Developmental and Comparative Immunology 45 (2014) 56–66

Contents lists available at ScienceDirect

Developmental and Comparative Immunology

journal homepage: www.elsevier .com/locate /dc i

Review

No more non-model species: The promise of next generation sequencingfor comparative immunology

http://dx.doi.org/10.1016/j.dci.2014.01.0220145-305X/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author at: Université de Perpignan Via Domitia, UMR 5244 CNRS, Ecologie et Evolution des Interactions, 52 Avenue Paul Alduy, 66860 PerpignaFrance. Tel.: +33 (0) 6 25253514.

E-mail address: ndheilly@hotmail.fr (N.M. Dheilly).

Nolwenn M. Dheilly a,b,⇑, Coen Adema c, David A. Raftos d, Benjamin Gourbal a,b, Christoph Grunau a,b,Louis Du Pasquier e

a CNRS, UMR 5244, Ecologie et Evolution des Interactions (2EI), Perpignan F-66860, Franceb Université de Perpignan Via Domitia, Perpignan F-66860, Francec Center for Evolutionary and Theoretical Immunology, Biology Department, University of New Mexico, Albuquerque, NM 87131, USAd Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australiae University of Basel, Institute of Zoology and Evolutionary Biology, Basel, Switzerland

a r t i c l e i n f o

Article history:Received 4 December 2013Revised 20 January 2014Accepted 21 January 2014Available online 6 February 2014

Keywords:Transcriptome sequencingAssociation studiesGene expressionEpigeneticImmune systemDiversification

a b s t r a c t

Next generation sequencing (NGS) allows for the rapid, comprehensive and cost effective analysis ofentire genomes and transcriptomes. NGS provides approaches for immune response gene discovery, pro-filing gene expression over the course of parasitosis, studying mechanisms of diversification of immunereceptors and investigating the role of epigenetic mechanisms in regulating immune gene expressionand/or diversification. NGS will allow meaningful comparisons to be made between organisms from dif-ferent taxa in an effort to understand the selection of diverse strategies for host defence under differentenvironmental pathogen pressures. At the same time, it will reveal the shared and unique components ofthe immunological toolkit and basic functional aspects that are essential for immune defence throughoutthe living world. In this review, we argue that NGS will revolutionize our understanding of immuneresponses throughout the animal kingdom because the depth of information it provides will circumventthe need to concentrate on a few ‘‘model’’ species.

� 2014 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

1.1. Our current understanding of invertebrate immune systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571.2. Next generation sequencing and it role in addressing the key questions facing evolutionary immunology. . . . . . . . . . . . . . . . . . . . . . . . . 58

2. Transcriptome sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

2.1. De novo transcriptome assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592.2. Dual de novo assemblies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3. Association studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.1. Whole genome association studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.2. Targeted sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4. Gene expression profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.1. Comparative transcriptomic studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614.2. micro RNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624.3. Alternative splicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624.4. Transcription factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5. Epigenetic profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.1. Histone modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.2. DNA methylation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

n Cedex,

N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66 57

6. Conclusions and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

1. Introduction

The vast majority of studies in immunology focus on medical orveterinary subjects, for obvious and justifiable reasons. The result-ing paucity of data on immune responses in non-mammalian spe-cies has skewed our understanding of host defence in the vastmajority of species on earth, leaving room for the erroneous inter-pretation that they are more ‘‘simple’’ than ourselves. However,work by comparative immunologists has revealed that the im-mune systems of non-mammalian species (particularly inverte-brate animals) are not only much more complex than previouslyassumed but can also vary much more among classes or phyla (Lo-ker et al., 2004). This fits with evidence from whole genomesequencing studies, which have shown that the number of ex-pressed genes per genome is roughly equivalent in most multicel-lular animals. The obvious conclusion is that the genomic playingfield available for the development of complex immune systemsin different taxa is much more level than previously assumed.

Despite advances at the level of genomics, accompanying infor-mation about the physiological function of immune response genesin non-model species is often lacking. In most cases, we still do notunderstand the biological relevance of the gene systems that ap-pear to be associated with host defense, how they help organismsto combat infection, how they evolved, or even whether they sup-port immune responses to infection that are vaguely comparable toour own. To date, partial answers to these questions have comefrom other experimental approaches. Ecological studies haveinvestigated interaction between hosts and their symbionts (rang-ing from parasites to mutualists) at the level of whole organisms orpopulations.

One of the main challenges now for evolutionary immunolo-gists is to link the molecular systems that they have detected usinggenomics (and transcriptomics) with these newly identified formsof immune response. There are three outstanding questions: (1)What is the fundamental genetic toolkit of the immune system?In other words, which are the core genes and gene networks thatunderpin immune responses throughout the animal kingdom?And by corollary, (2) Which immune response genes evolved denovo in individual taxa, and what do they do? For ecological immu-nologists, the main goal is (3) to link particular ecological interac-tions with the selection of immune response pathways(Schulenburg and Kurtz, 2009). That is, what are the core ecologicalinteractions (host–parasite, host–symbiont) that lead to the gen-ome structure of immune response genes?

We believe that the answers to these fundamental questionsabout the evolution of immune systems will come from a combina-tion of data from next generation nucleotide sequencing (NGS) andexperimental ecology. To support that argument, this article dis-cusses our current understanding of invertebrate immune systemsand then describes how different applications of NGS can be usedto further that understanding. We place special emphasis on inver-tebrate immune responses because our knowledge of these organ-isms has been limited by a lack of genome and transcriptome datawhen compared to mammals.

Invertebrates represent roughly 90% of the planet’s animal spe-cies. The basic understanding of invertebrate immune responseswill have significant impacts for many of the major societal chal-lenges that we face today. This has already been the case whenone considers for instance that a recent attribution of Nobel prizes

in physiology and medicine acknowledges the importance of com-parative immunology for medical sciences (Imler and Ferrandon,2011). There are also new challenges, such as global warming. Inthe near future, we can expect global environmental changes to af-ford new environments that will favor the apparition, or spread ofnumerous diseases that will impact all marine and terrestrial eco-systems (and a great majority of invertebrate species) (Patz et al.,2000; Harvell et al., 2002; Lafferty, 2009). These changes will affecthuman health and society (Campbell-Lendrum et al., 2007): vectorborne diseases transmission, agriculture (biological pests controls,and pests), and aquaculture will be impaired to some unpredict-able extent. More than ever, comparative approaches will bewelcome.

1.1. Our current understanding of invertebrate immune systems

The study of invertebrate immune systems has a broad and richhistory. It is almost obligatory to point out that the first experi-ments in modern immunology were conducted by Metchnikoff,not in humans or mice, but in sea star larvae, waterfleas and newtsand that his work led to the discovery of encapsulation and phago-cytosis (Metchnikoff, 1893). In the intervening 120 years, the fieldof comparative immunology made substantial advances by investi-gating invertebrate immune responses at a cellular level (includingstudies of phagocytosis, cytotoxicity and the production of antimi-crobial peptides) and by tracing the evolutionary history of specificimmune response gene families, such as complement componentC3 and its related thioester containing proteins (TEP) (Lokeret al., 2004).

However, until very recently, comparative immunology hasbeen hindered by the lack of broad sequence data that can be usedto make comparisons across the level of entire genomes, ratherthan just specific genes (Adams et al., 2000). In the past five years,our most significant breakthroughs have been the direct conse-quence of studying the diversity of invertebrate immune responsesat the genomic level (Rast and Messier-Solek, 2008; Messier-Soleket al., 2010). At first, and in accordance with the limited number ofsequenced genomes, our view of animal immunity and its evolu-tion came almost entirely from investigations in mammals birds,amphibians fish and insects (especially Drosophila melanogaster).This selective representation of deuterostome vertebrates andecdysozoan protostome invertebrates, to the exclusion of prebila-terian and lophotrochozoan protostomes and invertebrate deuter-ostomes together with incomplete physiological approaches toimmunity, gave the impression that vertebrates had highly com-plex immune systems combining innate and adaptive immunity,whilst invertebrates were ‘‘poor cousins’’ with simple innate im-mune system.

The wave of new nucleotide sequences that have become avail-able in the past five years (Fig. 1) has provided a very different andcomplex picture of the evolution of immune systems throughoutthe animal kingdom (Rast and Messier-Solek, 2008). Briefly, themost up-to-date evidence suggests that early metazoans such assponges and cnidarians (Putnam et al., 2007; Chapman et al.,2010; Shinzato et al., 2011) already possessed all the gene familiesusable by immune systems, signifying a common eumetazoaninheritance for the basic immune response toolkit (Hemmrichet al., 2007; Miller et al., 2007; Kvennefors et al., 2008; Srivastavaet al., 2010). In this context, it appears that the less elaborate

1

8

1

2030

1

4

16

2 3

4369

1 1

2 2

1

15

4

1

45

2

1 1

6

101183

1 1

4 6

4

1

25

2

6

2

94

4

1 1

7

183

342

1

10

100

1000

para

zoa

pori

fera

cnid

aria

Plat

yhel

min

thes

mol

lusc

a

myr

iapo

da

Nem

atod

a

anne

lida

chel

icer

ata

crus

tace

a

Inse

cta

echi

node

rmat

a

hem

icho

rdat

a

ceph

aloc

hord

ata

uroc

hord

ata

Chor

data

Tota

l

Protostomia Deuterostomia

Metazoa

2000 2005 2010 2013

Fig. 1. Total number of sequenced genome on the 9th of October 2013 as referenced in NCBI (2013).

58 N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66

innate immune systems evident in the genomes of some uro-chordate deuterostomes (Ciona intestinalis, Oikopleura dioica), cer-tain ecdysozoans (such as Caenorhabditis elegans), and someinsect species, are the result of gene loss within these lineages(Kortschak et al., 2003; Denoeud et al., 2010). When the broaderphylogenetic picture revealed by genome sequencing is taken intoaccount, a remarkable expansion of innate immune receptors andeffectors is evident in basal deuterostomes, such as sea urchins(echinoderms), and basal chordates, such as amphioxus (cephalo-chordates) and a number of protostome taxa (Hibino et al., 2006;Rast et al., 2006; Huang et al., 2008). For instance, a significantexpansion of Toll-like receptor diversity has been found in annelidworms (protostome invertebrates), suggesting that selection pres-sures to massively expand receptor repertoires have been presentthroughout the protostome and deuterostome lineages of animalevolution (Davidson et al., 2008).

This trend toward diversification is evident among not justreceptors associated with ‘‘innate’’ immune systems, but also inhypervariable recognition molecules generated somatically akinto the antibodies and T-cell receptors of vertebrates. Over the past10 or so years, highly variable molecules that seem to participate innew forms of inducible, pathogen specific immune responses havebeen found in a broad range of invertebrate taxa, including seaurchins, molluscs, insects and crustaceans even though their roleis far from being fully understood. These molecules include 185/333 proteins from sea urchins (Nair et al., 2005; Terwilliger et al.,2007), FREPs from molluscs (Adema et al., 1997; Zhang and Loker,2004; Zhang et al., 2008), penaeidins in crustaceans (Cuthbertsonet al., 2002), and DSCAMs in insects and crustaceans (Watsonet al., 2005; Schmucker and Chen, 2009; Dong et al., 2012). Theirdiscovery raise fundamental questions such as where (and whenfrom an evolutionary point of view) does innate immunity stopand adaptive immunity begins? (Ziauddin and Schneider, 2012).All of these systems are now being thoroughly investigated tounderstand their evolutionary origins, the ecological circum-stances of their diversification and their functional links with otherimmune regulatory pathways.

At the same time that molecular genetic technologies wererevealing the existence of highly variable immune response genesin invertebrates, studies of host–parasite and host–symbiont co-evolution by ecological immunologists began to reveal unexpected

results at the level of whole organisms and populations. These datahave allowed us to propose a model that predicts how host–path-ogen interactions shape the immune system. This model suggeststhat symbionts (parasites, commensalists or mutualists) all exertsome negative effects on the physiology of their host (Fig. 2). Insome cases, they also provide beneficial effects that may compen-sate for their negative impact on the host fitness. Confronted tothis situation the host offers a proportional evolutionary responseto counter infection, or limit its pathogenicity. Within this gamut,active immune responses to prevent infection are seen as expen-sive physiologically. They place demands on the energy budget ofcells and individuals, reducing the availability of energy that canbe allocated to reproduction, growth and other key functions (Shel-don and Verhulst, 1996; Stahlschmidt et al., 2013). Hence, from anecological perspective, hosts are most likely to evolve immune re-sponses that trade-off regulation of their populations of symbionts(from parasites to mutualists) with conserving an optimum fitness.

Such a trade-off can sustain both the elimination of pathogensand the tolerance of symbionts (Medzhitov et al., 2012). The armsrace of host response and pathogen virulence that plays out duringhost–pathogen co-evolution drives the diversity of gene familiesand gene polymorphism within populations (Eizaguirre et al.,2012; Kubinak et al., 2012; Mitta et al., 2012). Host–mutualistco-evolution has led to a distinct phenomenon, specialization ofimmune responses (Reynolds and Rolff, 2008; Login et al., 2011).Thus, the intensity and type of host response result from trade-offsthat are required to assure host fitness during their co-evolutionwith pathogens, or other commensals (Fig. 2). However, the directdemonstration that host symbiont coevolution lead to either diver-sification of specialization of the immune system are very scarceand the proposed model on host parasite evolution is based on onlyfew model species. More comprehensive sampling is now neces-sary to confirm that this model point to generalities.

1.2. Next generation sequencing and it role in addressing the keyquestions facing evolutionary immunology

We have shown that even though our current understanding ofinvertebrate immune systems at the molecular level is stronglybiased towards few model species, genome sequencing has provenits power in revealing the diversity of immune system genome

Mutualism Commensalism Parasitism

Host

Host

Specialisa�on Diversifica�on

Symbiont effect on host fitness

Mutualism Commensalism Parasitism

Host efforts to control symbiont population

Fig. 2. Model of host-symbiont co-evolution to compare the strength (thickness ofarrows) of negative (red) and positive (green) components of interactions betweensymbiont and host that result in specialization and diversification of immuneresponses mechanisms in mutualistic and parasitic interactions, respectively. (Forinterpretation of the references to color in this figure legend, the reader is referredto the web version of this article.)

N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66 59

structures. Ecological immunology has provided some evidencethat ecological factors have a key role in the evolution of immunity,which result in very diverse immune systems (Schulenburg et al.,2009). However, in most cases, the molecular mechanisms respon-sible for the observed phenotypes have not been characterize dueto limited access to molecular biology approaches for non-modelspecies. Next generation sequencing is now emerging as an essen-tial tool for such cross-disciplinary research. The comprehensivesampling, with indication of abundance levels, at modest per readeffort renders NGS superior to Sanger sequencing because it facil-itates integrative approaches and broad, large scale comparisonsamong different organisms (Table 1). It can be applied to popula-tion biology, ecology, evolutionary biology, and molecular biology.NGS is now dominated by four main techniques developed by dif-

Table 1Comparison of characteristics of next generation sequencing versus traditional Sanger seq

Cost per millions baseRead lengthAccuracyAmount of template needed/sequenceMultiplexing of samples (individuals/treatment) (or simultaneous analysis of multiplReconstruction of full length contigGenome/transcriptome assemblyRecovery of rare sequencesSampling of unknown sequencesRepresentation of members of genefamiliesSampling of variant sequencesRecovery of symbiont/pathogen sequencesRecovery of methylated sequences***

Protein–nucleic acid interactions***

Information on expression levelmiRNA profilingComprehensive sequence comparison among species

* read-lengths for NGS approaching high throughput Sanger sequencing [BASED ON (ES** ‘‘complete’’ within confines of sequencing bias by different techniques.*** using appropriately prepared template.

ferent companies: Roche Applied Science (454 Genome SequencerFLX System, Branford, CT, USA), Illumina (Genome analyser, SanDiego, CA, USA), Helicos BioSciences (HeliScop Single Molecule Se-quencer, MA, USA) and Life technologies (Sequencing by Oligonu-cleotide Ligation and Detection; SOLID, Carlsbad, CA, USA). Thetechnical details of these different systems have already beenextensively reviewed elsewhere and so they will not be describedfurther here (Hudson, 2008; Morozova and Marra, 2008; Shendureand Ji, 2008). The length of individual sequences (reads) producedby NGS is shorter than Sanger sequencing, but millions of reads canbe generated in a short period of time, providing massively in-creased depth of sequencing (Table 1). NGS can generate three tofour order of magnitude more sequences than traditional methodsand is considerably less expensive (Table 1). As a result, NGS willbenefit numerous fields of research, especially with the continuedimprovement of computational resources to manage and analyzethe large datasets collected. We believe that the contribution ofNGS will be especially important in our understanding of evolu-tionary immunology (Fig. 3).

2. Transcriptome sequencing

2.1. De novo transcriptome assembly

The most straightforward contribution that NGS will make toevolutionary biology is to provide either genomic or transcriptomicdatasets for a very broad diversity of species across all phyla. Even-though NGS has now replaced standard Sanger sequencing as goldstandard for whole genomic DNA sequencing (Li et al., 2010), thisapplication remains relatively expensive and requires considerableexpertise, as well as complex infrastructure for data collection andanalysis. As an alternative, de novo assembly of NGS transcriptomedata significantly reduces the size of the target space because it re-lies on sequencing cDNA (representing transcribed genes) ratherthan genomic DNA. Initially, NGS transcriptomes had to be assem-bled de novo using the genome of a closely related species as a ref-erence or ‘‘scaffold’’ (e.g. the wasp Polistes metricus (Toth et al.,2007)). However, continued computational developments have fol-lowed in rapid order to improve both the length of NGS sequencereads and the capability of assembler software. This has led to sev-eral software packages that can assemble transcriptomes fromshort sequence reads without a reference genome, allowing deeptranscriptomic analyses of numerous species that do not have clo-sely related reference genomes. The first of these de novo assem-

uencing, considering same budget/effort.

NGS Sanger

0.07–10 $ <<< 2400 $35–900 bp <= 400–900 bp98–99.9% < 99.999%NGS < << Sanger

e samples) NGS >>> SangerNGS <=Sanger*

NGS >>> SangerNGS >> SangerNGS > SangerNGS >> Sanger‘‘complete’’** versus randomly selectiveNGS > SangerNGS > SangerNGS > SangerNGS > SangerNGS >>> SangerNGS >> Sanger

T average = �400 bp Sanger, close to 454)].

Mechanisms of diversifica�on of Highly

variable molecules

Regulatorynetworks

Evolu�on of Immune response genes

PRIMARY ANALYSIS

DOWNSTREAM ANALYSIS

INTEGRATIVE STUDIES

Genomeassembly

Transcriptomeassembly

Point muta�ons

Discovery of symbionts/pathogens

Genomestructure

SNV discovery

Gene expression

Protein-nucleic acidinterac�ons Alterna�ve splicing sites

Methyla�ons

Genome annota�on

Associa�on studies

Developement of molecular markers

Co-evolu�on

Recovery of rare sequences

Recovery of miRNA

RNA-seq

Compara�ve analyses

DNA-seq

Fig. 3. Scheme showing the workflow from primary sequencing of DNA or RNA toapplication of NGS in comparative immunology.

60 N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66

blies was performed in transcriptome data for various life historystages of the Glanville fritillary butterfly (Melitaea cinxia) (Veraet al., 2008).

In many invertebrate species, multigenic families with a direct orindirect role in immunity present a greater number of loci than thesame families in vertebrates, which increases the difficulty in reli-ably assemble de novo the transcripts or genes. Effective de novoassembly is now also feasible for short NGS reads with high sequencesimilarity that may encode transcripts from multiple alleles of thesame gene, different members of the same multilocus family, orhighly variable immune response genes generated by post-genomicprocesses. For example, the diversity of fibrinogen-related proteins(FREPs) in several strains of Biomphalaria glabrata had previouslybeen assessed by extensive Sanger sequencing of cloned PCR ampli-cons and BAC inserts (Zhang and Loker, 2003, 2004). In a single wholetranscriptome NGS experiment (Illumina sequencing), bioinformat-ics analysis led to doubling the number of FREP gene subfamilies thathad been detected previously by traditional Sanger sequencing(Dheilly et al., Submitted). Moreover, the high levels of sequencediversity among the FREP sequence fragments that were detectedby NGS from an additional strain of B. glabrata suggested that yetmore gene families remain to be discovered.

These results are highly significant for comparative immunol-ogy because they confirm that the levels of diversity within im-mune response gene families in invertebrates may approachthose of comparable gene systems in mammals. In addition, thedata demonstrate that de novo NGS transcriptome sequencingcan detect and help us to identify novel, highly diversified immuneresponse gene families in other taxa from which complete genome

sequence are lacking. For example, a great diversity of antimicro-bial peptides was recently discovered in the ladybeetle Harmoniaaxyridis (Vilcinskas et al., 2013). Now, with NGS, such descriptiveapproaches can be coupled to studies aiming at finding out the roleof this diversity at the biological level. However, we wish to remindhow important it is to couple genome analysis with functionalanalysis. For instance, gene from the same family may participatein distinct physiological functions, such as Tolls and Dscams thatmay be involved in immune response but also in neurones devel-opment and central nervous system functions (Schmucker et al.,2000; Dong et al., 2006; McIlroy et al., 2013).

2.2. Dual de novo assemblies

‘Dual-de novo’ assemblies, in which both parasite and host tran-scriptome are assembled from the same sample, may also be usefulto provide a more comprehensive understanding of the immuneprocesses that play out during host–pathogen interactions.

Dual assemblies will allow inducible immune response genes tobe identified by comparing the transcriptomes of host organismscollected from naturally-infected populations, or from host exper-imentally exposed to pathogens with the resting transcriptome ofunchallenged hosts that include ‘‘only’’ constitutively-expressedinnate response factors. For species that host parasites with com-plex life-cycles, the dual de novo assembly approach will allowthe transcriptomes for different stages of parasite life-cycle to becompared. From a more integrated point of view, a comprehensiveunderstanding of immune gene expression at a given time wouldin theory necessitate characterizing all symbionts (microbial com-munity, viruses and eucaryote parasites) in the individual understudy. It is often difficult to diagnose latent pathogens in manyinvertebrates because most pathogens can neither be cultured,purified, nor be found outside their host organisms. Hence, it isanticipated that many novel pathogens or symbionts will be iden-tified as ‘‘stowaway passengers’’ while sequencing the transcripto-mes or genomes of their hosts. This approach has already beenused to identify a novel totivirus associated with cardiomyopathysyndrome in salmon (Lovoll et al., 2010) and is currently beingused to search for unidentified pathogens associated with mortal-ities in pearl oysters (DA Raftos, personnal communications).

Despite the success of this approach, it is acknowledged that thepresence of transcripts from multiple species of distant phyloge-netic identity within a single sample may negatively impact thequality of the de novo transcriptome assembly. It may also be dif-ficult to reliably assign a transcript to either the host or symbiont(Hraber and Weller, 2001; DeJong et al., 2004). Taxonomic assign-ment may be performed based on (i) tblastx e-value, (ii) the per-centage of GC base content and (iii) species specific signaturesbased on CLaMS (Sabourault et al., 2009; Pati et al., 2011; Zhuanget al., 2012; Vidal-Dupiol et al., 2013). Moreover, the routine puri-fication of transcripts for analysis of eukaryote transcriptomes fre-quently relies on capture of polyA tails, whereas the study ofprokaryotes relies on purification of micro-organisms followedwith sequencing of total RNA. Hence, it remains to be demon-strated whether multiple de novo assembly of host and microbiotacan be performed routinely on samples from eucaryote hosts con-taining prokaryotic pathogens, since it will necessitate enrichmentof different RNA species (Westermann et al., 2012).

3. Association studies

3.1. Whole genome association studies

One of the primary reasons for the use of NGS is the re-sequenc-ing of whole genomes to identify variations or mutations associ-

N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66 61

ated with a particular phenotypic trait. However, such approachremain costly and limited to model species for which the genomehas been sequenced. At lower cost, whole transcriptome sequenc-ing also allows the identification of single nucleotide polymor-phism (SNP) and estimation of allele frequency in genes withsufficiently high expression level (when performed in the appro-priate tissue for the trait of interest) (Cirulli et al., 2010). Numeroussoftware tools are now available for the detection of SNPs in NGSdata sets (Marth et al., 1999; Li et al., 2008; Quinlan et al., 2008;Koboldt et al., 2009; Li et al., 2009; Shen et al., 2010). The associa-tion between specific SNPs and specific immune traits means thatcomparative immunologists need to pay particular attention toSNPs within host populations. Such phenotype-association studieshave yielded a broader understanding of the genetic complexity inresponses to a range of immune insults, including vaccination(Biscarini et al., 2010; Pankratz et al., 2010), Lipopolysaccharides(Biscarini et al., 2010), herpesvirus (Kongchum et al., 2010), cancer(Ho-Pun-Cheung et al., 2010), meningitis (Da Silva et al., 2011), andmastitis in cattle (Carvajal et al., 2013). However, among inverte-brates immune-related SNPs have been localized but phenotype-association studies have been rarely undertaken (Cohuet et al.,2008; Nunez-Acuna and Gallardo-Escarate, 2013). The use of NGSwill provide a more convenient and cost-effective method for dis-covery of SNP in non-model species and so could provide impor-tant information about the genetic basis of inter-individualvariation in immune responses. More specifically, populationgenomic approaches that incorporate SNP analysis can identify ge-netic markers for disease resistance, as demonstrated by the NGSapproach taken by Bangham et al. (2007). The authors identifieda mutation associated with resistance to sigma virus in Drosophila(Bangham et al., 2007). Such approaches may be extremely valu-able for selective breeding programs in industries. In addition,transcriptome analysis allows identifying Single nucleotide vari-ants (SNV) within individuals and has recently revealed extensiveRNA editing in humans (Chepelev et al., 2009; Peng et al., 2012;Lee et al., 2013). Again phenotype-association studies has beenused to reveal its role in cancers (Chepelev et al., 2009).

3.2. Targeted sequencing

NGS may also be used in a selective fashion to sequence a lim-ited number of loci, yielding a high read coverage of the sequenc-ing targets. This powerful approach has been employed tocharacterize the olfactory receptor gene family, the largest multi-gene family in mammals (Hughes et al., 2013). It can also be usedfor population level genetic analyses. Primer tags facilitate barcod-ing transcripts from different samples for multiplex sequencing inorder to genotype loci of interest in a large number of individuals(Binladen et al., 2007). This approach has been used to characterizethe highly polymorphic major histocompatibility complex (MHC)of the bank vole, Myodes glareolus (Babik et al., 2009). It allowed al-leles that were present at very low frequency within the popula-tion to be detected and identified a significant associationbetween certain MHC alleles and the intensity of infection by thepinworm parasite, Aspiculuris tetraptera.

Similar approaches may be particularly useful among inverte-brates. Recent evidence suggests that invertebrates encode numer-ous categories of immune-related factors that appear to use highlevels of sequence diversity to recognize the antigenic proteins orMicobial-associated molecular patterns (MAMPs) of parasites(Ausubel, 2005). This diversity often appears to be associated withpolymorphic, multigenic families that are further diversified bypost-genomic mechanisms such as somatic mutations (includingrecombination, gene conversion), alternative splicing and RNAediting. NGS is ideal for studying such systems whether at theRNA or DNA levels. For instance, targeted NGS sequencing of the

highly diverse CDR3 (complement determining region 3) of T-cellreceptors in human peripheral blood leukocytes increased by anorder of magnitude the number of known variants from 3187 to33,664 TCRb mRNA sequences (Freeman et al., 2009). Comparableapproaches could be used to investigate the diversity of highly var-iable immune-related proteins of invertebrates. In particular, NGScould test whether the repertoire of antigen receptors in inverte-brates is static, or whether it is continually modified over time orin response to immune challenge. Several candidate systems,which already have detailed information from one species andindications of the presence of homologous genes in close relatives,are already available for such analyses. For instance, Anophelesgambia responds to infection by producing antigen-specific spliceforms of Dscams (Dong et al., 2012). In the purple sea urchin,Strongylocentotus purpuratus, different repertoires of 185/333 pro-teins are expressed in response to different challenges (Terwilligeret al., 2007; Dheilly et al., 2009), whilst in the pond snail, B. glabra-ta, there is evidence that sequences from different FREP familiesare expressed in response to different forms of immune challenge(Zhang et al., 2008; Adema et al., 2010). In these systems, the use oftargeted NGS to identify SNP and SNV to profile the diversity ofreceptor repertoires over time, in different tissues, and inresponse to immune challenge will provide information aboutthe levels of specificity in these defence responses and the mecha-nisms by which those repertoires change (alternative splicing, RNAediting, etc.).

4. Gene expression profiling

4.1. Comparative transcriptomic studies

Perhaps the biggest contribution that NGS will make to ourunderstanding of invertebrate immune responses is its capacity toidentify genes from across entire genomes that are either up- ordown-regulated during immune responses to particular types ofinfectious agent. For the first time, NGS affords textured, genome-wide assessment of the intricacies of complex immune responsesat the level of the transcriptome. One shortfall of genome sequenc-ing and subsequent gene annotation in invertebrates is that sub-stantial proportions of the putatively expressible genes are novelor are not sufficiently conserved so that orthologues can be easilyidentified in sequence databases. Accordingly, no function can beassigned based on similarity with previously studied genes. In thesecases, the increased expression of a novel transcript in response toimmune challenge detected by NGS-based high throughput tran-scriptomic profiling can be used to provide an initial indication thatthe encoded protein has an immune responsive function. Such tran-scriptomic profiling can also be used to provide information onother physiological functions that are impacted over the course ofan immune challenge (including the messages that are down regu-lated). Previously, microarray analyses were performed extensivelyto provide these types of transcriptional profiles (De Gregorio et al.,2001; Aujame et al., 2002; Hutton et al., 2004; Schweitzer et al.,2010) and identify candidate sequences of putative immune factorsfor downstream functional studies. In those downstream studies,expression profiles of specific target genes have most often been as-sessed over the course of immune responses using quantitativeRT-PCR (qRT-PCR). However, this approach has been limited bythe relatively shallow depth of sequence diversity that can beassessed using cDNA microarrays. In contrast, mRNA sequencingby NGS generates millions of cDNA fragments (reads) and mRNAexpression can be rapidly evaluated by counting the number ofreads that match the gene or transcript of interest in the corre-sponding genome or transcriptome, even though candidatesequences still require functional validation of immune function

62 N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66

and involvement (Galinier et al., 2013). The ever decreasing cost ofRNA sequencing also implies that kinetic studies are being possible,which will considerably enhance biological significance of geneexpression studies.

In parallel to the dual-de novo assembly of transcriptomeapproach discussed above, it will also be useful to adopt ‘dualRNA-seq’ approaches, in which the expression profiles of bothpathogens and their hosts can be assessed simultaneously(Westermann et al., 2012). The complexity of such studies willdepend on the nature of the host/pathogen interaction, due tothe need for different RNA enrichment protocols for different typesof pathogens (eukaryote, prokaryote or virus), and the sequencingdepth needed for accurate coverage of both host and pathogen(Westermann et al., 2012). For example, Juranic Lisnic et al.(2013) recently demonstrated the utility of this approach by study-ing virus–host cell interactions of murine Cytomegalovirus.

4.2. micro RNA

Noncoding RNAs (small nuclear or nucleolar RNAs and microR-NAs), which produce functional RNA molecules rather than encod-ing proteins are involved in regulating a broad range ofphysiological processes, including immune responses (Eddy,2001; Carpenter et al., 2013; Curtale and Citarella, 2013; Minton,2013). It has recently been demonstrated that small non-codingRNAs are responsible for post-transcriptional regulation of geneexpression either via the degradation of target mRNAs or the inhi-bition of protein translation. For instance, exposure of human celllines to LPS has been shown to induce microRNAs (Taganovet al., 2006) that specifically regulate the NF-jB signalling pathway(Taganov et al., 2006). More recently, a microRNA (miR-29) wasfound to specifically target IFN- in humans and to suppress im-mune response to intracellular pathogens (Ma et al., 2011). Antivi-ral immunity in bacteria, plants and invertebrates also involves theproduction of small RNAs that interfere with viral replication (Dingand Voinnet, 2007; Sorek et al., 2008). The ability of host microR-NAs to evolve rapidly (Meunier et al., 2013) makes them ideal can-didates for the control of host–pathogens interactions. However,pathogens, including viruses also produce microRNA that can helpthem evade the immune system (Sullivan, 2008). Hence thereseems to be a molecular arms race between virus and host gen-omes involving microRNAs (Ding and Voinnet, 2007).

NGS strategies already exist to capture and analyze the comple-ment of microRNA sequences in non-model species (Buermanset al., 2010). The identification of microRNA sequences relies onappropriate data analysis. Since all sequence reads produced fromany given NGS platform are now longer than the average microR-NA, it initialy relies on finding the 30 and 50 ends of the microRNAin the same read and then to perform secondary structure analysis.Using this approach, RNA-seq successfully identified novel microR-NAs in tomatoes (Moxon et al., 2008), human embryonic stem cells(Bar et al., 2008), chicken embryos (Glazov et al., 2008) and mono-nuclear cells from peripheral blood (Vaz et al., 2010). These NGSstudies confirmed the identities of almost all previously knownmicroRNA, and identified hundreds of new candidate microRNAs.In the context of host-pathogen interactions, RNAseq has also beenemployed to study microRNAs in HeLa cells responding to chal-lenge by Salmonella (Schulte et al., 2011).

4.3. Alternative splicing

Alternative splicing of mRNAs is one of the best known mecha-nisms responsible for the generation of post-genomic diversity(Nadal-Ginard et al., 1991). However, we are only starting tounderstand the regulatory processes that govern splicing decisionsand how this regulation shapes the splicing phenotypes observed

in different tissue types and developmental stages. Cell surfacereceptors involved in the nervous system and in immune re-sponses are among the genes that are most frequently associatedwith alternative splicing (Modrek et al., 2001). In plants, alterna-tive splicing plays a key role in providing transcriptome plasticitynecessary to better cope with stress and pathogens (Mastrangeloet al., 2012). In mammals, alternative splicing plays an extensiverole in regulation of T-cell activation (Lynch, 2004; Ip et al.,2007), whilst in insects Dscams exhibit pathogen-specific splice-form expression following infection with different pathogens(Dong et al., 2006). Both insects and crustaceans use alternativesplicing to produce tens of thousands of different Dscams sequencevariants (Watson et al., 2005; Brites et al., 2008). Similarly, in thepond snail, B. glabrata, alternatively spliced FREPs have been iden-tified (Zhang and Loker, 2003), yet their functional significance re-mains to be analyzed.

The systematic analysis of alternative splicing used to be under-taken using expressed sequence tags (EST) or specialized micro-arrays. However, EST are subject to cloning biases and generallyprovide only low coverage, and the specificity of microarrays is af-fected by cross-hybridization. NGS RNA-seq data can now be usedas a more effective alternative to identify novel splice junctions.NGS has been employed previously to identify new alternativesplicing sites in humans (Pan et al., 2008; Wang et al., 2008), C. ele-gans (Ramani et al., 2010) and Plasmodium falciparum (Sorber et al.,2011). Specialized tools for splice junction identification are avail-able but most necessitate a reference genome (Trapnell et al.,2009). Only a few software packages, such as SplitSeek, are ableto identify splice sites in uncharacterized transcripts (Ameuret al., 2010). In the future, the development of additional softwarethat allow more automated identification of splicing sites from thede novo assembled NGS transcriptomes will have significantoutcomes.

Other approaches, such as cross-linking immunoprecipitationsequencing (CLIP-Seq) and RNA immunoprecipitation sequencing(RIP-Seq) have been used to map RNA-binding sites for splicingfactors (Sanford et al., 2009; Yeo et al., 2009; Zisoulis et al.,2010). These techniques have great potential to examine mRNAprocessing in a large range of invertebrate species that generatehighly variable molecules over the course of an immune response(see above).

4.4. Transcription factors

Chromatin immunoprecipitation coupled with NGS, known asChIP-Seq, has been employed to identify protein–DNA interactions.In particular, it has been extensively used to identify the DNA bind-ing sites of transcription factors. Transcription factors are requiredfor activation of immune cells, and so it is necessary to identifytheir targets to comprehensively understand how they influencegene transcription and cell fate. For example, ChIP-Seq has beenused to identify the binding sites of the NFjB factor p65 on humanchromosome 22 (Martone et al., 2003) and the targets of STAT4and STAT6 transcription factors involved in T helper cell differenti-ation (Wei et al., 2010).

5. Epigenetic profiling

Epigenetic modifications are mitotically or meiotically heritablechanges in gene expression that are not based on alterations ofnucleotide sequence and are potentially reversible (metastable).Such modifications can impact gene expression, alternative splic-ing and other mechanisms of diversification in immune responsegenes. To date, two mechanisms of epigenetic change are known

N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66 63

to play key roles in the regulation of immune responses: histonemodifications and DNA methylation.

5.1. Histone modifications

NGS techniques related to CHIPseq such as native-ChIP (N-ChIP)and crosslink-ChIP (X-ChIP) capture DNA–histone interactions(O’Neill and Turner, 2003; Cosseau et al., 2009). The distributionof histone modifications identified using these methods can nowbe found in several databases e.g. (Zhang et al., 2010). Histonemodifications are employed by both host and pathogens to regu-late gene expression. For instance, the bacterial pathogen, Listeriamonocytogenes, has been shown to induce dephosphorylation ofhistone H3 and a deacetylation of histone H4 during infection inmammals, thus reducing the transcriptional activity of some keyimmune genes (Hamon et al., 2007). Similarly, it has been shownthat histone H3 phosphorylation by IKK-a is a key step in the NFjBpathway of immune activation (Yamamoto et al., 2003). ChIP-seqhas also revealed that histone modifications are involved inantigen variations among pathogens such as Schistosoma mansoni(Perrin et al., 2013).

5.2. DNA methylation

In mammals, DNA methylation is associated with the control ofgene expression and the maintenance of cell lineages. It is neces-sary for T cell activation, proliferation, memory cell formationand activation. DNA methylation appears rapidly post infectionand is maintained thereafter (Weng et al., 2012; Kondilis-Mangumand Wade, 2013). They constitute stable markers of immunostim-ulation that facilitate more rapid cellular responses upon reinfec-tion. The role of DNA methylation in mammalian B cell lineagesformation is also being investigated and may be linked to activa-tion-induced deaminase (Kondilis-Mangum and Wade, 2013). Inaddition, substantial DNA methylation has been detected in manyinvertebrate species ranging from deuterostomes (S. purpuratus(Bird et al., 1979)) to molluscs (C. gigas (Gavery and Roberts,2010) and B. glabrata (Fneich et al., 2013)), insects (A. mellifera(Lyko et al., 2010)), and tunicates (C. intestinalis (Suzuki et al.,2007)). In contrast, DNA methylation appears to be more limitedin other invertebrates, such as D. melanogaster (Gowher et al.,2000) and C. elegans (Simpson et al., 1986). Although the data arelimited, DNA methylation may also play important roles in thedevelopment and phenotypic plasticity of invertebrates (Regevet al., 1998; Roberts and Gavery, 2012). However, to date, the roleof DNA methylation in the regulation of invertebrate immune re-sponse genes has not been investigated.

Three NGS techniques have been developed in order to studythe DNA methylome. In BS-Seq, bisulphite conversion of the DNAis followed by whole genome sequencing (Laurent et al., 2010).In MeDIP-Seq, DNA is fragmented and regions containing methyl-ated DNA are immunoprecipitated with 5-methylcytosine antibod-ies before NGS (Down et al., 2008), whilst in MBD-Seq, DNAfragments containing CpG’s specifically interact with methyl-bind-ing domain conjugated on beads and are sequenced with NGS(Serre et al., 2010). The last two techniques are cost-effectivemethods to identify methylation-enriched genomic regions. Forexample, MeDIP-Seq has very recently been employed to identifyaberrantly methylated genes in skin lesions from human patientswith Psoriasis vulgaris infections (Zhang et al., 2013).

The ratio of observed to expected CpG dinucleotides has alsobeen employed to predict methylation status in genomes and tran-scriptomes from many taxonomic groups (Shimizu et al., 1997;Elango and Yi, 2008; Elango et al., 2009; Gavery and Roberts,2010). In invertebrates, CpG methylations are preferentially lo-cated within coding regions (Suzuki et al., 2007). Therefore, the ra-

tio of CpG dinucleotides can be calculated from transcriptomes.Recently, this approach was used to predict the methylation statusof B. glabrata from a transcriptome generated de novo from RNAseqdata (Fneich et al., 2013). This suggests that generating de novo ahigh number of transcritpomes from various species will also sig-nificantly enhance our understanding of the evolution of DNAmethylation.

6. Conclusions and perspectives

The power and comparatively low cost of NGS is revolutionizinggenomic and transcriptomic research. It provides opportunities toundertake comprehensive analyses of the biology of any organismnot just a limited number of ‘‘model’’ species. For example, com-parison of RNA-seq data from closely related species with differentlife history traits, lifespan, diet, reproductive strategies, symbiontsand pathogens will provide significant information to linkphenotypic traits such as the diversity of the immune system toevolution. The frequently documented rapid evolution ofimmune-related genes and the identification of novel highly vari-able immune response gene families from invertebrates (Ghoshet al., 2011) have challenged the technical capabilities of tradi-tional investigative research methods in ways that limited our per-spectives on the evolution of immune systems. The advent of NGSnow provides for a new generation of comprehensive and integra-tive studies in comparative immunology. These encompass studiesranging from the investigation of immune regulation via epigeneticmechanisms, through gene discovery, to studies of gene expressionand the diversification of immune factors. We also wish to empha-size that every time a immunome of a new species is characterized,someone drops on something special that could have medicalimplications. It is still daunting to consider the effort that will beneeded to collect and analyze NGS datasets from numerous speciesto provide a broad representing of metazoan taxa. This may requireconcerted effort through the establishment of consortia ofresearchers interested in comparative immunology across a broadrange of phyla. However, the technical feasibility afforded by NGSholds great promise and opportunity. In the future of comparativeimmunology there will be no more non-model species.

Acknowledgments

CMA acknowledges support from NIH Grant NumberP20GM103452 from the National Institute of General Medical Sci-ences (NIGMS). NMD was supported by the Agence Nationale de laRecherche Blanc, SVSE7, project Bodyguard.

References

Adams, M.D., Celniker, S.E., et al., 2000. The genome sequence of Drosophilamelanogaster. Science 287 (5461), 2185–2195.

Adema, C.M., Hanington, P.C., et al., 2010. Differential transcriptomic responses ofBiomphalaria glabrata (Gastropoda, Mollusca) to bacteria and metazoanparasites, Schistosoma mansoni and Echinostoma paraensei (Digenea,Platyhelminthes). Mol. Immunol. 47 (4), 849–860.

Adema, C.M., Hertel, L.A., et al., 1997. A family of fibrinogen-related proteins thatprecipitates parasite-derived molecules is produced by an invertebrate afterinfection. Proc. Natl. Acad. Sci. 94 (16), 8691–8696.

Ameur, A., Wetterbom, A., et al., 2010. Global and unbiased detection of splicejunctions from RNA-seq data. Genome Biol. 11 (3), R34.

Aujame, L.U.C., Burdin, N., et al., 2002. How microarrays can improve ourunderstanding of immune responses and vaccine development. Ann. N. Y.Acad. Sci. 975 (1), 1–23.

Ausubel, F.M., 2005. Are innate immune signaling pathways in plants and animalsconserved? Nat. Immunol. 6 (10), 973–979.

Babik, W., Taberlet, P., et al., 2009. New generation sequencers as a tool forgenotyping of highly polymorphic multilocus MHC system. Mol. Ecol. Resour. 9(3), 713–719.

Bangham, J., Obbard, D.J., et al., 2007. The age and evolution of an antiviralresistance mutation in Drosophila melanogaster. Proc. R. Soc. London, B 274(1621), 2027–2034.

64 N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66

Bar, M., Wyman, S.K., et al., 2008. MicroRNA discovery and profiling in humanembryonic stem cells by deep sequencing of small RNA libraries. Stem cells 26(10), 2496–2505.

Binladen, J., Gilbert, M.T.P., et al., 2007. The use of coded PCR primers enables high-throughput sequencing of multiple homolog amplification products by 454parallel sequencing. PLoS One 2 (2), e197.

Bird, A.P., Taggart, M.H., et al., 1979. Methylated and unmethylated DNAcompartments in the sea urchin genome. Cell 17 (4), 889–901.

Biscarini, F., Bovenhuis, H., et al., 2010. Across-line SNP association study of innateand adaptive immune response in laying hens. Anim. Genet. 41 (1), 26–38.

Brites, D., McTaggart, S., et al., 2008. The Dscam homologue of the crustaceanDaphnia is diversified by alternative splicing like in insects. Mol. Biol. Evol. 25(7), 1429–1439.

Buermans, H., Ariyurek, Y., et al., 2010. New methods for next generationsequencing based microRNA expression profiling. BMC Genomics 11 (1), 716.

Campbell-Lendrum, D., Corvalan, C., et al., 2007. Global climate change:implications for international public health policy. Bull. Wolrd Health Organ85 (3), 161–244.

Carpenter, S., Aiello, D., et al., 2013. A long noncoding RNA mediates both activationand repression of immune response genes. Science 341 (6147), 789–792.

Carvajal, A., Huircn, P., et al., 2013. Single nucleotide polymorphisms in immunity-related genes and their association with mastitis in Chilean dairy cattle. Genet.Mol. Res. 12 (3), 2702–2711.

Chapman, J.A., Kirkness, E.F., et al., 2010. The dynamic genome of Hydra. Nature 464(7288), 592–596.

Chepelev, I., Wei, G., et al., 2009. Detection of single nucleotide variations inexpressed exons of the human genome using RNA-Seq. Nucleic Acids Res. 37(16), e106.

Cirulli, E., Singh, A., et al., 2010. Screening the human exome: a comparison of wholegenome and whole transcriptome sequencing. Genome Biol. 11 (5), R57.

Cohuet, A., Krishnakumar, S., et al., 2008. SNP discovery and molecular evolution inAnopheles gambiae, with special emphasis on innate immune system. BMCGenomics 9 (1), 227.

Cosseau, C.l., Azzi, A., et al., 2009. Native chromatin immunoprecipitation (N-ChIP)and ChIP-Seq of Schistosoma mansoni: critical experimental parameters. Mol.Biochem. Parasitol. 166 (1), 70–76.

Curtale, G., Citarella, F., 2013. Dynamic nature of noncoding RNA regulation ofadaptive immune response. Int. J. Mol. Sci. 14 (9), 17347–17377.

Cuthbertson, B., Shepard, E., et al., 2002. Diversity of the penaeidin antimicrobialpeptides in two shrimp species. Immunogenetics 54 (6), 442–445.

Da Silva, T.A., Fontes, F.L., et al., 2011. SNPs in DNA repair genes associated tomeningitis and host immune response. Mutat. Res., Fundam. Mol. Mech.Mutagen. 713 (1–2), 39–47.

Davidson, C.R., Best, N.M., et al., 2008. Toll-like receptor genes (TLRs) from Capitellacapitata and Helobdella robusta (Annelida). Dev. Comp. Immunol. 32 (6), 608–612.

De Gregorio, E., Spellman, P.T., et al., 2001. Genome-wide analysis of the Drosophilaimmune response by using oligonucleotide microarrays. Proc. Natl. Acad. Sci. 98(22), 12590–12595.

DeJong, R.J., Emery, A.M., et al., 2004. The mitochondrial genome of Biomphalariaglabrata (Gastropoda: basommatophora), intermediate host of Schistosomamansoni. J. Parasitol. 90 (5), 991–997.

Denoeud, F., Henriet, S., et al., 2010. Plasticity of animal genome architectureunmasked by rapid evolution of a pelagic tunicate. Science 330 (6009), 1381–1385.

Dheilly, N.M., Nair, S.V., et al., 2009. Highly variable immune-response proteins(185/333) from the sea urchin, Strongylocentrotus purpuratus: proteomicanalysis identifies diversity within and between individuals. J. Immunol. 182(4), 2203–2212.

Ding, S.-W., Voinnet, O., 2007. Antiviral immunity directed by small RNAs. Cell 130(3), 413–426.

Dong, Y., Cirimotich, C.M., et al., 2012. Anopheles NF-jB-regulated splicing factorsdirect pathogen-specific repertoires of the hypervariable pattern recognitionreceptor AgDscam. Cell Host Microbe 12 (4), 521–530.

Dong, Y., Taylor, H.E., et al., 2006. AgDscam, a hypervariable immunoglobulindomain-containing receptor of the Anopheles gambiae innate immune system.PLoS Biol. 4 (7), e229.

Down, T.A., Rakyan, V.K., et al., 2008. A Bayesian deconvolution strategy forimmunoprecipitation-based DNA methylome analysis. Nat. Biotechnol. 26 (7),779–785.

Eddy, S.R., 2001. Non-coding RNA genes and the modern RNA world. Nat. Rev.Genet. 2 (12), 919–929.

Eizaguirre, C., Lenz, T.L., et al., 2012. Rapid and adaptive evolution of MHC genesunder parasite selection in experimental vertebrate populations. Nat. Commun.3, 621.

Elango, N., Hunt, B.G., et al., 2009. DNA methylation is widespread and associatedwith differential gene expression in castes of the honeybee, Apis mellifera. Proc.Natl. Acad. Sci. 106 (27), 11206–11211.

Elango, N., Yi, S.V., 2008. DNA methylation and structural and functional bimodalityof vertebrate promoters. Mol. Biol. Evol. 25 (8), 1602–1608.

Fneich, S., Dheilly, N.M., et al., 2013. 5-Methyl-cytosine and 5-hydroxy-methyl-cytosine in the genome of Biomphalaria glabrata, a snail intermediate host ofSchistosoma mansoni. Parasites and Vectors 6, 167.

Freeman, J.D., Warren, R.L., et al., 2009. Profiling the T-cell receptor beta-chainrepertoire by massively parallel sequencing. Genome Res. 19 (10), 1817–1824.

Galinier, R., Portela, J., et al., 2013. Biomphalysin, a new b Pore-forming toxininvolved in Biomphalaria glabrata immune defense against Schistosoma mansoni.PLoS Pathog. 9 (3), e1003216.

Gavery, M., Roberts, S., 2010. DNA methylation patterns provide insight intoepigenetic regulation in the Pacific oyster (Crassostrea gigas). BMC Genomics 11(1), 483.

Ghosh, J., Lun, C.M., et al., 2011. Invertebrate immune diversity. Dev. Comp.Immunol. 35 (9), 959–974.

Glazov, E.A., Cottee, P.A., et al., 2008. A microRNA catalog of the developing chickenembryo identified by a deep sequencing approach. Genome Res. 18 (6), 957–964.

Gowher, H., Leismann, O., et al., 2000. DNA of Drosophila melanogaster contains 5-methylcytosine. EMBO J. 19 (24), 6918–6923.

Hamon, M.A., Batsché, E., et al., 2007. Histone modifications induced by a family ofbacterial toxins. Proc. Natl. Acad. Sci. 104 (33), 13467–13472.

Harvell, C.D., Mitchell, C.E., et al., 2002. Climate warming and disease risks forterrestrial and marine biota. Science 296 (5576), 2158–2162.

Hemmrich, G., Miller, D.J., et al., 2007. The evolution of immunity: a low-lifeperspective. Trends Immunol. 28 (10), 449–454.

Hibino, T., Loza-Coll, M., et al., 2006. The immune gene repertoire encoded in thepurple sea urchin genome. Dev. Biol. 300 (1), 349–365.

Ho-Pun-Cheung, A., Assenat, E., et al., 2010. A large-scale candidate gene approachidentifies SNPs in SOD2 and IL13 as predictive markers of response topreoperative chemoradiation in rectal cancer. Pharmacogenomics J. 11 (6),437–443.

Hraber, P.T., Weller, J.W., 2001. On the species of origin: diagnosing the source ofsymbiotic transcripts. Genome Biol. 2 (9), 0037.

Huang, S., Yuan, S., et al., 2008. Genomic analysis of the immune gene repertoire ofamphioxus reveals extraordinary innate complexity and diversity. Genome Res.18 (7), 1112–1126.

Hudson, M.E., 2008. Sequencing breakthroughs for genomic ecology andevolutionary biology. Mol. Ecol. Res. 8 (1), 3–17.

Hughes, G.M., Gang, L., et al., 2013. Using illumina next generation sequencingtechnologies to sequence multigene families in de novo species. Mol. Ecol.Resour. 13 (3), 510–521.

Hutton, J., Jegga, A., et al., 2004. Microarray and comparative genomics-basedidentification of genes and gene regulatory regions of the mouse immunesystem. BMC Genomics 5 (1), 82.

Imler, J.L., Ferrandon, D., 2011. Le printemps de l’immunité innée couronné àStockholm - Pris Nobel de Médecine 2011: Bruce A. Beutler, Jules A. Hoffmannet Ralph M. Steinman Med. Sci. 27, 1019-1024.

Ip, J.Y., Tong, A., et al., 2007. Global analysis of alternative splicing during T-cellactivation. RNA 13 (4), 563–572.

Juranic Lisnic, V., Babic Cac, M., et al., 2013. Dual analysis of the murinecytomegalovirus and host cell transcriptomes reveal new suspects of thevirus-host cell interface. PLoS Pathog. 9 (9), e1003611.

Koboldt, D.C., Chen, K., et al., 2009. VarScan: variant detection in massively parallelsequencing of individual and pooled samples. Bioinformatics 25 (17), 2283–2285.

Kondilis-Mangum, H.D., Wade, P.A., 2013. Epigenetics and the adaptive immuneresponse. Mol. Aspects Med. (0).

Kongchum, P., Palti, Y., et al., 2010. SNP discovery and development of geneticmarkers for mapping innate immune response genes in common carp (Cyprinuscarpio). Fish Shellfish Immunol. 29 (2), 356–361.

Kortschak, R.D., Samuel, G., et al., 2003. EST analysis of the Cnidarian Acroporamillepora reveals extensive gene loss and rapid sequence divergence in themodel invertebrates. Curr. Biol. 13 (24), 2190–2195.

Kubinak, J.L., Ruff, J.S., et al., 2012. Experimental viral evolution to specific host MHCgenotypes reveals fitness and virulence trade-offs in alternative MHC types.Proc. Natl. Acad. Sci. 109 (9), 3422–3427.

Kvennefors, E.C.E., Leggat, W., et al., 2008. An ancient and variable mannose-bindinglectin from the coral Acropora millepora binds both pathogens and symbionts.Dev. Comp. Immunol. 32 (12), 1582–1592.

Lafferty, K.D., 2009. The ecology of climate change and infectious diseases. Ecology90 (4), 888–900.

Laurent, L., Wong, E., et al., 2010. Dynamic changes in the human methylome duringdifferentiation. Genome Res. 20 (3), 320–331.

Lee, J.-H., Ang, J.K., et al., 2013. Analysis and design of RNA sequencing experimentsfor identifying RNA editing and other single-nucleotide variants. RNA 19 (6),725–732.

Li, H., Ruan, J., et al., 2008. Mapping short DNA sequencing reads and calling variantsusing mapping quality scores. Genome Res. 18 (11), 1851–1858.

Li, R., Fan, W., et al., 2010. The sequence and de novo assembly of the giant pandagenome. Nature 463 (7279), 311–317.

Li, R., Li, Y., et al., 2009. SNP detection for massively parallel whole-genomeresequencing. Genome Res. 19 (6), 1124–1132.

Login, F.d.r.H., Balmand, S.v., et al., 2011. Antimicrobial peptides keep insectendosymbionts under control. Science 334 (6054), 362–365.

Loker, E.S., Adema, C.M., et al., 2004. Invertebrate immune systems – nothomogeneous, not simple, not well understood. Immunol. Rev. 198 (1), 10–24.

Lovoll, M., Wiik-Nielsen, J., et al., 2010. A novel totivirus and piscine reovirus (PRV)in Atlantic salmon (Salmo salar) with cardiomyopathy syndrome (CMS). Virol. J.7 (1), 309.

Lyko, F., Foret, S., et al., 2010. The honey bee epigenomes: differential methylationof brain DNA in queens and workers. PLoS Biol. 8 (11), e1000506.

N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66 65

Lynch, K.W., 2004. Consequences of regulated pre-mRNA splicing in the immunesystem. Nat. Rev. Immunol. 4 (12), 931–940.

Ma, F., Xu, S., et al., 2011. The microRNA miR-29 controls innate and adaptiveimmune responses to intracellular bacterial infection by targeting interferon-[gamma]. Nat. Immunol. 12 (9), 861–869.

Marth, G.T., Korf, I., et al., 1999. A general approach to single-nucleotidepolymorphism discovery. Nat. Genet. 23 (4), 452–456.

Martone, R., Euskirchen, G., et al., 2003. Distribution of NF-kB-binding sites acrosshuman chromosome 22. Proc. Natl. Acad. Sci. 100 (21), 12247–12252.

Mastrangelo, A.M., Marone, D., et al., 2012. Alternative splicing: Enhancing ability tocope with stress via transcriptome plasticity. Plant Sci. 185, 40–49.

McIlroy, G., Foldi, I., et al., 2013. Toll-6 and Toll-7 function as neurotrophinreceptors in the Drosophila melanogaster CNS. Nat. Neurosci. 16 (9), 1248–1256.

Medzhitov, R., Schneider, D.S., et al., 2012. Disease tolerance as a defense strategy.Science 335 (6071), 936–941.

Messier-Solek, C., Buckley, K.M., et al., 2010. Highly diversified innate receptorsystems and new forms of animal immunity. Semin. Immunol. 22 (1), 39–47.

Metchnikoff, E., 1893. Lectures on the Comparative Pathology of Inflammation. N. Y.(Reprinted by Dover, 1968), (Translated by Starling, F.A., Starling, E. H.,).

Meunier, J., Lemoine, F.d.r., et al., 2013. Birth and expression evolution ofmammalian microRNA genes. Genome Res. 23 (1), 34–45.

Miller, D., Hemmrich, G., et al., 2007. The innate immune repertoire in Cnidaria –ancestral complexity and stochastic gene loss. Genome Biol. 8 (4), R59.

Minton, K., 2013. Immune regulation: long non-coding RNAs in the immune system.Nat. Rev. Immunol. 13 (9) (617–617).

Mitta, G., Adema, C.M., et al., 2012. Compatibility polymorphism in snail/schistosome interactions: from field to theory to molecular mechanisms. Dev.Comp. Immunol. 37 (1), 1–8.

Modrek, B., Resch, A., et al., 2001. Genome-wide detection of alternative splicingin expressed sequences of human genes. Nucleic Acids Res. 29 (13), 2850–2859.

Morozova, O., Marra, M.A., 2008. Applications of next-generation sequencingtechnologies in functional genomics. Genomics 92 (5), 255–264.

Moxon, S., Jing, R., et al., 2008. Deep sequencing of tomato short RNAs identifiesmicroRNAs targeting genes involved in fruit ripening. Genome Res. 18 (10),1602–1609.

Nadal-Ginard, B., Smith, C., et al., 1991. Alternative splicing is an efficientmechanism for the generation of protein diversity: contractile protein genesas a model system. Adv. Enzyme Regul. 31, 261–286.

Nair, S.V., Del Valle, H., et al., 2005. Macroarray analysis of coelomocyte geneexpression in response to LPS in the sea urchin. Identification of unexpectedimmune diversity in an invertebrate. Physiol. Genomics 22 (1), 33–47.

NCBI, 2013. 3556 Eukaryotic genome sequencing projects, from <http://www.ncbi.nlm.nih.gov/genomes/leuks.cgi>.

Nunez-Acuna, G., Gallardo-Escarate, C., 2013. Identification of immune-relatedSNPs in the transcriptome of Mytilus chilensis through high-throughputsequencing. Fish Shellfish Immunol. (0).

O’Neill, L.P., Turner, B.M., 2003. Immunoprecipitation of native chromatin: NChIP.Methods 31 (1), 76–82.

Pan, Q., Shai, O., et al., 2008. Deep surveying of alternative splicing complexity in thehuman transcriptome by high-throughput sequencing. Nat. Genet. 40 (12),1413–1415.

Pankratz, V.S., Vierkant, R., et al., 2010. Associations between SNPs in candidateimmune-relevant genes and rubella antibody levels: a multigenic assessment.BMC Immunol. 11 (1), 48.

Pati, A., Heath, L.S., et al., 2011. ClaMS: a classifier for metagenomic sequences.Stand. Genomic Sci. 5 (2), 248–253.

Patz, J.A., Graczyk, T.K., et al., 2000. Effects of environmental change on emergingparasitic diseases. Int. J. Parasitol. 30 (12, Äì13), 1395–1405.

Peng, Z., Cheng, Y., et al., 2012. Comprehensive analysis of RNA-Seq data revealsextensive RNA editing in a human transcriptome. Nat. Biotechnol. 30 (3), 253–260.

Perrin, C., Lepesant, J.M.J., et al., 2013. Schistosoma mansoni mucin gene (SmPoMuc)expression: epigenetic control to shape adaptation to a new host. PLoS Pathog. 9(8), e1003571.

Putnam, N.H., Srivastava, M., et al., 2007. Sea anemone genome reveals ancestraleumetazoan gene repertoire and genomic organization. Science 317 (5834), 86–94.

Quinlan, A.R., Stewart, D.A., et al., 2008. Pyrobayes: an improved base caller for SNPdiscovery in pyrosequences. Nat. Methods 5 (2), 179–181.

Ramani, A.K., Calarco, J.A., et al., 2010. Genome-wide analysis of alternative splicingin Caenorhabditis elegans. Genome Res. 21 (2), 342–348.

Rast, J.P., Messier-Solek, C., 2008. Marine invertebrate genome sequences and ourevolving understanding of animal immunity. Biol. Bull. 214 (3), 274–283.

Rast, J.P., Smith, L.C., et al., 2006. Genomic insights into the immune system of thesea urchin. Science 314 (5801), 952–956.

Regev, A., Lamb, M.J., et al., 1998. The role of DNA methylation in invertebrates:developmental regulation or genome defense? Mol. Biol. Evol. 15 (7), 880.

Reynolds, S., Rolff, J., 2008. Immune function keeps endosymbionts under control. J.Biol. 7 (8), 28.

Roberts, S.B., Gavery, M.R., 2012. Does DNA methylation facilitate phenotypicplasticity in invertebrates? Front Physiol. 2.

Sabourault, C., Ganot, P., et al., 2009. Comprehensive EST analysis of the symbioticsea anemone, Anemonia viridis. BMC Genomics 10 (1), 333.

Sanford, J.R., Wang, X., et al., 2009. Splicing factor SFRS1 recognizes a functionallydiverse landscape of RNA transcripts. Genome Res. 19 (3), 381–394.

Schmucker, D., Chen, B., 2009. Dscam and DSCAM: complex genes in simpleanimals, complex animals yet simple genes. Genes Dev. 23 (2), 147–156.

Schmucker, D., Clemens, J.C., et al., 2000. Drosophila Dscam is an axon guidancereceptor exhibiting extraordinary molecular diversity. Cell 101 (6), 671–684.

Schulenburg, H., Kurtz, J., et al., 2009. Introduction. Ecological immunology. Phil.Trans. R. Soc. B, 364(1513), pp. 3–14.

Schulte, L.N., Eulalio, A., et al., 2011. Analysis of the host microRNA response toSalmonella uncovers the control of major cytokines by the let-7 family. EMBO J.30 (10), 1977–1989.

Schweitzer, B., Meng, L., et al., 2010. Immune response biomarker profilingapplication on ProtoArray� protein microarrays. Urinary Proteome 641, 243–252.

Serre, D., Lee, B.H., et al., 2010. MBD-isolated genome sequencing provides a high-throughput and comprehensive survey of DNA methylation in the humangenome. Nucleic Acids Res. 38 (2), 391–399.

Sheldon, B.C., Verhulst, S., 1996. Ecological immunology: costly parasite defencesand trade-offs in evolutionary ecology. Trends Ecol. Evol. 11 (8), 317–321.

Shen, Y., Wan, Z., et al., 2010. A SNP discovery method to assess variant alleleprobability from next-generation resequencing data. Genome Res. 20 (2), 273–280.

Shendure, J., Ji, H., 2008. Next-generation DNA sequencing. Nat. Biotechnol. 26 (10),1135–1145.

Shimizu, T.S., Takahashi, K., et al., 1997. CpG distribution patterns in methylatedand non-methylated species. Gene 205 (1,Äì2), 103–107.

Shinzato, C., Shoguchi, E., et al., 2011. Using the Acropora digitifera genome tounderstand coral responses to environmental change. Nature 476 (7360), 320–323.

Simpson, V.J., Johnson, T.E., et al., 1986. Caenorhabditis elegans DNA does not contain5-methylcytosine at any time during development or aging. Nucleic Acids Res.14 (16), 6711–6719.

Sorber, K., Dimon, M.T., et al., 2011. RNA-Seq analysis of splicing in Plasmodiumfalciparum uncovers new splice junctions, alternative splicing and splicing ofantisense transcripts. Nucleic Acids Res. 39 (9), 3820–3835.

Sorek, R., Kunin, V., et al., 2008. CRISPR – a widespread system that providesacquired resistance against phages in bacteria and archaea. Nat. Rev. Microbiol.6 (3), 181–186.

Srivastava, M., Simakov, O., et al., 2010. The Amphimedon queenslandica genomeand the evolution of animal complexity. Nature 466 (7307), 720–726.

Stahlschmidt, Z.R., Rollinson, N., et al., 2013. Are all eggs created equal? Foodavailability and the fitness trade-off between reproduction and immunity. Func.Ecol. (n/a–n/a).

Sullivan, C.S., 2008. New roles for large and small viral RNAs in evading hostdefences. Nat. Rev. Genet. 9 (7), 503–507.

Suzuki, M.M., Kerr, A.R.W., et al., 2007. CpG methylation is targeted to transcriptionunits in an invertebrate genome. Genome Res. 17 (5), 625–631.

Taganov, K.D., Boldin, M.P., et al., 2006. NF-kB-dependent induction of microRNAmiR-146, an inhibitor targeted to signaling proteins of innate immuneresponses. Proc. Natl. Acad. Sci. 103 (33), 12481–12486.

Terwilliger, D., Buckley, K., et al., 2007. Distinctive expression patterns of 185/333genes in the purple sea urchin, Strongylocentrotus purpuratus: an unexpectedlydiverse family of transcripts in response to LPS, b-1,3-glucan, and dsRNA. BMCMol. Biol. 8 (1), 16.

Toth, A.L., Varala, K., et al., 2007. Wasp gene expression supports an evolutionarylink between maternal behavior and eusociality. Science 318 (5849), 441–444.

Trapnell, C., Pachter, L., et al., 2009. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25 (9), 1105–1111.

Vaz, C., Ahmad, H., et al., 2010. Analysis of microRNA transcriptome by deepsequencing of small RNA libraries of peripheral blood. BMC Genomics 11 (1),288.

Vera, J.C., Wheat, C.W., et al., 2008. Rapid transcriptome characterization for anonmodel organism using 454 pyrosequencing. Mol. Ecol. 17 (7), 1636–1647.

Vidal-Dupiol, J., Zoccola, D., et al., 2013. Genes related to ion-transport and energyproduction are upregulated in response to CO2-driven pH decrease in corals:new insights from transcriptome analysis. PLoS One 8 (3), e58652.

Vilcinskas, A., Mukherjee, K., et al., 2013. Expansion of the antimicrobial peptiderepertoire in the invasive ladybird Harmonia axyridis. Proc. R. Soc., B 280 (1750).

Wang, E.T., Sandberg, R., et al., 2008. Alternative isoform regulation in human tissuetranscriptomes. Nature 456 (7221), 470–476.

Watson, F.L., Puttmann-Holgado, R., et al., 2005. Extensive diversity of Ig-superfamily proteins in the immune system of insects. Science 309 (5742),1874–1878.

Wei, L., Vahedi, G., et al., 2010. Discrete roles of STAT4 and STAT6 transcriptionfactors in tuning epigenetic modifications and transcription during T helper celldifferentiation. Immunity 32 (6), 840–851.

Weng, N.-P., Araki, Y., et al., 2012. The molecular basis of the memory T cellresponse: differential gene expression and its epigenetic regulation. Nat. Rev.Immunol. 12 (4), 306–315.

Westermann, A.J., Gorski, S.A., et al., 2012. Dual RNA-seq of pathogen and host. Nat.Rev. Microbiol. 10 (9), 618–630.

Yamamoto, Y., Verma, U.N., et al., 2003. Histone H3 phosphorylation by IKK-a iscritical for cytokine-induced gene expression. Nature 423 (6940), 655–659.

Yeo, G.W., Coufal, N.G., et al., 2009. An RNA code for the FOX2 splicing regulatorrevealed by mapping RNA–protein interactions in stem cells. Nat. Struct. Mol.Biol. 16 (2), 130–137.

Zhang, P., Zhao, M., et al., 2013. Whole-genome DNA methylation in skin lesionsfrom patients with Psoriasis vulgaris. J. Autoimmunity 41, 17–24.

66 N.M. Dheilly et al. / Developmental and Comparative Immunology 45 (2014) 56–66

Zhang, S.-M., Loker, E.S., 2003. The FREP gene family in the snail Biomphalariaglabrata: additional members, and evidence consistent with alternative splicingand FREP retrosequences. Dev. Comp. Immunol. 27 (3), 175–187.

Zhang, S.-M., Loker, E.S., 2004. Representation of an immune responsive genefamily encoding fibrinogen-related proteins in the freshwater molluscBiomphalaria glabrata, an intermediate host for Schistosoma mansoni. Gene341, 255–266.

Zhang, S.-M., Yong, Z., et al., 2008. Expression profiling and binding properties offibrinogen-related proteins (FREPs), plasma proteins from the schistosome snailhost Biomphalaria glabrata. Innate Immunity 14 (3), 175–189.

Zhang, Y., Lv, J., et al., 2010. HHMD: the human histone modification database.Nucleic Acids Res. 38 (Suppl. 1), D149–D154.

Zhuang, X., McPhee, K., et al., 2012. Rapid transcriptome characterization andparsing of sequences in a non-model host-pathogen interaction; pea-Sclerotiniasclerotiorum. BMC Genomics C7 – 668 13 (1), 1–19.

Ziauddin, J., Schneider, D.S., 2012. Where does innate immunity stop and adaptiveimmunity begin? Cell Host Microbe 12 (4), 394–395.

Zisoulis, D.G., Lovci, M.T., et al., 2010. Comprehensive discovery of endogenousArgonaute binding sites in Caenorhabditis elegans. Nat. Struct. Mol. Biol. 17 (2),173–179.