Omics in fish mucosal immunity

Accepted Manuscript Omics in fish mucosal immunity Salinas Irene, Susana Magadán PII:

S0145-305X(17)30087-3

DOI:

10.1016/j.dci.2017.02.010

Reference:

DCI 2818

To appear in:

Developmental and Comparative Immunology

Received Date: 5 December 2016 Revised Date:

15 February 2017

Accepted Date: 16 February 2017

Please cite this article as: Irene, S., Magadán, S., Omics in fish mucosal immunity, Developmental and Comparative Immunology (2017), doi: 10.1016/j.dci.2017.02.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT 1

Omics in fish mucosal immunity

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Irene Salinasa and Susana Magadána,b

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Keywords

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Center for Evolutionary and Theoretical Immunology (CETI), Department of Biology, MSC03 2020, University of New Mexico, Albuquerque, NM 87131, USA; Tel.: +1-505-277-0039; Fax: +1-505-2770304.

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Current address: Immunology Laboratory, Biomedical Research Center (CINBIO), University of Vigo, Campus Lagoas Marcosende, Vigo, Pontevedra, 36310, Spain, Tel +34 986 812 626, Fax +34 986 812 556

New Generation Sequencing, Mucosal immunity, Teleost

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Abstract

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The mucosal immune system of fish is a complex network of immune cells and

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molecules that are constantly surveilling the environment and protecting the host from

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infection. A number of “omics” tools are now available and utilized to understand the

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complexity of mucosal immune systems in non-traditional animal models. This review

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summarizes recent advances in the implementation of “omics” tools pertaining to the

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four mucosa-associated lymphoid tissues in teleosts. Genomics, transcriptomics,

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proteomics, and “omics” in microbiome research require interdisciplinary collaboration

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and careful experimental design. The data-rich datasets generated are proving really

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useful at discovering new innate immune players in fish mucosal secretions, identifying

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novel markers of specific mucosal immune responses, unraveling the diversity of the B

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and T cell repertoires and characterizing the diversity of the microbial communities

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present in teleost mucosal surfaces. Bioinformatics, data analysis and storage platforms

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should be developed to facilitate rapid processing of large datasets, especially when

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mammalian tools such as bioinformatics analysis software are not available in fishes.

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ACCEPTED MANUSCRIPT 1. Introduction

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Mucosal surfaces are continuously exposed to the external environment and can be

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considered the main route of entry for pathogens in all living organisms. Fish, compared

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with terrestrial vertebrates, live in an ideal medium (fresh or seawater) for microbial

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growth and their mucosal surfaces are constantly colonized by a variety of commensals,

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opportunistic and obligate pathogens. To maintain homeostasis, mucosal barriers are

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armed with an associated lymphoid tissue (MALT), with innate and adaptive

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components, responsible for the protection against the continuous challenge of microbes

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and antigens but also for the tolerance towards beneficial symbiont colonization

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(Gomez et al., 2013).

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In general, research on the immune system of fish has been limited not only by its

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complexity, but also by the lack of reagents suitable for classical cellular immunology

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studies. However, the current development of high throughput methodologies for global

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data acquisition, computational tools for data analysis, data storage and mathematical

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algorithms has significantly moved this field forward. In fact, the advance in new

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generation sequencing (NGS) technologies has drastically reduced cost and time

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requirements for sequencing, triggering an exponential growth of vertebrate complete

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genome sequences availability (Spaink et al., 2014; Goodwin et al., 2016). The NGS

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has improved the assembly and gene annotation of genomes from model fish species,

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like zebrafish (Danio rerio) (Howe et al., 2013) and medaka (Oryzias latipes) (Kasahara

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et al., 2007), and also generated a number of fish genomes from important species for

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aquaculture, like rainbow trout (Oncorhynchus mykiss), common carp (Cyprinus

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carpio), Atlantic cod (Gadus morhua) or Atlantic salmon (Salmo salar) (Berthelot et al.,

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2014; Star et al., 2011; Xu et al., 2014; Lien et al., 2016). Furthermore, these genomic

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and bioinformatics approaches have been instrumental in enlightening how diverse

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ACCEPTED MANUSCRIPT cycles of whole-genome duplication, as well as gene losses and expansions, taken place

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during vertebrate evolution have affected to teleost immune system (Pasquier et al.,

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2016). One intriguing example is the Atlantic cod genome (Star et al., 2011), whose

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analysis confirmed the hypothesis suggested by Pilstrom (Pilstrom et al., 2005)

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regarding MHC class II deficiency as an explanation for the lack of specific antibody

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responses but high levels of “natural” antibodies in non-immunized cod (Magnadóttir et

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al., 2001; Solem, 2006). Recently, low-coverage genome sequencing and comparative

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analysis of 66 teleost species genomes have shown that the MHC II pathway as well as

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the CD4 receptor are missing in the entire Gadiforme lineage (Malmstrøm et al., 2016).

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Thus, this teleost group has adopted alternative strategies in order to deal with bacterial

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and parasitic infections that are remarkably distinct from the adaptive molecular

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machinery used by most vertebrates (Buonocore and Gerdol, 2016).

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Besides Genomics, other throughput approaches or “omic” technologies have emerged

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to provide a holistic view of all cellular components and their interactions. Perhaps the

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most used in fish immunology is transcriptomics, which can be applied to measure the

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expression of all immune genes within a sample (immunome) and also allow the

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discovery of new immune genes, including those fish specific that lack homologs in

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other vertebrates (Qian et al. 2014). As a consequence, “omics” tools can provide

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meaningful answers when trying to characterize and interpret the complex behavior of

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fish mucosal immune system, in which multiple and reciprocal interactions occur

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between immune cells, epithelial cells, neuroendocrine cells, mucus secreting cells,

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antigens and the microbiota (Brandtzaeg et al., 2008; Macpherson et al., 2008).

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The goal of this review is to present the most recent advances on “omics” tools in the

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context of teleost mucosal immunity. First we provide a general overview of the teleost

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mucosal immune system (section 2) highlighting the contributions of omic approaches

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ACCEPTED MANUSCRIPT to the current body of knowledge in the field. Sections 3 and 4 summarize the

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transcriptomic and proteomic studies regarding innate and adaptive immune genes in

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fish mucosal tissues and secretions. Next we discuss the use of high throughput

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sequencing approaches for the study of fish microbiomes (section 5). The microbiota is

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known as the “extended-self”, affecting its composition to the development and

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functioning of the mucosal and systemic immune systems (Maynard et al., 2012). For

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these reasons, “omics” of fish microbiomes is an emerging area of research that is

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relevant for the understanding of teleost mucosal immune responses. Finally, an

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overview of the use of NGS and its applications to the understanding of teleost Ig and

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TCR repertoires will be provided in section 6. Although still at its infancy, recent

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advances in this field are promising and will greatly contribute to our current knowledge

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on mucosal B and T cells.

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2. The teleost mucosal immune system

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2.1 Organization of teleost MALT

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According to their anatomical location, four different MALTs have been described in

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teleosts: gut-associated lymphoid tissue (GALT); gill-associated lymphoid tissue

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(GIALT), skin-associated lymphoid tissue (SALT) and, the recently characterized

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nasopharynx-associated lymphoid tissue (NALT) (Tacchi et al., 2014). GIALT includes

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the diffuse leucocyte populations found in the gills as well as the interbranchial immune

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tissue (ILT) so far described in salmonids (Ballesteros et al., 2012; Haugarvoll et al.,

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2008; Koppang et al., 2010). Fish MALTs share many characteristics with type I

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mucosal surfaces of mammals despite some functional and structural differences (see

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review by Gomez et al., 2013). In contrast to endotherms, teleost MALTs do not present

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ACCEPTED MANUSCRIPT organized lymphoid structures, such as lymph nodes, Peyer´s patches. Thus, teleost

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MALT apparently lacks germinal center formation.

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Despite the morphological differences between species, common features can be noted

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in all teleost MALT including the presence of a mucus layer, which acts as a physical

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and chemical barrier; of the presence of innate and adaptive immune components,

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including secretory Igs (sIgs) with a preponderant role of IgT antibodies; the transport

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of antibodies across the epithelium by the polymeric Ig receptor (pIgR) and the presence

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of a complex and diverse commensal bacterial community that is coated by sIgs and is

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involved in the development and regulation of the mucosal immune system (Parra et al.,

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2015; Rombout et al., 2014; Salinas, 2015; Sepahi et al., 2016).

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2.2 Teleost mucus

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The mucus exerts its protective role not only by immobilizing or inhibiting pathogen

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binding, but also, by acting as a vehicle for mucins and humoral immune factors

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(lectins, antimicrobial peptides, cytokines, immunoglobulins). Mucins are high

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molecular weight glycoproteins important for viscosity, trapping pathogens and

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physical barrier (Johansson and Hansson, 2016). A recent in silico analysis of 28 teleost

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genomes suggests that the 5 secreted gel-forming mucins present in mammals (Muc2,

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Muc5AC, Muc5B, Muc6, and Muc19) can be also found in fishes, but Muc6 is missing

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in most teleosts studied (Lang et al., 2016). In the intestine, two secreted mucins, Muc2

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and Muc2-like, are expressed in gilthead seabream (Sparus aurata) (Pérez-Sánchez et

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al., 2013), while only one, Muc2, has been described in common carp (van der Marel et

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al., 2012). Micallef et al. (2012) described the expression of mucins type 2 and 5 in skin

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transcriptome of Atlantic salmon. They highlight the difficulty to characterize mucin

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full-length sequences due to the large repetitive units in mucin mRNAs. Despite the

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biological relevance of mucins in teleost mucosal immunity, gene expression studies of

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ACCEPTED MANUSCRIPT mucins in mucosal sites, and how they respond to pathogens and commensals, are

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lacking in fish.

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2.3 Innate immune receptors in teleost mucosal surfaces

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When a mucosal barrier is disturbed not only resident immune cells are activated, but

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there is also a tissue specific recruitment of leucocytes from blood, which is directed by

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the expression of adhesion molecules and chemokines. In general, an immediate innate

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immune response is activated by the recognition of evolutionarily conserved structures

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on microorganisms, named microbe-associated molecular patterns (MAMPs), through

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the germ-line-encoded pathogen recognition receptors (PRRs) (Mogensen, 2009).

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MAMPs are not exclusive to pathogens and are abundantly produced by the normal

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resident microbiota. Thus, the recognition of those MAMPs specifically expressed by

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pathogens triggers intracellular signaling cascades to stimulate the release of cytokines

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and transmit signals to neighboring cells (Akira et al., 2006; Sellge and Kufer, 2015;

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Wells et al., 2010). Among the four main PRRs described to date, the TLRs are the first

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and best characterized. In 2003, Sttaford et al described the first teleost TLR gene was

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from an expressed sequence tag (EST) screening of a goldfish (Carassius auratus)

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(Stafford et al., 2003), later assigned to be TLR22, but the emergence of

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genomics/transcriptomics research led to the discovery of at least 20 TLR genes, being

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some of them fish-specific (Pietretti and Wiegertjes, 2014). Using a number of pathogen

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models and routes of exposure, transcriptomic evaluation of mucosal and other tissues

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has revealed putative roles of TLRs during immune responses against bacteria (Aoki et

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al., 2013; Li et al., 2013), virus (Encinas et al., 2010; Wan and Su, 2015), and

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ectoparasites (Valenzuela-Muñoz et al., 2016).

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A recently discovered family of cytoplasmic PRRs, the NOD like receptors (NLRs), has

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also been identified in teleosts, like zebrafish (Laing et al., 2008), channel catfish

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ACCEPTED MANUSCRIPT (Ictalurus punctatus) (Rajendran et al., 2012), rainbow trout and Japanese flounder

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(Paralichthys olivaceus) (Unajak et al., 2011). In mammals, their role is not only

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restricted to microbial and danger signal recognition, but also there is a clear evidence

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for an interplay between NLRs and TLRs in the regulation of inflammatory response

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against microbes (Chen et al., 2009). As described for TLRs, omic tools have allowed

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the identification of new NLR subfamily member and shown NLR gene expansion in

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teleosts (Howe et al., 2016; Li et al., 2016; Rajendran et al., 2012). Clearly, fish-specific

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PRRs will require further studies with respect to their tissue distribution, function,

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determination of ligand binding and signaling functions. Most likely, the total number

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of ‘non-mammalian’ PRRs will increase even further with future “omics” research.

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2.4 Teleost mucosal antigen presenting cells

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In mammalian immune responses, dendritic cells (DCs) are essential to perform the

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antigen sampling across the epithelial barrier and prime the adaptive immunity through

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the interaction with T cells. However, in teleosts the existence of DCs is still

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questioned. Recently, DC-like cells have been identified in the intestinal epithelium of

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Atlantic salmon (Fuglem et al., 2010) as well as in zebrafish (Lewis et al., 2014; Lugo-

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Villarino et al., 2010), rainbow trout and channel catfish (Granja et al., 2015; Kordon et

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al., 2016; Shao et al., 2015). Nevertheless, the lack of comprehensive data regarding the

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functional role of DCs in the mucosa of teleost fish, and the fact that they are not as

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abundant as their mammalian counterparts, may suggest that other cells like

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macrophages or B cells, play a main role in priming teleost mucosal adaptive responses

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(Parra et al., 2015). In addition to DCs, teleost mucosal surfaces are able to uptake

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antigen via local macrophages as well as by endocytosis in enterocytes (Rombout et al.,

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2014; Lewis et al., 2014).

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2.5 Teleost mucosal T cells

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ACCEPTED MANUSCRIPT T cells are abundant in mucosal tissues of teleost (Abelli et al., 1997; Araki et al., 2005;

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Bernard et al., 2006; Nuñez Ortiz et al., 2014; Picchietti et al., 2011; Tacchi et al.,

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2014). Genomic studies performed in different teleost species have contributed to the

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identification of T cell components associated with already characterized mammalian T

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cell subsets (Tc, Th1, Th2 , Th17 and Treg) and the four TCR chains (αβ, γδ) (Castro et

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al., 2011; Yamaguchi et al., 2015). The analysis of the whole transcriptome from gills of

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naïve European sea bass (Dicentrarchus labrax) detected transcripts for cytokines and T-

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cell associated transcription factors suggesting the presence of Th1/Th2/Th17/Treg cell

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subpopulations in this tissue (Nuñez Ortiz et al., 2014). However, whether these

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different subsets exist in all fish mucosal and systemic tissues and whether they exert

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similar roles than those present in mammals is still unknown. A transgenic CD4-1

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zebrafish line that was recently generated has shed some insights into mucosal CD4 T

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cells (Dee et al., 2016). Specifically, a subspecialization of mucosal CD4-1 T cells was

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observed, with a skewed Treg phenotype in gut and a novel Th2-like population in gills

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that expresses gata3 and il4/13b.

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In mucosal microenvironments, where there is a complex microbial symbiotic

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community, T cells such as Th17 and Treg cells play a critical role in creating tolerance

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or immunity against these symbionts (Nutsch and Hsieh, 2012; Round and Mazmanian,

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2010). Th17 cells are characterized by IL17 production that promotes tissue

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inflammation while Treg cells have been identified as immune suppressors and for

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maintaining peripheral tolerance. Recent data suggest the interconversion between these

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T cell subsets, a process that can be affected by environmental signals and microbiota

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(Omenetti et al., 2015). Moreover, fish mucosal tolerance is thought to be one of the

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reasons why repeated booster vaccinations by oral or bath routes may lead to tolerance

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ACCEPTED MANUSCRIPT against the corresponding pathogen, making very difficult to establish long-lasting

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protection using these vaccination routes (Munang’andu et al., 2015).

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Functional studies on mucosal T cells of teleosts are scant. We currently do not know

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how mucosal APCs educate mucosal T cell subsets and how they control the production

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of mucosal Igs. The best characterized mucosal T cells are those present in gut. For a

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recent review see Tafalla et al (2016). In gills, there are few studies in which the

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immune response mediated by T cells has been evaluated. For instance, rainbow trout

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CD3 expression is up-regulated in response to viral hemorrhagic septicemia (VHS)

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infection (Aquilino et al., 2014). In Japanese flounder, immersion vaccination

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with Vibrio anguillarum results in increased CD4-1, CD4-2 and CD8α expression in

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the gills (Kato et al., 2013). In addition, in ILTs of Atlantic salmon challenged with

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infectious salmon anemia virus, levels of CD3ζ transcripts increased indicating that

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this molecule (and therefore T cells) plays a role in the ILT antiviral immune

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response (Aas et al., 2014). Furthermore, gill T cells appear to be involved in the

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response against amoebic gill disease since an up-regulation of IL-4/13 expression,

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potentially involved in the Th2 pathway, was noted (Benedicenti et al., 2015; Norte dos

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Santos et al., 2014). However, contradictory results were found in other studies

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regarding skin immune responses against parasitic infection in rainbow trout and Baltic

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salmon (Salmo salar). While a decrease of CD8+ cells and a Th1/Th2 switch were

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observed in trout, an increase of skin CD8+ T cells was detected in salmon (Chettri et

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al., 2014; Kania et al., 2007). Finally, a very recent report proposed T cells as major

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players in rainbow trout skin defense against viral infection with VHS (Leal et al.,

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2016). Future studies will benefit from using “omics” approaches in purified immune

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cell subsets to better define immune cell subpopulations and their changes during

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infection and vaccination.

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ACCEPTED MANUSCRIPT 2.6 Teleost mucosal B cells

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IgM + and IgT/Z+ B cells are present in fish gut, gills, skin and olfactory organ

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(Tacchi et al., 2014; Xu et al., 2016, 2013; Zhang et al., 2010). The role of IgT/Z in

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mucosal immunity has been revealed very recently with several works mainly

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developed in rainbow trout. A number of review papers have recently summarized the

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state of the art knowledge on teleost mucosal B cells and Igs. Please see Salinas et al.

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(2011), Rombout et al. (2014), Parra et al. (2015 and 2016).

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In general, the combined body of works suggest a compartmentalization of IgT/Z and

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IgM responses in mucosal and systemic sites respectively (Austbø et al., 2014; Xu et al.,

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2013; Zhang et al., 2010). Interestingly, Xu et al. (2016) showed the generation of

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local IgT+ B cell proliferative and pathogen-specific IgT responses in rainbow

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trout gills, demonstrating for the first time the ability GIALT to mount locally

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induced B-cell and sIg responses. However, the importance of local Ig repertoire at

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mucosal sites versus that resulting from the infiltration and concentration of Ag-

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specific IgT+ B cells from other lymphoid organs remains unknown. It seems

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unlikely that diversity of mucosal resident IgT+ B cells can be sufficient to match

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any possible pathogen; hence, IgT+ B cells likely would have to gather at

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inflammatory sites and the Ag-specific cells sampled and locally amplified. The

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integration of deep analysis of systemic and mucosal IgM and IgT repertoires will

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provide insights into the sites of B cell activation and migration to mucosal tissues.

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3. Use of transcriptomics in fish mucosal immunity

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The expression of large number of genes in an organism, tissue, cell population or

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single cell can be analyzed using oligomicroarrays (Miller and Maclean, 2008), which

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ACCEPTED MANUSCRIPT were first developed in the 1990’s, and the whole transcriptomic sequencing or RNA-

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seq (Table 1).

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Despite the benefits of RNA-seq over oligomicroarray, like no previous gene sequence

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is required, RNA-seq can detect novel transcripts/genes, splice variants and differences

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in allele-specific expression; oligomicroarrays are still the more common choice to

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study teleost fish immune responses. In this sense, one of the major challenges to

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improve the biological interpretation of RNA-seq experiments is the reduced

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availability of well-annotated teleost genomes. Additionally, when studying mucosal

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tissues, bacterial, fungal or parasitic RNAs will be also sequenced and therefore

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bioinformatics approaches may be required to eliminate reads not derived from the host.

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For a summary of the pipelines used in microarray and RNA-seq as well as a review

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focused on RNA-seq in fish see Martin et al. (2016) and Qian et al. (2014), respectively.

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Only few studies have taken advantage of the whole transcriptome approach to

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investigate changes in immune gene expression in fish intestine, gills, skin and olfactory

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organ (Table 1), being the gut the most studied (see review by Martin et al., 2016). In

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some cases, pools of tissues (mucosal and non-mucosal) are used and therefore we

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cannot distinguish transcriptional changes that are specific to MALT. Studies with

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mixed tissues were not included in Table 1. However, it is worth mentioning that

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transcriptomes from non-mucosal immune tissues can been used for molecular and

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functional discovery of mucosal-relevant immune molecules.

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From the transcriptomic studies performed on control individuals, comparisons between

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MALT and main lymphoid organs have been made. Additionally, the notion of the

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posterior part of the gut as most immune relevant region of the gut has been confirmed

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in seabass by measuring the constitutive expression of immune genes (Calduch-Giner et

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ACCEPTED MANUSCRIPT al., 2016). This finding highlights the importance of collecting the same portion of the

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intestine in all specimens of one experiment.

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With respect to the olfactory organ only four transcriptomic studies have been

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performed in fish to date. In all cases it is clear that the olfactory organ has a high level

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of expression of immune genes in the absence of any antigenic stimulation. From the

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only intranasal vaccination study performed, significant changes (>2-fold change) in the

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expression of ∼8,000 genes including genes involved in innate and adaptive immunity

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were recorded 4 days after nasal vaccination with live attenuated infectious

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hematopoietic necrosis virus (Tacchi et al., 2014). Assuming that features printed in

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microarray are representative of the entire transcriptome, this means that ∼18% of the

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olfactory transcriptome is affected by one single nasal vaccine delivery. This supports

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the idea that nasal vaccines induce potent local NALT immune responses.

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Although distilling information from all the teleost mucosal transcriptomic studies is a

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complex task, generally speaking, transcriptomic approaches have revealed important

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aspects of microbial pathogenesis, immune evasion, host immune responses, host tissue

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remodeling responses as well as the kinetics of these responses. Additionally, these

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studies have revealed immune molecular markers of mucosal vaccination. Yet, our

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current view is limited to a few teleost species and a few pathogens. Moreover, studies

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evaluating transcriptomic changes in multiple MALTs in the same model system are

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lacking.

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4. Use of proteomics in fish mucosal immunity

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Proteomics is the study of the proteome, which is the entire set of proteins expressed by

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a biological entity (cell, tissue or whole organism) at a specific time. All fish mucosal

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surfaces are covered by abundant mucosal secretions. Although some of the proteins

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ACCEPTED MANUSCRIPT present in fish mucus have been known for a long time, proteomic approaches allow

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capturing a snapshot of all the different proteins present in a mucus sample, including

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immune-related proteins. There are a number of proteomic techniques available,

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including gel-based and shotgun proteomics (Chandramouli and Qian, 2009). Gel-based

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approaches such as 2-DE or 2DE-DIGE can be used to determine the relative quantity

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of proteins. Coupling with mass spectrometry can identify which proteins are present or

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differentially expressed in different samples. However, the many limitations of gel-

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based techniques make the more reproducible and sensitive gel-free techniques the

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method of choice for most (Chandramouli and Qian, 2009). To date, no protein arrays

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are available for any fish species. Thus far, proteomics have been used in a number of

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fish species to establish baseline protein levels or describe changes in the mucosal

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proteome in response to infection. Reference skin mucus proteome maps have been

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published for European seabass (Cordero et al., 2015), gilthead seabream (Jurado et al.,

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2015), Atlantic cod (Rajan et al., 2011) and discus fish (Symphysodon aequifasciata)

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(Chong et al., 2006). The skin mucus proteome of Atlantic cod infected with Vibrio

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anguilarum and the skin mucus proteome of Altantic salmon infected with sea lice were

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studied using 2D followed by LC-MSMS (Provan et al., 2013; Rajan et al., 2013). In

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Atlantic salmon has been also confirmed an effect of dietary yeast cell wall extract on

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the skin mucus proteome, specially an increase of Calreticulin- like protein, which is

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involved in the synthesis of mucins (Micallef et al., 2017). Proteome changes in the skin

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mucus of seabream in response to probiotic administration or crowding stress showed

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changes

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and nonspecific cytotoxic cell receptor protein 1 (Cordero et al., 2016). For a review on

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skin mucus proteomics of fish see Brichmann 2016 (Brinchmann, 2016). Other than the

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skin mucus, the proteome of the buccal mucus of tilapia (Oreochromis niloticus) has

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in

lysozyme,

complement

C3,

natural

killer cell enhancing

factor

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ACCEPTED MANUSCRIPT been studied (Iq and Shu-Chien, 2011) as well as the proteome of Atlantic salmon

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affected by amoebic gill disease, in which proteins that were differentially expressed in

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skin and gill mucus were identified (Valdenegro-Vega et al., 2014). To our knowledge,

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there is only one work in which the gut mucus proteome of fish has been characterized

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(Wu et al., 2016). This interesting transcriptome-proteome integrative work was

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conducted in tilapia and shed new light on gut-liver immunity mechanisms involved in

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both homeostasis and inflammation. Additionally, a few proteomic studies have been

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performed in rainbow trout gut epithelium (not mucosal secretions) with the goal to

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evaluate the effect of starvation and feed diet composition in this tissue but not in

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disease or vaccination contexts (Baumgarner et al., 2013; Wulff et al., 2012).

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5. Omics in the study of fish microbiomes

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The microbiota is considered the “extended-self” and contributes to the host’s array of

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defense molecules. The relationships between the microbiota and the host immune

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system are complex and essential for the correct development and function of all

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animals. Teleost fish are known to have complex microbial communities present in

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every mucosal surface of the body (Lowrey et al., 2015; Llewellyn, 2014). Amplicon

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sequencing of the variable region of the 16s rDNA is the gold standard approach for the

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taxonomic identification of microbial communities. This approach, which was

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underused in the field of fish microbiology until recently, has gained popularity over the

343

past three years. Thus, the composition of the bacterial communities living in

344

association with a number of teleost species is now available (Gajardo et al., 2016;

345

Llewellyn et al., 2014; Lowrey et al., 2015; Tarnecki et al., 2016). Yet, how these

346

communities change in response to environmental changes, infection or vaccination

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348

(2014).

349

An example of how to utilize 16s rDNA sequencing to gain insights into mucosal

350

immunity of fish, taxonomic surveys can be used to identify key bacterial species

351

present in different mucosal sites. In the case of culturable bacteria, further experimental

352

work can unveil the mechanisms by which a symbiotic species can modulate the fish

353

immune system. For instance, Flectobacillus major, identified in a trout microbiome

354

survey as one of the main species present in the skin and the gills, has been shown to

355

specifically modulate gill IgT expression and secretion, control B cell populations and

356

influence the growth of other commensal bacteria from trout (Sepahi et al., 2016).

357

In order to gain deeper insights about the taxonomic and functional attributes of

358

microbiota, integrative approaches known as “multi-omics” have been developed at the

359

metagenomic, metatranscriptomic and metaproteomic levels (Heintz-Buschart et al.,

360

2016). This approach, applied so far to the study of human gut microbiomes, can link

361

the expression of disease-associated microbial functions to distinct taxa. Specifically,

362

authors found that type-1 diabetes mellitus patients have unique signatures of pancreatic

363

enzymes in their stools and that the abundances of those proteins correlated with the

364

presence of microbial taxa expressing genes involved in metabolism transformations

365

relevant to the disease (Heintz-Buschart et al., 2016). This multi-omics approach also

366

showed that the functionality of microbiomes is determined by specific microbial

367

populations and that only if multiple omic approaches are integrated host-microbe

368

phenotypic associations are unveiled.

369

In fish, a study has used PICRUSt (Phylogenetic Investigation of Communities by

370

Reconstruction of Unobserved States) (Lokesh and Kiron, 2016), a bioinformatics

371

software package that predicts metagenome functional content from marker gene (e.g.,

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373

salmon skin microbiome. However, caution should be taken when using this software in

374

fish studies since the predictions are made based on available genome sequences for

375

some bacteria and therefore OTUs lacking closely related genomes are less easily

376

predicted. This is likely a high proportion of bacteria living in association with less

377

studied fish species or particular environments.

378

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6. Mucosal Ig and T cell repertoire in fish: Use of deep-sequencing technologies

380

Fishes share the basic components of their immune system with all other jawed

381

vertebrates, including an adaptive immune system based on antigen specific receptors

382

(Igs and TCRs) (Flajnik , 2013; Fillatreau et al., 2013). The Ig or TCR repertoire of an

383

organism at any given time is the result of diversity generation during development

384

combined with expansions of clones during each pathogenic encounter and,

385

theoretically, the set of Ig or TCR encoding genes has the potential to create more than

386

1011 clonotypes. Thus, regular sequencing techniques may fail to capture the vast

387

diversity of the repertoire and we need the combination of different technical

388

approaches as well as NGS to get a comprehensive understanding of systemic or

389

mucosal Ig/TCR responses.

390

Several NGS platforms are available including Roche 454, Illumina and IonTorrent,

391

which offer benchtop sequencers with reduced cost and a setup that fits with immune

392

repertoire analysis, in terms of read length and depth (Georgiou et al., 2014; Six et al.,

393

2013). Table 2 shows a comparative summary of the features of each NGS platform. To

394

date, the main challenge while performing a thorough characterization of teleost

395

immune repertoires is the data analysis and interpretation. There is a considerable lack

396

of bioinformatics approaches to process and analyze non-human or mouse immune

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ACCEPTED MANUSCRIPT repertoire, resulting in a need for in-house developed scripts. However, a few recent

398

initiatives like IMGT/HighV-QUEST that offers online tools for the analysis of some

399

teleost Ig and TCR sequences from NGS (Alamyar et al., 2012) has been published. For

400

instance, VDJviz is a web tool that can be used to browse and analyze the results

401

generated by various pre-processing immune repertoire softwares (Bagaev et al., 2016)

402

or IgDiscover, that identifies germline V genes from expressed repertoires (Corcoran et

403

al., 2016).

404

There are only few studies in which the diversity of teleost B/T cell repertoire under

405

normal conditions or after antigen challenge has been characterized and, most of them

406

did not use NGS (Bernard et al., 2006; Boudinot, 2004; Castro et al., 2013; Mashoof et

407

al., 2014). One exception is the zebrafish, the first and only vertebrate in which a full

408

organism Ig and TCRαß repertoire analysis has been performed using 454 and Illumina

409

MiSeq, respectively (Covacu et al., 2016; Weinstein et al., 2009; Jiang et al., 2011). In

410

rainbow trout, a similar technology was used to investigate the clonal structure of the

411

secondary B-cell response against VHS. In this case, the analysis of the Ig repertoire

412

showed highly diverse IgM responses with few large public clones (shared by all fishes)

413

and suggested the participation of IgT in the systemic immune response (Castro et al.,

414

2013).

415

Currently, teleost mucosal IgM/IgT repertoires remain almost unknown. In human,

416

a recent study using NGS found that the intestinal IgA repertoire comprises a large

417

diversity of low-frequency clones in addition to the highly expanded ones (Lindner

418

et al., 2012). In fact, this likely reflects an equilibrium between the impact of gut

419

bacteria on the B-cell repertoire and the pressure of mucosal Igs to control the

420

microbiota composition, diversity and function (Wei et al., 2011). It will be

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ACCEPTED MANUSCRIPT interesting to determine if a similar available repertoire is found in the gut of naive

422

fish.

423

Regarding TCR repertoire, the analysis performed in whole zebrafish suggests that a

424

relative small TCR repertoire is enough to cover the antigenic diversity presented by

425

pathogens (Covacu et al., 2016). The teleost mucosal T cell repertoire has been only

426

studied with classical methods, like Immunoscope and Sanger sequencing. A diverse

427

lymphocyte population has been described not only for TCRß from systemic organs

428

(spleen, head kidney and thymus) of naïve rainbow trout, but also in gut intraepithelial

429

lymphocytes (IELs) from young adult trout (Bernard et al 2006). This repertoire was

430

found to be more restricted in older fish from farms, suggesting that IEL diversity

431

might be reduced due to external perturbations (Magadan et al., 2015). In addition,

432

significant modifications of the trout IEL TCRβ repertoire were observed after a

433

systemic infection with a VHSV and responding T-cell clonotypes were observed to

434

be shared between gut mucosa and other peripheral (non-mucosal) lymphoid

435

tissues (Bernard et al., 2006). As mentioned for mucosal Ig responses, further

436

studies would be necessary to clarify the mechanisms of IEL selection in fish and

437

also to determine the respective contributions of local T cells locally expanded in

438

the mucosa versus T cells recruited from other tissues.

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440

Conclusions

441

Mucosal surfaces represent heterogeneous and complex areas of the body in which the

442

environment, the host genomic background and the microbial communities form a

443

delicate homeostasis. Furthermore, each one of these elements harnesses a great degree

444

of cellular and molecular complexity within itself. Thus, classical immunological tools

445

such as ELISA, quantitative PCR or immunohistochemistry are not sufficient to

18

ACCEPTED MANUSCRIPT understand the entire mucosal immune response. Currently, each “omics” tool can

447

provide specific information about immune gene loss and expansion, single nucleotide

448

polymorphism (genomics), gene expression activation or repression in different

449

conditions/cells (transcriptomics), and the proteins involved in healthy or infected sates

450

(proteomics). However, to exploit the full capabilities of “omics” tools, we need to

451

establish how these elements interact with each other (interactome) and ultimately

452

define the immunome. The integration of various “omics” datasets is now possible

453

thanks to “multi-omics” approaches. The discipline of “systems biology” can aid

454

acquire a deeper understanding of complex biological processes happening at fish

455

mucosal surfaces and translate this information into better aquaculture practices that

456

favor fish mucosal health.

457

Acknowledgements

458

This work was funded by National Institutes of Health Grant 2R01GM085207-05 and

459

USDA AFRI Grant# 2DN70-2RDN7 to IS. SM has also received funding from the

460

People Programme (Marie Curie Actions) of the European Union's Seventh Framework

461

Programme (FP7/2007-2013) under REA grant agreement n° 600391.

464 465 466

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467 468 469 470

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Table 1: Fish studies using “omic” tools to characterize mucosal transcriptome. Treatment

Conditions / Sampling

Tissue

Channel catfish (Ictalurus punctatus) Channel catfish (Ictalurus punctatus) Channel catfish (Ictalurus punctatus) Channel catfish (Ictalurus punctatus) Channel catfish (Ictalurus punctatus) Asian seabass (Lates calcarifer)

Edwardsiella ictaluri, immersion

Infected vs control fish (0, 3, 24 h & 3 d)

Entire intestine

Edwardsiella ictaluri, immersion

Hsp40 multigene expression in infected vs control fish (0, 3, 24 h & 3 d) Cytochrome P450 multigene expression in infected vs control fish (0, 3, 24 h & 3 d) Claudin multigene expression in infected vs control fish (0, 3, 24 h & 3 d) Tumor suppressor multigene expression in infected vs control fish (0, 3, 24 h & 3 d) Infected vs control fish (40 h)

Multiple, including intestine

Gilthead sea bream (Sparus aurata) Gilthead sea bream (Sparus aurata)

Escherichia coli LPS, intraperitoneally & Vibrio harveyi, intraperitoneally Enteromyxum leei, immersion Enteromyxum leei, immersion

Turbot

Enteromyxum

SC

Multiple, including intestine

M AN U

Edwardsiella ictaluri, immersion

TE D

Edwardsiella ictaluri, immersion

EP

Edwardsiella ictaluri, immersion

Exposed infected and noninfected vs control fish (113 d) Infected fish fed vegetable (VO) or fish (FO) oil vs noninfected fish fed VO or FO (102 d) Infected vs control fish (7, 24

Platform

RI PT

Fish species

AC C

475

Intestine

Multiple, including intestine Intestine

RNA-seq, Illumina HiSeqTM 2000 Illumina-based RNA-seq datasets Illumina-based RNA-seq datasets Illumina-based RNA-seq datasets Illumina-based RNA-seq datasets RNA-seq, 454 GS FLX Titanium

References (Li et al., 2012) (Song et al., 2014) (Zhang et al., 2014) (Sun et al., 2015) (Mu et al., 2015) (Xia et al., 2013)

Multiple, including intestine Distal intestine

Microarray Microarray

(Davey et al., 2011) (CalduchGiner et al., 2012)

Multiple, including

RNA-seq,

(Robledo et

21

ACCEPTED MANUSCRIPT

Infected vs control fish (0, 2, 24, 48, 72, 96 & 120 h)

Multiple, including intestine

Vaccine against Vibrio anguillarum, orally

Vaccinated vs control fish (135 d)

Multiple including hind gut

DNA vaccine against infectious pancreatic necrosis virus (Ipnv), orally 2 diets with and without immunostimulants Enteromyxum scophthalmi, oral infection Control

Vaccinated vs control fish

Multiple including pyloric caeca

Control

Illumina HiSeqTM 2000 RNA-seq, Illumina HiSeqTM 2000 RNA-seq, 454 GS FLX Titanium (Roche) Microarray

M AN U

SC

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pyloric caeca

al., 2014) (Shi et al., 2014) (Sarropoulou et al., 2012)

(Ballesteros et al., 2012)

Fish fed immunostimulant vs control diets (4 weeks)

Multiple including intestine

Microarray

(Doñate et al., 2010)

24 days postinoculation vs control

Pyloric ceaca

(Ronza et al., 2016)

Cultured versus wild

Olfactory organ

RNA-seq, Illumina HiSeqTM 2000 RNA-seq, Illumina based Capillary Sequencer systems, ABI 3730 XL (Applied Biosystems) and MegaBase 4500 (GE) Healthcare,

TE D

Rainbow trout (Oncorhynchus mykiss) Turbot (Scophthalmus maximus) Senegalese sole (Solea senegalensis) Goldfish (Carassius auratus)

& 42 d)

EP

Rainbow trout (Oncorhynchus mykiss)

scophthalmi, oral intubation Reovirus (GCRV), immersion

Control

AC C

(Scophthalmus maximus) Grass carp (Ctenopharyngodon idella) European sea bass (Dicentrarchus labrax)

Olfactory organ

(Fatsini et al., 2016) (Kolmakov et al., 2008)

22

ACCEPTED MANUSCRIPT

wild anodromous vs nonanodromous

Olfactory organ

Live attenuated IHNV vaccinated, intranasally Ammonia exposed

Vaccinated vs control fish (4 d and 14d)

Olfactory organ

Gill

Control

0.1 mg L_1 ammonia (control) and 20 mg L_1 ammonia for 48h Control

Atlantic cod (Gadus morhua)

Control

Control

Atlantic cod (Gadus morhua) Different Rainbow trout strains

Smoltification / Sea water transfer Aeromonas salmonicida, intraperitoneal AGD infection

1 and 3 weeks post sea water transfer Day 3 postinfection

44, 114, and 189 h pi

Gill

Microarray

AGD infection

19 days post infection

Gill

Microarray

4 h, 24 h, and 48 h

Gill

1 day and 18 days at 12C

Gill

RNA-seq, Illumina HiSeqTM 2000 Microarray

Flavobacterium columnare, immersion Hypothermia

SC Gill

M AN U

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EP

Atlantic salmon (Salmo salar) Atlantic salmon (Salmo salar) Channel catfish (Ictalurus punctatus) Zebrafish (Danio rerio)

RNA-seq, Illumina HiSeqTM 2000 Microarray

RI PT

Control

AC C

Japanese Grenadier Anchovy (Coilia nasus) Rainbow trout (Oncorhynchus mykiss) Blunt snout bream (Megalobrama amblycephala) Fugu (Takifugu rubripes)

Gill

Head kidney, Gut, Gill Gill

RNA-seq, Illumina HiSeqTM 2000 RNA-seq, Illumina HiSeqTM 2000 RNA-seq, 454 GS FLX Titanium (Roche) Microarray Microarray

(Zhu et al., 2014) (Tacchi et al., 2014) (Sun et al., 2016) (Cui et al., 2014) (Małachowicz et al., 2015)

(Johansson et al., 2016) (Rebl et al., 2014) (Morrison, 2006) (Wynne et al., 2008) (Sun et al., 2012) (Chou et al., 2008)

23

ACCEPTED MANUSCRIPT

Coho salmo (Oncorhynchus kisutch) and Atlantic salmon (Salmo salar) 476

Microarray

Fasting

7 days

Gills and skin

ISAV cohabitation

3, 7 and 14 days after outbreak vs control

Gill

RNA-seq, Illumina HiSeqTM 2500 RNA-seq, Illumina MiSeq

Control

Control

Nervous necrosis virus (NNV), in vitro

6, 12, 24, 48 h post-inoculation

Epithelial cell line

Control

Control

Skin

Freshwater and saltwater Aeromonas hydrophila, immersion Caligus rogercresseyi, immersion

Freshwater vs saltwater

Skin

2, 12, 24

Skin

RNA-seq, Illumina HiSeqTM 2000 RNA-seq, Illumina HiSeqTM 2000 RNA-seq llumina GA IIx platform RNA-seq Rochr 454 GS Microarray

Head kidney and skin

RNA-seq, Illumina MiSeq

SC

RI PT

Gill

M AN U

Gill

TE D

Mud loach (Misgurnus anguillicaudatus) Atlantic salmon (Salmo salar) Blue catfish (Ictalurus furcatus)

8C and 23C

EP

Naked carp (Gymnocypris przewalskii) Asian seabass epithelial cell line

Thernal stress

7 and 14 days post-infestation

AC C

Different Rainbow trout strains Channel catfish (Ictalurus punctatus) Atlantic salmon (Salmo salar)

(Rebl et al., 2013) (Liu et al., 2013) (ValenzuelaMiranda et al., 2015) (Tong et al., 2015) (Liu et al., 2016) (Long et al., 2013) (Micallef et al., 2012) (Li et al., 2013) (ValenzuelaMuñoz et al., 2016)

477

24

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478

Table 2: High Throughput sequencing platforms used in Ig/TCR repertoire analysis

Read length (bp)

Roche 454 GS FLX

Pyrosequencing

Up to 600

Dye Terminator Sequencing

Up to 2x300

>107

Dye Terminator Sequencing Semiconductor

Up to 2x250 Up to 200

>108

Illumina MiSeq

Illumina HiSeq

105-106

490 491 492

References 480

Homopolymer associated indels ≈ 1%

a (Weinstein et al., 2009) 481 (Lindner et al., 2012) (Castro et al., 2013)a 482

Random substitutions > 1%

a (Covacu et al., 2016) 483

Homopolymer associated indels ≈ 1%

484 (Gao et al., 2015) 485 (He et al., 2014) 486

EP

489

Studies performed in teleost.

Error type/rate

AC C

488

a

TE D

Ion Torrent 487

Reads /Unit 105-106

SC

Mechanism

M AN U

Platform

RI PT

479

25

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Aas, I.B., Austbø, L., König, M., Syed, M., Falk, K., Hordvik, I., Koppang, E.O.,

494

2014. Transcriptional Characterization of the T Cell Population within the

495

Salmonid Interbranchial Lymphoid Tissue. J. Immunol. 193, 3463–3469. Abelli, L., Picchietti, S., Romano, N., Mastrolia, L., Scapigliati, G., 1997.

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Immunohistochemistry of gut-associated lymphoid tissue of the sea bass

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Alamyar,E., Giudicelli,V., Li,S., Duroux,P., and Lefranc,M.P. (2012). IMGT/HighV-

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QUEST: the IMGT(R) web portal for immunoglobulin (IG) or antibody and T cell

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Aoki, T., Hikima, J., Hwang, S.D., Jung, T.S., 2013. Innate immunity of finfish:

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Primordial conservation and function of viral RNA sensors in teleosts. Fish

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509

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Araki, K., Suetake, H., Kikuchi, K., Suzuki, Y., 2005. Characterization and expression

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513

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Ballesteros, N.A., Saint-Jean, S.S.R., Encinas, P.A., Perez-Prieto, S.I., Coll, J.M.,

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523

(Ipnv) induces different immune gene expression profiles in head kidney and

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Baumgarner, B.L., Bharadwaj, A.S., Inerowicz, D., Goodman, A.S., Brown, P.B.,

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Berthelot, C., Brunet, F., Chalopin, D., Juanchich, A., Bernard, M., Noël, B., Bento,

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“Omics” tools can provide meaningful answers to characterize the complex behavior of teleost mucosal immunity.

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Teleost mucosal transcriptome, proteome and microbiome analysis performed in teleost are reviewed. Deep sequencing technologies are useful approaches to get a comprehensive

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understanding of mucosal adaptive immune responses.

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