the remaining 14 known catfish CC chemokines (SCYA101- SCYA114) using RT-PCR in the head kidney and spleen tissues from both healthy fish and fish challenged with the bacterial pathogen Edwardsiella ictaluri. In order to be 36 able to compare information on expression of all 26 known catfish CC chemokines, we present here the novel expression data combined with previously published expression data on SCYA115-126. As shown in Fig. 3, and summarized in Tables 5 and 6, four main expression patterns were observed. The majority (16) of the 26 CC chemokines were, on the whole, constitutively expressed with no effect observed after bacterial infection (Fig. 3). These included SCYA102, SCYA104, SCYA107, SCYA110, SCYA111, SCYA119, SCYA120, SCYA122, SCYA123, SCYA124, SCYA126, SCYA101, SCYA106, SCYA108, SCYA114, and SCYA103. Of these, SCYA101, SCYA106, SCYA108, and SCYA114 may be slightly up-regulated, and SCYA103 may be slightly down-regulated, but the extent of up- or down-regulation was minor, and for the purpose of discussion here, we categorized them into the constitutively expressed group. Fig. 3. Expression analysis of the 26 catfish CC chemokines using RT-PCR. RT-PCR reactions were conducted as described in the Materials and Methods. RT-PCR products were analyzed by agarose gel electrophoresis. Two tissues, spleen and head kidney (Hd kidney), were used in the study, as indicated at the top of the figure. The names of the 37 38 catfish CC chemokines were indicated on the left margins of each panel of the gels. Samples from healthy fish (0) and infected fish at 4h (4), 24h (24), and 72h (72) were used. Molecular marker (M) was 1-kb ladder purchased from Invitrogen. Arrows indicate the expected positions of the catfish CC chemokine RT-PCR products. The RT-PCR product of the internal control, beta-actin, was not indicated, but in all cases, it was the upper band on the gel. Note that RT-PCR reactions were conducted for one gene at a time, and the images of agarose gels were compiled together into a single figure and, therefore, expression levels can only be analyzed separately for each gene. Note also that 32 PCR cycles were used for SCYA119 and SCYA121, whereas 29 cycles were used for the remaining chemokines. Table 5 Up-regulated CC chemokines. NC denotes no change in expression; 0 indicated no expression detected; ?+? indicates slightly up, ?++? indicates intermediately up; and ?+++? indicates greatly up. All comparisons of expression levels are within each individual gene and not among the other genes Spleen Head kidney 4h 24h 72h 4h 24h 72h SCYA105 ++ +++ NC ++ +++ NC SCYA117 ++ +++ + ++ +++ + SCYA109 + + +++ 0 0 0 SCYA112 NC NC ++ NC NC ++ SCYA113 NC NC NC + ++ +++ SCYA115 NC NC NC NC + ++ SCYA125 NC ++ + NC ++ + Table 6 Down-regulated CC chemokines. The asterisk (*) indicated the presence of additional PCR bands for SCYA116. All comparisons of expression levels are within each individual gene and not among the other genes Spleen Head kidney 4h 24h 72h 4h 24h 72h SCYA116 NC NC NC NC NC -* SCYA121 NC NC - - - - 39 Seven of the 26 CC chemokines were up-regulated upon bacterial infection (Fig. 3 and Table 5). These included SCYA105, SCYA109, SCYA112, SCYA113, SCYA115, SCYA117, and SCYA125. Of these up-regulated CC chemokines, the most interesting were SCYA105, SCYA109, and SCYA117, which were expressed at very low levels before infection, but their expression was dramatically induced after challenge (Fig. 3, Peatman et al. 2005). SCYA116 and SCYA121 were down-regulated upon bacterial infection (Fig. 3 and Table 6). The down-regulation was more evident with SCYA121, which showed a significant reduction of RT-PCR products in both the head kidney and spleen, but the response was more rapid in head kidney than in spleen (Fig. 3). SCYA116 expression was lower three days after infection; extra bands were detected using RT-PCR, possibly from unspliced products. With SCYA118 and SCYA126, no expression was detected at any time point of the analysis. In order to confirm the lack of expression for SCYA118 and SCYA126, PCR amplification was repeated with extended cycles, but no products were observed. Differences were observed in the time points and tissues involving up-regulation and down-regulation. For instance, SCYA105, SCYA117 and SCYA125 were rapidly and highly induced after bacterial infection in both the spleen and the head kidney tissues, whereas SCYA109 was only induced in spleen, but no expression was detected from head kidney. In contrast, SCYA115 was moderately upregulated only in the head kidney, but not in the spleen. SCYA121 expression was down-regulated in both spleen and head kidney tissues, but more rapidly in head kidney (Fig. 3). 40 Discussion In this study, all 26 previously identified catfish CC chemokine cDNAs were mapped to BAC clones, setting the foundation for comparative genome analysis in the genomic regions containing chemokine genes. Through a combination of cDNA probe hybridizations and fluorescent fingerprinting, 18 fingerprinted contigs were assembled from BACs containing catfish CC chemokine genes. The catfish CC chemokine genes were found to be not only extensively clustered in the catfish genome, but also highly duplicated at various levels. As many as six copies of a single catfish chemokine were found from separate genomic regions. Although a draft genome is not available for catfish, our approach allowed us to study the local genomic architecture of the catfish CC chemokines in order to better understand the origins and orthologies of these important immune molecules. With genome-enablement still years away in many economically important species, our methods may serve as an important model for researchers working with other similar species who want to harness genome information on a limited budget. Here we have also analyzed the expression of the transcripts of the 26 catfish CC chemokines in head kidney and spleen in response to bacterial infection of Edwardsiella ictaluri, an economically devastating catfish pathogen. Such analysis will allow us to concentrate research efforts on the CC chemokines most likely involved in inflammatory responses. The clustering of CC chemokine genes on chromosomes was previously revealed in human, mouse, and chicken (Nomiyama et al. 2001, Wang et al. 2005). In humans, the largest group of CC chemokine genes is located on chromosome 17, and 41 several clusters of CC chemokines genes are also found on chromosomes 7, 9, and 16. Chicken has a large cluster of CC chemokines on chromosome 19, with member genes orthologous to CC chemokines on human chromosome 17. A segment of mouse chromosome 11 additionally corresponds to human chromosome 17. In the case of both chicken and mouse, however, syntenies are only partially conserved with humans. For example, chicken has three genes on chromosome 19 corresponding to a single gene, CCL13, on human chromosome 17. Likewise, mouse has two genes CCL9 and CCL6 on Chromosome 11 that lack orthologues in human despite conservation of the genomic neighborhood. This phenomenon, coupled with the high sequence similarity between the non-orthologous CC chemokines of a given species, is highly suggestive of a pattern of species-specific gene duplications and changes after species divergence. An expectation of distinct expansions of the CC chemokine family within each species means that the identification of orthologues by phylogenetic analysis will be largely unsuccessful. Identification of a smaller ancestral set of CC chemokines and comparisons of genomic organization and architecture across species, therefore, may be more realistic aims for those describing novel sets of chemokines in lower vertebrates. Duplication of CC chemokines within the human genome, before largely unanalyzed or ignored, has become an important matter for research only lately. The discovery that CC chemokine receptor 5 (CCR5) is an entry point for infection of cells by HIV-1 (Alkhatib et al. 1996), and that CCL3 and CCL4, by binding to CCR5, limit infection by HIV-1 (Nibbs et al. 1999), increased interest in understanding the chemokine repertoire and their functions. More recently, researchers have focused on CCL3 and CCL4 duplications and their correlation in disease severity (Townson et al. 42 2002). CCL3L1 and CCL4L1 have been discovered in segmental duplications on chromosome 17 (Modi 2004). Of greatest note has been a recent study strongly correlating copy number of a segmental duplication encompassing the gene encoding CCL3L1 with HIV/AIDS susceptibility. Possession of a copy number of CCL3L1 lower than the population average markedly enhances susceptibility (Gonzalez et al. 2005; reviewed by Julg and Goebel, 2005). Additional correlations have been made between copy numbers or chemokine loci and other diseases such as tuberculosis (Jamieson et al. 2004). Chemokine architecture and duplication, therefore, is an important matter for investigation in studies of the innate immune components of lower vertebrates. Comparison of the major CC chemokine clusters across vertebrate species may reveal important patterns of divergence or conservation and help to pinpoint similar disease quantitative trait loci (QTL) in agricultural species. Sequence and phylogenetic analyses are currently not capable of establishing orthologies between the majority of mammalian CC chemokines and fish CC chemokines (He et al. 2004; Laing and Secombes 2004b; Peatman et al. 2005), probably because of the pattern of duplication and divergence described above. Nonetheless, we attempted to match the genomic segments containing catfish CC chemokine genes we obtained through fingerprinting with the largest of the human chromosome cluster of CC chemokines (Fig. 4). It must be noted that we are still missing the larger genomic context of these chemokine-containing contigs. Since only BAC clones positive for CC probes were included in the fingerprinting, we lack the surrounding genomic regions. A physical map of the catfish genome, currently under construction, when linked with available linkage maps, will tell us whether these contigs are distinct contigs on the 43 same chromosome or on entirely different chromosomes. Based on our current knowledge, therefore, we aligned the larger of the catfish clusters with the human chromosomal segments using their top BLASTX identities. Some of the genomic segments of catfish appeared loosely conserved, in that all chemokine gene members shared highest identities with CC chemokines on the same chromosomal stretch in humans (i.e. SCYA108, SCYA123, SCYA120, SCYA124, and SCYA104). Other segments, such as the one containing SCYA107, SCYA101, SCYA112, SCYA105, SCYA116, SCYA118 and SCYA115, showed no discernible pattern of conservation. From this contig, SCYA101 and SCYA107 appear to be fish specific CC chemokines (He et al. 2004) while SCYA112 shares clear orthology with human CCL20 which is localized on Chromosome 2. Other chemokines from this contig share highest identities with CC chemokines on human chromosome 17 (Fig. 4). One notable feature of this ?comparative map? was that many of the catfish CC chemokines share highest identity with CCL3 or CCL14. These CC chemokines may represent part of the ancestral repertoire before species-specific duplications and divergence. 44 17q12 17q11.2 SCYA121 SCYA110 SCYA117 SCYA111 SCYA122 SCYA126 CCL22 CCL17 50 kb 16q13 9p13.3 CCL27 CCL19 28 kb CCL21 20 kb SCYA107 SCYA101 SCYA112 SCYA105 SCYA116 SCYA118 SCYA115 SCYA102 SCYA119 SCYA114 SCYA109 SCYA106 CCL2 CCL7 CCL11 CCL8 CCL13 CCL1 CCL5 CCL16 CCL14/15 CCL23 CCL18 CCL3 CCL4 CCL3L1 15 kb 15 kb 34 kb 37 kb 30 kb 4kb 1.5 Mb 6kb 51 kb 25 kb 105 kb 15 kb 198 kb SCYA108 SCYA123 SCYA120 SCYA124 SCYA104 CCL26 CCL24 42 kb 7q11.23 45 Fig. 4. A comparison of genomic contigs containing catfish CC chemokines with clusters of human CC chemokines on several human chromosomal segments. Chromosomal segment names for human are given on the left. Distances between the human CC chemokines are noted in kilo-bases (kb) or mega-bases (Mb). Orientation of the catfish contigs is unknown, and was arranged based on BLASTX identities as described in the text. Dashed lines between the catfish and human CC chemokines indicate especially low BLASTX identity. Boxes indicate catfish genomic contigs. Dashed lines within the catfish contigs surround SCYA105 and SCYA108, whose genomic contigs were merged with those shown to avoid showing duplicate copies in the figure. Note that the order and orientation of individual catfish CC chemokine genes were not determined and their relative positions shown in the contig (box) were arbitrary. Sequence similarities between the catfish CC chemokines correlated strongly with genomic architecture (Table 4, Fig. 2) strongly suggesting tandem and segmental gene duplications as the evolutionary mechanism responsible for the diversity of these molecules presently in catfish. The 26 CC chemokine cDNAs with the additional genomic copies revealed by fingerprinting leaves us with a tentative total of at least 75 genes. However, due to the relatively small pool of catfish ESTs in the GenBank, it is not possible yet to provide solid EST evidence for these genes. Using the 26 genes as queries against catfish ESTs allowed the identification of 186 catfish CC chemokine- related ESTs. Cluster analysis using relatively stringent overlapping (90 bp) allowed us 46 to identify 18 additional CC chemokines with protein sequences different from the queries. Thus, at the molecular level, it appeared that EST evidence as available now supports the presence of additional genes in channel catfish, consistent with our conclusions made from the BAC-based contig analysis. It is noteworthy that BAC- based physical analysis is accurate in answering whether sequences similar to CC chemokine genes physically exist in the catfish genome. Whether such sequences are transcribed requires further analysis using transcriptome approaches. Future work, including BAC sequencing and FISH, will help resolve questions related to the ontogeny of this large catfish gene family. Most of the inducible/inflammatory human CC chemokines are highly clustered on chromosome 17, while the constitutive/homeostatic CC chemokines are on other chromosomes (Moser et al. 2004). Despite the extensive clustering of the catfish CC chemokines, a similar correlation between inducible expression and genomic architecture was not observed after infection with Edwardsiella ictaluri in head kidney and spleen tissues. Patterns of expression of genes within the same genomic clusters often differed (i.e. SCYA117, SCYA121, SCYA122, SCYA126). It is possible that the highly duplicated nature of the catfish CC chemokine genes has allowed division of roles that may be manifested in spatial, temporal, or functional differences. Further functional studies are needed to pinpoint the catfish CC chemokines integral to successful innate immune responses against bacterial and viral pathogens, particularly with regard to their function in directing leukocyte traffic after infection. 47 Acknowledgements This project was supported by a grant from USDA NRI Animal Genome Basic Genome Reagents and Tools Program (USDA/NRICGP 2003-35205- 12827), in part by E-Institute of Shanghai Municipal Education Commission, Project number E03009, and in part by a Specific Cooperative Agreement with USDA ARS Aquatic Animal Health Laboratory under the Contract Number 58-6420-5-030. Baolong Bao, Peng Xu, Puttharat Baoprasertkul, Yolanda Brady, and Zhanjiang Liu were coauthors on the manuscript. We thank Renee Beam, Karen Veverica, Esau Arana, and Randell Goodman for their excellence in the production and maintenance of fish used in this study and their assistance during challenge experiments. 48 III. MICROARRAY-BASED GENE PROFILING OF THE ACUTE PHASE RESPONSE IN CHANNEL CATFISH (ICTALURUS PUNCTATUS) AFTER INFECTION WITH A GRAM NEGATIVE BACTERIUM 49 Abstract The acute phase response (APR) is a set of metabolic and physiological reactions occurring in the host in response to tissue infection or injury and is a crucial component of the larger innate immune response. The APR is best characterized by dramatic changes in the concentration of a group of plasma proteins known as acute phase proteins (APP) which are synthesized in the liver and function in a wide range of immunity-related activities. Utilizing a new high-density in situ oligonucleotide microarray, we have evaluated the APR in channel catfish liver following infection with Edwardsiella ictaluri, a bacterial pathogen that causes enteric septicemia of catfish. Our catfish microarray design (28K) builds upon a previous 19K channel catfish array by adding recently sequenced immune transcripts from channel catfish along with 7159 unique sequences from closely-related blue catfish. Analysis of microarray results using a traditional two-fold change in gene expression cutoff and a 10% false discovery rate revealed a well-developed APR in catfish, with particularly high up-regulation (>50- fold) of genes involved in iron homeostasis (i.e. intelectin, hemopexin, haptoglobin, ferritin, and transferrin). Other classical APP genes upregulated greater than two-fold included coagulation factors, proteinase inhibitors, transport proteins, and complement components. Up-regulation of the majority of the complement cascade was observed including the membrane attack complex components and complement inhibitors. A number of pathogen recognition receptors (PRRs) and chemokines were also differentially expressed in the liver following infection. Independent testing of a selection of up-regulated genes with real-time RT-PCR confirmed microarray results. 50 1. Introduction The acute phase response (APR) is a group of rapid physiological responses to infection or injury (Bayne and Gerwick et al. 2001; Gabay and Kushner 1999) and is one of several components which comprise the innate immune system. The molecular signals leading to the induction of the APR following infection, best characterized in mammals, can now be traced from initial recognition of pathogen associated molecular patterns (PAMPs) such as lipopolysaccharide (LPS) by host Toll-like receptors (TLRs), through a resulting signaling cascade, to the ultimate activation of target genes encoding pro-inflammatory cytokines such as IL-1, IL-6, and TNF-? (Pandey and Agrawal 2006). The release of these cytokines has long been known to stimulate the APR and rapidly alter rates of synthesis of a group of plasma proteins known as acute phase proteins (APP) Fey and Gauldie 1990). As the site of synthesis of the majority of plasma proteins, the liver is commonly considered the center of the APR. APPs are an established diagnostic tool as early indicators of inflammation and disease (Schillaci and Pirro 2006), but many are now known to play beneficial roles in mediating the complex inflammatory response and seeking to restore homeostasis(Gabay and Kushner 1999). Research on the APR and the larger innate immune response of teleost fish has received more attention only recently as a growing worldwide aquaculture industry faces disease outbreaks resulting in devastating losses (Meyer 1991). The acute nature of these infections has drawn attention to the importance of the innate immune response in fish. The APR has been best characterized previously in rainbow trout, 51 Oncorhynchus mykiss, using gene and protein-based techniques (Bayne et al. 2001; Gerwick et al. 2002; Russell et al. 2006) and recently using a small oligo-based microarray (Gerwick et al. 2007). Expression of a number of acute phase reactants has also recently been measured in zebrafish Danio rerio using real-time PCR (Lin et al. 2007). No information is known, however, about the nature of the APR in channel catfish (Ictalurus punctatus), the predominant aquaculture species in the United States and one of the best characterized teleost immune models to-date (Bengten et al. 2006). Catfish production suffers heavy losses due to enteric septicemia of catfish (ESC), caused by the Gram-negative, intracellular bacterium Edwardsiella ictaluri (USDA 2003; Hawke et al. 1981). ESC in its acute form is characterized by gastroenteric septicemia and, under artificial challenge, often results in heavy mortalities as early as four days after onset of infection (Newton et al. 1989; Wolters et al. 1994). To better understand the crucial innate immune response of catfish in the context of ESC, we have previously identified and characterized a large number of cytokines, chemokines, antimicrobial peptides, and Toll-like receptors from catfish (He et al. 2004; Baoprasertkul et al. 2004; Chen et al. 2005a; Baoprasertkul et al. 2005; Peatman et al. 2005; Bao et al. 2006a; 2006b; Peatman et al. 2006; Wang et al. 2006a; 2006b; 2006c; Bao et al. 2005; Xu et al. 2005; Baoprasertkul et al. 2006; 2007) and identified additional immune-related genes through EST sequencing (Ju et al. 2000; Cao et al. 2001; Karsi et al. 2002; Kocabas et al. 2002). To study the expression of these important immune components in the larger context of the catfish transcriptome following ESC infection, we have developed a high-density in situ oligonucleotide 52 microarray for catfish based upon a previous 19K channel catfish array (Li and Waldbieser 2006). By adding 7,159 additional transcripts from blue catfish (Ictalurus furcatus), a closely-related species to channel catfish sharing greater than 98% nucleotide similarity within cDNA transcripts (He et al. 2003), along with additional immune and non-immune transcripts from channel catfish, the new 28K microarray design should capture a large proportion of the catfish transcriptome. Here we describe the utilization of this 28K microarray for gene profiling of the acute phase response of channel catfish following infection with E. ictaluri. 2. Materials and Methods 2.1. Experimental fish, disease challenge and sampling All procedures involving the handling and treatment of fish used during this study were approved by the Auburn University Institutional Animal Care and Use Committee (AU-IACUC) prior to initiation. Blue (D&B strain) and channel catfish (Kansas Random strain) fry were artificially spawned at the hatchery of the Auburn University Fish Genetics Research Unit. At one week post-hatch, they were transferred to troughs or aquaria at the USDA ARS Aquatic Animal Health Unit in Auburn, AL or the Auburn University Fish Pathology wet lab. In both locations, the use of recirculating systems and municipal or well water sources ensured that the catfish fingerlings remained na?ve to E. ictaluri during grow-out. 53 Catfish fingerlings were grown out for 4 months to approximately 15 cm before artificial bacterial challenges. Challenges followed established detailed protocols for ESC (Dunham et al. 1993; Baoprasertkul et al. 2004) with modifications. Water temperature before challenge was gradually (over the course of 1 week) brought from 18?C to 27?C by mixing in heated water. Fish were challenged in 30-L aquaria with 6 control and 8 treatment aquaria used. Sixty fish were placed in each aquaria, 30 channel and 30 blue catfish each. Aquaria were divided randomly into replicates of sampling timepoints?24 h control (3 aquaria), 24 h treatment (3 aquaria), 3 d control (3 aquaria), 3 d treatment (3 aquaria), and moribund (2 aquaria). E. ictaluri bacteria were cultured from a single isolate (MS-S97-773) and used in a small test infection of several channel catfish. Bacteria were re-isolated from a single symptomatic fish and biochemically confirmed to be E. ictaluri, before being inoculated into brain heart infusion (BHI) medium and incubated in a shaker incubator at 28?C overnight. The bacterial concentration was determined using colony forming unit (CFU) per ml by plating 10 ?l of 10-fold serial dilutions onto BHI agar plates. At the time of challenge, the bacterial culture was added to the aquaria to a concentration of 4X10 8 CFU/ml. Water was turned off in the aquaria for 2 h of immersion exposure, and then continuous water flow-through resumed for the duration of the challenge experiment. Control aquaria were treated similarly with an identical volume of sterile BHI. Fish were fed lightly during challenge. At 24 h and 3 d post-infection, 25 fish from each species were collected from each of the appropriate control and treatment aquaria, euthanized with MS-222 (300 mg/L), and their tissues and organs were collected and pooled. Pooling was carried out due to tissue constraints in the juvenile fish and to reduce variability 54 between arrays to allow assessment of broad expression changes (see discussion). Collected tissues and organs included head kidney, spleen, trunk kidney, liver, gill, and skin. Samples were flash frozen in liquid nitrogen during collection and stored at -80 ?C until RNA extraction. Procedures were the same for moribund fish except that they were collected over the course of the challenge as they lost equilibrium in the water. During the challenge, symptomatic treatment fish and control fish of each species were collected and confirmed to be infected with E. ictaluri and pathogen-free, respectively, at the Fish Disease Diagnostic Laboratory, Auburn University. 2.2. RNA extraction and labeling Due to financial constraints, only channel catfish liver control and treatment replicates at the 3 d time point were used for initial microarray analysis. Accordingly, the pooled livers (n=25) from each replicate (3 control replicates, 3 treatment replicates) were ground in liquid nitrogen by mortar and pestle to a fine powder and thoroughly mixed. Approximately 30 mg of tissue powder was homogenized in Buffer RLT Plus by passing the lysate several times through a 20-gauge needle fitted to a syringe according to the protocol of the RNeasy Plus Mini Kit (Qiagen, Valencia, CA). Samples were filtered through a genomic DNA elimination column included in the RNeasy Plus kit. Following the manufacturer?s instructions, approximately 35 ?g of total RNA was obtained from each extraction. RNA quality and concentration was checked by spectrophotometer analysis and gel electrophoresis. All extracted samples had an A260/280 ratio of greater than 1.8, and were diluted to 1 ?g/?L. RNA labeling, 55 array hybridization, washing, and scanning were carried out by NimbleGen Systems, Inc. (Madison, WI). Briefly, total RNA was converted to double-stranded cDNA using a SuperScript II cDNA synthesis kit (Invitrogen) and an oligo-dT primer containing the T7 RNA polymerase promoter. In vitro transcription (IVT) was carried out to produce biotin- labeled cRNA from cDNA using the MEGAscript T7 kit (Ambion, Austin, TX). Briefly, 3 ?L double-stranded cDNA was incubated with 7.5 mM ATP and GTP, 5.6 mM UTP and CTP, 1.875 mM bio-11-CTP and bio-16 UTP (Enzo) and 1x T7 enzyme mix in 1x reaction buffer for 16 h at 37?C. The cRNA was then purified using an RNeasy mini kit (Qiagen, Valencia, CA). Before hybridization, cRNA was fragmented to an average size of 50 to 200 bp by incubation in a buffer of 100 mM potassium acetate, 30 mM magnesium acetate, and 40 mM tris-acetate for 35 min at 94?C. Fragmentation was measured using a Bioanalyzer 1000 (Agilent Technologies, Palo Alto, CA). 2.3. Microarray fabrication, hybridization and image acquisition A high-density in situ oligonucleotide microarray was constructed, building on a previously-published 19K catfish design (Li and Waldbieser 2006). Newly sequenced transcripts including many ESTs related to immune functions from channel catfish were added bringing the number of sequences from that species to 21,359. Additionally, 7,159 unique ESTs from the closely-related species blue catfish (Ictalurus furcatus) were added to the microarray to increase the number of informative genes on the array 56 in cases where blue catfish ESTs contained a gene not present in the channel catfish ESTs or to allow better eventual comparisons between the species in cases where putative orthologues are present. To obtain a unique set of blue catfish ESTs, all sequences available in the NCBI GenBank for the species as of March 2005 were downloaded in FASTA format, added into the ContigExpress program of the Vector NTI software suite (Invitrogen, Carlsbad, CA) and assembled. Singletons (non- clustering sequences) and representative clones from contigs were selected and reassembled in ContigExpress to ensure a unique gene set as described previously by Peatman et al. (2004). A total of 28,518 sequences were used, therefore, to construct the new catfish microarray. The added channel catfish and blue catfish sequences were compared by BLASTX against the non-redundant (nr) protein database at NCBI, with a cutoff E-value=0.00001 for annotation. A record of all sequences contained on the 28K catfish microarray, their putative identities, expression values on each slide, and other experimental data have been deposited in the NCBI Gene Expression Omnibus (GE0; http://www.ncbi.nlm.nih.gov/geo/) accessible through the GEO series accession number GSE6105. Nimblegen Systems produced the physical microarrays utilizing an in situ maskless array synthesis technology to synthesize 24 base pair (24mer) oligos on the surface of the microarray slides (Singh-Gasson et al. 1999; Nuwaysir et al. 2002). At least twelve 24-mer oligonucleotides were designed for each EST present on the microarray. Half of these were perfect-match (PM) oligos selected along the length of the sequence, while the other half were duplicates of the first but with two mismatched (MM) bases at the #6 and #12 positions. 57 The microarrays were prehybridized with a solution of 2x MES hybridization buffer (100 mM 2-morpholinoethanesulfonic acid, 1.0 M Na + , 20 mM EDTA, 0.01% Tween 20), 50 ?g of herring sperm DNA, and 250 ?g of acetylated bovine serum albumin (BSA) at 45?C for 15 min followed by hybridization with 10 ?g of denatured and fragmented cRNA per microarray, 3.5 ?l of CPK6 control oligo, 35 ?g of herring sperm DNA, 175 ?g of acetylated BSA, and 2X MES buffer at 45?C for 16 h with constant rotation in a hybridization oven. After hybridization, the microarrays were washed twice with nonstringent buffer (6x SSPE, 0.01% Tween 20) at room temperature followed by two stringent washes (0.1 M Na + , 0.01% Tween 20) at 45?C for 15 min each. After a final one minute rinse with nonstringent buffer, the arrays were placed into a 1x stain solution (100 mM MES, 1 M Na + , 0.05% Tween 20, 50 mg/ml BSA, and 1 ?g/?l Cy3-streptavidin) at room temperature for 15 min, agitating every few minutes. The microarrays were removed from the stain solution and placed in fresh nonstringent wash buffer for one minute. They were then placed into Nimblegen?s proprietary final wash buffer for 30 s, and then immediately dried under a stream of argon gas and scanned using an Axon GenePix 4000B scanner (Molecular Devices, Union City, CA) at 5-?m resolution. Six microarrays were used in the experiment, corresponding to the 3 control pools and 3 treatment pools of RNA isolated three days after infection. 58 2.4. Microarray data analysis After extraction of data from raw images using the NimbleScan software (Nimblegen, Inc.), gene calls (a single expression intensity value based on the multiple probes for each gene) were generated using the Robust Multichip Average (RMA) algorithm (Irizarry et al. 2003) which takes into account only the perfect match oligos. RMA takes a background adjustment on the raw intensity scale, carries out quantile normalization (Bolstad et al. 2003), takes the log2 of the normalized background adjusted PM values, and then uses a linear model to estimate expression values on the log scale. Both programs are available in the affy package of the Bioconductor project (http://www.bioconductor.org). The normalized intensity values from the three control sample microarrays and the three E. ictaluri-infected sample microarrays were then analyzed using the Significance Analysis of Microarrays method (Tusher et al. 2001) in the two-class unpaired mode (SAM version 2.23A:http://www- stat.stanford.edu/~tibs/SAM/). SAM assigns each gene a relative difference score based on its change in gene expression relative to the standard deviation of replicate measurements for that gene. For genes falling above an adjustable threshold, permutations of repeated measurements are used to determine a percentage of genes identified by chance, the false discovery rate (FDR) (Benjamini and Hochberg 1995), which is presented as a q-value for each gene in the final list of significant genes (Tusher et al. 2001; Pawitan et al. 2005; Larsson et al. 2005). The q-value, therefore, reflects the variability present in the data set for a given gene. A list of differentially expressed genes with at least 2-fold expression changes between treatment and control 59 and a global false discovery rate of <10% was produced, and sorted according to fold- change. BLASTX searches were conducted for each sequence on the list. In order to provide insight into the potential identities of the differentially expressed genes, a less stringent cutoff E-value (0.0001) was used, and the hit with the most negative E-value was noted. Those sequences possessing no significant similarity to peptide sequences within the nr database were assembled in ContigExpress to identify and remove any redundant sequences. When a putative gene identity was shared by multiple sequences, further sequence analysis was carried out to remove redundancies. In cases where channel catfish and blue catfish putative orthologues of the same gene were differentially expressed, the channel catfish transcript was selected to represent this gene. If multiple channel catfish transcripts were determined to be derived from the same gene, the transcript with the lowest q-value was chosen. In cases where two differentially-expressed transcripts shared the same putative gene identity but likely represented paralogues, both transcripts were kept on the unique list. A list of all sequences (including redundancies) meeting the threshold parameters can be found as Supplementary Tables S1 and S2. 2.5. Real-time RT-PCR analysis The RNA prepared for microarray analysis was also used for confirmation of the expression pattern of selected genes of interest by quantitative real-time reverse- transcription polymerase chain reaction (qRT-PCR). The three control pools and three treatment pools of RNA, each representing 25 fish, were utilized for each tested gene. 60 One-step qRT-PCR was carried out using a LightCycler 1.0 instrument (Roche Applied Science, Indianapolis, IN) and the Fast Start RNA Master SYBR Green I reagents kit (Roche Applied Science) following manufacturer's instructions with modifications. Briefly, all qRT-PCR reactions were performed in a 10 ?l total reaction volume (9 ?l master mix and 1 ?l (100 ng) RNA template). The master mix contained 4.3 ?l H 2 O, 0.6 ?l Mn[OAc] 2 , 0.3 ?l of each primer (0.1 ?g/ ?l), and 3.5 ?l of the SYBR Green mix. The same cycling parameters were used for all tested genes: (i) reverse transcription, 20 min at 61 ?C; (ii) denaturation, 30 s at 95 ?C; (iii) amplification repeated 50 times, 5 s at 95 ?C, 5 s at 58 ?C, 20 s at 72 ?C; (iv) melting curve analysis, 5 s at 95 ?C, 15 s at 65 ?C, then up to 95 ?C at a rate of 0.1 ?C per second; (v) cooling, 30 s at 40 ?C. Primers were designed using either the FastPCR program (http://www.biocenter.helsinki.fi/bi/Programs/fastpcr.htm) or the PriFi sequence alignment and primer design program (Fredslund et al. 2005; http://cgi- www.daimi.au.dk/cgi-chili/PriFi/main). Primer names, accession numbers, and sequences are listed in Table 1. The 18S ribosomal RNA gene was selected for normalization of expression levels due to its stable expression levels over a variety of tissues and treatment conditions in catfish (Murdock et al. 2006). The triplicate (biological) fluorescence intensities of the control and treatment products for each gene, as measured by crossing-point (Ct) values, were compared and converted to fold differences by the relative quantification method (Pfaffl, 2001) using the Relative Expression Software Tool 384 v. 1 (REST) and assuming 100% efficiencies. Expression differences between control and treatment groups were assessed for statistical significance using a randomization test in the REST software. The mRNA 61 expression levels of all samples were normalized to the levels of 18S ribosomal RNA gene in the same samples. Expression levels of 18S were constant between all samples (<0.35 change in Ct). Each primer set amplified a single product as indicated by a single peak present for each gene during melting curve analysis. Table 1 Primers used for qRT-PCR confirmation (5?-3?) Gene Accession Forward Reverse TLR5 CV993724 ATTAGCACGCCTTCCACAGC AGAGGTTCTGCAAGCCGGTC Intelectin TC6845 TCGGAGCTGCCGGGACATCAA GGAG CCCTGCTCGCTTGACCAGCGA TCAC Hemopexin TC8425 TGACCGCTGTGAGGGCATCGA G TGTGCATGCGGAAGGCTGCAT CCA SCYA113 AY555510 TCCACAAAGCCTGGTGGAATC C AGTTGTTCTTTGTCGCACGAGG Ferritin CK404798 CAGAGCGTGACGAGTGGGGCA G AGGCGCTCCCATACGGCGCAG G 18S BE469353 TGCGCTTAATTTGACTCAACAC CGATCGAGACTCACTAACATC G 3. Results 3.1. Bacterial challenge, microarray sample selection and hybridization The artificial challenge with virulent E. ictaluri resulted in widespread mortality of infected fish at day 5 after exposure. No control fish manifested symptoms of ESC, and randomly-selected control fish were confirmed to be negative for E. ictaluri by standard diagnosis procedures. Dying fish manifested behavior and external signs associated with ESC infection including hanging in the water column with head up and tail down and petechial hemorrhages along their ventral surface. E. ictaluri bacteria were successfully isolated from randomly-selected treatment fish. While two timepoints (24 h and 3 d) were selected for sampling, only the 3 d time point was chosen for 62 microarray analysis, due to financial restraints and a desire to include sufficient biological replicates to allow robust statistical analysis. As liver is central to the APR and is an important organ to innate immunity, it was selected for microarray analysis. Six RNA samples were successfully extracted from the livers of the three control replicate pools (n=25) and the three treatment replicate pools (n=25), labeled, and hybridized to six high-density in situ oligonucleotide microarrays for catfish. The catfish microarray contains 28,518 expressed sequences from channel catfish and blue catfish, each represented by at least six probe pairs of 24 oligonucleotides each. 3.2. Analysis of catfish gene expression profiles after ESC infection The expression levels of the 28,518 catfish transcripts in liver three days after infection with E. ictaluri were compared with the levels seen in uninfected catfish. After data normalization and gene expression calculation in the Robust Multichip Average program (Irizarry et al. 2003), the resulting expression intensity values were analyzed in SAM (Significance Analysis of Microarrays) (Tusher et al. 2001). The criteria of a two-fold or greater change in expression and a global false discovery rate (FDR) of 10% were chosen to determine upregulated or downregulated genes in the infected replicates. Using these criteria, 301 transcripts were significantly upregulated, and 6 were significantly downregulated (Supplemental Tables 1 and 2?see appendices). Of the 301 upregulated catfish transcripts, 207 of these are believed to represent unique genes, and 5 of the 6 significantly downregulated transcripts were unique. The redundant transcripts resulted either from blue and channel putative 63 orthologues of the same gene or multiple transcripts from non-overlapping regions of a large cDNA being included on the microarray. A wide range of levels of gene upregulation was observed. Fourteen genes were upregulated from 10-85 fold following infection; 16 genes were upregulated from 5-10 fold; 27 genes were upregulated from 3-5 fold; and 150 genes were upregulated from 2-3 fold (Table 2). Table 2 Profile of significant, differentially-expressed genes in catfish following E. ictaluri infection. Transcripts on the array 28,518 Number of upregulated transcripts 301 Number of unique upregulated genes 207 Number of unique genes upregulated >10 fold 14 Number of unique genes upregulated 5-10 fold 16 Number of unique genes upregulated 3-5 fold 27 Number of unique genes upregulated 2-3 fold 150 Number of downregulated transcripts 6 Number of unique downregulated genes 5 3.3. Putative identities of differentially expressed genes after infection with Edwardsiella ictaluri Of the 207 unique, significantly upregulated transcripts after infection, 127 could be annotated based on sequence similarity by BLASTX searches while 80 had no significant similarity to protein sequences in the nr database (cutoff E-value=0.0001; see Supplemental Tables 3 and 4 for unique upregulated transcripts with and without annotation). Thirty catfish genes were upregulated 5-fold or greater, and their putative functions, as obtained by PubMed and UniProt (http://www.pir.uniprot.org/) searches, are listed in Table 3. 64 Table 3 Catfish genes upregulated 5-fold or greater in the liver following E. ictaluri infection. Accession refers to the GenBank accession number or TIGR consensus number of the sequence on the microarray. Putative Id is the hit with the most negative E-value. q-value is the false-discovery rate for the particular gene. Function is putative function of top BLAST hit Accession Putative identity Fold Change q- value Function CF970955 Intelectin 85.4 1.25 Pathogen recognition Iron metabolism CK408483 Haptoglobin precursor 34.3 0.00 Binds hemoglobin; APP BM438750 Microfibrillar-associated protein 4 32.9 1.25 Unknown; lectin similar to ficolin and tachylectin?initiates complement? TC6845 Intelectin 28.0 2.36 Same as above? putative paralogues BM438689 Microfibrillar-associated protein 4 25.6 0.00 Same as above? putative paralogues TC8425 Warm-temperature-acclimation-related- 65kda-protein-like-protein 23.4 0.00 Similar to hemopexin? sequesters heme TC7475 CC chemokine SCYA113 21.5 3.27 Unknown; putative catfish orthologue of human CCL19/ MIP- 3-beta CK406396 Neurotoxin/C59/Ly-6-like protein 21.3 1.25 Unknown; possible phospholipase inhibitor or complement membrane attack complex inhibitor CV994031 Catechol-O-methyltransferase domain containing 1 14.8 0.00 Unknown; putative O- methyltransferase TC9205 Hypothetical protein XP_683888 14.4 1.25 Unknown TC8426 Hemopexin precursor 13.6 2.36 Sequesters heme to liver CV996638 Apolipoprotein Apoa4 protein 13.0 2.36 Lipid binding and transport CV993724 Toll-like receptor 5 11.8 0.00 Pathogen recognition receptor--flagellin CV987901 Complement C3-H1 10.0 1.71 Complement pathway; inflammation EE993362 Complement protein component C7-1 9.7 0.00 Membrane attack complex component TC9637 Fibrinogen alpha chain 9.6 0.00 Coagulation factor; APP TC9194 Complement regulatory plasma protein 8.9 3.27 Factor H; complement inhibition CV992853 Ceruloplasmin 8.5 0.00 Iron transport; APP TC9833 Microfibrillar-associated protein 4 8.4 1.25 Same as above? putative paralogues TC8765 Transferrin 7.7 0.00 Transports iron?APP 65 TC8306 Fibrinogen gamma polypeptide 6.1 0.00 Coagulation factor; APP CV989503 CXCL14 5.7 0.00 Chemokine? stimulates monocytes, NK cells CV997126 Complement C3 5.6 0.00 Complement pathway; inflammation TC7892 Ceruloplasmin 5.4 0.00 Same as above? putative paralogues CV992447 Complement component C8 beta 5.4 3.27 Membrane attack complex component TC7741 Complement factor B/C2-A3 5.4 3.74 Complement pathway BM494620 Serum/glucocorticoid regulated kinase 5.3 1.25 Cellular stress response CV995884 Solute carrier family 31 (copper transporters), member 1 5.3 0.00 Copper ion transport TC8490 Fibrinogen, B beta polypeptide 5.2 1.71 Coagulation factor; APP EE993545 Erythroblast membrane-associated protein 5.0 3.74 Cell adhesion or receptor molecule of erythroid cells; Ig superfamily member 3.4. Profiling of the APR in catfish A conserved APR was evident in the significantly upregulated catfish transcripts following infection. At least 35 of the 127 unique, annotated transcripts (Supplemental Table 3) represented APPs (Bayne et al. 2001), including coagulation factors, proteinase inhibitors, transport proteins, and complement components. Many of the APPs were upregulated greater than 5-fold (Table 3). Two subgroups of APP, iron transport/homeostasis proteins and complement components, were represented by particularly high numbers of upregulated transcripts. Transcripts representing at least 15 unique complement components or inhibitors were upregulated 2-fold or greater following infection. These included: a short transcript likely representing C1q (CV996365) upregulated 15.3-fold (Supplemental Table 1); 66 ficolin-like genes upregulated as much as 32-fold (BM438750); complement C2/Bf; several C3 isoforms; complement component C4; complement component C5; complement components C7, C8, and C9 active in the membrane attack complex; and several complement regulatory proteins including MAC inhibitor CD59, C1 inhibitor, and Factor H. The most highly-upregulated group of functionally-related catfish genes was composed of genes involved in iron homeostasis. These included intelectin, the most highly-upregulated gene observed at >85-fold, haptoglobin (>34 fold), hemopexin (>25- fold), ceruloplasmin (8.5-fold), transferrin (>7-fold), and ferritin (>2-fold). 3.5. Additional upregulated genes with putative immune functions A number of additional genes believed to play important roles in the innate immune response, inflammation, and/or cellular responses to infection were upregulated after infection. These included Toll-like receptor 5, CC chemokine SCYA113, CXC chemokine CXCL14, selenoprotein Pa, selenoprotein X, selenium binding protein, chemotaxin, and several lectins (Table 3; Supplementary table S3). 3.6. Downregulated genes A much smaller number of catfish transcripts were significantly downregulated following infection with a narrow range of suppression (Table 4). These included liver- expressed antimicrobial peptide-2, which is believed to be involved in the defense 67 response to bacteria (Bao et al. 2006b), and thioredoxin-interacting protein which functions in the oxidative stress response in mammals. Table 4 Unique, significantly downregulated catfish transcripts in liver after E. ictaluri infection. Accession refers to the GenBank accession number or TIGR consensus number of the sequence on the microarray. Putative Id is the hit with the most negative E-value. q-value is the false-discovery rate for the particular gene. Function is putative function of top BLAST hit Accession Putative identity Fold Change q- value Function TC7457 Eukaryotic translation initiation factor 3, subunit 6 interacting protein 0.42 6.5 Translation regulation CK404061 No significant similarity 0.44 5.2 NA AY845143 Liver-expressed antimicrobial peptide 2 0.45 6.5 Defense response to bacteria CK403219 No significant similarity 0.47 7.5 NA TC6758 Thioredoxin interacting protein 0.49 1.7 Oxidative stress mediator 3.7. Real-time RT-PCR confirmation of microarray results Expression patterns of five genes identified by microarray analysis as differentially expressed following infection were selected for confirmation using qRT- PCR. Genes upregulated ranging from 2-fold to 85-fold in the microarray experiment were selected and primers designed (Table 1). qRT-PCR results (Table 5) generally confirmed the microarray results, with all tested genes showing statistically significant upregulation greater than 2-fold (P<0.05). Fold changes measured by qRT-PCR were larger than those measured by microarray likely due to the more specific binding conditions of the PCR reaction (Table 5), and perhaps also due to the greater accuracy in quantitation by qRT-PCR than by microarrays. 68 Table 5 Confirmation of microarray results by qRT-PCR Gene Accession Microarray Fold Change qRT-PCR Fold Change Intelectin TC6845 +85.4 +545 (p=0.001) Hemopexin TC8425 +23.4 +65 (p=0.001) SCYA113 AY555510 +21.5 +235 (p=0.001) TLR5 CV993724 +11.8 +71 (p=0.013) Ferritin CK404798 +2.3 +10 (p=0.03) 4. Discussion Utilization of a new catfish microarray led to the identification of 212 unique, differentially expressed transcripts in the liver of channel catfish following infection with Gram negative bacterium E. ictaluri. The challenge inherent to microarray expression analysis is to move inward from large sets of raw data to a smaller set of significant results and, finally, to answers to biological questions. Our aims in the present experiment were to: a) validate a new catfish in situ oligonucleotide microarray design which included larger numbers of immune transcripts; b) capture and quantify the APR of catfish and compare it with previously described classical mammalian and fish APRs; and c) identify further immune-relevant transcripts from catfish as potential biomarkers for stress and disease (Rise et al. 2004b; Meijer et al. 2005; Kurobe et al. 2005; Mancia et al. 2006) and for future functional characterization, genetic mapping, and QTL analysis (Xu et al. 2006). Our results will allow us to fulfill these aims and move towards the long-term goal of improving disease resistance in catfish broodstocks. Microarray-based transcriptomic profiling of the liver in teleost fish has been utilized for measuring gene responses to a wide range of stimulants, in addition to 69 disease, including environmental toxicants, growth hormone transgenesis, and hypoxia (Lam et al. 2006; Krasnov et al. 2005c; Williams et al. 2006; Rise et al. 2006; Ju et al. 2006) making it an ideal tissue for comparison of conserved expression patterns. The catfish APR as measured in liver three days after infection included many of the genetic components of the classical mammalian APR and also had overlapping results with a recent APR study in rainbow trout liver (Gerwick et al. 2007) and other previous salmonid and carp microarray experiments measuring expression in liver after application of a variety of stressors (Tilton et al. 2005; Ewart et al. 2005; Martin et al. 2006; Reynders et al. 2006). A number of informative transcripts were shared between the compared experiments and a potentially conserved set of both mammalian and teleost acute phase reactants could be identified. Among the mammalian APP (Gabay and Kushner 1999) upregulated greater than 2-fold in catfish were haptoglobin, hemopexin, ferritin, transferrin, ceruloplasmin, fibrinogen, thrombin, alpha-2- macroglobulin, trypsin inhibitor, plasmin inhibitor, plasminogen, and angiotensinogen, and a large number of complement components and inhibitors (Fig. 1). Smaller subsets of APP were reported to be differentially expressed in rainbow trout (Gerwick et al. 2007) and as measured by real-time PCR in zebrafish (Lin et al. 2006), indicating the likely conservation of function of the vast majority of APP between mammals and teleost fish. Comparative expression analysis under specific conditions could provide fish biologists with important shortcuts towards a better understanding of function of genes related to immunity and inflammatory responses. 0 5 10 15 20 25 30 35 40 Haptoglobin Hemopexin Complement C3-H1 Complement C7-1 Fibrinogen Complement factor H Ceruloplasmin Transferrin Complement C8 beta Complement B/C2-A3 Angiotensinogen Complement C5-1 Complement C4-2 Complement C3-H2 Complement C9 Complement C3-S Alpha-2-macroglobulin Complement C3-Q1 C1 inhibitor Thrombin Plasmin inhibitor Ferritin Complement B/C2B Trypsin inhibitor CD59 Plasminogen A c ut e p h ase p r o t ei n Fold Upregulation Fig. 1 APP genes upregulated two-fold or greater in channel catfish following infection with E. ictaluri. 70 A number of genes not classically considered acute phase reactants were also observed to be shared between our results from catfish and several of the other 71 microa ar- ; Lin microarray were up - d errin in nd was also identified to be highly upregulated in carp liver af ing rray experiments involving teleost liver, and some of these warrant further comment as they may represent novel immunoregulators in fish. They included intelectin (also reported by Reynders et al. 2006; Gerwick et al. 2007), microfibrill associated protein 4 (Reynders et al. 2006), Toll-like receptor 5 (Ewart et al. 2005 et al. 2007), neurotoxin/differentially regulated trout protein (Tilton et al. 2005; Gerwick et al. 2007; Martin et al. 2006; Ewart et al. 2005), SEC31/high affinity copper uptake protein (Martin et al. 2006), and SEC61 (Gerwick et al. 2007). Intelectin was the most highly upregulated catfish transcript in liver following infection (Table 3). Five transcripts representing intelectin on the catfish regulated greater than 5-fold (Supplemental Table 1). qRT-PCR showed a 545 fold upregulation in gene expression following infection (Table 5). In mammals, intelectin is believed to be involved in pathogen defense mechanisms, recognizing galactofuranose in carbohydrate chains of bacterial cell walls (Tsuji et al. 2001) an may function as a receptor for lactoferrin, an iron sequestering homologue of transf (Suzuki et al. 2001). We are currently investigating the function of catfish intelectins the context of iron and disease. Microfibrillar-associated protein 4 (mfap4) was also highly upregulated in catfish following ESC infection a ter cadmium exposure (Reynders et al. 2006). Multiple transcripts represent several mfap4 genes are on the catfish microarray. Mfap4 is represented by a multi-gene family in zebrafish and has strong similarity to ficolin and tachylectin. It may be functioning in pathogen recognition and initiation of the lectin complement pathway (Boshra et al. 2006). 72 th wirtz et al. 2001; Tsujita et al. 2004). TLR5 has been well- charact was this s carp ntially regulated trout protein was upregulated greater than 20-fold following ty with table. This result, though dissimilar to ur ased Toll-like receptor 5 is a well-characterized pathogen recognition receptor in bo mammals and fish (Ge erized in catfish previously and was shown to be upregulated in the liver following ESC infection (Bilodeau and Waldbieser 2005; Bilodeau et al. 2006) and upregulated in the spleen after LPS exposure (Li and Waldbieser et al. 2006). In study, TLR5 was observed to be upregulated greater than 11-fold by microarray analysis. A catfish transcript similar to neurotoxin/C59/Ly-6-like protein from gras and differe infection. The upregulation of this gene has been reported in several salmonid microarray experiments on liver to-date and has been tentatively suggested to be an APP (Bayne et al. 2001). While its function is unknown, it shares some similari membrane attack complex inhibitor CD59. The absence of the iron regulatory hormone hepcidin (Park et al. 2001) from the transcriptomic profile of catfish liver was no what was found in several other teleost expression studies in liver (Gerwick et al. 2007; Tilton et al. 2005; Ewart et al. 2005; Martin et al. 2006; Lin et al. 2007), confirmed o previous expression studies which showed that hepcidin was not upregulated three days after infection (Bao et al. 2005). In mammals, an inflammatory stimulus (IL-6) induces production of hepcidin in the liver. Hepcidin then blocks the release of iron from macrophages, hepatocytes, and enterocytes by internalizing and degrading ferroportin, the site of cellular iron export (Nemeth et al. 2004a). This leads to drastically decre plasma iron levels during infection, a potential host defense mechanism to deny bacteria 73 tfish ogical explanations can be suggested to explain the high in genes gical gh the tion Fe access to the critical metal (Schaible et al. 2002). As plasma iron levels decrease, a feedback mechanism is believed to downregulate hepcidin production in the liver (Nemeth et al. 2004b). Regulation of iron homeostasis was a key aspect of the APR observed in ca (Fig. 2). Several physiol upregulation of a large group of iron regulatory genes following infection. Liver iron stores are known to be significantly increased by hepcidin, even as plasma iron concentrations decline (Rivera et al. 2005). One would expect, therefore, an increase iron storage, binding, and transport proteins such as haptoglobin, hemopexin, transferrin, ceruloplasmin, and ferritin as seen in the results. Hepatocytes, which account for 80% of the liver mass, are the primary site of synthesis for all these (Anderson and Frazer 2005). In mammals many of these genes are active in sequestering iron to restrict its availability to invading bacteria, and several are now known to possess immunoregulatory and antioxidant properties under patholo conditions which may supersede the importance of their roles in normal iron metabolism (Melamed-Frank et al. 2001; Gueye et al. 2006; Tolosano et al. 2002; Legrand et al. 2005; Giurgea et al. 2005; Anderson and Frazer 2005). Althou exact mechanisms of gene upregulation in catfish are unknown at present, upregula of a large number of genes involved in iron homeostasis suggests conservation of the homeostasis pathway in dealing with infectious bacteria. Further investigation of this important group of upregulated catfish transcripts is currently underway in our lab to elucidate their functions and place them on catfish physical and linkage maps. Fig. 2 Putative relationships of a set of upregulated catfish genes involved in iron regulation highlighted on a diagram of pathways of iron homeostasis in mammalian hepatocytes [adapted from Anderson and Frazer (2005)]. Upregulated catfish genes are of indicated in larger text with arrows and fold-upregulation nearby. The conservation these pathways in catfish is currently unknown. DMT1, divalent metal transporter 1; FPN1, ferriportin 1; SFT, stimulator of Fe transport; TfR1, transferrin receptor 1; TfR2, transferrin receptor 2; HO-1, heme oxygenase 1; LRP, low density lipoprotein receptor- related protein. 74 75 Peatman et al. 2006; Bao et al. 2006a; Baoprasertkul et al. 2005). atfish ). ser itial icroa sing et only a 10% Two chemokines from catfish previously characterized in our laboratory, SCYA113 and CXCL14, were upregulated greater than 5-fold after infection in liver (He et al. 2004; SCYA113 is a catfish CC chemokine most similar to mammalian CCL19, while c CXCL14 is the putative orthologue of mammalian CXCL14 (Baoprasertkul et al. 2005 Upregulation of CCL19-like genes after infection has also been recently reported in rainbow trout and Atlantic salmon (Morrison et al. 2006; Martin et al. 2006). Interestingly, both chemokines are among a small handful of CC and CXC chemokines traditionally considered to have homeostatic rather than inflammatory functions (Mo et al. 2004). Their roles in the catfish immune response are still unclear. The desire to capture genes that are involved in disease response at the species level rather than inter-individual variations, financial cost, and small tissue sample sizes were all factors that entered into our decision to create replicate pools for in m rray analysis. The debate over pooling of biological samples for microarray analysis has been contentious (Kendziorski et al. 2005; Jolly et al. 2005). However, a consensus has emerged recently recognizing the advantages of pooling for decrea variability between arrays and cost, if multiple pools are analyzed per group (Allison al. 2006). We utilized RNA samples from three distinct treatment pools and three distinct control pools for microarray analysis and were able to identify a large, reproducible set of differentially expressed transcripts. We did observe that variability between pools was noticeably larger among downregulated transcripts, resulting in a small number of transcripts being declared significantly downregulated using FDR cutoff. A similarly small number of transcripts were reported to be significantly 76 he infection d l ted the innate immune response of primitive teleost fish. he ma downregulated in the head kidney, spleen, and liver of Atlantic salmon following a Gram negative bacterial infection (Ewart et al. 2005). This may reflect the nature of the late-stage inflammatory response and/or be the result of the more transitory downregulation of genes being masked in the pooled samples. Genes that are differentially expressed in a sustained manner were more likely to be identified as significant, given that the pooled fish were potentially at different stages of t (Ewart et al. 2005). Analysis of the 3 d rather than 24 h timepoint likely favore identification of upregulated genes, as the fish were likely in the process of termina infection. We hope to conduct more comprehensive expression analysis in the future to better understand this phenomenon. The larger set of non-significant downregula genes, while not allowing global conclusions, will still yield candidates for further genome mapping and analysis. In summary, microarray analysis of transcriptomic changes in channel catfish liver following an infection with Gram negative bacterium E. ictaluri indicated a conserved APR occurs as part of T jority of classical APPs were strongly upregulated in catfish along with a set of putative ?teleost? acute phase reactants. Several transcripts involved in iron homeostasis were highly induced, suggesting that catfish may attempt to limit free iron availability to inhibit bacterial growth and avoid metal-induced cellular damage. Strong upregulation of the complement cascade, pathogen recognition receptors and chemokines indicated that the catfish liver plays an integral role in pathogen recognition and defense as well as inflammatory signaling. Ongoing functional characterization, 77 as supported by a grant from USDA NRI Animal Genome Tools nd Resources Program (award # 2006-35616-16685), and in part by a seed grant from the AA ing Li, re coauthors r ase genetic mapping, and QTL analysis of many of these immune-related genes from catfish will add to our understanding of the teleost immune system. Acknowledgments This project w a ES Foundation. We are grateful for an equipment grant from the National Research Initiative Competitive Grant no. 2005-35206-15274 from the USDA Cooperative State Research, Education, and Extension Service. Puttharat Baoprasertkul, Jeffery Terhune, Peng Xu, Samiran Nandi, Huseyin Kucuktas, P Shaolin Wang, Benjaporn Somridhivej, Rex Dunham, and Zhanjiang Liu a on the manuscript. We thank Renee Beam, Esau Arana, and Randell Goodman for thei excellence in the production and maintenance of fish used in this study and their assistance during challenge experiments. We are grateful to Karl Hayden of the Fish Disease Diagnostic Laboratory for his assistance in the diagnosis of the ESC dise and the identification of the bacterial pathogen. 78 IV. TRANSCRIPTOMIC PROFILING OF THE LIVERS OF BLUE CATFISH (ICTALURUS FURCATUS) FOLLOWING INFECTION WITH EDWARDSIELLA ICTALURI 79 Abstract The acute nature of disease outbreaks in aquaculture settings has emphasized the importance of the innate immune response of fish for survival and led to the recent identification and characterization of many of its components. Catfish, the predominant aquaculture species in the United States, serves as an important model for the study of the teleost immune system. However, tr gene expression in catfish have only recently been initiated, and understanding of immune responses to pathogen infections is limited. Here, we have developed and utilized a 28K in situ oligonucleotide microarray composed of blue catfish (Ictalurus furcatus) and channel catfish (Ictalurus punctatus) transcripts. While channel catfish accounts for the majority of commercial production, the closely related blue catfish possesses several economically important phenotypic traits. Microarray analysis of gene expression changes in blue catfish liver after infection with Gram negative bacterium Edwardsiella ictaluri indicated the strong upregulation of several pathways involved in the inflammatory immune response and potentially in innate disease resistance. A multifaceted response to infection could be observed, encompassing the complement cascade, iron regulation, inflammatory cell signaling, and antigen processing and presentation. The induction of several components of the MHC class I- related pathway following infection with an intracellular bacterium is reported here for the first time in fish. Our results add to the understanding of the teleost immune responses and provide a solid foundation for future functional characterization, genetic mapping, and QTL analysis of immunity-related genes from catfish. anscriptomic-level studies of disease-related 80 ction Studies of acute inflammation in mammals have often focused on the liver, a major target for proinflammatory cytokines and the center of the acute phase response (APR) component of innate immunity (Olivier et al. 1999; Gabay and Kushner 1999). The APR in mammals is characterized by rapid, dramatic changes in the concentrations of a set of plasma proteins termed the acute phase proteins (APP). Acute phase proteins are an established diagnostic tool as early indicators of inflammation and disease (Schillaci and Pirro 2006), and many are now known to play beneficial roles in mediating the complex inflammatory response and seeking to restore homeostasis following infection or injury (Gabay and Kushner 1999). Recent studies using genomic approaches in teleost fish have indicated that the liver is an important source of immune transcripts (Martin et al. 2006; Ewart et al. 2005) and mediates a powerful, conserved APR (Bayne et al. 2001; Bayne and Gerwick 2001; Gerwick et al. 2007; Lin et al. 2007). Research on the APR and the larger innate immune response of teleost fish has taken on new importance as a growing worldwide aquaculture industry faces disease outbreaks resulting in devastating losses (Meyer, 1991). The outcome of these acute infections in fish appears to depend heavily on non- specific immune responses (Camp et al. 2000). Characterization of the gene components and pathways of the teleost innate immune system, therefore, has become an area of particular focus in fish immunology, and has resulted in the identification of large numbers of cytokines, complement components, pathogen recognition receptors (PRR), and antimicrobial peptides from several aquaculture species (reviewed by 1. Introdu 81 6). Genome-wide comparative studies of immune components and their expression after infection provide basic assessment and understanding of disease resistance relevant to both basic research and practical applications. The advent of microarray technology has allowed fish researchers to conduct simultaneous expression analysis on tens of thousands of gene transcripts in organisms subjected to a variety of diseases and environmental conditions (Rise et al. 2004b; Ewart et al. 2005; Meijer et al. 2005; Martin et al. 2006; MacKenzie et al. 2006; Purcell et al. 2006; Morrison et al. 2006; Matsuyama et al. 2006; Li and Waldbieser, 2006; Roberge et al. 2007; Gerwick et al. 2007). Microarray studies provide important context for the study of the immune response, connecting known immune components to a broader set of genes with similar expression patterns. As more microarray studies have been conducted using a variety of pathogens, host response profiles have begun to highlight a number of genes with conserved expression patterns following infection (Roberge et al. 2007), many of which like play important yet unknown roles in teleost immunity. These genes serve as natural targets for further functional characterization, development as molecular biomarkers for disease progression, and genetic mapping. Catfish (Ictalurus spp.), the predominant aquaculture species in the United States, serves as an important model for the study of the teleost immune system (Bao et al. 2006a; Bengten et al. 2006). While channel catfish (I. punctatus) accounts for the majority of commercial production, the closely related blue catfish (I. furcatus) possesses several economically important phenotypic traits that have led to the production of an interspecific hybrid (channel female x blue male) recently available for commercial use (He et al. 2003; Chatakondi et al. 2005). Catfish production suffers Magnadottir, 200 82 , artificial challen s (Liu large p d heavy losses due to enteric septicemia of catfish (ESC), caused by the Gram-negative intracellular bacterium Edwardsiella ictaluri (USDA 2003; Hawke et al. 1981). ESC in its acute form is characterized by gastroenteric septicemia and, under ge, often results in heavy mortalities as early as four days after infection (Newton et al. 1989; Wolters and Johnson 1994). In trials, blue catfish had significantly higher resistance to ESC than either channel catfish or hybrid catfish (Wolters et al. 1996). To move toward the goal of eventually identifying molecular contributors to this increased resistance in blue catfish, we have previously developed extensive EST resources for both catfish species and have developed interspecific mapping panel et al. 2003b, He et al. 2003). In order to study the transcriptomic responses of blue catfish following infection with E. ictaluri and develop important immune-related markers for characterization and genetic mapping, we have developed a 28K in situ oligonucleotide microarray composed of blue catfish and channel catfish transcripts based upon a previous 19K channel catfish array (Li and Waldbieser 2006). By adding 7,159 additional transcripts from blue catfish, along with additional immune and non- immune transcripts from channel catfish, the new 28K microarray design represents a roportion of the catfish transcriptome. Here we describe the microarray-base transcriptomic profiling of the livers of blue catfish following infection with E. ictaluri. 83 e and 8 treatment aquaria used. Sixty fish were placed in each aquaria, 30 channel and 30 blue catfish each. Aquaria were divided randomly into replicates of sampling timepoints?24 hr control (3 aquaria), 24 hr treatment (3 aquaria), 3 d control (3 aquaria), 3 d treatment (3 aquaria), and moribund (2 aquaria). E. ictaluri bacteria were cultured from a single isolate (MS-S97-773) and used in a small test infection of several 2. Materials and methods 2.1. Disease challenge All procedures involving the handling and treatment of fish used during this study were approved by the Auburn University Institutional Animal Care and Use Committee (AU-IACUC) prior to initiation. Blue catfish (D&B strain) and channel catfish (Kansas Random strain) fry were artificially spawned at the hatchery of the Auburn University Fish Genetics Research Unit. At one week post-hatch, they were transferred to troughs or aquaria at the USDA ARS Aquatic Animal Health Unit in Auburn, AL or the Auburn University Fish Pathology wet lab. In both locations, th use of recirculating systems and municipal or well water sources ensured that the catfish fingerlings remained na?ve to E. ictaluri during grow-out. Catfish fingerlings were grown out for 4 months to approximately 15 cm before artificial bacterial challenges. Challenges followed established detailed protocols for ESC (Dunham et al. 1993; Baoprasertkul et al. 2004) with modifications. Water temperature before challenge was gradually (over the course of 1 week) brought to 27?C by mixing in heated water. Fish were challenged in 30-L aquaria with 6 control 84 lated from a single symptomatic fish and biochemically onfirmed to be E. ictaluri, before being inoculated into brain heart infusion (BHI) n a shaker incubator at 28 ?C overnight. The bacterial oncentration was determined using colony forming unit (CFU) per ml by plating 10 ?l of 10- l nd to reduce variability h ion. catfish. Bacteria were re-iso c medium and incubated i c fold serial dilutions onto BHI agar plates. At the time of challenge, the bacteria culture was added to the aquaria to a concentration of 4X10 8 CFU/ml. Water was turned off in the aquaria for 2 h of immersion exposure, and then continuous water flow-through resumed for the duration of the challenge experiment. Control aquaria were treated similarly with an identical volume of sterile BHI. Fish were fed lightly during challenge. At 24 hr and 3 d post-infection, 25 fish from each species were collected from each of the appropriate control and treatment aquaria, euthanized with MS-222 (300 mg/L), and their tissues and organs were collected and pooled. Pooling was carried out due to tissue constraints in the juvenile fish a between arrays to allow assessment of global expression changes. For the studies described here, liver tissues were collected at day 3 after infection. Samples were flas frozen in liquid nitrogen during collection and stored at -80 ?C until RNA extract Procedures were the same for moribund fish except that they were collected over the course of the challenge as they lost equilibrium in the water. During the challenge, symptomatic treatment fish and control fish were collected and confirmed to be infected with E. ictaluri and pathogen-free, respectively, at the Fish Disease Diagnostic Laboratory, Auburn University. 85 ding on a e ay To ase ues on data have been deposited in the NCBI Gene xpression Omnibus (GE0; http://www.ncbi.nlm.nih.gov/geo/) accessible through the EO series accession number GSE6350 2.2. Oligonucleotide microarray construction A high-density in situ oligonucleotide microarray was constructed, buil previously-published 19K catfish design (Li and Waldbieser, 2006). Newly sequenced transcripts including many ESTs related to immune functions from channel catfish wer added bringing the number of sequences from that species to 21,359. Additionally, 7,159 unique ESTs from blue catfish (Ictalurus furcatus) were added to the microarr to increase the number of informative genes on the array in cases where blue catfish ESTs contained a gene not present in the channel catfish ESTs or to allow better eventual comparisons between the species in cases where orthologues are present. obtain a unique set of blue catfish ESTs, all sequences available in the NCBI GenBank for the species as of March 2005 were downloaded in FASTA format, added into the ContigExpress program of the Vector NTI software suite (Invitrogen, Carlsbad, CA) and clustered. Singletons (non-clustering sequences) and representative clones from contigs were selected and reclustered to ensure a unique gene set as described previously by Peatman et al. (2004). A total of 28,518 sequences were used, therefore, to construct the new catfish microarray. The added channel catfish and blue catfish sequences were compared by BLASTX against the non-redundant (nr) protein datab at NCBI, with a cutoff e-value=0.00001 for annotation. A record of all sequences contained on the 28K catfish microarray, their putative identities, expression val each slide, and other experimental E G . 86 cal microarrays utilizing an in situ askless array synthesis technology to synthesize 24 base pair (24mer) oligos on the surface ed y ral times sis. out by Nimblegen Systems produced the physi m of the microarray slides (Singh-Gasson et al. 1999; Nuwaysir et al. 2002). At least twelve 24-mer oligonucleotides were designed for each EST present on the microarray. Half of these were perfect-match (PM) oligos selected along the length of the sequence, while the other half were duplicates of the first but with two mismatch (MM) bases at the #6 and #12 positions. 2.3. RNA extraction and labeling Blue catfish liver control and treatment replicates at the 3 d time point were used for initial microarray analysis. Accordingly, the pooled livers (n=25) from each replicate (3 control replicates, 3 treatment replicates) were ground in liquid nitrogen b mortar and pestle to a fine powder and thoroughly mixed. Approximately 30 mg of tissue powder was homogenized in Buffer RLT Plus by passing the lysate seve through a 20-gauge needle fitted to a syringe according to the protocol of the RNeasy Plus Mini Kit (Qiagen, Valencia, CA). Following the manufacturer?s instructions, approximately 35 ?g of total RNA was obtained from each extraction. RNA quality and concentration was checked by spectrophotometer analysis and gel electrophore All extracted samples had an A260/280 ratio of greater than 1.8, and were diluted to 1 ?g/?L. RNA labeling, array hybridization, washing, and scanning were carried NimbleGen Systems, Inc. (Madison, WI). 87 cript The cRNA was then purified using an RNeasy mini kit (Qiagen, Valencia, CA). Before hybridization, cRNA was fragmented by incubation in a buffer of 100 mM potassium cetate, 30 mM magnesium acetate, and 40mM tris-acetate for 35 min at 94?C. Fragme g tion, the microarrays were washed twice with nonstringent buffer (6x SSPE, 0.01% Tween 20) at room temperature followed by Briefly, total RNA was converted to double-stranded cDNA using a SuperS II cDNA synthesis kit (Invitrogen) and an oligo-dT primer containing the T7 RNA polymerase promoter. In vitro transcription (IVT) was carried out to produce biotin- labeled cRNA from cDNA using the MEGAscript T7 kit (Ambion, Austin, TX). Briefly, 3 ?L double-stranded cDNA was incubated with 7.5 mM ATP and GTP, 5.6 mM UTP and CTP, 1.875 mM bio-11-CTP and bio-16 UTP (Enzo) and 1x T7 enzyme mix in 1x reaction buffer for 16 h at 37?C. to an average size of 50 to 200 bp a ntation was measured using a Bioanalyzer 1000 (Agilent Technologies, Palo Alto, CA). 2.4. Hybridization and image acquisition The oligonucleotide microarrays were prehybridized with a solution of 2x MES hybridization buffer (100 mM 2-morpholinoethanesulfonic acid, 1.0 M Na + , 20 mM EDTA, 0.01% Tween 20), 50 ?g of herring sperm DNA, and 250 ?g of acetylated bovine serum albumin (BSA) at 45?C for 15 min followed by hybridization with 10 ?g of denatured and fragmented cRNA per microarray, 3.5 ?l of CPK6 control oligo, 35 ? of herring sperm DNA, 175 ?g of acetylated BSA, and 2X MES buffer at 45?C for 16? 20 h with constant rotation. After hybridiza 88 two str 15 ly ed in A. ray and bioinformatic data analysis images using the NimbleScan software imblegen, Inc.), gene calls (a single expression intensity value based on the multiple probes s. ingent washes (100 mM MES salt and free acid solution, 0.1 M Na + , 0.01% Tween 20) at 45?C for 15 min each. After a final one minute rinse with nonstringent buffer, the arrays were placed into a 1x stain solution (100 mM MES, 1 M Na + , 0.05% Tween 20, 50 mg/ml BSA, and 1 ?g/?l Cy3-streptavidin) at room temperature for min, agitating every few minutes. The microarrays were removed from the stain solution and placed in fresh nonstringent wash buffer for one minute. They were then placed into Nimblegen?s proprietary final wash buffer for 30 s, and then immediate dried under a stream of argon gas and scanned using an Axon GenePix 4000B scanner (Molecular Devices, Union City, CA) at 5-?m resolution. Six microarrays were us the experiment, corresponding to the 3 control pools and 3 treatment pools of RN 2.5. Microar After extraction of data from raw (N for each gene) were generated using the Robust Multichip Average (RMA) algorithm (Irizarry et al. 2003) which takes into account only the perfect match oligo RMA takes a background adjustment on the raw intensity scale, carries out quantile normalization (Bolstad et al. 2003), takes the log2 of the normalized background adjusted PM values, and then uses a linear model to estimate expression values on the log scale. Both programs are available in the affy package of the Bioconductor project (http://www.bioconductor.org). The normalized intensity values from the three contr sample microarrays and the three ESC-infected sample microarrays were then analyzed ol 89 o-using the Significance Analysis of Microarrays method (Tusher et al. 2001) in the tw class unpaired mode (SAM version 2.23A:http://www-stat.stanford.edu/~tibs/SAM/). SAM assigns each gene a relative difference score based on its change in gene expression relative to the standard deviation of replicate measurements for that gene. For genes falling above an adjustable threshold, permutations of repeated measur are used to determine a percentage of genes identified by chance, the false discovery rate (FDR; Benjamini and Hochberg, 1995), which is presented as a q-value for each gene in the final list of significant genes (Tusher et al. 2001; Pawitan et al. 2005; Larsson et al. 2005). The q-value, therefore, reflects the variability present in the data set for a given gene. A list of significant genes with at least 2-fold expression changes ements between treatment and control and a global false discovery rate of <10% was produced, quence on the list. In order to provide insight into the potential identities of the differen d the s. and sorted according to fold-change. BLASTX searches were conducted for each se tially expressed genes, a less stringent cutoff E-value (0.0001) was used, an top informative hit was noted. Those sequences possessing no significant similarity to peptide sequences within the nr database were clustered to identify and remove any redundant (blue-channel) sequences. When a putative gene identity was shared by multiple sequences, further sequence analysis was carried out to remove redundancie In cases where blue catfish and channel catfish orthologues of the same gene were differentially expressed, the blue catfish transcript was selected to represent this gene. If multiple blue catfish transcripts were found to be derived from the same gene, the transcript with the lowest q-value was chosen. In cases where two 90 a et al. m y odes differentially-expressed transcripts shared the same putative gene identity but likely represented paralogues, both transcripts were kept on the unique list. Gene annotation was carried out using the BLAST2GO program (Cones 2005), a Java application which enables Gene Ontology (GO) based data mining on sequences for which no GO annotation is currently available. FASTA-formatted sequences representing the unique upregulated transcripts were uploaded to the progra and BLASTX searches carried out. GO terms associated with the hits were retrieved b the program and queries were annotated based on hit similarity and GO evidence c (EC). Some query sequences were not annotated by the BLAST2GO process due to uninformative top BLAST hits. These sequences were therefore searched against the UniProt database (http://www.pir.uniprot.org/) and manually annotated in the BLAST2GO program where appropriate. 2.6. Real-time RT-PCR analysis The RNA prepared for microarray analysis was also used for validation of selected genes of interest by real-time RT-PCR. The three control pools and three treatment pools of RNA, each representing 25 fish, were utilized for each tested gene. One-step quantitative RT-PCR was carried out using a LightCycler 1.0 instrument (Roche Applied Science, Indianapolis, IN) and the Fast Start RNA Master SYBR Green I reagents kit (Roche Applied Science) following manufacturer's instructions with modifications. Briefly, all real time RT-PCR reactions were perform ed in a 10 ?l total reaction volume (9 ?l master mix and 1 ?l (100 ng) RNA template). The master mix 91 of ested genes: (i) reverse s, 5 g, 30 contained 4.3 ?l H 2 O, 0.6 ?l Mn[OAc] 2 , 0.3 ?l of each primer (0.1 ?g/?l), and 3.5 ?l the SYBR Green mix. The same cycling parameters were used for all t transcription, 20 min at 61 ?C; (ii) denaturation, 30 s at 95 ?C; (iii) amplification repeated 50 times, 5 s at 95 ?C, 5 s at 58 ?C, 20 s at 72 ?C; (iv) melting curve analysi s at 95 ?C, 15 s at 65 ?C, then up to 95 ?C at a rate of 0.1 ?C per second; (v) coolin s at 40 ?C. Primers were designed using either the FastPCR program (http://www.biocenter.helsinki.fi/bi/Programs/fastpcr.htm) or the PriFi sequence alignment and primer design program (Fredslund et al. 2005; http://cgi- www.daimi.au.dk/cgi-chili/PriFi/main). Primer names, accession numbers, and sequences are listed in Table 1. The 18S ribosomal RNA gene was selected for normalization of expression levels due to its stable expression levels over a var tissues and treatment conditions in catfish (M iety of urdock et al. 2006). The triplicate uorescence intensities of the control and treatment products for each gene, as values, were compared and converted to fold differences by the relative quantification method (Pfaffl, 2001) using the Relative Expres A fl measured by crossing-point (Ct) sion Software Tool 384 v. 1 (REST) and assuming 100% efficiencies. Expression differences between control and treatment groups were assessed for statistical significance using a randomization test in the REST software. The mRNA expression levels of all samples were normalized to the levels of 18S ribosomal RN gene in the same samples. Expression levels of 18S were constant between all samples (<0.30 change in Ct). Each primer set amplified a single product as indicated by a single peak present for each gene during melting curve analysis. 92 chemokine SCYA106; LAMP3, Lysosomal-associated membrane protein 3; MMP13, Gene Accession Forward Reverse Table 1 Primers used for real-time RT-PCR validation (5?-3?). SCYA106, CC matrix metalloproteinase 13; LY6E2, lymphocyte antigen 6 complex, E2 SCYA106 AY555503 GTCTCTTGGAGAGCAAGCACT G CATCAGCTCTCTGACCCAGTC G Intelectin CF970955 TCGGAGCTGCCGGGACATCAA GGAG CCCTGCTCGCTTGACCAGCGA TCAC LAMP3 TC7925 TCTGAGGTGTTTCTGAACCAG G CCATGCCGAACCTGGC C Hemopexin CK406564 TGACCGCTGTGAGGGCATCGA G TGTGCATGCGGAAGGC CCA MMP13 CF972078 GCTGGCATCGGTGGAGACGCT C ACGTTGGAATGC G G C 18S BE469353 TGCGCTTAATTTGACTCAACA C CGATCGAGACTCACTAACATC G CATCA TGCAT TCAAGGCCT LY6E2 CK404046 GGACACGTCATGACGAGCTCT CCTTCAGGCACAGGCAGATGA 3. Results 3.1. Bacterial challenge and microarray hybridization The artificial challenge with virulent E. ictaluri resulted in mortality of infe fish beginning at day 5 after exposure. No control fish manifested symptoms o and randomly-selected control fish were confirmed to be negative for E. ictaluri by standard diagnostic procedures. Dying fish manifested behavior and external signs associated with ESC infection including hanging in the water column with head up and tail down and petechial hemorrhages along their ventral surface. E. ictaluri bacteria were successfully isolated from randomly-selected treatment fish. While two time points (24 hr and 3 d) were selected for sampling, only the 3 d time point was chosen cted f ESC for microarray analysis, due to financial restraints and a desire to include sufficient 93 nt organ ples were e rom o ol treatment replicate pools (n=25), labeled, and hybridized to six high-density in situ oligonucleotide microarrays for catfish. The catfish microarray contains 28,518 expressed sequences from channel catfish and blue catfish, each represented by at least s i go 3.2. Microarray analysis of blue catfish expression following challenge expression profile of blue catfish liver three days after infection with E. taluri were compared with the levels seen in uninfected blue catfish. After data Irizarry et al. 2003), the resulting expression intensity values were analyzed in SAM (Tusher et al. 2001). The criteria ed, nel f a biological replicates to allow robust statistical analysis. As liver is an importa to innate immunity, it was selected for microarray analysis. Six RNA sam xtracted f the livers f the three control replicate po s (n=25) and the three ix probe pa rs of 24 oli nucleotides each. The ic normalization and gene expression calculation in RMA ( of a two-fold or greater change in expression and a global false discovery rate (FDR) of 10% were chosen to determine upregulated or downregulated genes in the infected replicates. Using these criteria, 126 transcripts were significantly upregulat and 5 were significantly downregulated (Supplemental Tables 5 and 6?see appendices). Of the 126 upregulated catfish transcripts, 98 of these are believed to represent unique genes. The redundant transcripts resulted either from blue and chan orthologues of the same gene or multiple transcripts from non-overlapping regions o large gene being included on the microarray. 94 ould 01; and by searches against the UniProt databas . Annotation results are summarized in Fig. 1. GO terms were ultimately iological rocesses assigned to the upregulated transcripts revealed that many shared putative functio 76 al n, 3.3. Bioinformatic analysis of induced transcripts following infection Of the 98 unique, significantly upregulated transcripts after infection, 76 c be annotated based on sequence similarity by BLASTX searches while 22 had no significant similarity to protein sequences in the nr database (cutoff E-value=0.00 Table 2 and Supplemental Table 7). Gene ontology annotation was carried out using the BLAST2GO program (Conesa et al. 2005) e assigned to 70 sequences. Analysis of specific (>level 6) GO terms for b p ns related to ion homeostasis and immune responses. Other large categories included those related to protein modification, folding, and transport (Fig. 2). The sequences with significant BLASTX hits were divided into similar broad function categories in Table 2. The majority of the upregulated transcripts were grouped into six categories each with at least 5 members?acute phase response; complement activation; metal ion binding/transport; immune/defense response; protein processing, localizatio folding; and protein degradation. 95 ng ESC infection. Accession, GenBank accession number or TIGR consensus number of the sequence on e microarray; Putative Id, top informative BLASTX hit; q-value, false-discovery rate for the particular gene; Functional Classification, putative functions assigned based on gene on response encompasses bold transcripts included in other categories. Transcripts were transcripts could be classified into multiple categories but are listed under the most . Genes were sorted by fold change within functional categories Classification Change ) Table 2 Catfish transcripts upregulated in the blue catfish liver followi th tology annotations and Uniprot entries of top BLAST hits. *Acute phase grouped into broad functional categories of at least 5 unique transcripts. Some specific category. Gene names appearing more than once should represent paralogues Functional Accession Putative Id Fold q-value (% Acute phase response* CK407841 Fibrinogen gamma polypeptide 5.7 5.63 CF971953 Fibrinogen, beta chain isoform 4 5.6 9.82 TC8490 Fibrinogen, B beta polypeptide 4.2 7.00 CK408173 Pentraxin (Serum amyloid P-like) 4.1 8.70 CF971852 Fibrinogen alpha chain 3.8 9.82 BM438634 Angiotensinogen 2.4 9.82 Complement activation EE993177 Complement factor H precursor 14.5 0.00 EE993354 Complement component 7 precursor 10.1 9.18 CV997126 Complement C3 6.1 8.97 EE993343 Complement C4 3.9 5.63 CV987901 Complement C3-H1 3.2 5.63 CK406493 Complement C3-Q1 3.1 5.63 Metal ion binding/transport CF970955 Intelectin 48.6 9.82 CK408483 Haptoglobin precursor 20.4 9.82 CF971550 Warm relat TC7660 Complement component C9 5.2 8.70 CF971897 Intelectin 2 37.7 5.63 -temperature-acclimation- ed-65kDa (Hemopexin-like) 12.3 8.70 CK406564 Hemopexin precursor 9.7 5.63 CK408512 Solute carrier family 31 (copper transporters), member 1 7.1 8.70 CF971219 Ceruloplasmin 4.7 9.18 CK408666 Transferrin 4.1 9.82 CK418197 Cytochrome P450 3A 3.6 7.00 Immune/defense response AY555503 CC chemokine SCYA106 105.1 9.18 TC7475 CC chemokine SCYA113 12.5 8.97 TC7925 Lysosomal-associated membrane protein 3 (CD208) 5.7 9.82 TC9859 MHC class I alpha chain 4.7 8.70 CF972295 Thioredoxin 3.8 9.82 96 9.82 T Lectin, galac g, soluble, 9 (galectin 9)-like 1 rocessing, calization, lding TC9330 ER-resident chaperone calreticulin 4.8 5.63 (IRL685) ) CK405569 beta 2.8 9.82 BM438439 nce receptor, alpha 2.6 8.70 se) 94) osaccharide- ase (48kDa) ) activator PA28 subunit, ) iscellaneous ociated protein 4 enosyltransferase II tein 4 7 nsporting lysosomal BM438717 Tumor necrosis factor, alpha-induced protein 9 isoform 2 3.4 7.00 CF971576 Tumor necrosis factor, alpha-induced protein 9 3.1 9.18 EE993326 CD63 2.6 9.82 TC7043 CCAAT/enhancer binding protein (C/EBP), beta 2.6 7.00 CK404046 Lymphocyte antigen 6 complex, locus E ligand isoform 2 2.5 CK401855 MHC class I alpha chain 2.5 5.63 TC6716 Beta-2 microglobulin precursor 2.3 9.82 C8645 toside-bindin 2.3 9.82 Protein CV989503 CXCL14 2.2 9.82 p lo fo CK407547 Protein disulfide isomerase associated 4 (Erp72) 4.7 5.63 TC7345 Fetuin-B precursor 3.3 9.18 CK411755 Integral membrane protein 1 (STT3 3.0 5.63 Translocon-associated protein (SSR2) Signal seque (SSR1) TC8981 FK506 binding protein 2 (PPIa 2.6 5.63 CK415655 Endoplasmin (GRP 2.4 9.82 TC9170 Dolichyl-diphosphoolig protein glycosyltransfer Dnajb11 protein (ERdj3 2.3 9.82 CK406459 2.1 9.18 Protein egradation d CK409611 Proteasome alpha Proteasome (p 3.2 9.82 TC9755 rosome, macropain) subunit, alpha type, 3 3.1 9.18 CF972078 Matrix metalloproteinase 13 Proteasome activator PA28 subunit, 2.8 5.63 TC6963 beta 2.3 9.82 TC7388 Proteasome (prosome, macropain subunit, beta type, 6 2.2 9.82 M CK406362 Microfibrillar-ass 27.1 5.63 CK405246 Methionine ad alpha subunit 8.3 9.82 CK 407588 Catechol-O-methyltransferase domain containing 1 7.9 8.70 BM438689 Microfibrillar-associated pro 7.5 8.97 CK CK408412 408535 Apolipoprotein Apoa4 protein Microfibrillar-associated protein 4 4.1 4.1 8.70 5.63 CK40531 Beta-actin 4.0 5.63 TC6790 Atpase H+ tra 3.0 5.63 97 e family, member V, 6 se 1 TC9648 sor protein- 2 2.4 9.82 9 e 5 CV995433 ent-binding EBP-2) 2.2 9.82 2 CK407421 Glutaredoxin (thioltransferase) 2.0 9.82 CF971597 Hypothetical protein XP_683888 20.6 9.82 CK407596 3.7 9.82 TC9161 tical protein LOC641319 2.0 5.63 vacuolar proton pump Armet protein TC 7903 CK406132 2.9 2.9 9.18 9.82 Alpha-1-tubulin CK404348 H2A histon isoform 1 WW domain binding protein 2 2.7 8.97 CK40168 2.5 9.82 CK424035 Neuronal myosin light chain kina Amyloid beta (A4) precur 2.5 8.97 binding, family B, member CV98794 Fructose-1,6-bisphosphatase 1, lik 2.3 8.70 CV99099 Coactosin-like 1 Sterol regulatory elem 2.3 9.82 protein 2 (SR Unknown CV99516 Alcohol dehydrogenase 5 2.2 9.82 CK401799 MGC68649 protein LOC407646 protein 3.8 5.63 TC6930 LR8 protein 2.3 9.18 CK406492 Similar to family with sequence similarity 46, member A isoform 1 Hypothe 2.3 0.00 22% 6% 41% 31% No BLASTX identity BLASTX identity, no GO a tionnnota BLASTX identity, BLAST2GO annotated BLASTX identity, manually annotated F g. 1. Analysis a t ique, ificantl u regulated transc e i nd Gene On ology (GO) annotation of 98 un sign y p ripts in blu catfish. 02468101 cellular protein metabolism cation homeostasis metal ion homeostasis di-, tri-valent inorganic cation homeostasis humor 2 respon c ivati transition sta i respon establishment zati bi cati p cati nsp protein foldi cal sta sta nsp macrom ynthes cellular mac i tati ripti transcri nd y intracel por argeti Bi ol a l pr oc e ss ( G O ) Number of transcripts al immune se omplement act metal ion homeo on sis nflammatory se of cellular locali on opolymer modifi on electron trans ort protein modifi on intracellular tra ort ng siscium ion homeo iron ion homeo sis protein tra ort olecule bios romolecule c is smatabol antigen presen on transc on ption, DNA-depe ent proteol lular protein trans sis t protein t ng ogi c Fig. 2. Significantly upregulated transcripts assigned to lower level (>6) GO biological process categories. 53 sequences had a biological process GO term. 3.4. Conserved acute phase response in blue catfish lated t least 20 of the 98 unique, annotated transcripts represented likely acute phase proteins (APP; Bayne et al. 2001), divided A conserved acute phase response was evident in the significantly upregu catfish transcripts following infection. A 98 99 among the acute phase response, complement activation and metal ion binding/transport categories in Table 2 (bold names). Transcripts falling within these categories were among the most highly upregulated following ESC infection. An active complement response to infection was observed, with three forms of complement C3 upregulated along with C4 and members of the membrane attack complex (C7, C9). The complement regulatory protein factor H was also strongly upregulated (>14 fold). Genes involved in iron binding and transport in mammals were strongly induced following infection. These included intelectin, haptoglobin, hemopexin/warm- temperature-acclimation- related, ceruloplasmin, and transferrin. Other upregulated APP included pentraxin (serum amyloid P-like), fibrinogen, and angiotensinogen (Table 2). 3.5. Protein processing, localization, folding and degradation after ESC infection A large number of transcripts with likely functions in protein modifications and rotein response (UPR) which upregulates chaperones and genes for protein ins during stress (Szegezdi et al. 006), or to the degradation and processing of antigens for the MHC class I molecule. At leas degradation were upregulated in the liver following infection. Members of these two groups of genes were likely connected to the endoplasmic reticulum?s (ER) unfolded p degradation upon the accumulation of unfolded prote 2 t 15 unique transcripts were upregulated in these two categories including chaperones, proteasome activators, and proteasome subunits (Table 2). 100 e fish liver. C , among Downregulated transcripts following ESC infection ion with E. ictaluri (Table 3). Interestingly, two of the three transcripts 3.6. Induction of immune/defense response related transcripts Upregulated transcripts with established roles in immune responses comprised another large functional category, indicating that active immunosurveillance, immun signaling, and immune cell activation were occurring in the infected blue cat These included the most highly upregulated transcript observed, CC chemokine SCYA106, at 105-fold. Other induced immune genes included two types of MH class I alpha chain, CD63, CC chemokine SCYA113, CXCL14, and galectin 9 others (Table 2). 3.7. A smaller number of catfish transcripts were significantly downregulated following infect with known identities were catfish selenoproteins P1b and selenoprotein H which may possess antioxidant properties (Steinbrenner et al. 2006). A cell cycle gene, anaphase promoting complex subunit 13, was also downregulated. 101 Table 3 Unique, significantly downregulated catfish transcripts in blue catfish liver after SC infection. Accession refers to the GenBank accession number or TIGR consensus number of the sequence on the microarray. Putative Id is the top informative BLASTX hit. q-v function of top BLAST hit Change value E alue is the false-discovery rate for the particular gene. Function is putative Accession Putative identity Fold q- Function CK41760 0 No significant similarity 0.4 9.57 NA TC9079 Anaphase promoting complex subunit 13 0.5 9.57 Cell cy CB94079 Selenoprotein H 0.5 9.57 Stress o response CF971521 Selenoprotein P, plasma, 1b 0.5 9.57 Stress or cle 0 r defense TC9060 No significant similarity 0.5 9.57 NA defense response ll tested genes except intelectin showing statistically significant upregulation greater than 2-fold (P<0.05). A strong upregulation of intelectin following infection was confirmed, despite the p-value falling slightly above the set threshold due to greater variations among the biological plicates. Fold changes measured by real time RT-PCR were larger than those easured by microarray likely due to the more specific binding conditions of the PCR 3.8. Real-time RT-PCR confirmation of microarray results Expression patterns of six genes identified by microarray analysis as differentially expressed following infection were selected for validation using real-time RT-PCR. Genes upregulated ranging from 2.5-fold to 105-fold in the microarray experiment were selected and primers designed (Table 1). Real-time RT-PCR results (Table 4) generally confirmed the microarray results, with a re m 102 action, and perhaps also due to the greater accuracy in quantitation by real-time PCR Table 4 Validation of microarray results by QRT-PCR. SCYA106, CC chemo SCYA106; LAMP3 embrane pro M m m ote phocyte antigen 6 comp E2 ene ld Change RT-PC ld re than by microarrays. kine , Lysosomal-associated m inase 13; LY6E2, lym tein 3; M P13, atrix etallopr lex, G Accession Microarray Fo Q R Fo Change SCYA106 3 +105 +74 0.0AY55550 1 (p= 47) Intelectin CF970955 +48 +455 (p=0.0 +5.7 +90.9 0.0 +9.7 +39 .0 MMP13 CF972078 +2.8 +2.6 (p=0.0 LY6E2 CK404046 +2.5 +4.3 (p=0.047) 55) LAMP3 TC7925 (p= 47) Hemopexin CK406564 (p=0 47) 47) n luri et al. 2005; Peatman et al. 2005; Bao et al. 2006a; Peatman et al. 2006;Wang et al. 4. Discussio We have utilized a high-density oligonucleotide microarray for catfish in order to study the transcriptomic responses of blue catfish following infection with E. icta and to identify and develop important immune-related markers for future characterization and genetic mapping. Microarray analysis of the transcriptome profile of the blue catfish liver following infection with the Gram negative bacterium led to the identification of 103 differentially expressed transcripts. The generation of a large set of catfish ESTs has aided the rapid identification and characterization of many innate immune components including cytokines and chemokines (He et al. 2004; Baoprasertkul et al. 2004; Chen et al. 2005a; Baoprasertkul 103 ke receptors (Baoprasertkul et al. 2006; 2007). To better utilize atfish EST resources and to analyze the expression of these important immune on, a tfish array provides a reasonably comprehensive platform from w o study express t tissues and organs of catfish species. liver was s the center acute phase res likely contributor to the acute inflammatory reaction observed in the catfish response to irulent E. ictaluri. The catfish APR as measured three days after infection included any of the components of the mammalian APR and also contained commonalities PR study in rainbow trout liver (Gerwick et al. 2007) and other previous lmonid and carp microarray experiments measuring expression in liver after ay and s of APP were reported to be - n of 2006c), antimicrobial peptides (Bao et al. 2005; 2006b; Wang et al. 2006a; 2006b; Xu et al. 2005), and Toll-li c components in the larger context of the catfish transcriptome following ESC infecti 28K in situ oligonucleotide microarray was designed. The 28K ca hich t ion in importan The targeted a of the ponse and as a v m with a recent A sa application of a variety of stressors (Tilton et al. 2005; Ewart et al. 2005; Martin et al. 2006; Reynders et al. 2006). Acute phase proteins composed a significant percentage of upregulated transcripts in blue catfish. Among the mammalian APP (Gab Kushner, 1999) upregulated greater than 2-fold in blue catfish were haptoglobin, hemopexin, transferrin, ceruloplasmin, fibrinogen, angiotensinogen, pentraxin and several complement components (Table 2). Similar subset differentially expressed in rainbow trout (Gerwick et al. 2007) and as measured by real time PCR in zebrafish (Lin et al. 2007), indicating the likely conservation of functio the vast majority of APP between mammals and teleost fish. 104 n multiple forms in fish, possibly serving as an eost d the . lved rmation that could amplify all the differentially expressed transcripts. Many of APP observed to be upregulated in blue catfish liver were likely serving important functions in host defense. Pentraxin, upregulated 4.1-fold in the current study, has recently been shown to be capable of initiating the complement cascade and possesses opsonizing activity in the snapper Pagrus auratus (Cook et al. 2003; 2005). The complement system of teleost fish plays conserved roles in sensing and clearing pathogens (Boshra et al. 2006). C3, as the central component of the complement system, is present i expanded pathogen recognition mechanism (Sunyer et al. 1998). We detected three upregulated forms of C3 in blue catfish liver, emphasizing its importance in the tel innate immune response. Complement C4, important for the activation of the lectin an classical complement pathways, was also upregulated strongly. Two components of membrane attack complex which carries out cell lysis, C7 and C9, were both upregulated greater than 5-fold. Interestingly, the highest upregulation among complement-related factors (14.5-fold) was seen for complement factor H which may inactivate C3b in the alternative complement pathway (Boshra et al. 2006), suggesting that the host fish were attempting to modulate the complement response (Table 2) Intelectin was the most highly upregulated gene among several likely invo in iron homeostasis, binding, and transport (Table 2). Induction of intelectin was previously reported in carp (Reynders et al. 2006) and rainbow trout (Gerwick et al. 2007). Four transcripts representing intelectin on the catfish microarray were highly upregulated (Supplemental Table 5) and these transcripts appear to represent at least two genes. Primers were designed for real-time RT-PCR confi 105 ial plasma a access ns seen in f may metabolism Real-time RT-PCR showed a 455-fold upregulation in gene expression following infection (Table 4). In mammals, intelectin is believed to be involved in pathogen defense mechanisms, recognizing galactofuranose in carbohydrate chains of bacter cell walls (Tsuji et al. 2001) and may function as a receptor for lactoferrin, an iron sequestering homologue of transferrin (Suzuki et al. 2001). We are currently investigating the function of catfish intelectins in the context of iron and disease. Regulation of iron homeostasis was a key component of the acute phase response observed in blue catfish. Iron regulation also plays an important role in the mammalian host response to pathogens. In mammals, interleukin-6 induces production of hepcidin in the liver. Hepcidin then blocks the release of iron from macrophages, hepatocytes, and enterocytes by internalizing and degrading ferroportin, the site of cellular iron export (Nemeth et al. 2004a). This leads to drastically decreased iron levels during infection, a potential host defense mechanism to deny bacteri to the critical metal (Schaible et al. 2002). Liver iron stores are known to be significantly increased by hepcidin, even as plasma iron concentrations decline (Rivera et al. 2005). The increase in expression of iron storage, binding, and transport protei the results (Table 2) may be the result of increasing iron concentrations in the liver. Hepatocytes, which account for 80% of the liver mass, are the primary site o synthesis for haptoglobin, hemopexin, transferrin, and ceruloplasmin (Anderson and Frazer, 2005). In mammals many of these genes are active in sequestering iron to restrict its availability to invading bacteria, and several are known to possess immunoregulatory and antioxidant properties under pathological conditions which supersede the importance of their roles in normal iron 106 and et ed to the rved to 7; in is late infection ns een Martin et als, CXCL14 is (Melamed-Frank et al. 2001; Gueye et al. 2006; Tolosano and Altruda, 2002; Legr al. 2005; Giurgea et al. 2005; Anderson and Frazer, 2005). Further efforts are need elucidate the role iron regulation plays in the catfish defense response. The absence of the iron regulatory hormone hepcidin (Park et al. 2001) from set of upregulated genes in blue catfish liver was notable given that it was obse be highly upregulated in other teleost expression studies in liver (Gerwick et al. 200 Tilton et al. 2005; Ewart et al. 2005; Martin et al. 2006; Lin et al. 2007). Hepcid represented by at least three transcripts on the microarray, none of which showed significant upregulation. This result, however, agrees with our previous investigation of hepcidin expression in channel catfish (Bao et al. 2005) which showed that hepcidin expression was minimally upregulated in liver at 3 d post ESC infection. We specu that by the 3 d time point after infection in blue catfish, hepcidin may have returned to basal levels after an earlier induction. A large group of transcripts with putative roles in immune responses to were upregulated (Table 2). Two CC chemokines, SCYA106 and SCYA113, previously identified from catfish were highly induced (He et al. 2004; Peatman et al. 2006; Bao et al. 2006a). SCYA106 was the most highly upregulated transcript in this study (>105-fold). Both SCYA106 and SCYA113 are most similar to mammalian CCL19 (MIP-3?), a regulator of dendritic cell trafficking to secondary lymphoid orga (Humrich et al. 2006). Upregulation of CCL19-like genes after infection has also b recently reported in rainbow trout and Atlantic salmon (Morrison et al. 2006; al. 2006). A catfish orthologue of CXCL14 chemokine (Baoprasertkul et al. 2005) also showed heightened expression in the liver after infection. In mamm 107 induced in t- roles proliferation of catfish B cells (Khayat et al. 2001), hocyte . known as a chemoattractant for activated monocytes, immature dendritic cells, and NK cells (Starnes et al. 2006). Two lesser known immune transcripts observed in catfish were also zebrafish following infection with Mycobacterium marinum (Meijer et al. 2005). Lysosomal-associated membrane protein 3 (DC-LAMP) was upregulated strongly both in the microarray analysis and in real-time RT-PCR confirmation and is associated with the endosomal/lysosomal MHC II compartments of dendritic cells in humans (de Sain Vis et al. 1998; Arruda et al. 2006). Galectin-9 has recently been reported to play in both innate and adaptive immunity--it possesses eosinophil chemoattractant activity, induces superoxide production, induces dendritic cell maturation, and promotes Th1 immune responses (Dai et al. 2005). Thioredoxin, upregulated 3.8-fold in this study, has been reported previously to have important roles in the activation and and may be also protecting the catfish liver against oxidative stress-induced damage (Isoda et al. 2006). A catfish transcript with highest similarity to lymp antigen 6 complex, locus E (LY6E) was also induced. Interestingly, this gene in chicken has been identified as a putative disease resistance gene for Marek?s disease virus by protein binding assays, linkage analysis, and microarrays (Liu et al. 2003a) The upregulation of CCAAT/enhancer binding protein beta (C/EBP) was likely linked to the active acute phase response observed (Table 2). This transcription factor is induced by pro-inflammatory cytokines, and in turn regulates the expression of many acute phase reactants (Poli, 1998). 108 d active antigen processing and presentation were likely occurri t atterns following pathogen infections. Similarly, minima as ays The upregulation of two different MHC class I alpha chains and beta-2- microglobulin (? 2 m) indicate ng in the catfish liver 3 days after infection as part of a cell-mediated immune response. E. ictaluri, as an intracellular bacterium, has been observed by electron microscopy in vacuoles within liver macrophages 48 hr post infection and within the vacuoles of hepatocytes 72 hr post infection. The bacterium was also observed to survive and replicate within phagocytic cells (Baldwin and Newton, 1993). A MHC class I and CD8 + cytotoxic T lymphocyte (CTL)-mediated response, therefore, would be an expected response to E. ictaluri-infected cell types in the liver. The MHC class I genes from catfish have been extensively characterized (Antao et al., 1999; 2001), bu little is known about their expression p l expression analysis of MHC class I-related genes has been conducted in teleost species following infection with intracellular bacteria. In mammalian systems, Listeria monocytogenes is an intracellular bacterial pathogen that has been well characterized a model organism for the study of cell-mediated immunity. Several recently described characteristics of the host response to L. monocytogenes may help to explain the expression patterns observed in blue catfish. After exposure to L. monocytogenes, hepatocytes upregulate MHC class I heavy chain and ? 2 m, producing a rapid influx of newly generated peptides into the endoplasmic reticulum (Chen et al. 2005b). CD8 + T cells have been found to serve an important role in the innate immune response 3 d after infection by L. monocytogenes by rapidly secreting IFN- in response to IL-12 an IL-18 (Berg et al. 2003). This rapid d CD8 + T cell IFN- response has been associated 109 ith host s with lower bacterial burdens in the liver 3 days post infection and is correlated w resistance in mice (D?Orazio et al. 2006). In mammals, antigenic peptides presented on MHC class I molecules to CTL are generated in the cytosol by degradation in the proteasome, translocated into the endoplasmic reticulum, and loaded onto the MHC molecule with the help of several protein components. Genes associated with the generation of peptides and peptide- loading for the MHC class I molecules were also observed to be upregulated in blue catfish liver (Table 2). Studies of intracellular bacterium L. monocytogenes again provide insights into these expression patterns. Khan et al. (2001) reported the replacement of constitutive proteasomes with immunoproteasomes in mice livers starting two days after infection with L. monocytogenes. Immunoproteasomes support the generation of MHC class I epiptopes and shape immunodominance hierarchies of CD8 T cells (Chen et al. 2001). This switch in mice is marked by the upregulation of proteasome activator PA28? and PA28? subunits (Khan et al. 2001) + , which alter the fragmentation of polypeptides through the proteasome and are inducible by IFN- (Ahn et al. 1995; Groettrup et al. 1996). Both PA28 nits ng cells. ? and ? proteasome activator subu were observed to be upregulated in blue catfish (Table 2), suggesting a shift toward MHC class I antigen processing. This pathway has recently been reported to be particularly important for protection against L. monocytogenes in hepatocytes, where infection triggers expression of immunoproteasomes and eventual generation of CD8 + T-cell epitopes needed for bacterial clearance (Strehl et al. 2006). The author?s data supported the view that, during infection, hepatocytes act as effective antigen presenti 110 urther evidence of an active MHC class I- ediated lpha cea) rt of , ol f d per group. Most of the catfish transcripts differentially expressed greater than Two ER chaperones, calreticulin and endoplasmin (GRP94), were also induced in the blue catfish liver, providing f m response (Table 2). Among ER chaperones, GRP94 and calreticulin are apparently unique in their ability to bind peptides suitable for assembly on to MHC class I molecules (Nicchitta and Reed, 2000). We also noted that tapasin, another molecule involved in MHC class I antigen loading, was upregulated 2.3-fold on the microarray, but, with a q-value of 11%, was excluded from the set of genes declared significantly upregulated. Recently, the coordinated upregulation of MHC class I a chain, ? m, and PA28-? was reported in large yellow croaker (Pseudosciana cro following poly I:C injection (Liu et al. 2007). Our findings represent the first repo the coordinated upregulation of these and several other MHC class I-related components following a bacterial infection in fish. Further gene and cellular-based studies are needed in catfish to understand the importance of MHC class I/CTL- mediated responses to ESC infection. Pooling and variation, in relation in microarray analysis, have been subjects of vigorous debate (Kendziorski et al. 2005; Jolly et al. 2005). In the present experiment we utilized RNA samples from three distinct treatment pools and three distinct contr pools for microarray analysis and were able to identify a large, reproducible set o differentially expressed transcripts. Our interest in primary transcriptome analysis lies more with global expression patterns rather than inter-individual variations. In a recent review article in Nature Genetics, Allison et al. (2006) recognized the advantages of pooling for decreasing both variability between arrays and cost, if multiple pools were analyze 2 111 ed bles far response ned ed as significant, given that the pooled fish were tion two-fold had q-values of 5% or greater, indicating a certain level of variation exist even between the replicate pools. Real time RT-PCR, however, confirmed that transcripts approaching the 10% FDR cutoff were still significantly upregulated (Ta 2 and 4). In addition, the induction of multiple components belonging to the same pathways and biological processes (described above) provided validation of the new catfish microarray as a powerful tool for immune-related transcriptomic analysis. A smaller number of transcripts were declared significantly downregulated than significantly upregulated, a seemingly characteristic result of transcriptomic analyses of bacterial infections. A similarly small number of transcripts were reported to be significantly downregulated in Atlantic salmon following a Gram negative bacterial infection (Ewart et al. 2005). This may reflect the nature of the inflammatory in liver and/or be the result of the more transitory downregulation of genes being masked in the pooled samples. Genes that are differentially expressed in a sustai manner were more likely to be identifi potentially at different stages of the infection. The larger set of non-significant downregulated genes may still yield candidates for further mapping and analysis. In conclusion, microarray analysis of gene expression changes in blue catfish liver after infection with Gram negative bacterium E. ictaluri indicated the upregula of several pathways likely involved in the inflammatory immune response. A multifaceted response to infection could be observed, encompassing the complement cascade, iron regulation, inflammatory cell signaling, and antigen processing and presentation. The induction of several components of the MHC class I-related pathway following infection with an intracellular bacterium is reported here for the first time in 112 ols m oodm osis of fish. Taken together, the microarray results add to our understanding of the teleost immune responses and will provide a solid foundation for future functional characterization, genetic mapping, and QTL analysis of immunity-related genes from catfish. Acknowledgments This project was supported by a grant from USDA NRI Animal Genome To and Resources Program (award # 2006-35616-16685), and in part by a seed grant fro the AAES Foundation. We are grateful for an equipment grant from the National Research Initiative Competitive Grant no. 2005-35206-15274 from the USDA Cooperative State Research, Education, and Extension Service. Jeffery Terhune, Puttharat Baoprasertkul, Peng Xu, Samiran Nandi, Shaolin Wang, Benjaporn Somridhivej, Huseyin Kucuktas, Ping Li, Rex Dunham, and Zhanjiang Liu were coauthors on the manuscript. We thank Renee Beam, Esau Arana, and Randell G an for their excellence in the production and maintenance of fish used in this study and their assistance during challenge experiments. We are grateful to Karl Hayden of the Fish Disease Diagnostic Laboratory for his assistance in the diagn the ESC disease and the identification of the bacterial pathogen. 113 V. CONCLUSIONS he development of genetically-superior catfish brood stocks resistant to major diseases nificant, lasting economic stimulus to the U.S. catfish industry. rogress toward this goal will be made more rapidly as genomic tools and reagents are develop ork ome s rtant rinted stered in the catfish enome, but also extensively duplicated at various levels. Additionally, the expression atterns of the transcripts of the 26 catfish CC chemokines were analyzed in head idney and spleen in response to bacterial infection of E. ictaluri. T would represent a sig P ed and implemented for catfish. These tools will automate and simplify once overwhelming tasks and serve to connect phenotypic differences with their genotypic origins. Expressed sequence tags (ESTs) are one such genomic tool. Additional w is required, however, to utilize effectively the short transcript sequences for gen research. This may take the form of: development of markers from microsatellite contained within ESTs; sequencing, expression analysis, and mapping of impo genes or gene families identified from ESTs; or construction of microarrays for transcriptome analysis based on the EST sequences. In this work, 26 CC chemokines from catfish were mapped to BAC clones. Through a combination of hybridization and fluorescent fingerprinting, 18 fingerp contigs were assembled from BACs containing catfish CC chemokine genes. The catfish CC chemokine genes were found to be not only highly clu g p k 114 ESTs were also utilized in the development of a 28K in situ oligonucleotide microarray composed of blue catfish (Ictalurus furcatus) and channel catfish (Ictalurus punctatus) transcripts. Initial microarray analysis in channel catfish liver following an fection with E. ictaluri captured 212 unique, differentially expressed transcripts, and indicated a conserved acute phase response occurs as part of the innate immune ique, strong upregulation of several pathwa to n of r description of in response of primitive teleost fish. The majority of classical acute phase proteins were strongly upregulated in catfish along with a set of putative ?teleost? acute phase reactants. Several transcripts involved in iron homeostasis were highly induced, suggesting that catfish may attempt to limit free iron availability to inhibit bacterial growth and avoid metal-induced cellular damage. Strong upregulation of the complement cascade, pathogen recognition receptors and chemokines indicated that the catfish liver plays an integral role in pathogen recognition and defense as well as inflammatory signaling. In a parallel study to validate the microarray for blue catfish, analysis of gene expression changes in blue catfish liver after E. ictaluri infection captured 103 un differentially expressed transcripts, and indicated the ys involved in the inflammatory immune response. A multifaceted response infection could be observed, encompassing the complement cascade, iron regulation, inflammatory cell signaling, and antigen processing and presentation. The inductio several components of the MHC class I-related pathway following infection with an intracellular bacterium was reported for the first time in fish. The CC chemokine family from catfish, as the largest chemokine family characterized from fish to-date, has served as a reference source fo 115 hemok ). ides a 2007). n s family ith resistance or susceptibility to ESC. The investigation of the ave cleotide microarrays from ch re, c ines from other fish species (i.e. Gonzalez et al. 2007; Peatman and Liu 2006 More concrete comparisons can be made between catfish and other fish chemokines than with mammalian chemokines. Additional functional information will be gained as orthologues of catfish CC chemokines are identified in other fish species during the course of infection studies. The mapping of the CC chemokine gene family prov useful dataset for the study of gene duplication and divergence in catfish. A database- searchable, BAC-based physical map for catfish will soon be available (Xu et al. This will allow further analysis of the extent of clustering of the CC chemokines i catfish. Additionally, microsatellites identified from sequencing of the 26 CC chemokine genes (Bao et al. 2006) are currently being utilized for mapping thi to the catfish linkage map. This will provide information as to the chromosomal arrangement of the chemokines and will serve to integrate the two maps as well as testing for correlations w catfish CC chemokines also led to the subsequent identification of a large number of unannotated CC chemokine genes in zebrafish (Peatman and Liu 2006). Analysis of the CC chemokines of catfish and zebrafish confirmed that lower teleost fish appear to h a large number of species-specific tandem and segmental duplications of CC chemokine loci. Further investigation of this phenomenon is needed given that similar genomic regions in humans are increasingly recognized as hot spots for gene copy number variation and are often linked to disease (Redon et al. 2006). The construction and utilization of high-density oligonu annel catfish and blue catfish ESTs represent a strong foundation for futu widespread use of microarrays in catfish research. As technological breakthroughs 116 he h. , , the re ish oth species manifested high in continue to increase gene spotting densities and detection sensitivity and decrease cost/array, microarrays will become a more practical tool for agricultural research. T initial studies presented here produced informative, reproducible results. They also helped to identify areas for improvement for future array designs and studies in catfis The microarray results demonstrated the importance of a multifaceted, integrated approach to genome research. A number of chemokines, Toll-like receptors and antimicrobial peptides previously characterized in our lab were differentially expressed on the microarrays. This made results more informative and facilitates integrating expression candidates into mapping and QTL analysis. Additionally current studies revealed a number of genes which may play important, but previously undescribed, roles in catfish innate immune responses. A number of these genes a currently being mapped to the BAC-based physical map, analyzed for expression patterns in additional tissues, and sequenced to identify microsatellites for linkage mapping. Besides making the catfish genome maps more informative, this will allow QTL analysis of these genes in regards to ESC resistance. The entire set of differentially expressed transcripts is also currently being searched for SNPs that would allow them to be genetically mapped. These projects will lend greater biological relevance to the data obtained by microarray analysis. Comparison of sets of differentially expressed transcripts from channel catf and blue catfish revealed more similiarities than differences. B duced expression of acute phase proteins, complement components, and genes involved in iron homeostasis. A MHC class I-related response was evident in blue catfish but not in channel catfish. Overall, however, expression patterns may differ 117 tain conduc ture a issues nate ed nce ray s for be significantly higher than the situ a es more between the two species in their timing than in the presence or absence of cer gene components. Studies are underway to examine and compare expression patterns of a subset of genes at the 24 hr timepoint in both species using quantitative real time RT-PCR. Obviously, future studies could improve on the ones described here by ting microarray analysis over several timepoints. This would likely cap larger set of genes, and earlier timepoints would probably include more downregulated genes than were seen at the 3 d timepoint. In the same vein, studies of additional t would capture different sections of the transcriptome that are contributing to the in immune response. While liver is fairly homogenous with regards to cell types it contains, isolation of individual cell populations from various tissues may be more informative than whole tissue analysis. Additional changes in the future may lend greater power to microarray-bas expression profiling in catfish. An ongoing Joint Genome Institute project to seque 300,000 ESTs from blue catfish and channel catfish will provide a significantly larger set of unique transcripts. This will necessitate construction of a new microar incorporating these transcripts. Since the vast majority of catfish genes should then be included in the transcript set, it may prove feasible to construct spotted oligo array catfish. While initial start-up costs for these arrays will in rrays used here, long-term cost/array will be less, allowing more in-depth microarray studies in catfish. 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Accession refers to the GenBank accession number or TIGR consensus number of the sequence on the microarray. Putative Id is the hit with the most negative E-value along with the E-value of that hit. q-value is the false-discovery rate for the particular gene Accession Putative Id E-value Fold Change q-value(%) CF970955 Intelectin [Ctenopharyngodon idella] 7.00E-32 85.4 1.25 CF971897 Intelectin 2 [Danio rerio] 3.00E-117 40.0 3.27 CK407451 Intelectin 2 [Danio rerio] 8.00E-120 36.0 1.25 BM439193 No significant similarity 35.5 3.27 CK408483 Haptoglobin precursor [Danio rerio] 1.00E-56 34.3 0.00 CK409144 No significant similarity 33.3 3.27 BM438750 Microfibrillar-associated protein 4 [Danio rerio] 1.00E-35 32.9 1.25 CF970863 Microfibrillar-associated protein 4 [Danio rerio] 1.00E-57 30.0 1.25 TC6845 Intelectin [Ctenopharyngodon idella] 4.00E-128 28.0 2.36 BM438689 Microfibrillar-associated protein 4 [Rattus norvegicus] 3.00E-68 25.6 0.00 TC8425 Warm-temperature-acclimation-related- 65kda- protein-like-protein [Oryzias latipes] 2.00E-118 23.4 0.00 CK406362 Microfibrillar-associated protein 4 [Danio rerio] 2.00E-75 23.2 0.00 AY555510 CC chemokine SCYA113 [Ictalurus punctatus] 4.00E-37 22.7 3.27 TC7475 CC chemokine SCYA113 [Ictalurus punctatus] 5.00E-43 21.5 3.27 CK406396 Neurotoxin/C59/Ly-6-like protein [Ctenopharyngodon idella] 6.00E-21 21.3 1.25 CV995517 CC chemokine SCYA113 [Ictalurus punctatus] 2.00E-39 17.7 3.27 CV996365 No significant similarity 15.3 1.25 CV994031 Catechol-O-methyltransferase domain containing 1 [Danio rerio] 3.00E-76 14.8 0.00 TC9205 Hypothetical protein XP_683888 [Danio rerio] 7.00E-48 14.4 1.25 CK406832 No significant similarity 14.2 0.00 CF971597 Hypothetical protein XP_683888 [Danio rerio] 4.00E-44 14.0 1.25 TC8426 Hemopexin precursor [Danio rerio] 5.00E-75 13.6 2.36 CV996638 Apoa4 protein [Danio rerio] 1.00E-93 13.0 2.36 CK406564 Hemopexin precursor [Danio rerio] 1.00E-90 12.9 6.47 CF971550 Warm-temperature-acclimation-related- 65 [Oryzias latipes] 7.00E-104 12.4 0.00 CV993724 Toll-like receptor 5 [Ictalurus punctatus] 2.00E-118 11.8 0.00 EE993358 Complement C3 [Ctenopharyngodon 8.00E-85 11.3 1.25 149 Complement C io] 3 10 1.7 EE9 Complement prot nent C7- 6. 0.00 asma protein lasma protein .00E-27 5 0 CK407588 -O-methyltransferase domain 2.00E-87 7.5 0.00 CK407841 en gamma polypeptide [Danio 5.00E-109 7.3 3.27 CV996644 omponent factor H [Rattus 7.00E-05 7.3 3.27 CK406972 nt similarity 7.2 1.71 CF971378 beta polypeptide [Danio 1.00E-21 6.4 1.71 9 ide [Danio .00E-162 2 BM439121 rity 5.6 5.24 idella] EE993177 Complement factor H precursor [Danio rerio] 2.00E-26 10.9 1.25 CF971448 Catechol-O-methyltransferase domain containing 1 [Danio rerio] 9.00E-48 10.5 0.00 EE993360 Toll-like receptor 5 [Ictalurus punctatus] 1.00E-84 10.0 0.00 CV987901 93362 3-H1 [Cyprinus carp ein compo .00E-59 00E-104 .0 9.7 1 1[Danio rerio] Fibrinogen alpha chain [Danio rerio] TC9637 2.00E-95 9.6 0.00 CK407984 Complement regulatory pl [Paralabrax nebulifer] 1.00E-14 8.9 3.27 TC9194 Complement regulatory p [Paralabrax nebulifer] io] 7 8.9 3.27 CV992853 Ceruloplasmin [Danio rer 8.00E-98 8.5 0.00 TC9833 Microfibrillar-associated protein 4 [Danio rerio] 1.00E-57 8.4 1.25 TC8307 Fibrinogen gamma polypeptide [Danio rerio] 6.00E-162 8.4 1.25 CK40735 Complement regulatory plasma protein [Paralabrax nebulifer] 2.00E-41 8.1 3.27 CF970899 No significant similarity 7.9 3.27 BM43904 TC8765 No significant similarity Transferrin [Salvelinus fontinalis] Catechol 1.00E-160 7.8 7.7 3.27 0.00 containing 1 [Danio rerio] Fibrinog rerio] Complement c norvegicus] No significa BM438893 No significant similarity 6.9 0.00 CK406644 Apoa4 protein [Danio rerio] Fibrinogen, B 2.00E-19 6.9 6.47 rerio] CV989967 Complement C3 [Ctenopharyngodon idella] 4.00E-100 6.3 1.25 CK40660 Fibrinogen gamma polypeptide [Ictalurus punctatus] 2.00E-63 6.2 6.47 TC8306 Fibrinogen gamma polypept rerio] No significant similarity 6 6.1 0.00 CF971612 6.0 3.74 CK40814 No significant similarity 5.9 1.71 CV997096 Apoa4 protein [Danio rerio] 5.00E-55 5.9 5.24 CV996287 No significant similarity 5.7 3.27 CV989503 CXCL14 [Ictalurus punctatus] No significant simila 2.00E-52 5.7 0.00 CV997126 Complement C3 [Ctenopharyngodon idella] 3.00E-44 5.6 0.00 150 CF971852 gen alpha chain [Danio rerio] 7.00E-68 5.5 0.00 4 ursor ociated 00E-16 5. rters), member 1 [Danio rerio] ntinalis] .00E-83 atus] .00E-52 o] BM438634 sinogen [Danio rerio] 6.00E-57 4.5 1.25 2 CF971024 ificant similarity 4.4 3.27 8 beta .00E-67 [Cyprinus io] 2 [Danio rerio] .00E-12 o] Fibrino BM438459 Fibrinogen gamma polypeptide [Danio rerio] 5.00E-28 5.4 1.71 TC7741 Complement factor B/C2-A3 [Cyprinus carpio] 2.00E-114 5.4 3.74 CV992447 Complement component C8 beta [Oncorhynchus mykiss] ss] 4.00E-38 5.4 3.27 BM425349 TC7892 Complement C4 [Oncorhynchus myki Ceruloplasmin [Danio rerio] 2.00E-18 2.00E-104 5.4 5.4 3.27 0.00 CV99588 Solute carrier family 31 (copper transporters), member 1 [Danio rerio] Serum/glucocorticoid regulated kinase 4.00E-67 5.3 0.00 BM494620 [Danio rerio] LOC407646 protein [Danio rerio] 5.00E-73 5.3 1.25 CK407596 3.00E-67 5.3 3.74 EE993354 Complement component 7 prec [Danio rerio] Transferrin [Oncorhynchus tshawytscha] 5.00E-105 5.3 0.00 CF971561 3.00E-35 5.2 5.24 TC8490 Fibrinogen, B beta polypeptide [Danio rerio] 0 5.2 1.71 BM438919 Intelectin [Ctenopharyngodon idella] Erythroblast membrane 2.00E-11 5.0 0.00 EE993545 -ass protein [Danio rerio] 8. 0 3.74 TC7498 No significant similarity 4.9 1.25 TC7249 LOC407646 protein [Danio rerio] 2.00E-84 4.9 0.00 BM438712 Tryptophan 2,3-dioxygenase [Danio rerio] 1.00E-13 4.8 0.00 CK408512 Solute carrier family 31 (copper transpo 8.00E-77 4.8 1.25 CK406912 Complement C4 [Oncorhynchus mykiss] Transferrin [S 6.00E-57 4.8 3.27 CK408638 AY836756 alvelinus fo CXCL14 [Ictalurus punct 8 2 4.7 4.7 1.71 3.27 CK406849 Angiotensinogen [Danio reri No significant similarity 4.00E-90 4.7 3.74 TC9712 4.6 3.27 BM438818 No significant similarity 4.5 0.00 CF971802 LOC407646 protein [Danio rerio] Angioten 1.00E-24 4.5 0.00 CK420078 Selenoprotein P, plasma, 1a [Danio rerio] 5.00E-67 4.4 7.53 BM02794 Selenoprotein P, plasma, 1a [Danio rerio] No sign 4.00E-55 4.4 6.47 EE993162 Complement component C [Oncorhynchus mykiss] 9 4.4 3.27 BM438200 Complement component C5-1 carpio] No significant similarity 1.00E-43 4.4 1.25 CF971526 4.2 3.74 TC9358 Complement C4-2 [Cyprinus carp 1.00E-87 4.2 0.00 BM438717 TNFAIP9 protein isoform 2 4.1 1.25 CK407254 Complement component 9 [Danio reri 7.00E-75 4.1 3.27 151 4 rpio] 2 sociated 4 ] o 8 r E-13 16 0 us us ember 2 .00E-12 4 ykiss] e associated 4 .00E-122 BM438848 No significant similarity 4.0 1.25 BQ097411 No significant similarity Fibrino 4.0 3.27 CF971664 BM43865 gen alpha chain [Danio rerio] Complement factor H precursor 5.00E-46 3.00E-21 4.0 4.0 1.25 3.27 [Oncorhynchus mykiss] Complement C3-H2 [Cyprinus caEE993174 5.00E-84 4.0 1.25 CK408253 Uridine phosphorylase, like [Danio rerio] 2.00E-63 3.9 3.74 CB93968 Protein disulfide isomerase-as [Danio rerio] No significant similarity 8.00E-76 3.9 1.71 BM438858 3.9 3.27 CK407775 Fibrinogen, B beta polypeptide [Danio rerio] 2.00E-25 3.9 1.71 CK409245 Fibrinogen alpha chain [Danio rerio 2.00E-32 3.8 3.27 CK407193 Complement factor H-related 1 [Hom sapiens] 9.00E-23 3.8 3.27 TC7660 CK40732 Complement component C9 [Ctenopharyngodon idella] Coagulation factor XIII B chain precurso 1.00E-19 1.00 3.8 1.71 [Canis familiaris] Tumor necrosis factor, alpha-induced 3.8 6.47 TC7576 protein 9 No significant similari 4.00E-74 3.7 0.00 BM4391 CK41727 ty Cytochrome b5 [Danio re 1 3.7 3.7 3.27 1.71 rio] Protein disulfide-isomerase [Cricet .00E-36 0 TC10030 ul griseus] No sign 3.6 7.53 CF971645 BM438615 ificant similarity Complement C3-S [Danio rerio] 4.00E-69 3.6 3.6 3.27 3.74 TC8237 Protein disulfide isomerase associated 4 [Danio rerio] No significant similarity 6.00E-108 3.5 1.71 CK407075 3.5 3.27 TC9113 No significant similarity 3.5 3.74 BQ097150 SSR alpha subunit [Oncorhynch mykiss] 1.00E-83 3.4 0.00 CK405481 Solute carrier family 38, m [Xenopus tropicalis] 4 3.4 3.27 CF970857 Protein disulfide isomerase associated [Danio rerio] 1.00E-57 3.4 6.47 CK406974 No significant similarity 3.4 1.71 BM438944 No significant similarity 3.4 3.27 CK423432 No significant similarity 3.4 3.27 EE993343 Complement C4 [Oncorhynchus m Chemotaxin [Oncorhynch 3.00E-80 3.4 1.71 TC8567 TC7903 us mykiss] Armet protein [Xenopus laevis] 2.00E-46 3.00E-60 3.4 3.4 1.71 7.53 CK407547 Protein disulfide isomeras [Danio rerio] 1 3.3 6.47 CF972223 No significant similarity 3.3 1.71 TC6684 CV988633 Jun B proto-oncogene, like [Danio rerio] Apoa4 protein [Danio rerio] 3.00E-14 2.00E-65 3.3 3.3 3.27 5.24 152 [Cyprinus .00E-63 nio anio rerio] 00E-92 2 CV992516 B/C2B [Danio rerio] 3.00E-25 3.1 6.47 ed ursor (TRAP- ykiss] a E-88 anio rerio] .00E-11 io rerio] 7 niloticus] .00E-68 4 rio] 00E-82 io rerio] .00E-100 2 protein 4 [Danio .00E-17 BM438229 Alpha-2-macroglobulin-2 carpio] 3 3.3 3.74 CV988413 Alpha-2-macroglobulin-2 [Cyprinus carpio] 1.00E-92 3.3 1.71 BM438317 Tryptophan 2,3-dioxygenase [Da rerio] 4.00E-77 3.2 2.36 CK406955 No significant similarity 3.2 3.27 CV996636 No significant similarity 3.2 5.24 CK407024 Complement C3-S [Cyprinus carpio] Coagulation fa 7.00E-05 3.2 3.74 BM438664 ctor 10 [D 4. 3.2 2.36 CK421169 Can CK414114 opy1 [Danio rerio] Solute carrier family 38, member 2 1.00E-69 5.00E-112 3. 3.2 1 3.27 0.00 SLC38A2 [Danio rerio] CV989134 Calumenin isoform 2 isoform 3 [Canis familiaris] No signif 3.00E-75 3.1 3.74 CF97086 K407645 icant similarity No significant similarity 3.1 3.1 7.53 2.36 C CK409512 No significant similarity Complement factor 3.1 3.74 TC7895 SSR alpha subunit/Translocon-associat protein subunit alpha prec alpha) [Oncorhynchus m 3.00E-75 3.0 3.74 TC7025 No significant similarity 3.0 0.00 CF971953 Fibrinogen, beta chain isoform 4 [Macac mulatta] 5.00 3.0 3.27 CK406847 Fibrinogen alpha chain [D 9 3.0 3.74 CV994441 Complement C3-Q1, partial [Dan 4.00E-09 3.0 0.00 CK40750 Type III iodothyronine deiodinase [Oreochromis 2.00E-90 2.9 3.74 CK408666 Transferrin [Salmo trutta] 2 2.9 3.74 CF97139 No significant similarity 2.9 3.27 EE993209 No significant similarity No signif 2.9 3.27 TC8545 CF971799 icant similarity No significant similarity 2.9 2.9 1.71 3.74 CK409240 Coagulation factor 10 [Danio rerio] Ceruloplasmin [Da 1.00E-73 2.9 3.27 CF971219 CV987559 nio re SEC61, alpha subunit [Dan 7. 5 2.9 2.8 5.24 3.27 TC7371 No significant similarity 2.8 5.24 CK407648 No significant similarity 2.8 5.24 CK406724 No significant similarity 2.8 7.53 CK41336 No significant similarity 2.8 3.27 CV994570 CK425818 No significant similarity 2.8 1.71 No significant similarity 2.8 1.71 CK408535 Microfibrillar-associated rerio] Apolipoprotein B-100 precursor [Danio 2 2.8 1.71 CV995270 rerio] 1.00E-76 2.8 6.47 153 aryngodon idella] protein 10 [Danio rerio] ng protein 10 [Danio rerio] .00E-76 .00E-141 sor [Danio 00E-19 2. nio rerio] .00E-87 1 (NADP+), .00E-20 BM438622 1.00E-43 2.6 3.74 nctatus] .00E-51 C8843 2.5 3.74 9 ulus] 8 rerio] .00E-106 anio rerio] .00E-79 olivaceus] .00E-44 [Canis .00E-21 ember 14/Oatp .00E-121 CK406836 Alpha-2-macroglobulin [Ctenoph 1.00E-76 2.8 3.27 CK406859 Transmembrane emp24 domain- containing 3.00E-91 2.7 0.00 CK410283 Transmembrane emp24 domain- containi 9.00E-101 2.7 1.71 CV996617 Transferrin [Salmo trutta] 1 2.7 3.27 TC12946 Transferrin [Salmo trutta] 3 2.7 3.74 EE993484 Selenium binding protein 1 isoform 4 [Danio rerio] 2.00E-76 2.7 7.53 CK406562 No significant similarity 2.7 3.74 CK406432 N CV989409 o significant similarity Dnajb11 protein [Danio rerio] 2.00E-65 2.7 2.7 1.71 1.71 TC9511 Calreticulin [Ictalurus punctatus] Alpha-2-an 0 2.7 0.00 CK407198 tiplasmin precur rerio] 7. 7 3.27 TC8542 SEC11-like 1/SPC18 [Da 2 2.6 3.74 CV990480 Isocitrate dehydrogenase soluble [Danio rerio] 7 2.6 1.25 TC8074 Deleted in malignant brain tumors 1 [Danio rerio] C1 inhibitor [Oncorhynchus mykiss] 9.00E-28 2.6 3.74 TC6883 Thioredoxin [Ictalurus pu 2 2.6 3.74 CV993716 SAR1a-like protein 2 [Ictalurus punctatus] 4.00E-27 2.5 0.00 CK407814 Plasminogen [Danio rerio] 1.00E-149 2.5 5.24 CB939893 No significant similarity 2.5 6.47 BM029249 No significant similarity No significant similarity 2.5 0.00 T CK409289 No significant similarity 2.5 3.27 CV994205 No significant similarity 2.5 5.24 CV993853 No significant similarity 2.5 5.24 CK40910 No significant similarity 2.5 3.27 CK404043 No significant similarity 2.5 1.71 TC8981 FK506 binding protein 2 [Mus musc 9.00E-56 2.5 3.74 CB937094 Eif4e1a protein [Danio rerio] 8.00E-99 2.5 1.25 TC7047 DC2 protein [Xenopus tropicalis] 3.00E-76 2.5 0.00 CV99034 Calreticulin, like 2 [Danio 5 2.5 3.74 CV995732 Asparagine synthetase [D 3 2.5 0.00 CF971399 Transferrin [Paralichthys 4 2.4 7.53 CK410712 Sorting nexin-17 isoform 7 familiaris] 1 2.4 1.71 TC9317 Solute carrier family 21, m [Danio rerio] No significant similarity 4 2.4 3.27 CK408024 2.4 5.24 CK422256 No significant similarity 2.4 3.74 154 CV995916 2.4 3.74 protein CV993838 drogenase 1 (NADP+), 1e-109 2.4 6.47 ide [Danio .00E-162 hain anio anio rerio] [Danio 00E-52 2. 4 , member E .00E-43 e-related io rerio] .00E-64 ylocentrotus .00E-78 tatus] 1 (NADP+), .00E-146 anio rerio] .00E-77 CK407036 No significant similarity No significant similarity 2.4 0.00 CV992084 BM424895 2.4 2.4 1.25 2.36 No significant similarity CV990387 No significant similarity No significant similarity 2.4 5.24 CF970874 No significant similarity 2.4 3.74 CK407673 Leucine-rich alpha-2-glyco [Ctenopharyngodon idella] Isocitrate dehy 3.00E-91 2.4 1.71 soluble [Danio rerio] TC8308 Fibrinogen gamma polypept rerio] Complement C3 precursor alpha 6 2.4 1.71 EE993353 c [Danio rerio] 6.00E-27 2.4 1.25 BM438581 Colipase preproprotein [Homo sapiens] Coagul 2.00E-23 2.4 6.47 CK409467 ation factor II (thrombin) [D rerio] 4.00E-12 2.4 7.53 TC8712 Bsg protein [Danio rerio] Alpha-2-antiplasmin p 3.00E-06 2.4 3.74 CV994347 recursor [Danio rerio] Unnamed prot 2.00E-17 2.4 3.27 CV987909 ein product [Tetraodon nigroviridis] 3.00E-12 2.3 3.27 TC7791 Asparagine-linked glycosylation 5 homolog [Xenopus tropicalis] Tumor necrosis factor, alpha-induced 6.00E-46 2.3 7.53 CF971576 protein 9 [Homo sapiens] 5.00E-53 2.3 1.25 CV992736 Integral membrane protein 1 [D 8.00E-140 2.3 0.00 CK405052 SEC11-like 1 (S. Cerevisiae) rerio] 5. 3 1.71 CK40915 Ras homolog gene family [Danio rerio] 5 2.3 5.24 CV991869 Protein disulfide isomeras protein P5 precursor [Dan 9 2.3 0.00 CK411358 Phosphoethanolamine N- methyltransferase [Strong purpuratus] No significant similarity 3 2.3 1.25 CK418743 2.3 2.36 CK421480 CK407038 No significant similarity 2.3 0.00 No significant similarity 2.3 1.25 CK410384 TC9282 No significant similarity 2.3 5.24 No significant similarity 2.3 3.74 CK402366 No significant similarity 2.3 3.27 CK412705 No significant similarity 2.3 1.71 CK425589 No significant similarity 2.3 5.24 TC6827 NADH dehydrogenase subunit 1 [Ictalurus punc 7.00E-124 2.3 0.00 CK401667 Isocitrate dehydrogenase soluble [Danio rerio] Glucose transporter 1A [D 8 2.3 1.25 BM027884 4 2.3 5.24 155 talurus .00E-95 , subfamily B 00E-71 2. [Cyprinus .00E-76 omo sapiens] .00E-21 us s] s 2 e [Danio rerio] CK415655 3.00E-42 2.2 7.53 s] .00E-58 soform 3 [Danio .00E-48 eus] .00E-64 s .00E-82 mma gamma) e-86 n 1 [Danio rerio] .00E-24 io rerio] .00E-98 CK404798 Ferritin heavy subunit [Ic punctatus] 1 2.3 6.47 CK407169 Cytochrome P450, family 8 [Gallus gallus] 4. 3 2.36 CV991514 Complement factor B/C2B carpio] 6 2.3 3.74 CF971843 Colipase preproprotein [H 3 2.3 7.53 CV988651 Coated vesicle membrane protein [M musculus] 3.00E-91 2.3 0.00 CF971415 V994492 Canopy1 [Danio rerio] Apoa4 protein [Danio rerio] 2.00E-23 1.00E-54 2.3 2.3 3.27 3.74 C TC9203 Inter-alpha trypsin inhibitor [Fundulus heteroclitu 1.00E-54 2.2 5.24 TC8465 WW domain binding protein 2 [Ictaluru punctatus] 4.00E-95 2.2 3.74 CK425032 Translocon-associated protein beta/SSR beta [Danio rerio] Translocon-associated protein beta/SSR 2.00E-89 2.2 0.00 CF972062 beta [Danio rerio] 1.00E-40 2.2 3.27 TC7124 Stress-associated endoplasmic reticulum protein 1 [Homo sapiens] 5.00E-27 2.2 5.24 TC7513 CK407263 Sec61 gamma subunit [Homo sapiens] No significant similarity 1.00E-13 2.2 2.2 5.24 6.47 CV99471 No significant similarity No significant similarity 2.2 3.74 CK404612 CK418449 2.2 2.2 3.74 7.53 No significant similarity CK403577 No significant similarity 2.2 3.27 CK425361 No significant similarity No sig 2.2 3.74 BM424296 CB939641 nificant similarity No significant similarity 2.2 2.2 3.74 3.27 TC8290 NADH dehydrogenase subunit 5 [Ictalurus punctatus] 0.00 2.2 1.71 TC6955 Jun B proto-oncogene, lik Endoplasmin [Aedes aegypti] 1.00E-54 2.2 0.00 TC9110 CD59 [Ictalurus punctatu 6 2.2 0.00 TC8069 C type lectin receptor A i rerio] 2 2.2 5.24 CV992060 Transposase [Bacillus cer 2 2.1 6.47 TC6893 Signal sequence receptor delta [Ictaluru punctatus] 4 2.1 3.74 TC7064 Signal sequence receptor, ga (translocon-associated protein Integral membrane protei 2 2.1 6.47 CK411755 4 2.1 1.71 BM438221 SEC61, beta subunit [Dan 4 2.1 0.00 TC7429 SEC22 vesicle trafficking protein homolog B [Danio rerio] Ribosomal protein L23a [Ictalurus punctatus] 9.00E-98 2.1 3.74 TC8540 2.00E-69 2.1 6.47 CV987930 Plasminogen [Danio rerio] 3.00E-111 2.1 7.53 156 CF972234 ficant similarity 2.1 3.74 ] CK408501 r transport protein 20 [Danio 9.00E-55 2.1 1.71 norvegicus] rcatus] BM438646 nio rerio] 1.00E-22 2.1 3.74 5 3 n 1 [Danio rerio] .00E-171 rerio] .00E-48 ae) [Danio .00E-08 like [Danio .00E-96 o] .00E-51 hus mykiss] .00E-11 philin B 113 8 8 6 BQ096912 lated protein 58kd [Bos 8.00E-45 2.0 3.74 CK404833 6.00E-54 2.0 7.53 ily A, 4 s] CV994121 Nucleobindin 2a [Danio rerio] No significa 2.00E-75 2.1 0.00 BM438887 CK417505 nt similarity No significant similarity 2.1 2.1 3.74 3.27 CK407464 No significant similarity No signi 2.1 3.27 CB936516 Low density lipoprotein receptor-related protein associated protein 1 [Danio rerio Intraflagella 7.00E-98 2.1 0.00 rerio] TC8637 Hypothetical protein [Rattus 9.00E-16 2.1 1.25 TC10083 Glucose regulated protein 58kd [Bos taurus] 0 2.1 6.47 TC9827 Cytochrome c oxidase subunit 1 [Ictalurus fu 0 2.1 0.00 TC8986 Cytochrome b5 [Danio rerio] Ceruloplasmin [Da 2.00E-42 2.1 3.27 EE993184 C type lectin receptor A isoform 3 [Danio rerio] Cathepsin L preproprotein [Cyprinus 1.00E-72 2.1 3.27 CK40653 carpio] Transducer of ERBB2, 1a [Danio rerio] 1.00E-144 2.1 1.71 BM43815 2.00E-32 2.0 6.47 TC9714 Integral membrane protei 3 2.0 0.00 EE993544 Selenoprotein X, 1 [Danio 6 2.0 3.74 CF971017 SEC13-like 1 (S. Cerevisi rerio] 5 2.0 3.74 CK402358 Retinol dehydrogenase 1, rerio] Profilin 2 like [Danio reri 9 2.0 7.53 TC8057 2 2.0 3.74 CF971814 Plasminogen [Oncorhync 2 2.0 6.47 TC9540 Peptidylprolyl isomerase B/cyclo [Ictalurus punctatus] No significant similar 9.00E- 2.0 3.74 CK421111 CV99169 ity No significant similarity 2.0 2.0 7.53 3.27 6 CK406865 No significant similarity 2.0 3.74 CV99134 No significant similarity 2.0 3.27 CK40693 No significant similarity 2.0 3.27 CK407170 No significant similarity 2.0 5.24 CK40515 No significant similarity Glucose regu 2.0 1.71 taurus] Ferritin heavy subunit [Ictalurus punctatus] CK408710 Cytochrome P450, family 3, subfam polypeptide 65 [Danio rerio] Calpain-like protease [Gallus gallu 4.00E-90 2.0 5.24 CK42212 1.00E-100 2.0 3.74 157 ta can fish transcripts in liver afte ictalu ection. ref cession number r TIGR c nsus er of the on ive Id is the hit with the most negative E-value along -va discovery rate the pa lar gene value F Change q-value Supplemen l Table 2 All signifi tly downregulated cat r E. ri inf Accession ers to the GenBank ac o onse numb sequence the microarray. Putat with the E lue of that hit. q-value is the false- for rticu Accession Putative identity E- old TC7457 Eukaryotic translation initiation factor 3, sub 1.0 74 0.42 6.5 unit 6 interacting protein [Danio rerio] 0e- CK404061 0.44 5.2 ide 2 [Icta us 5.0 29 0.45 6.5 9 0.47 7.5 n [Danio rerio 0.0 0.49 1.7 o 0.0 0.50 1.7 No significant similarity AY845143 Liver-expressed antimicrobial pept punctatus] lur 0e- CK40321 No significant similarity TC6758 Thioredoxin interacting protei ] TC6756 Thioredoxin interacting protein [Danio reri ] 158 upplemental Table 3 p-regulated catfish transcripts in liver after E. ictaluri infection F Chan al S Unique, significantly u that could be annotated by sequence similarity. Accession refers to the GenBank accession number or TIGR consensus number of the sequence on the microarray. Putative Id is the hit with the most negative E-value. q-value is the false-discovery rate for the particular gene Accession Putative identity old ge q-v ue CF970955 Intelectin [Ctenopharyngodon idella] 85.4 1.25 CK408483 H 0.00 M Danio rerio] 32 9 1.25 I 2.36 BM438689 M -associated protein 4 [Rattus norvegicus] 25.6 0.00 W ion-related-65kda- protein- l 23.4 0.00 C 3.27 K406396 Neurotoxin/C59/Ly-6-like protein [Ctenopharyngodon idella] 21.3 1.25 V994031 Catechol-O-methyltransferase domain containing 1 [Danio rerio] 14.8 0.00 C9205 Hypothetical protein XP_683888 [Danio rerio] 14.4 1.25 C8426 Hemopexin precursor [Danio rerio] 13.6 2.36 V996638 Apolipoprotein Apoa4 protein [Danio rerio] 13.0 2.36 V993724 Toll-like receptor 5 [Ictalurus punctatus] 11.8 0.00 V987901 Complement C3-H1 [Cyprinus carpio] 10.0 1.71 E993362 Complement protein component C7-1[Danio rerio] 9.7 0.00 C9637 Fibrinogen alpha chain [Danio rerio] 9.6 0.00 C9194 Complement regulatory plasma protein [Paralabrax nebulifer] 8.9 3.27 V992853 Ceruloplasmin [Danio rerio] 8.5 0.00 C9833 Microfibrillar-associated protein 4 [Danio rerio] 8.4 1.25 C8765 Transferrin [Salvelinus fontinalis] 7.7 0.00 C8306 Fibrinogen gamma polypeptide [Danio rerio] 6.1 0.00 V989503 CXCL14 [Ictalurus punctatus] 5.7 0.00 V997126 Complement C3 [Ctenopharyngodon idella] 5.6 0.00 C7892 Ceruloplasmin [Danio rerio] 5.4 0.00 V992447 Complement component C8 beta [Oncorhynchus mykiss] 5.4 3.27 C7741 Complement factor B/C2-A3 [Cyprinus carpio] 5.4 3.74 M494620 Serum/glucocorticoid regulated kinase [Danio rerio] 5.3 1.25 V995884 Solute carrier family 31 (copper transporters), member 1 [Danio rerio] 5.3 0.00 C8490 Fibrinogen, B beta polypeptide [Danio rerio] 5.2 1.71 E993545 Erythroblast membrane-associated protein [Danio rerio] 5.0 3.74 C7249 LOC407646 protein [Danio rerio] 4.9 0.00 aptoglobin precursor [Danio rerio] otein 4 [ 34.3 .BM438750 icrofibrillar-associated pr TC6845 ntelectin [Ctenopharyngodon idella] icrofibrillar 28.0 TC8425 arm-temperature-acclimat ike-protein [Oryzias latipes] TC7475 C chemokine SCYA113 [Ictalurus punctatus] 21.5 C C T T C C C E T T C T T T C C T C T B C T E T 159 -dioxygenase [Danio rerio] 4.8 0.00 sis factor, alpha-induced protein 9 isoform 2 [Danio rerio] 4.1 1.25 Complement C3-H ] 4 [Danio rerio] rio] 0 Homo 3 carpio] 4 C38A2 [Danio [Macaca mulatta] a) [Oncorhynchus 7 is ] 9 erio] CV989409 protein [Danio rerio] 2.7 1.71 4 io] 2 0 ), soluble [Danio BM438712 Tryptophan 2,3 BM438634 Angiotensinogen [Danio rerio] 4.5 1.25 BM438200 Complement component C5-1 [Cyprinus carpio] 4.4 1.25 BM027942 Selenoprotein P, plasma, 1a [Danio rerio] 4.4 6.47 TC9358 Complement C4-2 [Cyprinus carpio] 4.2 0.00 BM438717 Tumor necro EE993174 2 [Cyprinus carpio 4.0 1.25 CB939682 Protein disulfide isomerase-associated 3.9 1.71 CK408253 Uridine phosphorylase, like [Danio re 3.9 3.74 CK407328 Coagulation factor XIII B chain precursor [Canis familiaris] 3.8 6.47 TC7660 Complement component C9 [Ctenopharyngodon idella] 3.8 1.71 CK41727 Cytochrome b5 [Danio rerio] Tumor necrosis factor, alpha-in 3.7 1.71 TC7576 duced protein 9 [ sapiens] 3.7 0.00 BM438615 Complement C3-S [Danio rerio] Protein 3.6 3.74 TC10030 TC7903 disulfide-isomerase [Cricetulus griseus] 3. Armet protein [Xenopus laevis] 3. 6 4 7.53 7.53 TC8567 Chemotaxin [Oncorhynchus mykiss] 3. 1.71 CV98841 Alpha-2-macroglobulin-2 [Cyprinus 3.3 1.71 CV988633 Apolipoprotein Apoa4 protein [Danio rerio] 3.3 5.24 CK421169 Canopy1[Danio rerio] 3.2 3.27 BM438664 Coagulation factor 10 [Danio rerio] 3. 2.36 CV989134 Calumenin isoform 2 isoform 3 [Canis familiaris] 3.1 3.74 CK41411 Solute carrier family 38, member 2 SL rerio] Complement C3-Q1, partial [Danio rerio] 3. 3.1 0.00 CV994441 0 0.00 CF971953 Fibrinogen, beta chain isoform 4 3.0 3.27 TC7895 SSR alpha subunit/Translocon-associated protein subunit alpha precursor (TRAP-alph mykiss] Type III iodothyronine deiodinase [Oreochrom 3.0 3.74 CK40750 niloticus] 2.9 3.74 CV995270 Apolipoprotein B-100 precursor [Danio rerio 2.8 6.47 CV98755 SEC61, alpha subunit [Danio r 2. 3.27 TC9511 Calreticulin [Ictalurus punctatus] Dnajb11 2. 0.00 EE99348 Selenium binding protein 1 isoform 4 [Danio rer 2.7 7.53 TC12946 Transferrin [Salmo trutta] 2. 3.74 CK410283 Transmembrane emp24 domain containing protein 10 [Danio rerio] 2.7 1.71 BM43862 C1 inhibitor [Oncorhynchus mykiss] 2.6 3.74 TC8074 Deleted in malignant brain tumors 1 [Danio rerio] 2.6 3.74 CV99048 Isocitrate dehydrogenase 1 (NADP+ 2.6 1.25 160 io] 8 TC8712 [Danio rerio] 2.4 3.74 7 piens] 3 4/Oatp [Danio 5 homolog [Xenopus us] , subfamily B [Gallus ctatus] CK409154 2.3 5.24 ] 2. ulus heteroclitus] io rerio] ] omo sapiens] ] R beta [Danio rerio] TC8542 SEC11-like 1/SPC18 [Danio rer 2.6 3.74 TC6883 Thioredoxin [Ictalurus punctatus] 2. 3.74 CV995732 Asparagine synthetase [Danio rerio] 2.5 0.00 CV99034 Calreticulin, like 2 [Danio rerio] 2. 3.74 TC7047 DC2 protein [Xenopus tropicalis] 2.5 0.00 CB937094 Eif4e1a protein [Danio rerio] 2.5 1.25 TC8981 FK506 binding protein 2 [Mus musculus] 2.5 3.74 CV993716 SAR1a-like protein 2 [Ictalurus punctatus] 2.5 0.00 CV994347 Alpha-2-antiplasmin precursor [Danio rerio] Bsg protein 2.4 3.27 CK40946 Coagulation factor II (thrombin) [Danio rerio] 2.4 7.53 BM438581 Colipase preproprotein [Homo sa 2.4 6.47 CK40767 Leucine-rich alpha-2-glycoprotein [Ctenopharyngodon idella] 2.4 1.71 TC9317 Solute carrier family 21, member 1 rerio] 2.4 3.27 CK410712 Sorting nexin-17 isoform 7 [Canis familiaris] 2.4 1.71 TC7791 Asparagine-linked glycosylation tropicalis] 2.3 7.53 CV988651 Coated vesicle membrane protein [Mus muscul 2.3 0.00 CV991514 Complement factor B/C2B [Cyprinus carpio] 2.3 3.74 CK407169 Cytochrome P450, family 8 gallus] Ferritin heavy subunit [Ictalurus pun 2.3 2.36 CK404798 2.3 6.47 BM027884 Glucose transporter 1A [Danio rerio] 2.3 5.24 TC6827 NADH dehydrogenase subunit 1 [Ictalurus punctatus] 2.3 0.00 CK411358 Phosphoethanolamine N-methyltransferase [Strongylocentrotus purpuratus] Protein disulfide isomerase-related protein P5 2.3 1.25 CV991869 precursor [Danio rerio] Ras homolog gene family, member E [Danio rerio] Unnamed 2.3 0.00 CV987909 TC8069 protein product [Tetraodon nigroviridis C type lectin receptor A isoform 3 [Danio rerio] 3 2.2 3.27 5.24 TC9110 CD59 [Ictalurus punctatus] 2. 0.00 CK415655 Endoplasmin [Aedes aegypti] 2.2 7.53 TC9203 Inter-alpha trypsin inhibitor [Fund 2.2 5.24 TC6955 Jun B proto-oncogene, like [Dan 2.2 0.00 TC8290 NADH dehydrogenase subunit 5 [Ictalurus punctatus 2.2 1.71 TC7513 SEC61, gamma subunit [H 2.2 5.24 TC7124 Stress-associated endoplasmic reticulum protein 1 [Homo sapiens 2.2 5.24 CK425032 Translocon-associated protein beta/SS rerio] WW domain binding protein 2 [Ictalurus punctatus] 2.2 0.00 TC8465 Cathepsin L preproprotein [Cyprinus carpio] 2.2 3.74 CK406535 2.1 1.71 161 TC9827 rome c oxidase subunit 1 [Ictalurus furcatus] 2.1 0.00 aurus] icus] nio rerio] elated protein ] 2. [Danio 1 o rerio] tus] ocon- 4 eptide s Cytoch TC10083 Glucose regulated protein 58kd [Bos t 2.1 6.47 TC8637 Hypothetical protein [Rattus norveg 2.1 1.25 CK408501 Intraflagellar transport protein 20 [Da 2.1 1.71 CB936516 Low density lipoprotein receptor-r associated protein 1 [Danio rerio] 2.1 0.00 CV994121 Nucleobindin 2a [Danio rerio] 2.1 0.00 CV987930 Plasminogen [Danio rerio] 2. 7.53 TC8540 Ribosomal protein L23a [Ictalurus punctatus 1 6.47 TC7429 SEC22 vesicle trafficking protein homolog B rerio] 2.1 3.74 BM43822 SEC61, beta subunit [Dani 2.1 0.00 TC6893 Signal sequence receptor delta [Ictalurus puncta 2.1 3.74 TC7064 Signal sequence receptor, gamma (transl associated protein gamma) [Danio rerio] Transposase [Bacillus cereus] 2. 2.1 6.47 CV992060 1 6.47 CK42212 Calpain-like protease [Gallus gallus] Cytoch 2.0 3.74 CK408710 rome P450, family 3, subfamily A, polyp 65 [Danio rerio] 2.0 5.24 TC9714 Integral membrane protein 1 [Danio rerio] Peptidylpro 2.0 0.00 TC9540 lyl isomerase B/cyclophilin B [Ictaluru punctatus] 2.0 3.74 TC8057 Profilin 2 like [Danio rerio] 2.0 3.74 CK402358 Retinol dehydrogenase 1, like [Danio rerio] SEC13- 2.0 7.53 CF971017 like 1 (S. Cerevisiae) [Danio rerio] 2.0 3.74 EE993544 Selenoprotein X, 1 [Danio rerio] 2. 3.74 BM438153 Transducer of ERBB2, 1a [Danio rerio] 2.0 6.47 162 ent ign r r E. ictalu ection ld n ity. Accessio fers to the Bank nu sequenc the microarray. q-value he false-d ery rate for the parti n tity Fold Chan q-value Supplem al Table 4 Unique, s ificantly up-regulated catfish transcripts in live afte ri inf that cou ot be annotated by sequence similar n re Gen accession mber or TIGR consensus number of the e on Putative Id is the hit with the most negative E-value. cular gene is t iscov Accessio Putative iden ge BM439193 .5 3.27 No significant similarity 35 CV996365 1.25 CK406832 significant similarity 14.2 0.00 7.9 3.27 0 3.27 3 9 0.00 6.0 3.74 5.9 1.71 3.27 BM439121 t similarity 5.6 5.24 4.9 1.25 3.27 8 4.5 0.00 4.2 3.74 4.0 1.25 4.0 3.27 3.9 3.27 M439116 No significant similarity 3.7 3.27 F971645 No significant similarity 3.6 3.27 K407075 No significant similarity 3.5 3.27 C9113 No significant similarity 3.5 3.74 M438944 No significant similarity 3.4 3.27 K423432 No significant similarity 3.4 3.27 F972223 No significant similarity 3.3 1.71 K406955 No significant similarity 3.2 3.27 V996636 No significant similarity 3.2 5.24 F970862 No significant similarity 3.1 7.53 K409512 No significant similarity 3.1 3.74 C7025 No significant similarity 3.0 0.00 F971394 No significant similarity 2.9 3.27 E993209 No significant similarity 2.9 3.27 C8545 No significant similarity 2.9 1.71 F971799 No significant similarity 2.9 3.74 C7371 No significant similarity 2.8 5.24 K407648 No significant similarity 2.8 5.24 No significant similarity 15.3 No CF970899 No significant similarity BM43904 No significant similarity 7.8 BM43889 No significant similarity 6. CF971612 No significant similarity CK408142 No significant similarity CV996287 No significant similarity 5.7 No significan TC7498 No significant similarity TC9712 No significant similarity 4.6 BM43881 No significant similarity CF971526 No significant similarity BM438848 No significant similarity BQ097411 No significant similarity BM438858 No significant similarity B C C T B C C C C C C T C E T C T C 163 icant similarity 2.8 7.53 ficant similarity 2.5 6.47 M significant 7 6 2 5 1 5 CK406724 No signif CK413362 No significant similarity 2.8 3.27 CV994570 No significant similarity 2.8 1.71 CK425818 No significant similarity 2.8 1.71 CK406432 No significant similarity 2.7 1.71 CB939893 No signi B 029249 No similarity 2.5 0.00 TC8843 No significant similarity 2.5 3.74 CK409289 No significant similarity 2.5 3.27 CV994205 No significant similarity 2.5 5.24 CV993853 No significant similarity 2.5 5.24 CK409109 No significant similarity 2.5 3.27 CK404043 No significant similarity 2.5 1.71 CK422256 No significant similarity 2.4 3.74 CK407036 No significant similarity 2.4 0.00 CV992084 No significant similarity 2.4 1.25 BM424895 No significant similarity 2.4 2.36 CV99038 No significant similarity 2.4 5.24 CV99591 No significant similarity 2.4 3.74 CF970874 No significant similarity 2.4 3.74 CK418743 No significant similarity 2.3 2.36 CK421480 No significant similarity 2.3 0.00 CK410384 No significant similarity 2.3 5.24 TC9282 No significant similarity 2.3 3.74 CK402366 No significant similarity 2.3 3.27 CK412705 No significant similarity 2.3 1.71 CK425589 No significant similarity 2.3 5.24 CV99471 No significant similarity 2.2 3.74 CK404612 No significant similarity 2.2 3.74 CK418449 No significant similarity 2.2 7.53 CK403577 No significant similarity 2.2 3.27 CK425361 No significant similarity 2.2 3.74 BM424296 No significant similarity 2.2 3.74 CB939641 No significant similarity 2.2 3.27 BM438887 No significant similarity 2.1 3.74 CK41750 No significant similarity 2.1 3.27 CK407464 No significant similarity 2.1 3.27 CF972234 No significant similarity 2.1 3.74 CK42111 No significant similarity 2.0 7.53 CV991696 No significant similarity 2.0 3.27 CK40686 No significant similarity 2.0 3.74 CV991348 No significant similarity 2.0 3.27 164 CK406938 No significant similarity 2.0 3.27 CK407170 No significant similarity 2.0 5.24 CK405156 No significant similarity 2.0 1.71 Supplemental T y upregulated transcripts in blue catfish liver. refers ssion number IGR cons number of the is the top inf rmative BLASTX hit. q-value is iscov lar gene eId E-value Fold Change q- value (%) able 5 All significantl Accession to the GenBank acce or T ensus sequence on the m the false-d icroarray. Putative Id ery rate for the particu o Accession Putativ AY555503 C alurus furcat 2.00E-5 105.1 9.18 C chemokine SCYA106 [Ict us] 9 CF970955 I on idella] 7.00E-32 48.6 9.82 I 3.00E-1 37.7 5.63 H 83888 [Danio re 7.00E-4 30.9 5.63 I on idella] 4.00E-1 30.0 9.82 I 8.00E-1 28.4 9.18 M otein 4 [Danio 2.00E-7 27.1 5.63 M otein 4 [Danio 1.00E-5 22.9 5.63 H 83888 [Danio re 4.00E-4 20.6 9.82 3 H nio rerio] 1.00E-5 20.4 9.82 C rsor [Danio reri 2.00E-2 14.5 0.00 C alurus puncta 5.00E-4 12.5 8.97 W n-related-65 [ 7.00E-1 12.3 8.70 W n-related-65k p atipes] 2.00E-1 11.9 9.82 C 2.00E-1 11.4 5.63 N 11.2 5.63 C recursor [Dani rerio] 5.00E-1 10.1 9.18 H io rerio] 1.00E-9 9.7 5.63 M ase II alpha su [ 8.00E-66 8.3 9.82 C ase domain cont g 1 2.00E-87 7.9 8.70 H io rerio] 5.00E-7 7.5 5.63 M otein 4 [Rattus n 3.00E-68 7.5 8.97 N 7.4 9.82 S pper transporter m 8.00E-77 7.1 8.70 ntelectin [Ctenopharyngod CF971897 ntelectin 2 [Danio rerio] 17 TC9205 ypothetical protein XP_6 rio] 8 TC6845 ntelectin [Ctenopharyngod 28 CK407451 ntelectin 2 [Danio rerio] 20 CK406362 icrofibrillar-associated pr rerio] 5 CF970863 icrofibrillar-associated pr rerio] 7 CF971597 ypothetical protein XP_6 rio] 4 CK40848 aptoglobin precursor [Da 6 EE993177 omplement factor H precu o] 6 TC7475 C chemokine SCYA113 [Ict tus] 3 CF971550 arm-temperature-acclimatio Oryzias latipes] 04 TC8425 arm-temperature-acclimatio da- rotein-like-protein [Oryzias l 18 TC7892 eruloplasmin [Danio rerio] 04 CK406832 o significant similarity EE993354 omplement component 7 p o emopexin precursor [Dan 05 CK406564 0 CK405246 ethionine adenosyltransfer bunit Mus musculus] CK407588 atechol-O-methyltransfer ainin [Danio rerio] emopexin precursor [DanTC8426 5 BM438689 icrofibrillar-associated pr orvegicus] CK407645 o significant similarity CK408512 olute carrier family 31 (co s), ember 1 [Danio rerio] 165 C ma protein [ 7.00E-27 6.8 8.97 C ase domain cont 1 3.00E-76 6.7 8.70 V992853 Ceruloplasmin [Danio rerio] 8.00E-98 6.2 5.63 V997126 Complement C3 [Ctenopharyngodon idella] 3.00E-44 6.1 8.97 K407841 Fibrinogen gamma polypeptide [Danio rerio] 5.00E-109 5.7 5.63 C7925 Lysosomal-associated membrane protein 3 [Canis familiaris] 5.00E-24 5.7 9.82 82 C Complement inhib [Rattus norvegicus] 3.0 9 alurus 9 nctatus] 1 rerio] io] .00E-61 io] BQ096774 nificant similarity 4.1 9.18 6 anio rerio] 00E-68 TC9194 omplement regulatory plas Paralabrax nebulifer] CV994031 atechol-O-methyltransfer [Danio rerio] aining C C C T CF971953 Fibrinogen, beta chain isoform 4 [Macaca mulatta] 5.00E-88 5.6 9. CF972223 No significant similarity 5.5 7.00 TC7660 Complement component C9 [Ctenopharyngodon idella] 1.00E-19 5.2 8.70 K407260 itory factor H 0E-24 5.2 .18 82 CK407984 Complement regulatory plasma protein [Paralabrax nebulifer] 1.00E-14 5.0 9. TC9330 ER-resident chaperone calreticulin [Ict punctatus] 0 4.8 5.63 TC8545 No significant similarity 4.8 0.00 CF97121 Ceruloplasmin [Danio rerio] alurus pu 7.00E-82 4.7 9.18 TC9859 MHC class I alpha chain [Ict 1.00E-130 4.7 8.70 CK407547 Protein disulfide isomerase associated 4 [Danio rerio] 1.00E-122 4.7 5.63 CV995884 Solute carrier family 31 (copper transporters), member 1 [Danio rerio] 4.00E-67 4.7 7.00 CF971394 No significant similarity 4.5 8.97 CB939682 Protein disulfide isomerase-associated 4 [Danio rerio] 8.00E-76 4.5 5.63 TC8306 Fibrinogen gamma polypeptide [Danio rerio] 6.00E-162 4.4 7.00 CK406609 Fibrinogen gamma polypeptide [Ictalurus punctatus] 2.00E-63 4.4 9.82 BM43912 No significant similarity e [Danio 4.3 9.18 TC8490 Fibrinogen, B beta polypeptid 0 4.2 7.00 CK408412 Apoa4 protein [Danio rer 9 4.1 8.70 CK408535 Microfibrillar-associated protein 4 [Danio rer No sig 2.00E-17 4.1 5.63 CK408173 Pentraxin [Salmo salar] 5.00E-42 4.1 8.70 CK408666 Transferrin [Salmo trutta] 2.00E-68 4.1 9.82 CK405317 EE993343 Beta-actin [Tigriopus japonicus] Complement C4 [Oncorhynchus mykiss] 7.00E-90 3.00E-80 4.0 3.9 5.63 5.63 BM43911 No significant similarity 3.9 5.63 CK403482 No significant similarity Fibrinogen al 3.9 8.70 CF971852 pha chain [D 7. 3.8 9.82 CK401799 CF972295 MGC68649 protein [Danio rerio] Thioredoxin [Ictalurus punctatus] 7.00E-67 1.00E-25 3.8 3.8 5.63 9.82 166 ein 9 7 TC7345 (IRL685) [Danio rerio] 1.00E-50 3.3 9.18 rinus carpio] .00E-59 1 CF972140 t similarity 3.1 9.18 t, alpha 6 E-53 sosomal vacuolar .00E-57 5 TC7903 ein [Xenopus laevis] 3.00E-60 2.9 9.18 CK405569 con-associated protein beta [Danio rerio] 3.00E-89 2.8 9.82 8 TC12946 Salmo trutta] 3.00E-141 2.7 9.82 [Ictalurus furcatus] .00E-07 eta E-82 us musculus] .00E-56 lpha [Danio rerio] cus E ligand tus] ain kinase 1 [Danio .00E-77 us in-binding, TC7576 Tumor necrosis factor, alpha-induced prot LOC407646 protein [D 4.00E-74 3.8 8.70 CK407596 K418197 anio rerio] Cytochrome P450 3A [Ctenopharyngodon idella] 3.00E-67 5.00E-79 3.7 3.6 9.82 7.00 C CF972133 No significant similarity 3.6 5.63 CK414572 No significant similarity 3.5 9.82 TC6882 Thioredoxin [Ictalurus punctatus] 5.00E-51 3.4 9.18 BM43871 TNFAIP9 protein isoform 2 [Danio rerio] Fetuin-B precursor 2.00E-12 3.4 7.00 CV993853 No significant similarity 3.3 9.82 CV987901 Complement C3-H1 [Cyp 3 3.2 5.63 CK40961 Proteasome activator PA28 subunit [Cyprinus carpio] 7.00E-98 3.2 9.82 CK406493 Complement C3-Q1 [Cyprinus carpio] No significan 6.00E-54 3.1 5.63 TC9755 Proteasome (prosome, macropain) subuni type, 3 [Xenopus tropicalis] Tumor necrosis factor, alpha-induced protein 9 1.00E-126 3.1 9.18 CF97157 [Homo sapiens] Atpase H+ transporting ly 5.00 3.1 9.18 TC6790 proton pump [Pagrus major] 5 3.0 5.63 CK41175 Integral membrane protein 1 [Danio rerio] 4.00E-24 3.0 5.63 CK406132 Alpha-1-tubulin [Gecarcinus lateralis] Armet prot 3.00E-24 2.9 9.82 CK405386 No significant similarity 2.9 9.82 CF971092 No significant similarity 2.9 9.82 CF972078 Matrix metalloproteinase 13 [Danio rerio] Translo 5.00E-80 2.8 5.63 CK40434 H2A histone family, member V, isoform 1 [Homo sapiens] Transferrin [ 7.00E-55 2.7 8.97 TC7398 CC chemokine SCYA106 1 2.6 8.97 TC7043 CCAAT/enhancer binding protein (C/EBP), b [Danio rerio] 2.00 2.6 7.00 EE993326 CD63 [Oncorhynchus mykiss] 2.00E-62 2.6 9.82 TC8981 FK506 binding protein 2 [M 9 2.6 5.63 CF972066 No significant similarity 2.6 9.82 BM438439 Signal sequence receptor, a 3.00E-68 2.6 8.70 CK404046 Lymphocyte antigen 6 complex, lo isoform 2 [Danio rerio] MHC class I alpha chain [Ictalurus puncta 1.00E-12 2.5 9.82 CK401855 2.00E-27 2.5 5.63 CK424035 Neuronal myosin light ch rerio] 2 2.5 8.97 CV995916 No significant similarity 2.5 9.82 CK401686 WW domain binding protein 2 [Ictalur punctatus] Amyloid beta (A4) precursor prote 1.00E-93 2.5 9.82 TC9648 7.00E-107 2.4 9.82 167 pti] .00E-42 t 1 [Danio rerio] .00E-100 talurus rerio] nio rerio] [Danio rerio] nit, beta -binding protein 2 iseus] .00E-48 9 1 ] family B, member 2, partial [Danio rerio] BM438634 Angiotensinogen [Danio rerio] 6.00E-57 2.4 9.82 CV989409 Dnajb11 protein [Danio rerio] 2.00E-65 2.4 9.18 CK415655 Endoplasmin [Aedes aegy 3 2.4 9.82 TC8526 Proteasome activator subuni 7 2.4 5.63 TC6716 Beta-2 microglobulin precursor [Ic punctatus] 3.00E-52 2.3 9.82 CV990995 Coactosin-like 1 [Danio rerio] saccharide-protein 1.00E-59 00E-61 2. 2.3 9.82 TC9170 Dolichyl-diphosphooligo glycosyltransferase [Danio 2. 3 9.82 CV987949 Fructose-1,6-bisphosphatase 1, like [Da 3.00E-85 2.3 8.70 TC8645 Lectin, galactoside-binding, soluble, 9 (galectin 9)-like 1 1.00E-108 2.3 9.82 TC6930 LR8 protein [Danio rerio] 1.00E-52 2.3 9.18 CK423344 No significant similarity 2.3 9.82 TC6963 Proteasome activator subunit 2 [Danio rerio] Similar to family with sequence similar 1.00E-87 2.3 9.82 CK406492 ity 46, member A isoform 1 [Danio rerio] WW domain bind 1.00E-92 2.3 0.00 TC8465 ing protein 2 [Ictalurus punctatus] Alcohol dehydrogenase 5 [Dan 4.00E-95 2.3 9.82 CV995162 io rerio] 8.00E-93 2.2 9.82 CV989503 CXCL14 [Ictalurus punctatus] ubu 2.00E-52 2.2 9.82 TC7388 Proteasome (prosome, macropain) s type, 6 [Xenopus tropicalis] 2.00E-89 2.2 9.82 CV995433 Sterol regulatory element (SREBP-2) [Cricetulus gr 5 2.2 9.82 CK406459 Dnajb11 protein [Danio rerio] 1.00E-26 2.1 9.18 CK404782 Methionine adenosyltransferase II alpha subunit [Mus musculus] 2.00E-80 2.1 5.63 CK410925 No significant similarity 2.1 9.18 CV994277 6 No significant similarity 2.1 9.82 CV99663 No significant similarity 2.1 8.70 CK40898 CK424843 No significant similarity No significant similarity 2.1 2.1 9.18 8.70 BM425334 No significant similarity 2.1 9.82 CK40742 Glutaredoxin (thioltransferase) [Danio rerio 2.00E-40 2.0 9.82 CV997128 No significant similarity 2.0 9.18 CK403934 No significant similarity 2.0 8.70 TC9161 hypothetical protein LOC641319 [Danio rerio] 4.00E-62 2.0 5.63 168 Supplemental d transcripts in blue catfish liver. refe n number or TIG sus n er of t n th the top informative BLASTX hit. q-val isco gene n Fold Cha q-va Table 6 All significant, downregulate Accession rs to the GenBank accessio R consen umb he sequence o e microarray. Putative Id is ue is the false-d very rate for the particular Accessio Putative Identity E-value nge lue CK417600 No 0.4 9.57 significant similarity TC9079 An unit 13 [Danio rerio] 0.5 9.57 0 Sel 0.5 9.57 No 0.5 9.57 1 Sel 0.5 9.57 aphase promoting complex sub enoprotein H [Danio rerio] 2.00E-35 CB94079 4.00E-29 TC9060 significant similarity CF97152 enoprotein P, plasma, 1b [Danio rerio] 2.00E-60 ntal ficantly up-regulated blue catfish transcripts in liver C infe uenc ity. A sion r Ban num e sequ e on t P X hi is th lse- y rate Accession Putative Id Fold Chan q-value (% Suppleme Table 7 Unique, signi after ES ction that could not be annotated by seq e similar cces efers to the Gen microarray. k accession number or TIGR consensus utative Id is the top informative BLAST ber of th t. q-value enc e fa he discover for the particular gene ge ) CK406832 No significant similarity 11.2 5.63 CF972223 5.5 7.00 CF971394 4.5 8.97 4.3 9.18 3.9 5.63 3.6 5.63 CK414572 ilarity 3.5 9.82 3.3 9.82 3.1 9.18 2.9 9.82 2.9 9.82 2.6 9.82 2.5 9.82 2.3 9.82 2.1 9.18 2.1 9.82 6 2.1 8.70 K408989 No significant similarity 2.1 9.18 K424843 No significant similarity 2.1 8.70 M425334 No significant similarity 2.1 9.82 V997128 No significant similarity 2.0 9.18 K403934 No significant similarity 2.0 8.70 No significant similarity No significant similarity BM439121 No significant similarity BM439116 No significant similarity CF972133 No significant similarity No significant sim CV993853 No significant similarity CF972140 No significant similarity CK405386 No significant similarity CF971092 No significant similarity CF972066 No significant similarity CV995916 No significant similarity CK423344 No significant similarity CK410925 No significant similarity CV994277 No significant similarity CV99663 No significant similarity C C B C C