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All issues Volume 32 (2025) Parasite, 32 (2025) 19 Full HTML
Open Access
Issue
Parasite
Volume 32, 2025
Article Number 19
Number of page(s) 9
DOI https://doi.org/10.1051/parasite/2025012
Published online 07 March 2025

© Z.-Q. Gao et al., published by EDP Sciences, 2025

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction

Cryptosporidium is a significant zoonotic pathogen capable of infecting a wide range of hosts, including humans and wildlife [18]. Infections in humans and animals typically occur through the ingestion of water or food contaminated with Cryptosporidium oocysts [17]. While Cryptosporidium infections are often asymptomatic, they can sometimes lead to diarrhea and dehydration [10]. To date, over 170 species and genotypes of Cryptosporidium have been identified across various animal species [11, 36, 38]. Among these, more than 26 species and 56 genotypes have been detected in wild rodents [11]. Cryptosporidium muris was first identified in rodents in 1907 and has since been widely reported [4, 5, 47, 48]. Other species, such as C. parvum, C. ubiquitum, rodent genotype II, C. hominis, C. meleagridis, C. andersoni, C. viatorum, C. alticolis, C. microti, and C. apodemi, have also occasionally been reported in rodents [9, 21]. Recently, C. ditrichi was found in Apodemus species [11]. Notably, several of these species, including C. ditrichi [3], C. parvum, C. hominis, C. muris [6], C. meleagridis, C. viatorum, C. andersoni, and C. ubiquitum, have also been described in humans [12, 41].

Wild rodents are widely distributed all over the world, and can transmit many pathogens to humans and other animals, such as Helicobacter spp. [53], tick-borne encephalitis virus [2], and Toxoplasma gondii [32]. Although several studies have explored the prevalence of Cryptosporidium in wild rodents across different regions [31], data on certain rodent species remain limited. This study investigated the prevalence and risk factors associated with Cryptosporidium in nine wild rodent species from Yunnan, Guangxi, and Hunan Provinces in China, while also identifying the specific genotypes and species involved.

Materials and methods

Ethics statement

This study complied with the Animal Ethics Guidelines of China and was approved by the Institutional Animal Care and Use Committee of Yancheng Teachers University. All wild rodents were handled according to international welfare standards and university protocols to ensure humane treatment.

Collection of samples

From August 2023 to May 2024, a total of 510 samples were collected from three provinces: Yunnan (n = 88), Hunan (n = 319), and Guangxi (n = 103). The species composition included Microtus fortis (n = 287), Niviventer lotipes (n = 23), Rattus norvegicus (n = 41), Apodemus agrarius (n = 22), Rattus flavipectus (n = 39), Bandicota indica, (n = 39), Rattus rattus sladeni (n = 5), Rattus losea (n = 41), and Mus musculus (n = 13). Rodents were trapped at 6-meter intervals, with melon seeds used as bait. Fresh fecal samples were then collected directly from the rectum of each captured rodent. During the sampling process, detailed information was recorded, including the species, gender, collection time, location, and specific habitat type, such as field, lake beaches, or mountainous area. The collected samples were promptly transported to the laboratory under controlled conditions with ice and stored at −20 °C for subsequent analysis.

DNA extraction and PCR amplification

DNA was extracted from stool samples using an EZNA® Stool DNA Kit (OMEGA Biotek Inc., Norcross, GA, USA) and stored at −20 °C until PCR amplification. The SSU rRNA gene of Cryptosporidium was amplified using nested PCR, with primers consistent with those used in previous studies [37]. The primer sequences were as follows: external primers F1 (5′-GACATATCATTCAAGTTTCTGACC-3′) and R1 (5′-CTGAAGGAGTAAGGAACAACC-3′), and internal primers F2 (5′-CCTATCAGCTTTAGACGGTAGG-3′) and R2 (5′-TCTAAGAATTTCACCTCTGACTG-3′). The PCR cycling conditions were as follows: 5 min at 95 °C, followed by 35 cycles of 30 s at 94 °C, 45 s at 58 °C, and 1 min at 72 °C, with a final extension at 72 °C for 10 min. All secondary PCR products were electrophoresed on 1% agarose gels containing Biosharp and visualized under UV light.

Sequencing and phylogenetic analyses

All PCR products showing positive bands were sent to General Biology Co., LTD (Anhui, China) for sequencing. Cryptosporidium genotypes were determined by aligning the sequences with reference sequences in GenBank (http://www.ncbi.nlm.nih.gov/BLAST/). Phylogenetic trees were constructed using the neighbor-joining (NJ) method in MEGA 11 (Version 11.0.13), with bootstrapping performed using 1,000 replicates to assess the genetic relationships among Cryptosporidium species/genotypes.

Statistical analysis

Univariate analysis was conducted using SPSS software (IBM Corp., Armonk, NY, USA), with Fisher’s exact test employed to assess the association between variables. Statistical significance was defined as p < 0.05, and effect sizes were expressed as odds ratios (OR) with corresponding 95% confidence intervals (95% CI).

Multivariable regression analysis was performed using SAS (SAS Institute Inc., Cary, NC, USA, Version 9.4) to assess the potential associations between Cryptosporidium infection and five independent variables: region (x1), species (x2), gender (x3), environment (x4), and season (x5). Each variable was included as an independent variable in the binary Logit model for multivariate regression analysis. The best model was selected based on Fisher’s scoring algorithm. All tests were two-sided, and results were considered statistically significant if p < 0.05 [52].

Results

Prevalence of Cryptosporidium

Among the 510 samples analyzed, the overall prevalence of Cryptosporidium was 1.8% (9/510, 95% CI 0.81–3.32). The prevalence varied across the three provinces: 2.3% in Yunnan (2/88, 95% CI 0.04–6.73), 4.85% in Guangxi (5/103, 95% CI 1.39–10.01), and 0.6% in Hunan (2/319, 95% CI 0.01–1.88) (Fig. 1, Table 1). Among rodent species, R. losea exhibited the highest prevalence at 9.8% (4/41, 95% CI 2.20–21.07), followed by R. flavipectus at 5.1% (2/39, 95% CI 0.09–14.84), N. lotipes at 4.4% (1/23, 95% CI 0.00–17.73), and Microtus fortis at 0.7% (2/287, 95% CI 0.01–2.09). The prevalence rates between male and female rodents were 1.8% (5/272, 95% CI 0.52–3.84) and 1.7% (4/238, 95% CI 0.36–3.79), respectively. Rodents in mountainous areas had the highest infection rate at 4.9% (5/103, 95% CI 1.39–10.01), followed by field rodents at 2.1% (2/95, 95% CI 0.03–6.24) and those from lake beach habitats at 0.6% (2/312, 95% CI 0.01–1.92) (Table 1).

thumbnail Figure 1

Map of the prevalence of Cryptosporidium in wild rodents in China.

Table 1

Factors associated with the prevalence of Cryptosporidium in wild rodents in China.

Association between positivity and exposure

Table 1 outlines the association between Cryptosporidium positivity and exposure variables such as age and region based on univariate analysis. Results of multivariate analysis showed that species emerged as the only significant factor retained in the final model, indicating its strong influence on Cryptosporidium infection. In this model, species significantly impacted infection risk, with N. lotipes (OR 6.24, 95% CI 0.55–71.42), R. flavipectus (OR 7.36, 95% CI 1.01–53.74), and R. losea (OR 14.00, 95% CI 2.49–78.86) showing greater susceptibility to Cryptosporidium compared to Microtus fortis.

Cryptosporidium species/genotypes

This survey identified six species/genotypes of Cryptosporidium in wild rodents: C. viatorum, C. muris, Cryptosporidium vole genotype VII, C. ratti, rat genotype II, and rat genotype IV. The zoonotic species C. viatorum and C. muris were found in rodents from field and mountainous regions, respectively (Table 2). The nucleotide sequences of the Cryptosporidium SSU rRNA gene obtained in this study matched those of C. viatorum, C. muris, Cryptosporidium vole genotype VII, and C. ratti, rat genotypes II, and IV in GenBank. Phylogenetic analysis using the NJ method clustered these sequences with the corresponding species/genotypes (Fig. 2).

thumbnail Figure 2

Phylogenetic relationships among Cryptosporidium spp. based on a neighbor-joining tree of the SSU rRNA gene. Cluster reliability was assessed through bootstrap analysis with 1,000 replicates; values above 50% are not shown. Black circles mark the Cryptosporidium species/genotypes identified in this study.

Table 2

Distribution of Cryptosporidium species/genotypes.

Discussion

Rodents are associated with over 60 zoonotic infectious diseases, posing a significant public health threat [29, 33]. Cryptosporidium are commonly found in wild rodents; furthermore, rodents can serve as carriers of Cryptosporidium to humans and livestock [39]. This study aimed to investigate the prevalence and genotype distribution of Cryptosporidium in wild rodents across three provinces in China, to improve our understanding of its occurrence in these animals.

The study revealed a prevalence of Cryptosporidium in wild rodents of 1.8%. A previous study from China reported a similar prevalence of 2.2% (11/498) [34]. In Yunnan Province, the prevalence was 2.3%, marking the first survey of Cryptosporidium in wild rodents in this region. In Guangxi Province, the prevalence was 4.9%, lower than the 9.5% reported in China (7/74) [34]. The prevalence observed in this study is significantly lower than the global average of 19.8% (4,589/23,142) [51]. Other countries have reported higher prevalence rates, such as France with 15.4% (18/117) [14], Spain with 12.3% (22/179) [15], and Australia with 7.6% (19/250) [13]. In North America and Europe, the combined prevalence was 33.2% (362/1,089) [42]. However, the prevalence in Portugal was 1.0% (3/290), which is lower than that observed in this study [31]. Factors such as rodent species, sampling time, and detection methods may influence the prevalence of Cryptosporidium, but the key factors remain unknown and require further investigation [49].

Cryptosporidium was detected in three of the nine wild rodent species examined, with the highest prevalence observed in R. losea (9.8%, 4/41), followed by R. flavipectus (5.1%, 2/39), N. lotipes (4.4%, 1/23), and Microtus fortis (0.7%, 2/287), indicating statistically significant differences. A study in Zhejiang Province, China, found a similarly high infection rate of Cryptosporidium in R. losea (11.1%, 2/18) [24]. Different rodent species exhibit varying rates of Cryptosporidium infection in China, with rates ranging from 1.4% to 100% in pet rodents, 4.0% to 73.9% in wild rodents, 2.1% to 29.5% in field rodents, and 0.6% to 8.6% in laboratory rodents [24, 34, 51]. Although the risk of Cryptosporidium infection and transmission in rodents is significant, no strong correlation has been established between infection rates and specific rodent species.

Seasonal variations were observed, with Cryptosporidium infections in rodents significantly higher during winter compared to summer and autumn (p < 0.05). Previous studies have also reported higher infection rates in rodents during winter [22]. However, some evidence suggests that infection rates gradually increase from spring to summer, peaking in autumn [25]. Additionally, prevalence rates varied across different environments, with the highest incidence in mountainous areas (4.9%), followed by field areas (2.1%), and no detections at lake beaches. The higher prevalence in mountainous areas may be due to the presence of grazing domestic animals, which facilitate mutual transmission between domestic animals and wild rodents [20, 29, 33]. Thus, caution should be exercised when selecting grazing areas to avoid locations frequented by wild rodents.

Over 26 species and 59 genotypes of Cryptosporidium have been reported in rodents [11]. In this study, six Cryptosporidium species/genotypes were identified, including C. muris, C. viatorum, Cryptosporidium vole genotype VII, and C. ratti, rat genotypes II, and IV. Cryptosporidium muris and C. ratti were detected in Yunnan Province, C. viatorum and rat genotypes II and IV were found in Guangxi Province, and Cryptosporidium vole genotype VII was identified in Hunan Province. Cryptosporidium viatorum and Rat genotype II had previously been reported in rodents from Guangxi [34]. While the pathogenicity of C. ratti, rat genotypes II and IV in humans or animals remains unclear, these genotypes have been documented in multiple countries, and their host range is expanding as research progresses [7, 19, 23, 27]. Cryptosporidium vole genotype VII was first detected in wild-caught common voles in 2018 [21]. Fortunately, Cryptosporidium vole genotype VII and C. ratti, rat genotypes II and IV have not been reported in human samples to date, suggesting limited public health significance, though further research is needed to confirm this.

The study identified two significant zoonotic Cryptosporidium species: C. viatorum and C. muris. Cryptosporidium viatorum has only been detected in humans, wild rodents, and municipal wastewater, and its origin – whether from humans or rodents – remains uncertain [26, 8, 55]. The detection of C. viatorum in wild rodents in Guangxi is particularly notable, given its prior identification in humans in Guangxi in 2020, suggesting a high likelihood of transmission between humans and wild rodents in the region [50]. In China, C. viatorum has also been reported in wild rodents from Hainan and Liaoning Provinces, underscoring the potential transmission risk posed by these rodents [55, 30]. Globally, C. viatorum has been identified in countries such as Australia [26], France [14], India [40], Ethiopia [46], and Denmark [44], indicating its widespread distribution.

gp60 is considered a key genetic marker for C. viatorum and Cryptosporidium parvum, and multiple studies have used the gp60 gene to subtype C. viatorum and C. parvum carried by wild rodents [18, 24, 43]. For example, among 25 wild rodent C. viatorum-positive samples, 17 were successfully subtyped, revealing their classification into the XVa and XVc subtype families [8]. However, in this study, only one C. viatorum positive sample was detected, and attempts to further subtype it using gp60 were unsuccessful. This issue may be attributed to the low concentration of DNA templates or the limited number of positive samples. Therefore, future studies should aim to expand the sample size to enhance the success rate of genotyping and to provide more comprehensive insights into the genetic diversity of C. viatorum in wild rodents.

Cryptosporidium muris shows a similar distribution pattern to C. viatorum in China, with no reported human infections in the country [55, 34, 30]. However, human infections by C. muris have been reported in countries such as Peru [45], Kenya [16], Slovakia [35], and Nigeria [1]. Cryptosporidium muris exhibits a broad host range, encompassing cats, dogs, and non-human primates [19]. Its prevalence is generally higher in Mus musculus, Rattus rattus, and R. norvegicus among wild rodents [28, 54]. This study represents the first detection of C. muris in R. flavipectus in Yunnan, expanding its known host range. The detection of C. viatorum in R. losea and C. muris in R. flavipectus suggests these rodent species may play a role in the transmission dynamics of zoonotic Cryptosporidium. Their widespread defecation in natural habitats significantly amplifies the risk of zoonotic Cryptosporidium transmission by wild rodents [33, 51]. Therefore, it is imperative that preventive measures be implemented from a “one health” perspective to disrupt both direct and indirect routes of zoonotic disease transmission mediated by wild rodents [51].

Conclusion

This study highlights the prevalence of Cryptosporidium in wild rodents across China, identifying six genotypes/species, including two zoonotic species, C. viatorum and C. muris. Notably, this is the first identification of the zoonotic C. muris in R. flavipectus in Yunnan Province. Additionally, the study showed a genetic link between C. viatorum found in wild rodents in Guangxi and in humans, underscoring the role of wild rodents as reservoirs of Cryptosporidium with potential transmission to humans and domestic animals. Therefore, continuous monitoring of Cryptosporidium prevalence in wild rodents is essential.

Acknowledgments

We would like to extend our deepest gratitude to Chair Professor Hany Elsheikha from the University of Nottingham, UK for his invaluable assistance in revising and improving our manuscript. His insightful feedback, expert advice, and meticulous attention to detail have greatly enhanced the quality of our work.

Conflicts of interest

The authors declare that they have no conflict of interest.

Data availability statement

The gene sequences obtained in the present study have been submitted to GenBank (accession nos. PP732555–PP732561, PQ268965).

Author contribution statement

Zhen-Qiu Gao and Hai-Tao Wang: Methodology, Software, Writing – original draft. Qing-Yu Hou: Data curation, Methodology, Writing – review & editing. Si-Yuan Qin and He Ma: Data curation, Resources, Visualization, Writing – review & editing. Yi-Xuan Song and Jing-Hao Li: Supervision, Writing – review & editing. Ya Qin and Quan Zhao: Conceptualization, Resources, Supervision, Writing – review & editing.

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Cite this article as: Gao Z-Q, Wang HT, Li J-H, Song Y-X, Hou Q-Y, Qin S-Y, Ma H, Zhao Q & Qin Y. 2025. Prevalence and genotype analysis of Cryptosporidium spp. in nine species of wild rodents in China. Parasite 32, 19. https://doi.org/10.1051/parasite/2025012.

All Tables

Table 1

Factors associated with the prevalence of Cryptosporidium in wild rodents in China.

In the text
Table 2

Distribution of Cryptosporidium species/genotypes.

In the text

All Figures

thumbnail Figure 1

Map of the prevalence of Cryptosporidium in wild rodents in China.
In the text
thumbnail Figure 2

Phylogenetic relationships among Cryptosporidium spp. based on a neighbor-joining tree of the SSU rRNA gene. Cluster reliability was assessed through bootstrap analysis with 1,000 replicates; values above 50% are not shown. Black circles mark the Cryptosporidium species/genotypes identified in this study.
In the text

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