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Geographic strain differentiation of Schistosoma japonicum in the Philippines using microsatellite markers

  • Kharleezelle J. Moendeg,

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliations National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido, Japan, Department of Biology, School of Science and Engineering, Ateneo de Manila University, Quezon City, Manila, Philippines

  • Jose Ma M. Angeles,

    Roles Conceptualization, Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review & editing

    Affiliation National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido, Japan

  • Ryo Nakao,

    Roles Formal analysis, Investigation, Software, Validation, Writing – review & editing

    Affiliation Laboratory of Parasitology, Department of Disease Control, Graduate School of Veterinary Medicine, Hokkaido University, Sapporo, Hokkaido, Japan

  • Lydia R. Leonardo,

    Roles Conceptualization, Project administration, Resources, Writing – original draft, Writing – review & editing

    Affiliation Department of Parasitology, College of Public Health, University of the Philippines Manila, Philippines

  • Ian Kendrich C. Fontanilla,

    Roles Formal analysis, Investigation, Software, Validation

    Affiliation Institute of Biology, University of the Philippines Diliman, Quezon City, Manila, Philippines

  • Yasuyuki Goto,

    Roles Resources

    Affiliation Laboratory of Molecular Immunology, Department of Animal Resource Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan

  • Masashi Kirinoki,

    Roles Methodology, Resources

    Affiliation Department of Tropical Medicine and Parasitology, Dokkyo Medical University School of Medicine, Tochigi, Japan

  • Elena A. Villacorte,

    Roles Methodology

    Affiliation Department of Parasitology, College of Public Health, University of the Philippines Manila, Philippines

  • Pilarita T. Rivera,

    Roles Resources

    Affiliation Department of Parasitology, College of Public Health, University of the Philippines Manila, Philippines

  • Noboru Inoue,

    Roles Resources

    Affiliation National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido, Japan

  • Yuichi Chigusa,

    Roles Resources

    Affiliation Department of Tropical Medicine and Parasitology, Dokkyo Medical University School of Medicine, Tochigi, Japan

  • Shin-ichiro Kawazu

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

    skawazu@obihiro.ac.jp

    Affiliation National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido, Japan

    ORCID http://orcid.org/0000-0003-4326-4025

Geographic strain differentiation of Schistosoma japonicum in the Philippines using microsatellite markers

  • Kharleezelle J. Moendeg, 
  • Jose Ma M. Angeles, 
  • Ryo Nakao, 
  • Lydia R. Leonardo, 
  • Ian Kendrich C. Fontanilla, 
  • Yasuyuki Goto, 
  • Masashi Kirinoki, 
  • Elena A. Villacorte, 
  • Pilarita T. Rivera, 
  • Noboru Inoue
PLOS
x

Abstract

Background

Microsatellites have been found to be useful in determining genetic diversities of various medically-important parasites which can be used as basis for an effective disease management and control program. In Asia and Africa, the identification of different geographical strains of Schistosoma japonicum, S. haematobium and S. mansoni as determined through microsatellites could pave the way for a better understanding of the transmission epidemiology of the parasite. Thus, the present study aims to apply microsatellite markers in analyzing the populations of S. japonicum from different endemic areas in the Philippines for possible strain differentiation.

Methodology/ Principal findings

Experimental mice were infected using the cercariae of S. japonicum collected from infected Oncomelania hupensis quadrasi snails in seven endemic municipalities. Adult worms were harvested from infected mice after 45 days of infection and their DNA analyzed against ten previously characterized microsatellite loci. High genetic diversity was observed in areas with high endemicity. The degree of genetic differentiation of the parasite population between endemic areas varies. Geographical separation was considered as one of the factors accounting for the observed difference between populations. Two subgroups have been observed in one of the study sites, suggesting that co-infection with several genotypes of the parasite might be present in the population. Clustering analysis showed no particular spatial structuring between parasite populations from different endemic areas. This result could possibly suggest varying degrees of effects of the ongoing control programs and the existing gene flow in the populations, which might be attributed to migration and active movement of infected hosts from one endemic area to another.

Conclusions/ Significance

Based on the results of the study, it is reasonable to conclude that genetic diversity could be one possible criterion to assess the infection status in highly endemic areas. Genetic surveillance using microsatellites is therefore important to predict the ongoing gene flow and degree of genetic diversity, which indirectly reflects the success of the control program in schistosomiasis-endemic areas.

Author summary

Schistosomiasis is one of the important neglected tropical diseases endemic in 78 countries throughout the world. The disease is caused by the parasitic worms known as schistosomes. In the Philippines, S. japonicum is the causative agent of the disease. The prevalence of the disease varies in endemic areas, suggesting that the parasite populations might differ in their genetic composition and infectivity to the human host. In this study, DNA samples of adult worms from seven endemic municipalities were analyzed. Characterization of S. japonicum samples in different endemic sites with varying prevalence provides information on the genetic diversity of the parasite. Results of this study showed that samples in high prevalence endemic areas like Irosin, Catarman and Socorro were genetically diverse as compared to other areas. Information on parasite genetic diversity is therefore important in planning disease control strategies. The results suggest ongoing parasite transmission across geographic endemic areas which should be monitored and used as reference for genetic diversity of the schistosomes attributed to geographic areas, thus a safeguarding precaution should be implemented to ensure localized elimination of the disease.

Introduction

Schistosomiasis is one of the most important neglected tropical diseases affecting almost 240 million people throughout the world with more than 700 million considered to be at risk of infection [1]. Five species of schistosomes are known to cause human infection: Schistosoma haematobium, S. mansoni, S. mekongi, S. intercalatum, and S. japonicum. Among these species, S. japonicum is considered the most virulent because of the larger number of eggs it can produce as compared to other species, causing severe disease pathology. In addition, the zoonotic nature of S. japonicum contributes to increased disease transmission, making schistosomiasis control more difficult [2, 3].

Microsatellite markers have recently been used in determining S. japonicum genetic diversity and in estimating the levels of gene flow in the population. Previous studies have recommended the use of microsatellite markers to determine schistosome genetic diversity because of their codominant expression and their ability to serve as neutral markers [4, 5]. The easy observation of heterozygosity and the reasonable number of alleles per polymorphic locus in the samples make microsatellite analysis a more powerful tool in genetic studies than the use of rapid amplified polymorphic DNA (RAPD) and mitochondrial DNA [6]. Studying the genetic variation of S. japonicum populations provides an opportunity to link some genotypes associated with disease prevalence, which can then be used in formulating effective control measures. Previous studies using microsatellite markers suggested that the prevalence of S. haematobium and S. mansoni infections could be closely related with the parasite’s genetic variation. High prevalence of infection was observed in those areas with high genetic diversity and low prevalence in those areas with low genetic diversity [7, 8]. This information makes DNA microsatellites useful in population genetics studies.

In addition, microsatellite markers had been used in the identification of different geographical strains of S. japonicum in China [5]. Previous studies suggested that these markers could provide useful information for assessing the efficacy of mass drug administration (MDA) [9]. In the Philippines, schistosomiasis has shown considerable variations in the intensity and prevalence of the disease [10]. We hypothesized that the population genetic structure of schistosome parasites might partly contribute to these differences. The parasite population in each endemic area was therefore characterized for their genetic backgrounds using the microsatellite markers. The information found in this study can therefore provide basic information on the population genetic structure of S. japonicum in the Philippines that can be used as basis to evaluate and modify the widely used current control strategies for human schistosomiasis. For example, cognizant of the genetic types of the parasites, interventions can be modified so as to address differences in disease prevalence.

Materials and methods

Ethics statement

Animal experiments done in this study were conducted according to ethical guidelines for the use of animal samples permitted by the University of the Philippines Manila Institutional Animal Care and Use Committee (2011–009), as well as by Obihiro University of Agriculture and Veterinary Medicine (Permit No. 28–30). Infected mice were anesthesized using isoflurane before they were sacrificed. Perfusion method with normal saline solution was done to collect the adult S. japonicum.

Parasite samples

The snail intermediate host, O. h. quadrasi, were collected from seven municipalities (in seven provinces) in 2013 to 2015 where the disease is endemic, namely Catarman (in Northern Samar), Gonzaga (in Cagayan Province), New Corella (in Davao del Norte), Irosin (in Sorsogon), Talibon (in Bohol), Alang-Alang (in Leyte) and Socorro (in Oriental Mindoro) (Fig 1). The snails were crushed between glass slides to examine the presence of cercariae under the microscope. The cercariae were then pooled separately for each endemic municipality and used for mice infection. Ten BALB/c mice were infected percutaneously with 50 cercariae from each municipality. The infected mice were sacrificed six weeks after the infection, and the adult worms were collected from their mesenteric veins and washed with saline for DNA extraction.

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Fig 1. Study areas.

The map shows the seven endemic municipalities in the Philippines included in this study. They are clustered into three major island groups Luzon, Visayas and Mindanao.

https://doi.org/10.1371/journal.pntd.0005749.g001

DNA extraction

Genomic DNA was extracted from individual male and female adult worms using the DNeasy Blood & Tissue Kit (QIAGEN, Japan) following the manufacturers’ protocol. Table 1 showed the total number of DNA samples tested in each endemic municipality.

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Table 1. Population genetic analysis of S. japonicum from seven endemic municipalities in the Philippines.

https://doi.org/10.1371/journal.pntd.0005749.t001

PCR amplification

PCR amplifications were performed using Veriti 96-well Thermal Cycler (Applied Biosystems, Carlsbad, CA). Amplifications were performed in 10 μl reactions containing 1 μl of 10X PCR buffer, 0.4 μl of 1.5 mM MgCl2, 0.2 μl of 2.5 mM dNTP, 0.2 μl each of 10 pmol/μl primer, 0.1 μl of 5 U/μl Taq DNA polymerase (Takara, Otsu, Japan), and 1 μl of template DNA. The conditions for thermal cycling were as follows: 5 minutes at 94°C, followed by 30 cycles of 1 minute at 94°C, 1 minute at locus-specific temperature, 1 minute at 72°C, with a final extension at 72°C for 10 minutes [11].

Microsatellite genotyping

The DNA of each individual S. japonicum worm was genotyped using the previously characterized microsatellite loci RRPS, M5A, TS2, MPA, 2AAA, J5, SJP1, SJP5, SJP6, and SJP9 [11, 12]. In our study, we have screened twenty microsatellite markers but among which only ten worked well. The 5’ end of the forward primer for each locus was fluorescently labeled with 6-FAM, VIC and Ned dyes. Different dyes were used for those loci with overlapping fragment size. Two μl of the PCR product with LIZ 600 labeled size standard (Applied Biosystems) was subjected to the 3500 ABI Prism Genetic Analyzer for fragment analysis assay. The allele sizes were determined using the Gene Mapper software version 4.0 (Applied Biosystems). In each run, S. japonicum sample from Gonzaga, Cagayan, which has good DNA volume and concentration, served as the reference genotype for which the microsatellite sizes for the 10 loci had been determined by sequencing. S. japonicum Yamanashi strain (Japanese isolate) was also genotyped as a control group to confirm that the microsatellite markers could differentiate between samples from different origin. A total of 201 DNA samples were tested; however, only 186 were successfully genotyped due to poor DNA quality.

Data analysis

For each population, the genetic diversity was examined by calculating the number of alleles using rarefaction analysis. Expected heterozygosity (gene diversity) (He) and observed heterozygosity (Ho) were determined using the GenAlEx 6.5 software [13]. Rarefaction analysis was performed to make the alleles comparable in the population. Genetic differentiation was determined using Wright’s F-statistics (Fst) in Arlequin, and the significance of the Fst values was tested at p value <0.05 [14]. The following qualitative guidelines were used for the interpretation of Fst genetic differentiation: 0–0.05 (little), 0.05–0.15 (moderate), 0.15–0.25 (great), and >0.25 indicate (very great genetic differentiation) [5]. The Analysis of Molecular Variance (AMOVA) was used to partition the genetic variation within and among populations using the software Arlequin version 3.5. The inbreeding coefficient (FIS), which measures the extent of nonrandom mating, was computed in the study. Nonrandom mating occurs when there is inbreeding.

Principal Coordinate Analysis (PCoA) was done to determine the clustering pattern of S. japonicum population based on their genetic distance using GenAlEx 6.5. Cercariae derived from a snail infected with only a single miracidium is assumed to be genetically identical. Hence, duplicate multi locus genotypes in a population are a consequence of clonal replication within snails [12, 15]. The presence of duplicate multilocus genotypes in adult worms was identified as one possible source of bias [15]. Duplicate multilocus genotype (MLG) was therefore removed, leaving a single representative of each in the dataset. Recent studies revealed that removal of clones in the dataset improved the assignment and clustering pattern of S. japonicum population [15]. The GENECLASS software 2.0 was used to identify migrant individuals [16].

To visualize relationships among populations, a Neighbor-joining tree was constructed based on FST genetic distance using 100 bootstrap replications in POPTREE2 [17]. The FST is one of the well-known parameters used in measuring genetic differentiation between populations using microsatellite data [18]. The S. japonicum Yamanashi strain was used as an outgroup.

Accession numbers

Sequences of microsatellite loci reported here have been deposited in GenBank with the following accession numbers, RRPS (U22167), M5A (AF244896), TS2 (AF244896), MPA (U11895), 2AAA (M32280), J5 (M26212), SJP1 (EU262604), SJP5 (EU262608), SJP6 (EU262609) and SJP9 (EU262612).

Results

Genetic diversity

A total of 186 individual S. japonicum worms collected from seven endemic municipalities were analyzed. Highest gene diversity indices (He) was observed in Catarman (0.727) followed by Irosin (0.694), Socorro (0.677), and Gonzaga (0.605) while the lowest was in Alang-Alang (0.495). Those from New Corella (0.587) and Talibon (0.566) were comparable. Similarly, allelic richness after sample size correction was highest in Irosin (4.630) followed by Catarman (4.500) and Socorro (4.280) and lowest in Alang-Alang (2.570) followed by Talibon (2.920) (Table 1).

Population-specific inbreeding coefficient was determined in this study to measure the extent of nonrandom mating. Highest inbreeding coefficient values (FIS) was observed in Irosin (0.239) while the lowest was in Catarman (0.012) (Table 1). The lowest inbreeding coefficient values in Catarman may be related to the increased heterozygosity in this area. Inbreeding increases the homozygosity of the alleles. The pairwise FST values ranged from 0.019 to 0.0188, indicating varied levels of pairwise population genetic differentiation (Table 2). Great genetic differentiation was observed in the New Corella samples. The AMOVA showed that greater genetic variation in the samples occurred within the population (91.95%) rather than among populations (8.05%) (Table 3).

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Table 2. Pairwise genetic differentiation (FST) among populations based on 10 microsatellite loci a.

https://doi.org/10.1371/journal.pntd.0005749.t002

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Table 3. Analysis of molecular variance (AMOVA) for S. japonicum population from different endemic municipalities.

https://doi.org/10.1371/journal.pntd.0005749.t003

Population structure

The PCoA showed no particular geographical structuring among the S. japonicum populations (Fig 2). The neighbor-joining tree method showed clustering of the samples into two groups. Populations from Catarman, New Corella, Gonzaga and Talibon grouped together (86% NJ bootstraps), whereas populations from Alang-Alang Socorro and Irosin belong to a separate cluster (61% NJ bootstraps) (S1 Fig). However, there was no correlation between the clustering of populations and their geographic distribution as shown in the neighbor joining tree (S1 Fig). These findings further support the results of the PCoA (Fig 2), suggesting the existing gene flow in the population. Two subgroups were observed in Catarman (Northern Samar) using the PCoA analysis (Fig 2). The presence of two subgroups in the Catarman samples may account for the high genetic variation within population (Table 1). Six samples, namely 3 from Gonzaga, 2 from Irosin and 1 from New Corella, were identified by GENECLASS as migrants (Table 4). These individuals showed a probability below 0.05.

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Fig 2. A two-dimensional plot of the Principal Coordinate Analysis (PCoA) of microsatellite data showing the clustering of Schistosoma japonicum populations after removal of duplicate genotypes.

The proportion of variation is explained by each axis in parenthesis.

https://doi.org/10.1371/journal.pntd.0005749.g002

Discussion

In this study, based on the hypothesis that the population genetic structure of S. japonicum might explain the variations in the intensity and prevalence of schistosomiasis in the Philippines, the genetic polymorphism of the parasite population from different endemic areas was examined. A large number of different alleles were observed in the samples examined, especially in Irosin, Catarman and Socorro where high prevalence of infections was reported [10] (S1 Table). There is a greater potential for these populations to possess the alleles responsible for the parasite infectivity, causing high infection [7, 19]. These findings were in agreement with that of previous studies where the prevalence of infection was directly proportional to the number of alleles [7, 8, 22]. This situation somehow follows a general pattern in our current study where high prevalence of infection either in humans and snail hosts was observed in those areas with high allelic richness while low or zero prevalence in those areas with low allele numbers (S1 Table). However, this is in contrast to our results in Alang-Alang where low number of different alleles has been observed where high prevalence of the infection was also reported. This could be due to the prolonged utilization of praziquantel from the annual MDA since Leyte has been one of the oldest endemic foci in the Philippines. To confirm such findings the effect of drug selective pressure brought by praziquantel on the parasite genetic diversity should be analysed. It has been known in other parasitic infections such as malaria that selective drug pressure brought by extensive drug use can lead to a reduction in genetic diversity of the parasite [20, 21]. Currently, there are no microsatellite markers that can be linked with parasite infectivity. The alleles that might contribute to the high infection rate might be present in those area with high prevalence, however further studies should be done to confirm this.

In this study, it should be noted that the prevalence data presented in S1 Table was collected from 2013 to 2015 while, our samples obtained from Talibon (Bohol) were collected prior to this period. Bohol is considered as a near-elimination area based on the absence of human cases for many years now. However, the presence of infection in water buffaloes continues to indicate an ongoing transmission even if there are no more human cases (S1 Table). Hence, the possibility of human infection is always present.

Among the seven endemic municipalities analyzed, the Catarman samples showed the highest gene diversity indices (Table 1). Catarman has been reported with high prevalence of infection both in humans and snail intermediate hosts (S1 Table). In addition, this study also revealed that water buffaloes and dogs in this municipality had high prevalence of infection (S1 Table). High infection rate in humans and animal hosts will then increase the probability of snail infection. A study by Rudge et al. (2008) in the Philippines using S. japonicum larval stages, found high levels of parasite gene flow between humans and dogs suggesting strongly the frequent transmission of S. japonicum infection across host species and between villages [23]. Furthermore, the role of animals in disease transmission was further supported by a population genetics study in China, where S. japonicum from cattle showed high genetic diversity in the marshland areas, whereas parasites from humans and dogs were more diverse in the hilly region [24, 25]. These previous studies have therefore demonstrated the contribution of animal host species in the genetic diversity, and gene flow pattern of the parasite. Thus, the zoonotic nature of S. japonicum infecting animals should be seriously considered in the increased disease transmission [26, 27, 28]. Currently, we are now performing direct genotyping of stool-derived eggs collected from humans and animals particularly in water buffaloes and dogs using microsatellites. We will measure the infection intensities in humans and animals together with the parasite’s genetic variation in our ongoing study. Moreover, in this study we chose to use adult worms isolated from mice experimentally infected with snail-derived cercariae for genotyping. This is because adult worms can generally provide DNA with higher quality and quantity suitable for genotyping than snail-derived cercariae or eggs in stool samples. However, fitness of parasites to mice may serve as a bias to the genetically diverse population, leading to bottlenecking of genotypes [29, 30]. The S. japonicum cercariae shed from infected snails collected from endemic areas will also be analyzed in our future studies. Because there are several studies on parasite’s population genetics by using cercariae-derived samples [23, 24, 25], such the direct genotyping may be feasible and provide an advantage to skip in vivo passage.

The level of genetic differentiation differs between endemic areas. Great genetic differentiations were observed in the New Corella samples than those from other endemic sites (Table 2). The large geographical distance separating New Corella from other endemic sites could possibly limit the contact between the hosts, eventually resulting to high genetic differentiation in this municipality. New Corella is located in Southern Mindanao and is expected to be more genetically differentiated because of its geographic location (Fig 1). As seen in Fig 1, New Corella is the farthest of the endemic municipalities being separated by a wide distance from other endemic municipalities. Previous studies showed that the high genetic differentiation observed among peripheral populations such as those of New Corella can be explained by their strong spatial isolation [31].

Furthermore, the possibility of the snail hosts influencing the genetic variation of the parasite population should also be taken into consideration. Presently, there is no study using microsatellites on the genetic variation of the snail population in the sampling areas. However, previous studies on mitochondrial DNA had provided some insights into the genetic variation of the snail population of S. japonicum, and suggested that examination of naturally infected snails may exhibit co-evolutionary relationships with their parasites [32]. Thus, a snail population may reflect the population genetic parameters of their parasites [33, 34]. Nevertheless, it is worth mentioning that these previous studies on the genetic diversity of Oncomelania populations are based on mitochondrial markers. Hence, future studies using microsatellites on the snail populations from each endemic area is essential to obtain results which can be analyzed together with those of S. japonicum population.

The genetic variation observed using AMOVA was greater within each S. japonicum population (91.95%) than the variation among the populations (8.05%) (Table 3). This might be due to the snails being infected by genetically different cercariae having multiple genotypes within the endemic areas [7, 22]. Mixing of infected snails and of their parasites brought about by flooding may explain the higher genetic variation within the population [5, 9]. Also, the continuous rainfall and subsequent floods in these endemic areas might facilitate host-parasite contact, exposing people and animals to contaminated waters that result to higher infection. Thus, people and animals moving from one village to another to escape flooding, take advantage of employment opportunities and there is also animal trade where water buffaloes are exported to other areas could facilitate parasite transmission, contributing to high genetic variation within each endemic area. Another reason could be due to the snail sampling being done in three villages for each endemic municipality where a high village-level variation might exist. Genetic variance within population was accounted for most of the genetic diversity of S. japonicum population in endemic provinces in China [5].

S. japonicum samples obtained from different endemic areas did not form a particular spatial structuring. The lack of geographical structuring suggests that there is still an ongoing gene flow among the S. japonicum populations in all the study areas despite execution of control measures [22]. These findings might imply that there is a continuing transmission of S. japonicum across geographic areas, and therefore reflect the inadequate effect of MDA implementation. The current national control strategy for schistosomiasis in the Philippines is annual MDA using 40 mg/kg of praziquantel in all schistosomiasis-endemic villages including the sampling areas. However, the compliance rate was reported to be <50% [35, 36].

The ongoing gene flow in the populations might be attributed to migration and movement of infected hosts as also suggested otherwise by previous studies done on S. mansoni [22]. The infected hosts could therefore serve as means of allele dispersal in endemic sites. Therefore, the existence of gene flow among the schistosome populations might increase the opportunity for the spread of alleles conferring parasite traits such as infectivity, virulence and drug resistance [8, 22].

Two subgroups were observed in the Catarman samples using the PCoA analysis (Fig 2), indicating that co-infection with several genotypes of the parasite might be infecting the hosts in this endemic site. Catarman is surrounded by other endemic areas such as Leyte, Negros Occidental, Bohol in the Visayas, so the possibility of intermixing of the parasite is very high leading to high genetic variation within the area. A higher transmission and infection success is expected to occur more in mixed parasite genotypes than in single-genotype infection as reported in previous studies [37]. There might be a decrease in the effectiveness of the host immune system to cope with the infection due to the simultaneous attack of the parasite with different genotypes leading to a higher infection success [37]. Furthermore, the high genetic diversity in Catarman may be explained by the lowest inbreeding coefficient values. Inbreeding increases the similarity of the alleles in the parasite population. Previous studies suggested that co-infection by multiple genotypes decreases the possibility of inbreeding [37].

Some parasite populations in Gonzaga, New Corella and Irosin were identified as migrants using Gene Class 2.0 software (Table 4). Gonzaga has just been identified as a new endemic focus at the start of the 21st century [38], and it is presumed that some parasite populations are introduced in this area from Talibon and Catarman. Infected people or animals might have moved from these areas and started the disease transmission in Gonzaga. The theory proposed for the emergence of schistosomiasis in Gonzaga was based on the history of a big geothermal project by PNOC (Philippine National Oil Company) in Gonzaga that recruited workers from the Visayas and Mindanao (Fig 1). The movement of people from these endemic areas into Gonzaga brought in cases, and with the presence of snail hosts in the area, the emergence of the disease became just a matter of time [38]. Interestingly, migrants detected in Irosin were rooted from Gonzaga, further providing evidence for the continuous transmission flow of the parasite among the endemic municipalities in the Philippines (Table 4). Migrant detection in the study supports the clustering analysis using the neighbor joining method wherein the population, including those individuals that were considered to be migrants, clustered together with their source population; for instance, Gonzaga received migrants from Talibon, which clustered together in the NJ tree (S1 Fig).

Genetic diversity found in this study among the parasite population in each endemic site in the Philippines is vital in the parasites’ ability to survive the effect of selective pressures such as those brought by drug treatment. At the same time, selection pressures increase the frequency of favorable alleles across all populations [4]. In this sense, the present finding of high diversity among the parasite populations imply that the MDA with praziquantel has varying degree of impact in interrupting the parasite’s life cycle. Aside from looking at several factors that can contribute to possible treatment failures including low compliance and the quality of the drugs used, it is also important that the effects of MDA be monitored on schistosome populations for its genetic background. This can be done by using microsatellite markers to measure the genetic diversity parameters which include the allelic richness and the heterozygosity of the alleles in the parasite population before and after MDA implementation. Alleles contributing to the severity of the disease such as the ones responsible for the fecundity and survival of S. japonicum inside the host should be identified and need to be further studied.

In conclusion, the use of microsatellites in this study has shown that there is an ongoing gene flow among the S. japonicum population from different endemic areas, indicating the active movement of infected humans and animals from one endemic area to another. Aside from the control programs being implemented in each endemic area, an effective surveillance to monitoring these movements in humans and animals in each endemic site should be in place. Thus, a better cooperation between the medical and veterinary sectors would be highly recommended to ensure a strengthened control program for schistosomiasis. In addition, the diversity will indirectly explain the varying degree of the effects of the ongoing control programs done in these endemic areas. A regular MDA should be implemented and monitored regularly for its efficacy in endemic areas. Considering that only 10 microsatellite markers were analyzed in this study for determining genetic diversity and gene flow of the parasite, we therefore recommend the use of additional highly polymorphic microsatellite markers not only for S. japonicum, but for S. mansoni and S. haematobium to be used in future studies for more precise analysis.

Supporting information

S1 Table. Prevalence of S. japonicum infection in snails, humans and animal hosts from different endemic areas in the Philippines (2013–2015).

https://doi.org/10.1371/journal.pntd.0005749.s001

(DOC)

S1 Fig. Neighbor-joining tree based on FST genetic distance showing the phylogenetic relationships of S. japonicum originating from each endemic site.

Values on nodes are percentage bootstrap supports based on 100 bootstrap samples. Scale bar represents FST genetic distance of 0.05.

https://doi.org/10.1371/journal.pntd.0005749.s002

(TIF)

S1 Dataset. Microsatellite data showing the allele sizes of the samples tested using ten markers.

https://doi.org/10.1371/journal.pntd.0005749.s003

(XLSX)

Acknowledgments

We thank the staff of the Department of Parasitology, College of Public Health, University of the Philippines Manila, to Raffy Jay Fornillos and to Pebbles Grayle Tamayo for assisting in the experiments done in the Philippines; Dr. Gohta Kinoshita of the Laboratory of Parasitology, Department of Disease Control, Graduate School of Veterinary Medicine, Hokkaido University for his help in the analysis of the data.

References

  1. 1. WHO Fact Sheet on Schistosomiasis (2016) Schistosomiasis. http://www.who.int/schistosomiasis/en/. Accessed 14 April 2016
  2. 2. He Y, Salafsky B, Ramaswamy K (2001) Host-parasite relationships of Schistosoma japonicum in mammalian hosts. Trends Parasitol 17: 320–324 pmid:11423374
  3. 3. Angeles JM, Kawazu S (2014) Insights into animal schistosomiasis: from surveillance to control. In: Miele AE (ed) Schistosomiasis epidemiology, Diagnosis and treatment. Nova Science Publishers, Inc., New York, pp 87–109
  4. 4. Curtis J, Minchella DJ (2000) Schistosome Population Genetic Structure: When Clumping Worms is not Just Splitting Hairs. Parasitol Today 6 (2)
  5. 5. Shrivastava J, Qian BQ, McVean G, Webster JP (2005) An insight into the genetic variation of Schistosoma japonicum in mainland China using DNA microsatellite markers. Mol Ecol 14: 839–849 pmid:15723675
  6. 6. Saeed AF, Wang R, Wang S (2016) Microsatellites in Pursuit of Microbial Genome Evolution. Front Microbiol.
  7. 7. Aemero M, Boissier J, Climent D, Mone H, Mouahid G, Berhe N, et al. (2015) Genetic diversity, multiplicity of infection and population structure of Schistosoma mansoni isolates from human hosts in Ethiopia. BMC Genet 16:137 pmid:26630932
  8. 8. Ezeh C, Yin M, Li H, Zhang T, Xu B, Sacko M, et al. (2015) High Genetic Variability of Schistosoma haematobium in Mali and Nigeria. Korean J Parasitol. 53: 129–134 pmid:25748721
  9. 9. Yin M, Li H, Blair D, Xu B, Feng Z, Hu W (2016) Temporal genetic diversity of Schistosoma japonicum in two endemic sites in China revealed by microsatellite markers. Parasit Vector 9: 36
  10. 10. Leonardo L, Pilarita R, Saniel O, Villacorte E, Lebanan MA, Crisostomo B, et al. (2012) A National Baseline Prevalence Survey of Schistosomiasis in the Philippines Using Stratified Two-Step Systematic Cluster Sampling Design. J Tropical Med. http://dx.doi.org/10.115/2012/93612
  11. 11. Shrivastava J, Barker GC, Johansen MV, Xiaonong Z, Aligui GD, McGarvey ST, et al. (2003) Isolation and characterization of polymorphic DNA microsatellite markers from Schistosoma japonicum. Mol Ecol 3: 406–408
  12. 12. Yin M, Hu W, Mo X, Wang S, Brindley PJ, McManus DP, et al. (2008) Multiple near-identical genotypes of Schistosoma japonicum can occur in snails and have implications for population-genetic analyses. Int J Parasitol 38: 1681–1691 pmid:18590733
  13. 13. Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28: 2537–2539 pmid:22820204
  14. 14. Excoffier L, Lischer H (2015) Arlequin ver 3.5 An Integrated Software Package for Population Genetics Data Analysis
  15. 15. Huo GN, Liu L, He HB, Attwood SW (2016) An investigation into the potential effects of infrapopulations structure and other sources of sampling error, on population genetic studies of the transmission of Schistosoma japonicum (Trematoda: Digenea). Parasit Vectors Vectors 9:165
  16. 16. Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, Estoup A (2004) GeneClass2: A Software for Genetic Assignment and First-Generation Migrant Detection. J Hered 95: 536–539 pmid:15475402
  17. 17. Takezaki N, Nei Masatoshi, Tamura K (2014) POPTREEW: Web Version of POPTREE for Constructing Population Trees from Allele Frequency Data and Computing Some Other Quantities. Mol Biol Evol pmid:24603277
  18. 18. Wang R, Zheng L, Toure YT, Dandekar T, Kafatos FC (2001) When genetic distance matters: Measuring genetic differentiation at microsatellite loci in whole-genome scans of recent and incipient mosquito species. PNAS 98:10769–10774 pmid:11553812
  19. 19. Glenn TC, Lance SL, McKee AM, Webster BL, Emery AM, Zerlotini A, et al. (2013) Significant variance in genetic diversity among populations of Schistosoma haematobium detected using microsatellite DNA loci from a genome-wide database. Parasit Vector 6: 300
  20. 20. Mendes C, Salgueiro P, Gonzalez V, Berzosa P, Benito A, do Rosario VE, et al. (2013) Genetic diversity and signatures of selection of drug resistance in Plasmodium populations from both human and mosquito hosts in continental Equatorial Guinea. Malar J 12: 114 pmid:23537170
  21. 21. Razak MRMA, Sastu UR, Norahmad NA, Abdul-Karim A, Muhammad A, Muniandy PK, et al. (2016) Genetic Diversity of Plasmodium falciparum Populations in Malaria Declining Areas of Sabah, East Malaysia. PLoS ONE 11 (3): e0152415. pmid:27023787
  22. 22. Agola LE, Steinauer ML, Mburu DN, Mungai BN, Mwangi IN, Magoma GN, et al. (2009) Genetic diversity and population structure of Schistosoma mansoni within human infrapopulations in Mwea, central Kenya assessed by microsatellite markers. Acta Tropica 111: 219–225 pmid:19427295
  23. 23. Rudge JW, Carabin H, Balolong E Jr, Tallo V, Shrivastava J, Lu DB, et al. (2008) Population Genetics of Schistosoma japonicum within the Philippines Suggest High Levels of Transmission between Humans and Dogs. PLoS Negl Trop Dis 2 (11): e340. pmid:19030225
  24. 24. Lu DB, Rudge JW, Wang TP, Donnelly CA, Fang GR, Webster JP (2010) Transmission of Schistosoma japonicum in Marshland and Hilly Regions of China: Parasite Population Genetic and Sibship Structure. PLoS Negl Trop Dis 4(8): e781. pmid:20689829
  25. 25. Wang TP, Shrivastava J, Johansen MV, Zhang SQ, Wang FF, Webster JP (2006) Does multiple hosts mean multiple parasites? Population genetic structure of Schistosoma japonicum between definitive host species. Int J Parasitol. 36:1317–1325 pmid:16876170
  26. 26. Angeles JM, Goto Y, Kirinoki M, Asada M, Leonardo LR, Rivera PT, et al. (2012) Utilization of ELISA using Thioredoxin Peroxidase-1 and tandem repeat proteins for diagnosis of Schistosoma japonicum infection among water buffaloes. PLoS Negl Trop Dis 6 (8):e1800. pmid:22953018
  27. 27. Moendeg KJ, Angeles JM, Yasuyuki G, Leonardo LR, Masashi K, Villacorte EA, et al. (2015) Development and optimization of cocktail-ELISA for a unified surveillance of zoonotic schistosomiasis in multiple host species. Parasitol Res 114: 1225–1228 pmid:25595656
  28. 28. Gray DJ, Williams GM, Li Y, Chen H, Li RS, Forsyth SJ, et al. (2007) A Cluster-Randomized Bovine Intervention Trial against Schistosoma japonicum in the People's Republic of China: design and baseline results. Am J Trop Med Hyg 77: 866–874 pmid:17984344
  29. 29. Gower CM, Shrivastava J, Lamberton PHL, Rollinson D, Webster BL, Emery A, et al. (2007) Development and application of an ethically and epidemiologically advantageous assay for the multi-locus microsatellite analysis of Schistosoma mansoni. Parasitology 134:523–536 pmid:17096873
  30. 30. Shrivastava J, Gower CM, Balolong E Jr, Wang TP, Qian BZ, et al. (2005) Population genetics of multi-host parasites-the case for molecular epidemiological studies of Schistosoma japonicum using larval stages from naturally infected hosts. Parasitology 131: 617–626. pmid:16255820
  31. 31. Wagner V, Durka W, Hensen I (2011) Increased Genetic Differentiation But No Reduced Genetic Diversity in Peripheral Vs. Central Populations of A Steppe Grass. Am J Bot 98: 1173–1179 pmid:21700808
  32. 32. Attwood SW, Ibaraki M, Saitoh Y, Nihei N, Janies DA (2015) Comparative Phylogenetic Studies on Schistosoma japonicum and Its Snail Intermediate Host Oncomelania hupensis: Origins, Dispersal and Coevolution. PLoS Negl Trop Dis 9(7): e0003935. pmid:26230619
  33. 33. Webster JP, Shrivastava J, Johnson PJ, Blair L (2007) Is host- schistosome coevolution going anywhere? BMC Evol Biol 7: 91 pmid:17567519
  34. 34. Saijuntha W, Jarilla B, Leonardo AK, Sunico LS, Leonardo LR, Andrews RH, et al. (2014) Genetic Structure Inferred from Mitochondrial 12S Ribosomal RNA Sequence of Oncomelania quadrasi, the Intermediate Snail Host of Schistosoma japonicum in the Philippines. Am J Trop Med Hyg 90: 1140–1145 pmid:24686739
  35. 35. Ross ALGP, Olveda RM, Chy D, Olveda DU, Li Y, Harn DA, et al. (2014) Can Mass Drug Administration Lead to the Sustainable Control of Schistosomiasis? J Infect Dis 211: 283–9 pmid:25070942
  36. 36. Tallo VL, Carabin H, Alday PP, Balolong E Jr, Olveda RM, McGarvey ST (2008) Is mass treatment the appropriate schistosomiasis elimination strategy? Bull World Health Organ 86: 10
  37. 37. Karvonen A, Rellstab C, Louhil KR, Jokela J (2012) Synchronous attack is advantageous: mixed genotype infections lead to higher infection success in trematode parasites. Proc R Soc B 279: 171–176 pmid:21632629
  38. 38. Leonardo L, Pilarita R, Saniel O, Solon JA, Chigusa Y, Villacorte E, et al. (2015) New endemic foci of schistosomiasis infections in the Philippines. Acta Tropica 141: 354–360 pmid:23583862