Introduction

Cryptosporidium infects various vertebrates, including humans (Fayer 2010). It is one of the four major pathogens responsible for severe diarrhea in infants and young children (Kotloff et al. 2013), the second leading cause of diarrhea and death in children after rotavirus (Zhao et al. 2019), and the fifth most important foodborne parasite worldwide (Ryan et al. 2021). Individual animals with weakened immune function are more susceptible to Cryptosporidium infection than healthy adult animals (Feng et al. 2018), and Cryptosporidium infection in cattle may manifest as persistent watery diarrhea (Wang et al. 2018), significant weight loss (Sarkar et al. 2014), and decreased growth rate after recovery (Thomson et al. 2019). During the environmental stage, the oocyst is highly resistant and difficult to inactivate (Blanchard 2012), and no vaccine or effective therapeutic drug is currently available (Ashigbie et al. 2021).

Since the first report of Cryptosporidium in 1907 (Tyzzer 1907), at least 44 valid Cryptosporidium species and approximately 120 genotypes have been described globally (Uran-Velasquez et al. 2022). Cattle are an important host (Ryan et al. 2014), and at least 12 Cryptosporidium species (Wang et al. 2011b), predominantly Cryptosporidium parvum, C. andersoni, C. ryanae, and C. bovis, have been reported in cattle worldwide (Fayer 2010; Wang et al. 2018; Yang et al. 2020). C. parvum is the most important cause of zoonotic cryptosporidiosis (Feng et al. 2018). Over 20 subtype families of C. parvum gp60 have been identified, with IIa, IIc, and IId being the most predominant subtype families (Wang et al. 2022). Only C. parvum infections caused by the IId subtype family have been identified in cattle in China, with IIdA15G1 and IIdA19G1 being the most common subtypes (Cai et al. 2017; Wang et al. 2017).

The global prevalence of cryptosporidiosis in cattle ranges from 6.25 to 39.65% (Tarekegn et al. 2021), with a global pooled prevalence of 29.1% (Hatam-Nahavandi et al. 2019). In China, the pooled prevalence has been estimated as 14.50% (Wang et al. 2017) and 11.9% (Gong et al. 2017). To date, only the infection rate of Cryptosporidium in rodents (3.8%, 2/53) has been reported in Shanxi province, China (Ni et al. 2021). We performed the molecular epidemiological survey of cattle in Shanxi to determine the prevalence and molecular characterization of Cryptosporidium.

Materials and methods

Study areas and sample collection

From March 2021 to September 2022, 858 fresh stool samples (30 g each) were collected from Lingqiu and Yingxian in Shanxi province, China (37°27′–38°25′N, 111°30′–113°09′E). The samples were collected from 48 preweaned calves (0–60 days old), 128 postweaned calves (61–180 days old), 186 young cattle (181–450 days old), and 496 adult cattle (>450 days old). Overall, 148 cattle had diarrhea. Each stool sample was placed inside a clean, labeled, and sterile plastic zipper bag, and the information was recorded (sampling date, age, and fecal consistency). Then, all samples were transported back to the laboratory for storage at 4°C and were processed as soon as possible.

DNA extraction, polymerase chain reaction, and sequence analysis

Total genomic DNA was extracted from each sample using E.Z.N.A Stool DNA Kit (Omega Bio-Tek Inc., Norcross, GA, USA) according to the manufacturer’s instructions. The final volume of each DNA sample was 200 μL. The samples were stored at −20°C until polymerase chain reaction (PCR) amplification.

To determine the prevalence of Cryptosporidium spp., a fragment of the small subunit (SSU) rRNA gene (~840 bp) was amplified via nested PCR (Feng et al. 2007). The 25-μL PCR mixture comprised 12.5 μL of Premix Taq (TaKaRa Taq Version 2.0 plus dye; TaKaRa Bio Inc., Kusatsu, Shiga, Japan), 0.5 μL each of forward and reverse primers, 1 μL of template DNA or first-round PCR product, and 10.5 μL of sterilized double distilled water. The first-round PCR parameters were as follows: predenaturation at 94°C for 5 min; followed by 35 cycles of denaturation at 94°C for 45 s, annealing at 55°C for 45 s, and extension at 72°C for 1 min; extension at 72°C for 7 min; and insulation at 4°C. The second-round PCR conditions were identical to those of the first-round PCR, except that the annealing temperature was 58°C. All rounds of PCR amplification included positive and negative controls. The products from the second-round PCR were analyzed via 1.5% agarose gel electrophoresis, and all positive PCR products were sent to a commercial company for bidirectional sequencing (Sangon Biotech, Shanghai, China). The sequencing results were assembled and corrected using SeqMan software (DNASTAR Inc., Madison, USA) for sequence analysis.

Subsequently, restriction fragment length polymorphism (RFLP) analysis of PCR-positive products was performed using SspI and MboII restriction endonucleases to further determine the species and status of mixed infections of Cryptosporidium. The presence of C. parvum DNA was confirmed via the abovementioned enzyme digestion and sequence analysis, and gp60 nested PCR was performed (Alves et al. 2003) at annealing temperatures of 52°C and 50°C for the first- and second-round PCR, respectively. A PCR amplification product of approximately 850 bp was identified as C. parvum, and the subtype was identified following sequencing (Sulaiman et al. 2005).

All sequences were aligned with reference sequences downloaded from GenBank (https://www.ncbi.nlm.nih.gov/genbank/) using MEGA v11.0 software (http://www.megasoftware.net/). The sequencing results were analyzed using the BLAST online platform (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome). To comprehensively investigate the relationship among different isolates, we constructed phylogenetic trees using the neighbor-joining algorithm based on a matrix of evolutionary distances, which were calculated using the Kimura two-parameter model via MEGA. We performed bootstrap analysis (1000 replicates) to assess the robustness of the clusters.

Statistical analysis and GenBank accession numbers

Chi-square test was performed, and 95% confidence interval (CI) values were determined using SPSS Statistics v21.0 (IBM Corp., New York, NY, USA) to compare Cryptosporidium infection rates among different sampling sites, age groups, breeds, and feeding methods as well as between diarrheal and nondiarrheal groups. A two-tailed P-value of <0.05 was considered to indicate statistical significance.

Results

Prevalence of Cryptosporidium spp.

Based on SSU rRNA PCR results, we revealed that 11.19% (96/858) of the samples were positive for Cryptosporidium. The overall prevalence of Cryptosporidium in cattle was 13.30% (62/466) and 8.67% (34/392) in Lingqiu and Yingxian, respectively, with a statistically significant difference between the two regions (odds ratio [OR]: 1.616, 95% CI: 1.039–2.514, P = 0.032). The overall prevalence of Cryptosporidium in dairy and beef cattle was 10.78% (40/371) and 11.50% (56/487), respectively, with no significant difference between them (OR: 0.930, 95% CI: 0.605–1.430, P = 0.741). Among the four age groups, postweaned calves (25.78%, 33/128) had the highest infection rate, followed by preweaned calves (18.75%, 9/48). Young (14.52%, 27/186) and adult (5.44%, 27/496) cattle showed the lowest infection rates. The overall prevalence of Cryptosporidium in preweaned calves, postweaned calves, and young cattle was significantly higher than that in adult cattle (OR: 4.009, 95% CI: 1.762–9.120, P < 0.001; OR: 6.034, 95% CI: 3.466–10.504, P < 0.001; and OR: 2.950, 95% CI: 1.680–5.179, P < 0.001, respectively). The overall prevalence of Cryptosporidium in diarrhea and nondiarrhea samples was 18.24% (27/148) and 9.72% (69/710), respectively, with the infection rate being significantly higher in diarrhea samples (OR: 2.073, 95% CI: 1.276–3.368, P = 0.003). The overall prevalence of Cryptosporidium in intensively farmed and free-range cattle was 17.40% (83/477) and 3.41% (13/381), respectively, with the infection rate being significantly higher in intensively farmed cattle (OR: 5.963, 95% CI: 3.267–10.884, P < 0.001) (Table 1).

Table 1 Cryptosporidium prevalence and information regarding Cryptosporidium species
Full size table

Distribution of Cryptosporidium species and its subtypes

Sequencing of 96 Cryptosporidium PCR-positive products yielded 95 sequences from 5 Cryptosporidium species (C. andersoni, 49; C. bovis, 30; C. parvum, 8; C. ryanae, 7; and C. ubiquitum, 1) (Table 1). Phylogenetic analysis of the PCR products revealed five branches (Fig. 1). C. ubiquitum was only identified in Yingxian, whereas the other four Cryptosporidium species were detected in both Lingqiu and Yingxian. C. andersoni was the dominant species in both regions. Further, C. andersoni and C. parvum were identified across all four age groups and were the dominant species in preweaned calves. C. bovis was the dominant species in postweaned calves, whereas C. andersoni was the dominant species in young and adult cattle. Furthermore, C. andersoni was the dominant species in dairy and beef cattle as well as diarrhea and nondiarrhea samples. Additionally, RFLP analysis of the PCR products revealed two cases of mixed infections in Lingqiu: C. andersoni + C. bovis in one sample from dairy cattle and C. andersoni + C. parvum in one sample from beef cattle.

Fig. 1
figure 1

Phylogenetic analyses of Cryptosporidium species in cattle in Shanxi, China. Species identified in the present study are indicated using black triangles (▲)

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The six gp60 sequences obtained in our study were analyzed and successfully identified as C. parvum subtype IIdA17G1.

Discussion

In recent decades, an increasing number of epidemiological investigations have been conducted both domestically and internationally to assess the prevalence of cryptosporidiosis(Cai et al. 2019; Gong et al. 2017; Hatam-Nahavandi et al. 2019; Wang et al. 2017). The prevalence of bovine cryptosporidiosis varies considerably across different countries, which is associated with factors such as animal age, sampling season, sanitation conditions, total sample number, study design, diagnostic method, geographical conditions, and climate (Gong et al. 2017; Hatam-Nahavandi et al. 2019). The present study revealed that the overall prevalence of Cryptosporidium in cattle was 11.19%, which was higher than that reported in Bangladesh (5%, 31/623) (Ehsan et al. 2015) and Egypt (10.2%, 49/480) (Ibrahim et al. 2016) but lower than that reported in Italy (38.8%, 57/147) (Díaz et al. 2018), USA (24.2%, 60/248) (Peng et al. 2003), and Estonia (23.0% (112/486) (Santoro et al. 2019); further, it was lower than the global pooled prevalence in cattle (29.1%) (Hatam-Nahavandi et al. 2019). Regarding the domestic market, the prevalence of Cryptosporidium in cattle in the present study was lower than that in Taiwan (37.6%, 173/460) (Watanabe et al. 2005), Shaanxi (20.2%, 52/258) (Qi et al. 2015a), Hubei (15.6%, 53/339) (Fan et al. 2017), and Jiangxi (12.8%, 71/556) (Li et al. 2021); moreover, it was lower than that observed in two reports of pooled prevalence in Chinese cattle: 14.5% (5265/36316) (Wang et al. 2017) and 11.9% (2623/22,051) (Gong et al. 2017).

In the present study, the Cryptosporidium infection rate in dairy cattle was 10.78% (40/371), which was lower than that reported in Heilongjiang (47.68%, 72/151) (Zhang et al. 2013); Xinjiang (38.4%, 39/232 (Wu et al. 2020); 16.0%, 86/514 (Qi et al. 2015b)); Shanghai (37%, 303/818) (Cai et al. 2017); Henan (21.5%, 172/801) (Wang et al. 2011b); Anhui, Jiangsu, and Shanghai (18.82%, 387/2056) (Chen and Huang 2012); and Sichuan (14.4%, 40/278) (Zhong et al. 2018). Further, it was lower than the previously reported pooled prevalence of Cryptosporidium in Chinese dairy cattle of 11.7% (3901/33,313) (Cai et al. 2019) and 13.98% (4405/31,504) (Wang et al. 2017). The prevalence of Cryptosporidium in dairy cattle in the present study was higher than that in Ningxia (1.61%, 22/1366) (Huang et al. 2014), Beijing (2.55%, 21/822) (Li et al. 2016), Gansu (4.2%, 60/1414) (Wang et al. 2020b), Guangdong (4.38%, 63/1440) (Liang et al. 2019), Northwest China (Gansu and Ningxia; 5.09%, 150/2945) (Zhang et al. 2015), Henan (7.9%, 104/1315) (Wang et al. 2011a), and Shaanxi (2.61%, 32/1224) (Zhao et al. 2013); moreover, it was higher than the pooled prevalence of Cryptosporidium in Chinese dairy cattle (10.44%, 1330/12743) (Cai et al. 2019). The Cryptosporidium infection rate in beef cattle in our study was 11.50% (56/487), which was lower than the reported prevalence in beef cattle in Henan (26.5%, 44/166) (Ma et al. 2015) and Heilongjiang (17.53%, 71/405) (Zhao et al. 2014), and higher than the pooled prevalence in Chinese beef cattle (8.09%, 82/1013 (Gong et al. 2017); 10.47%, 122/1165 (Wang et al. 2017)). Thus, geographical differences exist in Cryptosporidium distribution.

Host age is an important factor affecting the pathogenicity of Cryptosporidium (Helmy et al. 2013), and the prevalence of Cryptosporidium in preweaned calves is generally higher than that in other age groups (Liang et al. 2019). Compared with calves, the incidence of cryptosporidiosis is several times lower in older cattle (Santín and Trout 2007). However, our study revealed that the Cryptosporidium infection rate of postweaned calves was higher than that of preweaned calves, possibly due to higher levels of maternal antibodies in preweaned calves (Cai et al. 2019). Additionally, the age groups varied among studies. In our study, the preweaned calves were <60 days old, whereas the preweaned calves in some studies were 0–90 days old (Watanabe et al. 2005; Liu et al. 2009; Zhao et al. 2013; Zhang et al. 2015; Wang et al. 2020b); thus, some of them would have been considered postweaned calves in our study.

In the present study, the overall prevalence of Cryptosporidium in bovine diarrhea samples (18.24%) was significantly higher than that in nondiarrhea samples (9.72%). This is consistent with a comprehensive report in China revealing that the prevalence of Cryptosporidium was higher in cattle with diarrhea than in those without diarrhea (Cai et al. 2019; Wang et al. 2017). In our study, the overall prevalence of Cryptosporidium (17.40%) in intensively farmed cattle was significantly higher than that in free-range cattle (3.41%), which might be attributed to a high feeding density. Several studies have reported that pathogen transmission is considerably more likely within a concentrated animal feeding operation than in extensive farming systems (Hatam-Nahavandi et al. 2019). As reported in previous reviews in China, owing to the emergence of mega farms in some regions, the prevalence of C. parvum increased from 26.0% in 2011–2016 to 46.8% in 2017–2021 (Guo et al. 2022).

The Cryptosporidium species detected in our study were C. andersoni, C. bovis, C. parvum, C. ryanae, and C. ubiquitum, which included the top four species infecting cattle worldwide (Fayer 2010; Yang et al. 2020). C. andersoni was the dominant species in dairy and beef cattle in our study, consistent with reports from many regions of China and India (Liu et al. 2009; Paul et al. 2009; Wang et al. 2011b; Zhao et al. 2013). In contrast, C. bovis has been reported as the dominant species in beef cattle in Australia, Japan, and France (Abeywardena et al. 2013; Murakoshi et al. 2012; Rieux et al. 2013). C. andersoni usually infects adult cattle (Thomson et al. 2019), consistent with our study. This species is associated with gastritis, decreased milk production, and poor weight gain (Wang et al. 2020a). Additionally, although the C. ubiquitum infection rate in our study was low, its importance must not be neglected. In Ethiopia, C. ubiquitum was detected in a 12-year-old girl who had daily contact with adult cattle and sheep (Kifleyohannes et al. 2022), and investigations in the same study area revealed the presence of C. ubiquitum in calves, lambs, and goat kids (Kifleyohannes et al. 2021), indicating that C. ubiquitum poses a threat to human health. Additionally, C. ubiquitum has been detected in urban sewage in Harbin, China (Liu et al. 2011). Therefore, our study supports the fact that the impact of C. ubiquitum on humans cannot be neglected in China.

Several reports of age-related infections in different Cryptosporidium species have been published (Huang et al. 2014; Langkjær et al. 2007; Liang et al. 2019). In China, preweaned calves are mainly infected with C. bovis (Wang et al. 2017), as reported in Henan (Wang et al. 2011b), Heilongjiang (Zhang et al. 2013), and Shaanxi (Qi et al. 2015a). Some studies have suggested that C. parvum is the most common cause of Cryptosporidium infection in preweaned calves in various countries (Geurden et al. 2007; Plutzer and Karanis 2007). A similar phenomenon was observed in Ningxia (Cui et al. 2014; Huang et al. 2014) and Xinjiang (Qi et al. 2020) in China. In our study, the prevalence of C. parvum was similar to that of C. andersoni in preweaned calves. In the present study, C. bovis was the dominant species in postweaned calves, which differed from the findings of Wang et al. (2011a) and Qi et al. (2015b), who reported that C. andersoni was the dominant species in preweaned calves in China. Our study revealed that C. andersoni was the dominant species causing Cryptosporidium infections in both young and adult cattle, consistent with the finding of a previous study in Heilongjiang, China (Liu et al. 2009). Therefore, differences in the age distribution of samples from different studies might have led to differences in the identified dominant species.

In the present study, mixed infections were detected in Lingqiu. Mixed infections of different Cryptosporidium species are common, as observed in our previous study in Inner Mongolia (Zhao et al. 2023). The high prevalence of Cryptosporidium in Lingqiu also contributes to the emergence of mixed infections. A previous study stated that the first parasite to arrive can gain a marked reproductive advantage or induce cross-reaction immunity; however, it can also pave the way for subsequent infections by disrupting the associated defenses and through immunosuppression (Herczeg et al. 2021).

There are 14 subtype families of C. parvum, of which IIa and IId are the two main zoonotic subtype families in humans and animals (Wang et al. 2014), with other subtype families occasionally appearing in humans and animals (Wang et al. 2011b). In most industrialized countries, the IIa subtype family is relatively common (Holzhausen et al. 2019; Smith et al. 2014), particularly IIaA15G2R1 (Gharieb et al. 2019; Kváč et al. 2011), which is a common zoonotic subtype (Feng et al. 2013; Heckler et al. 2015; Imre et al. 2011). IIdA15G1 and IIdA19G1 are the dominant subtypes detected in dairy cattle infected with C. parvum in China (Feng and Xiao 2017; Wang et al. 2017). The C. parvum subtype in our study was identified as IIdA17G1, which was first detected in dairy cattle in Beijing, China, in 2016 (Li et al. 2016). Thus, our study verified the existence of this subtype in China. Additionally, IIdA17G1 was detected in British European hedgehogs (Sangster et al. 2016), preweaned lambs, and goat kids in northwestern Spain (Díaz et al. 2015) as well as raw and treated water in Portugal (Lobo et al. 2009). It has also been detected in humans in the UK (Chalmers et al. 2011), Qatar (Boughattas et al. 2017), Portugal (Alves et al. 2006), and Northern Spain (Azcona-Gutiérrez et al. 2017). Thus, this subtype is associated with a risk of zoonotic disease.

In summary, Cryptosporidium and C. parvum are widely distributed in cattle in China. Effective measures must be taken to prevent the spread of Cryptosporidium and C. parvum IId subtype in China as well as restrict the introduction of the C. parvum IIa subtype. Further biological and molecular epidemiological research should be conducted to improve our understanding of the host specificity and transmission of Cryptosporidium.