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The genetic diversity of Oncomelania hupensis robertsoni, intermediate hosts of Schistosoma japonicum in hilly regions of China, using microsatellite markers

Parasites & Vectors volume 17, Article number: 147 (2024) Cite this article

Abstract

Background

The elimination of schistosomiasis remains a challenging task, with current measures primarily focused on the monitoring and control of Oncomelania hupensis (O. hupensis) snail, the sole intermediate host of Schistosome japonicum. Given the emerging, re-emerging, and persistent habitats of snails, understanding their genetic diversity might be essential for their successful monitoring and control. The aims of this study were to analyze the genetic diversity of Oncomelania hupensis robertsoni (O. h. robertsoni) using microsatellite DNA markers; and validate the applicability of previously identified microsatellite loci for O. hupensis in hilly regions.

Methods

A total of 17 populations of O. h. robertsoni from Yunnan Province in China were selected for analysis of genetic diversity using six microsatellite DNA polymorphic loci (P82, P84, T4-22, T5-11, T5-13, and T6-27).

Results

The number of alleles among populations ranged from 0 to 19, with an average of 5. The average ranges of expected (He) and observed (Ho) heterozygosity within populations were 0.506 to 0.761 and 0.443 to 0.792, respectively. The average fixation index within the population ranged from – 0.801 to 0.211. The average polymorphic information content (PIC) within the population ranged from 0.411 to 0.757, appearing to be polymorphic for all loci (all PIC > 0.5), except for P28 and P48. A total of 68 loci showed significant deviations from Hardy-Weinberg equilibrium (P < 0.05), and pairwise Fst values ranged from 0.051 to 0.379. The analysis of molecular variance indicated that 88% of the variation occurred within snail populations, whereas 12% occurred among snail populations. Phylogenetic trees and principal coordinate analysis revealed two distinct clusters within the snail population, corresponding to “Yunnan North” and “Yunnan South”.

Conclusions

O. h. robertsoni exhibited a relatively high level of genetic differentiation, with variation chiefly existing within snail populations. All snail in this region could be separated into two clusters. The microsatellite loci P82 and P84 might not be suitable for classification studies of O. hupensis in hilly regions. These findings provided important information for the monitoring and control of snail, and for further genetic diversity studies on snail populations.

Graphical Abstract

Background

Schistosomiasis is a neglected tropical parasitic disease that has imposed a severe burden worldwide, affecting almost 240 million people [1]. It is a disease endemic to China, where the causative agent is Schistosoma japonicum. After decades of major efforts in schistosomiasis control, substantial progress has been achieved. For example, the estimated number of infected humans was reduced from approximately 11.6 million in the 1950s to 37,601 in 2017 [2]. Thus, aligning with the updated targets set by the World Health Organization, the Chinese central government proposed the goal of eliminating schistosomiasis transmission by 2030 [3]. Despite this progress and ambitious goals, several major challenges remain, one of which is the emerging, re-emerging, and persistent habitats of Oncomelania hupensis, the only intermediate host snails of S. japonicum [4,5,6].

In mainland China, O. hupensis is primarily distributed in the drainage basins of the middle and lower reaches of the Yangtze River across 12 provinces, encompassing four subspecies: Oncomelania hupensis hupensis, O. h. robertsoni, O. h. tangi and O. h. guangxiensis [4]. Oncomelania hupensis populations in different regions exhibit diverse morphological and genetic characteristics, evolving in distinct directions [7]. Previous studies have highlighted regional variations in snail morphology, genetic characteristics, and susceptibility to S. japonicum [8]. Yunnan Province was previously a severe hotspot for schistosomiasis endemism in hilly regions of China [9] and still harbors a substantial population of snails, i.e. O. h. robertsoni [10]. Population genetic experiments have revealed that Yunnan Province was the likely place of origin for O. hupensis in mainland China [11, 12]. Smooth-shelled O. hupensis, introduced from India through Yunnan, gradually migrated eastward to the Yangtze River Watershed. During this process, genetic mutations occurred, giving rise to ribbed-shelled O. hupensis. Therefore, studying the genetic diversity of O. h. robertsoni is crucial for tracing the origin of O. hupensis in the Chinese mainland. Currently, O. h. robertsoni is predominantly found in Yunnan Province in areas with elevations ranging from 1350 to 2466 m, a region that represents the highest altitude endemic area in China. Additionally, the breeding environment of this population is highly complex, occurring in ditches, grassland, field ridges, wasteland, and dry land. Its distribution is relatively isolated and patchy, with substantial fragmentation [13, 14]. High mountain barriers or other forms of isolation exist between some distribution areas, creating geographically separated and non-contiguous regions [15]. Given this unique geographical environment, the genetic diversity of O. h. robertsoni could exhibit a certain degree of heterogeneity, posing challenges to monitoring and controlling these snails [16,17,18]. Therefore, fully understanding the genetic diversity of O. h. robertsoni is crucial, especially concerning snail control through focal molluscicides [19].

Microsatellite DNA is a short nucleotide tandem (STR) or simple sequence repeat (SSR) DNA, which is widely distributed in the genomes of eukaryotic organisms [20]. Given its characteristics of easy detection in populations, high heterozygosity, codominance, and wide distribution, microsatellite DNA has become a crucial molecular marker for studying population genetic diversity [21, 22]. Although the microsatellite DNA library of O. hupensis has been established [20, 23], few studies have used microsatellite markers to analyze the population genetic diversity and genome mapping of this species. Moreover, previous studies have primarily focused on O. hupensis from marshland and/or lake areas endemic for schistosomiasis in China [6, 20, 24], and there is a lack of population genetic analyses using microsatellites for O. hupensis population in schistosomiasis-endemic areas in hilly regions.

Previous studies have revealed the existence of O. h. robertsoni based on morphological characteristics, biological traits, and molecular markers [4]. This subspecies inhabits hilly schistosomiasis-endemic regions in both Yunnan and Sichuan Provinces and displays significant genetic variation compared with O. hupensis in the Yangtze River Watershed [25, 26]. The suitability of using the same microsatellite DNA loci for O. hupensis in hilly regions remains unknown, and this merits more research attention.

Therefore, this study aimed to analyze the genetic diversity of O. h. robertsoni using microsatellite DNA markers and to validate the applicability of previously identified microsatellite loci for O. hupensis in hilly regions. Our results may provide important information for monitoring and control of snails and further genetic diversity studies on snail populations.

Methods

Sources of O. hupensis

A total of 17 populations of O. h. robertsoni were collected from formerly endemic schistosomiasis villages in Yunnan Province of China considering various factors such as snail density, geographical location, altitude, water system, and environmental type, etc. (Table 1, Fig. 1).

Table 1 Location of Oncomelania hupensis robertsoni collection
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Fig. 1
figure 1

Distribution of sampling sites of Oncomelania hupensis robertsoni in Yunnan Province of China

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The selected villages were Lianyi (HQ1) and Jinsuo (HQ2) in Heqing County, Caizhuang (MD1) in Midu County, Xiaoqiao (XY1) in Xiangyun County, Qiandian (EY1) and Yongle (EY2) in Eryuan County, Liuguanchang (DL1), Wuxing (DL2), Machang (DL3), and Fengming (DL4) in Dali City, Dianzhong (WS1) in Weishan County, Huilong (JC1) in Jianchuan County, Cangling (CX1) and Luhe (CX2) in Chuxiong County, Biyuan (NL1) in Ninglang County, Dongyuan (GC1) in Lijiang City, and Yangwu (YS1) in Yongsheng County. Approximately 100 snails were collected from each sampling site. The habitat characteristics of O. h. robertsoni in the sampling site for each environmental type are presented in Additional file 1: Fig. S1.

Extraction of total DNA from O. hupensis

The collected O. h. robertsoni snails were fed for 1 week, and we identified whether snails were infected with S. japonicum by observation of cercaria emergence from snails [27]. Thirty randomly selected S. japonicum-negative snails from each sampling site were used in this study. Genomic DNA was extracted from the head-foot muscle of each snail using the Qiagen extraction kit [Paisennuo Biotechnology (Shanghai) Co., Ltd.].

Gene scan

Six microsatellite DNA polymorphic loci were selected from a previously published microsatellite loci library (i.e. P82, P84, T4-22, T5-11, T5-13, T6-27) [27]. Primer sequences and related information are presented in Table 2.

Table 2 Primers of the six microsatellite loci in Oncomelania robertsoni
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PCR amplification was performed. The amplification reaction system was 20 μl, including 1 μl template DNA, 1 μl forward fluorescent primer, 1 μl reverse primer, 2 μl 10*buffer, 0.5 μl dNTP, 0.5 μl Taq enzyme, and 14 μl DdH20. The reaction conditions for PCR amplification were as follows: 95 °C, 5 min; 95 °C, 30 s, 62–52 °C, 30 s, 72 °C, 30 s, 10 cycles; 95 °C, 30 s, 52 °C, 30 s, 72 °C, 30 s, 25 cycles; 72 °C, 7 min for final extension. After the completion of PCR, a portion of the samples was subjected to agarose gel electrophoresis to determine the concentration and fragment size range of the samples. The samples were adjusted based on electrophoresis results, and 70% ethanol was added to a final volume of 50 μl. After thorough mixing, centrifugation was performed at 3700 rpm and 4 °C for 30 min, followed by sample purification. Ethanol was then removed after inversion. Finally, PCR products were mixed with LIZ500 and Hi-Di, denatured at 95 °C for 4 min and detected using an automated genetic analyzer (3730XL, ABI, USA).

Analysis of microsatellite diversity

The length of the amplified fragments of microsatellite DNA loci were determined and subsequently exported as an Excel table for genetic diversity analysis.

First, genetic differentiation analysis was conducted within populations, calculating the number of alleles (Na), expected heterozygosity (He), observed heterozygosity (Ho), and fixation index (F) for each population at six microsatellite DNA loci, respectively. Hardy-Weinberg equilibrium (HEW) tests were also performed. The values of Na, Ho, and F represent the degree of population genetic differentiation, with higher values of Na and Ho and negative values of F indicating greater genetic diversity [28,29,30]. When He exceeds Ho, it suggests a lack of heterozygosity within the population [31, 32]. Additionally, the polymorphic information content (PIC) was calculated, with higher PIC values indicating greater genetic variation. Higher values represent higher polymorphism where the locus was classified based on their value (i.e. low polymorphic = < 0.25, moderately polymorphic = 0.25–0.50, and highly polymorphic = > 0.50) [33].

Second, genetic differentiation among populations was assessed using pairwise Fst, which measures the probability that two gametes randomly selected from two populations are homologous. The value ranged from 0 to 1, with a higher value representing greater genetic differentiation between the populations [25, 34].

Third, based on Nei's genetic distance, phylogenetic trees were constructed using the minimum evolution (ME) method, neighbor-joining (NJ) method, and unweighted pair-group (UPG) method. Based on genetic distance, principal coordinate analysis (PCoA) was performed to determine the genetic variation structure, and analysis of molecular variance (AMOVA) was conducted to analyze the sources of variation in the snail population.

All analyses were conducted using GenAlEx (version 6.5), MEGA (version 11), and Cervus (version 3.0.7) software. HEW test and AMOVA considered P value < 0.05 as statistically significant.

Results

Gene scan

A total of 507 specimens from the 17 sampled populations of O. h. robertsoni were scanned at the genetic level across six polymorphic loci of microsatellite DNA, resulting in 6084 microsatellite DNA loci data (Additional file 2: Dataset S2).

Genetic differentiation within populations

As shown in Table 3, the Na of the 17 O. h. robertsoni populations ranged from 0 to 19, with a mean of 5. The CX2 and WS1 populations had the minimum and maximum average Na values, respectively. The mean ranges of He and Ho within populations were 0.506 to 0.761 and 0.443 to 0.792, respectively. The HQ2 and WS1 populations had the minimum and maximum mean Ho values, respectively. Most populations exhibited lower Ho than He, indicating a loss of heterozygosity. The mean of F within the populations ranged from – 0.801 to 0.211. The mean of PIC within population ranged from 0.411 to 0.757, with a mean of 0.557. Significant deviation from HWE was observed in 68 out of 102 (24.48%) possible single exact locus tests (P < 0.05). Except for the YS1 population, which had only one locus deviating from HWE, the remaining 16 populations had three to five loci deviating from HWE.

Table 3 Coefficients of genetic diversity of Oncomelania hupensis robertsoni at different loci
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In summary, there is a high degree of genetic differentiation in snail samples of O. h. robertsoni. Among these, the highest genetic differentiation was observed in the WS1 population.

Genetic differentiation among populations

As shown in Table 4, across the 17 O. h. robertsoni populations, the lowest estimates of pairwise Fst were observed between WS1 population and JC1 population, with 0.051, whereas the highest estimates of pairwise Fst were observed between CX2 population and DL2 population, with 0.379.

Table 4 Pairwise Fst among Oncomelania hupensis robertsoni populations
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Population structure

The phylogenetic trees constructed using the ME, NJ, and UPG methods (Fig. 2a–c) consistently divided the 17 O. h. robertsoni populations into two major branches, tentatively named “Yunnan North Branch” and “Yunnan South Branch,” based on their geographical locations. The topological structures of the trees constructed using ME and NJ methods were consistent, whereas those resulting from the UPG method differed in the clustering of the HQ1 population. Based on the ME and NJ cluster analyses, the Yunnan South Branch included populations distributed in the Lancang River Watershed (i.e. JC1, EY1, EY2, DL1, DL2, DL3, and DL4), Yuan River Watershed (i.e. WS1, MD1, and XY1), and Longchuan River Watershed (i.e. CX1 and CX2), located in central and southern Yunnan Province. The Yunnan North Branch included populations distributed in the Jinsha River Watershed (i.e. NJ1, YS1, GC1, HQ1, and HQ2), located in northern Yunnan Province. However, based on UPG method, the HQ1 population was clustered in the Yunnan South Branch by UPG method. There are clear geographic barriers between the Jinsha River Watershed and the Lancang River, Yuan River, and Longchuan River Watershed, as shown in Fig. 3.

Fig. 2
figure 2

Phylogenetic tree constructed by minimum evolution (ME) method (a), neighbor-joining (NJ) method (b), and unweighted pair-group (UPG) method (c)

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Fig. 3
figure 3

Geographical locations of north-south Yunnan branch of Oncomelania hupensis robertsoni

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The PCoA (Fig. 4) based on the covariance of the genetic distance matrix revealed that individuals from each population were relatively concentrated, with some populations such as HQ1 and MD1 showing relatively dispersed individuals, corresponding approximately to the branches in the constructed phylogenetic tree.

Fig. 4
figure 4

A two-dimensional plot of the principal coordinate analysis (PCoA) of microsatellite data showing the clustering of Oncomelania hupensis robertsoni

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AMOVA results (Table 5) indicated that the source of variation was mainly within O. h. robertsoni populations, accounting for 88% of the total variation, compared with variation among O. h. robertsoni populations, which accounted for 12%. Fst among the 17 O. h. robertsoni populations was 0.170 (P < 0.001), indicating significant genetic differentiation between at least two populations.

Table 5 Analysis of molecular variance (AMOVA) for Oncomelania hupensis robertsoni populations
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Discussion

Eliminating schistosomiasis remains a challenging public health endeavor worldwide [35, 36]. Given the sustainable parasitism of S. japonicum in 42 nonhuman mammalian species, control strategies for this parasite infection in wild animals (e.g. rodents) might not be practical [37, 38]; thus, monitoring and controlling its only intermediate host snail to interrupt the transmission chain is a more feasible approach [39]. However, the natural environment of hilly areas of schistosomiasis endemism in China is complex, with scattered snail breeding grounds, making it difficult to further compress the snail’s breeding areas and rendering snail monitoring less effective [40]. In this study, we collected 17 representative O. h. robertsoni snail populations from hilly endemic schistosomiasis areas in Yunnan Province of China and analyzed the genetic diversity of this population using microsatellite DNA markers, which are considered suitable, neutral Mendelian markers [41]. Our results may provide theoretical support for the targeted monitoring and control of O. h. robertsoni in such regions.

In this research, the Ho of the 17 O. h. robertsoni populations ranged from 0.506 to 0.761 (all exceeding 0.500), with most populations exhibiting higher Ho than He and deviating from HWE at six microsatellite DNA polymorphic loci. These results indicated a significant deficiency of heterozygotes in the O. h. robertsoni population, reflecting a relatively high degree of genetic differentiation [42, 43]. High genetic differentiation could be associated with higher fitness of hosts and decreased susceptibility to parasite infection [44, 45], whereas the causes of low diversity may be related to low degrees of habitat disturbance, large population sizes, or persistently habituated sites [24]. We hypothesize that the observed high diversity of O. h. robertsoni may mainly result from one or multiple factors mentioned above. In fact, the O. h. robertsoni breeding environment in Yunnan Province is complex, presenting a patchy distribution, and snail control measures mainly rely on spraying of molluscides, such as niclosamide and metaldehyde [46]. The effectiveness of such an approach is limited by geographical constraints and higher water source requirements [47], resulting in relatively low habitat disturbance and a large population size.

Additionally, in this study, the average PIC in the 17 O. h. robertsoni population ranged from 0.411 to 0.757. In contrast, another study using the same microsatellite loci for O. h. hupensis population reported average PIC values ranging from 0.511 to 0.850 [27]. This suggests that the level of genetic diversity previously reported for the O. h. hupensis population may be higher than that for the study O. h. robertsoni population.

Among the 17 O. h. robertsoni populations analyzed, the WS1 population had the highest average Na, He, Ho, and PIC, indicating the highest level of genetic differentiation, possibly because of a larger population size [13]. In contrast, the CX2 population had the lowest mean number of alleles, the HQ2 population had the lowest Ho, and the EY1 population had the lowest PIC, indicating relatively lower genetic differentiation. Low host diversity can be associated with increased susceptibility to parasite infection [48, 49]. For example, in the Senegal River Basin, perturbations (i.e. construction of the Diama Dam) in population genetics that lead to decreased intrapopulation diversity could have contributed to the major Schistosome mansoni outbreak [50, 51]. These findings may provide important information for targeted formulation of schistosomiasis prevention and control measures.

The pairwise Fst values ranged from 0.051 (between WS1 population and JC1 population) to 0.379 (between CX2 population and DL2 population), indicating a certain degree of genetic differentiation among all pairwise comparisons of the O. h. robertsoni populations. Notably, compared with those from the marshland and/or lake areas endemic for schistosomiasis in China, the pairwise Fst values reported here were relatively elevated. This suggested a heightened genetic variation among snails in these hilly terrains. AMOVA revealed that most of the variation was within populations in O. h. robertsoni, i.e. close consanguinity within rather than among populations.

The phylogenetic tree results showed that the snails in this region could be divided into two branches: the northern branch in the Jinsha River Watershed and the southern branch in the Lancang River, Yuanjiang, and Longchuan River Watersheds. This distribution pattern correlates with river basins rather than presenting an island-like distribution. However, to the best of our knowledge, the difference of morphology or biology between the northern and southern branch has not been investigated so far; this merits further research attention. Notably, the HQ1 population, located at the junction of the Jinsha River and Lancang River Watersheds, shows clustering results using the ME and NJ methods that differ from those obtained using the UPG method, and this population showed relatively scattered positions in the PCoA plot. This may be due to the limited representativeness of the six microsatellite loci selected in this study, suggesting the need for further verification with increased sampling points and microsatellite loci.

Furthermore, the PIC value is directly proportional to both the abundance of heterozygotes and the amount of genetic information, with higher PIC values indicating a larger proportion of heterozygotes and richer genetic information [52, 53]. Among the six loci used in this study, all but two, P28 and P48, were highly polymorphic (all PIC > 0.5), with the mean of PIC for P28 and P48 being 0.393 and 0.296, respectively. However, previous research using the P28 and P48 loci for genetic differentiation of snails in marshland and/or lake endemic areas showed them to be high polymorphic. This suggested significant differences in P28 and P48 between lake- and hill-type snail populations, making them important markers for distinguishing between these types. However, they might not be suitable for studying subspecies of O. hupensis in hilly regions of China, providing valuable information for future genetic differentiation studies in O. hupensis snail populations.

Considering that only six microsatellite DNA polymorphic loci were analyzed in this study for determining genetic diversity, we therefore recommend the use of additional highly polymorphic microsatellite markers not only for O. hupensis but also for Bulinus species (Bulinus sp.), the sole intermediate host of Schistosome mansoni and Biomphalaria species (Biomphalaria sp.), the sole intermediate host of Schistosome haematobium, to be used in future studies for more precise analysis. This study, based on microsatellite markers, may provide genetic evidence to help monitoring and control of snails.

Conclusions

This study, using microsatellite DNA markers, first demonstrated that the O. h. robertsoni snail populations generally had relatively high genetic diversity, and the variation mainly originates from within rather than among populations; the snails within populations have closer consanguinity. The notable north-south genetic differentiation of snail populations in this region was likely to be associated with specific watersheds. These findings provided crucial information for understanding the genetic structure and distribution patterns of O. h. robertsoni in hilly regions of China. Ultimately, our results might also have implications for the development of more effective strategies for the control of schistosomiasis in these regions.

Additionally, the microsatellite DNA loci P82 and P84 showed relatively low polymorphism in this study, suggesting that these loci were not be suitable for classification study of O. hupensis in hilly regions. This finding has significance for subsequent investigations into the genetic diversity of O. hupensis populations.

Availability of data and materials

All data other than the ones included in the main manuscript are available as Additional file 2: Dataset S2.

Abbreviations

STR:

Short nucleotide tandem

SSR:

Imple sequence repeat

O. h. robertsoni :

Oncomelania hupensis robertsoni

Na:

Number of alleles

He:

Expected heterozygosity

Ho:

Observed heterozygosity

F:

Fixation index

HEW:

Hardy–Weinberg equilibrium

PIC:

Polymorphic information content

ME:

Minimum evolution

NJ:

Neighbor-joining

UPG:

Unweighted pair-group

PCoA:

Principal coordinate analysis

AMOVA:

Analysis of molecular variance

References

  1. Organization WH: Schistosomiasis (Bilharzia). In., vol. 2023.

  2. Zhou Y, Chen Y, Jiang Q. History of human schistosomiasis (bilharziasis) in China: from discovery to elimination. Acta Parasitol. 2021;66:760–9.

    Article  PubMed  Google Scholar 

  3. Wang W, Bergquist R, King CH, Yang K. Elimination of schistosomiasis in China: current status and future prospects. PLoS Negl Trop Dis. 2021;15:e9578.

    Article  Google Scholar 

  4. Li ZJ, Ge J, Dai JR, Wen LY, Lin DD, Madsen H, et al. Biology and control of snail intermediate host of Schistosoma japonicum in The People’s Republic of China. Adv Parasitol. 2016;92:197–236.

    Article  PubMed  Google Scholar 

  5. Liu MM, Feng Y, Yang K. Impact of micro-environmental factors on survival, reproduction and distribution of Oncomelania hupensis snails. Infect Dis Poverty. 2021;10:47.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Qiu C, Lu DB, Deng Y, Zou HY, Liang YS, Webster JP. Population genetics of Oncomelania hupensis snails, intermediate hosts of Schistosoma japonicum, from emerging, re-emerging or established habitats within China. Acat Trop. 2019;197:105048.

    Article  PubMed  Google Scholar 

  7. Wang CZ, Lu DB, Guo CX, Li Y, Gao YM, Bian CR, et al. Compatibility of Schistosoma japonicum from the hilly region and Oncomelania hupensis hupensis from the marshland region within Anhui. China Parasitol Res. 2014;113:4477–84.

    Article  PubMed  Google Scholar 

  8. Webster JP, Shrivastava J, Johnson PJ, Blair L. Is host-schistosome coevolution going anywhere? BMC EVOL BIOL. 2007;7:91.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Yun Z, Xiguang F, Mingshou W, Mengtao X, Meifen S, Jing S. Current prevalence situation and control strategy of schistosomiasis in Yunnan Province Chinese Journal of Schistosomiasis. Control. 2015;27:618–20.

    Google Scholar 

  10. Xiaonong Z, Yi Z, Qingbiao H, Jide X, Tianping W. Science on Oncomelania Snail. Beijing: China Science Publishing and Media Ltd; 2005.

    Google Scholar 

  11. Leonardo L, Varona G, Fornillos RJ, Manalo D, Tabios IK, Moendeg K, et al. Oncomelania hupensis quadrasi: snail intermediate host of Schistosoma japonicum in the Philippines. Acta Trop. 2020;210:105547.

    Article  PubMed  Google Scholar 

  12. Zhou XN, Kristensen TK. Genetic and morphological variations in populations of Oncomelania spp in China. Southeast Asian J Trop Med Public Health. 1999;30:166–76.

    CAS  PubMed  Google Scholar 

  13. Xiaonong Z. Report on nationwide survey of Oncomelania hupensis in China. Shanghai: Shanghai Science and Technical Literature Press; 2022.

    Google Scholar 

  14. Xiaonong Z, Shizhu L, Zhongjie L, Jing X. Handbook for Eliminating of Schistosoma japonicum. Shanghai: Shanghai Science and Technology Press; 2021.

    Google Scholar 

  15. Prevention YIFE: Map of the distribution and prevalence of schistosomiasis in Yunnan Province. Kunming: Yunnan Science and Technology Press.

  16. Yang K, Wang XH, Yang GJ, Wu XH, Qi YL, Li HJ, et al. An integrated approach to identify distribution of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, in a mountainous region in China. INT J PARASITOL. 2008;38:1007–16.

    Article  PubMed  Google Scholar 

  17. Xu N, Zhang Y, Du C, Song J, Huang J, Gong Y, et al. Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models. Parasit Vectors. 2023;16:377.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jin S, Meifen S, Yi D. The effect analysis of comprehensive governance for schistosomiasis in Yunnan Province from 2004 to 202. J Torp Dis Parasitol. 2022;20:210–4.

    Google Scholar 

  19. Wang LD, Guo JG, Wu XH, Chen HG, Wang TP, Zhu SP, et al. China’s new strategy to block Schistosoma japonicum transmission: experiences and impact beyond schistosomiasis. Trop Med Int Health. 2009;14:1475–83.

    Article  PubMed  Google Scholar 

  20. Guan W, Li SZ, Abe EM, Webster BL, Rollinson D, Zhou XN. The genetic diversity and geographical separation study of Oncomelania hupensis populations in mainland China using microsatellite loci. Parasit Vectors. 2016;9:28.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Schlötterer C, Amos B, Tautz D. Conservation of polymorphic simple sequence loci in cetacean species. Nature. 1991;354:63–5.

    Article  ADS  PubMed  Google Scholar 

  22. Saeed AF, Wang R, Wang S. Microsatellites in pursuit of microbial genome evolution. Front Microbiol. 2015;6:1462.

    PubMed  Google Scholar 

  23. Li SZ, Wang YX, Yang K, Liu Q, Wang Q, Zhang Y, et al. Landscape genetics: the correlation of spatial and genetic distances of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum in mainland China. Geospat Health. 2009;3:221–31.

    Article  PubMed  Google Scholar 

  24. Cheng YH, Sun MT, Wang N, Gao CZ, Peng HQ, Zhang JY, et al. Population genetics of Oncomelania hupensis snails from new-emerging snail habitats in a currently Schistosoma japonicum non-endemic area. Trop Med Infect Dis. 2023;8:42.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shrivastava J, Qian BZ, Mcvean G, Webster JP. An insight into the genetic variation of Schistosoma japonicum in mainland China using DNA microsatellite markers. Mol Ecol. 2005;14:839–49.

    Article  CAS  PubMed  Google Scholar 

  26. Hauswald AK, Remais JV, Xiao N, Davis GM, Lu D, Bale MJ, et al. Stirred, not shaken: genetic structure of the intermediate snail host Oncomelania hupensis robertsoni in an historically endemic schistosomiasis area. Parasit Vectors. 2011;4:206.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Shizhu L, Li Z, Qin L, Shan L, Qiang W, Yinjun Q, et al. Study on the genetic differences among Oncomelania hupensis population in middle and lower reaches of Yangtze River using microsatellite DNA markers. Chin J Parasit DIs. 2012;30:268–73.

    Google Scholar 

  28. Jain A, Kumar A, Sharma PC. Morphometric and microsatellite diversity in Seabuckthorn (Hippophae rhamnoides L.) natural populations originating from the different geographical regions of the Indian Himalayas. Plant Mol Biol Report. 2022;40:566–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zeng J, Wang Z, Zhou S, Bai J, Zheng H. Allozyme variation and population genetic structure of Betula alnoides from Guangxi, China. Biochem Genet. 2003;41:61–75.

    Article  CAS  PubMed  Google Scholar 

  30. Jawasreh KI, Ababneh MM, Ismail ZB, Younes A, Sukhni IA. Genetic diversity and population structure of local and exotic sheep breeds in Jordan using microsatellites markers. Vet World. 2018;11:778–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Vdovina NV, Yuryeva IB. Monitoring for the genetic structure of Mezen breed of horses in terms of DNA microsatellites. Vavilovskii Zhurnal Genet Selektsii. 2021;25:202–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Amjadi MA, Yeganeh HM, Sadeghi M, Abbas RS, Yang J, Najafabadi HA, et al. Microsatellite analysis of genetic diversity and population structure of the Iranian Kurdish horse. J Equine Vet Sci. 2021;98:103358.

    Article  PubMed  Google Scholar 

  33. Khan HA, Arif IA, Bahkali AH, Al FA, Al HA. Bayesian, maximum parsimony and UPGMA models for inferring the phylogenies of antelopes using mitochondrial markers. Evol Bioinform Online. 2008;4:263–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lu DB, Rudge JW, Wang TP, Donnelly CA, Fang GR, Webster JP. Transmission of Schistosoma japonicum in marshland and hilly regions of China: parasite population genetic and sibship structure. PLoS Negl Trop Dis. 2010;4:e781.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Colley DG, Bustinduy AL, Secor WE, King CH. Human schistosomiasis. Lancet. 2014;383:2253–64.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lo NC, Bezerra F, Colley DG, Fleming FM, Homeida M, Kabatereine N, et al. Review of 2022 WHO guidelines on the control and elimination of schistosomiasis. LANCET INFECT DIS. 2022;22:e327–35.

    Article  PubMed  Google Scholar 

  37. Nelwan ML. Schistosomiasis: life cycle, diagnosis, and control. Curr Ther Res Clin Exp. 2019;91:5–9.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Jenkins-Holick DS, Kaul TL. Schistosomiasis. Urol Nurs. 2013;33:163–70.

    Article  PubMed  Google Scholar 

  39. Zhang JY, Gu MM, Yu QF, Sun MT, Zou HY, Zhou ZJ, et al. Genetic diversity and structure of Oncomelania hupensis hupensis in two eco-epidemiological settings as revealed by the mitochondrial COX1 gene sequences. Mol Biol Rep. 2022;49:511–8.

    Article  CAS  PubMed  Google Scholar 

  40. Shen MF, Feng XG, Huang NB, Zhang Y, Wu MS, Song J, et al. Analysis of Oncomelania hupensis status in schistosomiasis surveillance sites of Yunnan Province in 2015. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi. 2016;28:54–7.

    PubMed  Google Scholar 

  41. Jarne P, Lagoda PJ. Microsatellites, from molecules to populations and back. Trends Ecol Evol. 1996;11:424–9.

    Article  CAS  PubMed  Google Scholar 

  42. Terrones A, Juan A. Seventeen new microsatellites for Tamarix gallica and cross-amplification in Tamarix species. APPL Plant Sci. 2020;8:e11317.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Churcher TS, Schwab AE, Prichard RK, Basáñez MG. An analysis of genetic diversity and inbreeding in Wuchereria bancrofti: implications for the spread and detection of drug resistance. PLoS Negl Trop Dis. 2008;2:e211.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Coltman DW, Pilkington JG, Smith JA, Pemberton JM. Parasite-mediated selection against inbred soay sheep in a free-living island populaton. Evolution. 1999;53:1259–67.

    PubMed  Google Scholar 

  45. Jarne P, Théron A. Genetic structure in natural populations of flukes and snails: a practical approach and review. Parasitol. 2001;123:S27–40.

    Article  Google Scholar 

  46. Xiguang F, Binggui L, Wenbao L, Mingshou W, Ningbo H, Yun Z, et al. Molluscicidal effect of 5% powder of niclosamide ethanolamine saltgranules(NEG)in field of mountainous areas in Yunnan Province. Chin J Schistosomiasis Control. 2015;27:129–37.

    Google Scholar 

  47. Binggui L, Wenbao L, Xiguang F, Yi D, Mingshou W, Xingqi D. Observation on molluscicidal effect of META-Li and fish toxicity inmountainous area of Yunnan. Parasitic diseases and infectious diseases. 2013;11:180–4.

    Google Scholar 

  48. Cross JH, Zaraspe G, Lu SK, Chiu KM, Hung HK. Susceptibility of Oncomelania hupensis subspecies to infection with geographic strains of Schistosoma japonicum. Southeast Asian J Trop Med Public Health. 1984;15:155–60.

    CAS  PubMed  Google Scholar 

  49. Chiu JK, Ong SJ, Yu JC, Kao CY, Iijima T. Susceptibility of Oncomelania hupensis formosana recombinants and hybrids with Oncomelania hupensis nosophora to infection with Schistosoma japonicum. Int J Parasitol. 1981;11:391–7.

    Article  CAS  PubMed  Google Scholar 

  50. Campbell G, Noble LR, Rollinson D, Southgate VR, Webster JP, Jones CS. Low genetic diversity in a snail intermediate host (Biomphalaria pfeifferi Krass, 1848) and schistosomiasis transmission in the Senegal River Basin. MOL ECOL. 2010;19:241–56.

    Article  CAS  PubMed  Google Scholar 

  51. Meunier C, Tirard C, Hurtrez-Boussès S, Durand P, Bargues MD, Mas-Coma S, et al. Lack of molluscan host diversity and the transmission of an emerging parasitic disease in Bolivia. MOL ECOL. 2001;10:1333–40.

    Article  CAS  PubMed  Google Scholar 

  52. Serrote C, Reiniger L, Silva KB, Rabaiolli S, Stefanel CM. Determining the polymorphism information content of a molecular marker. Gene. 2020;726:144175.

    Article  CAS  PubMed  Google Scholar 

  53. Torutaeva E, Asanaliev A, Prieto-Linde ML, Zborowska A, Ortiz R, Bryngelsson T, et al. Evaluation of microsatellite-based genetic diversity, protein and mineral content in chickpea accessions grown in Kyrgyzstan. Hereditas. 2014;151:81–90.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank schistosomiasis control institutions in Yunnan Province for their valuable helps in Oncomelania hupensis robertsoni collection.

Funding

This work was supported by National Key Research and Development Program of China (nos. 2021YFC2300800, 2021YFC2300803), Open project of Key Laboratory of Parasite and Vector Biology of National Health Commission (NHCKFKT2023-08), Dali Prefecture Science and Technology Plan Project (D2022ZA0115), Youth Science and Technology Talent Training Project of Yunnan Institute of Endemic Disease Control and Prevention (YIEDC-G202101, YIEDC-G202102), and Project supported by the National Parasite Resource Bank (2019–194-30).

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Author notes

  1. Jing Song and Hongqiong Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Schistosomiasis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention, Dali, 671000, China

    Jing Song, Hongqiong Wang, Chunhong Du, Meifen Shen, Zongya Zhang, Jihua Zhou, Yun Zhang & Yi Dong

  2. Yunnan Key Laboratory of Natural Focus Disease Control Technology, Dali, 671000, China

    Jing Song, Hongqiong Wang, Chunhong Du, Meifen Shen, Zongya Zhang, Jihua Zhou, Yun Zhang & Yi Dong

  3. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Tropical Diseases Research; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Center for Tropical Diseases; National Center for International Research on Tropical Diseases, National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, Shanghai, 200025, China

    Shizhu Li, Peijun Qian, Wenya Wang & Yuwan Hao

  4. School of Global Health, Chinese Center for Tropical Diseases Research-Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Shizhu Li

  5. School of Public Health, Kunming Medical University, Kunming, 650500, China

    Chunying Li

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  1. Jing Song
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Contributions

JS, HW, YH, YD conceived and designed the study. YD, JS, ZZ, JZ, CL collected samples. ZZ, YZ drew sample map. JS, CD, MS Performed the experiments. PQ, WW, JS, HW, YD analyzed, and interpreted the data. JS, HW, SL, YH, YD drafted the manuscript. All authors critically revised the manuscript for scientific content and approved the final version of the article.

Corresponding authors

Correspondence to Yuwan Hao or Yi Dong.

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Supplementary Information

Additional file 1: Fig. S1.

Habitat characteristics of Oncomelania hupensis robertsoni in sampling site for each environmental type.

Additional file 2: Dataset S2.

Microsatellite DNA loci data of the samples tested.

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Song, J., Wang, H., Li, S. et al. The genetic diversity of Oncomelania hupensis robertsoni, intermediate hosts of Schistosoma japonicum in hilly regions of China, using microsatellite markers. Parasites Vectors 17, 147 (2024). https://doi.org/10.1186/s13071-024-06227-3

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Keywords

Parasites & Vectors

ISSN: 1756-3305

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