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Cryptosporidium is a protozoan parasite that causes diarrheal illness in a wide range of hosts including humans. Two species, C. parvum and C. hominis are of primary public health relevance. Genome sequences of these two species are available and show only 3-5% sequence divergence. We investigated this sequence variability, which could correspond either to sequence gaps in the published genome sequences or to the presence of species-specific genes. Comparative genomic tools were used to identify putative species-specific genes and a subset of these genes was tested by PCR in a collection of Cryptosporidium clinical isolates and reference strains.
The majority of the putative species-specific genes examined were in fact common to C. parvum and C. hominis. PCR product sequence analysis revealed interesting SNPs, the majority of which were species-specific. These genetic loci allowed us to construct a robust and multi-locus analysis. The Neighbour-Joining phylogenetic tree constructed clearly discriminated the previously described lineages of Cryptosporidium species and subtypes.
Most of the genes identified as being species specific during bioinformatics in Cryptosporidium sp. are in fact present in multiple species and only appear species specific because of gaps in published genome sequences. Nevertheless SNPs may offer a promising approach to studying the taxonomy of closely related species of Cryptosporidia.
At least eight Cryptosporidium species infect humans ; however, only two species are of major significance to public health by causing the majority of human cases both sporadic and outbreak related cases, C. hominis and C. parvum [2-5]. Cryptosporidium parvum is zoonotic and infects a wide range of animal hosts including humans, whereas C. hominis is generally restricted to humans . Therefore, the main phenotypic difference between C. hominis and C. parvum is the host range [1-3]. In addition, these two Cryptosporidium species differ in geographical and temporal distribution and pathogenicity [7,8]. Differential risk factors and transmission routes have also been identified [3,7,9]. However human infections are not solely linked to these two species and other species and genotypes have been associated with illness . These additional species and genotypes are therefore considered emergent. This was the case of the rabbit genotype, the aetiological agent in an outbreak of waterborne human cryptosporidiosis in Northamptonshire, East Midlands, England [11,12]. Subsequent characterization studies revealed that the rabbit genotype, which caused this outbreak, corresponds to Cryptosporidium cuniculus (Inman and Takeuchi, 1979) .
The public health relevance of C. parvum and C. hominis has driven a bias in Cryptosporidium research towards these two species. Indeed, the genomes of C. parvum and C. hominis (IOWA and TU502 reference strains, respectively) have been sequenced [14,15]. The genome sequencing of C. muris, a less relevant Cryptosporidium species from a public health perspective, is underway . The genomic data for all 3 genome representatives is available online http://CryptoDB.org. The genome sizes for C. parvum and C. hominis are 9.11 and 9.16 Mb, respectively. The GC content is ~ 30% and the coding region is of about 6 Mb . The number of published genes is slightly higher in C. hominis than in C. parvum: 3,994 genes versus 3,952 genes. The significance of these 42 missing genes is not clear. The average gene length is comparable between the 2 species: 1.57 kb and 1.72 kb, for C. hominis and C. parvum, respectively. Genome comparison showed that C. hominis and C. parvum are very similar. This high level of sequence similarity limited the ability of comparative genomics to improve annotation, identify conserved non-coding sequence elements and study gene and protein evolution . More importantly, this high sequence similarity hindered better understanding of host specificity and virulence mechanisms as was anticipated from the genome projects . In fact, C. hominis and C. parvum genomes exhibit only 3-5% sequence divergence, with no large insertions, deletions or rearrangements . The authors stated that the gene complements of the two species are essentially identical because the few C. parvum genes not found in C. hominis are proximal to known sequence gaps. However, uncertainty about the amount of sequence variation between C. parvum and C. hominis persists due to the incomplete status of the C. hominis genome. Nevertheless, it has been concluded that the phenotypic differences between C. hominis and C. parvum are caused by polymorphisms in coding regions and differences in gene regulation [15,18]. The role of this minimal genetic variability between C. hominis and C. parvum in the phenotypic differences is now much more accessible for investigation. In fact, these genes may include hitherto valuable epidemiological markers and previously unnoticed genetic determinants of host specificity and virulence. In addition, such markers would also serve as typing targets.
The aim of this study was to survey the published C. parvum and C. hominis genomes for incomplete regions and missing genes in order to identify novel genotyping markers. These genes are likely to contribute to the phenotypic differences between C. parvum and C. hominis and therefore might be potential genetic determinants of host tropism.
Initial screening by Reciprocal Blast and retention of coding sequences showing a level of similarity below 10% (and supported by significant p values) identified 117 and 272 putative species-specific genes for C. hominis and C. parvum, respectively. The majority of C. parvum putative specific genes were annotated, while C. hominis putative specific genes corresponded mainly to hypothetical proteins. Subsequently, the secondary screen decreased the number of the predicted genes to 93 and 211 genes for C. hominis and C. parvum, respectively.
Initially, a subset of ten genes was selected semi-randomly with preference to annotated genes (Table (Table1).1). This subset of genes was tested experimentally by PCR in a collection of Cryptosporidium clinical isolates and reference strains (Table (Table2).2). Surprisingly, 90% (9/10) of the genes tested were present in both C. hominis and C. parvum. PCR results for Cgd2_80 and Chro.50330 genes are shown in Figure Figure1.1. There was no discernable difference between PCR results of C. parvum and C. hominis clinical isolates and reference strains by agarose gel electrophoresis. DNA from isolate Cp4 did not amplify using Chro.30149 primers. Further testing of other putative species-specific genes confirmed the general trend. The majority of the predicted genes were therefore common to both Cryptosporidium species. Consequently, we considered whether the observed ubiquity of the predicted specific genes represented the closeness between C. hominis and C. parvum or whether these primers would also amplify orthologous genes from other Cryptosporidium species. C. meleagridis DNA was amplified by PCR for 8/10 genes (80%), only, Cgd2_2430 and Chro.20156 PCR reactions were negative (Table (Table33).
Interestingly, for Cgd2_2430 gene, only C. andersoni DNA was amplified by PCR. For Cgd6_5020, only C. felis DNA was PCR positive and for Chro.30149 primers, cervine genotype DNA was amplified. C. andersoni, cervine genotype and C. felis DNA was amplified by 10% (1/10) of primers tested. C. baileyi DNA was not amplified by any of the primers tested (Table (Table33).
All positive PCR products were sequenced. PCR product sequences are available online [GenBank: GU904212-GU904405]. The alignments of PCR product sequences for each gene are shown [additional file 1]. One PCR product of C. meleagridis DNA using Chro.50330 primers did not generate good sequence and was therefore excluded from the analysis. In addition, PCR products for C. andersoni, C. felis and cervine genotype did not generate good quality sequences and they were not included in the analysis.
Sequence analysis of these novel genetic loci showed interesting genetic polymorphisms and 78 Single nucleotide polymorphisms (SNP) were detected. These SNPs were detected from a total number of 4150 nucleotides, corresponding to an average of 1 SNP every 53 bp. The number of SNPs was variable for each gene, ranging from 1 SNP every 30 bp for Cgd2_2430 to less than one SNP per 330 bp for Chro.30149. The SNP results for each gene are summarized in Table Table4.4. Of the 78 SNPs, 61 (78.3%) were species-specific, thus defining an interesting feature of this subset of genes identified by comparative genomics. The proportion of species-specific SNPs ranged from 66.7% for Cgd8_2370 and Chro.50317 genes to 100% for Chro.50330 and Chro.50457 (Table (Table4).4). In addition, 64.2% (50/78) of the SNPs detected were synonymous, thus maintaining the protein sequence. The 28 non-synonymous SNPs were not evenly distributed between the loci. In fact, the proportion of non synonymous SNPs was low for the majority of the genes ranging from 0% to 25% for Chro.50330 and Cgd6_200, respectively (Table (Table4).4). On the contrary, for Chro.50317 and Chro.20156 genes, 66.7% and 83.4% of the SNPs were non-synonymous. The annotations of these genes are RNA polymerase and hypothetical proteins, respectively. The significance and effect of these mutations would need to be investigated experimentally. In addition to the 61 species-specific SNPs allowing discrimination between C. hominis and C. parvum, the sequence analysis showed 5 SNPs specific for C. cuniculus isolates and 3 SNPs specific for the anthroponotic C. parvum subtype. The newly identified SNPs were confirmed experimentally by PCR-RFLP, as sequence alignments were used to identify differential restriction endonuclease recognition sites between the main species tested (Data not shown).
SNP analysis was performed in a pair-wise manner between isolate groups and subtypes using the logical function "IF" of the Microsoft Excel software to discriminate between variables. When the SNPs were identical between the 2 groups, the value "0" was attributed, while if the 2 SNPs were different, the value "1" was assigned and the values summed for each group. The number of base pair differences between the groups is shown in Table Table5.5. These scores represent the genetic variability between the main isolate groups. The newly identified SNPs showed clear genetic difference patterns between species and subtypes of Cryptosporidium. It is noticeable that the genetic differences of C. hominis and C. parvum to C. meleagridis were comparable (5.50 and 5.05%, respectively). This analysis showed a minimal genetic variability between C. hominis and C. parvum (1.72%) (Table (Table5).5). Interestingly, the genetic difference between C. parvum and C. parvum anthroponotic subtype was 0.13%, while a slightly higher genetic difference was observed between C. hominis and C. cuniculus isolates (0.27%).
Sequences of the ten genetic loci and of the COWP (Cryptosporidium oocyst wall protein) gene were used for Multi-locus Analysis (MLA). All the retrieved sequences allowed comparison of a total 4469 bp. A Neighbour-Joining Tree was generated based on these sequences using MEGA software. The tree showed clear discrimination between C. parvum and C. hominis isolates (Figure (Figure2A).2A). Within each group, there were two clusters corresponding to isolate subtypes: C. parvum and C. parvum anthroponotic subtype and C. hominis and C. cuniculus. All groups and clusters were supported by high bootstrap values. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) phylogenetic method was also tested to construct phylogenetic trees and gave the same topology with similar bootstrap values (data not shown). There was no discrimination between the different isolates belonging to the main species groups, despite distinct gp60 subtypes. However, TU502 strain showed some sequence divergence and was grouped separately within the C. hominis cluster. This is due to the presence of a unique SNP at position 132 on Cgd8_2370 gene, which was confirmed by 3 independent rounds of sequencing. Cryptosporidium meleagridis sequences were included in the MLA and used as an out group. Cryptosporidium meleagridis DNA did amplify 8/10 loci tested, however, for 2 loci (Cgd8_2370 and Chro.50330 genes) the generated sequences were not of high quality and were not used for analysis. Therefore, the differences between this strain and the other isolates were based only on 2853 bp comparisons for 7 genetic loci. The phylogenetic tree with C. meleagridis as the out group also allowed discrimination of Cryptosporidium species and subtypes in a similar manner than the tree presented in Figure Figure2A.2A. The two phylogenetic trees showed similar bootstrap values (Figure (Figure2A2A and and2B2B).
In this study, comparative genomic tools were used to identify putative species-specific genes for C. hominis and C. parvum based on published genome sequences. The initial bioinformatics primary and secondary screening allowed the identification of 93 and 211 genes for C. hominis and C. parvum, respectively. This finding is somewhat lower than the number of orthologous gene clusters for C. parvum and C. hominis reported previously in a study of the Apicomplexa . Initially, 10 of these genes were tested by PCR in a collection of Cryptosporidium clinical isolates and reference strains. PCR screening of the predicted putative species-specific genes showed that the majority of the genes were not as predicted. In fact, 90% of the genes tested were present in both C. hominis and C. parvum isolates. This would suggest caution when using lineage-specific genes for taxonomic analysis at least until published genomes are known to be complete .
The discrepancy between bioinformatics and PCR is likely to be caused, at least in part, by the fact that the C. hominis TU502 genome is neither completed nor fully assembled, which is consistent with the smaller number of putative C. hominis specific genes as compared to those specific to C. parvum. However, this seems to be in disagreement with the finding that the C. hominis genome has 42 genes more than the C. parvum genome. Nevertheless, it is plausible that the status of the C. hominis genome had hindered the accuracy of the initial comparative genomic analysis because the selected genes may correspond to sequence gaps reported by the authors . Further testing of an additional ten predicted putative specific genes for each species confirmed the general trend of similar amplification from both species. Therefore, the majority of the genes seem to be common to both species. However, an improved comparative genomic analysis has been made possible by the fast progress made towards the completion of C. muris genome. At the time of writing, 8.9 Mb from the C. muris genome have been made available for download from CryptoDB, of which 7.2 Mb corresponding to coding sequences. Based on these newly added genomic sequences, 7/10 (70%) of the selected putative species-specific genes appear to have orthologs in C. muris. This information, if known previously, would have decreased dramatically the number of putative species-specific genes predicted by comparative genomics. Despite this limitation, only one C. parvum and one C. hominis gene were shown experimentally by PCR to be putatively specific, the characterisation of these genes is ongoing.
We considered whether the observed ubiquity of the predicted specific genes represented the closeness between C. hominis and C. parvum or whether these primers would also amplify orthologous genes from other Cryptosporidium species by testing DNA from C. andersoni, C. felis, cervine genotype, C. meleagridis and C. baileyi. Cryptosporidium meleagridis DNA amplified using 80% of the primers tested, while, C. andersoni, cervine genotype and C. felis DNA amplified with only 10% of primers. This result is in accordance with the taxonomy and evolution of Cryptosporidium species . In fact, amongst the species tested, C. meleagridis is the closest species to the cluster formed by C. hominis, C. parvum and C. cuniculus based on partial SSU rRNA gene . Cryptosporidium meleagridis DNA did not amplify with primers of Cgd2_2430 and Chro.20156. This could be explained by either nucleotide mismatch in the primer region or that the genes were missing.
PCR screening and sequencing of genes found experimentally to be common to both species provided de novo sequence information at incomplete regions of the Cryptosporidium genomes and was used to examine polymorphism in these regions. PCR product sequence analysis revealed interesting genetic variation as SNPs. In this study, 78 SNPs were detected, 78.3% (61) of which were species-specific. The presence of species-specific SNPs was reported previously from several genetic markers and has been exploited for Cryptosporidium genotyping and subtyping . PCR-RFLP of the SSU rRNA , COWP , dihydrofolate reductase (DHFR) gene , thrombospondin related adhesive protein of Cryptosporidium-1 (TRAP-C1)  and TRAP-C2 , polythreonine (Poly-T) repeats and heat shock protein 70 (HSP70)  genes allow discrimination between Cryptosporidium species from various sources. In a similar manner, the newly identified SNPs could be also used for Cryptosporidium genotyping, especially by PCR-RFLP and/or sequencing. The majority of the SNPs detected (64.2%) were synonymous. It has long been assumed that synonymous SNPs are inconsequential as the primary sequence of the protein is preserved. However, it has been demonstrated that synonymous mutations can alter the structure, function and expression level of the protein by affecting messenger RNA splicing, stability, protein folding and structure . In addition, Ge and colleagues  used a genome wide analysis and described a high number of nucleotide substitution patterns in C. parvum and C. hominis orthologous protein coding genes. The authors also reported a high number of non-synonymous SNPs in genes involved in host-parasite interactions, mainly genes with transmembrane domains or signal peptides .
The sequence analysis of C. meleagridis PCR products allowed data enrichment as this species is distant from C. hominis and C. parvum. In fact, among the genes assessed here, C. meleagridis species had 108 additional SNPs, 20 of which are in the Chro.30149 gene. For Chro.30149 gene, C. meleagridis has in average 1 SNP every 15 nucleotide. Surprisingly, all C. meleagridis SNPs are synonymous. Interestingly, no SNP was detected in this gene from C. hominis and C. parvum DNA. Chro.30149 has a predicted function as Ubiquitin ligase. This gene is a housekeeping gene and shows a low level of sequence divergence between species and isolates when compared to contingency genes consistently under environmental pressure and characterized by higher spontaneous mutation rates .
The newly identified SNPs were used to determine genetic differences between the main Cryptosporidium species and subtypes tested. This analysis showed that the genetic difference between C. hominis and C. parvum was only 1.72%. Within C. parvum group, the anthroponotic subtype isolates showed only 0.12% from the main zoonotic C. parvum isolates. The C. cuniculus isolates exhibited 0.27% genetic differences to C. hominis isolates. In addition, extremely low sequence variability between C. hominis and C. cuniculus was observed using the common genotyping loci . Based on these data and supported by morphological analysis and experimental infection, rabbit genotype was considered synonymous with C. cuniculus .
In addition, sequence analysis allowed us to perform a robust and novel MLA. The Neighbour-Joining phylogenetic tree clearly grouped and discriminated with high bootstrap values the previously described lineages of Cryptosporidium subtypes. Therefore, these genetic loci represent potential powerful targets for Cryptosporidium genotyping and subtyping purposes. Especially since these genes are stable and slow mutating, unlike the currently used Cryptosporidium typing targets (gp60, mini- and microsatellites).
Mini and Microsatellites are repetitive versatile DNA repeats known to influence the structure and expression of protein-coding genes and to be responsive to environmental signals [32,33]. The microsatellites abundance and high variability made them the genetic markers of choice for several applications (individual identity, forensics, parentage, genetic structure, epidemiology and phylogenetics . However, because of the instability of microsatellite markers, extra care should be taken when interpreting microsatellite-based typing data . Similarly, gp60 is hypervariable and under selective pressure as it mediates parasite attachment to host cells . In fact, discrepancies and limitations of these markers for Cryptosporidium typing have been reported. Hunter and colleagues  described the difficulty in interpreting the presence of different subtypes in outbreak setting and Widmer  reported that gp60 might not be a reliable marker of C. parvum and C. hominis population structure. The ten novel loci, described in this study, showed excellent discriminatory power and consistency to assess phylogenetic relationships at the species and infra-species levels. These findings suggest that these loci could be alternative valuable genotyping and subtyping targets for Cryptosporidium. However, their stability should be assessed in an extensive collection of isolates from different subtype families and geographical locations to validate their discriminatory power.
In this study, comparative genomics were used to identify putative C. parvum and C. hominis species-specific genes. Despite the fact that the majority of the predicted genes were common to both species and some to C. meleagridis, experimental evidence was found for one specific gene for each species. The ten novel genetic loci studied showed an interesting polymorphism. In fact, sequence analysis of PCR products revealed multiple SNPs, the majority of which were species-specific. These SNPs were stable and consistent across Cryptosporidium species and subtypes. These results showed that the ten novel genetic loci can potentially be used to assess the phylogenetic distance and relationships at the species and infra-species level of human infective Cryptosporidium isolates. In addition, the paired SNP analysis was found to be a good strategy to assess the genetic divergence of the isolates tested.
Reciprocal Blast was used to identify genes with high sequence variability between C. parvum and C. hominis. This is a variant of Blast (Basic local alignment search tool), originally described by Altschul and colleagues  and is a common computational tool for predicting putative orthologs http://www.ncbi.nlm.nih.gov/blast/blast_overview.shtml. Subsequently, each of the ~ 3900 genes of C. parvum and C. hominis was assigned a similarity score. Only sequences which returned genes with less than 10% sequence similarity from the other genome were considered. These coding sequences are putatively species-specific genes. A secondary screen was performed as follows: each gene was individually tested using Blastn algorithm http://blast.ncbi.nlm.nih.gov/Blast.cgi to confirm specificity and reveal any sequence similarity to genes from other Cryptosporidium species. Furthermore, orthology queries were performed using CryptoDB database. Whenever a gene showed sequence similarity, it was eliminated from the selection. This secondary screen increased the prediction stringency.
Amongst the putative species-specific genes, initially 10 genes were selected with preference to annotated genes and tested experimentally by PCR. For each gene, a pair of primers was designed using OligoPerfect™ Designer software http://www.invitrogen.com and supplied by Operon/Eurofins MWG (Cologne, Germany). Table Table11 details the genes selected, the primer sequences and the PCR product sizes for each gene tested. In addition, reference primers Cry15 and Cry9 amplifying a 555 bp of the COWP gene  were used as a positive control. PCR conditions were carried out as described previously . PCR screening of putative species genes was performed by testing a panel of DNA clinical samples isolated as described previously  and archived in the national collection at the UK Cryptosporidium Reference Unit (CRU) . Each isolate was characterised initially by PCR-RFLP of the Cryptosporidium oocyst wall protein (COWP) gene  and by real-time PCR using simplex Lib 13 primers for C. parvum and C. hominis  prior to sequencing part of the SSU rRNA and gp60 genes [44,45]. A total number of 14 Cryptosporidium clinical isolates was tested (Table (Table2).2). This includes DNA from three C. hominis isolates (Ch2, Ch3 and Ch4), 3 C. parvum isolates (Cp2, Cp3, and Cp4) and 4 C. parvum anthroponotic subtype isolates (W7265, W7266, W7267 and W7270). The anthroponotic C. parvum group isolates were previously identified as gp60 subtype family IIc (CRU unpublished data). This subtype family was reported to infect only humans, and was never reported in an animal species . The anthroponotic nature of the IIc subtype family was supported by extensive subtyping investigations of human and bovine cryptosporidiosis in Portugal, USA, Canada, UK, Ireland, Slovenia, the Netherlands and Australia [1,46-48]. In addition, the DNA of one rabbit genotype (C. cuniculus) isolate from the Northamptonshire outbreak  and three sporadic cases (Chalmers et al., manuscript in preparation) were also analysed. These DNA samples originated from patients with cryptosporidial diarrhoea from different geographical locations in UK and were chosen as a representative collection of the different strains circulating in the country. Furthermore, the genomic DNA of 3 reference strains C. parvum Iowa (ATCC/LGC Promochem, Teddington, UK), C. parvum Moredun (Moredun Research Institute, Midlothian, UK) and C. hominis TU502 (BEI Resources, Manassas, USA) were tested. Table Table55 details the origin and the genotyping data of the tested isolates. In addition, we considered whether the designed primers would amplify orthologous genes from other Cryptosporidium species, therefore, DNA from other Cryptosporidium species and genotypes was kindly donated by CRU and tested; this includes C. andersoni (W13086), C. felis (W14508), cervine genotype (W15916), C. meleagridis (W10509) and C. baileyi (W14184).
Positive PCR products were purified using QIAquick® PCR purification Kit (Qiagen Ltd., Crawley, UK). Purified PCR products were sequenced in both directions using PCR primers. We used 2 independent sequencing facilities: the genome lab, John Innes Centre http://www.jicgenomelab.co.uk and the sequencing service at the University of Dundee http://www.dnaseq.co.uk, both using Dye-terminator chemistry technology and Applied Biosystems automated capillary DNA sequencer (3770 and 3730 model, respectively). Sequences were assembled using CAP3 software http://pbil.univ-lyon1.fr/cap3.php and aligned using AlignX® application of Vector NTI Advance™ 10 software http://www.Invitrogen.com. Phylogenetic analysis was performed using MEGA (Molecular Evolutionary Genetic Analysis) software http://www.megasoftware.net.
MB carried out the experimental testing of the predicted putative species-specific genes, sequence alignment and data analysis and drafted the manuscript. KMT conceived the study, provided technical guidance, coordinated the study and helped to draft the manuscript. RC performed the comparative genomic analysis. RMC participated in the design of the study and helped to draft the manuscript. KE carried out DNA extraction from clinical samples and genotyping and subtyping of the isolates at CRU and helped to draft the manuscript. PRH coordinated the study and carried out data analysis and MLA. All authors read and approved the final manuscript.
Alignment of PCR product sequences of Cryptosporidium clinical isolates and reference strains. This file shows the PCR product sequences for the ten novel genetic loci and the COWP gene. The sequences are available online (see result section). The alignment shows the position of each SNP detected. The totality of the SNPs was used for MLA and calculation of genetic differences between Cryptosporidium species and isotypes tested.
The authors thank Dr Stephen Hadfield and Dr Guy Robinson, CRU for scientific support. Thanks are also extended to Dr Brent Emmerson, School of Biological sciences, University of East Anglia for scientific discussions.
This work was partially supported by funds from the European Commission for the HEALTHY WATER project (FOOD-CT-2006-036306). The authors are solely responsible for the content of this publication. It does not represent the opinion of the European Commission. The European Commission is not responsible for any use that might be made of data appearing therein.