Skip to main content
NCBI home page
Poultry Science logoLink to Poultry Science
. 2026 Aug 3;105(12):107554. doi: 10.1016/j.psj.2026.107554

Antimicrobial resistance characteristics of enterococcus and analysis of intra-farm transmission associations in large-scale laying hen production

Yuying Bao a,b,1, Ge Zhao a,1, Tongxu Tian a,1, Yubin Gao a, Xiumei Huang a, Jianmei Zhao a, Kailong Wang a, Zhihua Qin b, Xiaojing Hao c, Junwei Wang a,⁎
PMCID: PMC13634699  PMID: 42777350

Abstract

The emergence and transmission of antimicrobial resistance (AMR) in layer production has drawn considerable attention. Enterococcus spp. are recognized as indicator bacteria for AMR, and systematic investigation of these organisms can help elucidate the dynamics of AMR risks. In this study, a total of 1,016 samples were collected at multiple time points and from various sites across the production chain on a large-scale layer farm. Enterococci were isolated and cultured, and selected strains were subjected to antimicrobial susceptibility testing and whole-genome sequencing. The overall isolation rate of Enterococcus was 80.0%, with the highest rate observed in floor samples (92.9%). The predominant resistance phenotypes were to macrolides, tetracyclines, and sulfonamides. Genome sequencing revealed that all sequenced E. faecalis strains harbored tet(L), lsa(A), and optrA, whereas all E. faecium strains carried aac(6′)-Ii and msr(C). Mobile genetic elements were present in 97.2% of the isolates, and the transposon Tn554 was identified in 13 strains across all production stages. Multilocus sequence typing (MLST) identified 12 sequence types (STs) among E. faecalis, with ST1024 predominating, and 7 STs among E. faecium, with ST92, ST1091, and ST160 being dominant. Single nucleotide polymorphism (SNP) analysis revealed high genetic homology between ST1024 strains isolated from farm workers and layer cloacal samples, between ST1091 strains from water and floor samples, and between ST160 strains from air and eggshell surfaces. These findings demonstrate genetic relatedness across different niches, suggesting potential within-farm transmission and pointing to cross-transmission risks of resistant Enterococcus between humans and layers, between feed/water and the environment, and between the environment and eggs. Furthermore, high homology detected between ST92 strains isolated from flies and workers five months apart indicates persistent transmission and colonization via biological vectors. Resistance phenotypes for enrofloxacin and sulfisoxazole were strongly associated with environmental samples, highlighting the farm environment as a critical potential control point for AMR risk mitigation. Leveraging whole-genome sequencing, this study explores the transmission risk of antimicrobial-resistant Enterococcus in layer production and provides insights for preventing AMR risks originating from layer farms.

Keywords: Laying hens, Enterococcus, Antimicrobial resistance, Risk transmission

Introduction

Antimicrobial resistance (AMR) in bacteria has become a major global public health threat (Adhikary et al., 2025). In layer production, the long production cycle, enclosed high-density housing, and inappropriate antimicrobial use contribute to the emergence of bacterial resistance. Mounting evidence indicates that antimicrobial-resistant bacteria and resistance genes originating from laying hens can spread not only through airborne transmission and direct contact but also via fecal contamination of soil and water (Lancet, 2017; Tu, 2020; Gao et al., 2022; Bengtsson-Palme et al., 2023; Peng et al., 2023). Furthermore, resistance risks can be transmitted along the food chain via egg products, posing threats to the health of occupationally exposed farm workers and public health security (Fu, 2019).

Enterococcus, as intestinal symbiotic bacteria and opportunistic pathogens in humans and animals, can obtain antimicrobial resistance genes (ARGs) through horizontal transfer, and is usually used as indicator bacteria for AMR research (Witte, 2000). The emergence of vancomycin-resistant Enterococcus (VRE) poses a severe challenge to public health and safety. Enterococcus coexists in the intestines of poultry and serves as a carrier for spreading AMR (Bai et al., 2022). Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium) are the dominant strains to spread AMR, so it is necessary to monitor their AMR to prevent and control the spread of AMR risk. During the investigation of laying hen farms in Zambia from 2020 to 2021, it was found that 86% of Enterococcus (especially E. faecalis and E. faecium) had multidrug resistance (MDR) (Mudenda et al., 2023). At the same time, it was also found in laying hen farms in Hungary from 2022 to 2023 that Enterococcus were resistant to various antibiotics, especially to vancomycin, with the resistance rate as high as 27.9% (Kerek et al., 2024). In early 2012, 15 strains of VRE were detected in a study of 258 clinical isolates of Enterococcus from human in China, and their resistant phenotypes were MDR, and the resistant genotypes of these strains revealed also suitable for the analysis of animal-derived strains (Ma et al., 2012).

Currently, the prevalence and transmission of Enterococcus resistance across various sample types in layer production remain poorly understood. To address this gap, we conducted a one-year longitudinal surveillance of Enterococcus resistance in a single batch of laying hens at a large-scale farm. Whole-genome sequencing (WGS) was performed on the isolates to assess critical control points for resistance risk, thereby informing more targeted prevention and control strategies during production.

Materials and methods

Main reagents

Transport medium, Enterococcus chromogenic agar, Tryptic Soy Agar (TSA), LB broth (Qingdao Hope Bio-Technology Co., Ltd., China); Antimicrobial susceptibility testing panel (Shanghai Fosun Diagnostics Technology Co., Ltd., China); E. faecalis ATCC 29212 (China Animal Health And Epidemiology Center, China).

Sample collection

From July 2024 to June 2025, cloacal swabs, feeding products, environmental surface swabs, eggshell surface swabs, and media-related samples were collected from the same batch of laying hens at different growth stages (80, 110, 170, 260, and 400 days of age) in a farm in Qingdao, Shandong Province. These samples were placed in transport medium and sterile bags, and then transported to the laboratory at low temperature for subsequent tests. A total of 150 cloacal swabs of laying hens, 98 surface swabs of eggshells, 116 feeding products (including feed and water), 529 environmental surface swabs (from cages, floors, walls, fecal belts, and egg belts), and 123 media-related samples (air, flies, and personnel hand swabs) were collected.

Isolation and identification of E. faecalis and E. faecium

Upon arrival at the laboratory, samples were transferred into Buffered Peptone Water (BPW) and incubated at 37°C for 24–48 h. The enriched cultures were streaked onto Enterococcus chromogenic agar plates using a three-zone streaking method and incubated at 37°C for 24–48 h. Purple, presumptive Enterococcus colonies were selected, re-streaked onto the same chromogenic agar, and incubated at 37°C for 24–48 h; this purification procedure was repeated twice. The purified colonies were then inoculated onto Tryptic Soy Agar (TSA) plates and incubated at 37°C for 24–48 h. Single colonies were picked with sterile pipette tips, evenly smeared onto a 96-well target plate, and overlaid sequentially with lysis buffer and matrix solution. After air-drying, species identification was performed using a matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS; Autof ms1000, Autobio Diagnostics, Zhengzhou, China) with its corresponding analysis software.

Detection of antimicrobial resistance phenotype

To account for sampling time and sample type, a stratified subset of 257 isolates was selected from the 813 recovered strains for antimicrobial susceptibility testing. The broth microdilution method recommended by the Clinical and Laboratory Standards Institute (CLSI) was employed, using 96-well panels containing 18 antimicrobial agents to determine minimum inhibitory concentrations (MICs). The quality control strain, Enterococcus faecalis ATCC 29212, was tested in parallel. Test results were considered valid when the MIC values for the QC strain fell within the acceptable range and no growth was observed in the negative control wells. MICs were interpreted according to the Enterococcus-specific breakpoints provided in CLSI document M100-S33 (Clinical and Laboratory Standards Institute, 2023), and isolates were categorized as susceptible (S), intermediate (I), or resistant (R). For agents lacking Enterococcus-specific breakpoints, MICs were interpreted using the general Gram-positive breakpoints; these results were used exclusively for comparing population resistance patterns and should not be directly applied as clinical treatment references. Isolates exhibiting resistance to three or more antimicrobial classes were defined as multidrug-resistant (MDR). The antimicrobial classes and agents tested in this study were as follows: β-lactams: penicillin (PEN), amoxicillin/clavulanic acid (A/C), ceftiofur (CEF), cefoxitin (CFX), oxacillin (OXA); macrolides: erythromycin (ERY), clindamycin (CLI), tilmicosin (TIL); quinolones: enrofloxacin (ENR), ofloxacin (OFL); glycopeptides: vancomycin (VAN); sulfonamides: trimethoprim/sulfamethoxazole (SXT), sulfafurazole (SF); aminoglycosides: gentamicin (GEN); tetracyclines: tetracycline (TET); amphenicols: florfenicol (FFC); pleuromutilins: tiamulin (TIA); oxazolidinones: linezolid (LZD).

WGS and analysis

From these, 36 isolates exhibiting resistance to five or more antimicrobials were selected as a highly resistant subset for whole-genome sequencing (WGS). This sequenced subset was enriched for resistant strains; accordingly, its characteristics do not reflect the overall population distribution of Enterococcus across the farm. Each isolate was cultured on Enterococcus chromogenic agar and purified by subculturing 5–6 times. A single colony was picked, streaked onto a TSA plate, and then inoculated into 7 mL of Lysogeny Broth (LB). The culture was incubated at 37°C for 7 h in a shaking incubator. Subsequently, the bacterial suspension was inactivated at 65°C for 20 min in a water bath and sent to Sangon Biotech (Shanghai) Co., Ltd. for WGS and assembly.

Antibiotic resistance genes (ARGs) were identified using the ResFinder database (version 4.7.2) from the Center for Genomic Epidemiology (CGE). Plasmids in the sequenced isolates were detected with PlasmidFinder (version 3.0.3). Mobile genetic elements and their associations with ARGs were identified using MobileElementFinder (v1.0.3) from CGE. Sequence types (STs) were determined by querying the PubMLST database. A minimum spanning tree based on MLST data was constructed using Bionumerics v7.6. Sequence alignment and SNP calling were performed via the BacWGSTdb server (http://bacdb.cn/BacWGSTdb/Tools.php). Core-genome phylogenetic trees were constructed with E. faecalis AMB05 (CP015998, ST21) and E. faecium ATCC 700221 (CP014449, ST17) as reference genomes. The resulting clustering data in Newick format were imported into Chiplot (https://www.chiplot.online) for visualization.

Risk assessment of dominant AMR Enterococcus

Statistical analysis was performed using IBM SPSS Statistics (version 26.0; IBM Corp., Armonk, NY, USA). For the predominant Enterococcus resistance phenotypes, Spearman's rank correlation coefficients were calculated to evaluate the correlations between eggs and each of the following sample categories: layers, feed/water, environment, and biological vectors. One-sample t-tests were conducted to assess the statistical differences in the carriage rates of individual resistance phenotypes across different sample types. P value < 0.05 was considered to indicate statistical significance.

Results and analysis

Isolation results of Enterococcus strains

A total of 813 Enterococcus strains (80.0%) were isolated from 1016 samples, including 312 E. faecium (30.7%) and 501 E. faecalis (49.3%). From the sampling time (Fig. 1A), the isolation rates from high to low was October 2024 (92.5%), June 2025 (86.3%), August 2024 (82.6%), January 2025 (76.9%), July 2024 empty laying hen house (65.2%), and July 2024 broiler hens (55.1%). The isolation rate of E. faecium (30.6%) was higher than E. faecalis (24.5%) only in July 2024, and the highest isolation rate peaked at 60% in August 2024.

Fig. 1.

Fig. 1

Open in a new tab

Isolation of E. faecalis and E. faecium during the laying hens breeding. A: Variation in detection rates of E. faecalis and E. faecium across different time points; B: Distribution differences of E. faecalis and E. faecium among sample categories (T1.1: 2024.7 brooder farm; T1.2: 2024.7 layer farm (vacant); T2: 2024.8 layer farm; T3: 2024.10 layer farm; T4: 2025.1 layer farm; T5: 2025.6 layer farm).

The results of different sample categories (Fig. 1B) showed that the E. faecalis isolation rate from laying hens was always higher than E. faecium in all growth stages, with the highest isolation rate of 70% in August 2024 and the lowest of 13.3% in July 2024. At the same time, the E. faecalis isolation rate was always higher than E. faecium from eggs at all growth stages, and the highest isolation rate reached 87.5% in August 2024. The E. faecium isolation rate in feeding products was significantly higher than E. faecalis. In the environmental surface, only the empty house surface swabs in laying hen farms was found that the E. faecium isolation rate (42.2%) was higher than E. faecalis (28.9%). E. faecalis was still the dominant strain in the media-related sample, with the highest isolation rate of 78.1% in October.

Detection results of antimicrobial resistance phenotype and MDR

According to the sampling time (Fig. 2A), the Enterococcus in the empty house of laying hens were resistant to cefoxitin (100.0%), oxacillin (92.6%), clindamycin (88.9%), and ofloxacin/tiamulin (85.2%). During laying hens breeding, the Enterococcus resistance rate to cefoxitin was over 98.0%; the resistance rate to clindamycin, oxacillin, and ofloxacin was always over 75.0%; and the resistance rate to tiamulin, erythromycin, tilmicosin, and tetracycline was always over 50.0%. Especially in the brooding phase, the resistance rate to clindamycin, ofloxacin, cefoxitin, and tiamulin was 100%. In the late stage of laying hens, the Enterococcus resistance rate to sulfisoxazole increased by more than 50%. Moreover, in October 2024, the resistance to penicillin (23.1%) and amoxicillin-clavulanic acid (13.5%) also emerged, which reflected that the problem of AMR was prominent throughout the whole process of laying hens, and the AMR rate of some drugs increased with the breeding process, and new antimicrobial resistance types appeared.

Fig. 2.

Fig. 2

Open in a new tab

Phenotypic characteristics of bacterial antimicrobial resistance. A: Antibiotic resistance rates of 257 Enterococcus at different time points (T1.1: 2024.7 brooder farm; T1.2: 2024.7 layer farm (vacant); T2: 2024.8 layer farm; T3: 2024.10 layer farm; T4: 2025.1 layer farm; T5: 2025.6 layer farm); B: Antibiotic resistance rates of 257 Enterococcus across different stages; C: Antibiotic resistance rates of E. faecalis and E. faecium; D: Proportion of MDR in E. faecalis and E. faecium; E: Antimicrobial resistance profile of 257 Enterococcus Strains.

According to the sample category (Fig. 2B), the Enterococcus isolated from laying hens, eggshells, feeding products, environmental surfaces, and media samples were all resistant to clindamycin, ofloxacin, cefoxitin, oxacillin, and tiamulin, with the AMR rates over 75.0%. Among them, the AMR rate to cefoxitin was 100.0% in all kinds of samples except laying hens, and oxacillin (94.0%−100.0%) and ofloxacin (86.5%−98.0%) also maintained high AMR levels in all kinds of samples. The AMR rate of Enterococcus from laying hens to 8 kinds of antimicrobials was over 80%, among which the AMR rate to β-lactams was the most prominent. The resistance characteristics of eggshell-derived Enterococcus were highly correlated with the source of laying hens, indicating a potential transmission link between them. The Enterococcus from feeding products were found high resistant to cefoxitin, ofloxacin, and oxacillin, Enterococcus from environmental surface were found high resistant to most kinds of antibiotics, Enterococcus from media showed the highest AMR rate with almost full resistant to core antibiotics. Taken together, the highly overlapping and specific characteristics of Enterococcus resistance patterns in various samples suggested that there was a potential cross-transmission pathway of AMR strains between "feeding products-laying hens-eggshells-environmental surface-media".

The isolated E. faecalis and E. faecium were only sensitive to vancomycin, to other antibiotics, there were significant differences (Fig. 2C). E. faecium was mainly resistant to cefoxitin (99.2%), ofloxacin (97.6%), and oxacillin (94.4%), and AMR rate to 7 kind was over 75%. However, the AMR of E. faecalis was more serious, with the AMR rate to clindamycin, oxacillin, and cefoxitin reaching 100%, and the AMR rate to 10 antibiotics, such as tiamulin (98.5%), ofloxacin, tetracycline, and tilmicosin (all 90.2%), being over 50%. Besides penicillin, augmentin, and ofloxacin, the AMR rate of E. faecalis was higher than E. faecium, especially to tilmicosin.

From the results of MDR (Fig. 2D), E. faecium was mainly concentrated in 4-5 kinds of resistance (19.4% and 21.0%, respectively), while E. faecalis was mainly concentrated 7 kinds of resistant (33.8%). There were 3 E. faecium strains and 12 E. faecalis strains resistant to all antibiotics except vancomycin. Among 257 Enterococcus isolates there were total 129 resistance patterns, without fully sensitive strains (Fig. 2E). There were 76 patterns in 124 E. faecium isolates, with the dominant pattern of ERY-CLI-OFL-CEF-CFX-OXA-TIA (6.5%); there were 66 patterns in 133 E. faecalis, with the predominant pattern of ERY-CLI-OFL-CFX-SF-OXA-SXT-DOX-TIA-TIL-GEN (9.8%).

Analysis results of ARGs based on WGS

Through the genomes of 36 Enterococcus strains, six kinds of ARGs (tetracyclines, oxazolidinones, amphenicol, aminoglycosides, trimethoprim, and fosfomycin) and two kinds of cross-resistant genes (macrolide-linconamide-streptogramin B, linconamide-streptogramin A-oxazolidinone) were detected, totaling 27 unique ARGs (Fig. 3A). The data showed that the detection rates of tet(L) and optrA genes were the highest (91.7%), followed by tet(M) (86.1%), fexA (83.3%), erm(A) (80.6%), aph(3′)-III (72.2%), erm(B) (69.4%), dfrG (61.1%), and aac(6′)-aph(2″) and Isa(A) (both 52.8%). The genes with high detection rates (30%−50%) included ant(9)-Ia, aac(6′)-II, and msr(C) (47.2%), Isa(E) (44.4%), lnu(B) and ant(6)-Ia (41.7%). Genes with low detection rates (<30%) included aadD, aph(2″)-Ia, Cfr(D), fexB, cat, and fosB. Functional correlation analysis revealed that tet(L) was the main ARG for tetracycline, optrA was the main ARG for oxazolidinone, fexA for amphenicol, erm(A) for macrolide-lincoamide cross-resistance, and aph(3′)-III and aac(6′)-aph(2″) for aminoglycoside. The resistance to trimethoprim is related to dfrG.

Fig. 3.

Fig. 3

Open in a new tab

Antimicrobial resistant genes of Enterococcus isolates. A: Binary Matrix of Antibiotic Resistance Gene Distribution; B: Box plot of antibiotic resistance gene counts distribution in Enterococcus isolates stratified by sampling month, and sampling categories (T1: 2024.7 brooder farm; T2: 2024.8 layer farm; T3: 2024.10 layer farm; T4: 2025.1 layer farm; T5: 2025.6 layer farm).

E. faecium carried 23 kinds of ARGs, and E. faecalis carried 19 kinds of ARGs. Among them, aac(6′)-Ii, msr(C), fexB, lnu(G), fosB, cat(pC194), and dfrE were only carried by E. faecium, while Isa(A), Cfr(D), str, and cat were only carried by E. faecalis. At the same time, the carrying rate of aac(6′)-Ii and msr(C) ARGs in E. faecium was 100%, and the carrying rate of tet(L), Isa(A), and optrA ARGs in E. faecalis was 100%.

The number of ARGs showed significant differences at different times and in different links (Fig. 3B). The number of ARGs in July 2024 and August 2024 was between 10 and 13; and in October 2024, it was found higher and wider (12 to 17), among which the cloacal swabs (N416) showed the highest, reaching 17. There were significant differences (2 to 13) among eggshell, air, and personnel samples in January 2025, and 9 to 13 in June 2025. From the sample type, the distribution of ARG abundance is as follows: laying hen source (cloaca) > media source (flies) > egg source (eggshell) > environmental source (walls, egg belts, etc.).

Comparison of MGEs based on WGS

Thirty-five strains of 36 Enterococcus with WGS carried mobile genetic elements (MGEs), among which 12 MGEs carried by 27 Enterococcus co-existed with ARGs in the same contig, including 3 insertion sequences (IS), 6 transposons (Tn), and 3 plasmids (Fig. 4). ISSsu5 and Tn917 were only carried by E. faecalis, while Tn6260 and Tn6261 were only carried by E. faecium. ISSsu5 was carried by only one E. faecalis from flies, and Tn6261 was carried by only one E. faecium from cloacal swabs. Tn554 was carried by 13 Enterococcus strains, which throughout the growing stage and involved various sample categories. The carrier rate of Tn554 (63.2%) was the highest in E. faecalis, and Tn558 and Tn6260 (both accounting for 29.4%) were the highest in E. faecium.

Fig. 4.

Fig. 4

Open in a new tab

Sampling Time, Sampling Spot, MGEs, and ARGs in Enterococcus: Alluvial Diagram.

Different MGEs carried different ARGs, Tn554 mainly carried ARGs optrA, fexA, erm(A), and ant(9)-Ia; ISS1N mainly carried ARGs tet(M), tet(L), str, and cat; and Tn6260 mainly carried the ARG lnu(G). The composite transposons ISEnfa1 and cn_5536_ISEnfa1 carried erm(B) and aph(3′)-III in E. faecalis, while they tet(M) and tet(L) in E. faecium. The ARGs optrA and fexA were mainly carried by transposons Tn554 and Tn558, and erm(A) and ant(9)-Ia were mainly carried by transposon Tn554.

Multilocus sequence type (MLST) profiling results

A total of 12 STs were identified from 19 strains of E. faecalis, among which ST1024 (21.1%) was the dominant. Seven STs were identified from 17 strains of E. faecium, and another 7 strains were non-assignable (NA), among which ST92, ST160, and ST1091 (all accounting for 11.8%) were dominant. From the sampling links (Fig. 5A, C), E. faecalis identified 6 STs in cloacal swabs, and the closest genetic relationship was observed between ST1177 and ST618, which differed by only one allele. However, the difference between STs of E. faecium was less than 2 alleles. According to the sampling times (Fig. 5B, D), E. faecalis had the most STs in August, with 5 STs were identified while E. faecium isolates were identified 7 STs in October.

Fig. 5.

Fig. 5

Open in a new tab

Minimum Spanning Tree of MLST. A:E. faecalis by sampling spot; B:E. faecalis by sampling time; C:E. faecium by sampling spot; D:E. faecium by sampling time.

Core genome phylogenetic tree results

The core genome phylogenetic tree based on single nucleotide polymorphisms (SNPs) was constructed to illustrate the similarities (separated by <300 SNPs) (Gouliouris et al., 2018) and differences among highly resistant Enterococcus isolates. E. faecalis (Fig. 6A) from cloacas at different times had homology with that from simultaneous flies, eggshells, and egg belts. E. faecium (Fig. 6B) from cloacas at different times had homology with that from air and walls, so that between eggshell and air, and between water and floors. Additionally, genetic homology was found between isolates from floor and egg belt, as well as between isolates from flies and personnel hands at different time.

Fig. 6.

Fig. 6

Open in a new tab

SNP-based Phylogenetic tree. A:E. faecalis; B:E. faecium.

Correlation analysis of predominant resistance phenotypes across the production chain

Through Spearman correlation coefficient analysis, the correlations between four predominant AMR risks in Enterococcus isolated from egg products with that from laying hens, feeding products, environmental surfaces and media (Fig. 7). The results showed that as to anti-tetracycline risk, the media was found correlated; as to anti-enrofloxacin risk, the feeding products (P = 0.019) and environmental surfaces (P = 0.017) were found significantly high-correlated; as to anti-sulfonamides risk, environmental surfaces were found significantly high-correlated (P = 0.02). No other Enterococcus significantly related to the origin of egg products was found in the risk of AMR to tiamulin.

Fig. 7.

Fig. 7

Open in a new tab

Correlation coefficients between different antibiotics. A: Tetracycline; B: Enrofloxacin; C: Sulfonamide; D: Tiamulin.

Discussion

In layer production systems, Enterococci are common intestinal commensals. resistance genes they harbor—particularly those conferring multidrug resistance or carried by mobile genetic elements—can undergo cross-species dissemination via pathways such as the consumption of egg products and environmental transmission. This poses potential risks to the health of occupationally exposed farm workers and to public health security. This study focused on characterizing the antimicrobial resistance profiles of Enterococci and their intra-farm transmission associations on large-scale layer farms.

Analysis of isolation rate of Enterococcus

Isolation and culture results revealed an overall Enterococcus faecalis isolation rate of 49.3%, which was higher than that of Enterococcus faecium (30.7%). Wigmore et al. (Wigmore et al., 2024) reported isolation rates of 42.8% for E. faecalis and 32.28% for E. faecium in Australian broilers, findings consistent with the present study. Temporally, the total Enterococcus isolation rate peaked at 92.5% in October 2024, coinciding with the onset of the hens’ high-production period; concurrent hormonal changes and feed formulation adjustments may have triggered fluctuations in the gut microbiota. Notably, E. faecium exhibited a higher isolation rate than E. faecalis only during the brooding stage. After the hens entered the growing stage, E. faecalis isolation rates increased to varying extents and remained consistently above those recorded in the brooding stage. In contrast, E. faecium isolation remained relatively stable, ranging between 22.6% and 35.4%. Therefore, it is recommended that E. faecium be intensively monitored during the brooding stage, while surveillance should focus on E. faecalis during and after the growing stage.

Spatially, E. faecium was more frequently isolated than E. faecalis from feeding materials collected at different growth stages. Similarly, Yamagami et al. (2024) analyzed 57 whole feed samples and 275 feed ingredient samples collected between 2018 and 2020 and found E. faecium to be the predominant species (isolation rate: 33.2%), followed by E. faecalis (14.2%). However, in our study, E. faecalis was significantly more prevalent than E. faecium in samples from hens, eggs, and vectors, whereas the two species showed comparable isolation rates in environmental surface swabs.

Analysis of AMR and MGEs

Multidrug-resistant Enterococci contribute to the multidirectional dissemination of antimicrobial resistance genes across clinical, animal, environmental, and food-associated reservoirs (Li et al., 2025). In this study, antimicrobial susceptibility testing of 257 randomly selected Enterococcus isolates revealed a high degree of consistency in resistance patterns among layers, eggs, feed, environment, and vector samples; all exhibited high-level resistance to tiamulin, tetracycline, erythromycin, and tilmicosin. This indirectly suggests that resistance genes carried by Enterococci are excreted in feces and may undergo horizontal dissemination within the farm through the “environment-feed-livestock” cycle. Lanza et al. (2022) found that feeding layers antibiotic-supplemented diets could induce the emergence of Enterococcal resistance on eggshells. In our study, the resistance rate to sulfisoxazole increased during the later production stage, which may be associated with on-farm antimicrobial usage. Spearman correlation analysis captures only statistical associations and does not establish causality. Accordingly, our findings may provide clues and generate hypotheses for the development of resistance containment strategies but should not be taken as definitive evidence for risk management. Comparison of phenotypic antimicrobial susceptibility with acquired resistance gene data showed that Enterococcus isolates displayed high levels of phenotypic resistance to β-lactams, lincosamides, macrolides, and tetracyclines throughout the production period. Cefoxitin, oxacillin, and certain other β-lactams are intrinsically inactive against Enterococci (Hollenbeck and Rice, 2012). The high resistance rates observed here are consistent with this intrinsic trait; therefore, these data were used solely for cross-sectional comparison of resistance profiles among different farm compartments. Furthermore, the detection frequency of the tetracycline resistance gene tetL reached 91.7%, and that of the macrolide-lincosamide cross-resistance gene ermA reached 80.6%. In the United States, tetracycline resistance was detected in retail meat isolates as early as 2014, with rates of 67.5% for E. faecalis and 53.7% for E. faecium (Tyson et al., 2018). Similarly, the oxazolidinone resistance gene optrA was detected in 91.7% of our isolates. Zhai (2024) reported that optrA can co-transfer with fexA and erm(A), thereby conferring resistance to florfenicol and erythromycin in recipient bacteria, a finding consistent with our observations.

Previous studies have demonstrated that transposons Tn554 and Tn558 play critical roles in the spread of optrA. In our study, these two transposons were never found together in a single isolate but were each present across all sample categories. Tn554 was predominantly carried by E. faecalis, whereas Tn558 prevailed in E. faecium. Tn554 consistently harbored the resistance genes optrA, fexA, erm(A), and ant(9)-Ia. Tn558 mainly carried fexA and optrA; however, two isolates contained only fexA, lacking optrA.

Linezolid, a synthetic oxazolidinone antibiotic, was once considered the last line of defense against vancomycin-resistant enterococcal infections. Since the first detection of optrA in a clinical E. faecalis isolate in China in 2015 (Wang et al., 2015), continuous surveillance has revealed even higher carriage rates in Enterococci from animal and food sources. E. faecalis not only exhibits intrinsic resistance but also possesses a specialized conjugation transfer mechanism that can be readily induced, facilitating highly efficient horizontal gene transfer. It can even transfer resistance determinants to other Gram-positive species, such as Staphylococcus aureus. The emergence and spread of such resistance would pose a substantial threat to human health and environmental ecological safety (Yang et al., 2021).

It should be noted that whole-genome sequencing in this study was performed exclusively on highly resistant enrichment isolates. Consequently, conclusions regarding resistance genes and mobile genetic elements are applicable only to this highly resistant subpopulation and cannot fully represent the entire Enterococcus population on the farm. Moreover, the co-localization of resistance genes and MGEs provides only indirect molecular evidence for horizontal transfer; whether these elements possess actual conjugative transfer capability requires further validation through functional assays such as conjugation experiments.

Analysis of risk transmission based on MLST and SNP

MLST analysis of 19 Enterococcus faecalis isolates identified ST1024 (21.1%) as the predominant sequence type, which was primarily recovered from cloacal, fly/mosquito, and farm worker samples. This high genetic homology among strains from distinct compartments suggests a potential intra-farm transmission link and a risk of cross-transmission between layers and vectors. Although the E. faecalis population displayed considerable allelic diversity, ST1177 and ST618 differed by only a single allele, indicating close genetic relatedness. These three strains were isolated from a wall (October 2024), a cloaca (January 2025), and an egg surface (June 2025), respectively, implying a potential transmission pathway among layers, eggs, and the farm environment. Chen and Wan (2025) analyzed global E. faecalis STs and reported ST108 as the dominant type in poultry, a pattern that contrasts with the predominance of ST1024 observed in our layer farm. This discrepancy may be attributable to regional or source-related factors. Among 17 E. faecium isolates, ST92, ST160, and ST1091 were the predominant STs (each accounting for 11.8%). ST92 was detected in flies/mosquitoes and workers, ST160 in air and on eggshell surfaces, and ST1091 in floor and drinking water samples collected in 2024, pointing to a potential transmission link between the environment and feed/water supplies. Moreover, the limited allelic divergence among these STs indicated high genetic relatedness of E. faecium strains across different time points and sampling sites, highlighting a risk of cross-transmission along the layer–environment–vector–egg continuum. Yan et al. (2025) performed a comprehensive analysis and found that ST17 predominates globally among E. faecium, whereas ST78 is the most prevalent ST in China. Although these dominant types differ from those identified in our study, ST160 was present in our isolates and, consistent with the findings of Yan et al., uniformly carried the aminoglycoside resistance gene aac(6′)-Ii and the macrolide resistance gene msr(C). The detection of this ST160 in air and on eggshell surfaces raises concerns about potential occupational exposure for farm workers. Therefore, continuous surveillance of antimicrobial resistance in Enterococci is essential to guide rational antimicrobial use and timely control interventions.

SNP-based analysis plays a pivotal role in constructing cross-ecosystem ARG transmission networks and revealing potential transmission links among different farm compartments. Although SNP-based homology analysis only reflects the genetic relatedness of strains and cannot directly confirm the direction, route, or causality of transmission, the transmission pathways proposed in this study represent reasonable inferences grounded in homology data. These inferences advance our understanding of ARG evolution and dissemination, help elucidate the mechanisms underlying the emergence and spread of resistance, and inform the development of strategies to control the transmission of resistant bacteria. A core genome phylogenetic tree constructed in this study revealed that Enterococcus faecalis ST1024 isolates from farm workers and layer cloacal samples were highly homologous, indicating that occupational contact may serve as a potential route for cross-compartment strain transmission. Moreover, homology was observed among strains isolated at different time points on the same farm, implying that flies and mosquitoes may function as biological vectors capable of persistently transmitting resistant strains to layers and subsequently contaminating eggs. Persistent contamination was also detected on egg conveyor belts. Regarding Enterococcus faecium, ST92 isolates from flies/mosquitoes and farm workers sampled at a five-month interval exhibited high homology, suggesting long-term dissemination and colonization of the strain across the vector–human interface. ST1091 displayed high homology between water and floor samples, whereas ST160 was highly homologous between eggshell surfaces and air. Collectively, these findings point to cross-contamination between feed/water supplies and the production environment, as well as between eggs and the environment. However, such cross-contamination was observed only during limited time windows; the strains likely failed to maintain genetic stability in these compartments, thereby precluding persistent transmission. Further tracer experiments are warranted to validate these proposed transmission pathways.

Evaluation of dominant AMR phenotype of enterococcus from different samples

Analysis of the correlations between eggs and layers, feed, environment, and vectors revealed that the significance of these associations varied across antimicrobial categories. Notably, a statistically significant positive correlation was detected between eggs and the environment. This finding aligns with the high genetic homology between egg surface and wall isolates observed in MLST analysis and is supported by studies identifying the farm environment as a decisive factor in antimicrobial resistance gene dissemination (Luiken et al., 2022; Bai et al., 2023). Hence, the farm environment should be prioritized in resistance control efforts targeting eggs. Additionally, enrofloxacin resistance exhibited a significant correlation between eggs and feed, which may be attributable to prior drug administration through feed or drinking water, leading to sustained persistence of resistant bacteria in layers and subsequent transfer to egg surfaces (Looft et al., 2012; Caddey et al., 2025). These findings provide clear directions for the targeted optimization of resistance mitigation strategies in layer production, such as strengthening environmental management and regulating antimicrobial use in feed.

Conclusion

Enterococci isolated from laying hen farms exhibited antimicrobial resistance and carried the corresponding resistance genes. Up to 97.2% of the isolates harbored MGEs, which provide the genetic basis for the horizontal transfer of resistance determinants, indicating the potential for horizontal gene transfer. Analysis of the predominant STs, such as ST1024 and ST92, further revealed potential transmission links. These links encompassed multiple pathways throughout the entire production chain, including human contact, vector-borne transmission (via flies and mosquitoes), and horizontal cross-contamination among environmental sources (water, air, floor) and products (eggshells). The pronounced antimicrobial resistance and widespread transmission risk of Enterococcus along the laying hen production chain pose substantial threats to farm biosecurity and public health, thus warranting stringent attention.

Ethical statement

All procedures involving live animals in this study were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of the China Animal Health and Epidemiology Center (Approval No. [20221102]). The research strictly adhered to the institutional guidelines for the ethical treatment of animals and complied with relevant national regulations on animal welfare.

Funding information

This work was supported by the Qingdao Science and Technology Benefit the People Demonstration Project "Key Technological Innovations and Standardized Demonstration for Antimicrobial Reduction in Layer Hens" [grant number 24-1-8-xdny-21-nsh].

CRediT authorship contribution statement

Yuying Bao: Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis, Data curation, Conceptualization. Ge Zhao: Writing – review & editing, Writing – original draft, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. Tongxu Tian: Writing – review & editing, Visualization, Investigation. Yubin Gao: Writing – review & editing, Software, Formal analysis, Data curation. Xiumei Huang: Writing – review & editing, Resources, Data curation. Jianmei Zhao: Writing – review & editing, Methodology, Formal analysis. Kailong Wang: Writing – review & editing, Resources, Formal analysis. Zhihua Qin: Writing – review & editing, Validation, Investigation. Xiaojing Hao: Writing – review & editing, Visualization, Supervision, Resources, Investigation, Funding acquisition. Junwei Wang: Writing – review & editing, Supervision, Software, Project administration, Investigation, Conceptualization.

Disclosures

This work was supported by the Qingdao Science and Technology Benefit the People Demonstration Project "Key Technological Innovations and Standardized Demonstration for Antimicrobial Reduction in Layer Hens" [grant number 24-1-8-xdny-21-nsh]. The funders had no role in study design, data collection, analysis, interpretation, or manuscript preparation. The authors declare no other competing interests. All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper; Yuying Bao and Ge Zhao contributed equally to this work.

Data availability

  • The whole-genome sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number [PRJNAXXXXXX]. All data are publicly available.

References

  1. Adhikary K., Ganguly K., Chakrabarty S., Sarkar R., Nag R., Srivastava S., Barman S., Banerjee P., Karak P., Ashique S., Hussain M.S. Understanding antimicrobial resistance: from mechanisms to public health implications. Curr. Pharm. Biotechnol. 2025;26 doi: 10.2174/0113892010406135250828050228. e13892010406135. [DOI] [PubMed] [Google Scholar]
  2. Bai H., He L.Y., Wu D.L., Gao F.Z., Zhang M., Zou H.Y., Yao M.S., Ying G.G. Spread of airborne antibiotic resistance from animal farms to the environment: dispersal pattern and exposure risk. Environ. Int. 2022;158 doi: 10.1016/j.envint.2021.106927. [CrossRef] [DOI] [PubMed] [Google Scholar]
  3. Bai Y., Sun X., Guo Y., Qiu T., Xin H., Yu A., Wang X., Gao M. Particle-size stratification of airborne antibiotic resistant genes, mobile genetic elements, and bacterial pathogens within layer and broiler farms in Beijing, China. Environ. Sci. Pollut. Res. Int. 2023;30:112799–112812. doi: 10.1007/s11356-023-29975-8. [CrossRef] [DOI] [PubMed] [Google Scholar]
  4. Bengtsson-Palme J., Abramova A., Berendonk T.U., Coelho L.P., Forslund S.K., Gschwind R., Heikinheimo A., Jarquín-Díaz V.H., Khan A.A., Klümper U., Löber U., Nekoro M., Osińska A.D., Ugarcina Perovic S., Pitkänen T., Rødland E.K., Ruppé E., Wasteson Y., Wester A.L., Zahraab R. Towards monitoring of antimicrobial resistance in the environment: for what reasons, how to implement it, and what are the data needs? Environ. Int. 2023;178 doi: 10.1016/j.envint.2023.108089. [CrossRef] [DOI] [PubMed] [Google Scholar]
  5. Caddey B., Shaukat W., Tang K.L., Barkema H.W. Vancomycin-resistant Enterococcus prevalence and its association along the food chain: a systematic review and meta-analysis. J. Antimicrob. Chemother. 2025;80:908–918. doi: 10.1093/jac/dkaf008. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chen Y., Wan H. Distribution of van genes and sequence typing of Enterococcus faecium based on NCBI database. Chin. J. Clin. Lab. Sci. 2025;43:457–460. [Google Scholar]
  7. CLSI . 33rd Edition. Clinical and Laboratory Standards Institute; Malvern, PA: 2023. Performance Standards for Antimicrobial Susceptibility Testing. [Google Scholar]
  8. Fu X. M. East China Normal University; 2019. Study on the Pollution of Antibiotic Resistance Bacteria and Antibiotic Resistance Genes on Commercial Poultry Eggs. [Google Scholar]
  9. Gao M., Zhang X., Yue Y., Qiu T., Wang J., Wang X. Air path of antimicrobial resistance related genes from layer farms: emission inventory, atmospheric transport, and human exposure. J. Hazard. Mater. 2022;430 doi: 10.1016/j.jhazmat.2022.128417. [CrossRef] [DOI] [PubMed] [Google Scholar]
  10. Gouliouris T., Raven K.E., Ludden C., Blane B., Corander J., Horner C.S., Hernandez-Garcia J., Wood P., Hadjirin N.F., Radakovic M., Holmes M.A., de Goffau M., Brown N.M., Parkhill J., Peacock S.J. Genomic surveillance of Enterococcus faecium reveals limited sharing of strains and resistance genes between livestock and humans in the United Kingdom. mBio. 2018;9 doi: 10.1128/mBio.01780-18. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Hollenbeck B.L., Rice L.B. Intrinsic and acquired resistance mechanisms in enterococcus. Virulence. 2012;3:421–569. doi: 10.4161/viru.21282. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Kerek Á., Szabó Á., Jerzsele Á. Antimicrobial susceptibility profiles of commensal Enterococcus spp. isolates from chickens in Hungarian poultry farms between 2022 and 2023. Antibiotics. 2024;13:1194. doi: 10.3390/antibiotics13121194. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. The Lancet Food industry must act to safeguard the future of antibiotics. Lancet. 2017;390:2216. doi: 10.1016/S0140-6736(17)32900-8. [CrossRef] [DOI] [PubMed] [Google Scholar]
  14. Lanza I.P., Silva G.R., Menezes L.D.M., Assis D.C.S., Figueiredo H.C.P., Lana A.M.Q., Lara L.J.C., Figueiredo T.C., Souza M.R., Cançado S.V. Research note: antimicrobial resistance profile of Enterococcus spp. isolated from the eggshell of laying hens submitted to pharmacological treatment. Poult. Sci. 2022;101 doi: 10.1016/j.psj.2021.101606. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Li J., Mao X., Song Y., Hu X., Zhao G., Tang B. Urban trash bins as reservoirs of multidrug-resistant Enterococcus with shared resistance genes across clinical and environmental niches. J. Hazard. Mater. 2025;499 doi: 10.1016/j.jhazmat.2025.140064. [CrossRef] [DOI] [PubMed] [Google Scholar]
  16. Looft T., Johnson T.A., Allen H.K., Bayles D.O., Alt D.P., Stedtfeld R.D., Sul W.J., Stedtfeld T.M., Chai B., Cole J.R., Hashsham S.A., Tiedje J.M., Stanton T.B. In-feed antibiotic effects on the swine intestinal microbiome. Proc. Natl. Acad. Sci. U. S. A. 2012;109:1691–1696. doi: 10.1073/pnas.1120238109. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Luiken R.E., Heederik D.J., Scherpenisse P., Van Gompel L., van Heijnsbergen E., Greve G.D., Jongerius-Gortemaker B.G., Tersteeg-Zijderveld M.H., Fischer J., Juraschek K., Skarżyńska M., Zając M., Wasyl D., EFFORT-group. Wagenaar J.A., Smit L.A., Wouters I.M., Mevius D.J., Schmitt H. Determinants for antimicrobial resistance genes in farm dust on 333 poultry and pig farms in nine European countries. Environ. Res. 2022;208 doi: 10.1016/j.envres.2022.112715. [CrossRef] [DOI] [PubMed] [Google Scholar]
  18. Ma, J., Zhang, P., Chu, M.L., Liu, H.Y., Ren, W., W, L., X, W.C., 2012. Antibiotic resistance of 258 Enterococci isolates and detection of Vancomycin resistant genes. Int. J. Lab. Med. 33, 2507–2509.
  19. Mudenda S., Bumbangi F.N., Yamba K., Munyeme M., Malama S., Mukosha M., Hadunka M.A., Daka V., Matafwali S.K., Siluchali G., Mainda G., Mukuma M., Hang'ombe B.M., Muma J.B. Drivers of antimicrobial resistance in layer poultry farming: evidence from high prevalence of multidrug-resistant Escherichia coli and enterococci in Zambia. Vet. World. 2023;16:1803–1814. doi: 10.14202/vetworld.2023.1803-1814. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Peng K., Liu Y.X., Sun X., Wang Q., Du P., Zhang Y., Wang M., Wang Z., Li R. Long-read metagenomic sequencing reveals that high-copy small plasmids shape the highly prevalent antibiotic resistance genes in animal fecal microbiome. Sci. Total Environ. 2023;893 doi: 10.1016/j.scitotenv.2023.164585. [CrossRef] [DOI] [PubMed] [Google Scholar]
  21. Tyson G.H., Nyirabahizi E., Crarey E., Kabera C., Lam C., Rice-Trujillo C., McDermott P.F., Tate H. Prevalence and antimicrobial resistance of Enterococci isolated from retail meats in the United States, 2002 to 2014. Appl. Environ. Microbiol. 2018;84 doi: 10.1128/AEM.01902-17. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Tu Q. Occurrence and risk assessment of antibiotic resistance genes in typical chicken farms and surrounding soils. M. Chinese Acad. Agric. Sci. 2020;1:1–4. [Google Scholar]
  23. Wang Y., Lv Y., Cai J., Schwarz S., Cui L., Hu Z., Zhang R., Li J., Zhao Q., He T., Wang D., Wang Z., Shen Y., Li Y., Feßler A.T., Wu C., Yu H., Deng X., Xia X., Shen J. A novel gene, optrA, that confers transferable resistance to oxazolidinones and phenicols and its presence in Enterococcus faecalis and Enterococcus faecium of human and animal origin. J. Antimicrob. Chemother. 2015;70:2182–2190. doi: 10.1093/jac/dkv116. [CrossRef] [DOI] [PubMed] [Google Scholar]
  24. Wigmore S.M., Greenhill A.R., Bean D.C. Isolation and characterization of Enterococci from poultry reveals high incidence of Enterococcus thailandicus in Victoria, Australia. J. Appl. Microbiol. 2024;135 doi: 10.1093/jambio/lxae194. [CrossRef] [DOI] [PubMed] [Google Scholar]
  25. Witte W. Ecological impact of antibiotic use in animals on different complex microflora: environment. Int. J. Antimicrob. Agents. 2000;14:321–325. doi: 10.1016/s0924-8579(00)00144-8. [CrossRef] [DOI] [PubMed] [Google Scholar]
  26. Yamagami Y., Asao M., Takahashi A., Hashimoto Y., Okuyama N., Arai E., Arihara W., Masui R., Shimazaki Y. Prevalence and antimicrobial resistance of Enterococcus spp. isolated from animal feed in Japan. Front. Vet. Sci. 2024;10 doi: 10.3389/fvets.2023.1328552. [CrossRef] [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Yan R., Shen H., Cao X. Analysis of global drug-resistant genes, sequence types, and epidemic characteristics in Enterococcus faecium. Chin. J. Clin. Lab. Manag. 2025;13:27–34. [Google Scholar]
  28. Yang Y., Yang X., Wang S., Xue B., Li C., Zhao C., Zhang X., Shen Z., Wang J., Qiu Z. Mechanism of pheromone-responsive plasmids mediated resistance genes conjugation transfer in Enterococcus faecalis. Asian J. Ecotoxicol. 2021;16:160–167. [Google Scholar]
  29. Zhai Y. Kunming Medical University; 2024. Studies on the Mechanism of Transmission of the Linezolid Resistance Gene OptrA Among Clinical Enterococcus faecalis. [Google Scholar]

Associated Data

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

Data Availability Statement

  • The whole-genome sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number [PRJNAXXXXXX]. All data are publicly available.

Articles from Poultry Science are provided here courtesy of Elsevier

RESOURCES