Abstract

In the last decade, the rise of affordable high-throughput sequencing technologies has led to rapid advances across the biological sciences. At the time of writing, annotated reference genomes are available within most clades of eukaryotic pathogens, and including un-annotated sequences over 550 genomes are available in total. This has greatly facilitated studies in many areas of parasitology. In addition, the volume of functional genomics data, including analysis of differential transcription and DNA-protein interactions, has increased exponentially. With this unprecedented increase in publicly available data, tools to search and compare datasets are also becoming ever more important. A number of database resources are available, and access to these has become fundamental for a majority of research groups. This chapter discusses the current state of genomics research for a number of eukaryotic parasites, discussing the genome and functional genomics resources available at the time of writing and highlighting functionally important or unique aspects of the genome for each group. In addition publicly accessible database resources pertaining to eukaryotic parasites are also discussed.

Introduction

Arguably the field of genomics began when Friedrich Miescher first isolated DNA in 1869 [1], paving the way for the work of many scientists in understanding the role of this material in heredity [2], discovering its double-helical structure [3] and deciphering the genetic code [4]. However, the technological advance that the entire field of genomics rests on is sequencing [5–7]. The ability to read the genetic code is relatively new, having only been developed in the last 50 years. Sanger sequencing, which relies on dideoxy chain termination, remained the method of choice for several decades; however, early implementations of dideoxy chain termination methods were not well parallelized and analysis was initially a painstaking manual process. Later, data analysis was carried out computationally, but limited by the processing capacity of computers of the era. These factors combined to limit early sequencing to individual genes, small genomic fragments or the genomes of small viruses and organelles. The emergence of techniques such as fluorescence-based cycle sequencing and the polymerase chain reaction in addition to the increased use of computational power to automatically read and analyze results, allowed larger scale genome projects to be undertaken [8]. Indeed within a few years of this marriage of techniques and fields the first bacterial, protozoan, fungal, plant and animal genomes were sequenced [9–12] [13] [14]. Despite these advances, sequencing of whole genomes remained relatively costly and time consuming. As an example, sequencing the human genome took roughly 10 years at a price tag of 3 billion US dollars (https://www.genome.gov/11006943) [15].

The first forays into high-throughput analysis of sequence data came in the form of microarrays. A microarray consists of a panel of oligo-nucleotide probes bonded to a solid surface such as a glass slide. Hybridisation of nucleic acids from a specimen to individual probes is detected by the intensity of a fluorescent signal. This technique was the first to make querying of sequence polymorphisms, transcript expression levels and segmental duplications possible on a genomic level, and cheap enough to be widely available. In addition, microarrays forced the development of computational tools and techniques to handle data on a genomic scale. However, an important limitation of microarrays is the requirement for prior knowledge of the genome and the coincident inability to make de novo discoveries (i.e., one can query the presence of known SNPs, but not discover new SNPs). A large volume of functional genomics data has been obtained using microarray technologies, but with a small number of exceptions (such as diagnostics), microarrays have for the most part been superseded by next-generation sequencing technologies.

Two factors have been instrumental in enabling sequencing to be taken to the next level: continued growth of computer processing capacity following Moore’s law [16] and the development of “next-generation” sequencing (NGS) methods (also known as second generation sequencing), which enable massively parallel sequencing of millions of fragments by synthesis [17, 18]. One of the major advantages of next generation sequencing is that it can be applied to a wide variety of methodologies including (readers are directed to an excellent series of manuscripts http://www.nature.com/nrg/series/nextgeneration/index.html) and unlike microarrays, does not require any prior knowledge of the sample:

• DNA sequencing: High-throughput technology makes sequencing for de novo assembly of new genomes ever more affordable. Comparison of resequenced isolates against a reference is a common technique for discovery of sequence polymorphisms, while analysis of coverage depth and mapping topology can reveal information about structural variations such as chromosomal translocations and segmental duplications.
• RNA sequencing: Sequencing of RNA can provide important information about gene structure such as the locations of UTRs and intron/exon boundaries, and the presence of alternative or trans- splice variants. Analysis of RNAseq coverage depth over a time course or under different experimental conditions reveals information about transcription of genes under differing conditions and combination of this technique with ribosomal profiling enables identification of the translational status of the genome. Specialised sample preparation techniques enable the sequencing of non-coding RNA species such as those involved in the RNAi-mediated translational-silencing.
• Epigenomics: Chromatin immunoprecipitation (ChIP)-sequencing is a powerful technique that allows determination of the “footprint” of DNA-binding proteins. This can be used to examine promotor-binding sites, transcription, replication and repair mechanisms and factors such as histone-modification that can affect transcription. Other techniques are available, such as bisulfite sequencing which enables profiling of DNA methylation.
• Metagenomics: Sequencing of DNA extracted from samples that contain mixed populations of organisms can be used to survey populations in environmental samples (such as soil) or biological samples (such as gut microbiomes). Metagenomics techniques can be used to determine the makeup of populations and to survey how this changes over time or under different conditions. Metagenomics analysis is a fast-growing field in which the problems of analysis have not yet been solved.

It is not surprising that the dawn of large scale sequencing projects necessitated an expansion in the field of bioinformatics and data management. As high-throughput sequencing has become cheaper it has moved from being a specialist technique to a tool used daily in labs across the world. This has necessitated the development of user-friendly tools that can run on desktop machines, and thrust the field of bioinformatics into the foreground. The expansion of massively parallel sequencing has also led to a revolution in the teaching of biology, with computational techniques for management and analysis of genomic-scale datasets now being taught in many undergraduate courses. Data warehousing is also becoming a priority, with data repositories such as the National Center for Biotechnology Information (NCBI) [19] having to rethink both their submissions procedures and their approaches to storage.

Parasite genomics

The field of parasite genomics has benefited tremendously from the sequencing revolution. While only a handful of parasite genomes were sequenced by 2005, the number has exploded to over 550 genomes (http://genomesonline.org) [20] by 2015. This number reflects both annotated and unannotated genomes and will already be out of date by the time this chapter is in print. Besides the technological advances, this increase in sequences has been aided by a number of initiatives with parasitology components. These include projects supported by the Wellcome Trust Sanger Institute in the United Kingdom and a number of parasite specific genome sequencing white papers supported by the National Institute of Allergy and Infectious Diseases (NIAID) Genomic Centers for Infectious Diseases (GCID) in the United States. Together these centers have generated sequence, assemblies and annotation from many important human and veterinary parasites. All data from these projects are available via project specific websites (ie. GeneDB: http://genedb.org) [21] and/or through the International Nucleotide Sequence Databases (GenBank, EMBL Nucleotide Sequence Database, and the DNA Data Bank of Japan [22–24]).

General features of protozoan parasite genomes

Entamoebae

The described species of Entamoeba are generally obligate parasites or commensals. They have simple life cycles consisting of a vegetative stage, the trophozoite, which lives in the host’s large intestine and feeds upon bacteria and a transmissible stage, the cyst, which allows survival outside the host and transmission to a new host. Possible exceptions to these rules include two species (Entamoeba moshkovskii and Entamoeba bangladeshi) that can survive outside of the host and may be primarily free-living organisms, and one species (Entamoeba gingivalis) that colonises the mouth and may have lost the ability to form cysts instead being transmitted directly in the trophozoite form.

The human pathogen Entamoeba histolytica is the most studied species of the genus. A draft genome assembly was first published in 2005, with subsequent updates, though it remains fragmented and chromosomes cannot be defined [27–29]. Unusual features of the E. histolytica genome include an unusual organisation of tRNA genes, which occur in arrays of sets of tRNA genes separated by repetitive intergenic DNA [30], and rRNA genes encoded on extrachromosomal circular DNA occurring in multiple copies per cell [31]. Two features of Entamoebae associated with their anaerobic environments are the loss of the function and genome of the mitochondrion, which occurs as a relict organelle, the mitosome, and the related lateral transfer of genes, many involved in anaerobic metabolic processes and apparently derived from anaerobic bacteria [32].

Genomic re-sequencing suggests little nucleotide diversity among E. histolytica, even among lineages derived from widely separated geographical locations [33]. In contrast, gene copy number variation appears to be extensive [33], which may be associated with the genomic plasticity observed among E. histolytica lineages [34]. Studies using tRNA repetitive intergenic DNA or SNP markers also suggest very little linkage disequilibrium among markers, which suggests extensive outcrossing among parasite lineages [33, 35, 36]. Genetic diversity in other Entamoeba species is largely unknown, apart from studies of the 18S ribosomal RNA gene, which indicate that some ‘species’ may in fact be species complexes [37].

Genomic data exist for four other species of Entamoeba: E. nuttalli, E. dispar, E. moshkovskii and E. invadens. For the first three of these, the data are available but no reference publication yet exists. Most closely related to E. histolytica, Entamoeba nuttalli is a pathogen of macaques [38–40]. Entamoeba dispar infects humans and is of primary interest as a relative of E. histolytica (only recently defined as a separate species) that appears to be non-pathogenic [41]. Entamoeba moshkovskii is of uncertain status as a parasite or a free-living organism and has recently been associated with disease in humans [42, 43]. Entamoeba invadens, a pathogen of reptiles, is of primary interest as a model species for the process of encystation (which cannot be induced in axenic E. histolytica cultures). The genome of E. invadens is considerably larger than that of E. histolytica [44]. Genomic data for a number of E. histolytica strains, from a range of geographical locations and associated with different disease manifestations, are available via AmoebaDB (http://AmoebaDB.org) [45] (Table 1.1).

Table 1.1

Genome datasets of amoebae available in AmoebaDB.

Cryptosporidium

Cryptosporidium are protozoan parasites with significant impact to the health of humans and livestock. They infect the intestinal and gastric epithelium of a variety of vertebrates, causing a disease known as cryptosporidiosis. Human cryptosporidiosis is responsible for diarrhea-induced death of young children in developing countries, and in immune-compromised adults it constitutes an acute, usually self-limiting, diarrheal illness that results in significant morbidity and sometimes death. A recent study found Cryptosporidium to be the second leading cause of moderate-to-severe diarrhea in developing countries, and diarrheal diseases to be the second leading cause of death among children under five globally [60].

There are no licensed vaccines against Cryptosporidium and the only FDA-approved drug (Nitazoxanide) is only effective in immunocompetent patients. Thus, the development of alternative therapeutic agents and vaccines against this disease is urgently required, and remains a high public health priority. The lack of a practical and reproducible axenic in vitro culture system for Cryptosporidium is a major limitation to the development of specific anti-cryptosporidial vaccines [61, 62]. Advances in next-generation sequencing technologies and in genome assembly and annotation methodologies [63–66] have facilitated the generation of -omics data for Cryptosporidium, with genomics resources now available for multiple Cryptosporidium species (Table 1.2, [67]). These developments prompted a shift to in silico studies aiming to identify a wide pool of potential vaccine targets, to be further filtered according to properties common to antigens [68]. This approach is similar to reverse vaccinology studies that have led to licensed vaccines in other organisms [69, 70], and is particularly promising in organisms that, like Cryptosporidium, are difficult to cultivate continuously in the laboratory.

Table 1.2

Cryptosporidium species with completed or draft genomes.

Apart from human, Cryptosporidium species infect other vertebrates including fish, birds and rodents, and some species are capable of zoonotic transmission [71, 72]. Some have a somewhat restricted host range, such as Cryptosporidium hominis, a human parasite that infects the small intestine, Cryptosporidium muris, a gastric parasite of rodents, and Cryptosporidium baileyi, an avian parasite. Cryptosporidium parvum and Cryptosporidium meleagridis have a wider host range and are known to infect both avian and mammalian species, including humans. C. parvum and C. hominis are considered class B agent of bioterrorism and are significant causes of gastrointestinal infections worldwide.

Cryptosporidium genomic resources

Cryptosporidium genomes are compact, with >75% consisting of protein-coding sequences, have an average size of approximately 8.5 to 9.5 mega base pairs (Mbp), and each encode ~4000 genes (Table 1.2). C. parvum (isolate IOWA II) was the first species for which a genome was published [73]. The genome was found to be 9.1 Mbp in length, assembled into thirteen supercontigs. Pulsed-field gel electrophoresis studies had shown the nuclear-encoded genome to consist of 8 chromosomes, and therefore the assembly includes five unresolved gaps. About 5% of the 3,807 predicted protein-coding genes in this assembly contained introns, and the average gene length was 1,795 base pairs (bp). At about the same time the genome of C. hominis (isolate TU502) was published [74]. Since the two species were known to be closely related, with about 95-97% DNA sequence identity between them, the C. hominis genome was sequenced to a much lower depth of coverage. The primary goal was to identify differences relative to C. parvum, rather than reconstruct a gold-standard genome assembly. Consequently, this assembly is much more fragmented, with the likely 8 chromosomes split among 1,413 contigs, which are grouped into ~240 scaffolds.

Table 1.2

Genome statistics for representative Cryptosporidium species.

There were some fundamental differences between the annotated gene sets in the two species. The average gene length of C. hominis was 1,360 bp, about 500 bp less than that of C. parvum, and about 5-20% of the C. hominis genes were predicted to contain introns, compared to 5% in C. parvum [73, 75]. In addition, only 60% of the C. hominis genome was estimated to be coding compared to 75% for C. parvum. These differences are remarkable for such closely related taxa and were thought to be due to erroneous gene models in C. hominis due to the high degree of genome fragmentation. To address these questions, the genome assembly for C. hominis has recently been re-sequenced, assembled and annotated, improving the assembly from draft to “nearly finished” form, with preliminary data available in CryptoDB.org. This effort increased the average gene length by 500 bp, bringing it to 1,845 bp, in line with gene length in C. parvum (Table 1.2). The improved genome assembly consists of only 120 contigs, a ten-fold reduction in contig number relative to the original C. hominis assembly. The genome assembly is more comprehensive, with an additional 370 Kb sequence, also now comparable in length to that of C. parvum. Finally, there was a 25% increase in the predicted fraction of the genome that encodes for proteins. The now marked similarities between the re-annotated C. hominis gene set and that of C. parvum provide encouraging evidence that the predicted genes are a significant improvement over the original annotation, but validation of gene structures awaits community effort. C. parvum IOWA II was also recently re-annotated, based on full-length cDNA clone sequences and RNA-Seq data (Table 1.2, [76]).

Both C. hominis and C. parvum are intestinal parasites. C. muris (isolate RN66), the third species sequenced, was chosen for two primary reasons: its evolutionary distance to C. hominis and C. parvum, and the fact that it is a gastric species, which is rare among Cryptosporidium parasites. Currently, the field is rapidly expanding, with the genome sequence for several isolates of C. parvum and of C. hominis now available, as well as the genomes of other species (Table 1.3).

Table 1.3

Cryptosporidium genomes available in CryptoDB.

The availability of multiple isolate genomes per species allows analyses that can shed light into species evolution, including age and population structure, and will facilitate studies that address key questions of great translational impact, including the amino acid sequence variations in current candidate vaccine antigens, and the identification of genomic correlates of virulence whenever isolates with different pathogenic potential are available. In an effort to support research that addresses key questions in the evolution of the Cryptosporidium genus, and the discovery of parasite-encoded factors that control host specificity, C. meleagridis UKMEL1 was sequenced, a species which appears to lack host specificity and that is considerably more distantly related to C. hominis and C. parvum than they are to each other, but a closer relative to them that is C. muris. C. baileyi can complete its life cycle in embryonated chicken eggs, of critical importance for the establishment of an avian model system of cryptosporidiosis, and C. baileyi TAMU-09Q1 was sequenced to support its development of such a system. Determining the proportion of Cryptosporidium infections that are caused by human-specific parasites rather than by zoonotic infections remains a critical question in the field. Accordingly, the genome of a zoonotic infection by a Cryptosporidium species with origin in the chipmunk was conducted with the goal of identifying genotyping markers that differentiate among Cryptosporidium subtypes [77].

A major challenge for the generation of Cryptosporidium whole genome sequence data has been the need to propagate the parasites in vertebrate hosts, a step needed to generate DNA material in sufficient quantity and of the quality need for use in high-throughput sequencing applications. A novel method for preparing genomic Cryptosporidium DNA directly from human stool samples that satisfies the criteria these applications has now been developed [78]. The authors used this approach to generate five assemblies each for C. parvum and C. hominis. Finally, a new C. hominis (isolate UdeA01) also isolated from human stool has been sequenced independently [76].

All the genomics data described above is publicly available through CryptoDB [67]. This database also provides a platform to easily query the annotation and a variety of pre-computed analysis data (including homology information across taxa). Multiple aspects of the data can be easily visualized, including synteny, polymorphism and expression data. CryptoDB also contains Cryptosporidium information other than genome sequences, including gene expression and proteomics data (Table 1.4).

Table 1.4

Other Cryptosporidium genomic resources available in CryptoDB.

Piroplasms

Piroplasms are a vast group of poorly characterized Haemosporidia that are named after their pyriform (pear-shaped) structure visible during intracellular stages in the host erythrocytes. They are found in numerous mammals, birds, and reptiles, and are often transmitted by ixodid ticks after parasite replication in the tick gut [88]. While little is known about the life cycle of most piroplasms, well-described species of Theileria commonly infect mammalian host leukocytes, followed by a tick-infective stage in red blood cells (RBCs), while Babesia do not have a leukocyte-infective stage [89, 90]. Some Babesia species are known to infect humans (B. microti, B. divergens, B. duncani), where they cause a malaria-like disease [89]. The diseases caused by these parasites can lead to fevers and even death in equid and ruminant livestock species, all around the world. Consequently, most of the genomics resources developed for piroplasm research to date have focused on species that infect bovids (Table 1.5). Most of these resources are available through PiroplasmaDB (http://PiroplasmaDB.com).

Table 1.5

The first publication of available piroplasm whole genome sequences, and a few features of their genomes. All of these are available at PiroplasmaDB, with the exception of B. divergens.

The first piroplasm genomes were published in 2005, and consisted of Theileria species of domestic cattle and wild buffalo. T. parva causes a tremendous economic impact in eastern, central and southern Africa [90], while T. annulata is distributed throughout much of southern Asia and southeast Europe [94]. Their genomes are small at ~8.3 Mbp in length, are AT-rich with GC-content of 33%, and contain ~4,000 nuclear protein-coding genes. These properties are similar to the genomes of other Piroplasmida that have been sequenced since (Table 1.6). Several genomic features were uncovered that are typical of other sequenced piroplasm genomes, such as the presence of telomeric multi-gene families, and several incomplete or absent biosynthetic pathways, implying a critical dependence on salvaging resources from their hosts [97]. These two piroplasms are unique, however, in their ability to transform host leukocytes to have cancer-like phenotype. This phenotype correlates with the expansion of two multi-gene families: the Subtelomere-encoded Variable Secreted Protein (SVSP) gene family and the T. annulata schizont AT-hook/T. parva Host Nucleus (TashAT/TpHN) gene families [98, 99]. Two other Theileria species have been sequenced, T. orientalis [98], an economically important pathogen of cattle in eastern Asia, and T. equi [95], which has a worldwide distribution and infects equids. These two genomes have many similar features, with the exception that the genome of T. equi is larger, mostly due to a significant increase in the number of species-specific genes, including antigen-encoding families such as the Equi Merozoite Antigen (EMA) family [95].

Table 1.6

Whole-genome data for several piroplasm species. These resources are not available at PiroplasmaDB, but can be found associated with their respective references.

With a genome size of ~8.2Mpb, the B. bovis genome sequence revealed a genomic organization that is remarkably similar to T. parva, with extensive synteny and multiple, large multi-gene families potentially contributing to host immune evasion [106]. However, the smallest apicomplexan genome sequenced to date is B. microti, the principal agent of human babesiosis and a common pathogen transmitted by blood transfusions [89, 101]. With a genome size of 6.5Mbp, B. microti represent the closest record of a natural representation of an apicomplexan “core genome” and comparative genomics with this reduced genome could yield insights into the most essential gene products of apicomplexans that could make excellent chemotherapeutic targets. B. microti is also the only example of an apicomplexan with a circular mitochondrial genome[101].

One apicomplexan with somewhat unclear phylogenetic position is Cytauxzoon felis. While originally considered a separate genus, the existence of exo-erythrocytic forms, particularly schizonts, in macrophages/monocytes indicates that this parasite might be more appropriately considered in the family Theileriidae. C. felis is an emerging pathogen of domestic cats (Felis catus) in the southern United States, and as such its genome was sequenced in an effort to identify potential vaccine targets [93]. With a 9.1 Mbp genome, it has more protein coding genes in common with T. parva than it does with B. bovis, and was found to encode a gene that is syntenic with a block of genes around the T. parva antigen, and vaccine candidate, p67 [93].

There are currently no licensed vaccines against apicomplexans for use in humans, although the RTS,S malaria vaccine is close to licensure. With a few notable exceptions, such as coccidiosis (Eimeria), toxoplasmosis (Toxoplasma), and East Coast Fever (Theileria parva) vaccines, very few vaccines against piroplasms have been used on a commercial scale, which may be due, in part, to antigenic diversity in these parasites [107]. Genomic resources have also recently started to become available for some piroplasms (Table 1.7). These data are critical for identification of potential virulence genes, mapping recombination hotspots, and estimate genome-wide variation among various isolates, including vaccine strains [105]. One weakness of piroplasm whole-genome datasets is their reliance on ab initio gene predictors for the majority of their structural annotations (determining where exons start and end in the genome). Given the fact that these genomes are smaller, denser, and more AT-rich than most eukaryotes sequenced to date, these gene predictors may not be optimal for gene prediction in these genomes, and experimental evidence should be rigorously incorporated into genome re-annotation efforts in order to take full advantage of the genome sequences that are present for these apicomplexans. The coupling of whole-genome variation data with gene expression data is a powerful method to give insight into gene structure, variation and function, and will hopefully assist the design of better prophylaxis against piroplasm-mediated diseases.

Table 1.7

Gene expression data not found at PiroplasmaDB for several piroplasm species. Most expression data, including more EST data, is found at PiroplasmaDB for piroplasms.

Plasmodium reference genomes

To date several complete reference genomes of Plasmodium, the aetiological agent of malaria, have been sequenced. Advances in technology have also led to the sequencing of many additional lab strains and clinical isolates. The first reference to be published in 2002 was P. falciparum 3D7 [12], the species responsible for the majority of human morbidity. Additional genomes of species that infect humans have been sequenced (P. vivax [114]) or are in the process of being sequenced and analysed (P. malariae and P. ovale). The simian-and human infecting P. knowlesi [115], the chimpanzee malaria P. reichenowi [116] and the simian malaria parasite P. cynomolgi [117] are also part of the reference genome collection. Draft genomes of three rodent malaria parasites that are widely used as model systems, P. yoelii yoelii [118], P. chabaudi chabaudi AS and P. berghei ANKA were initially sequenced and analysed in 2005 [119]. Due to the highly fragmented nature of these genomes, they were re-sequenced in 2014 [120]. Two avian malaria genomes, P. relictum and P. gallinaceum have been sequenced and are in the process of being analysed. They will provide a valuable missing link to understand the evolutionary context of human malaria. All of the published genomes mentioned above can be searched in PlasmoDB [121] and GeneDB [21].

The publication of P. falciparum 3D7 in 2002 was a major milestone [12]. It enabled the malaria community to systematically analyse the gene content and tailor their experiments based on genomic data. This is also shown by over 2000 citations of the genome paper since publication. After the initial publication, assembly and annotation of the P. falciparum 3D7 genome has been continuously improved over time. In 2011 a new P. falciparum 3D7 assembly (version 3) was made publicly available. This new version includes the correction of major mis-assemblies. The current genome version has a size of 23.3 Mb and encodes 5429 genes (Table 1.8). It is highly AT-rich with a GC-content of only 19.3%. The overall structure of Plasmodium genomes sequenced to date is very similar (Table 1.8). The nuclear genome consists of 14 chromosomes, the size ranges from 19Mb to 26Mb with a comparable number of genes. About three quarters of genes are conserved across all Plasmodium genomes, representing the core genome. Plasmodium genomes also exhibit a high degree of synteny.. The majority of the variation between Plasmodium species is found in the subtelomeric regions at the end of the chromosomes. In these regions, each of the Plasmodium species has a unique set of gene families that are often involved in immune evasion and virulence. The most important gene family in P. falciparum 3D7 is the VAR gene family that encodes the erythrocyte membrane protein 1 (PfEMP1). PfEMP1 plays a role in antigenic variation. Of around 60 gene family members, only one protein is expressed on the surface of infected red blood cells at a time. PfEMP1 can also bind to host endothelial receptors and therefore plays an important role in pathogenicity. Additional gene families include rifins and stevors. It has been recently shown that rifins are expressed on the surface of infected red blood cells where they mediate microvascular binding of infected red blood cells [122]. The function of stevors is unknown. Both, rifins and stevors belong to the PIR (Plasmodium interspersed repeats) superfamily. This superfamily is the only subtelomeric gene family found so far that is present in all of the Plasmodium species.

Table 1.8

Plasmodium reference genomes.

Closely related to P falciparum is the chimpanzee malaria parasite P. reichenowi. A comparative genomics analysis only showed minor differences between these two genomes. There is an almost complete co-linearity in the core areas of the genome. The organisation of var genes and other virulence-associated genes is also conserved. Differences were found in the reticulocyte-binding proteins, a gene family involved in invasion. These genes encode ligands that are important for the recognition of host erythrocytes. Members of this gene family are located on chromosome 13, where two almost identical genes are present in P. falciparum (RH2a and RH2b). P. reichenowi lacks RH2a, but encodes a new reticulocyte-binding protein, RH7. The most significant difference between P. reichenowi and P. falciparum was found in the rifin and stevor multigene families. There are currently 463 rifins and 66 stevors annotated in P. reichenowi, while P. falciparum only encodes 185 rifins. The difference in this multigene family also explains the difference in the overall number of genes found in the nuclear genome (Table 1.8).

P. vivax is the major source of human malaria outside of Africa. In contrast to P. falciparum this species has a dormant stage in the human liver and can stay inactive for years. The nuclear genome of the Salvador I strain P. vivax has a size of 26.8 Mb and encodes 5433 genes (Table 1.8). With a GC-content of 42.3% P. vivax has the highest GC-content found so far in Plasmodium. Unique to P. vivax is an isochore structure. Chromosomes have AT-rich chromosome ends and internal-regions of high GC-content.

Closely related to P. vivax is the malaria parasite P. knowlesi. P. knowlesi is primarily a simian infecting malaria parasite, but has also been reported to cause natural infections in humans mainly in South East Asia. The nuclear genome has a size of 24.4 Mb, a GC-content of 38.6%, the number of protein-coding genes is 5290 (Table 1.8). There are two novel features in the P. knowlesi genome. The major variant gene families that are usually located in subtelomeres, are found in chromosome-internal regions dispersed on all 14 chromosomes. These regions are often also associated with intrachromosomal telomeric repeats. Another unusual feature unique to P. knowlesi is a phenomenon called molecular mimicry. KIR proteins that are part of the PIR superfamily contain stretches of sequences that are identical to the host proteins AHNAK and CD99, which has a critical immunoregulatory role in host T-cell function. It is speculated that these proteins might interfere with host recognition processes. Another important gene family is the SICAvar (schizont infected cell agglutination) gene family. SICAvars are expressed on the surface of infected erythrocytes and are the largest family of variable surface antigens in P. knowlesi.

Phylogenetically related to P. knowlesi and P. vivax is the simian malaria parasite P. cynomolgi. P. cynomolgi is used as a model organism for human P. vivax infections. Both share the ability to form a dormant liver stage. Strain B of P. cynomolgi has been sequenced and published in 2011 [117]. The genome has a size of 26.2 Mb and encodes 5722 genes (Table 1.8). Of those, around 90% have 1:1 orthologs to P. vivax and P. knowlesi. P. cynomolgi and P. vivax share a common isochore structure, while the presence of intrachromosomal telomeric repeats is common to P. cynomolgi and P. knowlesi. Comparative genome analysis found a number of copy-number variants in multigene families, e.g. in reticulocyte-binding proteins.

Of particular interest are the rodent malaria parasites, P. berghei, P. chabaudi chabaudi and P. yoelii yoelii. They are used as model organisms for experimental studies of human malaria. The genome size of the rodent malaria parasite genomes ranges from 18.8 Mb to 22.7 Mb (Table 1.8). The GC-content is around 22%. P. yoelii yoelii has the highest number of genes in the nuclear genome, mostly due to a large expansion of PIR genes (980). Gene synteny is conserved along the 14 chromosomes, with only one known synteny breakpoint. Analysis of gene families in the rodent-infective species reveals that the gene family is the PIR gene family. [120]. The second largest gene family encodes fam-a proteins. Fam-a proteins are exported to the infected red blood cell and are expanded in the rodent malaria parasites. All other Plasmodium genomes sequenced to date have only one fam-a family gene. The number ranges from 161 in P. yoelii yoelii, to 148 in P. chabaudi chabaudi and 74 in P. berghei.

Trypanosomatids

Trypanosomatids are a group of parasitic unicellular flagellate eukaryotes. Their range of hosts is diverse and includes humans and as well as a wide variety of species from both the animal and plant kingdoms. Trypanosomatids belong to the kinetoplastida, which is included in the phylum Euglenozoa, a branch that diverged early in the eukaryotic tree [123, 124]. While a number of Kinetoplastida are pathogenic parasites most are free-living organisms found in soils and aquatic habitats. The name Kinetoplastida derives from the presence of large amounts of mitochondrial DNA, visible by light microscopy as a dense mass known as the kinetoplast with its contained DNA referred to as kDNA. Trypanosomatids are obligate parasites that can be monoxenous or dixenous (usually an insect vector and other animal or plant [125]).

Trypanosomatid Genomes

The nuclear genome of trypanosomatids has some unusual characteristics when compared with other eukaryotic genomes. Their genome is organized in polycistronic transcriptional units (PTUs) and the production of individual mRNAs from PTUs requires trans-splicing of a splice leader (SL) sequence [126]. PTUs are well conserved and exhibit a high degree of synteny between species. The kDNA has an unusual physical structure, being arranged in circles of DNA that are interlocked in a chain-mail like network. These mitochondrial mRNAs require post-processing in the form of insertion and deletion of uridines before being translated into proteins, a process known as RNA editing [127, 128]. Other peculiarities of trypanosomatid genomes include the almost complete lack of introns, kinetoplastid-specific histone modifications and histone variants, unique origins of replication in some genera, a special DNA base (Base J) [129], and the transcription of protein-coding genes by RNA pol I in African trypanosomes, a behavior unique among eukaryotes [130]. Although none of these unusual features seem to be exclusive of trypanosomatids and are also present, at least in some basic form, in other free-living kinetoplastids, they may be related to the development of parasitism in trypanosomatids [124, 131].

Regulation of Gene Expression in Polycistronic Transcriptional Units

One of the most striking characteristics of trypanosomatid genomes is the organization of their protein-coding genes into long polycistronic transcriptional units (PTUs) that contain tens to hundreds of genes in the same orientation. Individual mRNAs are produced from the precursor mRNA by the 5’ trans-splicing of a capped mini-exon or splice leader sequence (SL), followed by the polyadenylation of the 3’ end. The 5’ trans-splicing is linked to the polyadenylation of the upstream gene. Gene order within PTUs is highly conserved among trypanosomatids and the main differences are usually in the regions between the PTUs and at the ends of the chromosomes [126, 132].

The genes included in a PTU are functionally unrelated and can be expressed at different times of the cell cycle or in different life stages. Nonetheless, each PTU is transcribed from a single transcriptional start site (TSS), severely limiting the amount of regulation that could be provided by the induction or repression of promoters. In some cases correlation between the location of a gene in a PTU and its expression level has been described. For example, in T. brucei, genes downregulated after heat shock tend to be closer to the transcriptions start site (TSS), while upregulated genes tend to be more distal. Also, the position of the genes along the PTUs correlates with gene regulation during the different cell cycle stages. However, most of the genes do not seem to be ordered depending on their transcriptional regulation [123, 126, 133].

In most organisms, the start of transcription is a fundamental step in the regulation of gene expression. In trypanosomatids this layer is constrained, but a swift and specific regulation of gene expression is still needed. Dixenous species like T. brucei or L. major, have complex life cycles that require fast and extensive changes in morphology and metabolism. These changes depend, ultimately, on changes in gene expression. For example, the parasite has to quickly adapt to differences in temperature, energy sources and host immune system [130, 133]. Besides the regulation at the start of transcription, it is possible to modulate other steps in the transcription and translation process. Additional levels of control include transcriptional elongation, mRNA processing (trans-splicing and polyadenylation), export from the nucleus, mRNA degradation (in the cytoplasm and nucleus), translation (start and elongation) and protein degradation [126, 132].

Both mRNA processing and the control of the mRNA stability are important regulatory steps in trypanosomatids. The stability of the mRNAs depends on elements present in the 3’ UTRs, for instance, duplicated genes in tandem arrays can be differentially regulated due to differences in their 3’ UTRs. In T. brucei, the range of half-lives of mature mRNAs is very diverse and is also determined by the life-cyle stage. In addition, the half-life of a mRNA not only depends on the stability of the mature mRNA but also on the rates of destruction of the precursor mRNA. If a mRNA undergoes a late or delayed polyadenylation it is more susceptible to being degraded, even before finishing maturation [132, 134].

Trypanosomatids contain a large number of RNA binding proteins (RBPs) that likely regulate expression levels by binding to regulatory elements in the 3’ UTRs of the mRNAs. The amount of RBPs is high compared with the number mRNAs. Consequently the current hypothesis proposes the binding of multiple RBPs to each 3’ UTR, which would compete or cooperate dynamically with other RBPs. The mix of RBPs would determine the stability of the mRNA and could also modulate the translation process [132, 135]. The expression of protein-coding genes can also be regulated at the translational level. In ribosome profiling studies it has been shown that there is a wide range in the density of ribosomes associated to mRNAs, with differences between life stages. In addition, trypanosome mRNAs can contain upstream open reading frames in their 5’ UTRs, which decrease the translation of the main ORF [132, 136, 137].

Multi-Copy Families of Surface Proteins

Genome reduction is frequent in parasites with functions that are essential for a free-living organism becoming obsolete inside a host. Surprisingly, compared with other single cell parasitic eukaryotes, trypanosomatid genomes do not appear to be specially reduced in size or function. On the contrary, in the evolution of parasitism in trypanosomatids the gain of new competences seems to have been more important than the loss of functions [134]. One example of this gain of functions is the presence of large multi-copy families that encode surface proteins. These families are specific to trypanosomatids and usually have a non-random distribution in the genome. A number of them have been implicated in pathogenesis and defence against the host immune system, such as the Major Surface Protease (MSP) family of metalloproteases involved in pathogenesis and conserved in all trypanosomatids. Other well-known examples are the Variant Surface Glycoprotein (VSG) and procyclin in T. brucei, delta-amastin and Promastigote Surface Antigen (PSA) in Leishmania and trans-sialidases in T. cruzi [134, 138, 139].

Epigenetic regulation

In eukaryotes, nuclear DNA is organized into a complex of DNA and proteins known as chromatin. The nucleosome is the basic unit of the chromatin, providing a sevenfold condensation. It comprises an octamer made of 2 copies of each of the core histones (H2A, H2B, H3 and H4) around which approximately 147 bp of DNA are wrapped. In addition, there is a histone (H1) in the DNA region between two nucleosomes that helps stabilize the chromatin. The chromatin is folded into a 30nm chromatin fiber that can be further compacted, up to the level of the distinct chromosomes that can be visualized during the eukaryotic mitosis [126, 129]. Although the nucleosomes are still the basic unit of chromatin in trypanosomatids, their histones are divergent from those found in yeast and vertebrates. DNA in trypanosomatids is not condensed into the 30nm chromatin fiber nor do chromosomes condense during mitosis. However, some differences in the level of condensation between life-cycle stages have been described [126, 130].

Mechanisms that influence the structure of chromatin have been implicated in the regulation of gene expression. In trypanosomatids, as in other eukaryotes, specific modifications of the N-terminal tails of histones, or the presence of histone variants correlate with regions of active or repressed transcription. As of yet, no conserved sequences have been identified in the transcription start sites (TSSs) of the PTUs. It has been proposed that TSSs could be determined by chromatin structure rather than the presence of conserved sequence motifs. Some of the histone modifications described in trypanosomatids are common in eukaryotes, but there are also some modifications and histone variations specific to trypanosomatids, such as H3V and H4V (probable markers of transcription termination sites) [129, 140].

Mitochondrial Genome: Architecture and RNA Editing

The kDNA is made up of circles of DNA that are interlocked in a chain-mail like network and are of two types: maxicircles and minicircles. Maxicircles store information for classical mitochondrial genes and proteins, but their transcripts require RNA editing, the insertion or deletion of uridines, before being translated. Minicircles encode guide RNAs (gRNA), that act as templates during the editing process. Unlike other eukaryotes, mitochondrial tRNAs are found in the nuclear genome and require specific target sequences to be transported into the mitochondria [123, 128]. The mitochondrial genome contains a few dozens of maxicircles, with identical sequence and a size of 20-40 kb, and thousands of minicircles. Minicircles differ in sequence content but their size is species specific and uniform, usually between 0.5-10 kb. Maxicircles are concatenated together and simultaneously interlinked with the minicircle network. The DNA network and associated proteins are organized in a dense disc visible by light microscopy. While all kinetoplastids contain maxi and minicircles, the concatenated network is unique to trypanosomatids [124, 141]. During the RNA editing uridines are inserted or deleted from mitochondrial mRNAs fixing errors in the sequence and restoring a viable coding sequence. The sequences to be used as templates are stored in the gRNAs (50-60nt). These encode only a small portion of the information needed to repair a mRNA, therefore multiple gRNA are required to edit each mRNA. RNA editing is catalysed by the RNA editing core complex or editosome. Several modules can combine to build different versions of the editosome, each with different specificities [127, 142].

Base J

Base J, or the modification of thymine to beta-D-glucosyl_hydroxymethyluracil, is enriched at the ends of PTUs, at potential transcription terminal sites (TTSs) and in repetitive DNA elements, such as the telomeric repeats [123, 129].

Transposable Elements

The two main classes of transposable element are DNA transposons and RNA retrotransposons. DNA transposons move by “cut and paste” and depend on a DNA intermediate, while RNA retrotransposons use a “copy and paste” strategy, with a RNA intermediate. DNA transposons have not been found in trypanosomatid genomes, butRNA retrotransposons have been shown to be present. For example, several classes of potentially active retrotransposons have been identified in T. brucei and T. cruzi, some of which could be involved in the regulation of gene expression, such as SIDER2, which localizes to the 3’UTRs of mRNAs and affects its stability [126, 143, 144].

Toxoplasma and related organisms

Toxoplasma gondii is a member of the tissue cyst-forming coccidian parasites, which include Neospora caninum, Hammondia hammondia and Sarcocystis spp. among others [162–165]. Of these T. gondii appears to be the most widely distributed both geographically and by host diversity, and is able to infect virtually any warm-blooded animal. While the diversity of T. gondii is restricted to three clonal lineages in Europe and North America, isolates from the southern hemisphere exhibit much wider genetic variability [166]. Amazingly, while T. gondii can infect a wide variety of warm-blooded organisms it can only undergo sexual recombination in Felidae. Cats shed infective sporozoites containing environmentally resistant cysts, which can be transmitted orally to other organisms such as rodents [165]. Following oral infection, sporozoites cross the small intestine and can infect a variety of cells where they undergo a developmental switch to fast growing tachyzoites [166]. Tachyzoites replicate through a process called endodyogeny where two daughter cells are formed within a mother cell by a combination of de novo building of cytoskeletal and secretory components, replication and segregation of mother cell components (i.e. nucleus, mitochondria and apicoplast) and recycling of mother cell components [167, 168]. Pressure from the host immune system forces tachyzoites to undergo another developmental change into bradyzoites [169]. These semi-quiescent cells form clusters called tissue cysts that settle in brain and/or muscle tissue where they may remain for the life of the host, althoughreactivation of bradyzoites can occur in immunocompromised individuals. Bradyzoites also serve as a reservoir of transmission if an infected host is eaten by another animal. Interestingly, the tissue cyst tropism varies markedly between hosts. The fast replicating tachyzoite stage is often asymptomatic, but can cause acute morbidity or mortality in immunocompromised individuals. Placental transmission is known to cause foetal mortality or serious congenital defects.

T. gondii contains a ~65 Mb nuclear genome comprising 14 chromosomes [170–172], a 35 Kb apicoplast genome [173] and a mitochondrial genome. T. gondii genomic scale data such as expressed sequenced tags, sequenced BAC clones and whole genome shotgun sequencing were first made available through ToxoDB beginning in 2001 [174]. Since then, additional genomic scale data have been generated including genome sequence and transcriptomic data from a large scale population sequencing project [172]. The genome of the closely related H. hammondia and N. caninum are ~65 Mb and ~62 Mb in size, respectively, and not surprisingly also comprise 14 chromosomes each (Table 1.9) [171, 172, 175]. The genome of the more divergent S. neurona is almost twice the size of those previously described at ~130Mb, while a GC content of roughly 53% is common across this group (Table 1.9) [176]. A high degree of genomic synteny is observed between T. gondii, H. hammondia and N. caninum. This level of synteny is not maintained with between this group and S. neurona. [171, 172, 176].

Table 1.9

Basic genome statistics for T. gondii and related organisms.

Apicomplexan parasites in general have evolved secretory systems that transport effector molecules into their host cells. These have a range of functions, including modification the intracellular environment, promotion of immune evasion and modulation of host-cell transcription [177]. Most information about secretory effectors in coccidian parasites comes from T. gondii where numerous studies have defined dense granule [178], rhoptry [179], microneme [180] and SAG1 related sequences (SRS) proteins [181]. Comparative genomic analysis revealed that one of the primary features differentiating both different species of coccidian parasite and different strains of T. gondii is sequence diversity and copy number variation (CNV) at secretory effector loci. [171, 172, 175]. A comparison of 62 isolates of T. gondii and one isolate of H. hammondia showed that secretory effectors are often found in genomic regions exhibiting tandem amplification [172]. A comparison of reference isolates from the 16 major Toxoplasma haplogroups showed that all possess a repertoire of secretory effectors with most diversity occurring in rhoptry and SRS genes. Further comparison of secretory effectors between T. gondii, H. hammondia and N. caninum revealed additional diversity and a T. gondii specific family (TgFAMs) of effectors, which may be important for host range and definitive host preferences [172]. Interestingly, a number of the TgFAMs are clustered in telomeric regions and contain a variable region, which may implicate them in immune evasion [172, 182] but they also may play a role in during sexual development since many are expressed in the cat and in oocysts [183].

Data integration and accessibility

Several databases exist that provide online and free access to parasite genomes, annotation and functional data (Table 2.0). The National Institutes of Allergy and Infectious Diseases (NIAID) in the United States initiated established bioinformatics resource centers (BRCs) in 2004 whose goal is to provide the global pathogen research community with free and online tools to mine genomic and functional genomic data, and additional data-types essential for pathogen surveillance and control [184]. The BRCs included one specifically tasked with providing support for the eukaryotic pathogen scientific community (EuPathDB, initially known as ApiDB) [185]. Now in its third five-year funding cycle, EuPathDB incorporates data from over 240 parasitic and evolutionarily related organisms spanning multiple phyla such as the Amoebozoa, Apicomplexa, Euglenozoa, Metamonada, Sarcomastigophora and numerous fungal phyla. Data includes genome sequence, structural and functional annotation, functional data covering the omics landscape including transcriptomic, proteomic and metabolomics. Most current database content can be accessed here: http://eupathdb.org/eupathdb/eupathGenome.jsp

Table 2.0

Online resources for genomic scale data.

Data within EuPathDB and its component sites are searchable via an intuitive graphical user interface that allows the development of complex in silico experiments to support hypothesis driven experiments. Data types include the underlying genomic sequences and annotations (close to 250 genomes represented), transcript level data (SAGE-tag, EST, microarray and RNA sequence data), protein expression data (including quantitative), epigenomic data (ChIP-chip and ChIP-seq), population-level (SNP) and isolate data, and host response data (antibody array). In addition, genomic analyses provide the ability to search for gene features, subcellular localization, motifs (InterPro and user defined), function (Enzyme commission annotation and GO terms) and evolutionary relationships based on gene orthology. Detailed tutorials and usage instructions are available through publications and online tutorials and exercises [121, 186]. A number of YouTube tutorials are available: https://www.youtube.com/user/EuPathDB/. EuPathDB resources provide Community annotation and curation via user comments (including images, files, PubMed records, etc) can be added to records in EuPathDB sites (Comments become immediately visible and searchable). A Graphical search system allows building complex searches in a step-wise manner that can be saved, modified and shared. An example strategy can be seem in figure 1 and accessed online by following this link: http://plasmodb.org/plasmo/im.do?s=df42a71ae3acbb1e. Browsing capability through a genome browser integrating genomes, annotation, analyses and functional data. Column and results analysis tools are also available to generate word cloud graphics, histograms, and GO term and pathways enrichment analyses.

Fig. 1

Screen shot from PlasmoDB depicting a search strategy that identifies putative phosphatases that are secreted and expressed in gametocytes based on proteomics, RNA sequence and microarray experiments. Search strategies are constructed by adding steps (more...)

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This is an Accepted Manuscript of a book chapter published by Springer-Verlag Wien in Molecular Parasitology: Protozoan Parasites and their Molecules, available online: https://www.springer.com/gp/book/9783709114155.