Abstract
The present study was designed to improve understanding of Theileria annulata in sympatric water buffalo and cattle in the Punjab province of Pakistan. The prevalence of tropical theileriosis is high, buparvaquone resistance is widespread, and vaccine protection is poor in the field. Better understanding is, therefore, needed of the factors that influence the genetics of T. annulata populations both within its hosts and in its overall populations. Here we utilise a panel of six satellites and a mitochondrial cytochrome b marker to explore the multiplicity of T. annulata infection and patterns of emergence and spread of different parasite genotypes. Parasite materials were collected from infected animals in defined regions, where water buffalo and cattle are kept together. Our results show that T. annulata is genetically more diverse in cattle-than in water buffalo-derived populations (the mean numbers of unique satellite alleles were 13.3 and 1.8 and numbers of unique cytochrome b locus alleles were 65 and 27 in cattle- and water buffalo-derived populations, respectively). The data show a high level of genetic diversity among the individual host-derived populations (the overall heterozygosity (He) indices were 0.912 and 0.931 in cattle, and 0.874 and 0.861 in buffalo, based on satellite and cytochrome b loci, respectively). When considered in the context of high parasite transmission rates and frequent animal movements between different regions, the predominance of multiple T. annulata genotypes, with multiple introductions of infection in the hosts from which the parasite populations were derived, may have practical implications for the spread of parasite genetic adaptations; such as those conferring vaccine cross-protection against different strains affecting cattle and buffalo, or resistance to antiprotozoal drugs.
1. Introduction
Vector-borne haemoprotozoa impact globally on the health, welfare, and production of livestock (Jabbar et al., 2015). The genus Theileria comprises of two important species, Theileria annulata and Theileria parva (Lawrence, 1979). T. parva typically causes East Coast Fever, Corridor Disease, or January Disease in southern and eastern Africa (Uilenberg et al., 1982), whereas, T. annulata causes tropical theileriosis in North Africa and South Asia (Nourollahi-Fard et al., 2015). Tropical theileriosis amongst the most important neglected tropical parasitic diseases of livestock (Sivakumar et al., 2014). Cattle and buffalo become infected with T. annulata following the transmission of sporozoites through ixodid ticks of the genus Hyalomma. These stages invade lymphocytes and develop by schizogony. Micromerozoites released from the lymphocytes enter erythrocytes and multiply by binary fission to produce merozoites. Piroplasms ingested during tick feeding, migrate to the arthropod gut where gametogeny and production of zygotes occurs. These enter the haemolymph and are carried to the salivary gland, where further replication by binary fission gives rise to sporozoites. The life cycle is completed when the next instar of the tick feeds (Gharbi and Darghouth, 2015). Clinically infected cattle and buffalo show high fever and lymphadenopathy, sometimes accompanied by haemolytic anaemia, respiratory and ocular lesions (Al-Hosary et al., 2010; Mahmmod et al., 2011).
Avoidance, removal or suppression of Hyalomma ticks or prophylactic use of antiprotozoal drugs are generally impractical in the control of tropical theileriosis. The unsustainability of these disease control measures highlights the potential for effective T. annulata vaccines. Attenuated vaccines against T. annulata have been used in many countries where tropical theileriosis is endemic (Brown, 1990; Darghouth et al., 1999; Tait and Hall, 1990). However, there are practical constraints to the widespread use of live T. annulata vaccines in control programmes: (i) a requirement for distribution in liquid nitrogen, which accounts for approximately 30% of the cost (Bouslikhane et al., 1998); (ii) the need to use the vaccine immediately after thawing; (iii) the difficulty and cost of quality control to ensure consistent efficacy and the absence of other pathogens; (iv) problems with post-vaccination reactions, which have been recorded in 3% of animals immunised with Chinese, Moroccan, Iranian or Tunisian stocks (Darghouth, 2008); and (v) the possibility of reversion to virulence. To overcome these problems, research for the development of a killed subunit vaccine against T. annulata has focused on antigens present on the surface of sporozoite and merozoite stages of the parasite. High levels of genetic diversity are a major hurdle for the development of subunit vaccines for use in different host species; for example, where cross-protection between cattle- and buffalo-derived T. parva strains may be incomplete (Young et al., 1973). Studies of T. parva in African buffalo have shown high genetic diversity when compared to cattle (Oura et al., 2005); but while T. annulata can infect both cattle and Asian buffalo (Nourollahi-Fard et al., 2015), genetic comparisons between strains infecting sympatric host populations have not been reported. Understanding of allelic variations of buffalo- and cattle-derived T. annulata and their circulation is needed to inform antigenicity and immunogenicity in vaccine development.
Control of T. annulata is heavily dependent on the prophylactic and therapeutic use of a single anti-protozoal drug, buparvaquone, but is now compromised by the emergence of resistance (Mhadhbi et al., 2015). Buparvaquone resistance has been reported with increasing frequency and now represents a serious challenge to efficient livestock production (Mhadhbi et al., 2010). Current understanding of the mechanisms and genetic basis of buparvaquone resistance in T. annulata is limited (Chatanga et al., 2019; Mhadhbi et al., 2015; Sharifiyazdi et al., 2012), with no functional link between mutations in the candidate cytochrome b locus and resistance phenotype having been demonstrated. Although previous studies have described multiple genotypes and high levels of genetic diversity (Al-Hamidhi et al., 2015; Weir et al., 2011), there is a lack of information on changes in response to drug selection pressure, fitness costs of mutations, and gene flow. It is, therefore, important to understand the genetic diversity and population sub-structure of T. annulata, with reference to the emergence and spread of buparvaquone resistance mutations. There is a need for a better understanding of the transmission of different T. annulata genotypes present in individual cattle and buffalo hosts as a proxy for multiplicity of infection when considering sustainable control strategies.
Population genetic studies of T. annulata have previously been performed using panels of satellite makers (Weir et al., 2007), but there are no reports based on mitochondrial cytochrome b sequence analysis using a post-genomic next-generation sequencing approach. Deep amplicon sequencing of metabarcoded mtDNA affords a practical and rhigh-throughput method when compared to conventional Sanger sequencing, to investigate genetic diversity between and within parasite populations. The Illumina MiSeq platform can provide 100,000 or more reads of up to 600 bp of loci of interest, depending on primer design. Recently, we have used these methods to study the population genetics of Calicophoron daubneyi (Sargison et al., 2019) and Fasciola gigantica infection in livestock keep in the United Kingdom and Pakistan.
In this paper, we describe the use of a mitochondrial cytochrome b locus and six polymorphic satellite markers investigate the population genetics of buffalo- and cattle-derived T. annulata. The results may help to: inform future rational vaccine design for the control of tropical theileriosis; predict the likely emergence and spread of genetic adaptations such as buparvaquone resistance; and identify patterns of infection with different genotypes.
2. Materials and Methods
2.1. Parasite resources and gDNA isolation
Blood samples were collected from the piroplasm-positive cattle and buffalo in veterinary clinics throughout the Punjab province of Pakistan between 2017 and 2019. The procedures involved jugular venipuncture and withdrawal of 5 ml of intravenous blood into EDTA tubes, followed by storage at −20°C. Samples were collected by para-veterinary staff under the supervision of local veterinarians following consent from the animal owners. The study was approved by the Institutional Review Board of the University of Veterinary and Animal Sciences (UVAS-24817). Peripheral blood smears were prepared and stained with 4% Giemsa, then visualised under 1000x oil immersion to detect piroplasm infection. ‘Haemoprotobiome’ sequencing was performed on piroplasm-positive blood samples to confirm the presence of T. annulata (Chaudhry et al., 2019). Genomic DNA was isolated from T. annulata positive samples by lysis with GS buffer and proteinase K as described in the TIANamp Blood DNA Kit (TIANGEN Biotech Co. Ltd, Beijing) and stored at −20°C.
Four cell-line stocks of T. annulata positive controls originally derived from Turkey, India, Tunisia, and Morocco (Katzer et al., 1994) were available at the University of Edinburgh Roslin Institute, UK.
2.2. Adapter and barcoded PCR amplification of T. annulata cytochrome b
A 517 bp region of the cytochrome b locus was selected for deep amplicon sequencing. One μl gDNA of each of 31 buffalo, 54 cattle and the 4 positive control derived T. annulata samples was used as templates for the 1st round adapter PCR amplification. A de novo primer set (Supplementary Table S1) was used under the following conditions: 13.25 μl ddH2O, 1 μl gDNA, 0.75 μl of 10 mM dNTPs mix, 5 μl of 5X HiFi Fidelity Buffer, 0.5 μl of 0.5U DNA polymerase enzyme (KAPA Biosystems, USA), and 0.75 μl of 10 μM forward adaptor primers and reverse adaptor primers. The thermocycling conditions were: 95°C for 2 min, 35 cycles at 98°C for 20 sec, 55°C for 15 sec, and 72°C for 2 min, followed by a final extension of 72°C for 2 min. The PCR product was purified with AMPure XP Magnetic Beads (1X) (Beckman coulter Inc., USA). A barcoded primer set was used in the 2nd round of PCR amplification to add a fragment of unique sequence into each purified product (Supplementary Table S2) under the following conditions: 13.25 μl ddH2O, 2 μl bead purified PCR products, 0.75 μl of 10 mM dNTPs mix, 5 μl of 5X HiFi Fidelity Buffer, 0.75 μl of 0.5U DNA polymerase enzyme (KAPA Biosystems, USA), and 1.25 μl of 10μM forward and reverse primers. The thermocycling conditions were: 98°C for 2 min, 7 cycles at 98°C for 20 sec, 63°C for 20 sec, and 72°C for 2 min. The PCR products were purified with AMPure XP Magnetic Beads (1X) (Beckman coulter Inc., USA).
2.3. Illumina Mi-Seq run and data handling
A pooled library was prepared with 10 μl of barcoded bead purification PCR product from each T. annulata sample and sent to Edinburgh Genomics, UK for deep amplicon sequencing. The size of the amplicon was measured by qPCR library quantification (KAPA Biosystems, USA), before running on an Illumina Mi-Seq sequencer using a 600-cycle pair-end reagent kit (Mi-Seq Reagent Kits v2, MS-103-2003) at a concentration of 15 nM with addition of 15% Phix control v3 (Illumina, FC-11-2003). The Illumina Mi-Seq post-run processing uses the barcoded indices to split all sequences by sample and generate FASTQ files. These were analysed using Mothur v1.39.5 software (Schloss et al., 2009) with modifications in the standard operating procedures of Illumina Mi-Seq (Kozich et al., 2013) in the Command Prompt pipeline. Briefly, the raw paired read-ends were run into the ‘make.contigs’ command to combine the two sets of reads for each sample. The command extracted sequence and quality score data from the FASTQ files, creating the complement of the reverse and forward reads, and then joining the reads into contigs. After removing the too long, or ambiguous sequence reads, the data were aligned with the T. annulata cytochrome b reference sequence library prepared from the positive controls (for more details Supplementary Data S1 and section 2.1) using the ‘align.seqs’ command. Any sequences that did not match with the T. annulata cytochrome b reference library were removed and the ‘summary.seqs’ command was used to summarise the 517 bp sequence reads of the T. annulata cytochrome b locus. The sequence reads were further run on the ‘screen.seqs’ command to generate the T. annulata cytochrome b FASTQ file. Once the sequence reads were classified as T. annulata, a count list of the consensus sequences of each population was created using the ‘unique.seqs’ command. The count list was further used to create FASTQ files of the consensus sequences of each population using the ‘split.groups’ command (for more details Supplementary Data S2).
2.4. Bioinformatics data analysis
The consensus sequences of T. annulata cytochrome b locus were aligned using the MUSCLE alignment tool in Geneious v10.2.5 software (Biomatters Ltd, New Zealand) and then imported into the FaBox 1.5 online tool (birc.au.dk) to collapse the sequences that showed 100% base pair similarity after corrections into a single genotype. The genotype frequency of each sample was calculated by dividing the number of sequence reads by the total number of reads. A split tree was created in the SplitTrees4 software (bio-soft.net) by using the neighbour-joining method in the JukesCantor model of substitution. The appropriate model of nucleotide substitutions for neighbour-joining analysis was selected by using the jModeltest 12.2.0 program (Posada, 2008). The tree was rooted with the corresponding cytochrome b sequence of T. parva. The branch supports were obtained by 1000 bootstraps of the data. The genetic diversity of cytochrome b was calculated within and between populations by using the DnaSP 5.10 software package (Librado and Rozas, 2009), and the following values were obtained: Heterozygosity (He), the number of segregating sites (S), nucleotide diversity (π), the mean number of pairwise differences (k), the mutation parameter based on an infinite site equilibrium model, and the mutations parameter based on segregating sites (Sθ).
2.5. Satellite genotyping and bioinformatics data analysis of T. annulata
Six satellite markers (TS6, TS8, TS20, TS31, TS12, TS16) were selected for use on each population as previously described by Weir et al. (2007). A summary of primer sequences and allele ranges is given in Supplementary Table S3. Eighteen buffalo and 35 cattle derived T. annulata populations were successfully analysed from six markers. PCR amplification was performed using a master mix containing 17.2 μl ddH2O, 0.3 μl of 100 μM dNTPs, 2.5 μl of 1X thermopol reaction buffer, 0.3 μl of 1.25U Taq DNA polymerase (New England Biolabs) and 0.3 μl of 0.1μM forward and reverse primers. The thermo-cycling parameters were 94°C for 2 min followed by 35 cycles of 94°C for 1 min, staying at the annealing temperatures of 60°C (TS6, TS20), 55°C (TS8, TS12, TS16), or 50°C (TS31) for 1 min and 65°C for 1 min, with a single final extension cycle of 65°C for 5 min. The forward primer of each microsatellite primer pair was 5’ end labelled with fluorescent dye (IDT, UK), and the ROX 400 internal size standard was used on the ABI Prism 3100 genetic analyser (Applied Biosystems, USA).
Individual chromatograms were analysed using Peak Scanner software version 2.0 (Thermo Fisher Scientific, USA) to determine the size of the alleles. These were combined across six markers to generate a multilocus genotype (MLG). From the MLG data, allelic variation (A) and heterozygosity (He) for individual populations were calculated using Arlequin 3.11 software (Guo and Thompson, 1992). Significance levels were calculated using the sequential method of Bonferroni correction for multiple comparisons in the same dataset (Rice, 1989). Analysis of molecular variance (AMOVA) was estimated through the partition of satellite diversity between and within populations (Excoffier et al., 1992) and fixation index (pairwise FST) values were calculated using Arlequin 3.11 to provide a measurement of population genetic sub-structure. Principal coordinate analysis (PCoA) was performed using GenALEx software to illustrate the extent of genetically distinct features of individual populations with plot coordinates (Peakall and Smouse, 2012).
The likelihood ratio test statistics (G-test) were calculated using Arlequin 3.11 software (Excoffier et al., 2005) to estimate genetic linkage equilibrium and deviations from Hardy-Weinberg equilibrium. There was no evidence to support linkage disequilibrium for any combination of satellite loci in the individual T. annulata populations of buffalo and cattle, indicating that alleles at these loci were randomly associating and not genetically linked (data on file). There was some significant departure from Hardy-Weinberg equilibrium, even after Bonferroni correction, in addition to relative P values for 108 loci combinations for buffalo and 210 loci combinations for cattle T. annulata populations. The bottleneck software (version 1.2.02) was, therefore, used to search for the evidence of heterozygosity excess and mode-shift. The Wilcoson signed-rank test was used to evaluate the statistical significance of any possible genetic drift equilibrium (Lefterova et al., 2015). The data showed that there was no heterozygosity excess according to the Sign Test and Wilcoxon Test (data on file). The mode shift analysis established most populations had a normal L-shaped distribution.
3. Results
3.1. Allelic diversity between cattle- and buffalo- derived T. annulata populations
The allelic diversity data show that T. annulata is more diverse in cattle as compared to water buffalo. The mean numbers (±SD) of satellite alleles in the cattle- and buffalo-derived populations were 34.7 (±9.1) and 23.7 (±5.2), respectively. The number of alleles (An) for each satellite marker in the cattle- and buffalo-derived populations ranged from 22 to 46 and 17 to 32, respectively. The mean numbers (±SD) of cattle- and buffalo-specific T. annulata satellite alleles (Au) per marker were 13.3 (±6.3) and 1.8 (±2.04), respectively (Table 1 and Fig. 1). Seventy-nine and 41 cytochrome b locus alleles (An) were identified in the cattle- and buffalo-derived T. annulata populations. The numbers of cattle- and buffalo-specific T. annulata alleles (Au) were 65 and 27, respectively (Table 1 and Fig. 1).
3.2. Genetic diversity within T. annulata populations derived from cattle and buffalo
Overall, the mean heterozygosities (He) (±SD) of the cattle- and buffalo-derived T. annulata populations were 0.912 (±0.056) and 0.874 (±0.028) respectively (Table 2). The heterozygosity (He) data reveal high levels of genetic diversity within each population of T. annulata; with mean values (±SD) for the six satellite markers ranging from 0.8 (±0.082) to 0.959 (±0.017) in individual cattle- and 0.738 (±0.196) to 0.956 (±0.034) in buffalo-derived populations (Table 2).
High levels of heterozygosity (He) were seen at the T. annulata cytochrome b locus, ranging from 0.556 to 0.923 (overall mean value 0.931) in cattle-derived populations and from 0.545 to 0.900 (overall mean value 0.861) in buffalo-derived populations (Table 3).
3.3. Genetic sub-structure between T. annulata populations derived from cattle and buffalo
Genetic differentiation was shown by comparing the fixation indices (FST) of two populations with each other in a pairwise manner. The FST values indicated a low level of genetic differentiation between T. annulata populations ranging from 0.001 to 0.114 in cattle and 0.003 to 0.132 buffalo, respectively (Supplementary Table S4).
An AMOVA was conducted using the panel of six satellite markers to estimate the genetic variation within and between the populations. This showed that genetic variation was distributed 97.91% and 97% and within; and 2.09% and 3% between cattle- and buffalo-derived T. annulata populations, respectively (data on file). PCoA was performed to illustrate as a two-dimensional plot the extent to which populations are genetically distinct. The two axes accounted for 72.5% (25.67 + 46.83) and 73.93% (27.6 + 46.33) of the variation in the cattle- and buffalo-derived populations, respectively; and showed that populations from different regions formed overlapping clusters, hence were not geographically sub-structured (Fig. 2).
3.4. Genotype distribution of T. annulata populations derived from cattle and buffalo
T. annulata satellite data were found to be highly polymorphic in each population, with overall numbers of genotypes per population ranging from 2 to 26 in cattle and 2 to 17 in buffalo. The mean numbers (±SD) of T. annulata genotypes in cattle- and buffalo-derived populations were 8.60 (±2.52) and 6.25, respectively (Table 4).
Seventy-nine T. annulata cytochrome b genotypes from individual cattle-derived populations were analysed separately. Fifteen populations had a single genotype at high frequencies ranging from 90 to 100%. These comprised of ten populations containing only one genotype; two populations containing two genotypes; one population with three genotypes; one population with four genotypes; and one population with seven genotypes (Fig. 3A). Thirty-nine populations had multiple genotypes at high frequencies. These comprised of five populations containing two genotypes; eight populations containing three genotypes; ten population containing four genotypes; seven populations with five genotypes; four populations with six genotypes; one population with seven genotypes; two populations with eight genotypes; one populations with 12 genotypes; and one with 14 genotypes (Fig. 3A). Phylogenetic analysis of the 79 cytochrome b genotypes of the cattle-derived T. annulata populations, showed that two genotypes for 22.9% and 10.8% of the total number of the sequence reads, being present in 36 and 8, populations, respectively. Seven genotypes accounted for between 5.0% and 7.0% of the sequence reads and 45 genotypes accounted for less than 1.0% of the sequence reads (Fig. 4A). The split tree shows that eight genotypes are shared between populations derived from cattle holdings in multiple locations of Lahore, Chakwal, Gujranwala, Okara, and Sahiwal. Seven genotypes are shared between populations derived from cattle holdings in any two locations of Lahore, Gujranwala, Okara, and Sahiwal. Sixty-one genotypes are present in a single location (Gujranwala=24; Qadirabad=12; Okara =14; Lahore=11 genotypes) (Fig. 4A).
Forty-one T. annulata cytochrome b genotypes from individual buffalo-derived populations were analysed separately. A single genotype predominated in 15 populations at a frequency of between 90 and 100%. These comprised of eight populations containing only one genotype; three populations containing two genotypes; one population with three genotypes; one population with five genotypes; one population with six genotypes; and one population with seven genotypes (Fig. 3B). Sixteen populations had high frequencies of multiple genotypes. These comprised of seven populations contained 2 genotypes; two populations contained 3 genotypes; one population contained 4 genotypes; three populations had 5 genotypes; two populations had 7 genotypes and one population contained a maximum of 8 genotypes (Fig. 3B). Phylogenetic analysis of the 41 cytochrome b genotypes of the buffalo-derived T. annulata populations, showed that three genotypes accounted for 39.1%, 10.8%, and 5.5% of sequence reads being present in 26, 15 and 3 populations, respectively. Eighteen genotypes accounted for between 1.0% and 3.0% of sequence reads and 20 genotypes accounted for less than 1.0% of sequence reads (Fig. 4B). The split tree shows that two genotypes are shared between T. annulata populations derived from buffalo holdings in multiple locations of Gujranwala, Hafizabad, Okara, and Sahiwal. Five genotypes are present in the populations derived from buffalo holdings in Gujranwala and Okara. One genotype is present in the populations derived from buffalo holdings in Hafizabad and Okara and one genotype from Okara and Sahiwal. Thirty-two genotypes are present in a single location (16 genotypes in Gujranwala; 13 genotypes in Okara; and 3 in Hafizabad) (Fig. 4B).
4. Discussion
Theleria annulata is considered to be the most economically important protozoan parasite of water buffalo and cattle in Asia and North Africa, causing high mortality and morbidity. The annual economic impact of the pathogen is estimated to be billions of USD, with a significant impact on both meat and milk production (Jabbar et al., 2015). Our results reveal a high level of genetic diversity within T. annulata infecting individual hosts, and between different host-derived populations; being greater in cattle-than in water buffalo-derived populations. Theileria annulata genotype distribution in the Punjab province of Pakistan is consistent with high parasite transmission rates and frequent animal movements between different regions. These results may have practical implications for the spread of parasite genetic adaptations.
4.1. Potential impact of T. annulata allelic diversity on immunisation cross-protection
The satellite and cytochrome b data reported in the present study show that T. annulata is genetically more diverse with more circulation of alleles in cattle as compared to water buffalo. The satellite data were informative due to the numbers of alleles present, showing more unique alleles in cattle-derived than in Asian water buffalo-derived T. annulata populations. The cytochrome b locus provided further evidence of allelic diversity and differences between cattle- and buffalo-populations. Similarly high levels of parasite heterogeneity have been reported in T. parva, albeit with higher allelic diversity in African buffalo-than in cattle-derived parasite populations (Oura et al., 2011). Such allelic variation may reflect the capacity of parasite to undergo transmission. For example, the differences between T. annulata and T. parva could have arisen if the buffalo-derived T. annulata transmission is more restricted than transmission between cattle-derived populations in the Punjab province of Pakistan; while T. parva transmission may be more efficient between infected buffalo than cattle in endemic east coast fever regions of Africa (Oura et al., 2011).
The high level of allelic variation described in T. annulata might have practical implications for the development of a sporozoite and merozoite antigen subunit vaccine. Immunisation of cattle with different subunit versions of the sporozoite stage surface protein (SPAG1, TaSP) has provided a degree of protection against cattle-derived T. annulata, probably through a reduction in the level of parasitaemia (Schnittger et al., 2002). However, little is known about the antigenic diversity of T. annulata candidate loci, which may be important in vaccine development (MacHugh et al., 2011), or about the impact of differences between cattle- and buffalo-derived populations. Our results highlight the need for better understanding of antigenic diversity in the development of a subunit vaccine for tropical theileriosis, capable of providing cross-protection against cattle- and buffalo-derived T. annulata genotypes across different regions.
4.2. Potential impact of T. annulata genetic diversity on the emergence of buparvaquone resistance
Our satellite and cytochrome b locus data show high levels of genetic diversity in both cattle- and buffalo-derived T. annulata populations, reflecting high effective population sizes (Al-Hamidhi et al., 2015) and implying high mutation rates across the T. annulata genome. The 8.35 Mb nuclear genome sequence of T. annulata spans four chromosomes that range from 1.9 to 2.6 Mb, with 3,792 putative protein-coding genes. In addition, a total of 49 transfer RNA and five ribosomal RNA genes were identified (Pain et al. (2005). The high level of genetic diversity in T. annulata may practical implications for the emergence of antiprotozoal drug resistance mutations. Only one drug, buparvaquone, is available for the treatment of tropical theileriosis: Pakistani livestock are treated frequently with buparvaquone and anecdotal evidence suggests that resistance is widespread.
4.3. Potential impact of the multiplicity of T. annulata infection on control strategies for tropical theileriosis
The presence of a single cytochrome b genotype in 15 cattle and 9 buffalo-derived populations suggests a single emergence and subsequent spread of T. annulata infection. The satellite data provide further evidence of low levels of genetic differentiation among cattle-and buffalo-derived populations and show overlapping clusters that are not geographically sub-structured, consistent with the high levels of gene flow due to livestock movements or translocations of ticks to a new region. This could influence the spread of drug-resistant alleles. Studies of the global genetic sub-structure of T. annulata have shown a high level of genetic differentiation between Turkey and Tunisia (Weir et al., 2007). Studies have also shown a low level of genetic differentiation in T. annulata within-countries including Turkey, Tunisia, Oman, China and Portugal (Al-Hamidhi et al., 2015; Gomes et al., 2016; Weir et al., 2011; Yin et al., 2018), reflecting high levels of animal movement, or translocation of ticks.
The predominance and the high proportions of multiple cytochrome b genotypes in 39 cattle- and 16 buffalo-derived populations implies multiple emergence of infection. The numbers of satellite genotypes per population provide further evidence for this pattern of emergence of different genetically adapted strains. Random cross mating of gametes and genetic recombination of T. annulata in ticks gives rise to the formation of new genotypes (Al-Hamidhi et al., 2015). The multiplicity of infection of T. annulata may be influenced by variations in the intensity of transmission due to levels of different tick species infestation, or prevalence of tick infection (Yin et al., 2018). Hyalomma scupense is the most common and economically important species and the major vector of T. annulata, but Hyalomma marginatum and Hyalomma anatolicum are also found in Tunisia (Bouattour et al., 1996). There are four Hyalomma species responsible for the transmission of T. annulata in Turkey, of which H. anatolicum is the major vector for T. annulata (Aktas et al., 2004). Two tick species, Hyalomma lusitanicum, and H. marginatum are the vectors of T. annulata in Portugal (Estrada-Pena and Santos-Silva, 2005). There are a few reports that H. anatolicum may be a vector of T. annulata in the Punjab province of Pakistan (Karim et al., 2017). The multiplicity of T. annulata infection may also be influenced by population bottlenecking effects, arising from seasonal effects of climatic conditions on tick transmission and completion of the parasite’s lifecycle. The T. annulata transmission season in Turkey is between May and September, with the peak of clinical cases occurring in mid-summer (Sayin et al., 2003). In a region of endemic stability of tropical theileriosis in Tunisia, there is a high level of multiplicity of T. annulata infection, but clinical disease is rare; while in a region of endemic instability, a proportion of the population becomes infected all year round, and the clinical disease occurs particularly in adult cattle (Gharbi et al., 2011).
Control strategies for tropical theileriosis need to consider factors such as: the reproductive isolation of parasite populations; management of host movements; control of tick vectors; mitigation of the impacts of climate change; and consequences of parasite exposure to antiprotozoal drugs. Understanding of the multiplicity of infection and of high levels of gene flow is, therefore, important in the educational dissemination and implementation of advice on sustainable parasite control.
Conflict of interest
None
Acknowledgement
The study was financially supported by the Carnegie Trust Scotland and Biotechnology and Biological Sciences Research Council (BBSRC). Work at the University of Veterinary and Animal Science Pakistan uses facilities funded by the Higher Education Commission of Pakistan. The authors of this study would like to thank Professor Dr. Masood Rabbani, Vice-Chancellor of the University of Veterinary and Animal Science Lahore Pakistan, for his great support in the arrangements for sample collections.