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  • Research article
  • Open Access

Phylotranscriptomics suggests the jawed vertebrate ancestor could generate diverse helper and regulatory T cell subsets

BMC Evolutionary Biology201818:169

https://doi.org/10.1186/s12862-018-1290-2

  • Received: 19 December 2017
  • Accepted: 2 November 2018
  • Published:

Abstract

Background

The cartilaginous fishes diverged from other jawed vertebrates ~ 450 million years ago (mya). Despite this key evolutionary position, the only high-quality cartilaginous fish genome available is for the elephant shark (Callorhinchus milii), a chimaera whose ancestors split from the elasmobranch lineage ~ 420 mya. Initial analysis of this resource led to proposals that key components of the cartilaginous fish adaptive immune system, most notably their array of T cell subsets, was primitive compared to mammals. This proposal is at odds with the robust, antigen-specific antibody responses reported in elasmobranchs following immunization. To explore this discrepancy, we generated a multi-tissue transcriptome for small-spotted catshark (Scyliorhinus canicula), a tractable elasmobranch model for functional studies. We searched this, and other newly available sequence datasets, for CD4+ T cell subset-defining genes, aiming to confirm the presence or absence of each subset in cartilaginous fishes.

Results

We generated a new transcriptome based on a normalised, multi-tissue RNA pool, aiming to maximise representation of tissue-specific and lowly expressed genes. We utilized multiple transcriptomic datasets and assembly variants in phylogenetic reconstructions to unambiguously identify several T cell subset-specific molecules in cartilaginous fishes for the first time, including interleukins, interleukin receptors, and key transcription factors. Our results reveal the inability of standard phylogenetic reconstruction approaches to capture the site-specific evolutionary processes of fast-evolving immune genes but show that site-heterogeneous mixture models can adequately do so.

Conclusions

Our analyses reveal that cartilaginous fishes are capable of producing a range of CD4+ T cell subsets comparable to that of mammals. Further, that the key molecules required for the differentiation and functioning of these subsets existed in the jawed vertebrate ancestor. Additionally, we highlight the importance of considering phylogenetic diversity and, where possible, utilizing multiple datasets for individual species, to accurately infer gene presence or absence at higher taxonomic levels.

Keywords

  • Cartilaginous fish
  • Elasmobranch
  • Shark
  • Immunity
  • T cell
  • Interleukin
  • Immune gene evolution
  • Site-heterogeneous models
  • Phylogenetic rooting
  • Transcriptome

Background

The cartilaginous fishes (Chondrichthyes) diverged from a common ancestor with other vertebrates around 450 million years ago (mya) and are comprised of Holocephali (chimaeras) and Elasmobranchii (sharks, skates, and rays), which likely split between 300 and 420 mya [1, 2]. They represent the most phylogenetically-distant relatives of mammals to have an adaptive immune system based on somatically-rearranging immunoglobulins (i.e. antibodies) and T cell receptors, as well as major histocompatibility complex molecules [3, 4]. Despite their key evolutionary position, the only high-quality genome assembly available for this group is that of the elephant shark (Callorhinchus milii); a chimaera [5]. This dataset has been used to infer the presence or absence of many genes in the cartilaginous fishes [5]. However, distinct scenarios of gene family evolution are likely to have played out within cartilaginous fish evolutionary history, most notably across the vast time separating chimaeras and elasmobranches (e.g [6]), questioning the use of a single species to infer the presence or absence of genes in an entire vertebrate class.

In this respect, an initial survey of the elephant shark genome suggested the immune gene repertoire of cartilaginous fishes was very different to that of bony jawed vertebrates, lacking many CD4+ T cell-associated genes present in mammals [5]. T cells expressing the CD4 co-receptor are vital for mounting an adaptive immune response [7], and are loosely split into two major groups: (i) so-called ‘helper’ T cells, required for antibody production and subsequent affinity maturation of the response, in addition to immunological memory [711], and (ii) ‘regulatory’ T cells that suppress immune responses, preventing autoimmunity [12, 13]. These two groups are further divided into functionally-specific subsets (i.e. TH1, TH2, TH9, TH17, T follicular helper [TFH], Treg, and Tr1 cells), dependent upon on the cytokines (soluble signalling molecules such as interleukins [ILs]) and transcription factors required for their development, combined with the cell surface receptors and effector cytokines they express (Fig. 1) [79, 1117]. The apparent absence of many of these molecules from elephant shark led to a proposal that cartilaginous fishes possess only a primordial T helper (TH) cell system, based upon the TH1 subset alone [5]. Subsequent analyses [18] suggested genes associated with the TH2 and Treg subsets may be present, although in some cases the evidence was weak [19]. The proposal that cartilaginous fishes lack a diverse set of T cell subtypes is at odds with evidence that elasmobranchs can generate a robust antibody response, incorporating immunological memory and affinity maturation, following immunization [20, 21].
Fig. 1
Fig. 1

Summary of the presence/absence of major mammalian CD4+ T-cell lineages and associated genes in the jawed vertebrate ancestor. The figure and gene selection are based on Fig. 5 from Venkatesh et al. [5]. Boxed lineages were predicted to have emerged in the ancestor of jawed vertebrates by Venkatesh et al. [5] (black boxed lineages), or by Dijkstra [18] (blue boxed lineages). Crossed out genes are those thought to be absent from cartilaginous fishes and the vertebrate ancestor, while blue encircled genes are those that Dijkstra later predicted to in fact be present [18]. Dotted box/circle edges indicate uncertainty of gene or lineage presence(e.g. for IL-2R, FOXP3 [18])

To robustly infer orthology, as well as gene duplication and loss events, when comparing cartilaginous fish and their sister bony vertebrates, highly complete datasets are required. Adding to the elephant shark genome data, draft genome sequences have also become available for little skate (Leucoraja erinacea) [6, 22, 23] and whale shark (Rhincodon typus) [24]. While extremely useful tools for comparative analyses, draft assemblies may often be limited by their fragmented nature, a problem compounded by the large and complex genomes of elasmobranches [25, 26]. Several cartilaginous fish transcriptomes are also available [6, 2736], but none include a complete set of immune tissues. For these reasons, one goal of the current study was to generate elasmobranch transcriptome data that includes a full set of immune tissues, in the hope of revealing immune genes missed by other studies.

Even with high-quality data at hand, characterising the evolution of immune gene families across deep timescales remains challenging. The divergence of cartilaginous and bony jawed vertebrates occurred a relatively short time after genome duplication event(s) [3740] that radically re-shaped the evolution of many immune gene families [41, 42]. This could result in limited or conflicting phylogenetic signals, especially for rapidly-evolving genes. Robust phylogenetic inference of immune gene families at deep evolutionary scales should therefore include measures to avoid poorly fitting models of amino acid substitution and the inclusion of inappropriate outgroups [4345]. Mixture models accounting for variation in the amino acid substitution process across sites (i.e. site-heterogeneous models), such as CAT [46], can improve phylogenetic inference at deep evolutionary timescales when compared to site-homogeneous models [44] and have been adapted for smaller datasets [47, 48]. We hypothesize that this property will transfer particularly well to immune genes, due to the presence of rapid evolutionary turnover at some amino acid positions, in a background of structural conservation.

The principal aim of this study was to robustly reconstruct the T cell subset diversity of cartilaginous fishes in the context of jawed vertebrate evolution. To achieve this aim, we first generated a normalised multi-tissue transcriptome dataset for small-spotted catshark, a model cartilaginous fish species [49]. Our approach aimed to produce a ‘genome proxy’ maximising representation of novel genes expressed from immune tissues. Second, we searched for homologues of gene families involved in CD4+ T cell development and function in multiple catshark transcriptome datasets, along with genome and transcriptome data from other species of cartilaginous fish. Third, we used these sequences for phylogenetic reconstructions employing relaxed clock and outgroup rooting approaches, and considering the relative and absolute fit of site-heterogeneous mixture models. The results allowed us, for the first time, to unambiguously identify several CD4+ T cell-associated genes in cartilaginous fishes, implying that the ancestral jawed vertebrate was, and cartilaginous fishes are, capable of producing an array of helper and regulatory T cell subsets comparable to that of modern mammals. We also provide important insights into the phylogenetic analysis of fast-evolving immune genes at deep evolutionary timescales.

Materials & methods

Sample preparation and sequencing

A captive-bred, female small-spotted catshark of approximately 3 years of age was overdosed with MS-222 prior to sacrifice and tissue harvest; all procedures were conducted in accordance with UK Home Office ‘Animals and Scientific Procedures Act 1986; Amendment Regulations 2012’ on animal care and use, with prior ethical approval from the University of Aberdeen’s Animal Welfare and Ethical Review Body (AWERB). Tissue samples were flash frozen at − 80 °C for stomach, liver, spleen, gill, and brain, as well as spiral valve (homologous to the large intestine), and the epigonal and Leydig organs (associated with the gonad and oesophagus, respectively; bone marrow equivalents of cartilaginous fishes: [50]). Total RNA isolation was performed using TRIzol (Sigma-Aldrich) according to the manufacturer’s instructions but washing the RNA pellets 4x with 70% ethanol. Homogenisation was performed using either mortar and pestle, or tungsten carbide beads (QIAGEN) and a TissueLyser II (QIAGEN). RNA Quantification was performed by broad range RNA assay on a Qubit 2.0 fluorimeters (Invitrogen). RNA integrity was assessed via the Agilent 2100 Bioanalyzer or 2200 TapeStation (Agilent technologies) platforms. RNA samples were pooled to create a single multi-tissue RNA sample which was used for subsequent cDNA synthesis and normalisation using the Evrogen Mint-2 and Trimmer-2 kits, respectively, performed according to manufacturer’s instructions. cDNA (both normalised and non-normalised) was quantified by Qubit dsDNA HS Assay, and quality assessed by TapeStation. An additional peak at 166 bp, only present in the normalised sample, and probably representing a primer dimer introduced in the normalisation process, was removed by AMPure XP clean-up and verified on the TapeStation.

The sequencing library was constructed with the Ion Xpress Plus gDNA Fragment Library preparation kit (Life Technologies). Size selection was performed using a BluePippen (Sage Science) with a 270 bp target size and confirmed via TapeStation. The Ion Library quantification kit (Life Technologies) was used to quantify the library by qPCR on an Illumina ECO qPCR machine, revealing the need for amplification, which was performed according to the library preparation protocol. AMPure XP clean-up of the amplified library was then performed along with final TapeStation quality assessment and quantification via qPCR. Sequencing was performed on the Ion Proton (Life Technologies) using 2x Ion PI v2 BC Chips (Life Technologies) to generate single-ended 200 bp reads. The Ion PI IC 200 kit (Life Technologies) was used, and chips were prepared by the Ion Chef (Life Technologies).

Transcriptome assembly

The quality of each dataset was examined using fastqc (v0.10.1) [51]. Adaptors were trimmed using fastq-mcf within the ea-utils package (v1.1.2) [52], along with low quality bases from the start and end of reads, and very short sequences (named ‘MCF’ dataset after the trimming protocol). A hard-trimming procedure was also performed using a custom Perl script, where the first 10 bases, and any bases after 250 bp were trimmed from every read (named ‘HTMCF’ dataset; ‘HT’ representing ‘hard trim’ in addition to the MCF procedure), given that reads of greater than this length are most likely spurious. De novo assemblies were performed in Trinity (r2013-11-10) [53] for both datasets, as well as for an untrimmed dataset (named ‘RAW’ dataset). Assemblies were evaluated using basic assembly metrics for transcripts > 300 bp using the Assemblathon2 Perl script Assemblathon_Stats.pl (downloaded from http://korflab.ucdavis.edu/datasets/Assemblathon/Assemblathon2/Basic_metrics/assemblathon_stats.pl) [54]. BUSCO (v 3.0.0) analyses were performed against vertebrate and metazoan datasets (odb9), with no transcript size cut-off [55].

Sequence searches and phylogenetic analyses

Characterized human, chicken, teleost, or putative elephant shark amino acid sequences (as identified by [18]) for CD4+ T cell-associated genes identified as ‘missing’ from cartilaginous fishes, were used as TBLASTN [56] queries against each of the three transcriptome assemblies (RAW, MCF, HTMCF), as well as against two existing small-spotted catshark transcriptomes: ‘MEA’, from Mulley et al. [30], and ‘KEA’, from King et al. [6] (e-value cut-off: 10). In addition to using multiple catshark datasets, searches were also performed against the recently released spiny dogfish (Squalus acanthias, a member of the distantly related Squaleomorphii) [57] and blue shark (Prionace glauca, as this included spleen) transcriptomes [36], and the recently improved whale shark genome assembly (Rhincodon typus; ASM164234v2) [24]. The top 10 hits for each search were translated using TransDecoder v2.1 [58] and used as BLASTP queries against the Swissprot reviewed database. Those sequences producing hits to the protein of interest or a close relative were retained for phylogenetic analysis. To complement the BLAST analyses, profile hidden Markov model (HMM) based searches were also applied in HMMER v3.1 [59], using an alignment of phylogenetically diverse and representative orthologues as a query. All HMMER analyses were performed against either predicted protein models for whale shark, or TransDecoder translated transcriptomes for other species. All hits above the default exclusion threshold in HMMER were extracted and used as BLASTP queries against Swissprot to detect homologs to sequences of interest.

The newly identified sequences were mainly interleukins (IL), which are cytokines (cell signalling molecules) of the immune system, and their receptors (IL-R), along with potential TH17 and Treg transcription factors. To verify the identity of hits by phylogenetic analysis, as well as to assess evidence for loss of other CD4+ T cell-associated genes, nine datasets were assembled. For IL-Rs three datasets were generated; an IL-6Rα family dataset based on [60], an IL-2Rα/IL-15Rα dataset, and a class 1 group 2 cytokine receptor family dataset, as defined by [61]. Two main datasets were generated for ILs; an IL-6 superfamily dataset with members of both the IL-6 and IL-12 families included (as some genes appear to co-occupy these families [6264]), and an IL-2 superfamily dataset. The IL-2 family dataset was further broken down into more focused subsets, one containing IL7 and IL9 (which are considered sister genes [65, 66]), including the closely related IL-2 superfamily members IL-4 and IL-13 as outgroups [66], and a second focusing on the aforementioned IL-4 and IL-13, using IL2, IL-15, and IL-21 as an outgroup [66]. Finally, two transcription factor family datasets were also assembled. A dataset of the retinoic acid receptor-related orphan receptor (ROR) transcription factor family was compiled, considering two other nuclear receptors as outgroups; the human retinoic acid receptors (RARs), as well as nuclear hormone receptor 3 (HR3) of fruit fly [67]. A dataset for the forkhead-box P (FOXP) family was also assembled, with the use of invertebrate FOXP sequences as the outgroup tested.

Multiple sequence alignments were generated using MAFFT v7 [68], and trimmed using the ‘gappyout’ approach in trimAl v1.2 [69]. Maximum likelihood phylogenetic analyses were performed in IQ-tree (omp-1.5.4) with 1000 ultrafast bootstrap replicates [70, 71]. Model selection was carried out in IQ-tree under the Bayesian information criterion (BIC), mainly considering the standard substitution models available in BEAST [72] and Phylobayes [73]. The fit of the phylogenetic mixture models C10, C20, C30, C40, C50 and C60 (empirical CAT models) [47] were also examined, as well as variants with ‘+F’, ‘+JTT/WAG/LG’, or ‘+JTT/WAG/LG + F’ for the empirical CAT models (‘+G’ is already included in IQ-tree). These combinations were applied as the CAT model has been shown to provide a better fit to many datasets when combined with GTR (yielding GTR-CAT) [43, 7476], and JTT/WAG/LG + C10/C20/30/40/50/60 might be viewed as providing a precomputed GTR-CAT mimic (see also: [77]).

Bayesian phylogenetics in Phylobayes 4.1b [73] were performed in cases where mixture models were better fitting in the IQ-tree analysis, but using the CAT model itself rather than the empirical derivation (for example, JTT + C10 and C40 would be replaced by JTT + CAT and CAT, respectively), as this, theoretically, should collapse to the most appropriate number of site categories, is commonly used in phylogenomics, and has been shown to perform well for gene family analyses elsewhere [78, 79]. While the CAT model has been applied in previous studies of immune genes [80, 81], its fit to such datasets has never been tested. As such, in addition to testing the relative fit of empirical CAT models in IQ-tree, posterior predictive simulations (PPS) [44] were performed in Phylobayes to test if CAT-based models offer an improved absolute fit (in terms of describing site-specific amino acid alphabets), over standard models for fast-evolving immune genes. A standard statistical cut-off for a two-sided test was applied (at P < 0.05), such that posterior predictive Z-scores > 1.96 or < − 1.96 significantly deviate from the observed value (i.e. from the real data) and the model is taken to be inadequate.

Bayesian phylogenetics incorporating an outgroup-free relaxed clock rooting approach, which we have previously applied successfully to root vertebrate immune gene family trees [8286], were performed in BEAST v1.8.3 [72], using an uncorrelated relaxed clock model [87], and a Yule speciation prior [88, 89]. Two Markov chain Monte Carlo samples were generated in all Bayesian analyses, with convergence of sampled chains assessed in Phylobayes as maxdiff < 0.3, and visually appraised in Tracer v1.6 (http://beast.bio.ed.ac.uk/tracer) for BEAST runs. To summarise these analyses, 50% majority rule consensus trees were generated for Phylobayes runs, while RootAnnotator [90] was used to obtain root probabilities and identify a maximum clade credibility tree from the BEAST Markov chain Monte Carlo sample.

Results

Small-spotted catshark transcriptome

Ion Proton sequencing returned 134,841,605 reads, from which three Trinity assemblies based on increasingly complex quality filtering strategies were generated: RAW, MCF, and HTMCF, respectively containing 623,430, 621,635, and 167,255 contigs (See Table 1 for extended statistics for contigs > 300 bp). Compared to the MCF and RAW datasets, the HTMCF dataset produced a reduced number of contigs (Table 1), a greater number of complete single-copy BUSCOs (Table 2), and a higher N50 contig length (Table 1), suggesting that more stringent read and base quality filtering improves assembly contiguity. However, the RAW and MCF assemblies contain fewer missing BUSCOs, suggesting that novel data was lost from the HTMCF assembly (Table 2).
Table 1

Basic assembly statistics (> 300 bp) compared to existing small-spotted catshark transcriptomes

 

KEA

MEA

RAW

MCF

HTMCF

Total base count

45,214,552

110,464,397

275,457,098

275,410,367

80,066,015

Total contig count

58,273

86,006

388,800

387,579

102,228

N50a contig length

965

2316

786

791

969

L50b contig count

12,721

13,518

89,641

88,973

20,814

GC content (%)

45.69

43.9

42.09

42.1

42.03

aN50: the length of the shortest contig included when the least number of contigs are used to make up 50% of the total assembly length

bL50: the total contig count when the least number of contigs are used to make up 50% of the total assembly length

Table 2

RAW, MCF and HTMCF, BUSCOs compared to existing catshark transcriptomes

 

KEA

MEA

RAW

MCF

HTMCF

Vertebrata (2586 BUSCOs)

 Complete Single-copy BUSCOs

1392 (53.83%)

1257 (48.61%)

997 (38.55%)

1000 (38.67%)

1533 (59.28%)

 Complete Duplicated BUSCOs

164 (6.34%)

788 (30.47%)

434 (16.78%)

435 (16.82%)

188 (7.27%)

 Fragmented BUSCOs

641 (24.79%)

277 (10.71%)

852 (32.95%)

855 (33.06%)

477 (18.45%)

 Missing BUSCOs

389 (15.04%)

264 (10.21%)

303 (11.72%)

296 (11.45%)

388 (15%)

Metazoa (978 BUSCOs)

 Complete Single-copy BUSCOs

735 (75.15%)

628 (64.21%)

583 (59.61%)

588 (60.12%)

787 (80.47%)

 Complete Duplicated BUSCOs

87 (8.9%)

300 (30.67%)

248 (25.36%)

238 (24.34%)

75 (7.67%)

 Fragmented BUSCOs

116 (11.86%)

33 (3.37%)

127 (12.99%)

130 (13.29%)

78 (7.97%)

 Missing BUSCOs

40 (4.09%)

17 (1.74%)

20 (2.04%)

22 (2.25%)

38 (3.89%)

Comparison with existing catshark datasets suggested that the total number of transcripts and bases in our assemblies is greater than generated elsewhere (Table 1). All assemblies lack some BUSCOs, however the MEA assembly contains the fewest missing BUSCOs. Both the MEA (liver, pancreas, and brain [30]) and KEA (pooled embryo [6]) datasets were sequenced on the Illumina platform, raising the possibility that the increased number of transcripts in our assemblies is linked to the increased error rates under Ion Torrent sequencing [91, 92]. In addition, the apparent increased contiguity of the MEA assembly is likely due to the use of paired-end reads, which was not done here. Biological explanations are also possible for the differences, such as the introduction of additional sequences and splice variants from multiple tissues [93], or the inclusion of very lowly expressed transcripts, due to normalisation.

Overall, a combination of sequencing approaches, as well as biological differences are likely to significantly impact the gene content of each assembly. For this reason, all five assemblies were carried forward to allow a robust search for T cell-associated genes in cartilaginous fishes.

CD4+ T cell subset-defining genes in cartilaginous fishes

To better understand T cell biology in cartilaginous fishes, we employed BLAST and HMM based searches of all five small-spotted catshark assemblies, as well as blue shark and spiny dogfish transcriptomes, and the whale shark predicted proteome, for the ILs and IL-Rs defining those T cell subsets reported missing from elephant shark [5]. These searches failed to identify putative homologs of IL-2 or its receptor IL-2R, IL-5, IL-9, or RORC. However, we identified putative sequences for IL-4/IL-13, IL-21, IL-23, IL-27 (p28), IL-6Rα, IL-23R, and FOXP3. Presence of the IL and IL-R sequences was variable between species, and between catshark transcriptome datasets (Table 3). To verify orthology of these sequences we assembled comprehensive datasets for each gene along with related family members and performed extensive phylogenetic analyses.
Table 3

Demonstration of putative orthologue content variation between datasets

 

KEA

MEA

RAW

MCF

HTMCF

IL-4/13

IL-27 (p28)

IL-6Rα

IL-23R

Site heterogeneous mixture models capture site-specific evolutionary processes in immune genes where standard models fail

The relative fit of empirical mixture models was tested for each of the assembled datasets, as these models may better accommodate the complex evolutionary history of fast-evolving genes. Simple empirical CAT models were never better-fitting than standard site-homogeneous models for any dataset according to the BIC (Table 4). However, when these were paired with a standard model (i.e. JTT, WAG, or LG), this resulted in improved fit over standard substitution models for the IL-6 superfamily, class-1 group-2 cytokine receptor, and FOXP datasets.
Table 4

IQ-tree BIC best-fit model results for all dataset

Dataset

Best overall

Best standard

IL-2 superfamily

JTT + F + G

JTT + F + G

IL-7/9 family no outgroup

JTT + G

JTT + G

IL-7/9 family outgroup

JTT + G

JTT + G

IL-4/13 family no outgroup

WAG+G

WAG+G

IL-4/13 family outgroup

WAG+G

WAG+G

IL-6 superfamily

JTT + C10 + F

LG + F + G

IL-6Rα family

JTT + I + G

JTT + I + G

IL-2Rα/IL-15Rα

WAG+I + G

WAG+I + G

Class-1 Group-2 cytokine receptors

JTT + C30 + F

WAG+F + I + G

ROR no outgroup

JTT + I + G

JTT + I + G

ROR HR3 outgroup

JTT + I + G

JTT + I + G

ROR RAR outgroup

JTT + G

JTT + G

FOXP no outgroup

JTT + C20 + F

JTT + I + G

FOXP invertebrate outgroup

JTT + C10 + F

JTT + F + I + G

To better understand the above findings, we also compared the absolute fit of standard substitution models and mixture models using PPS (Fig. 2) [44]. Specifically, for each dataset better-fit by a paired standard and empirical CAT model, we tested the ability of standard models and non-empirical CAT-based models (this was JTT + CAT in all cases here) to anticipate evolutionary process variation across sites. PPS of the number of amino acids per site revealed that standard models failed to adequately capture site-wise biochemical diversity in all tested datasets, but instead consistently overestimated the per site amino acid alphabet (Z-score > 1.96; P-value < 0.05) (Fig. 2). This means that the ability of standard models to accurately infer homoplasy in these datasets is impaired, which could mislead phylogenetic inference and result in errors in branch lengths and evolutionary relationships [44]. On the other hand, CAT-based models adequately captured per-site amino acid alphabet diversity in every dataset (1.96 > Z-score > − 1.96; P-value > 0.05) (Fig. 2), and as such should be more robust to error for these datasets.
Fig. 2
Fig. 2

Posterior predictive simulations show that standard models, but not site-heterogeneous mixture models, inadequately capture site-specific biochemical diversity in all tested vertebrate immune genes

Phylogenetic analyses of CD4+ T cell-associated genes

IL-2 superfamily

The IL-2 superfamily consists of three subfamilies, the IL-2 family, the IL-4/13 family, and the IL-7/9 family, and these were verified here by phylogenetic analysis (Fig. 3). Within the IL-2 subfamily there are two CD4+ T cell associated cytokines; IL-2 is a key inducer of Treg cells, which modulate immune responses, promote feto-maternal tolerance, and help avoid allergy and autoimmunity [17], while IL-21 is the effector cytokine of TFH cells, which contribute to antibody affinity maturation and immune memory [8]. Dijkstra [18] identified putative orthologues of mammalian IL-2 and IL-21 in elephant shark, despite initially being thought absent [5]. Our phylogenetic analyses of the IL-2 superfamily are consistent with these findings although support was weak (BEAST posterior probability [BPP] = 0.69 and ultrafast bootstrap [UB] = 62 for IL-21, and UB = 0.61 for IL-2) (Fig. 3). We also identified cartilaginous fish orthologues of IL-15 (BPP = 0.8; UB = 83), the other member of the IL-2 family, while the putative IL-21 identified in our homology searches turned out to instead group with the recently identified IL-15L (UB = 56) [94]. More focused analyses of the IL-2 family failed to provide improved phylogenetic resolution (results not shown), however this was not the case for the IL-7/9 and IL-4/13 families.
Fig. 3
Fig. 3

Phylogenetic analysis of the IL-2 superfamily. Branches are coloured according to the taxonomic key in the figure. Statistical support is shown for key nodes as per accompanying box, wherein the analysis shown in bold is the topology shown. Root Posterior Probabilities (RPP) > 0.05 from the BEAST analysis are shown

IL-7/9 family

IL-9 is the signature effector cytokine produced by mammalian TH9 cells, a subtype with a role in mucosal immunity that includes the expulsion of intestinal worms [9, 14]. IL-7 is considered the closest relative of IL-9 [65, 66], meaning that although we did not find a putative IL-9 orthologue in small-spotted catshark, the initial BLAST-based assignment of an IL-7 gene in elephant shark [5] implied either that IL-9 diverged from IL-7 after the divergence of cartilaginous fishes from other jawed vertebrates, or that IL-9 was lost or is yet to be found in cartilaginous fishes. To assess this, we performed phylogenetic analysis of the IL-7/9 family, using both outgroup and relaxed clock rooting approaches, to independently verify the root placement (root posterior probability [RPP] = 0.99). The putative IL-7 sequences from cartilaginous fishes form a clade with IL-7 from other vertebrates (BPP = 1.00; UB = 97%) (Fig. 4a). These results support a scenario where IL-7 and IL-9 both existed in the jawed vertebrate ancestor, with IL-9 subsequently being lost, or yet to be discovered, in cartilaginous fishes. This hypothesis is supported by the larger IL-2 superfamily analysis (Fig. 3).
Fig. 4
Fig. 4

Phylogenetic analyses of the (a) IL-7/9 family and the (b) IL-4/IL-13 family. Outgroup sequences are from human. BPP and RPP values are from analyses not including the outgroups. Other details as per Fig. 3

IL-4/13 family

The IL-4/13 family of cytokines are the effector cytokines produced by TH2 cells in mammals, a subtype central to antibody-mediated immunity and the clearance of extracellular pathogens [95]. Evidence for putative IL-4/13-like sequences in elephant shark was provided by Dijkstra [18] after Venkatesh et al. [5] originally reported the absence of these genes from cartilaginous fishes. Our results indicate that two IL4/13-like lineages are present and expressed in small-spotted catshark (IL-4/13A and IL4/13B), having duplicated in the ancestor of cartilaginous fishes (BPP = 0.88; UB = 77%), but do not support clear orthology of these genes, or those of teleosts, to mammalian IL-4 or IL-13 (Fig. 4b). The IL-2 superfamily analysis was also congruent with these findings (Fig. 3).

IL-6 superfamily

IL-27α (p28), thought to be absent from cartilaginous fishes [5], forms half of the Tr1 subset-inducing cytokine IL-27. Tr1 cells dampen autoimmunity and inflammation by promoting expression of the immunosuppressive cytokine IL-10 [16]. IL-23 (p19), which is also thought not to exist in cartilaginous fishes [5], is a pro-inflammatory TH17 cell effector cytokine [10, 12]. Phylogenetic analyses of the IL-6 superfamily, to which these cytokines belong [6264], reveal that the putative cartilaginous fish IL-27α sequence is sister to tetrapod IL-27α (BPP = 0.99; UB = 84%; PPP = 0.69), indicating orthology (Fig. 5). The phylogenetic analyses performed here are at odds with the prior assumption that IL-27 (p28), is closely related to pro-inflammatory IL-23 and the TH1-inducing cytokine IL-12 (p35) [96, 97]. Rather, IL-23 (p19), and IL-12 (p35) form a subfamily with the pro-inflammatory cytokine IL-6 (BPP = 0.99; UB = 97%; PPP = 0.98), and, depending upon root placement, IL-11, which modulates placentation, bone resorption, platelet production, as well as immune responses in mammals (Fig. 5). Phylogenetic analysis verified the identity of cartilaginous fish IL-23 (BPP = 0.95; UB = 97%; PPP = 0.97), as well as supporting the assignment of a cartilaginous fish IL-12 (p35)/IL-23 (p19) orthologue (BPP = 1.00; UB = 100; PPP = 1.00), indicating that these genes emerged in the jawed vertebrate ancestor (Fig. 5). Although IL-11 was not found in elephant shark by Venkatesh and colleagues [19], we found evidence of a cartilaginous fish orthologue which formed a clade with IL-11 of tetrapods (BPP = 1.00; UB = 96%; PPP = 0.88) (Fig. 5). Further, cartilaginous fish sequences form part of a clade with IL-6 from other vertebrates (BPP = 0.96; UB = 88%; PPP = 0.92), with evidence of lineage-specific duplication or triplication in cartilaginous fishes (Fig. 5). Beyond this, our analyses support the presence of orthologues to OSM/LIF (BPP = 0.59; UB = 85%; PPP = 0.85) and CNTF (BPP = 1.00; UB = 100%; PPP = 1.00) (Fig. 5). CNTF and CLC form a clade (UB = 86%; PPP = 0.94), which is sister to (BPP = 1.00; UB = 94%; PPP = 0.93) another clade containing Ct-1 and Ct-2 (BPP = 1.00; UB > = 92%; PPP = 0.9), suggesting that a CLC orthologue (and a Ct-1/Ct-2-like sequence) existed in the jawed vertebrate ancestor (Fig. 5).
Fig. 5
Fig. 5

Phylogenetic analysis of the IL-6 superfamily reveals orthologues of IL-23α (p19), IL-27α (p28), and IL-11 in cartilaginous fishes. Details as per Fig. 3

IL-6Rα family

In mammals, IL-6Rα is expressed on both TFH and TH17 subsets; TFH cells are important for antibody production, affinity maturation of the antibody response, and memory B cell differentiation [8], while TH17 cells play a pro-inflammatory role, helping to maintain the integrity of mucosal barriers in humans [10]. For phylogenetic analyses involving putative cartilaginous fish IL-6Rα sequences, we included the closely related IL-11Rα and CNTFRα proteins [60], and employed a relaxed clock rooting approach [87]. The results firmly place the root between IL-6Rα and the other two proteins (RPP = 0.98), indicating that IL-11Rα and CNTFRα are more closely related to each other than to IL-6Rα (BPP = 1.00; UB = 99%) (Fig. 6a). Both Bayesian and maximum likelihood phylogenetic analyses strongly support direct orthology of cartilaginous fish IL-6Rα sequences to those in other jawed vertebrates (BPP = 0.99; UB = 99%) demonstrating that an IL-6Rα gene was present in the jawed vertebrate ancestor (Fig. 6a). Moreover, this approach also robustly supports the existence of cartilaginous fish orthologues of IL-11Rα (BPP = 1.00; UB = 92) and CNTFRα (BPP = 1.00; UB = 100) (Fig. 6a).
Fig. 6
Fig. 6

Phylogenetic analysis of the (a) IL-6Rα family, and the (b) IL2Rα/IL-15Rα family. Details as per Fig. 3

IL-2Rα/IL-15Rα family

IL-2Rα forms part of the IL-2R heterotrimer, which is pivotal to maintenance and growth of the immunomodulatory Treg lineage [17], but is thought to be missing from cartilaginous fishes [5]. Dijkstra [18] suggested that IL-2Rα separated from IL-15Rα early in tetrapod evolution, and that IL-15Rα functionally accommodates the role(s) of IL-2Rα in teleost fishes. Our BLAST and HMMER searches identified putative orthologues of IL-15Rα, and while no appropriate outgroup is known, we performed relaxed clock rooted phylogenetic analyses of IL-2Rα and IL-15Rα. This result appears to verify the identity of cartilaginous fish IL-15Rα (BPP = 0.92; UB = 100) (Fig. 6b). Interestingly however, we found no evidence for IL-2Rα emerging from IL-15Rα, rather it seems that they diverged from a common ancestor prior to the divergence of cartilaginous fishes and bony vertebrates (RPP ≥ 0.97) (Fig. 6b).

IL-23R and the class 1 group 2 cytokine receptor family

IL-23R is a cytokine receptor specific to TH17 cells [8, 10, 12, 15]. To verify the putative IL-23R identified by BLAST in cartilaginous fishes, and to better understand the evolution of cytokine receptors, we carried out a phylogenetic analysis of the class 1 group 2 cytokine receptor family [61]. This revealed that putative cartilaginous fish IL-23R falls sister to IL-23R of tetrapods (BPP = 1.00; UB = 99%; PPP = 1.00) indicating the presence of an IL-23R orthologue in cartilaginous fishes (Fig. 7). The analyses support inclusion of IL-23R within a subfamily that also contains IL27Rα, and IL-12Rβ2 (BPP = 1.00; UB = 100; PPP = 1.00) (Fig. 7). IL27Rα and IL-12Rβ2 are involved in TH1 cell differentiation and, due to their relationships to bony vertebrate sequences, our data suggest that direct orthologues exist for these genes in cartilaginous fishes (BPP = 1.00; UB = 97%; PPP = 1.00, and BPP = 1.00; UB = 93%; PPP = 1.00, respectively) (Fig. 7).
Fig. 7
Fig. 7

Phylogenetic analysis of class 1 group 2 cytokine receptors reveals an IL-23R orthologue in cartilaginous fishes. Details as per Fig. 3

This analysis also provides insights into the evolution of the other included class 1 group 2 cytokine receptors. GP-130 (also known as IL-6Rβ or IL-6ST), which forms complexes with many IL-6 and IL-6R family members, plays a key role in both promoting and suppressing inflammation, and is essential for embryo survival in mammals [98]. Three copies of GP-130 exist in cartilaginous fishes [5], which evidently result from two lineage-specific duplications (BPP ≥ 0.97; UB ≥ 72%; PPP ≥ 0.9) (Fig. 7). A GCSFR clade, which also contains cartilaginous fishes (BPP = 1.00; UB = 98%; PPP = 1.00), falls sister to GP-130 (BPP = 1.00; UB = 93%; PPP = 1.00), which together are sister to the IL-23Rα, IL-27Rα, and IL-12Rβ2 clade (BPP = 1.00; UB = 93%; PPP = 0.93) (Fig. 7). Outside this grouping, a cartilaginous fish sequence falls within an OSMR (multifunctional) and LIFR (tumour metastasis suppressor [99]) clade (BPP = 1.00; UB = 1.00%; PPP = 1.00) (Fig. 7). Finally, cartilaginous fishes possess a putative orthologue of leptin receptor (LEP-R), a hypothalamic appetite-controlling hormone receptor, as this sequence formed a clade with bony vertebrate LEP-R (BPP = 1.00; UB = 100%; PPP = 1.00), and relaxed clock rooting analysis best places the root between LEP-R and the other family members (RPP = 0.66) (Fig. 7).

ROR transcription factor family

Having identified orthologues of two cytokine receptors associated with the TH17 subset (IL-23R and IL-6R), we performed a variety of phylogenetic rooting analyses to look for evidence of the transcription factor ROR-γ, the master regulator of TH17 cells [100]. ROR-γ is a member of the larger ROR family and was reported missing in elephant shark [5]. We tested a relaxed clock rooting method (Fig. 8a), and two alternative outgroups; fruit fly HR3 (Fig. 8b), and the human RAR family (Fig. 8c), both closely related nuclear receptors [67]. These approaches did not provide congruent support for any root position (Fig. 8), which may result from the major difference in evolutionary rate between ROR-γ and the other RORs (Fig. 8). However, our results are consistent with two new findings: (i) ROR-γ existed in the jawed vertebrate ancestor, though evidence for its presence in cartilaginous fishes depends on root placement in relaxed clock analyses (Fig. 8a), and (ii) a fourth member of the vertebrate ROR family, which falls sister to ROR-β (BPP = 0.99; UB ≥ 79%), exists, but is possibly lost in mammals and teleosts. We propose the name ROR-δ for this new family member (Fig. 8).
Fig. 8
Fig. 8

Phylogenetic analyses of the vertebrate ROR family shows that ROR-γ existed in the jawed vertebrate ancestor and reveals a new vertebrate ROR-β paralog not found in mammals (which we name ROR-δ). Alternative rooting strategies, using (a) a relaxed clock model, (b) fruit fly HR3 as outgroup, or (c) human RARs as outgroup, show that the root of the ROR phylogeny cannot be confidently placed. Gene level clades are collapsed in (b) and (c), but contain the same taxa as a Other details as per Fig. 3

FOXP transcription factor family

FOXP3 is the master regulator of Treg cell development and function in mammals [101]. A FOXP3 homologue was identified in elephant shark, but presumed non-functional by Venkatesh and colleagues [5] based on analysis of the DNA-binding domain. However, Dijkstra [18] has suggested that this inference may be premature. Our phylogenetic analyses suggest that cartilaginous fishes possess orthologues to all four mammalian FOXP family members (Fig. 9). Like the ROR family, the relationships between these genes are not easily resolved as different root positions are favored when the tree is rooted with either relaxed clocks or invertebrate FOXP sequences (Fig. 9). Another common feature between the FOXP and ROR families is a striking increase in evolutionary rate in the family member involved in T cell biology (i.e. immune functioning RORC and FOXP3), as compared to the other family members (Figs 8 and 9). We generated a multiple sequence alignment of cartilaginous fish FOXP3 DNA binding domains against those of other jawed vertebrates to explore the issue of FOXP3 functionality in cartilaginous fishes and the jawed vertebrate ancestor. This revealed that the sites predicted to lead to non-functionality in cartilaginous fishes by Venkatesh et al. [5] are not noticeably more divergent from human than those of other non-mammals, and certainly no more so than expected in the context of species phylogeny and divergence times [1, 2].
Fig. 9
Fig. 9

Phylogenetic analyses of the vertebrate FOXP family verifies the existence of cartilaginous fish orthologues to FOXP1–4, but alternative rooting strategies, using (a) a relaxed clock model, or (b) invertebrate FOXP sequences as an outgroup, show that the root of the FOXP phylogeny cannot be confidently placed. Other details for (a) and (b) as per Figs. 3 and 6. (c) Alignment of the FOXP3 DNA-binding domain from phylogenetically representative vertebrates suggests that cartilaginous fish FOXP3 is not atypical

Discussion

Transcriptomes, taxonomy, and gene discovery

As available genomic data remains relatively sparse for cartilaginous fish, we generated a normalised multi-tissue transcriptome for the small-spotted catshark, with the goal of maximizing representation of novel transcripts. We applied a variety of trimming approaches and tested subsequent assemblies using various statistical approaches. While some of the assemblies contained excessive numbers of transcripts considering the number of genes typical of a vertebrate genome, we did not introduce filters by coverage, length, or contamination, thus retaining as many novel transcripts as possible. The results indicate that while statistical methods can be useful to determine the most contiguous (e.g. high N50), or most complete assembly (e.g. fewest missing BUSCOs), choosing a ‘best’ assembly may lead to loss of interesting data, such as novel sequences or full-length transcripts. The findings similarly highlighted the differential presence of transcripts of interest in our datasets compared to those of past transcriptome studies of the same species [6, 30], to the genome of the distantly related elephant shark [5], and to genomic data from other shark species. Our study thus supports the notion that using a single genome [102] or transcriptome assembly [103], or species [104], is grossly insufficient to adequately assess gene presence or absence in a vertebrate class. Our results also suggest that paired-end data, or longer reads than those applied here, should also be utilised where possible. Despite this, the data generated in this study contains novel sequences for cartilaginous fishes, and other researchers should benefit from this resource.

Adequate phylogenetic modelling of fast-evolving immune genes

A precarious balance must be maintained in immune gene evolution to uphold structural integrity and functionality, while avoiding pathogen subversion. As such, immune genes evolve rapidly, but with strong site-specific evolutionary pressures; both of which can contribute to accumulation of hidden substitutions (homoplasy) over time, which is known to cause phylogenetic errors. In line with this, standard phylogenetic models inadequately predicted the diversity of amino acid alphabets across sites in the immune gene datasets tested in this study. This inadequacy to detect site-specific biochemical constraints indicates that a model has an impaired capacity to infer hidden substitutions in the data [44]. To the best of our knowledge this is the first report of the inadequacy of standard phylogenetic models for immune gene datasets, though this result is not surprising given the complex evolutionary pressures imposed on immune genes by the host-pathogen arms race. In stark contrast, and consistent with our hypothesis that site-heterogeneous models would better accommodate the rapid and complex evolutionary patterns of immune genes, CAT-based models adequately captured site-specific amino acid alphabet diversity for all tested datasets. These findings imply (based on [44]) that standard models will often fit poorly to immune gene datasets, and that CAT-based models should typically produce more accurate phylogenetic trees for immune genes in future studies.

The problem of rooting rate asymmetric phylogenetic trees

Increased attention has been given recently to the prevalence of asymmetric evolutionary rates between different members of gene families and the negative impact this has on phylogenetic inference [105, 106]. Here, for the ROR and FOXP transcription factor family phylogenetic analyses we found that the immune genes RORC and FOXP3 had drastically increased evolutionary rates compared to their relatives. In the case of outgroup-free relaxed clock rooting analyses, the root fell between the fast-evolving immune gene and the rest of the family, although this was never the case using outgroups. This suggests that clock rooting may be susceptible to error in the face of extreme rate asymmetry, even when an uncorrelated relaxed clock model [87] is applied. However, multiple alternate outgroups were tested for the ROR family and these resulted in different root positions, meaning that the root placement under the relaxed clock cannot be reliably dismissed. Interestingly, it appears that for families of immune genes with a shared fast-evolutionary rate this phylogenetic difficulty is not as prevalent, with clocks and outgroups supporting a common root position (e.g. IL-6R family, IL-7/9 family, IL-4/13 family). As such, while many factors may contribute to the phylogenetic incongruence in the transcription factor families analysed here (e.g. rediploidisation following genome duplication events prior to divergence of cartilaginous and bony vertebrates [37, 39, 107], or selective pressure changes associated with the functional shift to immune gene status inducing compositional heterogeneity among branches, heterotachy, and/or heteropecilly [108]) we nonetheless predict that rate asymmetry is likely a key player, promoting the case for it being a somewhat overlooked phenomenon [105]. We suspect that this may derive from standard substitution models being designed to accommodate rate asymmetry, and the resultant branching errors when this fails being less obvious at the level of genes than species, where there are often morphology-derived topological expectations.

CD4+ T cell subsets in cartilaginous fishes and the jawed vertebrate ancestor

Venkatesh et al. proposed that cartilaginous fishes have only basic or primordial T cell function [5]. Here, having employed detailed phylogenetic analyses, we identified orthologues of several additional genes integral to CD4+ T cell-subset induction and function in cartilaginous fishes. Combined with previous findings [5, 18, 19], these results show that cartilaginous fishes possess the molecules necessary to generate an array of CD4+ helper and regulatory T cells comparable to that of mammals. In fact, we present a new model of helper and regulatory T cell evolution wherein all key genes (in some form) and/or pathways found in mammals existed in the jawed vertebrate ancestor (Fig. 10).
Fig. 10
Fig. 10

A full set of T helper and T regulatory cell associated genes existed in the jawed vertebrate ancestor. The figure and gene selection are based on Fig. 5 from Venkatesh et al. [5], but here refer to the ancestral jawed vertebrate gene set rather than that of cartilaginous fishes. Boxed lineages were predicted to have emerged in the ancestor of jawed vertebrates by Venkatesh et al. [5] (black boxed lineages), by this study (red boxed lineages), or by this study and Dijkstra [18] (blue boxed lineages). All genes listed, except for IL-9 and IL-2Rα, have now been identified in cartilaginous fishes

We have provided new insights on the controversy surrounding the absence of IL-2R and FOXP3 functioning in cartilaginous fishes, both of which are required for the development and function of Treg cells, a subset that helps maintain self-tolerance by dampening inflammation and suppressing immune responses [12, 17]. For example, in teleost fishes a common IL-2/15 receptor binds both IL-2 and IL-15 [18, 94, 109]; in a similar manner IL-15R, which our study shows is present in cartilaginous fishes, could functionally compensate for the lack of IL-2R, which appears to be lost from both cartilaginous fishes and teleosts. Also, while cartilaginous fish FOXP3 shows poor conservation of the amino acids that facilitate DNA binding in mammalian FOXP3, we find that this is not unusual among non-mammals. Further, these residues vary naturally between FOXP subfamily members—all of which can bind DNA [110]—so lack of conservation of these elements in FOXP3 does not necessarily equate to an absence of Treg cells in cartilaginous fishes [18]. Further, while Venkatesh et al. used the apparent absence of T helper cell subsets in general, and TFH cells in particular, to explain the long lag-times associated with humoral immune responses in cartilaginous fishes [5], our results contradict this idea. Indeed, our data suggest cartilaginous fishes are capable of producing both TH2 and TFH cells, a finding that fits better with the antibody affinity maturation and immunological memory previously evidenced in cartilaginous fishes [20, 21].

While our data is consistent with the presence of a sophisticated, mammalian-like, set of T cell subtypes in cartilaginous fishes, several lineage-specific novelties were also observed; for example, GP-130 (IL-6Rβ/IL-6ST; the signalling component of the IL-6 receptor) is triplicated in cartilaginous fishes, potentially increasing the diversity of signalling that can be induced by IL-6. In line with this, IL-6 is also duplicated (and possibly triplicated) in cartilaginous fishes. Enigmatic orthology, as observed for the IL-4/13 family, may result from independent duplications in many lineages, combined with exon shuffling or conversion events [19]. Lineage-specific loss events have also played a role, for example the potential loss of IL-9 and IL-2Rα in cartilaginous fishes, or ROR-δ in mammals.

Finally, it must be noted that the data presented here do not provide conclusive evidence for the existence of any T cell subset in cartilaginous fishes, or the jawed vertebrate ancestor, but do strongly reject past conclusions regarding their absence. Importantly, although a canonical (i.e. mammalian-like) CD4 was reported as absent from cartilaginous fishes [5, 19], one of several CD4/LAG3-like molecules identified by Venkatesh et al. [5] has since been shown to have a CD4-like expression profile and thus may act as the functional equivalent in sharks (Martin F. Flajnik, personal communication). Together with our data, this suggests that a fully developed set of CD4+ helper and regulatory T cell subsets equivalent to that of mammals evolved in the jawed vertebrate ancestor and still exists, with lineage-specific modifications, in cartilaginous fishes today. While more work is required to fully understand T cell biology in cartilaginous fishes, our results show that this arm of their adaptive immune system is likely no more ‘primordial’ than that of mammals.

Abbreviations

BPP: 

BEAST Posterior Probability

IL: 

Interleukin

IL-R: 

Interleukin receptor

KEA: 

King et al.

MEA: 

Mulley et al.

PPP: 

Phylobayes Posterior Probability

RPP: 

Root Posterior Probability

UB: 

Ultrafast Bootstrap

Declarations

Acknowledgements

We are grateful to Dr. Elaina Collie-Duguid, Prof David Salt (University of Nottingham), Mr. Brennan Martin, Mrs. Diane Stewart, and the rest of the Centre for Genome-Enabled Biology and Medicine team at University of Aberdeen for their advice and/or technical assistance. We thank Dr. Abdullah Alzaid for advice on RNA isolation, and Dr. Rita Pettinello for assistance with tissue sampling (both University of Aberdeen). We acknowledge the support of the Maxwell compute cluster funded by the University of Aberdeen. Finally, we extend thanks to Dr. Tom Williams (University of Bristol) for helpful discussion, and Dr. Bui Quang Minh (Centre for Integrative Bioinformatics Vienna) for advice on IQ-tree model implementation.

Funding

This study was supported by The Royal Society Research Grant RG130789 awarded to HD, as well as by a University of Aberdeen Centre for Genome-Enabled Biology and Medicine PhD studentship and Marine Alliance for Science and Technology for Scotland (MASTS) research grant SG363 awarded to AKR.

Availability of data and materials

RNA-seq reads are available from NCBI Bioproject: PRJNA497467. De novo transcriptome assemblies, and immune gene sequences, alignments and tree files are available from figshare archive: https://doi.org/10.6084/m9.figshare.c.3956320.v1.

Authors’ contributions

AKR and HD conceived the study. AKR performed experiments and prepared the manuscript and figures. All authors contributed to experimental design, wrote, read, and approved the final manuscript.

Ethics approval and consent to participate

All animal procedures were conducted in accordance with UK Home Office ‘Animals and Scientific Procedures Act 1986; Amendment Regulations 2012’ on animal care and use, with prior ethical approval from the University of Aberdeen’s Animal Welfare and Ethical Review Body (AWERB).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Authors’ Affiliations

(1)
School of Biological Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, UK
(2)
Centre for Genome-Enabled Biology & Medicine, University of Aberdeen, Aberdeen, AB24 2TZ, UK
(3)
Department of Microbiology & Immunology, University of Maryland School of Medicine, Institute of Marine & Environmental Technology, 701 E Pratt St, Baltimore, MD21202, USA
(4)
Present address: Smurfit Institute of Genetics, Trinity College Dublin, University of Dublin, Dublin 2, Ireland

References

  1. Inoue JG, Miya M, Lam K, Tay BH, Danks JA, Bell J, et al. Evolutionary origin and phylogeny of the modern Holocephalans (Chondrichthyes: Chimaeriformes): a mitogenomic perspective. Mol Biol Evol. 2010;27:2576–86.PubMedView ArticleGoogle Scholar
  2. Irisarri I, Baurain D, Brinkmann H, Delsuc F, Sire J-Y, Kupfer A, et al. Phylotranscriptomic consolidation of the jawed vertebrate timetree. Nat Ecol Evol. 2017;1:1370–8.PubMedPubMed CentralView ArticleGoogle Scholar
  3. Dooley H, Flajnik MF. Antibody repertoire development in cartilaginous fish. Dev Comp Immunol. 2006;30:43–56.PubMedView ArticleGoogle Scholar
  4. Dooley H. Athena and the evolution of adaptive immunity. In: Smith S, Sim B, Flajnik MF, editors. Chapter 2 in Immunobiology of the Shark, Taylor and Francis Group, LLC. 2014. p. 24-50.Google Scholar
  5. Venkatesh B, Lee AP, Ravi V, Maurya AK, Lian MM, Swann JB, et al. Elephant shark genome provides unique insights into gnathostome evolution. Nature. 2014;505:174–9.PubMedPubMed CentralView ArticleGoogle Scholar
  6. King BL, Gillis JA, Carlisle HR, Dahn RD. A Natural Deletion of the HoxC Cluster in Elasmobranch Fishes. Science. 2011;334:1517.PubMedPubMed CentralView ArticleGoogle Scholar
  7. Luckheeram R, Zhou R, Verma A, Xia B. CD4+T cells: differentiation and functions. Clin Dev Immunol. 2012;2012:1–12.View ArticleGoogle Scholar
  8. Crotty S. Follicular helper CD4 T cells (TFH). Annu Rev Immunol. 2011;29:621–63.PubMedView ArticleGoogle Scholar
  9. Kaplan MH, Hufford MM, Olson MR. The development and in vivo function of T helper 9 cells. Nat Rev Immunol. 2015;15:295–307.PubMedPubMed CentralView ArticleGoogle Scholar
  10. Gaffen SL, Jain R, Garg AV, Cua DJ. IL-23-IL-17 immune axis: Discovery, Mechanistic Understanding, and Clinical Testing. Nat. Rev. Immunol. 2014;14:585–600.PubMedPubMed CentralView ArticleGoogle Scholar
  11. Berger A. Th1 and Th2 responses: what are they? BMJ. 2000;321:424.PubMedPubMed CentralView ArticleGoogle Scholar
  12. Omenetti S, Pizarro TT. The Treg/Th17 axis: a dynamic balance regulated by the gut microbiome. Front. Immunol. 2015;6:–639.Google Scholar
  13. Zeng H, Zhang R, Jin B, Chen L. Type 1 regulatory T cells: a new mechanism of peripheral immune tolerance. Cell Mol Immunol. 2015;12:566–71.PubMedPubMed CentralView ArticleGoogle Scholar
  14. Schmitt E, Klein M, Bopp T. Th9 cells, new players in adaptive immunity. Trends Immunol. 2014;35:61–8.PubMedView ArticleGoogle Scholar
  15. Annunziato F, Cosmi L, Liotta F, Maggi E, Romagnani S. Human Th17 cells: are they different from murine Th17 cells? Eur J Immunol. 2009;39:637–40.PubMedView ArticleGoogle Scholar
  16. Pot C, Apetoh L, Kuchroo VK. Type 1 regulatory T cells (Tr1) in autoimmunity. Semin. Immunol. 2011, 23:202–8.PubMedPubMed CentralView ArticleGoogle Scholar
  17. Corthay A. How do regulatory t cells work? Scand. J. Immunol. 2009;70:326–36.PubMedPubMed CentralView ArticleGoogle Scholar
  18. Dijkstra JM. T H 2 and T reg candidate genes in elephant shark. Nature. 2014;511:E7–9.PubMedView ArticleGoogle Scholar
  19. Venkatesh B, Lee AP, Swann JB, Ohta Y, Flajnik MF, Kasahara M, et al. Venkatesh et al reply. Nature. 2014;511:E9–10.PubMedView ArticleGoogle Scholar
  20. Dooley H, Stanfield RL, Brady RA, Flajnik MF. First molecular and biochemical analysis of in vivo affinity maturation in an ectothermic vertebrate. Proc Natl Acad Sci U S A. 2006;103:1846–51.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Dooley H, Flajnik MF. Shark immunity bites back: affinity maturation and memory response in the nurse shark, Ginglymostoma cirratum. Eur J Immunol. 2005;35:936–45.PubMedView ArticleGoogle Scholar
  22. Wyffels J, King BL, Vincent J, Chen C, Wu CH, Polson SW. SkateBase, an elasmobranch genome project and collection of molecular resources for chondrichthyan fishes. F1000Research. 2014;3:191.PubMedPubMed CentralGoogle Scholar
  23. Wang Q, Arighi CN, King BL, Polson SW, Vincent J, Chen C, et al. Community annotation and bioinformatics workforce development in concert-little skate genome annotation workshops and jamborees. Database. 2012;2012.Google Scholar
  24. Read TD, Petit RA III, Joseph SJ, Alam MT, Weil MR, Ahmad M, et al. Draft sequencing and assembly of the genome of the world’s largest fish, the whale shark: Rhincodon typus smith 1828. BMC Genomics. 2017;18:1–10.View ArticleGoogle Scholar
  25. Stingo V, Rocco L. Selachian cytogenetics: a review. Genetica. 2001;111:329–47.PubMedView ArticleGoogle Scholar
  26. Luo M, Kim H, Kudrna D, Sisneros NB, Lee S-J, Mueller C, et al. Construction of a nurse shark (Ginglymostoma cirratum) bacterial artificial chromosome (BAC) library and a preliminary genome survey. BMC Genomics. 2006;7:106.PubMedPubMed CentralView ArticleGoogle Scholar
  27. Goshima M, Sekiguchi R, Matsushita M, Nonaka M. The complement system of elasmobranches revealed by liver transcriptome analysis of a hammerhead shark, Sphyrna zygaena. Dev Comp Immunol. 2016;61:13–24.PubMedView ArticleGoogle Scholar
  28. Richards VP, Suzuki H, Stanhope MJ, Shivji MS. Characterization of the heart transcriptome of the white shark (Carcharodon carcharias). BMC Genomics. 2013;14:697.PubMedPubMed CentralView ArticleGoogle Scholar
  29. Gopalan TK, Gururaj P, Gupta R, Gopal DR, Rajesh P, Chidambaram B, et al. Transcriptome profiling reveals higher vertebrate orthologous of intra-cytoplasmic pattern recognition receptors in grey bamboo shark. PLoS One. 2014;9:e100018.View ArticleGoogle Scholar
  30. Mulley JF, Hargreaves AD, Hegarty MJ, Heller RS, Swain MT. Transcriptomic analysis of the lesser spotted catshark (Scyliorhinus canicula) pancreas, liver and brain reveals molecular level conservation of vertebrate pancreas function. BMC Genomics. 2014;15:1074.PubMedPubMed CentralView ArticleGoogle Scholar
  31. Marra NJ, Richards VP, Early A, Bogdanowicz SM, Pavinski Bitar PD, Stanhope MJ, et al. Comparative transcriptomics of elasmobranchs and teleosts highlight important processes in adaptive immunity and regional endothermy. BMC Genomics. 2017;18:87.PubMedPubMed CentralView ArticleGoogle Scholar
  32. Swift DG, Dunning LT, Igea J, Brooks EJ, Jones CS, Noble LR, et al. Evidence of positive selection associated with placental loss in tiger sharks. BMC Evol Biol. 2016;16:126.PubMedPubMed CentralView ArticleGoogle Scholar
  33. De Oliveira Júnior NG, Fernandes Gda R, Cardoso MH, Costa FF, Cândido Ede S, Neto DG, et al. Venom gland transcriptome analyses of two freshwater stingrays (Myliobatiformes: Potamotrygonidae) from Brazil. Sci. Rep. 2016;6:21935.PubMedView ArticleGoogle Scholar
  34. Nakamura T, Klomp J, Pieretti J, Schneider I, Gehrke AR, Shubin NH. Molecular mechanisms underlying the exceptional adaptations of batoid fins. Proc Natl Acad Sci. 2015;112:15940–5.PubMedView ArticleGoogle Scholar
  35. Lighten J, Incarnato D, Ward BJ, van Oosterhout C, Bradbury I, Hanson M, et al. Adaptive phenotypic response to climate enabled by epigenetics in a K-strategy species, the fish Leucoraja ocellata (Rajidae). R Soc Open Sci. 2016;3:160299.PubMedPubMed CentralView ArticleGoogle Scholar
  36. Machado AM, Almeida T, Mucientes G, Esteves PJ, Verissimo A, Castro LFC. De novo assembly of the kidney and spleen transcriptomes of the cosmopolitan blue shark, Prionace glauca. Mar Genomics. 2018;37:50–3.View ArticleGoogle Scholar
  37. Dehal P, Boore JL. Two rounds of whole genome duplication in the ancestral vertebrate. PLoS Biol. 2005;3:e314.PubMedPubMed CentralView ArticleGoogle Scholar
  38. Smith JJ, Kuraku S, Holt C, Sauka-Spengler T, Jiang N, Campbell MS, et al. Sequencing of the sea lamprey (Petromyzon marinus) genome provides insights into vertebrate evolution. Nat Genet. 2013;45:415–21 421-2.PubMedPubMed CentralView ArticleGoogle Scholar
  39. McLysaght A, Hokamp K, Wolfe KH. Extensive genomic duplication during early chordate evolution. Nat Genet. 2002;31:200–4.PubMedView ArticleGoogle Scholar
  40. Smith JJ, Keinath MC. The sea lamprey meiotic map improves resolution of ancient vertebrate genome duplications. Genome Res. 2015;25:1081–90.PubMedPubMed CentralView ArticleGoogle Scholar
  41. Okada K, Asai K. Expansion of signaling genes for adaptive immune system evolution in early vertebrates. BMC Genomics. 2008;9:218.PubMedPubMed CentralView ArticleGoogle Scholar
  42. Liongue C, O’Sullivan LA, Trengove MC, Ward AC. Evolution of JAK-STAT pathway components: mechanisms and role in immune system development. PLoS One. 2012;7:e32777.PubMedPubMed CentralView ArticleGoogle Scholar
  43. Pisani D, Pett W, Dohrmann M, Feuda R, Rota-Stabelli O, Philippe H, et al. Genomic data do not support comb jellies as the sister group to all other animals. Proc Natl Acad Sci. 2015;112:201518127.View ArticleGoogle Scholar
  44. Lartillot N, Brinkmann H, Philippe H. Suppression of long-branch attraction artefacts in the animal phylogeny using a site-heterogeneous model. BMC Evol. Biol. 2007;7(Suppl 1):S4.PubMedPubMed CentralView ArticleGoogle Scholar
  45. Feuda R, Rota-Stabelli O, Oakley TH, Pisani D. The comb jelly opsins and the origins of animal phototransduction. Genome Biol. Evol. 2014;6:1964–71.PubMedPubMed CentralView ArticleGoogle Scholar
  46. Lartillot N, Philippe H. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol Biol Evol. 2004;21:1095–109.PubMedView ArticleGoogle Scholar
  47. Le SQ, Gascuel O, Lartillot N. Empirical profile mixture models for phylogenetic reconstruction. Bioinformatics. 2008;24:2317–23.View ArticleGoogle Scholar
  48. Le SQ, Lartillot N, Gascuel O. Phylogenetic mixture models for proteins. 2008;Philos. Trans. R. Soc. Lond. B. Biol. Sci, 363:3965–76.View ArticleGoogle Scholar
  49. Coolen M, Menuet A, Chassoux D, Compagnucci C, Henry S, Lévèque L, et al. The dogfish Scyliorhinus canicula: a reference in jawed vertebrates. Cold Spring Harb Protoc. 2008;3.Google Scholar
  50. Honma Y, Okabe K, Chiba A. Comparative histology of the Leydig and Epigonal organs in SOme elasmobranchs. Japanese J Ichthyol. 1984;31:47–54.Google Scholar
  51. Andrews S. FastQC: A quality control tool for high throughput sequence data. available from http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. 2010;1.
  52. Aronesty E. Comparison of sequencing utility programs. Open Bioinforma J. 2013;7:1–8.View ArticleGoogle Scholar
  53. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.PubMedPubMed CentralView ArticleGoogle Scholar
  54. Bradnam KR, Fass JN, Alexandrov A, Baranay P, Bechner M, Birol I, et al. Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species. Gigascience. 2013;2:10.PubMedPubMed CentralView ArticleGoogle Scholar
  55. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2.PubMedPubMed CentralView ArticleGoogle Scholar
  56. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.View ArticleGoogle Scholar
  57. Chana-Munoz A, Jendroszek A, Sønnichsen M, Kristiansen R, Jensen JK, Andreasen PA, et al. Multi-tissue RNA-seq and transcriptome characterisation of the spiny dogfish shark (Squalus acanthias) provides a molecular tool for biological research and reveals new genes involved in osmoregulation. PLoS One. 2017;12:e0182756.PubMedPubMed CentralView ArticleGoogle Scholar
  58. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.PubMedView ArticleGoogle Scholar
  59. Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14:755–63.PubMedView ArticleGoogle Scholar
  60. Costa MM, Wang T, Monte MM, Secombes CJ. Molecular characterization and expression analysis of the putative interleukin 6 receptor (IL-6R?? And glycoprotein-130) in rainbow trout (Oncorhynchus mykiss): salmonid IL-6R?? Possesses a polymorphic N-terminal Ig domain with variable numbers of two rep. Immunogenetics. 2012;64:229–44.PubMedView ArticleGoogle Scholar
  61. Liongue C, Ward AC. Evolution of class I cytokine receptors. BMC Evol Biol. 2007;7:120.PubMedPubMed CentralView ArticleGoogle Scholar
  62. Rose-John S. Interleukin-6 family cytokines. Cold Spring Harb. Perspect. Biol. 2017;10.View ArticleGoogle Scholar
  63. Trinchieri G, Pflanz S, Kastelein RA. The IL-12 family of heterodimeric cytokines: New players in the regulation of T cell responses. Immunity. 2003;19:641–4.PubMedView ArticleGoogle Scholar
  64. Vignali DAA, Kuchroo VK. IL-12 family cytokines: immunological playmakers. Nat. Immunol. 2012;13:722–8.PubMedPubMed CentralView ArticleGoogle Scholar
  65. Boulay JL, Paul WE. Hematopoietin sub-family classification based on size, gene organization and sequence homology. Curr. Biol. 1993;3:573–81.PubMedView ArticleGoogle Scholar
  66. Kono T, Bird S, Sonoda K, Savan R, Secombes CJ, Sakai M. Characterization and expression analysis of an interleukin-7 homologue in the Japanese pufferfish, Takifugu rubripes. FEBS J. 2008;275:1213–26.PubMedView ArticleGoogle Scholar
  67. Bertrand S, Brunet FG, Escriva H, Parmentier G, Laudet V, Robinson-Rechavi M. Evolutionary genomics of nuclear receptors: from twenty-five ancestral genes to derived endocrine systems. Mol Biol Evol. 2004;21:1923–37.PubMedView ArticleGoogle Scholar
  68. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.PubMedPubMed CentralView ArticleGoogle Scholar
  69. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.PubMedPubMed CentralView ArticleGoogle Scholar
  70. Nguyen LT, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.PubMedView ArticleGoogle Scholar
  71. Minh BQ, Nguyen MAT, Von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30:1188–95.PubMedPubMed CentralView ArticleGoogle Scholar
  72. Drummond AJ, Suchard MA, Xie D, Rambaut A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 2012;29:1969–73.PubMedPubMed CentralView ArticleGoogle Scholar
  73. Lartillot N, Lepage T, Blanquart S. PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating. Bioinformatics. 2009;25:2286–8.PubMedView ArticleGoogle Scholar
  74. Campbell LI, Rota-Stabelli O, Edgecombe GD, Marchioro T, Longhorn SJ, Telford MJ, et al. MicroRNAs and phylogenomics resolve the relationships of Tardigrada and suggest that velvet worms are the sister group of Arthropoda. Proc Natl Acad Sci U S A. 2011;108:15920–4.PubMedPubMed CentralView ArticleGoogle Scholar
  75. Morgan CC, Foster PG, Webb AE, Pisani D, McInerney JO, O’Connell MJ. Heterogeneous models place the root of the placental mammal phylogeny. Mol Biol Evol. 2013;30:2145–56.PubMedPubMed CentralView ArticleGoogle Scholar
  76. Philippe H, Brinkmann H, Copley RR, Moroz LL, Nakano H, Poustka AJ, et al. Acoelomorph flatworms are deuterostomes related to Xenoturbella. Nature. 2011;470:255–8.PubMedPubMed CentralView ArticleGoogle Scholar
  77. Wang HC, Susko E, Roger AJ. An amino acid substitution-selection model adjusts residue fitness to improve phylogenetic estimation. Mol Biol Evol. 2014;31:779–92.PubMedView ArticleGoogle Scholar
  78. Hering L, Mayer G. Analysis of the opsin repertoire in the tardigrade Hypsibius dujardini provides insights into the evolution of opsin genes in Panarthropoda. Genome Biol Evol. 2014;6:2380–91.PubMedPubMed CentralView ArticleGoogle Scholar
  79. Domman D, Horn M, Embley TM, Williams TA. Plastid establishment did not require a chlamydial partner. Nat Commun. 2015;6.Google Scholar
  80. Li R, Redmond AK, Wang T, Bird S, Dooley H, Secombes CJ. Characterisation of the TNF superfamily members CD40L and BAFF in the small-spotted catshark (Scyliorhinus canicula). Fish Shellfish Immunol. 2015;47:381–9.PubMedView ArticleGoogle Scholar
  81. Mukherjee K, Korithoski B, Kolaczkowski B. Ancient origins of vertebrate-specific innate antiviral immunity. Mol Biol Evol. 2014;31:140–53.PubMedView ArticleGoogle Scholar
  82. Redmond AK, Pettinello R, Dooley H. Outgroup, alignment and modelling improvements indicate that two TNFSF13-like genes existed in the vertebrate ancestor. Immunogenetics. 2017;69:187–92.PubMedPubMed CentralView ArticleGoogle Scholar
  83. Zou J, Redmond AK, Qi Z, Dooley H, Secombes CJ. The CXC chemokine receptors of fish: Insights into CXCR evolution in the vertebrates. Gen. Comp. Endocrinol. 2015;215:117–31.PubMedView ArticleGoogle Scholar
  84. Pettinello R, Redmond AK, Secombes CJ, Macqueen DJ, Dooley H. Evolutionary history of the T cell receptor complex as revealed by small-spotted catshark (Scyliorhinus canicula). Dev Comp Immunol. 2017;74:125–35.PubMedView ArticleGoogle Scholar
  85. Redmond AK, Ohta Y, Criscitiello MF, Macqueen DJ, Flajnik MF, Dooley H. Haptoglobin Is a Divergent MASP Family Member That Neofunctionalized To Recycle Hemoglobin via CD163 in Mammals. J Immunol. 2018;2483-91.PubMedView ArticleGoogle Scholar
  86. Macqueen DJ, Wilcox AH. Characterization of the definitive classical calpain family of vertebrates using phylogenetic, evolutionary and expression analyses. Open Biol. 2014;4:130219.PubMedPubMed CentralView ArticleGoogle Scholar
  87. Drummond AJ, Ho SYW, Phillips MJ, Rambaut A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 2006;4:699–710.View ArticleGoogle Scholar
  88. Yule U. A mathematical theory of evolution, based on the conclusions of II. - a mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S. Source Philos. Trans. R. Soc. London. Ser. B. 1925;213:21–87.Google Scholar
  89. Gernhard T. The conditioned reconstructed process. J Theor Biol. 2008;253:769–78.PubMedView ArticleGoogle Scholar
  90. Calvignac-Spencer S, Schulze JM, Zickmann F, Renard BY. Clock rooting further demonstrates that Guinea 2014 EBOV is a member of the Zaïre lineage. PLoS Curr. 2014;6:1–9.Google Scholar
  91. Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, et al. A tale of three next generation sequencing platforms: comparison of ion torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics. 2012;13:341.PubMedPubMed CentralView ArticleGoogle Scholar
  92. Loman NJ, Misra RV, Dallman TJ, Constantinidou C, Gharbia SE, Wain J, et al. Performance comparison of benchtop high-throughput sequencing platforms. Nat. Biotechnol. 2012;30:434–9.Google Scholar
  93. Xu Q, Modrek B, Lee C. Genome-wide detection of tissue-specific alternative splicing in the human transcriptome. Nucleic Acids Res. 2002;30:3754–66.PubMedPubMed CentralView ArticleGoogle Scholar
  94. Dijkstra JM, Takizawa F, Fischer U, Friedrich M, Soto-Lampe V, Lefèvre C, et al. Identification of a gene for an ancient cytokine, interleukin 15-like, in mammals; interleukins 2 and 15 co-evolved with this third family member, all sharing binding motifs for IL-15R. Immunogenetics. 2014;66:93–103.PubMedView ArticleGoogle Scholar
  95. Allen JE, Sutherland TE. Host protective roles of type 2 immunity: Parasite killing and tissue repair, flip sides of the same coin. Semin. Immunol. 2014;26:329–40.PubMedPubMed CentralView ArticleGoogle Scholar
  96. Wang T, Husain M. The expanding repertoire of the IL-12 cytokine family in teleost fish: identification of three paralogues each of the p35 and p40 genes in salmonids, and comparative analysis of their expression and modulation in Atlantic salmon Salmo salar. Dev Comp Immunol. 2014;46:194–207.PubMedView ArticleGoogle Scholar
  97. Wang T, Husain M, Hong S, Holland JW. Differential expression, modulation and bioactivity of distinct fish IL-12 isoforms: implication towards the evolution of Th1-like immune responses. Eur J Immunol. 2014;44:1541–51.PubMedView ArticleGoogle Scholar
  98. Silver JS, Hunter CA. gp130 at the nexus of inflammation, autoimmunity, and cancer. J Leukoc Biol. 2010;88:1145–56.PubMedPubMed CentralView ArticleGoogle Scholar
  99. Chen D, Sun Y, Wei Y, Zhang P, Rezaeian AH, Teruya-Feldstein J, et al. LIFR is a breast cancer metastasis suppressor upstream of the hippo-YAP pathway and a prognostic marker. Nat Med. 2012;18:1511–7.PubMedPubMed CentralView ArticleGoogle Scholar
  100. Unutmas D. RORC2: the master of human Th17 cell programming. Eur J Immunol. 2009;39:1452–5.View ArticleGoogle Scholar
  101. Zheng Y, Rudensky AY. Foxp3 in control of the regulatory T cell lineage. Nat. Immunol. 2007;8:457–62.PubMedView ArticleGoogle Scholar
  102. Mcinerney JO, O’Connell MJ. Evolutionary developmental biology: ghost locus appears. Nature. 2014;514:570–1.PubMedView ArticleGoogle Scholar
  103. Fernandez-Valverde SL, Calcino AD, Degnan BM. Deep developmental transcriptome sequencing uncovers numerous new genes and enhances gene annotation in the sponge Amphimedon queenslandica. BMC Genomics. 2015;16:387.PubMedPubMed CentralView ArticleGoogle Scholar
  104. Fortunato SA, Adamski M, Ramos OM, Leininger S, Liu J, DEK F, et al. Calcisponges have a ParaHox gene and dynamic expression of dispersed NK homeobox genes. Nature. 2014;514:620–3.PubMedView ArticleGoogle Scholar
  105. Holland PWH, Marlétaz F, Maeso I, Dunwell TL, Paps J. New genes from old: asymmetric divergence of gene duplicates and the evolution of development. Philos Trans R Soc B Biol Sci. 2017;372.Google Scholar
  106. Manousaki T, Feiner N, Begemann G, Meyer A, Kuraku S. Co-orthology of Pax4 and Pax6 to the fly eyeless gene: molecular phylogenetic, comparative genomic, and embryological analyses. Evol Dev. 2011;13:448–59.PubMedView ArticleGoogle Scholar
  107. Robertson FM, Gundappa MK, Grammes F, Hvidsten TR, Redmond AK, SAM M, et al. Lineage-specific rediploidization is a mechanism to explain time-lags between genome duplication and evolutionary diversification. Genome Biol. 2017;18:1–14.View ArticleGoogle Scholar
  108. Gouy R, Baurain D, Philippe H. Rooting the tree of life: the phylogenetic jury is still out. Philos Trans R Soc B Biol Sci. 2015;370:20140329.View ArticleGoogle Scholar
  109. Wen Y, Fang W, Xiang LX, Pan RL, Shao JZ. Identification of Treg-like cells in tetraodon: insight into the origin of regulatory T subsets during early vertebrate evolution. Cell Mol Life Sci. 2011;68:2615–26.PubMedView ArticleGoogle Scholar
  110. Bandukwala HS, Wu Y, Feuerer M, Chen Y, Barboza B, Ghosh S, et al. Structure of a domain-swapped FOXP3 dimer on DNA and its function in regulatory T cells. Immunity. 2011;34:479–91.PubMedPubMed CentralView ArticleGoogle Scholar

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