Target gene mutation discovery and amplicon sequencing for evaluating maduramicin resistance in Eimeria tenella

Abstract

Anticoccidial drug resistance in Eimeria spp. presents a growing threat to global poultry health and food security, necessitating robust molecular tools for resistance surveillance. In this study, we report the identification and functional validation of a single nucleotide mutation (c.1255 C > T) in the ETH2_0402100 gene of Eimeria tenella that confers resistance to maduramicin, a widely used ionophore anticoccidial drug. Using genetic crossing, backcrossing, and QTL-seq, we narrowed the resistance locus to a 76 kb region on chromosome HG994964.1. CRISPR/Cas9-mediated site-directed mutagenesis and gene overexpression confirmed that the leucine-to-phenylalanine substitution in ETH2_0402100 confers maduramicin resistance. To translate this discovery into a practical tool, we developed a PCR-based amplification sequencing assay that enables rapid, quantitative detection of maduramicin-resistant alleles from field samples. Together, these findings establish the molecular basis and monitoring strategy for maduramicin resistance in Eimeria spp., and provide a field-deployable molecular diagnostic tool for detecting ionophore resistance.

Introduction

Coccidiosis, caused by Eimeria species, remains one of the most economically destructive parasitic diseases in poultry worldwide, with global losses of £10.4 billion annually^1,2,3,4,5. E. tenella, which invades and destroys the caecal mucosa, is among the most pathogenic species of chicken coccidia⁶. Ionophore anticoccidial drugs, such as maduramicin, have long been pillars of prophylactic control^7,8,9. However, the widespread and prolonged use of these drugs has led to the emergence of resistant strains, severely compromising prevention and treatment efficacy¹⁰. Apart from addressing drug resistance, it is crucial to urgently address environmental and food safety concerns associated with anticoccidial drugs. Traditional resistance assessments rely primarily on labor-intensive in vivo tests, which are time-consuming and lack sensitivity¹¹. Moreover, the absence of reliable molecular markers for resistance surveillance hampers timely intervention and informed drug management decisions on poultry farms. These challenges highlight the urgent need to develop sensitive and scalable molecular tools for early detection of resistance emergence in Eimeria populations.

In contrast, other apicomplexan parasites such as Plasmodium spp. and Toxoplasma gondii have successfully yielded multiple molecular markers associated with drug resistance, such as kelch13¹², pfcrt¹³, pfdhfr¹⁴, PRS¹⁵, pfgch1^16,17,18, and pfmdr1¹⁹. To date, only a limited number of resistance-associated markers have been identified in Eimeria, such as cyt b²⁰ and PRS²¹, highlighting the substantial untapped potential in elucidating the molecular mechanisms underlying drug resistance in coccidia. Bulk segregant analysis (BSA) has emerged as a powerful alternative to classical linkage methods in genetic research for detecting quantitative trait loci (QTL)²². BSA offers a straightforward and rapid approach to identifying loci associated with complex traits without the need for phenotyping and genotyping individual progeny. This technique has been successfully applied across various organisms, including yeast²³, Caenorhabditis elegans²⁴, Eimeria tenella²⁵, Schistosoma mansoni²⁶, and Plasmodium falciparum^22,27. BSA is often combined with whole-genome re-sequencing, a strategy known as QTL-Seq, to enhance mapping resolution and accuracy²⁸. For instance, genome-wide analysis of two genetic crosses in Haemonchus contortus identified a single QTL on chromosome V linked to ivermectin resistance across diverse populations²⁹, with cky-1 subsequently proposed as a potential contributor³⁰. Similarly, a forward genetic cross between multidrug-resistant and susceptible strains revealed a non-synonymous SNP (S168T) in the acetylcholine receptor subunit ACR-8 strongly associated with levamisole resistance, which was recently validated as a mechanistic contributor and molecular marker³¹.

Maduramicin, a polyether ionophore antibiotic, is widely used to control infections caused by various Eimeria species by disrupting transmembrane ion transport, ultimately leading to osmotic imbalance and parasite death³². Unfortunately, resistance to maduramicin has been reported in field populations, particularly when oocyst burdens exceed 20,000, with low-level resistance emerging at dosages ≥2 mg/kg and high-level resistance observed at 5 mg/kg³³. Recent studies suggest that E. tenella enolase 2 (EtENO2) may play a role in multiple anticoccidial drug resistance, including to maduramicin, based on its elevated expression levels and enhanced enzymatic activity in resistant strains; however, no functional validation has yet been performed³⁴.

Despite the widespread use of maduramicin, the specific genes and molecular mechanisms driving resistance remain largely undefined. This study aimed to optimize the BSA approach to identify loci associated with maduramicin resistance in E. tenella. To enrich the resistant allele within a sensitive genetic background, we developed introgression lines by strategically crossing maduramicin-resistant and -sensitive strains, followed by multiple rounds of backcrossing under drug selection, and subsequently performed genomic re-sequencing and QTL analysis to identify candidate resistance genes. These candidates were subsequently validated through gene overexpression and CRISPR/Cas9-mediated site-directed mutagenesis. Furthermore, transcriptomic profiling was conducted to elucidate the potential biological functions of the target gene. In addition, PCR amplification combined with amplicon sequencing was used to detect maduramicin resistance in field strains. This study translates genetic insights into a practical, field-deployable molecular assay for resistance monitoring and provides critical guidance for combating drug resistance in coccidia, which is essential for sustainable poultry health management strategies.

Results

Candidate molecular markers of maduramicin resistance

We compared the endogenous development of the maduramicin-sensitive E. tenella Houghton (H) strain and the maduramicin-resistant E. tenella (MRR) strain using H&E-stained tissue sections (Supplementary Fig. 1a). The MRR strain completed its entire endogenous development under drug pressure, similar to the H strain (Supplementary Fig. 1b). However, under maduramicin (5 mg/kg) selection pressure, the oocyst curve showed that MRR produced fewer oocysts at the peak on day seven compared to the H (0 mg/kg) strain (Supplementary Fig. 1c, Supplementary Data 1). Overall, MRR generated significantly fewer oocysts than the H strain, both without drug pressure (p < 0.05) and under drug pressure (p < 0.001). Notably, the H strain did not produce any oocysts under drug pressure (Supplementary Fig. 1d, Supplementary Data 1).

To identify the genetic basis of resistance, we established a classical genetic cross between the MRR and H strains and performed two rounds of backcrossing with drug selection to generate introgression lines (Fig. 1a). Due to the limited recovery of F₂ progeny under selection, the F₃ generation was used for further selection and propagation. Genomic DNA from H, MRR, F₁, F₃, BC₁F₁, BC₁F₂, BC₂F_1, and BC₂F₂ lines was separately subjected to whole-genome resequencing (mean depth >100×), and reads were aligned to the E. tenella reference genome (pEimTen1.1). Sequencing depth and quality met criteria for variant calling and linkage analysis (Supplementary Table 1).

Fig. 1: QTL mapping identifies genomic regions associated with maduramicin resistance in E. tenella.

a Strategy for generating maduramicin-resistant introgression lines through iterative backcrossing and drug selection. Resistance progeny were backcrossed with the sensitive parental strain over two generations under maduramicin pressure, yielding lines enriched for resistance-associated alleles within a largely sensitive genetic background. This figure was originally created by the authors using Adobe Illustrator. b Schematic overview of the QTL-seq approach on whole-genome resequencing of resistant and sensitive bulks to identify genomic regions under selection. This figure was originally created by the authors using Adobe Illustrator. c ΔSNP-index plots highlight genomic regions associated with maduramicin resistance across the E. tenella genome. The X-axis represents chromosomal positions, and the Y-axis shows the smoothed ΔSNP-index values (resistant minus sensitive) using 100kb sliding windows. Arrows mark significant peaks corresponding to candidate loci potentially involved in drug resistance.

SNP profiling across generations revealed approximately 10⁶ new mutations emerging every two generations (Supplementary Fig. 2a), reflecting extensive recombination and diversification during hybridization and selection. Notably, SNP density analysis revealed increasing fixation of variants within a defined region on chromosome HG994964.1 from the F₃ to the BC₂F₂ generations (Supplementary Fig. 2b), suggesting positive selection at this locus under maduramicin pressure.

We then applied QTL analysis by calculating ΔSNP-index values between each drug-selected progeny line and the sensitive H strain. Consistently, the candidate region with ΔSNP-index approaching 1.0 was located on chromosome HG994964.1 (Fig. 1b, c), suggesting strong genetic linkage to the resistance trait. Further comparison using the unselected F₁ population as an additional control confirmed the robustness of this region in all resistant lines across two generations (Supplementary Fig. 3). By intersecting ΔSNP-index peaks across all datasets, we refined the candidate interval to a ~76 kb region between positions 605,456 and 681,509 on chromosome HG994964.1.

Gene annotation within this region identified four candidate genes bearing nonsynonymous mutations (Table 1). These included: ETH2_0402100, encoding a hypothetical protein, with a Leu419Phe substitution; ETH2_0402400, encoding a leucine-rich repeat protein, with Phe117Val and Thr86Ala substitutions; ETH2_0402600, another hypothetical protein, with a Pro721Ser substitution; ETH2_0402700, annotated as trichohyalin, harboring three missense mutations: Thr1023Ile, Pro1081Leu, and Leu1108Pro. PCR amplification and Sanger sequencing confirmed the presence of these point mutations in the resistant lines, consistent with genome re-sequencing data (Supplementary Fig. 4). These mutations likely alter protein function and may contribute to the maduramicin resistance phenotype in E. tenella, thereby representing promising molecular markers for resistance surveillance.

Table 1 Mutations found in candidate genes identified by QTLseq analysis

Overexpression of mutated candidate genes confer maduramicin resistance in E. tenella

To validate the association between candidate gene mutations and maduramicin resistance, we tried overexpression of these selected genes. The candidate gene ETH2_0402700 (encoding trichohyalin) is 7065 bp long, contains 13 introns, and features repetitive sequences. Despite numerous attempts, we were unable to amplify the full length of this gene. Consequently, we developed overexpression strains for the ETH2_0402100 (encoding hypothetical protein), ETH2_0402400 (encoding leucine rich repeat), and ETH2_0402600 (encoding hypothetical protein) genes (Fig. 2a and Supplementary Figs. 5a, b). After transfecting these mutated genes into E. tenella H strain, F₁ generation oocysts exhibited red fluorescence (Fig. 2b). Drug resistance screening in the F₂ generation revealed red fluorescence exclusively in oocysts from ETH2_0402100 overexpression strain (ETH2_0402100^Mut-OE), with no fluorescence observed in the other groups. Using flow cytometry, we enriched the red fluorescent overexpression strain, achieving a fluorescence rate of 99.4% for the ETH2_0402100^Mut-OE strain (Supplementary Fig. 5c and Table 2). Our preliminary assessment suggests that the c.1255 C > T mutation in the ETH2_0402100 gene, resulting in the substitution of leucine with phenylalanine, contributes to maduramicin resistance. As shown in Fig. 2c, ETH2_0402100^Mut-OE-mCherry constitutively expressed throughout the parasites’ life cycle except the sporulating stage. Immunofluorescence assay (IFA) results confirmed the successful generation of overexpression strain, with ETH2_0402100^Mut-OE-mCherry localized in the cytoplasm of E. tenella (Fig. 2d). Western blot analysis further verified the expression of a 79.9 kDa protein corresponding to the Flag-tagged fusion protein, indicating the successful construction of the ETH2_0402100^Mut-OE strain (Fig. 2e, Supplementary Fig. 6). Comparison of the oocyst output curves between wild-type H strain and transgenic strain showed that the ETH2_0402100^Mut-OE transgenic parasites could complete their entire endogenous development under maduramicin pressure (5 mg/kg), although oocyst yield was relatively low (Fig. 2f, g and Supplementary Data 1).

Fig. 2: Overexpression validation and phenotypic characterization of candidate genes in transgenic parasites.

a Construction strategy of overexpression transgenic parasites. The purple regions indicate mutation sites. b Fluorescence observation of F₁ generation transgenic parasites. Scale bar, 50 µm. c mCherry expression across different stages of ETH2_0402100^Mut-OE strain. Unsporulated oocyst, sporulated oocyst, sporozoites, schizont, merozoite, and gametocyte. Scale bar, 5 µm. d Indirect immunofluorescence microscopy showing the localization of ETH2_0402100^Mut-OE-mCherry. Mouse anti-Flag mAb was used to detect the Flag-tagged ETH2_0402100^Mut protein followed by FITC-conjugated goat anti-mouse IgG. Scale bar, 10 µm. e Stable expression of the target protein in ETH2_0402100^Mut-OE strain validated by western blot. H was used as the wild-type control. f Oocyst output curves comparing ETH2_0402100^Mut-OE and wild-type H strains. “−” and “+” indicate treatment without and with maduramicin (5 mg/kg), respectively. g Total oocyst output measured during infection. Strain H served as the wild-type control. “−” and “+” indicate treatment without and with maduramicin (5 mg/kg), respectively. Statistical analysis was performed using an unpaired two-tailed Student’s t-test. P-values: ***p < 0.001; ns not significant. Data are presented as mean ± SD. Each dot represents an individual biological replicate (n = 3).

Table 2 Isolation of stable overexpression transgenic parasites under drug selection

CRISPR/Cas9-mediated single-point mutation (c.1255 C > T) in the ETH2_0402100 gene confers maduramicin resistance in E. tenella

To verify whether the c.1255 C > T mutation confers the resistance to maduramicin, we introduced a site-specific c.1255 C > T mutation into the wild-type ETH2_0402100 gene locus using the stable Cas9-expressing EtHCYA strain, which is sensitive to maduramicin (Fig. 3a and Supplementary Fig. 7). Through successive rounds of flow cytometry sorting of dual fluorescent proteins and under maduramicin selection, the ETH2_0402100^Mut-HR transgenic strain with a 80% double fluorescent rate were obtained (Supplementary Fig. 6c). PCR amplification and western blotting analysis confirmed the successful generation of the homologous recombination strain (Fig. 3b, c and Supplementary Fig. 8). Expression of the ETH2_0402100 gene was observed at the sporulated oocyst, sporozoite and schizont stages (Fig. 3d). IFA revealed that ETH2_0402100 is expressed in the cytoplasm of E. tenella (Fig. 3e). Under maduramicin selection pressure (5 mg/kg), the ETH2_0402100^Mut-HR transgenic parasites completed their entire endogenous development, although the oocyst yield was relatively lower than that of the EtHCYA strain (Fig. 3f, g and Supplementary Data 1).

Fig. 3: Cas9-mediated site-directed mutagenesis and phenotypic characterization of the ETH2_0402100^Mut gene conferring maduramicin resistance in E. tenella.

a Cas9-mediated strategy for introducing a single point mutation. b PCR amplification and c western blot analysis confirming successful generation of homologous recombination (HR) parasites. EtHCYA strain was used as the control. d ETH2_0402100^Mut protein expression across different developmental stages of ETH2_0402100^Mut-HR transgenic parasites: unsporulated oocyst, sporulated oocyst, sporozoites, schizont, merozoite, and gametocyte. Scale bar, 5 µm. e Indirect immunofluorescence microscopy showing localization of the ETH2_0402100^Mut protein. Rabbit anti-mCherry pAb was used to detect the ETH2_0402100^Mut protein, followed by Alexa Fluor^® 647 goat anti-rabbit IgG. Scale bar, 5 µm. f Oocyst output curves comparing ETH2_0402100^Mut-HR and EtHCYA strains. “−” and “+” indicate treatment without and with maduramicin (5 mg/kg), respectively. g Total oocyst outputs. EtHCYA was used as the control. “−” and “+” indicate treatment without and with maduramicin (5 mg/kg), respectively. Statistical analysis was performed using an unpaired two-tailed Student’s t-test. P-values: ***p < 0.001. Data are presented as mean ± SD. Each dot represents an individual biological replicate (n = 3).

Structural prediction and docking analysis reveal potential effects of mutation on maduramicin–protein interaction

The protein encoded by the ETH2_0402100 gene shares close evolutionary ties with homologs in Eimeria species, Cyclospora cayetanensis, and Besnoitia parasites (Fig. 4a). However, these proteins remain hypothetical and are not functionally characterized, limiting the available information. Based on bioinformatic predictions, the target protein appears to be hydrophobic (Supplementary Fig. 9a), lacking transmembrane domains (Supplementary Fig. 9b), signal peptides (Supplementary Fig. 9c), or conserved functional domains (Supplementary Fig. 9d), suggesting a likely cytoplasmic localization. Transcriptional analysis shows that it is highly expressed during the sporulated oocyst and sporozoite stages (Fig. 4b, Supplementary Data 1), while its translational expression remains low across all stages, except for a slight increase during the second-generation merozoites stage (Fig. 4c, Supplementary Fig. 10). Secondary structure prediction suggests the presence of α-helices, coils (or loops), and β-strands (Fig. 4d). To assess the potential impact of the ETH2_0402100 mutation on maduramicin binding, we modeled the three-dimensional structures of the wild-type and mutant proteins using AlphaFold2 (Fig. 4e). The predicted overall folds were similar, with only subtle local conformational differences. Docking simulations with AutoDock Vina yielded scores of –4.82 kcal/mol for the wild-type and –4.04 kcal/mol for the mutant, representing a modest difference in predicted binding energy. In both models, maduramicin interacted with hydrophobic residues within the binding pocket; however, the L419F substitution replaced a leucine with a bulkier aromatic phenylalanine side chain, accompanied by a change in the predicted ligand orientation relative to the wild-type (Fig. 4f). These in silico findings suggest a possible structural basis for reduced sensitivity of the mutant protein to maduramicin, but require experimental validation.

Fig. 4: Phylogenetic, expression pattern and molecular docking analysis of ETH2_0402100.

a Phylogenetic tree of the ETH2_0402100 protein in eukaryotes. b Transcriptional patterns of the ETH2_0402100 gene across different developmental stages. SO sporulated oocysts, UO unsporulated oocysts, MZ-2 second-generation merozoites, SZ sporozoites. The relative expression levels were normalized to the 18S rRNA internal control and calculated using the 2^−ΔΔCt method. Data are presented as mean ± SEM from three biological replicates. Statistical analysis was performed using an unpaired two-tailed Student’s t-test. P-values: *p < 0.05; **p < 0.01; ***p < 0.001. Data are presented as mean ± SD. Each dot represents an individual biological replicate (n = 3). c Expression of the ETH2_0402100 protein across different developmental stages. SO sporulated oocysts, UO unsporulated oocysts, MZ-2 second-generation merozoites, SZ sporozoites. d Secondary structure of the ETH2_0402100 protein. Mutated amino acids highlighted in red. e Structural comparison of wild-type and mutant ETH2_0402100 proteins. The mutated amino acid is highlighted in red. The inset on the left shows the side chain of the wild-type residue (leucine, L419), while the inset on the right illustrates the mutant residue (phenylalanine, F419), emphasizing the structural change from an aliphatic side chain to an aromatic benzene ring. f Molecular docking visualization between the ETH2_0402100^Mut protein and maduramicin drug molecule. The right panel shows the predicted binding interaction between the mutant protein and maduramicin, with the drug displayed in stick representation. The left inset is a magnified view, highlighting the spatial relationship between the drug and the mutated amino acid residue located near the putative binding pocket.

Gene expression dynamics unveil potential role of target gene in maduramicin resistance

To explore the effects of developmental stage and drug treatment on gene expression profiles, principal component analysis (PCA) was performed. PC1 (explaining 55% of the variance) primarily separates samples from the MZ-2 stage and the SZ stage, the major difference of gene expression between the two stages. Samples from different treatment groups of MRR (0 mg/kg, 5 mg/kg, 7 mg/kg) and H (0 mg/kg) show no significant separation along PC2 and PC1, indicating minimal gene expression divergence due to drug pressure within the MZ-2 stage. MRR samples with different treatment and H group within the SZ stage show significant separation along PC2, indicating that drug pressure induce substantial divergence in gene expression specifically during the SZ stage (Fig. 5a, Supplementary Data 2). Correlation analysis (Fig. 5b, Supplementary Data 2) demonstrated that samples with higher drug concentrations were closely clustered, indicating good inter-group reproducibility. Further analysis of the PC4 module revealed significant differences between the SZ and MZ-2 stages (Fig. 5c, Supplementary Data 2), highlighting distinct gene expression profiles. The PC4 module, which contained 75 genes, was divided into different clusters (Fig. 5d, Supplementary Data 2), supporting the identification of key functional gene groups. Clustering of all genes in the PC4 module (Fig. 5e, Supplementary Data 2) revealed that the target gene ETH2_0402100 clustered with genes indicated in the red box. GO annotation of this gene cluster revealed significant enrichment in two pathways: nucleotide-excision repair (GO:0006289) and endoplasmic reticulum to Golgi vesicle-mediated transport (GO:0006888). These findings imply that ETH2_0402100, localized in the cytoplasm (Fig. 3e), may participate in transport-related cellular processes.

Fig. 5: Functional exploration of the target gene through transcriptomic analysis.

a Principal component analysis (PCA) revealing variability among samples. Circles represent samples from the MZ-2 stage, and triangles represent samples from the SZ stage. Different colors indicate treatment groups: H (no drug treatment), MRR (no drug treatment), MRR (treated with 5 mg/kg maduramicin), and MRR (treated with 7 mg/kg maduramicin). b Pearson correlation analysis uncovers resistance patterns across developmental stages of SZ and MZ-2. c PCA highlights variance in resistance profiles across groups and stages. d Visualization of gene modules through dendrogram clustering and TOM similarity analysis. e Heatmap visualization of gene expression correlations across groups and stages. The asterisk marks the target gene. The red box highlights a co-expression module comprising genes with expression patterns similar to the target gene.

ETH2_0402100 ^Mut as a potential marker for monitoring maduramicin resistance in the field by PCR and amplicon sequencing

To evaluate the ETH2_0402100^mut as a molecular marker, we assessed its mutation frequency in strains with defined resistance ratios and in field-derived E. tenella populations. A strong correlation was observed between the frequency of the C > T substitution at nucleotide position 1255 and the population of resistant parasites, supporting the reliability of this mutation as a quantitative indicator of resistance. (Fig. 6a). Amplicon sequencing indicated that the mutation frequency at position 1255 closely correlated with the mixed resistance ratios (Fig. 6b). This suggests that detecting this genetic mutation site offers a highly accurate method for assessing E. tenella resistance to maduramicin.

Fig. 6: Detection of ETH2_0402100 mutation for monitoring maduramicin resistance in poultry farms.

a PCR amplification of a 306 bp fragment at the ETH2_0402100 locus using laboratory-prepared mixtures containing defined proportions (5–95%) of maduramicin-resistant E. tenella (MRR). b Amplicon sequencing of these PCR products revealed the C > T mutation at position 1255, with mutation frequencies increasing proportionally from 3 to 89%. c PCR amplification of the 306 bp fragment from 11 DNA samples collected from poultry farms. d Amplicon sequencing of field samples identified the C > T mutation at position 1255 in all eight PCR-positive farms, with mutation frequencies ranging from 1.43 to 100%.

Field validation using fecal samples from commercial farms demonstrated that detection of the E. tenella-specific PCR amplification band (306 bp, comprising a 239 bp target fragment plus adapter sequences) was diagnostic of E. tenella infection, whereas the absence of such band, as observed for samples 4, 5, and 11, indicated the absence of E. tenella oocysts in the corresponding farms (Fig. 6c). Subsequently, amplicon sequencing confirmed the presence of the resistance-associated mutation in the majority of positive samples, with a wide range of mutation frequencies detected (Fig. 6d). These findings demonstrate that ETH2_0402100^mut enables sensitive detection of resistant E. tenella subpopulations and can be effectively applied for resistance surveillance and the monitoring of maduramicin efficacy under field conditions.

Discussion

To our knowledge, we report the first validated molecular marker for maduramicin resistance in Eimeria, and the study also provides a field-deployable molecular diagnostic tool for ionophore resistance in eimerian parasites. Building on the successful use of BSA approaches in malaria parasites to map drug resistance loci^22,35,36,37, we combined forward genetics, CRISPR/Cas9-mediated site-directed mutagenesis validation, and a scalable amplicon sequencing-based assay to pinpoint the molecular basis of maduramicin resistance in E. tenella. This work not only advances our understanding of ionophore-resistant mechanisms but also addresses an urgent practical need in poultry health by enabling informed drug management and early detection of resistance in the field.

Genetic crossing in Eimeria is notoriously challenging due to the parasite’s strong tendency toward self-fertilization. Previous studies showed that cross-fertilization occurs at a frequency of only 11% under standard conditions, even using a high inoculum of 5000 oocysts per parent³⁸, significantly limiting the efficiency of conventional genetic mapping. Blake et al. addressed this challenge by employing population-based crossing and back-crossing, combined with self-bred, to identify candidate genes and novel antigens for anti-coccidia subunit vaccines²⁵. To overcome this low hybridization efficiency, we implemented an iterative backcrossing strategy. MRR progeny were serially backcrossed to the drug-sensitive parent line under drug pressure, producing introgression lines enriched for the resistant allele against an otherwise sensitive genetic background. This approach stabilized the resistance locus and enabled precise localization of the candidate interval via whole-genome re-sequencing. Rigorously controlling the parental inputs was critical—for example, we pre-treated the sensitive line with a high drug dose and confirmed it produced no oocysts under maduramicin exposure, ensuring that any oocysts after crossing originated from true hybrids. We observed that a clear candidate interval on the genome emerged as early as the BC₁F₂ generation and became fixed by BC₂F₂. These results suggest that future genetic analyses of drug resistance in Eimeria should focus on at least two rounds of backcrossing to reliably identify drug resistance loci. Overall, this refined crossing and selection methodology enhanced the precision of resistance gene mapping in Eimeria, which can support more targeted interventions in coccidiosis control.

To functionally validate the candidate genes within the identified QTL, we employed both gene overexpression and CRISPR/Cas9-mediated site-directed mutagenesis. Our previous work had established CRISPR-Cas9 as a robust tool for gene editing in Eimeria³⁹. Unlike trypanosomatids, Eimeria species primarily repair double-strand DNA breaks (DSBs) through the NHEJ pathway, mediated by a conserved KU70/KU80-dependent mechanism^40,41. However, the low transfection efficiency in Eimeria (~0.2%)^42,43 significantly limits the feasibility of strategies involving co-transfection of multiple plasmids, such as Cas9-gRNA and donor plasmids. Therefore, for screening multiple candidate genes, we prioritize overexpression for validation. After the identification of the target gene, we use Cas9-mediated site-directed mutagenesis to further validate, thereby enhancing efficiency. The issue of low cleavage efficiency has been addressed in Toxoplasma gondii research by deleting the NHEJ repair pathway via disruption of the KU80 gene⁴⁴ or by employing Cas9-mediated DSB followed by single tachyzoite cloning^45,46. In the future, knocking out the KU80 gene in Eimeria to establish a more robust Cas9 gene editing system would greatly expand the applications of genetic editing in this parasite.

Our data also reveal that the MRR mutation comes with a fitness cost for the parasites. Under drug pressure, the resistant parasites successfully completed their endogenous development but exhibited substantially reduced oocyst output compared to the control strain. This reduction in output likely indicates a potential trade-off between resistance and overall fitness⁴⁷. In the present study, we used oocyst output as the primary indicator for evaluating fitness cost. Compared to traditional in vivo assessments—which typically involve multiple parameters such as lesion scores, weight gain, survival rates, and the anticoccidial index (ACI)^48,49—oocyst output provides a rapid, quantifiable, and practical proxy for parasite viability and transmission potential²¹. While more comprehensive metrics may yield a fuller picture of resistance-associated fitness consequences, they require labor-intensive, time-consuming, and ethically demanding animal trials that are not feasible within the scope of this study. Future work integrating these parameters would offer deeper insights into the fitness landscape of drug-resistant Eimeria strains and contribute to the development of more accurate resistance surveillance strategies.

Given the field importance of this discovery, we translated it into a practical diagnostic assay for farm settings and performed preliminary field validation. Coccidiosis is ubiquitous in poultry operations, exacerbated by intensive farming and high stocking densities⁴⁸. Financial pressures in the poultry industry drive wide and intensive prophylactic use of anticoccidial drugs, but their effectiveness has waned globally due to increasing drug resistance^3,5,50. In this study, we employed PCR amplification and amplicon sequencing to detect maduramicin resistance in field strains, aiming to monitor drug resistance on poultry farms and provide insights into combating coccidia drug resistance. PCR is a widely used, rapid detection method in Eimeria research. Previous studies reported a 78.7% prevalence of coccidiosis using a species-specific PCR diagnostic method in fecal samples from 356 poultry farms, with E. acervulina and E. tenella being most common⁵¹. Amplicon sequencing offers a sensitive, scalable, and cost-effective method for detecting and quantifying resistance-associated mutations in complex parasite populations, making it a powerful tool for surveillance of drug resistance⁵². Unlike Sanger sequencing, amplicon sequencing can identify mixed mutations and determine mutation type ratios. Plummer et al.⁵³ utilized a custom amplicon sequencing approach to detect resistance-associated mutations and sequence types in Mycoplasma genitalium. In our study, fecal samples from eleven sites successfully amplified target fragments in eight locations, with amplicon sequencing revealing varying resistance gene mutations in seven farm sites, confirming the presence of maduramicin resistance. Although the resistance-associated mutation was supported by oocyst shedding under drug pressure in animal experiments, phenotypic validation was limited. Only qualitative detection of oocysts was performed using saturated salt flotation^54,55 and microscopy, without quantification of oocyst output or lesion scoring. Therefore, while the findings suggest the presence of field resistance, further experimental validation is warranted to confirm this observation. Overall, PCR combined with amplicon sequencing provides an efficient strategy for monitoring drug resistance. It enables sensitive detection of low-frequency mutations, which is critical for tracking resistance emergence. Clinically, this approach may allow earlier intervention by guiding treatment adjustments and informing public health decisions.

The Eimeria genome encodes approximately 8000 genes, the functions of most of which remain uncharacterized^56,57. Our study focused on a hypothetical protein. Through extensive bioinformatic analyses, we found that this protein is non-membrane-associated and lacks transmembrane regions, preventing its incorporation into cell or organelle membranes. Additionally, it lacks a signal peptide, indicating that it does not follow the classical secretion pathway. While transcriptomic analysis suggests that the target gene is involved in transport-related functions, the absence of predicted structural domains indicates limited available information regarding its function. Attempts to express and purify the protein in prokaryotic systems were unsuccessful, as were immunoprecipitation (IP) experiments, likely due to issues with protein solubility and low expression levels. Further investigation is needed to fully understand its function and mechanisms. Notably, we observed inconsistent transcript and protein levels of ETH2_0402100 across life stages. Although transcription was elevated in sporulated oocysts and sporozoites, protein levels remained low, except for a slight increase in second-generation merozoites. This suggests possible post-transcriptional regulation, such as limited translation efficiency or protein instability. Similar patterns have been reported in this parasite, where translational control plays an important role^58,59. These findings emphasize the need to combine transcriptomic and proteomic data to better understand gene function in Eimeria.

In the future, a fully detailed functional characterization of ETH2_0402100 will be a priority. This includes developing alternative expression systems or protein purification strategies to enable in vitro biochemical assays, as well as using advanced proteomics to identify any interacting partners or pathways. These efforts will deepen our understanding of how the c.1255 C > T mutation confers maduramicin resistance at a mechanistic level and may reveal whether the protein has a normal physiological role related to transport or other cellular processes. In conclusion, our study provides a critical first step by uncovering a genetic marker and a practical diagnostic for ionophore resistance in Eimeria. At the same time, it lays the groundwork for future investigations into the protein’s function, the fitness and virulence implications of resistance, and the broader application of molecular surveillance to combat drug resistance in coccidian parasites.

Methods

Animals and parasites

Arbor Acres (AA) broilers, aged 1–6 weeks, were obtained from Beijing Arbor Acres Poultry Breeding (Beijing, China) for oocyst proliferation and the development of introgression lines. Two-week-old specific-pathogen-free (SPF) chickens were obtained from Merial Animal Health Co., Ltd. (Beijing, China). All birds were provided with a coccidia-free diet and had access to water ad libitum.

E. tenella Houghton⁶⁰ strain used in this study was kindly provided by Dr. Fiona Tomley from the Royal Veterinary College, UK. The MRR strain was from Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, China. The stable Cas9-expressing EtHCYA strain was established and maintained in our laboratory³⁹. Sporulated oocysts of E. tenella were generated by orally infecting 3-week-old chickens with 1 × 10⁴ sporulated oocysts. Fresh unsporulated oocysts were collected from feces at 6–9 days post-infection and purified by sequential centrifugation and salt flotation to remove impurities. The purified unsporulated oocysts were washed thoroughly in sterile PBS and subjected to sporulation at 28 °C for 48 h with continuous aeration provided by a low-pressure aquarium pump. After sporulation, oocysts were washed, resuspended in 2.5% potassium dichromate, and stored at 4 °C until use⁵⁴.

Development of introgression lines for maduramicin resistance gene

The parental parasites used for this trial were the maduramicin-sensitive E. tenella Houghton (H) strain and the maduramicin-resistant E. tenella (MRR) strain. For developing introgression lines, comparative studies on their endogenous development were carried out with adjustments to established protocols⁶¹. Samples from chicken caeca were analyzed at specific time points (24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, and 156 h post-inoculation) for histological evaluation. At each designated time point, chickens were euthanized by CO₂ inhalation following AVMA-approved guidelines. Immediately after euthanasia, the abdominal cavity was opened, and both caeca were carefully excised. Each caecum was opened longitudinally, and the luminal contents were gently removed. The tissues were briefly rinsed in cold sterile PBS to eliminate residual debris and subsequently fixed in 4% paraformaldehyde (Solarbio, Beijing, China) at 4 °C for 24 h. Fixed caeca were then processed for paraffin embedding, sectioned at 5 µm thickness, and stained with hematoxylin and eosin (H&E) for microscopic examination of parasite developmental stages.

Based on these developmental characterizations, introgression lines were then constructed. Initially, three 3-week-old AA broilers were crop-inoculated with 500 fresh oocysts of the MRR strain and 5000 fresh oocysts of the H strain. Oocysts collected from days 6 to 9 were designated as the F₁ generation. Subsequently, three 4-week-old AA broilers each received an inoculum of 10,000 F₁ oocysts via the crop, and 5 mg/kg maduramicin (MedChemExpress, Monmouth Junction, NJ, USA) was supplemented in their feed. Oocysts collected thereafter were labeled as the F₂ generation, exhibiting resistance to maduramicin. Following this, three 3-week-old AA broilers were inoculated with 500 fresh F₂ oocysts and 5000 fresh H strain oocysts, generating the BC₁F₁ generation upon collection. This was followed by inoculating three 4-week-old AA broilers with 10,000 BC₁F₁ oocysts each, supplemented with 5 mg/kg maduramicin, resulting in the BC₁F₂ generation. This first backcross led to the development of MRR progeny strains. The BC₁F₂ generation underwent a second backcross with the H strain, producing introgression strains designated as BC₂F₂. The experiment was conducted with three replicates (as shown in Fig. 1a).

QTL analysis to identify genomic regions associated with maduramicin resistance in E. tenella

Extract genomic DNA from introgression lines and parental oocysts using the CTAB method as previously reported⁶². Briefly, purified oocysts were directly lysed in CTAB buffer (2% CTAB, 1.4 M NaCl, 0.2% β-mercaptoethanol, 20 mM EDTA, 100 mM Tris-HCl) (Solarbio, Beijing, China) and incubated at 60 °C to achieve efficient cell lysis, polysaccharide removal, and protein denaturation. The lysate was then subjected to phenol–chloroform extraction followed by ethanol precipitation. The resulting DNA pellet was washed, air-dried, and resuspended in nuclease-free water for downstream molecular analyses. Following quality assessment of the DNA samples— including purity evaluation using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), concentration measurement with a Qubit 3.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), and integrity verification by 1% agarose gel electrophoresis—genome re-sequencing was performed on the Illumina HiSeq PE150 platform.

We performed quality control on raw sequencing data using FastQC to assess its quality. Reads were aligned to the E. tenella reference genome (pEimTen1.1) using the mem subprogram in BWA. BAM files were sorted by alignment position with samtools, and alignment statistics were generated using samtools flagstat to ensure high-quality alignments. To remove duplicates from library construction and sequencing, the MarkDuplicates subprogram in Picard was employed. SNPs were called with GenotypeGVCFs, followed by filtering with the SelectVariants subprogram in GATK based on criteria: QD > 2.0, MQ > 40.0, FS < 60.0, MQRankSum > −12.5, and ReadPosRankSum > −8.0. The filtered VCF file was prepared for QTL analysis. We utilized the QTLseqr package in R, importing data via importFromGATK and importFromTable, then filtering and screening data to apply the SNP-index for identifying genetic regions associated with phenotypic variation. Finally, SNP annotations for mutation gene information were performed using SnpEff v5.0 to assess functional impact.

Construction of overexpression and homologous replacement transgenic parasites to validate candidate genes

Total RNA was isolated from E. tenella merozoites using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Complementary DNA (cDNA) was synthesized with random primers using a high-capacity cDNA reverse transcription kit (Vazyme, Nanjing, China). The exons of E. tenella candidate genes harboring mutations were amplified by PCR with specific primers. To construct the overexpression vector, the ETH2_0402100 coding sequence was fused at its 3′ end with a 3 × Flag tag, followed by a self-cleaving P2A peptide and the mCherry gene encoding red fluorescent protein. Expression of this fused gene was driven by the EtMIC2 promoter to ensure robust transcription. A specific point mutation was introduced into the coding region by amplifying the target gene directly from the maduramicin-resistant strain (MRR), which naturally carries the mutant allele. All fragments were assembled using seamless cloning and verified by Sanger sequencing. Primer sequences used are listed in Supplementary Table 2.

Restriction enzyme-mediated integration was used to transfect E. tenella sporozoites⁴³. In this process, 1 × 10⁶ freshly isolated E. tenella sporozoites were transfected with 20 µg of linear overexpression plasmid and 5 µL of SnaBI restriction enzyme (New England Biolabs, Ipswich, MA, USA). The transfected sporozoites were then inoculated into 1-week-old AA broiler chickens via the cloacal route to select for stably transfected oocysts under maduramicin stress. Stable E. tenella lines were further selected using a combination of maduramicin and fluorescence-activated cell sorting (FACS). For FACS purification, freshly oocysts expressing mCherry were filtered through a 40-μm cell strainer and analyzed using a BD FACSAria III cell sorter (BD Biosciences, San Jose, CA, USA). The gating strategy followed standard sequential filtering: (i) oocysts were first gated by forward scatter (FSC) and side scatter (SSC) to exclude debris; (ii) single cells were selected by FSC-A versus FSC-H to remove doublets; (iii) mCherry⁺ populations were identified using PE-Texas Red (610/20 nm) channel. Sorted parasites were collected into PBS and immediately used for downstream infection to amplify the selected transgenic lines.

To generate transgenic parasites carrying a site-directed mutation, we constructed a homologous replacement vector targeting the ETH2_0402100 gene. A 1434 bp fragment encompassing the mutation site was selected as the upstream homologous arm. A guide RNA (gRNA) targeting a sequence near the 3′ end of ETH2_0402100 was designed using the reference genome sequence available in the ToxoDB database (https://toxodb.org/toxo/app). The gRNA sequence used for CRISPR/Cas9 editing was 5′-GAAAATCGTTCAGGAGGCTGG-3′. The mCherry coding sequence was fused in-frame to the 3′ end of ETH2_0402100 via a flexible linker, with expression driven by the endogenous promoter of ETH2_0402100, serving as a positive selectable marker for identifying recombinant parasites. The 3′ untranslated region (3′UTR) of the 803 bp target gene was used as the downstream homologous arm. The homologous replacement vector also contained a U6-gRNA expression cassette, in which the gRNA was driven by the E. tenella U6 (EtU6) promoter. All PCR amplifications were carried out using Q5^® High-Fidelity DNA Polymerase (New England Biolabs, Ipswich, MA, United States), and the primers used are listed in Supplementary Table 3. The linearized homologous replacement plasmid was transfected into E. tenella sporozoites, with Cas9-mediated cleavage facilitating targeted integration, followed by in vivo selection and fluorescence-based enrichment of recombinant parasites. FACS was then used to isolate EYFP⁺/mCherry⁺ double-positive oocysts, ensuring the recovery of correctly integrated transgenic lines.

Immunoblotting, cecal smears, and indirect immunofluorescence assay

The soluble antigens from transgenic E. tenella sporozoites were analyzed using SDS-PAGE and immunoblot techniques, following standard protocols. Primary antibodies used were mouse anti-Flag tag monoclonal (1:1000, Merck, USA) or rabbit anti-mCherry polyclonal (1:1000, Abcam, UK). Secondary antibodies included HRP-conjugated goat anti-mouse IgG (1:2500, Proteintech, USA) and goat anti-rabbit IgG (1:2500, Abcam, UK).

To identify target protein expression across all stages of E. tenella, chickens were infected with 10,000 transgenic E. tenella oocysts. Cecal smears were prepared at 96, 120, 144, and 168 h post-infection to detect schizonts, merozoites, gametocytes, and unsporulated oocysts, respectively. Oocysts were collected and purified from feces, while sporozoites were purified from sporulated oocysts.

Indirect IFA was performed to evaluate the distribution and relative expression levels of the target protein. Parasites were fixed in 4% paraformaldehyde for 15 min, permeabilized with 0.1% Triton X-100 (Solarbio, Beijing, China) for 10 min, and blocked with 3% BSA (M&C Gene Technology, Beijing, China) for 1 h at room temperature. Samples were then incubated overnight at 4 °C with mouse anti-Flag monoclonal antibody (1:200, Sigma-Aldrich, MO, USA; Cat# F1804) or rabbit anti-mCherry polyclonal antibody (1:200, Abcam, Cambridge, UK; Cat# ab183628), followed by incubation with FITC-conjugated goat anti-mouse IgG (1:100, MACGENE Biotechnology, Beijing, China; Cat# IS001-100UG) or Alexa Fluor® 647-conjugated goat anti-rabbit IgG (1:100, Abcam, Cambridge, UK; Cat# ab150079) for 1 h at room temperature. After washing, nuclei were counterstained with DAPI (Solarbio, Beijing, China), and fluorescence images were captured using a confocal microscope.

Oocyst output curves and differential analysis among Eimeria strains

To evaluate the differences in oocyst production between MRR and -sensitive strains, we generated oocyst output curves. Two groups of five 3-day-old AA broilers were established. In the experimental group, chickens were inoculated with 2000 oocysts of the maduramicin-resistant MRR strain and treated with maduramicin at 5 mg/kg. In the control group, chickens were inoculated with 2000 oocysts of the sensitive H strain without maduramicin treatment.

To further analyze differences in oocyst production, we quantified the total oocyst output. Four groups of five 3-day-old AA broilers were established. In the experimental group, chickens were inoculated with 2000 maduramicin-resistant MRR strain oocysts and divided into two subgroups: maduramicin-treated and untreated. Similarly, control group chickens were inoculated with 2000 H strain oocysts, also divided into treated and untreated subgroups. Oocyst output was monitored daily from days 5 to 12 post-infection (d.p.i.), with total counts measured using a modified McMaster chamber⁶³.

In addition, oocyst output curves and total oocyst production were compared between the overexpression and wild-type H strains, as well as between the homologous replacement and EtHCYA strains, to assess the impact of target gene modification on the E. tenella life cycle.

Phylogenetic, molecular characteristics, expression pattern, and molecular docking analysis

To investigate the genetic relationships of the target gene, we compared its amino acid sequences using the VEuPathDB (https://veupathdb.org/veupathdb/app)⁶⁴ eukaryotic database. We analyzed gene conservation across different species with MEGA 11 software and constructed an evolutionary tree. The tree was then enhanced for presentation using iTol (https://itol.embl.de)⁶⁵.

Hydrophobicity analysis was performed using the Expasy website (https://web.expasy.org/protscale/)⁶⁶, while transmembrane region prediction was conducted through statistical analysis using the Tmbase database (https://embnet.vital-it.ch/software/TMPRED_form.html)⁶⁷. The protein sequence in FASTA format was submitted to SignalP-5.0 (https://services.healthtech.dtu.dk/services/SignalP-5.0/)⁶⁸ for signal peptide prediction, and finally, the sequence was analyzed using InterProScan (https://www.ebi.ac.uk/interpro/search/sequence/)⁶⁹ for domain prediction.

The expression profiles of the target genes were analyzed at the transcriptional level in four developmental stages of the homologous replacement strain of E. tenella (unsporulated oocysts, sporulated oocysts, sporozoites, and second-generation merozoites) using quantitative real-time PCR (qPCR). Total RNA was extracted from each stage, and reverse transcription was performed to synthesize cDNA using SuperScript™ II Reverse Transcriptase (Invitrogen, Carlsbad, CA, USA) and random primers. The housekeeping 18S rRNA gene was used as the internal control. qPCR reactions were conducted on the StepOnePlus™ Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) using the SYBR® Premix Ex Taq™ II kit (Takara, Tokyo, Japan). The relative expression levels of the target genes were calculated using the comparative 2^−ΔΔCt method. Concurrently, proteins were extracted from the homologous replacement strain at various developmental stages, and expression patterns at the translation level were assessed using Western blotting to detect mCherry protein.

For protein secondary structure prediction, the protein amino acid sequence was submitted to the PSIPRED database (http://bioinf.cs.ucl.ac.uk/psipred/)⁷⁰. PSIPRED will analyze the sequence and generate secondary structure results.

To assess whether the mutation affects the interaction between maduramicin and the target protein, we performed molecular docking based on structure prediction. The full-length amino acid sequence of the target protein was submitted to AlphaFold2 (locally installed), and the model with the highest confidence (ranked_0.pdb) was selected based on pLDDT scores⁷¹. The 2D structure of maduramicin was retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/)⁷² and converted to a 3D structure using Open Babel (http://openbabel.org)⁷³. Potential ligand-binding pockets and interacting residues were identified using CavityPlus (http://www.pkumdl.cn:8000/cavityplus/)⁷⁴. For molecular docking, the 3D structures of the wild-type and mutant ETH2_0402100 proteins were prepared by removing water molecules, adding polar hydrogens, and assigning Gasteiger charges using AutoDockTools (version 1.5.6). The predicted binding pocket from CavityPlus was defined as the docking search space, and the grid box was centered on the pocket with dimensions large enough to cover all potential interacting residues. Docking simulations were performed using AutoDock Vina with default parameters, and the output poses were ranked by binding affinity scores. The top-scoring binding conformations were selected for analysis, and ligand–protein interactions were visualized using PyMOL (version 2.5).

RNA sequencing and data analysis

We established four experimental groups: H (no drug treatment), MRR (no drug treatment), MRR (treated with 5 mg/kg maduramicin), and MRR (treated with 7 mg/kg maduramicin). Sporozoites and merozoites were collected from each group for transcriptome sequencing. Total RNA was extracted using Trizol™ reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. RNA degradation and contamination were assessed on 1% agarose gels, while RNA integrity was evaluated with the RNA Nano 6000 Assay Kit on a Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). For library preparation, mRNA was purified and fragmented, with first-strand cDNA synthesized using M-MuLV reverse transcriptase and second-strand synthesis performed using DNA polymerase I. After purification, cDNA fragments of 250–300 bp were selected, and the libraries were sequenced on the Illumina NovaSeq PE150 platform.

Raw sequencing reads were converted to FASTQ format and preprocessed by removing low-quality reads and adapter sequences. The cleaned reads were then aligned to the E. tenella reference genome (pEimTen1.1) using STAR, and transcript abundance was quantified with RSEM. Gene expression levels were normalized using transcripts per million, and differential expression analysis was performed with DESeq. Differentially expressed genes (DEGs) were identified based on an adjusted P-value < 0.05 and |log₂(fold change)| >1. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analysis, was conducted to characterize the biological relevance of DEGs.

To investigate the co-expression patterns of the candidate drug resistance gene and its associated genes, PCA was applied to filter expression data, resulting in six gene clusters that accounted for 99% of the variance. We then constructed a correlation matrix using the topological overlap measure method implemented in weighted gene co-expression network analysis. Genes highly correlated with the candidate drug resistance gene were identified, and their potential biological functions were inferred through enrichment analysis.

Monitoring maduramicin resistance

To evaluate maduramicin resistance, the resistant strain MRR and sensitive strain H were mixed at defined ratios (5, 10, 20, 30, 40, 50, and 95%). Genomic DNA was extracted from the mixtures and used as the template for a two-step PCR targeting a 200–300 bp region containing resistance-associated mutations. In the first round, gene-specific primers targeting the E. tenella maduramicin resistance–associated gene were used, with partial Illumina adapter sequences appended (primer sequences listed in Supplementary Table 4). The resulting amplicons were purified and subjected to a second round of PCR to incorporate sample-specific barcodes and complete the Illumina adapter sequences. Following amplification, the libraries were constructed and then subjected to quality assessment, including concentration measurement with Qubit 2.0 and fragment size analysis with the Agilent 2100 Bioanalyzer.

Sequencing was performed on an Illumina MiSeq platform (Tsingke Biotechnology, China). Raw reads were processed with FastQC for quality filtering and trimming, and clean paired-end reads were merged using FLASH⁷⁵ to reconstruct full-length amplicons. Sequence variants were analyzed by clustering identical sequences to assess diversity. The merged reads were aligned to the reference genome using BLAST, followed by multiple sequence alignment with MAFFT⁷⁶. SNPs and indels were identified and annotated to determine mutation types and allele frequencies.

Simultaneously, fecal samples were randomly collected from 11 chicken farms across different counties in four cities within Zhejiang Province, China. Coccidia oocysts were purified from these samples, and genomic DNA was extracted. Target regions containing putative resistance mutation was amplified by PCR and sequenced using the same amplicon sequencing workflow to assess field-level maduramicin resistance.

Statistics and reproducibility

All statistical analyses were performed using GraphPad Prism 10. Data are presented as mean ± SD. Comparisons between two groups were conducted using unpaired two-tailed Student’s t-tests. For all experiments, n = 3 biologically independent replicates unless otherwise stated, and each replicate represents an independent parasite preparation. FACS sorting, immunoblotting, and IFA were repeated at least twice with consistent results. No data were excluded, and all attempts at replication were successful.

Ethics statement

All animal procedures were conducted in strict accordance with the guidelines of the China Agricultural University Institutional Animal Welfare and Animal Experimental Ethical Inspection Committee, and the study protocol received specific ethical approval from this committee [approval number: AW22022202-1-1]. We have complied with all relevant ethical regulations for animal use.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.