PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
 
Int J Parasitol. 2017 March; 47(4): 189–202.
PMCID: PMC5354109

The genome of the protozoan parasite Cystoisospora suis and a reverse vaccinology approach to identify vaccine candidates[star]

Graphical abstract

An external file that holds a picture, illustration, etc.
Object name is fx1.jpg
Keywords: Apicomplexa, Coccidia, Vacceed, Transcriptome, Illumina sequencing, Antigens

Abstract

Vaccine development targeting protozoan parasites remains challenging, partly due to the complex interactions between these eukaryotes and the host immune system. Reverse vaccinology is a promising approach for direct screening of genome sequence assemblies for new vaccine candidate proteins. Here, we applied this paradigm to Cystoisospora suis, an apicomplexan parasite that causes enteritis and diarrhea in suckling piglets and economic losses in pig production worldwide. Using Next Generation Sequencing we produced an ~84 Mb sequence assembly for the C. suis genome, making it the first available reference for the genus Cystoisospora. Then, we derived a manually curated annotation of more than 11,000 protein-coding genes and applied the tool Vacceed to identify 1,168 vaccine candidates by screening the predicted C. suis proteome. To refine the set of candidates, we looked at proteins that are highly expressed in merozoites and specific to apicomplexans. The stringent set of candidates included 220 proteins, among which were 152 proteins with unknown function, 17 surface antigens of the SAG and SRS gene families, 12 proteins of the apicomplexan-specific secretory organelles including AMA1, MIC6, MIC13, ROP6, ROP12, ROP27, ROP32 and three proteins related to cell adhesion. Finally, we demonstrated in vitro the immunogenic potential of a C. suis-specific 42 kDa transmembrane protein, which might constitute an attractive candidate for further testing.

1. Introduction

Cystoisospora suis (syn. Isospora suis) is a protozoan parasite of the phylum Apicomplexa (subclass Coccidia, order Eucoccidiorida, family Sarcocystidae). This phylum contains almost exclusively obligate endoparasites of animals, including species of great medical and veterinary relevance such as Plasmodium falciparum and Toxoplasma gondii. According to recent reevaluations of the coccidian phylogeny (Ogedengbe et al., 2015, Samarasinghe et al., 2008), the position of C. suis in the family Sarcocystidae constitutes an outgroup of the cluster containing the genera Neospora, Hammondia and Toxoplasma. The closest outgroup genus of C. suis in the family Sarcocystidae is Sarcocystis, while the closest outgroup family of Sarcocystidae is Eimeridae, which contains the genus Eimeria.

Cystoisospora suis is responsible for neonatal porcine coccidiosis, a diarrheal disease of suckling piglets that causes significant economic losses in swine production worldwide (Lindsay et al., 1992, Meyer et al., 1999, Mundt et al., 2005, Aliaga-Leyton et al., 2011, Shrestha et al., 2015) (Torres, A., 2004. Prevalence survey of Isospora suis in twelve European countries. Proceedings of the 18th IPVS Congress, Hamburg, Germany, p. 243). The disease is commonly controlled with the triazinone toltrazuril, but drug costs and pressure to reduce the use of drugs in livestock production are increasing. Furthermore, resistance to toltrazuril has been described among parasites of the genus Eimeria (Stephen et al., 1997) and must be considered likely for C. suis. To date, no other drugs have been shown to be effective if administered in a way that is compatible with field use (Joachim and Mundt, 2011).

Alternative options for the control of this pathogen include vaccination. Immunological control measures (active or passive vaccination) commonly provide longer lasting protection than chemotherapeutic interventions and do not leave chemical residues in the host or the environment. Vaccine development for apicomplexan parasites has been hindered in part by their relatively complex life cycles and lack of in vitro and in vivo models for screening. Moreover, many apicomplexans have proved capable of evading immune killing by targeting immunoprivileged sites or through extensive antigenic diversity (Stanisic et al., 2013). Vaccination against some apicomplexan parasites such as the Eimeria spp. that infect chickens has been possible using formulations of live unmodified or attenuated parasites, but vaccine production requires passage and amplification in live animals with implications for cost, biosafety and animal welfare (Blake et al., 2015). For other protozoan parasites, success has been harder to achieve. However, progress towards antigen identification that could lead to development of recombinant or vectored vaccines has improved for several coccidian parasites in recent years (McAllister, 2014).

To date, no vaccine has been developed against C. suis although previous studies have shown that cellular and humoral immune responses are induced upon infection (Taylor, J., 1984. Immune Response of Pigs to Isospora suis (Apicomplexa, Eimeriidae), PhD Thesis, Auburn University, USA; Worliczek et al., 2010a, Schwarz et al., 2013, Gabner et al., 2014) and that superinfection of sows ante partum with high doses of oocysts can confer partial maternal protection (Schwarz et al., 2014). Since the use of live, virulent vaccines in large amounts is not practical and attenuated lines are not currently available for C. suis, a systematic search for proteins with antigenic properties is required to find appropriate vaccine candidates for testing and antigenic characterisation. A key step towards the identification of appropriate antigens for many apicomplexans has been the availability of genomic data, urging the development of a C. suis genome sequence assembly.

The approach of finding vaccine candidates using a genome sequence has been termed “reverse vaccinology”. This strategy has become a powerful way to identify proteins that can elicit an antigenic response with relevance to host/pathogen interaction. Reverse vaccinology is based on in silico screening of protein sequences to search for motifs and structural features responsible for inducing an immune response. Examples include transmembrane domains, signal peptides for excretion or surface membrane targeting and binding sites for Major Histocompatibility Complex (MHC) proteins. While this method has been successfully applied in bacterial pathogens (i.e. Pizza et al., 2000), only a few studies have been performed on eukaryotic pathogens. Examples include the apicomplexan species T. gondii (Goodswen et al., 2013, Goodswen et al., 2014b) and Neospora caninum (Goodswen et al., 2014a), as well as helminths such as Schistosoma (de Oliveira Lopes et al., 2013). Recently, the program Vacceed (Goodswen et al., 2014c) was developed, providing a high-throughput pipeline specifically designed to identify vaccine candidates in eukaryotic pathogens. This program was tested on the coccidian T. gondii, where it showed an accuracy of 97% in identifying proteins that corresponded to previously validated vaccine candidates (Goodswen et al., 2013).

In this work, we applied the reverse vaccinology paradigm to identify potential vaccine candidates in C. suis. To accomplish this, we used Illumina Next Generation Sequencing technology to sequence the C. suis genome and annotate protein-coding genes by combining ab initio and orthology predictions with gene models derived from a C. suis merozoite RNA-Seq library. Additionally, the annotation was manually curated at single gene resolution, greatly enhancing the quality of the gene models. Vacceed was then applied to perform a genome-wide screen for potential immunogenic proteins and identified 1,168 proteins with a high immunogenicity score. Finally, we validated the immunogenic potential of a C. suis-specific 42 kDa transmembrane protein (CSUI_005805) by performing an independent immunoblot analysis using positive polyclonal sera from infected piglets. These results show how reverse vaccinology, combined with comparative genomics and transcriptomics, can be applied to a eukaryotic pathogen to guide the identification of novel vaccine candidates as a starting point to develop a vaccine against C. suis. Moreover, the C. suis genome represents the first genomic sequence available for a member of the Cystoisospora group and it might serve as a reference for future studies involving Cystoisospora spp.

2. Materials and methods

2.1. Parasite material

For preparation of genomic DNA for sequencing, C. suis oocysts (strain Wien I) were isolated from experimentally infected piglets, left to sporulate in potassium bichromate and purified using a caesium chloride gradient (Worliczek et al., 2010b). DNA was extracted from 2.5 × 106 washed and pelleted sporulated oocysts using a Peqlab Microspin tissue DNA kit (Peqlab, Erlangen, Germany) following the manufacturer’s instructions. RNAse A digestion was performed on the DNA before final purification. Cystoisospora suis merozoites were maintained in IPEC-J2 cells as described earlier (Worliczek et al., 2013). Free merozoites (8 × 106) were harvested by collecting supernatant 6 days p.i. and purified on a Percoll® density gradient. Purified merozoites were then filtered through Partec CellTrics® disposable filters (50 μm), washed twice with PBS and pelleted by centrifugation at 1000g for 10 min. Total RNA was extracted from purified pelleted merozoites using a QIAamp RNA blood mini kit (Qiagen, Germany) according to the manufacturer’s instructions and was quantified using a spectrophotometer (NanoDrop 2000, Thermo Fisher Scientific, USA).

2.2. Genome assembly

A total of 80 million paired-end 100 bp reads were generated from an Illumina HiSeq 2000 platform (Source BioScience, UK) (reads were deposited at the Short Reads Archive – accession number SRS1672208). A genome sequence was assembled into contigs with CLC Genomics Workbench version 7.5 (CLC Bio-Qiagen, Aarhus, Denmark) using default parameters. In this study, we did not attempt to assemble chromosomes, as the main interest was in identifying proteins to screen for immunogenic features. To remove possible contaminants, we aligned the contigs against the non-redundant (nr) RefSeq protein database using BLASTN (E < 10−5) with default parameters and removed all contigs with a hit to non-apicomplexan organisms. Alignment of C. suis contigs to N. caninum and T. gondii genomes was performed with PROmer version 3.0.7 (Kurtz et al., 2004) (default parameters). Repetitive content was computed using RepeatMasker version 4.0.5 (parameters –species “toxoplasma apicomplexa”).

2.3. Genome annotation

Genes were annotated using Maker version 2.31.8 (Cantarel et al., 2008), which is a pipeline that combines different annotation tracks into a final set of gene models. Each annotation track was produced using the following programs:

2.3.1. Augustus

Augustus is an ab initio gene predictor that can be trained with an accurate set of gene models, if available. To construct the training set we started from the Cufflinks gene track (described in more detail in Section 2.3.4), as it was based on the most accurate evidence for transcription, namely RNA-Seq data. Transcript sequences from the Cufflinks track were given to ORFPredictor version 2.3 (Min et al., 2005) (parameters strand ‘+’) to predict the location of each Coding Sequence (CDS). Transcripts were then filtered according to the following criteria: (i) transcripts with incomplete CDS were removed; (ii) in the case of multiple isoforms, the isoform with the longest CDS was retained; (iii) genes without introns were removed; (iv) genes with at least one exon made entirely of untranslated regions (UTRs) were removed; (v) genes separated by at least 500 bp from the previous or next gene were retained; (vi) genes with containing ambiguous nucleotides (Ns) in the upstream or downstream 500 bp flanking regions were removed. The resulting gene set was used to train Augustus version 3.01 (Stanke et al., 2006) to generate a new species model that was provided as a species parameter for Augustus (default parameters) to predict gene locations on the contigs and generate the final track.

2.3.2. Snap

Snap is an ab initio gene predictor. We used the same training set constructed for Augustus to train Snap version 2006-07-28 (Korf, 2004) and generate the parameters file that was provided to Snap (default parameters) together with the contigs to produce the gene track.

2.3.3. Exonerate

Exonerate is a homology based gene predictor which generates gene models based on the assumption that gene sequence and structure is conserved among closely related species. To create this gene track, protein sequences from Eimeria tenella, N. caninum, Hammondia hammondi and T. gondii were downloaded from ToxoDB (Gajria et al., 2008) release 24 and aligned to the contigs with Exonerate version 2.2.0 (parameters -m protein2genome –softmasktarget –percent 20 –showtargetgff –bestn 1).

2.3.4. Cufflinks

Cufflinks is an evidence-based gene predictor which constructs gene models from RNA-Seq data. Total RNA was extracted from merozoites harvested from an in vitro culture (Worliczek et al., 2013) 6 days p.i. and sent for sequencing to GATC Biotech AG (Constance, Germany) using an Illumina sequencer HiSeq 2500 to generate paired-end reads of 100 bp (reads were deposited at the Short Reads Archive – accession number SRS1683974). A combined reference including the pig and C. suis genome was created, as the raw reads were likely to contain residual pig RNAs from the cell culture from which the merozoites were extracted. The Sus scrofa genome version 10.2 was downloaded from Ensembl (www.ensembl.org) and concatenated to the C. suis contigs. Reads were mapped to this combined reference with TopHat version 2.1.0 (Kim et al., 2013) (parameters –library-type fr-firststrand) (see Supplementary Table S1 for RNA-Seq mapping statistics). Reads mapped to the pig genome were filtered out and the resulting BAM file was provided to Cufflinks version 2.2.1 (Trapnell et al., 2010) (parameters –library-type fr-firststrand) to reconstruct gene models. Cufflinks additionally computes the expression level of each gene using the standard FPKM measure (Fragments per Kilobase of Exon per Million reads mapped), which normalises the number of reads mapped to a gene by gene length and the total number of reads in the dataset (for more details see Mortazavi et al. (2008)). Transcripts with low expression (FPKM < 1) were removed and the output was converted to GFF3 format using the cufflinks2gff3 script from the Maker pipeline. Additionally, junctions derived from TopHat were converted to GFF3 with the tophat2gff3 script from Maker and added to the Cufflinks GFF3 to get the final track.

After generating the four gene tracks, Maker was run to generate the uncurated Maker track with the following parameters that were provided in the configuration file:

  • genome = FASTA with contigs
  • augustus_species = species name (cystoisospora) from the Augustus training
  • snaphmm = HMM parameter file from the Snap training
  • protein = FASTA with proteins from E. tenella, N. caninum, H. hammondi and T. gondii
  • Furthermore, these additional parameters were specified: (est2genome = 1, protein2genome = 1, pred_flank = 500, alt_splice = 0, single_exon = 1, correct_est_fusion = 1).

2.4. Manual curation

The BAM file from TopHat, the four gene tracks (Augustus, Snap, Exonerate, Cufflinks) and the uncurated Maker gene track were loaded into the Integrative Genomics Viewer (IGV) (Thorvaldsdottir et al., 2013). Each gene was independently curated in the following way: in the case of incongruences among tracks, priority was given to the Cufflinks evidence, followed by Exonerate, Augustus and Snap. We decided to prioritise Augustus over Snap, as Snap produced a high number of very short terminal exons, which we cautiously regarded as unreliable. Gene models corresponding to lowly expressed genes were resolved only in some cases, as their exon–intron structure was often very fragmented. The genome and annotation of C. suis are available in the National Center for Biotechnology Information, (NCBI, USA) database under the accession number PRJNA341953.

2.5. Evaluation of annotation quality

To assess the quality of the annotation, cuffcompare (Trapnell et al., 2010) was run to compute the fraction of final gene models confirmed by each type of evidence (Cufflinks, Exonerate, Augustus, Snap). To evaluate the completeness of the annotation, the core eukaryotic proteins from Parra et al. (2007) were downloaded from http://korflab.ucdavis.edu/datasets/cegma/core/core.fa and aligned to the contigs with BLASTP (E < 10−5, query coverage [gt-or-equal, slanted] 30%), retaining only the best hit. Eukaryotic core proteins that did not align to contigs (unaligned proteins) were further analysed to check for their presence in the RNA-Seq dataset using the following steps: (i) RNA-Seq reads from merozoites were de novo assembled into transcripts using Oases (Schulz et al., 2012) with default parameters; (ii) unaligned proteins were aligned to the assembled transcripts with TBLASTN (E < 10−5).

2.6. Functional annotation

Functional annotation was performed with Blast2GO (Conesa et al., 2005) version 3.1.3 on the protein sequences using the following steps: (i) blastp-fast (E < 10−5) was run against the local version of the nr database downloaded on 01 December, 2015 and used to generate a first version of the Gene Ontology (GO) functional annotation; (ii) InterProScan (Jones et al., 2014) was run and results were merged to the initial GO annotation in order to extend it, (iii) ANNEX (Myhre et al., 2006) was run to further augment the GO annotation. The final functional annotation also allowed the identification of additional transposable elements that were not found by RepeatMasker.

2.7. Detection of vaccine candidates

Initially, 403 protein sequences containing unknown amino acids (‘X’) were filtered out. The software Vacceed (Goodswen et al., 2014c) was used to identify potential immunogenic proteins. Vacceed implements a machine learning approach that combines independent sources of evidence for immunogenic features computed by different tools: WoLf PSORT (Horton et al., 2007), SignalP (Petersen et al., 2011), TargetP (Emanuelsson et al., 2000), Phobius (Kall et al., 2007), TMHMM (Sonnhammer et al., 1998), MHC-I Binding and MHC-II Binding. The program finally assigns a score between 0 and 1 to each protein to rank the protein from low immunogenicity (score = 0) to high (score = 1). We excluded MHC-II Binding in our analyses as data about MHC-II allele binding affinity is unavailable for the pig. Moreover, prior to running Vacceed the tool MHC-I Binding was trained using known immunogenic proteins as input. Since no known immunogenic proteins were available for C. suis, we used T. gondii proteins from Goodswen et al. (2014c) to train MHC-I Binding for affinity against Swine Leukocyte Antigens (SLA) alleles, as T. gondii was the closest relative for which a dataset of immunogenic and non-immunogenic proteins was available. Moreover, as the computational burden of the MHC-I Binding predictor was very high, we divided the Vacceed analysis into two steps: (i) we ran Vacceed on all the protein sequences using each tool except MHC-I Binding and ranked the proteins according to score; (ii) we selected all proteins with a score > 0.75 and reran Vacceed on this subset using all the tools, including MHC-I Binding. Finally, from the score distribution obtained after step ii, we selected as candidates only proteins with a score [gt-or-equal, slanted] 0.998, corresponding to the top 25% of the score distribution.

2.8. Ortholog assignment for vaccine candidate genes

To assign orthologs of C. suis genes in the other coccidian species, protein sequences from E. tenella, Sarcocystis neurona, N. caninum, H. hammondi and T. gondii were downloaded from ToxoDB (Gajria et al., 2008) release 24 and clustered with the C. suis proteins using the Orthology MAtrix (OMA) software (Altenhoff et al., 2013) with parameters LengthTol = 0.30. For genes for which OMA did not detect an ortholog in any species, we manually screened for its presence in the ToxoDB database by looking at the section “Orthologs and Paralogs within ToxoDB” within the gene entry.

2.9. Two-dimensional (2D) gel electrophoresis

For 2D gel electrophoresis, 6 × 106 merozoites harvested from cell culture were purified and concentrated as described above and directly dissolved in DIGE buffer (7 M urea, 2 M thiuourea, 4% CHAPS, 1% (w/v) DTT, 20 mM Tris) and centrifuged again at 20,000g for 10 min at 4 °C. The protein concentration of each lysate was determined by Bradford assay (Bradford, 1976). For separation in the first dimension, an aliquot of 40 μg of protein was diluted in 300 μl of rehydration solution (8 M urea, 2% (w/v) CHAPS, 12.7 mM DTT, 2% immobilised pH gradient (IPG) buffer, 0.002% (w/v) bromophenol blue) and used to rehydrate 13 cm IPG strips with a non-linear gradient pH 3–10 (Immobiline, GE Healthcare Life Sciences, Uppsala, Sweden) for 18 h at room temperature. Isoelectric focusing (IEF) was carried out (300 V ascending to 3,500 V for 90 min, followed by 3,500 V for 18 h) using a Multiphor II electrophoresis chamber (GE Healthcare Life Sciences). After IEF, the IPG strips were equilibrated with 10 mg/ml of DTT in equilibration buffer (6 M urea, 30% glycerol, 2% (w/v) SDS, 0.002% (w/v) bromophenol blue, 1.5 M Tris–HCl) for 20 min and further incubated in the same buffer for another 20 min, replacing DTT with 25 mg/ml of iodoacetamide. The IPG strips were then washed with deionised water. In the second dimension, SDS–PAGE was performed using vertical slab gels (1.5 mm; T = 12%, C = 2.6%, 1.5 M Tris–HCl, 10% SDS) under reducing conditions at 15 mA for 15 min, followed by 25 mA in a Protean II electrophoresis chamber (Bio-Rad Laboratories, Hercules, USA). Each gel was stained with silver (Blum and Gross, 1987) and scanned using the program ImageMaster™ 2D platinum v.7.0.

2.10. Immunoblot analysis

Proteins separated by 2D gel electrophoresis were transferred onto a Trans-Blot® (Bio-Rad) nitrocellulose (NC) membrane (0.45 μm) for 150 min at 35 V, 150 mA and 6 W on a Nova Blot semi-dry transfer system (Pharmacia Biotech, USA). The membranes were dried overnight in the dark at room temperature. The next day, blots were blocked for 1 h using 2% (w/v) BSA. After three washes with TTBS buffer (100 mM Tris, 0.9% NaCl, 0.1% Tween 20), the blots were incubated with porcine anti-C. suis serum (Schwarz et al., 2013) (having an IFAT antibody titer [gt-or-equal, slanted] 10,000) diluted in TTBS buffer (1:300) under gentle agitation at room temperature for 30 min. After rinsing in TTBS for 15 min, blots were exposed to biotinylated goat anti-pig IgG (Vector laboratories, USA; 1:500 dilution) as secondary antibody for 30 min at room temperature, incubated with ABC solution (Vector Laboratories) and finally detected by 3,3′-5,5′-tetramethylbenzidine (TMB). Pre-colostral sera from non-infected piglets served as negative controls (Schwarz et al., 2013).

2.11. Mass spectrometry

The spot of interest from 2D gel was excised, washed, destained (Gharahdaghi et al., 1999), reduced with DTT and alkylated with iodoacetamide (Jimenez et al., 2001). In-gel digestion was performed with trypsin (Trypsin Gold, Mass Spectrometry Grade, Promega, Fitchburg, USA) according to Shevchenko et al. (1996) with a final trypsin concentration of 20 ng/μl in 50 mM aqueous ammonium bicarbonate and 5 mM calcium chloride. Dried peptides were reconstituted in 10 μl of 0.1% (v/v) trifluoroacetic acid (TFA). Nano-HPLC separation was performed on an Ultimate 3000 RSLC system (Dionex, Amsterdam, Netherlands). Sample pre-concentration and desalting were accomplished with a 5 mm Acclaim PepMap μ-precolumn (300 μm inner diameter, 5 μm particle size, and 100 Å pore size) (Dionex) with a flow rate of 5 μl/min using a loading solution (2% Acetonitrile (ACN) in 0.05% aqueous TFA). The separation was performed on a 25 cm Acclaim PepMap C18 column (75 μm inner diameter, 3 μm particle size, and 100 Å pore size) with a flow rate of 300 nl/min. The gradient started with 4% B (80% ACN with 0.1% formic acid) and increased to 35% B over 120 min. It was followed by a washing step with 90% B for 5 min. Mobile Phase A consisted of mQH2O with 0.1% formic acid. The injection volume was 1 μl partial loop injection mode. For mass spectrometric analysis, the LC was directly coupled to a high-resolution quadrupole time of flight mass spectrometer (Triple TOF 5600+, Sciex, USA) was used. For information-dependent data acquisition (IDA runs), MS1 spectra were collected in the range 400–1500 m/z. The 25 most intense precursors with charge state 2–4 which exceeded 100 counts per second were selected for fragmentation, and MS2 spectra were collected in the range 100–1800 m/z for 110 ms. The precursor ions were dynamically excluded from reselection for 12 s. The nano-HPLC system was regulated by Chromeleon 8.8 (Dionex) and the MS by Analyst Software 1.7 (Sciex). Processed spectra were searched via the software Protein Pilot (Sciex) against T. gondii extracted from the UniProt_TREMBL database as well as against our C. suis protein database using the following search parameters: Global modification: Cysteine Alkylation with Iodacetamide, Search effort: rapid, FDR analysis: Yes. Proteins with more than two matching peptides at > 95% confidence were selected.

3. Results

3.1. Genome assembly

We generated a total of 14,776 contigs from 80 M paired-end Illumina reads (see Supplementary Table S2 for mapping statistics). The assembly had an N50 of 29,979 bp, with minimum and maximum contig lengths of 89 bp and 285,055 bp. (N50 is the largest length L such that 50% of all nucleotides are contained in contigs of a size that is at least L.) A graphical distribution of contig lengths is shown in Fig.1A. To exclude bacterial and other contaminations, we aligned the contigs to the nr database and retained 14,630 contigs without matches to other organisms outside the Apicomplexa and covering a length of 83.6 Mb. We used this set of contigs for the remainder of the analyses. In Table 1 we report a comparison about genome size and GC content between C. suis and other coccidians.

Fig. 1
General contig-based statistics for the Cystoisospora suis genome. (A) Distribution of C. suis contig lengths. (B) Alignment of C. suis contigs to the chromosomes (chr) of Toxoplasma gondii; contigs are colored according to orientation (blue = plus, ...
Table 1
Comparison of genome features among Cystoisospora suis and its closest relatives from the subclass Coccidia. Organisms are sorted according to phylogenetic position (Fig. 2). Data for other coccidia were extracted from ToxoDB version 24 (for Sarcocystis ...

To investigate evolutionary divergence from other coccidians we aligned the C. suis contigs to the genomes of the closest relatives, T. gondii and N. caninum, in the coccidian phylogeny (Fig. 2). Only 27.8% and 28.1% of the C. suis bases aligned, respectively, to T. gondii and N. caninum (Fig.1B). This translated into 722 C. suis contigs successfully aligned to T. gondii, covering a total of 3,143 C. suis genes, and 733 contigs aligned to N. caninum, including 3,195 C. suis genes.

Fig. 2
Phylogenetic tree showing the relationships among the coccidian species analysed in this study (Toxoplasma gondii, Hammondia hammondi, Neospora caninum, Cystoisospora suis, Sarcocystis neurona, Eimeria tenella) reconstructed using the glyceraldehyde 3 ...

The GC content of the C. suis genome was 50.0%, similar to that of other coccidian species (Table 1). Among repetitive sequences 5.07% were simple repeats, 1.84% low complexity regions and 0.02% small RNAs. We identified 93 genes associated with transposons distributed as follows: 58 genes from the ty3-gypsy subclass, six tf2 retrotransposons, one FOG transposon and 28 genes encoding for retrotransposon accessory proteins, such as gag/pol.

Most apicomplexan parasites possess a special organelle called the apicoplast (Sato, 2011), which is a plastid acquired through secondary horizontal transfer from an algal ancestor and has functions related to secondary metabolism. We attempted to identify contigs corresponding to the apicoplast genome. By aligning the T. gondii apicoplast sequence to the C. suis contigs using BLASTN (default parameters), we found that 89.4% of the T. gondii sequence mapped to three C. suis contigs. Most of the apicoplast sequence was covered by contig 294 (~24 kb) (Fig.1C), with additional short fragments located on contigs 1252 (Fig.1C) and 6453 (not shown as it contained redundant sequence overlapping with contig 1252). Gene annotation confirmed the apicoplast origin of these contigs, with a total of 25 protein-coding genes all well conserved with T. gondii. The genes encode for 14 ribosomal proteins, the elongation factor tu, orf c, d, e, f, the caseinolytic protease C, four RNA polymerases and the cysteine desulfurase activator complex subunit. Four of the ribosomal proteins contain a premature stop codon, which might suggest that they are pseudogenes. Finally, five T. gondii genes did not have an ortholog in the C. suis apicoplast. To further characterise apicoplast genes we looked at RNA-Seq expression data from merozoites (Hehl et al., 2015), however we found none of the genes to be expressed in either C. suis or T. gondii at this stage. The complete list of apicoplast genes is available in Supplementary Table S3.

3.2. Genome annotation

To annotate protein-coding genes we applied the MAKER pipeline, as outlined in Section 2.3, followed by a manual curation of each individual gene. To quantify the effect of curation we computed the total number of genes, the percentage of exonic base pairs overlapping with exons generated from the RNA-Seq dataset (nucleotide level sensitivity – also described in Burset and Guigo (1996)) and the percentage of genes with UTRs before and after curation. The initial uncurated annotation contained 10,065 genes with a nucleotide level sensitivity of 68.8%, 5,553 genes with 5′ UTRs and 4,911 genes with 3′ UTRs. After curation, we obtained 11,572 genes, a nucleotide level sensitivity of 85.1%, 9,806 genes with 5′ UTRs and 8,485 genes with 3′ UTRs. These results showed that our curation greatly enhanced the quality of the annotation by increasing the concordance with transcriptional evidence and the number of genes with UTRs.

The total gene number appears to be higher in C. suis compared with other coccidians (Table 2), although lowly expressed genes might represent transcriptional noise and artificially increase the total gene count. To test this hypothesis, we removed genes with FPKM < 1, as this threshold is commonly used to distinguish transcriptional noise from real transcription. We detected 1,207 genes (10.4%) with FPKM < 1, implying that lowly expressed genes can only partially account for the higher number of genes in C. suis compared with other species.

Table 2
Comparison of annotation features among Cystoisospora suis and its closest relatives from the subclass Coccidia. Organisms are sorted according to phylogenetic position (Fig. 2). Data for other coccidia were extracted from ToxoDB version 24 (for Sarcocystis ...

To verify the completeness of our annotation, we checked for the presence of eukaryotic core genes that should be conserved in all eukaryotes (Parra et al., 2007). From 458 eukaryotic core genes, 396 (86%) were present in our gene catalogue. We looked for the presence of the missing genes in filtered contigs and in transcripts reconstructed de novo from the RNA-Seq dataset. Of the remaining 62 eukaryotic core genes, none were found in the filtered contigs and 16 were found in the de novo assembled transcripts. A total of 46 eukaryotic core genes was thus not found in our C. suis data. To shed light on whether these might constitute missing annotations or genuine losses, we looked for their presence in the proteomes of the coccidian species listed in Table 1. We found 29 genes present in at least one coccidian species. The remaining 17 genes were not found in any species, and thus were likely true gene losses in this clade (Supplementary Table S4). If this set of genes is excluded from the initial list of eukaryotic core genes a final completeness of (396 + 16)/(458 − 17) * 100 = 93% is obtained.

To further assess the quality of the annotation we assigned to each gene a binary vector summarizing the kind of evidence that was used to annotate it (transcriptional, orthology-based or ab initio). Generally, the most reliable source of annotation is transcription evidence, followed by orthology inference and ab initio predictions. Of a total of 11,572 genes, 10,452 (90.3%) were supported by transcription evidence (Cufflinks). Among the remaining 1,120 genes, 435 (3.8%) were supported by orthology evidence (Exonerate) and 338 (2.9%) only by ab initio predictors (either Augustus or Snap). Finally, 347 genes (3.0%) were only partially supported by any kind of evidence and would require further curation.

Next, we predicted CDS for 11,545 genes (99.8%) using ORFPredictor (Min et al., 2005). We classified genes according to the completeness of the CDS as follows: 7,801 genes (67.8%) had a complete CDS, i.e. with start and stop codon; 1,964 (17.0%) had only a start codon, 1,161 (10.0%) only a stop codon and 619 (5.4%) were CDS fragments, i.e. without start and stop codons. The complete annotation of C. suis including exons, introns, CDS and UTRs is available at the NCBI database under accession number PRJNA341953.

Finally, the phylogenetic distribution of C. suis genes was assessed using OrthoMCL and found that 6,387 (55.4%) of the protein coding genes were assigned to an ortholog group.

3.3. Detection of vaccine candidates

We applied the tool Vacceed to screen the C. suis proteome for vaccine candidates. Due to the high computation time of the MHC-I Binding tool, the Vacceed analysis was divided into two steps (see Section 2.7). Fig.3A shows the score distribution after the first step of Vacceed. This resulted in a clearly bimodal distribution from which 2,905 proteins with a score > 0.75 were selected. During the second step of Vacceed we refined this set including the MHC-I Binding tool and obtained 1,168 final candidate proteins (non-stringent set). By looking at the classification of candidates by functional class (Fig.3B), we observed that most of the proteins had no annotated function (403 proteins) or contained transmembrane domains but had unknown function (230 proteins). Among proteins with known function, the most abundant were channels and transporters (147 proteins), followed by proteins involved in metabolism and biosynthesis (118 proteins). Notable was the presence of apicomplexan-specific secretory organelles proteins (27 proteins) such as rhoptry kinases, microneme and dense granule proteins, which are involved in parasite motility, attachment, invasion and re-modelling of the intracellular parasite environment.

Fig. 3
General statistics for the non-stringent set of vaccine candidates. (A) Distribution of Vacceed scores for Cystoisospora suis proteins using all the tools except Major Histocompatibility Complex (MH-I) binding (see Section 2.7). (B) Functional classification ...

3.4. Contribution of immunogenic features

Next, we wanted to establish the contribution of different immunogenic features in defining a protein as immunogenic, as Vacceed utilises different sources of evidence from various tools: WoLf PSORT to predict protein subcellular localisation; SignalP, TargetP, Phobius for detecting transmembrane domains; TMHMM and MHC-I Binding. For this analysis, we excluded MHC-I Binding due to its high computational burden. For each tool, Vacceed was run on the C. suis proteome using all other tools except for the tool in question (i.e. all tools except WoLf, then all tools except TMHMM and so on) and the score distributions were compared with the one computed using all tools (Fig. 4). All correlations were very high, showing that removing any one of the tools from Vacceed did not significantly affect the score distribution. However, it appeared that TMHMM was the program that contributed most to the immunogenic score since, when it was omitted, the correlation with the score computed with all tools was the lowest (r = 0.94).

Fig. 4
Correlation plot of Vacceed score distributions computed with different combinations of predictors. Each square contains the Pearson correlation coefficient between two Vacceed runs on all the C. suis proteins. (All predictors = Vacceed ...

3.5. Comparison with vaccine candidates in other coccidians

Vaccine candidates in coccidians can point to homologous proteins that may also induce immune protection against other coccidians. For this reason, we collected all tested vaccine candidates from an extensive screen of the published literature (Lillehoj and Lillehoj, 2000, Jenkins, 2001, Nishikawa et al., 2002, Bhopale, 2003, Elsheikha and Mansfield, 2004, Williams and Trees, 2006, Dlugonska, 2008, Jongert et al., 2009, Nam, 2009, Reichel and Ellis, 2009, Monney et al., 2011, Cui et al., 2012, Blake and Tomley, 2014, Hiszczynska-Sawicka et al., 2014, Lim and Othman, 2014, Monney and Hemphill, 2014) and from the VIOLIN database (Xiang et al., 2008) for E. tenella, S. neurona, N. caninum and T. gondii. No previously tested candidates were found for H. hammondi. We found 13 candidates for E. tenella, one for S. neurona, 19 for N. caninum and 43 for T. gondii, giving a union set of 58 total candidates (Fig. 5). These mostly included apicomplexan-specific secretory organelle proteins and surface antigens, which are known to be directly involved in the invasion process, but also a heterogeneous set of other proteins with no overrepresented function. Out of the 58 candidates, 34 were also present in the C. suis proteome and seven overlapped with our set of C. suis vaccine candidates, namely proteins orthologous to microneme proteins TgMIC8 and TgMIC13, the dense granule antigen TgGRA1, the surface antigen TgSAG1, the cyclophilin CyP, the immune mapped protein IMP1 and the protein disulphide isomerase PDI. This enrichment of previously described vaccine candidates in our Vacceed set was highly significant (one-tailed Fisher’s Exact Test – P = 2.2 × 10−5). Notably, most previously described rhoptry and dense granule candidate vaccine proteins were absent in C. suis, as they originated before the split of T. gondii, H. hammondi and N. caninum. On the other hand, many microneme proteins were present in C. suis but they were not classified as vaccine candidates in our study: three of those (orthologues of TgMIC2, TgMIC5, TgMIC12) had a Vacceed score that was only slightly lower than our threshold, while the remaining three (TgMIC1, TgMIC3, TgMIC4) had a very low score.

Fig. 5
Orthology comparison of vaccine candidate proteins between Cystoisospora suis and other coccidian spp. Hammondia hammondi was excluded as no vaccination studies have been performed. Orthologs of C. suis genes were detected using the Orthology Matrix ( ...

3.6. Evolutionary conservation of vaccine candidates

Proteins that are phylogenetically restricted to C. suis or conserved only among closely related species might be more attractive candidates for experimental testing, since proteins with homologs and conserved epitopes in the host might induce an unwanted autoimmune response. To study the evolutionary conservation of vaccine candidates, we applied OrthoMCL and classified proteins according to taxonomic levels (Fig. 6). Approximately 28% of the proteins were very conserved in eukaryotes or shared by all organisms (set V1), another 29% were restricted to coccidians, apicomplexans or alveolates (set V2), while the largest fraction (43%) was not assigned to any ortholog group (set V3). If one would exclude the most conserved proteins (set V1) from in vitro testing, there is still a large set of candidates (839 proteins) to be investigated. By looking at GO functional enrichment of apicomplexan-specific and coccidian-specific proteins we observed a significant overrepresentation for “calcium ion binding” (P < 2.5 × 10−7) and “protein kinase activity” (P = 0.00067). Similarly, for coccidian-specific proteins the most prominent functional terms were “calcium ion binding” (P < 7.4 × 10−6) and “cyclic-nucleotide phosphodiesterase activity” (P = 0.00015).

Fig. 6
Phylogenetic distribution of the non-stringent set of vaccine candidates. Each circle contains the number of genes shared by that taxonomical group. See the Section 3.6 for the descriptions of the different sets (V1, V2 and V3).

3.7. Expression of vaccine candidates in merozoites

It is usually assumed that highly expressed genes are more likely to induce a sustained immunogenic response compared with lowly expressed ones (Flower et al., 2010, Goodswen et al., 2014a). To gain insight into the expression of genes encoding for immunogenic proteins we performed an RNA-Seq experiment using polyadenylated RNA purified from C. suis merozoites, as those constitute the primary intracellular reproductive stage of C. suis and interact directly with the host during invasion. Genes with functions related to invasion were the most highly expressed (Fig.7A) and included surface antigens, apicomplexan-specific secretory organelles, cell adhesion and motility, and parasitophorous vacuole-related genes. When looking at the total ranked set of candidates according to expression level (Fig.7B), we observed that more than 50% of the highly expressed candidates had unknown functions. Other proteins such as transporter abcg89, cytochrome b and c, were highly expressed and well characterised, but phylogenetically highly conserved and thus less suitable for further experimentation. Finally, 13 uncharacterised genes (unknown function or including generic transmembrane domains) with very high expression (FPKM > 30) and C. suis specificity might constitute attractive candidates for in vitro testing (Fig.7B – proteins annotated as “–NA–“ or “transmembrane protein”).

Fig. 7
Gene expression statistics for the non-stringent set of vaccine candidates (A) Merozoite expression distribution of vaccine candidates classified by functional category. (B) Gene expression of the top 20 most expressed vaccine candidates. FPKM, Fragments ...

3.8. Stringent list of vaccine candidates

To produce a more stringent list of candidates, we selected from the 1,168 vaccine candidates only those proteins that were highly expressed in merozoites (FPKM > 30, which corresponds to the top 25% of the expression distribution of vaccine candidates, see Fig.7A – category “All”) and without orthologs outside the Apicomplexa. This resulted in a set of 220 proteins. These include 152 proteins with unknown function, of which 88 contain transmembrane domains. Additionally, there were 17 surface antigens related to the TgSAG/SRS gene families, 12 apicomplexan-specific secretory organelles proteins including orthologues of TgAMA1, TgMIC6, TgMIC13, TgROP6, TgROP12, TgROP27, TgROP32, nine proteins involved in metabolism and biosynthesis, seven channels and transporters proteins and three proteins related to cell adhesion. For the complete list of candidates see Supplementary Table S5.

3.9. Validation of vaccine candidates by immunoblot analysis

To test whether vaccine candidate proteins interact with the host immune system and induce an immune response during C. suis infection, we performed a 2D immunoblot experiment using positive polyclonal sera from experimentally infected piglets. We resolved crude lysates from cultured C. suis merozoites using a broad range (pH 3–10) of 2D gels. These revealed 18 spots that were easily visualised by silver staining, likely corresponding to the most highly expressed proteins in the merozoite proteome (Fig.8A). To detect proteins that are recognised as antigenic by serum antibodies of infected hosts, we performed an immunoblot of the 2D gel probed with highly positive sera from experimentally infected piglets. This revealed one immuno-reactive spot (Fig.8B), whereas no reactive spots were detected in the immunoblot probed with precolostral sera (data not shown). Protein sequencing by mass spectrometry showed that the spot corresponded to a set of eight proteins (Table 3). Remarkably, one of these proteins overlapped with our set of vaccine candidates (CSUI_005805). This protein had no annotated function, but showed a very high expression level (FPKM = 10,244) (Fig.7B) and was C. suis-specific, according the OrthoMCL orthology assignment, making it a very attractive vaccine candidate. The protein was predicted to be short (389 amino acids), with a molecular weight of 42 kDa and encoded by a single-exon gene located on contig 2816. To further characterise this protein we analysed its sequence using Phobius (Kall et al., 2007), which identified two transmembrane domains interspersed by a short cytoplasmic region and followed by a longer extracellular tail (Fig.9A). Screening of this protein with the B-cell epitope predictor from the IEDB Analysis Resource tools (Larsen et al., 2006) revealed the presence of several putative epitopes along the sequence (Fig.9B). No additional information about the function of CSUI_005805 was currently available, as this protein lacks orthologs in other organisms. By virtue of all its features such as a high Vacceed score, high expression, species specificity and in vitro immunoreactivity, we conclude that the CSUI_005805 protein constitutes an attractive vaccine candidate for further experimental testing.

Fig. 8
Protein blots of Cystoisospora suis merozoites lysates (A) Two-dimensional proteome map of C. suis merozoite lysates visualised using silver stain; arrows mark the detectable proteins; the circle marks the spots identified by western blot (B). (B) Two-dimensional ...
Fig. 9
Structural information for the vaccine candidate protein CSUI_005805. (A) Domain prediction performed with the program Phobius on the protein CSUI_005805. Different colors indicate the regions of the protein in relationship to the cell membrane. The drawing ...
Table 3
Proteins identified in the immunoblot experiment with pig serum. Vaccine candidates are highlighted in gray.

4. Discussion

In this study, we sequenced, assembled and annotated the genome of C. suis, an apicomplexan species of worldwide veterinary relevance. We used this new resource to predict a panel of putative vaccine candidate proteins, which hopefully will serve to develop a novel subunit vaccine. We performed this analysis by combining in silico predictors of protein immunogenicity with transcription and comparative genomics data.

Comparison with publicly accessible genome sequences for other coccidian species identified a relatively large assembly for C. suis, with only S. neurona found to be bigger. To understand whether this discrepancy was due to expansion of intergenic regions within the C. suis genome, we compared the length of genic and intergenic regions in C. suis and T. gondii and found no significant difference in proportions between the two species (C. suis intergenic = 26.3 Mb, genic = 57.4 Mb; T. gondii intergenic = 16.5 Mb, genic = 49.2 Mb; Fisher’s Exact Test P = 0.4629). Similarly, the proportions of exon to intron lengths were also not significantly different between the two species (C. suis exons = 34.6 Mb, introns = 23.5 Mb; T. gondii exons = 29.7 Mb, introns = 19.9 Mb; Fisher’s Exact Test P = 1). Finally, repetitive regions were also not responsible for the difference in genome size (C. suis repetitive = 5.8 Mb, C. suis non-repetitive = 77.8 Mb, T. gondii repetitive = 2.6 Mb, T. gondii non-repetitive = 63.1 Mb; Fisher’s Exact Test P = 0.5371). However, we caution that different genome assembly technologies and annotation strategies for different species might bias the comparison of genomic features among assemblies. Features such as average GC content were also consistent with those reported for other coccidians (Kissinger and DeBarry, 2011). Alignment of the C. suis contigs to assemblies representing the closest coccidian relatives showed that less than 30% of the C. suis assembly could be aligned to T. gondii or N. caninum. It has previously been shown that 90% of the N. caninum contig base pairs could be aligned to the T. gondii assembly, indicating a greater evolutionary divergence between C. suis and T. gondii. The next step will be to estimate the evolutionary divergence in millions of years between C. suis and its sister species.

Our annotation of the predicted C. suis transcriptome suggests more than 11,000 genes, considerably higher than most other coccidians, but consistent with the larger genome size. We evaluated the quality of our annotation using a range of metrics and found 86% of the core eukaryotic genes to be present. Notably, if we exclude core eukaryotic genes that are absent in the whole coccidian clade, thus also expected to be absent in C. suis, this proportion rises to 93%. Expression data validated 90% of the gene models, pointing to a high degree of completeness and reliability of the gene structures. Additionally, we performed a gene-by-gene manual curation, which greatly enhanced the quality of the annotation, by increasing the concordance with transcriptional evidence by almost 20% and the number of genes with UTRs by ~30%. The larger number of genes predicted compared with most other coccidians might be a consequence of more orphan genes (Martens et al., 2008), supported by the fact that more than 40% of the C. suis genes could not be assigned to any orthologous group. Alternatively, fragmented gene models might have resulted in an overestimation of gene number. However, gene numbers in other coccidians might also have been underestimated since most recent RNA-Seq based annotations (Ramaprasad et al., 2015) have not yet been incorporated into ToxoDB. In our annotation, most of the genes identified (99%) had coding potential. While non-coding RNAs were not explicitly annotated, it is likely that polyadenylated non-coding RNA (Matrajt, 2010), such as long non-coding RNAs (lincRNAs), constitute a minor fraction of the gene catalogue of C. suis, as also previously shown for T. gondii (Hassan et al., 2012).

Another feature specific to the apicomplexan clade is the presence of the apicoplast organelle in most of its members, except the gregarine-like Cryptosporidium (Zhu et al., 2000). Two contigs were found to contain most the C. suis apicoplast genome, confirming the presence of this organelle in this species. Comparison with T. gondii revealed a high level of conservation for the C. suis apicoplast genes, although some genes contained premature stop codons, implying a recent pseudogenisation event. This phenomenon has also been described in T. gondii (Sato, 2011), where it was suggested that internal stop codons might be interpreted as tryptophan coding by the translation machinery. The absence of transcripts derived from these genes within the RNA-Seq data preclude confirmation for C. suis since transcription may simply have been low in the single merozoite lifecycle stage sampled. Consistent with these results, T. gondii orthologs of C. suis apicoplast genes also had very low expression levels in 3 days p.i. merozoites according to the expression data from the ToxoDB database (Hehl et al., 2015) (Supplementary Table S3).

Screening the predicted C. suis proteome, 1,169 putative vaccine candidates were identified using the software Vacceed. We further characterised the candidates according to function, conservation, expression and overlap with candidates that had been tested in other coccidians. Most of the candidates were annotated as of unknown function and remarkably many had no orthologs in other coccidian species. Such diversity might be due to accelerated evolution of proteins that interact with the immune system of the host, as formerly reported for other apicomplexan species (Templeton et al., 2004, Jackson et al., 2014, Blake et al., 2015). Vaccine candidate proteins involved in host interaction and invasion such as apicomplexan-specific secretory organelles proteins, surface antigens and cell adhesion proteins were highly expressed in merozoites, as might have been expected given their function in the C. suis lifecycle. Interestingly, when vaccine candidates identified in other coccidian species were compared, only 22% of the candidates with orthologs in C. suis had a high Vacceed score. To understand why some known candidates had a low score in C. suis we looked at the partial scores from the various tools that constitute the Vacceed pipeline. Many proteins had very low partial scores, indicating the absence of specific signals for membrane, secretion or MHC1-binding epitopes. Additionally, when we looked at protein domains from the InterPro database we did not find any domain related to membrane, secretion or interaction with the immune system (data not shown). This indicates that membrane-related signals might not always be required features for an anticoccidial vaccine candidate. A relatively high proportion (43%) of candidates identified in other coccidians had no orthologs in C. suis. By looking at the phylogenetic patterns of these proteins, these candidates were found to be either specific to E. tenella (gam56, gam82, GX3262, GX3264, MA16) or they were proteins that originated just before the split of N. caninum, H. hammondi and T. gondii, mostly rhoptry kinases, dense granules and some surface antigens of the SRS family. These results also reflect the likely fast evolution of immune-related proteins. Finally, by overlapping the vaccine candidates obtained by Vacceed with proteins identified from an immunoblot experiment of pig serum, we pinpointed a promising new vaccine candidate corresponding to a 42 kDa transmembrane protein with unknown function (CSUI_005805). However, only a few proteins were recognised by positive sera from infected piglets. More sensitive detection methods or increased amounts of proteins on the gel would certainly reveal more positive spots. To further confirm the usefulness of candidates identified by reverse vaccinology and immunoblotting, recombinant proteins must be generated and characterised in vitro and in vivo in further experiments

In summary, we combined reverse vaccinology with transcriptomics and comparative genomics to identify a list of vaccine candidate proteins for further experimental testing. In order to restrict this set of candidates, new indicators of immunogenicity could be incorporated into the Vacceed pipeline, which is feasible due to the modularity of this tool. Studies on putatively immunogenic proteins of C. suis will also greatly enhance our understanding of the immune mechanisms underlying protection (or immunopathology) in porcine cystoisosporosis. Lastly, the genome and annotation of C. suis constitute a new step in the genomic era of apicomplexans. As the genus Cystoisospora can also be found in other hosts such as dogs, cats and humans, we anticipate that these resources will help to unravel the evolutionary mechanisms of host specificity in apicomplexan parasites.

Acknowledgments

We acknowledge the Biotechnology and Biological Sciences Research Council (BBSRC), United Kingdom for our funding, reference BB/H020195/1.

Footnotes

[star]Note: Nucleotide sequence data reported in this paper are available in the GenBank™, EMBL and DDBJ databases under the accession number PRJNA341953.

Appendix ASupplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ijpara.2016.11.007.

Appendix A. Supplementary data

Supplementary Table S1:

Read mapping statistics from the RNA-Seq dataset used in this study.

Supplementary Table S2:

Read mapping statistics from the DNA-Seq dataset used in this study.

Supplementary Table S3:

List of genes in the Cystoisospora suis apicoplast genome.

Supplementary Table S4:

List of eukaryotic core genes that are absent in Cystoisospora suis due to gene loss before the split of the coccidian clade.

Supplementary Table S5:

Stringent list of Cystoisospora suis vaccine candidates.

References

Aliaga-Leyton A., Webster E., Friendship R., Dewey C., Vilaca K., Peregrine A.S. An observational study on the prevalence and impact of Isospora suis in suckling piglets in southwestern Ontario, and risk factors for shedding oocysts. Can. Vet. J. 2011;52:184–188. [PubMed]
Altenhoff A.M., Gil M., Gonnet G.H., Dessimoz C. Inferring hierarchical orthologous groups from orthologous gene pairs. PLoS One. 2013;8:e53786. [PubMed]
Bhopale G.M. Development of a vaccine for toxoplasmosis: current status. Microbes Infect. 2003;5:457–462. [PubMed]
Blake D.P., Tomley F.M. Securing poultry production from the ever-present Eimeria challenge. Trends Parasitol. 2014;30:12–19. [PubMed]
Blake D.P., Clark E.L., Macdonald S.E., Thenmozhi V., Kundu K., Garg R., Jatau I.D., Ayoade S., Kawahara F., Moftah A., Reid A.J., Adebambo A.O., Alvarez Zapata R., Srinivasa Rao A.S., Thangaraj K., Banerjee P.S., Dhinakar-Raj G., Raman M., Tomley F.M. Population, genetic, and antigenic diversity of the apicomplexan Eimeria tenella and their relevance to vaccine development. Proc. Natl. Acad. Sci. U.S.A. 2015;112:E5343–E5350. [PubMed]
Blum H.B.H., Gross H.J. Improved silver staining of plant proteins, RNA and DNA in polyacrylamide gels. Electrophoresis. 1987;8:93–99.
Bradford M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 1976;72:248–254. [PubMed]
Burset M., Guigo R. Evaluation of gene structure prediction programs. Genomics. 1996;34:353–367. [PubMed]
Cantarel B.L., Korf I., Robb S.M., Parra G., Ross E., Moore B., Holt C., Sanchez Alvarado A., Yandell M. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 2008;18:188–196. [PubMed]
Conesa A., Gotz S., Garcia-Gomez J.M., Terol J., Talon M., Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21:3674–3676. [PubMed]
Cui X., Lei T., Yang D., Hao P., Li B., Liu Q. Toxoplasma gondii immune mapped protein-1 (TgIMP1) is a novel vaccine candidate against toxoplasmosis. Vaccine. 2012;30:2282–2287. [PubMed]
de Oliveira Lopes D., de Oliveira F.M., do Vale Coelho I.E., de Oliveira Santana K.T., Mendonca F.C., Taranto A.G., dos Santos L.L., Miyoshi A., de Carvalho Azevedo V.A., Comar M. Identification of a vaccine against schistosomiasis using bioinformatics and molecular modeling tools. Infect. Genet. Evol. 2013;20:83–95. [PubMed]
Dlugonska H. Toxoplasma rhoptries: unique secretory organelles and source of promising vaccine proteins for immunoprevention of toxoplasmosis. J. Biomed. Biotechnol. 2008;2008:632424. [PubMed]
Elsheikha H.M., Mansfield L.S. Sarcocystis neurona major surface antigen gene 1 (SAG1) shows evidence of having evolved under positive selection pressure. Parasitol. Res. 2004;94:452–459. [PubMed]
Emanuelsson O., Nielsen H., Brunak S., von Heijne G. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 2000;300:1005–1016. [PubMed]
Flower D.R., Macdonald I.K., Ramakrishnan K., Davies M.N., Doytchinova I.A. Computer aided selection of candidate vaccine antigens. Immunome Res. 2010;6(Suppl 2):S1. [PMC free article] [PubMed]
Gabner S., Worliczek H.L., Witter K., Meyer F.R., Gerner W., Joachim A. Immune response to Cystoisospora suis in piglets: local and systemic changes in T-cell subsets and selected mRNA transcripts in the small intestine. Parasite Immunol. 2014;36:277–291. [PubMed]
Gajria B., Bahl A., Brestelli J., Dommer J., Fischer S., Gao X., Heiges M., Iodice J., Kissinger J.C., Mackey A.J., Pinney D.F., Roos D.S., Stoeckert C.J., Jr., Wang H., Brunk B.P. ToxoDB: an integrated Toxoplasma gondii database resource. Nucleic Acids Res. 2008;36:D553–D556. [PubMed]
Gharahdaghi F., Weinberg C.R., Meagher D.A., Imai B.S., Mische S.M. Mass spectrometric identification of proteins from silver-stained polyacrylamide gel: a method for the removal of silver ions to enhance sensitivity. Electrophoresis. 1999;20:601–605. [PubMed]
Goodswen S.J., Kennedy P.J., Ellis J.T. A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms. BMC Bioinformatics. 2013;14 [PMC free article] [PubMed]
Goodswen S.J., Kennedy P.J., Ellis J.T. Discovering a vaccine against neosporosis using computers: is it feasible? Trends Parasitol. 2014;30:401–411. [PubMed]
Goodswen S.J., Kennedy P.J., Ellis J.T. Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores. PLoS One. 2014;9:e115745. [PubMed]
Goodswen S.J., Kennedy P.J., Ellis J.T. Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology. Bioinformatics. 2014;30:2381–2383. [PubMed]
Hassan M.A., Melo M.B., Haas B., Jensen K.D., Saeij J.P. De novo reconstruction of the Toxoplasma gondii transcriptome improves on the current genome annotation and reveals alternatively spliced transcripts and putative long non-coding RNAs. BMC Genomics. 2012;13:696. [PubMed]
Hehl A.B., Basso W.U., Lippuner C., Ramakrishnan C., Okoniewski M., Walker R.A., Grigg M.E., Smith N.C., Deplazes P. Asexual expansion of Toxoplasma gondii merozoites is distinct from tachyzoites and entails expression of non-overlapping gene families to attach, invade, and replicate within feline enterocytes. BMC Genomics. 2015;16:66. [PubMed]
Hiszczynska-Sawicka E., Gatkowska J.M., Grzybowski M.M., Dlugonska H. Veterinary vaccines against toxoplasmosis. Parasitology. 2014;141:1365–1378. [PubMed]
Horton P., Park K.J., Obayashi T., Fujita N., Harada H., Adams-Collier C.J., Nakai K. WoLF PSORT: protein localization predictor. Nucleic Acids Res. 2007;35:W585–W587. [PubMed]
Jackson A.P., Otto T.D., Darby A., Ramaprasad A., Xia D., Echaide I.E., Farber M., Gahlot S., Gamble J., Gupta D., Gupta Y., Jackson L., Malandrin L., Malas T.B., Moussa E., Nair M., Reid A.J., Sanders M., Sharma J., Tracey A., Quail M.A., Weir W., Wastling J.M., Hall N., Willadsen P., Lingelbach K., Shiels B., Tait A., Berriman M., Allred D.R., Pain A. The evolutionary dynamics of variant antigen genes in Babesia reveal a history of genomic innovation underlying host-parasite interaction. Nucleic Acids Res. 2014;42:7113–7131. [PubMed]
Jenkins M.C. Advances and prospects for subunit vaccines against protozoa of veterinary importance. Vet. Parasitol. 2001;101:291–310. [PubMed]
Jimenez C.R., Huang L., Qiu Y., Burlingame A.L. In-gel digestion of proteins for MALDI-MS fingerprint mapping. In: Coligan J.E., Dunn B.M., Ploegh H.L., Speicher D.W., Wingfield, editors. Curr Protoc Protein Sci. John Wiley; New York: 2001. pp. 16.14.1–16.14.5.
Joachim A., Mundt H.C. Efficacy of sulfonamides and Baycox((R)) against Isospora suis in experimental infections of suckling piglets. Parasitol. Res. 2011;109:1653–1659. [PubMed]
Jones P., Binns D., Chang H.Y., Fraser M., Li W., McAnulla C., McWilliam H., Maslen J., Mitchell A., Nuka G., Pesseat S., Quinn A.F., Sangrador-Vegas A., Scheremetjew M., Yong S.Y., Lopez R., Hunter S. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–1240. [PubMed]
Jongert E., Roberts C.W., Gargano N., Forster-Waldl E., Petersen E. Vaccines against Toxoplasma gondii: challenges and opportunities. Mem. Inst. Oswaldo Cruz. 2009;104:252–266. [PubMed]
Kall L., Krogh A., Sonnhammer E.L. Advantages of combined transmembrane topology and signal peptide prediction–the Phobius web server. Nucleic Acids Res. 2007;35:W429–W432. [PubMed]
Kim D., Pertea G., Trapnell C., Pimentel H., Kelley R., Salzberg S.L. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36. [PubMed]
Kissinger J.C., DeBarry J. Genome cartography: charting the apicomplexan genome. Trends Parasitol. 2011;27:345–354. [PubMed]
Korf I. Gene finding in novel genomes. BMC Bioinformatics. 2004;5:59. [PubMed]
Kurtz S., Phillippy A., Delcher A.L., Smoot M., Shumway M., Antonescu C., Salzberg S.L. Versatile and open software for comparing large genomes. Genome Biol. 2004;5:R12. [PubMed]
Larsen J.E., Lund O., Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res. 2006;2:2. [PubMed]
Lillehoj H.S., Lillehoj E.P. Avian coccidiosis. A review of acquired intestinal immunity and vaccination strategies. Avian Dis. 2000;44:408–425. [PubMed]
Lim S.S., Othman R.Y. Recent advances in Toxoplasma gondii immunotherapeutics. Korean J. Parasitol. 2014;52:581–593. [PubMed]
Lindsay D., Blagburn B., Powe T. Enteric coccidial infections and coccidiosis in swine. Compend. Cont. Educ. Pract. 1992;14:698–702.
Loytynoja A., Goldman N. An algorithm for progressive multiple alignment of sequences with insertions. Proc. Natl. Acad. Sci. U.S.A. 2005;102:10557–10562. [PubMed]
Martens C., Vandepoele K., Van de Peer Y. Whole-genome analysis reveals molecular innovations and evolutionary transitions in chromalveolate species. Proc. Natl. Acad. Sci. U.S.A. 2008;105:3427–3432. [PubMed]
Matrajt M. Non-coding RNA in apicomplexan parasites. Mol. Biochem. Parasitol. 2010;174:1–7. [PubMed]
McAllister M.M. Successful vaccines for naturally occurring protozoal diseases of animals should guide human vaccine research. A review of protozoal vaccines and their designs. Parasitology. 2014;141:624–640. [PubMed]
Meyer C., Joachim A., Daugschies A. Occurrence of Isospora suis in larger piglet production units and on specialized piglet rearing farms. Vet. Parasitol. 1999;82:277–284. [PubMed]
Min X.J., Butler G., Storms R., Tsang A. OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 2005;33:W677–W680. [PubMed]
Monney T., Hemphill A. Vaccines against neosporosis: what can we learn from the past studies? Exp. Parasitol. 2014;140:52–70. [PubMed]
Monney T., Debache K., Hemphill A. Vaccines against a major cause of abortion in cattle, Neospora caninum infection. Animals (Basel) 2011;1:306–325. [PubMed]
Mortazavi A., Williams B.A., McCue K., Schaeffer L., Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods. 2008;5:621–628. [PubMed]
Mundt H.C., Cohnen A., Daugschies A., Joachim A., Prosl H., Schmaschke R., Westphal B. Occurrence of Isospora suis in Germany, Switzerland and Austria. J. Vet. Med. B Infect. Dis. Vet. Public Health. 2005;52:93–97. [PubMed]
Myhre S., Tveit H., Mollestad T., Laegreid A. Additional gene ontology structure for improved biological reasoning. Bioinformatics. 2006;22:2020–2027. [PubMed]
Nam H.W. GRA proteins of Toxoplasma gondii: maintenance of host-parasite interactions across the parasitophorous vacuolar membrane. Korean J. Parasitol. 2009;47(Suppl):S29–S37. [PubMed]
Nishikawa Y., Mikami T., Nagasawa H. Vaccine development against Neospora caninum infection. J. Vet. Med. Sci. 2002;64:1–5. [PubMed]
Ogedengbe J.D., Ogedengbe M.E., Hafeez M.A., Barta J.R. Molecular phylogenetics of eimeriid coccidia (Eimeriidae, Eimeriorina, Apicomplexa, Alveolata): a preliminary multi-gene and multi-genome approach. Parasitol. Res. 2015;114:4149–4160. [PubMed]
Parra G., Bradnam K., Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23:1061–1067. [PubMed]
Petersen T.N., Brunak S., von Heijne G., Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods. 2011;8:785–786. [PubMed]
Pizza M., Scarlato V., Masignani V., Giuliani M.M., Arico B., Comanducci M., Jennings G.T., Baldi L., Bartolini E., Capecchi B., Galeotti C.L., Luzzi E., Manetti R., Marchetti E., Mora M., Nuti S., Ratti G., Santini L., Savino S., Scarselli M., Storni E., Zuo P., Broeker M., Hundt E., Knapp B., Blair E., Mason T., Tettelin H., Hood D.W., Jeffries A.C., Saunders N.J., Granoff D.M., Venter J.C., Moxon E.R., Grandi G., Rappuoli R. Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science. 2000;287:1816–1820. [PubMed]
Ramaprasad A., Mourier T., Naeem R., Malas T.B., Moussa E., Panigrahi A., Vermont S.J., Otto T.D., Wastling J., Pain A. Comprehensive evaluation of Toxoplasma gondii VEG and Neospora caninum LIV genomes with tachyzoite stage transcriptome and proteome defines novel transcript features. PLoS One. 2015;10:e0124473. [PubMed]
Reichel M.P., Ellis J.T. Neospora caninum–how close are we to development of an efficacious vaccine that prevents abortion in cattle? Int. J. Parasitol. 2009;39:1173–1187. [PubMed]
Samarasinghe B., Johnson J., Ryan U. Phylogenetic analysis of Cystoisospora species at the rRNA ITS1 locus and development of a PCR-RFLP assay. Exp. Parasitol. 2008;118:592–595. [PubMed]
Sato S. The apicomplexan plastid and its evolution. Cell. Mol. Life Sci. 2011;68:1285–1296. [PubMed]
Schulz M.H., Zerbino D.R., Vingron M., Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28:1086–1092. [PubMed]
Schwarz L., Joachim A., Worliczek H.L. Transfer of Cystoisospora suis-specific colostral antibodies and their correlation with the course of neonatal porcine cystoisosporosis. Vet. Parasitol. 2013;197:487–497. [PubMed]
Schwarz L., Worliczek H.L., Winkler M., Joachim A. Superinfection of sows with Cystoisospora suis ante partum leads to a milder course of cystoisosporosis in suckling piglets. Vet. Parasitol. 2014;204:158–168. [PubMed]
Shevchenko A., Wilm M., Vorm O., Mann M. Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. Anal. Chem. 1996;68:850–858. [PubMed]
Shrestha A., Abd-Elfattah A., Freudenschuss B., Hinney B., Palmieri N., Ruttkowski B., Joachim A. Cystoisospora suis – a model of mammalian cystoisosporosis. Front. Vet. Sci. 2015;2:68. [PubMed]
Sonnhammer E.L., von Heijne G., Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc. Int. Conf. Intell. Syst. Mol. Biol. 1998;6:175–182. [PubMed]
Stanisic D.I., Barry A.E., Good M.F. Escaping the immune system: how the malaria parasite makes vaccine development a challenge. Trends Parasitol. 2013;29:612–622. [PubMed]
Stanke M., Keller O., Gunduz I., Hayes A., Waack S., Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34:W435–W439. [PubMed]
Stephen B., Rommel M., Daugschies A., Haberkorn A. Studies of resistance to anticoccidials in Eimeria field isolates and pure Eimeria strains. Vet. Parasitol. 1997;69:19–29. [PubMed]
Templeton T.J., Iyer L.M., Anantharaman V., Enomoto S., Abrahante J.E., Subramanian G.M., Hoffman S.L., Abrahamsen M.S., Aravind L. Comparative analysis of apicomplexa and genomic diversity in eukaryotes. Genome Res. 2004;14:1686–1695. [PubMed]
Thorvaldsdottir H., Robinson J.T., Mesirov J.P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 2013;14:178–192. [PubMed]
Trapnell C., Williams B.A., Pertea G., Mortazavi A., Kwan G., van Baren M.J., Salzberg S.L., Wold B.J., Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 2010;28:511–515. [PubMed]
Williams D.J., Trees A.J. Protecting babies: vaccine strategies to prevent foetopathy in Neospora caninum-infected cattle. Parasite Immunol. 2006;28:61–67. [PubMed]
Worliczek H.L., Buggelsheim M., Alexandrowicz R., Witter K., Schmidt P., Gerner W., Saalmuller A., Joachim A. Changes in lymphocyte populations in suckling piglets during primary infections with Isospora suis. Parasite Immunol. 2010;32:232–244. [PubMed]
Worliczek H.L., Ruttkowski B., Joachim A., Saalmuller A., Gerner W. Faeces, FACS, and functional assays–preparation of Isospora suis oocyst antigen and representative controls for immunoassays. Parasitology. 2010;137:1637–1643. [PubMed]
Worliczek H.L., Ruttkowski B., Schwarz L., Witter K., Tschulenk W., Joachim A. Isospora suis in an epithelial cell culture system - an in vitro model for sexual development in coccidia. PLoS One. 2013;8:e69797. [PubMed]
Xiang Z., Todd T., Ku K.P., Kovacic B.L., Larson C.B., Chen F., Hodges A.P., Tian Y., Olenzek E.A., Zhao B., Colby L.A., Rush H.G., Gilsdorf J.R., Jourdian G.W., He Y. VIOLIN: vaccine investigation and online information network. Nucleic Acids Res. 2008;36:D923–D928. [PubMed]
Zhu G., Marchewka M.J., Keithly J.S. Cryptosporidium parvum appears to lack a plastid genome. Microbiology. 2000;146(Pt 2):315–321. [PubMed]