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. 2026 Mar 13;16:11754. doi: 10.1038/s41598-026-40394-w

Epidemiology and genetic evolution of Anaplasma species in rodents from southeastern China

Zhiwei Zeng 1,#, Shiju Hu 1,2,#, Jiaxiong Wang 1, Shuheng Zhou 1, Tengwei Han 1, Weijun Liu 1, Fen Lin 1,2, Shenggen Wu 1, Fangzhen Xiao 1,2,✉
PMCID: PMC13061983  PMID: 41820423

Abstract

Anaplasma species are globally distributed and pose significant public health threats as zoonotic pathogens. However, the epidemiological characteristics and genetic diversity of Anaplasma in rodents from Fujian Province, southeastern China, remain poorly understood. From 2015 to 2024, we systematically collected 966 rodents across 22 counties and cities in Fujian. Screening was performed using real-time PCR and nested PCR targeting the groEL and 16S rRNA genes. Positive samples were subjected to sequencing, followed by phylogenetic analysis (MEGA11) and haplotype network construction (PopART). The overall infection rate of Anaplasma was 4.35% (42/966), and all positive samples were identified as Anaplasma phagocytophilum. Infection rates showed significant differences among rodent species, cities, habitat types, and sampling years (P < 0.05). Haplotype network analysis indicated that H1 was the dominant haplotype. This study confirms the circulation of A. phagocytophilum in rodents in Fujian, reveals its association with rodent age, seasonal and habitat factors, and underscores the necessity for enhanced ongoing surveillance and control of rodent-borne Anaplasma in this region.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-40394-w.

Keywords: Small mammals, Fujian Province, A. phagocytophilum, Prevalence, Phylogeny, Haplotype

Subject terms: Diseases, Ecology, Ecology, Genetics, Microbiology

Introduction

Anaplasma, first identified by Sir Arnold Theiler in 1910, comprises obligate intracellular Gram-negative bacteria belonging to the order Rickettsiales, family Anaplasmataceae, and genus Anaplasma. In 2001, Dumler and colleagues revised the taxonomy of the genus based on phylogenetic analyses of the 16S rRNA and groESL genes, incorporating species previously classified under the genus Ehrlichia. This reclassification expanded the genus to include six primary species: A. phagocytophilum, A. ovis, A. marginale, A. centrale, A. bovis, and A. platys1,2. With ongoing research, novel Anaplasma species continue to be discovered. For instance, Li et al.3 identified A. capra infecting patients in a hospital in Heilongjiang Province, China, while Cynthia M. Tate et al.4 isolated A. odocoilei from white-tailed deer. Additionally, several unclassified genetic variants of Anaplasma have been reported. Members of the genus Anaplasma primarily infect host erythrocytes, granulocytes, monocytes, or platelets, causing diseases in both humans and animals5. Among them, four species are recognized as human pathogens: A. phagocytophilum, A. ovis, A. bovis, and A. platys6–9.

Anaplasma species exhibit a global distribution. Their transmission primarily involves hard ticks (Ixodidae) and vertebrate hosts. Infected vertebrates often develop persistent infections and serve as reservoirs for ticks5. Ticks acquire the pathogen by feeding on infected reservoir hosts and subsequently transmit Anaplasma to new vertebrate hosts via salivary secretions during blood meals10. The dominant reservoir animals and tick vectors vary geographically. Key tick vectors implicated in transmission include Ixodes scapularis, I. pacificus, I. ricinus, Dermacentor nuttalli, Rhipicephalus sanguineus, and Haemaphysalis longicornis, among others5,11. The host range for Anaplasma is broad, encompassing humans, carnivores, ruminants, birds, reptiles, and rodents12–14. In China, Anaplasma pathogens have been detected in various tick species such as R. haemaphysaloides, R. microplus15, D. silvarum16, H. longicornis, and H. flava17, as well as in hosts including goats18, dogs19, cattle20, and rodents21.

Studies indicate that rodents constitute the largest reservoir group for Anaplasma22. Fujian Province boasts a rich diversity of rodent species, with reports indicating 27 species belonging to 15 genera across 7 families, accounting for approximately 16.7% of the national rodent species23. However, research on Anaplasma infection in rodents in Fujian Province has not been reported.To address this knowledge gap, we collected rodent samples from various regions across Fujian, China. This study aims to determine the prevalence of Anaplasma, describe its distribution, analyze influencing factors associated with infection in rodents, and characterize the genetic features of the detected Anaplasma strains. The findings are intended to provide public health evidence for the prevention and control of rodent-borne diseases in Fujian Province.

Materials and methods

Ethical statement

The collection of rodent tissues was approved by the Laboratory Animal Ethics Committee of the Fujian Provincial Center for Disease Control and Prevention, China (Approval No.: FJCDCNT1811-2015 and PZ2024036). All animal procedures were conducted in accordance with the American Veterinary Medical Association (AVMA) Guidelines for the Euthanasia of Animals (2020) and the National Standard of China Laboratory animal—Guidelines for euthanasia (GB/T 39760-2021). Euthanasia was performed scientifically and humanely to minimize physiological and psychological distress in the animals. This study was reported following the ARRIVE guidelines (https://arriveguidelines.org).

Sample collection

Fujian Province comprises nine prefecture-level cities where surveillance for plague and other rodent-borne diseases is conducted annually. Samples for this study were obtained from specimens preserved during previous surveillance activities, supplemented by newly collected samples. To ensure balanced geographical representation, specimens were randomly selected from 2–3 counties (or districts) within each prefecture-level city. A balanced ratio between commensal rodents (species dependent on human resources) and wild rodents (species inhabiting natural environments independent of human provision) was maintained. For certain prefectures with insufficient counties or imbalanced distribution of commensal and wild rodents, additional sampling was conducted in 2024 to supplement the collection. In total, specimens from 22 counties (or districts) were included in this study. (Supplementary Table 1, Fig. 1).

Fig. 1.

Fig. 1

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Geographical distribution of rodent capture sites in Fujian Province, southeastern China, from 2015 to 2024. ArcGIS Desktop (version 10.8; Esri, Redlands, CA, USA) was used to create the map.

Rodent trapping was performed seasonally: spring (March–May), summer (June–August), autumn (September–October), and winter (December–January)24. Sampling sites covered various habitats including residential areas, farmland, mountainous regions, and hilly lands. Rodents were captured using the cage-day method with corn as bait placed in rodent traps (live cage traps). Trapping was conducted over 3–4 consecutive days with 100 traps deployed daily; traps were set in the evening and collected the following morning. Captured rodents were euthanized using carbon dioxide inhalation, with concentrations gradually increased from 40 to 100%.

Rodent species were identified by experienced staff from the Fujian Provincial Center for Disease Control and Prevention based on morphological characteristics including body size, tail structure, fur color, and sexual maturity markers. For some specimens, species identification was confirmed by molecular analysis of the mitochondrial cytochrome c oxidase subunit I (COI) gene25. Data including species, age, sex, habitat, and capture time and location were recorded. Liver, lung, and kidney tissues were collected during dissection and stored at − 80 °C for subsequent analysis.

Reagents and equipment

The following reagents and equipment were used: Automated nucleic acid extraction system (Tianlong Technology, Xi’an, China), magnetic bead-based bacterial genomic DNA extraction kit (Tianlong Technology, Xi’an, China), spin-column blood/cell/tissue genomic DNA extraction kit (Tiangen Biotech, Beijing, China), mini high-speed refrigerated centrifuge (Haitai Biomedical, Qingdao, China), multifunctional tissue homogenizer (Bertin Instruments, France), gradient PCR thermocycler (Thermo Fisher Scientific, USA), real-time fluorescence quantitative PCR system (Applied Biosystems, USA); Premix Ex Taq™ (Probe qPCR), Premix Taq (TaKaRa Taq), primers, and probes (Sangon Biotech, Shanghai, China).

DNA extraction

Between 0.1 and 0.5 g of rodent liver, kidney, and lung tissues were placed in grinding tubes and homogenized. Genomic DNA was extracted strictly following the manufacturer’s instructions of the tissue DNA extraction kit and stored at − 20 °C for subsequent analysis.

Real-time fluorescence quantitative PCR for Anaplasma groEL gene detection

The groEL gene (a universal target for Anaplasma spp. Table 1) was amplified using primers and probes designed based on references. The 20-μL reaction mixture contained: Premix Ex Taq™ (Probe qPCR): 10 μL, forward primer (10 μmol/L): 0.4 μL, reverse primer (10 μmol/L): 0.4 μL, fluorescent probe (10 μmol/L): 0.2 μL, DNA template: 3 μL, nuclease-free water: 6 μL. Thermal cycling conditions: initial denaturation at 95 °C for 3 min (1 cycle); amplification for 40 cycles of 95 °C for 10 s (denaturation) and 52.7 °C for 30 s (annealing/extension). Each run included positive and negative controls. Samples with Ct ≤ 35 and a valid amplification curve were considered positive26,27 and were included in the subsequent calculation of the positive rate.

Table1.

Sequence of primers and TaqMan probes for Anaplasma.

Gene Primers and probes Size(bp) Reference
groEL Anap-F: GCGAGCATAATTACTCAGAG 75 26,27
Anap-R: CAGTATGGAGCATGTAGTAG
Anap-P: Cy5-CCACCTTATCATTACACTGAGACG-[BHQ2a-Q]
16S rRNA Eh-out1: TTGAGAGTTTGATCCTGGCTCAGAACG 653 28
Eh-out2: CACCTCTACACTAGGAATTCCGCTATC
Eh-gs1: GTAATAACTGTATAATCCCTG 282
Eh-gs2: GTACCGTCATTATCTTCCCTA
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Nested PCR amplification of Anaplasma 16S rRNA gene

For groEL-positive samples, genus-specific primers were used to amplify the Anaplasma 16S rRNA gene (Table 1). The primary PCR reaction mixture (25 μL) contained: 12.5 μL Premix Taq™, 1 μL forward primer (10 μmol/L), 1 μL reverse primer (10 μmol/L), 3 μL DNA template, and 7.5 μL deionized water. The secondary PCR mixture (25 μL) consisted of: 12.5 μL Premix Taq™, 1 μL forward primer (10 μmol/L), 1 μL reverse primer (10 μmol/L), 0.5 μL DNA template (primary PCR product), and 10 μL deionized water. Amplification conditions were: initial denaturation at 94 °C for 5 min; 40 cycles of 94 °C for 45 s (denaturation), 55 °C for 50 s (annealing), and 72 °C for 1 min (extension); followed by a final extension at 72 °C for 5 min. PCR products were analyzed by 1.5% agarose gel electrophoresis. For semi-nested PCR amplification of the 16S rRNA fragment, primers Eh-out1 and Eh-gs2 were used to generate a 447-bp product using the primary PCR product as template, with reaction components identical to the above system. Each run included positive and negative controls.

Sequencing and phylogenetic analysis of positive amplicons

The amplified target fragments were submitted to Sangon Biotech (Shanghai) Co., Ltd. For unidirectional Sanger sequencing. The obtained sequences were initially aligned using the Basic Local Alignment Search Tool (BLAST) on the National Center for Biotechnology Information (NCBI) website (https://blast.ncbi.nlm.nih.gov/). Reference sequences exhibiting high similarity (> 98%) and comparable fragment lengths, derived from diverse geographical regions or hosts, were selected. Rickettsia rickettsia was designated as the outgroup29, and other Anaplasma species within the same genus were also included. Multiple sequence alignment was subsequently performed using ClustalW. A phylogenetic tree was constructed with the Neighbor-joining method in MEGA11 software30, with nodal support assessed through 1000 bootstrap replicates.

For genetic diversity analysis, the Anaplasma phagocytophilum 16S rRNA gene sequences (both those utilized in the phylogenetic tree construction and those newly obtained in this study) were analyzed using DnaSP v6.0 software31. This analysis determined the number of haplotypes, haplotype diversity (Hd), and nucleotide diversity (Pi). Sequence grouping based on different geographical origins was conducted using Arlequin v3.5.2.232. Finally, a haplotype network for the A. phagocytophilum 16S rRNA gene across different regions was visualized with PopART software (version 1.7)33, employing the Templeton, Crandall, and Sing (TCS) algorithm.

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics (version 25.0; IBM Corp, Armonk, NY, USA), with the significance level set at α = 0.05 and a two-sided P value < 0.05 considered statistically significant.

Chi-square tests were used to analyze differences in Anaplasma positivity rates across rodent species and populations, cities and regions , sexes, age groups, habitats, seasons, and years. When the expected frequency did not meet the assumptions of the chi-square test, Fisher’s exact test was applied. Categories with zero outcome events were excluded from the chi-square analysis.

Using rodent positivity status as the dependent variable, univariate logistic regression was performed for each potential influencing factor (habitat, age, season, sex, region ). Variables with a significance level of P < 0.1 were entered into a preliminary multivariate model. Comparison of model fit indices (− 2 log-likelihood, Nagelkerke R2) and the Hosmer–Lemeshow goodness-of-fit test indicated limited explanatory power of this simplified model. Therefore, all predefined variables were simultaneously included in the final multivariate logistic regression model. Variance inflation factor (VIF) testing was conducted to exclude multicollinearity.

Geographic zones were classified as inland (Longyan, Nanping, Sanming) or coastal (Fuzhou, Putian, Quanzhou, Xiamen, Zhangzhou, Ningde) based on their geographical location.

Results

General characteristics of the animal specimens

A total of 966 rodent specimens, encompassing 3 families and 17 species, were included in this study. Among these, 551 were wild rodents and 415 were commensal rodents. The wild rodents were predominantly Rattus losea and Niviventer fulvescens, accounting for 30.43% (294/966) and 11.80% (114/966) of the total, respectively. Niviventer confucianus, Berylmys bowersi, and Apodemus agrarius constituted 3.52% (34/966), 2.90% (28/966), and 2.59% (25/966) of the sample population, respectively. Among the commensal rodents, Rattus norvegicus and Rattus tanezumi were the most prevalent, representing 26.60% (257/966) and 16.25% (157/966) of the total specimens, respectively. Other rodent species accounted for relatively minor proportions (Supplementary Table 2, Fig. 2).

Fig. 2.

Fig. 2

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The distribution of different rodent species. Rodent species with a count of less than 5 are grouped under the “Other” category. The “Other” category includes the following species: Sciurus sp. (1), Niviventer coxingi. (4), Micromys minutus (1), Rattus nitidus (1), Alexandromys fortis (3), Callosciurus erythraeus (1), and Mus musculus (1).

Anaplasma infection prevalence among rodent species

Among the 966 rodents examined, 42 tested positive for Anaplasma, yielding an overall positivity rate of 4.35% ( 95% CI: 3.20%–5.77%). The positivity rate was 5.81% (32/551; 95% CI: 4.08%–8.00%) in wild rodents and 2.41% (10/415; 95% CI: 1.25%–4.23%) in commensal rodents, with this difference being statistically significant (χ2 = 6.57, df = 1, P < 0.05).

Anaplasma infection was detected in seven rodent species. Among wild rodents, the highest positivity rate was observed in Niviventer confucianus at 14.71% (5/34; 95% CI: 5.84%–29.27%), followed by Bandicota indica at 11.76% (2/17; 95% CI: 2.53%–32.68%) and Rattus losea at 7.48% (22/294; 95% CI: 4.89%–10.91%). Berylmys bowersi showed a positivity rate of 3.57% (1/28; 95% CI: 0.39%–15.50%), while the lowest rate among wild rodents was recorded in Niviventer fulvescens at 0.88% (1/114; 95% CI: 0.09%–4.03%).Among commensal rodents, Rattus norvegicus exhibited the highest positivity rate of 3.89% (10/257; 95% CI: 2.02%–6.79%). The “Other” rodent category had a positivity rate of 8.33% (1/12; 95% CI: 0.91%–32.85%), with the positive specimen identified as Niviventer coxingi. A significant difference in Anaplasma positivity rates was observed among rodent species (χ2 = 16.08, P < 0.05 by Fisher’s exact test). (Supplementary Table 2, Fig. 3).

Fig. 3.

Fig. 3

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Analysis of Anaplasma positivity rates in rodents under different factors. (A) Comparison of positivity rates among different cities, geographical regions, rodent species, and populations (commensal vs. wild rodents). (B) Comparison of positivity rates across sampling years, seasons, habitats, age groups, and sex. Bar lengths and the numerical labels above them indicate the Anaplasma positivity rate. Statistically compared groups are connected by black square brackets, with “ns” indicating no significant difference; * indicates P < 0.05. In this study, commensal rodents included Rattus norvegicus, Rattus tanezumi, and Mus musculus; all other species were classified as wild rodents.

Geographical distribution of Anaplasma infection

Anaplasma infections were detected in all sampled cities of Fujian Province except Ningde. Fuzhou, Xiamen, and Nanping showed the highest positivity rates. Specifically, Fuzhou had a positivity rate of 10.95% (15/137; 95% CI: 6.54%–16.98%), while Xiamen and Nanping exhibited rates of 9.09% (4/44; 95% CI: 3.15%–20.18%) and 9.03% (13/144; 95% CI: 5.16%–14.52%), respectively. In contrast, Quanzhou showed the lowest positivity rate of 0.70% (1/142; 95% CI: 0.08%–3.24%). The differences in positivity rates across different cities were statistically significant (χ2 = 31.43, df = 7, P < 0.05). When analyzed by geographic grouping into coastal (Ningde, Fuzhou, Putian, Quanzhou, Zhangzhou, Xiamen) and inland (Longyan, Sanming, Nanping) regions, no statistically significant difference was observed (χ2 = 0.70, df = 1, P = 0.40). (Supplementary Table 2, Fig. 3).

Prevalence of Anaplasma infection in rodents of different age groups and sexes

A total of 965 valid sex records and 964 complete age records were obtained. The positivity rate was 4.55% (23/506; 95% CI: 2.98%–6.63%) in male rodents and 4.14% (19/459; 95% CI: 2.60%–6.26%) in female rodents, with no statistically significant difference between sexes (χ2 = 0.10, df = 1, P = 0.76). Based on body size, sexual maturity, and fur quality, the captured rodents were categorized into three age groups: aged, adult, and juvenile. The positivity rates were 5.80% (4/69; 95% CI: 1.99%–13.19%) in aged rodents, 4.68% (33/705; 95% CI: 3.30%–6.43%) in adults, and 2.63% (5/190; 95% CI: 1.01%–5.67%) in juveniles. No statistically significant difference was observed among age groups (χ2 = 1.88, df = 2, P = 0.39). (Supplementary Table 2, Fig. 3).

Prevalence of Anaplasma infection in different habitats

Based on capture locations, rodent habitats were classified into residential areas, farmland, hills, and mountainous areas for analysis. The highest positivity rate was observed in rodents captured from hills at 16.42% (11/67; 95% CI: 9.05%–26.61%), followed by farmland at 5.75% (10/174; 95% CI: 2.99%–9.95%) and mountainous areas at 3.90% (12/308; 95% CI: 2.15%–6.50%). The lowest positivity rate was found in residential areas at 2.16% (9/417; 95% CI: 1.07%–3.90%). The differences in positivity rates among different habitats were statistically significant (χ2 = 29.25, df = 3, P < 0.05). (Supplementary Table 2, Fig. 3).

Seasonal and annual variations in Anaplasma infection rates

Rodents were analyzed by capture season to assess seasonal variations in Anaplasma positivity rates. The positivity rates by season were as follows: winter 5.97% (4/67; 95% CI: 2.05%–13.57%), spring 4.71% (24/510; 95% CI: 3.12%–6.81%), and autumn 4.27% (14/328; 95% CI: 2.47%–6.87%), No positive cases were detected in summer (0/61). No statistically significant difference in positivity rates was observed among seasons (χ2 = 0.38, df = 2, P = 0.83). (Supplementary Table 2, Fig. 3).

Significant differences in Anaplasma positivity rates were detected among years (χ2 = 19.56, df = 6, P < 0.05). No positive specimens were detected in 2015 (0/20). The positivity rate was 6.16% (9/146; 95% CI: 3.09%–10.95%) in 2017, peaked at 9.15% (15/164; 95% CI: 5.44%–14.27%) in 2019, then declined to 0.81% (1/123; 95% CI: 0.09%–3.74%) in 2020. The rates in 2021 and 2022 were 4.37% (8/183; 95% CI: 2.09%–8.08%) and 4.11% (3/73; 95% CI: 1.17%–10.56%), respectively. The rate reached its lowest point in 2023 at 0.60% (1/168; 95% CI: 0.06%–2.75%), before rising to 5.62% (5/89; 95% CI: 2.18%–11.87%) in 2024. (Supplementary Table 2, Fig. 3).

Risk factors associated with Anaplasma infection in rodents

The regression analysis identified habitat, age, and season as potential risk factors for Anaplasma infection in rodents. Compared to rodents captured in residential areas, those from farmland and hills showed a significantly increased risk of Anaplasma infection (P < 0.05). In contrast, juvenile rodents showed a significantly lower risk of infection compared to aged rodents (P < 0.05). Furthermore, rodents captured in winter exhibited a significantly elevated risk of infection compared to those captured in spring (P < 0.05) (Table 2).

Table 2.

Logistic Regression Results for Factors Influencing Anaplasma Infection in Rodents.

Effect B S.E P value OR (95% CI) Vif Values
Habitat Residential a
Farmland 1.56 0.54  < 0.05 4.77 (1.67–13.64) 1.27
Hilly 2.86 0.59  < 0.05 17.38 (5.47–55.20) 1.18
Mountainous 0.43 0.49 0.38 1.54 (0.59–3.98) 1.46
Age Aged a
Adult − 0.96 0.60 0.11 0.38 (0.12–1.25) 3.27
Juvenile − 1.64 0.74  < 0.05 0.19 (0.05–0.83) 3.20
Season Spring a
Autumn 0.72 0.48 0.14 2.05 (0.80–5.26) 1.51
Winter 1.40 0.68  < 0.05 4.06 (1.07–15.38) 1.19
Sex Male a
Female 0.03 0.33 0.93 1.03 (0.54–1.97) 1.03
Region Coastal a
Inland 0.46 0.35 0.19 1.58 (0.80–3.11) 1.09
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“a” denotes the reference group; B represents the regression coefficient; SE indicates the standard error; OR (95% CI) refers to the odds ratio with its 95% confidence interval. The Omnibus test (χ2 = 30.32, df = 9, P < 0.05) and the Hosmer–Lemeshow test (χ2 = 11.31, df = 8, P = 0.18) were performed for the model.

Phylogenetic and Haplotype Analyses

BLAST analysis of selected sequences via NCBI confirmed that all Anaplasma identified based on the 16S rRNA gene belonged to Anaplasma phagocytophilum. The 11 sequences obtained in this study have been submitted to GenBank under accession numbers PV270150 to PV270160. A phylogenetic tree was constructed using representative sequences obtained from NCBI BLAST comparisons based on the 16S rRNA gene of Anaplasma, with Rickettsia rickettsia (DQ150682.1) serving as the outgroup. The results demonstrated that the A. phagocytophilum sequences detected in rodents from our province clustered within a major clade, showing close phylogenetic relationships to sequences derived from cattle and sheep hosts in Iran, tick vectors and domestic dog hosts in Japan, as well as sequences identified in rodents from Taiwan (China) and Henan Province, China (Supplementary Table 3 and 4, Fig. 4).

Fig. 4.

Fig. 4

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Phylogenetic tree constructed based on 16S rRNA gene sequences of Anaplasma. Note: Each branch includes sequence accession numbers, names, geographical origins, and host information. Branch lengths represent genetic distances, and bootstrap values are displayed at each node. Filled circles (●) represent sequences obtained in this study, while filled squares (■) indicate outgroup sequences.

The aligned sequences were trimmed to a length of 359 bp. Analysis of DNA polymorphism revealed seven haplotypes (H1–H7) among Anaplasma from different regions. The overall haplotype diversity (Hd) was 0.65, and the nucleotide diversity (Pi) was 0.01. Conservative sites accounted for 98.03% (349/359), variable sites for 2.79% (10/359), singleton variable sites for 0.84% (3/359), and parsimony-informative sites for 1.95% (7/359). The haplotype network indicated that H1 was the dominant haplotype. Sequences from our province shared the H1 haplotype with those from Taiwan (China) and Henan Province, China, suggesting a close phylogenetic relationship among these regional populations (Supplementary Table 3 and4, Fig. 5).

Fig. 5.

Fig. 5

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Haplotype network of A. phagocytophilum based on 16S rRNA gene sequences from different regions. Each circle represents a haplotype, with the circle size corresponding to haplotype frequency. Different colors indicate distinct geographical origins. Nucleotide differences/haplotype substitutions/mutations are denoted by hatch marks (vertical bars) on the connecting lines between haplotypes, with each bar representing a single nucleotide variant.

Discussion

Anaplasma infections in rodents have been reported globally, including in Mexico34, Italy35, Tunisia36, and Thailand37, with reported prevalence rates ranging from 0.6% to 36.96%. In recent years, rodent carriage of this pathogen has also been documented in several provinces of China, such as Shandong38, Guangxi39, Jiangxi40, and Yunnan41, with prevalence rates ranging from 0.16% to 6.25%. The overall prevalence observed in Fujian Province in this survey was 4.35% (42/966). These variations in prevalence rates may depend on the composition of rodent species across different regions.

Anaplasma infections were detected in rodents from all cities in Fujian Province except Ningde, and multiple rodent species were found to carry the pathogen. This indicates a wide distribution of Anaplasma within the province and diversity among the infected rodent hosts. The prevalence was higher in male rodents than in females in Fujian, a finding consistent with a study from Bangladesh which also suggested that male rodents are more susceptible to Anaplasma infection and that adult rodents are more vulnerable to pathogen infection42 . Another study reported that the Anaplasma infection rate initially increases and then decreases with individual age43, which aligns with our finding that juvenile rodents had a significantly lower risk of infection (P < 0.05). In addition, habitat and season were identified as significant risk factors for Anaplasma infection in rodents, suggesting potential variations linked to living environment and seasonality44. For example, a study from Senegal similarly reported that infection rates were generally lower in indoor rodents compared to outdoor rodents45, which aligns with our findings. While some research indicates a higher prevalence of Anaplasma in host animals during summer, attributed to increased vector tick activity46, and a study from Korea reported a peak in Anaplasma infection in ticks during warmer seasons47, our study found higher positivity rates in rodents during winter. This discrepancy might be related to uneven sampling time distribution.

Notably, Fujian Province is situated along the southeastern coast of China and experiences a subtropical monsoon climate characterized by hot, rainy summers and mild, relatively dry winters, with high humidity and ample precipitation. It boasts the highest forest coverage rate in the country. Such climatic and ecological conditions are conducive to the breeding and proliferation of ticks. This increases the risk that ticks, after feeding on infected hosts, may transmit pathogens to human populations, thereby elevating the risk of human infection with anaplasmosis48,49. Given the significant public health implications of anaplasmosis, continuous monitoring of the pathogen’s prevalence and heightened vigilance are warranted.

All Anaplasma strains identified in this study were confirmed as A. phagocytophilum through sequence alignment, with no other Anaplasma species detected. This suggests that A. phagocytophilum might be the predominant strain circulating among rodents in Fujian Province. The sequences showed close phylogenetic relationships to strains from cattle and sheep hosts in Iran and to sequences identified in rodents from Taiwan (China) and Henan Province, China. This suggests a potentially wide distribution of A. phagocytophilum among rodents in different regions of China, possibly related to the distribution of vector ticks and host migration50. The haplotype analysis indicated a haplotype diversity (Hd) greater than 0.5 for the A. phagocytophilum sequences51, reflecting a relatively high level of genetic diversity among A. phagocytophilum strains from different geographical regions. Multiple haplotypes (H1 to H7) were identified across regions, among which H1 may represent a stable haplotype formed during the course of evolution.

Climatic factors, including sunshine duration, precipitation, temperature, and humidity, can potentially influence pathogen transmission by affecting vectors and animal hosts. A study from Korea suggested that regional differences in rodent positivity rates might be influenced by temperature52. However, this study’s analysis is limited by the scope of the collected data and sample size, including a lack of ecological data and information on vector ticks, as well as uneven sample distribution. Future research should expand the sample size, systematically collect environmental parameters and climate variables, and conduct in-depth analyses of the relationships between vectors, animal hosts, and pathogen infection dynamics. Targeted studies are needed to further elucidate these associations.

Conclusion

This study elucidates the epidemiological profile of Anaplasma infections in rodents within Fujian Province. The findings reveal a geographically widespread distribution of the infection, characterized by host diversity and associations with age, seasonal, and habitat-related factors. Molecular identification confirms Anaplasma phagocytophilum as the predominant strain in Fujian. Phylogenetic analysis indicates genetic relatedness between local sequences and those reported from other domestic and international regions, suggesting potential regional transmission dynamics and a degree of genetic diversity. Given the ecological and climatic conditions in the province that are conducive to vector and host proliferation, our results underscore the necessity of implementing a systematic surveillance program.

Fundings

This work was supported by the Fujian Provincial Medical Innovation Project, Grant/Award Number: 2022CXA034; the Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare Number: (No.[2023]2839); and the Guiding Project of Fujian Provincial Science and Technology Plan, Grant/Award Number: 2025Y0040.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (42.8KB, docx)

Author contributions

F.X. is the Corresponding Author and was responsible for the design and coordination of the project. Z.Z. and S.H. were the main performers of this study, responsible for sample collection, experimental testing, data analysis, and manuscript drafting. J.W., S.Z., T.H., W.L., and F.L. participated in sample collection and experimental implementation. F.X. and S.W. provided financial support. All authors reviewed and approved the final manuscript.

Data availability

The experimentally obtained sequences of 16S rRNA have been uploaded to GenBank (https://blast.ncbi.nlm.nih.gov/) under accession numbers [PV270150–PV270160]. These accession numbers are also provided in Supplementary Table S3. The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Zhiwei Zeng and Shiju Hu contributed equally to this work.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (42.8KB, docx)

Data Availability Statement

The experimentally obtained sequences of 16S rRNA have been uploaded to GenBank (https://blast.ncbi.nlm.nih.gov/) under accession numbers [PV270150–PV270160]. These accession numbers are also provided in Supplementary Table S3. The data supporting the findings of this study are available from the corresponding author upon reasonable request.

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