AAVS_14_6_1141-1150

INTRODUCTION

Antimicrobial resistance (AMR) is a top global health priority and poses a serious threat to human and animal health (Salam et al., 2023). AMR occurs when pathogenic microorganisms lose their sensitivity to previously effective antimicrobials, leading to increased incidence of difficult-to-treat infections and increased morbidity, mortality, and the economic burden on health (Ahmed et al., 2024). Antimicrobial resistance can be transmitted between bacteria through the transfer of resistance genes (ARGs), either through vertical gene transfer (VGT) from parent to offspring or horizontal gene transfer (HGT) between unrelated bacteria (Han et al., 2022). Within the One Health framework, the spread of resistance involves interactions between humans, animals, and the environment (Bustamante et al., 2025).

Escherichia coli is a commensal bacterium widely used as an indicator of the reservoir of antimicrobial resistance genes (Chetri, 2025). This bacterium is naturally found in the gastrointestinal tract of humans and animals, but it can also act as an opportunistic pathogen, causing various diseases, particularly urinary tract infections in humans (Frankel and Ron, 2018). Globally, E. coli contributed to approximately 929,000 AMR-related deaths in 2019 (Murray et al., 2022). Some human E. coli strains have phylogenetic and virulence gene similarities to avian pathogenic E. coli (APEC), which implies a potential epidemiological connection between animal and human sources (Manges, 2016). Moreover, resistant E. coli from animals can be transmitted to humans through the food chain, the environment, or direct contact with farm workers (Li et al., 2019; Pitta et al., 2020). Consequently, poultry production systems can serve as important reservoirs of antimicrobial-resistant E. coli, which is a significant public health issue in the veterinary sector.

In poultry farming, enrofloxacin is commonly used to treat colibacillosis (Temmerman et al., 2021). Enrofloxacin belongs to the second-generation fluoroquinolone group and works by inhibiting essential bacterial DNA replication enzymes, namely DNA gyrAse and topoisomerase IV (Fàbrega et al., 2009). DNA gyrAse is encoded by the gyrA and gyrB genes, whereas topoisomerase IV is encoded by the parC and parE genes. Quinolone resistance is the result of mutations in the quinolone resistance-determining region (QRDR) of the gyrA and parC genes, which reduce the antibiotic’s affinity for the target enzyme (Hooper and Jacoby, 2015). Resistance can be induced by a variety of mechanisms, such as increased efflux pump activity, changes in membrane permeability, and plasmid-mediated quinolone resistance (PMQR) genes, including qnr, aac(6’)-Ib-cr, and oqxAB, in addition to chromosomal mutations (Azargun et al., 2019). However, mutations in gyrA and parC are widely recognized as major mechanisms contributing to fluoroquinolone resistance, although other mechanisms may also play important roles (Onseedaeng and Ratthawongjirakul, 2016).

Despite the extensive global documentation of enrofloxacin resistance in avian E. coli, there is still a lack of information regarding the correlation between phenotypic sensitivity levels and mutation patterns of the gyrA and parC target genes in Indonesia, particularly in layer chicken farms found in West Java. The phenotype-genotype link is essential for comprehending resistance mechanisms since antibiotic sensitivity levels may not consistently correlate with a single mutation. This information is needed to support AMR surveillance systems, more rational antibiotic use, and control the spread of resistance from animals to humans. Therefore, this study aims to analyze the correlation between the sensitivity levels of enrofloxacin and mutations in the DNA gyrAse (gyrA) and topoisomerase IV (parC) target genes in E. coli isolates from layer poultry farms in West Java.

MATERIALS AND METHODS

Ethics declaration

This study did not involve the use of any experimental animals, therefore research ethics approval was not required.

Escherichia coli isolate confirmation

This study employed a total of 15 Escherichia coli isolates. The isolates are an inventory of PT Medika Satwa Laboratoris that were collected from layer poultry farms in Sukabumi, West Java, between 2016 and 2020. Although the number of isolates analyzed was limited, the study was designed as an exploratory molecular investigation to evaluate the presence of QRDR mutations and their association with phenotypic resistance patterns in field isolates. All isolates were stored freeze-dried. The isolates were revitalized and cultured on MacConkey agar (Oxoid, UK) (Indrawati et al., 2019). The colonies were subsequently identified using Eosin Methylene Blue Agar (EMBA) (Oxoid, UK) (Bonnet et al., 2020) (Bonnet et al., 2020). The Indole, Methyl Red, Voges–Proskauer, and Citrate (IMViC) test was used to biochemically test isolates that tested positive for EMBA (Praja et al., 2025). Biochemically confirmed isolates were then subjected to molecular analysis for E. coli detection. DNA extraction was performed using the boiling method, which involves heating the sample at 95°C for 10 minutes. The supernatant containing DNA was employed as a template for further analysis. E. coli detection was performed using the uspA target gene with primers uspAF (5’-CCGATACGCTGCCAATCAGT-3’) and uspAR (5’-ACGCAGACCGTAGGCCAGAT-3’), resulting in an 884-bp amplicon (Hardiati et al., 2021).

Each PCR reaction was conducted by combining 5 µL of extracted DNA into a reaction mixture consisting of MyTaq™ HS Red Mix, forward primer, reverse primer, and nuclease-free water. The amplification process was carried out using a thermal cycler under the following conditions: denaturation at 95°C for 15 seconds, annealing at 55°C for 15 seconds, and extension at 72°C for 10 seconds, for a total of 30 cycles. PCR products were analyzed using 1.5% agarose gel electrophoresis stained with ethidium bromide (0.5 µg/mL). DNA fragment size determination was performed using a 100 bp DNA marker (VC 100 bp Plus DNA Ladder, Vivantis). Electrophoresis was run at 120 V for 35 minutes.

Antibiotic sensitivity testing

Antibiotic susceptibility testing was performed using the Minimum Inhibitory Concentration (MIC) method based on the European Committee on Antimicrobial Susceptibility Testing (EUCAST) standards using the Sensititre system (Thermo Fisher, USA), as described in previous studies using the same platform (Amaliah et al., 2026; Bellerose et al., 2021). Three to five colonies of isolates previously confirmed as E. coli were suspended in sterile water (ddH₂O) (Himedia, India) and then homogenized using a vortex. The turbidity of the suspension was adjusted to a 0.5 McFarland standard using a nephelometer. Next, 1 µL of the bacterial suspension was inoculated into 11 mL of Mueller–Hinton broth (Thermo Fisher, USA) and homogenized again. Fifty µL of the suspension was then added to each well of the Sensititre panel containing enrofloxacin. The panel was incubated at 37°C for 18–24 hours. MIC readings were performed manually by observing bacterial growth in each well. Interpretation of MIC values for enrofloxacin in Enterobacterales was based on the Clinical and Laboratory Standards Institute (CLSI) standards. Isolates were categorized as sensitive at MICs ≤0.25 µg/mL, intermediate at 0.5 ≤ MIC < 1 µg/mL, and resistant at MICs ≥1 µg/mL.

Detection of dna gyr Mutation detection was performed molecularly on all isolates, including those categorized as sensitive, intermediate, and resistant to enrofloxacin. Identification of target gene mutations was performed using the Multiplex Allele-Specific PCR (MAS-PCR) method targeting the Quinolone Resistance Determining Region (QRDR), including the gyrA gene at codons Ser83 and Asp87 and the parC gene at codons Ser80 and Glu84 (Bansal and Tandon, 2011; Onseedaeng and Ratthawongjirakul, 2016). Table 1 contains records of the primer sequences employed. The primers used in this study were adopted from previously published protocols targeting QRDR mutations in gyrA and parC (Bansal and Tandon, 2011; Onseedaeng and Ratthawongjirakul, 2016). All primers were combined in a multiplex allele-specific PCR (MAS-PCR) assay. The annealing temperature was standardized at 56°C based on the reported optimal conditions of the referenced studies and preliminary optimization to ensure consistent amplification of all target fragments.

Ase and topoisomerase iv target genes

Mutation detection was performed molecularly on all isolates, including those categorized as sensitive, intermediate, and resistant to enrofloxacin. Identification of target gene mutations was performed using the Multiplex Allele-Specific PCR (MAS-PCR) method targeting theQuinolone Resistance Determining Region (QRDR), including the gyrA gene at codons Ser83 and Asp87 and the parC gene at codons Ser80 and Glu84 (Bansal and Tandon, 2011; Onseedaeng and Ratthawongjirakul, 2016).

Table 1 contains records of the primer sequences employed The primers used in this study were adopted from previously published protocols targeting QRDR mutations in gyrA and parC (Bansal and Tandon, 2011; Onseedaeng and Ratthawongjirakul, 2016). All primers were combined in a multiplex allele-specific PCR (MAS-PCR) assay. The annealing temperature was standardized at 56°C based on the reported optimal conditions of the referenced studies and preliminary optimization to ensure consistent amplification of all target fragments.

Each PCR reaction was conducted by combining 5 µL of extracted DNA with a reaction mixture consisting of GoTaq® Green Master Mix, forward primers, reverse primers, and nuclease-free water. The amplification

Table 1: Primer Sequences Used for Multiplex Allele-Specific PCR (MAS-PCR) Targeting QRDR Mutations in gyrA and parC.

Target gene	Target codon	Primer name	Nucleotide sequence (5'–3')	Annealing temperature (°C)	Product size (bp)	Source
gyrA	–	gyrA-F	TACACCGGTCAACATTGAGG	56	647	(Bansal and Tandon, 2011)
	–	gyrA-R	TTAATGATTGCCGCCGTCGG	56	647	(Bansal and Tandon, 2011)
	Ser83	gyrA83-R	TACCATCCCCATGGTGACTC	56	440	(Onseedaeng and Ratthawongjirakul, 2016)
	Asp87	gyrA87-R	GCCATGCGGACAATCGTGTC	56	255	(Onseedaeng and Ratthawongjirakul, 2016)
parC	–	parC-F	AAACCTGTTCAGCGCCGCATT	56	395	(Bansal and Tandon, 2011)
	–	parC-R	GTGGTGCCGTTAAGCAAA	56	395	(Bansal and Tandon, 2011)
	Ser80	parC80-R	ATACCATCCGCACGGCGATAG	56	289	(Onseedaeng and Ratthawongjirakul, 2016)
	Glu84	parC84-R	CGCCATCAGGACCATCGGTT	56	153	(Onseedaeng and Ratthawongjirakul, 2016)

process was conducted using a thermal cycler under the following conditions: An initial denaturation at 95°C for 2 minutes, followed by 35 cycles, each of which consisted of denaturation at 95°C for 30 seconds, annealing for 30 seconds (corresponding to the annealing temperatures in Table 1), and extension at 72°C for 2 minutes. The amplification process was concluded with a final extension at 72°C for 5 minutes. The PCR products were subjected to 1.5% agarose gel electrophoresis and stained with ethidium bromide (0.5 µg/mL). A 100 bp DNA marker (VC 100 bp Plus DNA Ladder, Vivantis) was employed to determine the size of the DNA fragments. Electrophoresis was run at 120 V for 35 minutes.

In this MAS-PCR design, the allele-specific primers were designed to amplify the wild-type sequence at the target codons. Therefore, successful amplification indicates the presence of the wild-type allele, whereas the absence of amplification suggests a possible mutation at the primer-binding site. Positive amplification controls were included to ensure PCR performance, and each assay was conducted under optimized conditions to minimize amplification failure. Isolates that exhibited DNA bands that were consistent with the target size at the gyrA and parC codons were declared not to have mutations, while those that did not exhibit the target bands were interpreted as having mutations at those loci. A reference E. coli strain was used as a positive amplification control to confirm PCR performance, while mutation detection relied on allele-specific amplification patterns as described in the referenced protocols.

Data analysis

All data were analyzed using descriptive and inferential statistical approaches to evaluate the association between mutations in the gyrA and parC target genes and enrofloxacin sensitivity levels in E. coli isolates. Based on the Minimum Inhibitory Concentration (MIC) results, the sensitivity categories were classified as sensitive, intermediate, and resistant in accordance with the CLSI breakpoints. For association analysis, the sensitive and intermediate categories were combined into a non-resistant group. Although a three-category analysis (sensitive, intermediate, resistant) was considered; the very small number of isolates within each subgroup would have resulted in sparse contingency tables and unstable statistical estimates. A p-value <0.05 was considered statistically significant. Analysis was performed for both total mutations (gyrA, parC, and the gyrA+ parC combination) and specific mutations in target codons (gyrA Ser83, gyrA Asp87, parC Ser80, and parC Glu84).

The risk of resistance associated with mutations was calculated using odds ratios (OR) and 95% confidence intervals (CI) based on a 2x2 contingency table. An OR value >1 indicates an increased chance of resistance, while an OR <1 indicates no increased risk. To evaluate the relationship between the number of QRDR mutations and the level of phenotypic resistance, a Spearman correlation analysis was performed between the number of mutations (0–2) and the enrofloxacin MIC value. The correlation coefficient (r) was interpreted as a weak (r<0.3), moderate (0.3–0.6), or strong (>0.6) relationship.

The association between QRDR gene mutations (gyrA and parC) and resistance status was analyzed using Fisher’s exact test due to the relatively small sample size. All analysis results are presented in tabular form and then interpreted to assess the contribution of QRDR mutations to enrofloxacin resistance. Statistical analyses were performed using standard statistical software, and all tests were two-tailed.

RESULTS

Bacterial isolates

All bacterial isolates grew on MacConkey medium, forming pink colonies. These colonies were subsequently inoculated and incubated on EMBA medium. All isolates exhibited black colonies in the center on EMBA medium, which were characterized by a greenish metallic sheen. Colonies with these characteristics were then tested using IMViC medium and showed positive results for Indole (+), Methyl Red (+), Voges–Proskauer (-), and citrate (-). The DNA band at 884 bp was observed in all isolates during molecular testing using the E. coli-specific gene, uspA. Figure 1 illustrates the outcomes of the uspA gene amplification.

Sensitivity of the isolates

A total of 15 isolates were tested for antibiotic sensitivity to enrofloxacin. Of these, 53.33% (8/15) were enrofloxacin sensitive, 13.33% (2/15) were enrofloxacin intermediate, and 33.33% (5/15) were enrofloxacin resistant. Table 2 displays the enrofloxacin sensitivity results for each isolate.

Table 2: Enrofloxacin MIC values, susceptibility categories, and QRDR mutation profiles of Escherichia coli isolates detected by MAS-PCR.

No	Isolate ID	MIC (µg/mL)	Susceptibility	QRDR mutation profile (MAS-PCR)
No	Isolate ID	MIC (µg/mL)	Susceptibility	gyrA	Ser83	Asp87	parC	Ser80	Glu84
1	B012	>0.5	Sensitive	–	–	–	–	–	–
2	B013	<0.12	Sensitive	Mutated	Mutated	–	Mutated	–	–
3	B015	<0.12	Sensitive	–	–	–	–	–	–
4	B032	>2	Resistant	Mutated	–	–	–	–	–
5	B064	>2	Resistant	–	–	Mutated	–	Mutated	–
6	B065	>2	Resistant	–	–	Mutated	–	Mutated	–
7	B086	>0.25	Sensitive	–	Mutated	–	Mutated	–	–
8	B087	>2	Resistant	–	–	–	–	–	–
9	B088	<0.12	Sensitive	–	Mutated	–	–	–	–
10	B089	<0.12	Sensitive	–	Mutated	–	–	–	–
11	B098	>0.25	Sensitive	–	Mutated	–	–	–	–
12	B100	>2	Resistant	–	–	Mutated	–	Mutated	–
13	B101	>0.5	Intermediate	–	–	–	–	Mutated	–
14	B102	>0.5	Intermediate	–	Mutated	–	–	Mutated	–
15	B103	>0.25	Sensitive	–	–	Mutated	–	–	–

QRDR mutation profile determined by MAS-PCR targeting codons Ser83 and Asp87 of gyrA and codons Ser80 and Glu84 of parC

The MIC values were determined based on the growth pattern observed in the Sensititre broth microdilution panel, where the MIC was defined as the lowest concentration of enrofloxacin that inhibited visible bacterial growth.

MAS-PCR amplification of qrdr target genes

Molecular testing of the isolates was performed on target

genes that can undergo mutations leading to enrofloxacin resistance: the QRDR of gyrA at codons Ser83 and Asp87 and parC at codons Ser80 and Glu84. The results of MAS PCR amplification are shown in Figure 2. The MAS PCR results for each isolate are presented in Table 2.

In this PCR assay, all enrofloxacin-sensitive isolates did not exhibit mutations in the parC codons Ser80 and Glu84.

Intermediate isolates did not exhibit mutations in gyrA, gyrA codon Asp87, parC, and parC codon Glu84. Meanwhile, none of the enrofloxacin-resistant isolates exhibited mutations in the gyrA codon Ser83, parC, or parC codon Glu84. This affected the sensitivity and specificity of the MA primers for enrofloxacin susceptibility. Table 3 displays a comparison of the enrofloxacin sensitivity of each isolate with the results of the MAS PCR. In this assay, the sensitivity of MAS PCR was 80% to 100%, with the exception of the gyrA codon Asp87 and parC codon Ser80, which were rated at 40%. The specificity of MAS PCR ranged from 0% to 55.5%.

Table 3: Sensitivity and Specificity of MAS-PCR for Detection of Enrofloxacin Resistance in E. coli.

PCR MAS Results		MIC results		Sensitivity	Specificity
PCR MAS Results		Resistant	Nonresistant (sensi+inter)	Sensitivity	Specificity
gyrA	Mutated	4	9	80%	10%
	Not mutated	1	1	80%	10%
gyrA83	Mutated	5	4	100%	55,5%
	Not mutated	0	5	100%	55,5%
gyrA87	Mutated	2	9	40%	10%
	Not mutated	3	1	40%	10%
parC	Mutated	5	8	100%	20%
	Not mutated	0	2	100%	20%
parC80	Mutated	2	8	40%	20%
	Not mutated	3	2	40%	20%
parC84	Mutated	5	10	100%	0%
	Not mutated	0	0	100%	0%

Among the enrofloxacin-sensitive isolates, B012 and B015 did not show detectable mutations in the analyzed QRDR loci. However, two other sensitive isolates (B013 and B086) carried combined mutations in gyrA Ser83 and parC, indicating that the presence of these mutations did not necessarily result in phenotypic resistance. Three enrofloxacin-resistant isolates (B064, B065, and B100) exhibited combined mutations of the gyrA codon Asp87 and the parC codon Ser80. A combined mutation of gyrA codon Ser83 and parC codon Ser80 was observed in one intermediate isolate.

The correlation between enrofloxacin resistance and qrdr mutations in e. coli

According to Fisher’s exact test, there was no statistically significant correlation between enrofloxacin resistance and QRDR mutations in general (gyrA, p=1.000; parC, p=0.608). Nevertheless, isolates with combined mutations in gyrA and parC exhibited a trend toward increased resistance in comparison to isolates without the combined mutation (p=0.329). The odds ratio analysis indicated a trend toward increased resistance in isolates that contained QRDR mutations. Relationship between QRDR mutation and enrofloxacin resistance in E. coli are presented in Table 4. Mutations in the gyrA gene increased the odds of resistance by 1.71 times (OR=1.71; 95% CI: 0.23–12.73), while mutations in the parC gene increased the odds by 2.25 times (OR=2.25; 95% CI: 0.30–16.68). Isolates with a combination of gyrA and parC mutations showed the highest odds of resistance (OR=3.50; 95% CI: 0.39–31.32). The contribution of mutations to resistance varied at the codon level. Glu84 mutations in parC codon exhibited the strongest association with resistance (OR=13.50; 95% CI: 1.01–180.12; p=0.077), followed by mutations in gyrA codon Asp87 (OR=6.00; 95% CI: 0.62–58.30; p=0.251). In contrast, the risk of resistance was not increased by mutations in the gyrA codon Ser83 (OR=0.58; 95% CI: 0.05–6.72; p=1.000) and parC codon Ser80 (OR=0.32; 95% CI: 0.01–5.52; p=0.524). Spearman correlation analysis between the number of QRDR mutations and MIC values showed a weak and insignificant relationship (r=0.19; p=0.489). The lack of statistical significance may partly be influenced by the limited number of isolates included in the analysis.

DISCUSSION

Testing on MacConkey medium demonstrated that the study isolates were specific for Escherichia coli. E. coli colonies

Table 4: Relationship between QRDR mutations and enrofloxacin resistance in E. coli.

Mutation variable	Fisher exact p-value	Odds ratio (OR)	95% CI	Interpretation
gyrA	1.000	1.71	0.23–12.73	Not significant, there is a trend of increasing resistance
parC	0.608	2.25	0.30–16.68	Moderate risk of increasing resistance
gyrA + parC (combination)	0.329	3.50	0.39–31.32	Highest risk of resistance, indicating a cumulative effect
gyrA Ser83	1.000	0.58	0.05–6.72	Not correlated with increasing resistance
gyrA Asp87	0.251	6.00	0.62–58.30	Shows a trend of increasing resistance
parC Ser80	0.524	0.32	0.01–5.52	Not correlated with resistance
parC Glu84	0.077	13.50	1.01–180.12	Strongest correlation, approaching significance

developed pink growth on MacConkey medium as a result of their capacity to ferment lactose (Indrawati et al., 2019). Positive results on EMBA medium were indicated by dark to black colony centers with a greenish metallic sheen. These results are consistent with the IMViC test, which demonstrates specific characteristics of E. coli. Detection of the uspA gene, an E. coli-specific gene expressed under stress conditions, showed that all isolates produced an 884 bp band, thereby verifying their identity as E. coli (Hardiati et al., 2021).

The enrofloxacin resistance rate in this study was 33.33% (5/15), which is lower than the resistance rate of 72% reported in broiler poultry in Indonesia (Hardiati et al., 2021). However, this comparison should be interpreted cautiously because broiler and layer production systems differ substantially in management practices and antimicrobial exposure. Broiler chickens are typically raised in short production cycles with higher stocking densities and more intensive disease management, which may lead to more frequent antimicrobial use and stronger selection pressure for resistant bacteria. In contrast, layer production systems often involve longer production periods and different therapeutic strategies. Therefore, differences in resistance prevalence between broiler and layer populations may partly reflect variations in management practices and antimicrobial usage rather than solely genetic mutation patterns.

A high prevalence of plasmid-borne quinolone resistance genes (PMQR) in avian E. coli isolates was also demonstrated in another study conducted in Indonesia (Palupi et al., 2023). Globally, the prevalence of fluoroquinolone resistance in avian E. coli is reported to be higher in China (60–80%) and several Southern European countries, but relatively lower in Northern Europe due to stringent antimicrobial stewardship programs. The prevalence of resistance in Southeast Asia is commonly linked to intensive antibiotic use, variable drug quality, and inadequate antibiotic monitoring (Carascal et al., 2022). Enrofloxacin use in the Indonesian poultry sector has been reported to reach 49.4%, which contributes to the selection pressure that induces resistance (Directorate General of PKH 2019).

The intensive use of antibiotics for both therapy and prophylaxis can lead to the development of antibiotic resistance (Baldy-Chudzik et al., 2015). The movement of resistance between strains and species is facilitated by the ability of antimicrobial resistance genes (ARGs) to propagate through vertical gene transfer (VGT) and horizontal gene transfer (HGT) (Han et al., 2022). This explains the rapid spread of quinolone resistance in avian bacterial populations.

Molecularly, fluoroquinolone resistance is primarily caused by mutations in the quinolone resistance-determining region (QRDR) of the gyrA and parC genes (Redgrave et al., 2014). These mutations inhibit the antibiotic’s ability to bind to the DNA–DNA gyrAse and topoisomerase IV complexes, thereby reducing its efficacy. The biological model of quinolone resistance suggests a sequential process: initial mutations typically occur in gyrA (Ser83 or Asp87), conferring low-level resistance, followed by additional mutations in parC (Ser80 or Glu84), which increase resistance to high levels. In addition to target mutations, fluoroquinolone resistance is also influenced by the presence of the PMQR gene (qnr, aac(6’)-Ib-cr, qepA), decreased membrane permeability, and increased efflux pump activity (AcrAB-TolC).

This study identified resistant isolates without detectable QRDR mutations, suggesting the possible involvement of alternative mechanisms of fluoroquinolone resistance. These mechanisms may include plasmid-mediated quinolone resistance (PMQR) genes such as qnr, aac(6’)-Ib-cr, and qepA, increased efflux pump activity (e.g., AcrAB–TolC), decreased membrane permeability due to porin alterations, or mutations in other target regions not investigated in this study (Onseedaeng and Ratthawongjirakul, 2016).Interestingly, several isolates carrying mutations in QRDR-associated loci remained phenotypically sensitive to enrofloxacin. This observation suggests that the presence of a mutation does not necessarily guarantee phenotypic resistance, particularly when only a single mutation is present. Similar findings have been reported in previous studies and may reflect the influence of additional factors such as gene expression levels, efflux pump activity, membrane permeability, and compensatory mutations. A limitation of this study is that the nucleotide positions of the mutations could not be confirmed by sequencing, which may also contribute to the observed discrepancies between genotype and phenotype. Therefore, the mutation detection based on MAS-PCR should be interpreted as indicative screening rather than definitive confirmation of nucleotide substitutions.

The diagnostic performance of the MAS-PCR assay showed variable sensitivity and specificity across different codons. This variability may reflect inherent limitations of allele-specific PCR methods, which are primarily designed as rapid screening tools rather than definitive mutation identification techniques. Because MAS-PCR relies on primer–template specificity, mismatches or sequence variability in primer-binding regions may influence amplification efficiency and reduce assay specificity. Therefore, the mutation profiles detected in this study should be interpreted as indicative screening results rather than definitive confirmation of nucleotide substitutions. Future studies incorporating DNA sequencing would provide more accurate identification of QRDR mutations and improve the reliability of mutation detection.

Another limitation of this study is the relatively small number of isolates analyzed, which may reduce the statistical power of the analysis and limit the generalizability of the findings to the broader layer poultry population in West Java. Nevertheless, the isolates used in this study represent field isolates collected from commercial farms, providing valuable preliminary insight into QRDR mutation patterns associated with enrofloxacin resistance in avian E. coli. Further studies involving larger numbers of isolates and broader geographic sampling are required to confirm these findings and better estimate the epidemiological significance of QRDR mutations. Based on the effect size trends observed in the present dataset, follow-up investigations including approximately 60–100 isolates from multiple farms would provide improved statistical power and allow a more robust evaluation of the association between QRDR mutations and phenotypic resistance.

The grouping of sensitive and intermediate isolates into a non-resistant category for statistical analysis was also influenced by the limited number of isolates in each susceptibility group. While intermediate isolates may exhibit characteristics between susceptible and resistant phenotypes, separating all three categories would have resulted in very small subgroup sizes, reducing the robustness of statistical comparisons.

The findings indicate that single gyrA mutations may occur in phenotypically susceptible isolates. This observation is consistent with the evolutionary model of quinolone resistance, in which initial mutations in gyrA confer low-level resistance, while the accumulation of additional mutations contributes to higher resistance levels. In this study, isolates with combined gyrA and parC mutations exhibited higher MICs, which is consistent with previous reports that combined mutations substantially increase fluoroquinolone resistance (Jones et al., 2000; Lorestani et al., 2018).

Despite the fact that the statistical association between QRDR mutations and resistance was not statistically significant, the odds ratio pattern indicates a consistent biological trend in which the likelihood of resistance increases as mutation accumulation increases. The statistical insignificance is likely due to the small sample size and biological variability. The phenomenon of “statistically non-significant but biologically important” is frequently observed in antimicrobial resistance studies, particularly those with limited sample sizes. In these studies, the biological effect remains clinically relevant despite failing to achieve statistical significance. However, distinguishing true biological signals from random variation is important. In this study, the interpretation of biological relevance was not based solely on statistical outcomes but also on the consistency of mutation patterns with established models of fluoroquinolone resistance evolution. Previous studies have demonstrated that mutations in gyrA typically occur first, followed by additional mutations in parC that contribute to higher resistance levels (Redgrave et al., 2014). The mutation combinations observed in this study follow this hierarchical pattern and are accompanied by higher MIC values, supporting the possibility that the observed trend reflects a biologically meaningful mechanism rather than purely random variation. The role of target mutations in the evolution of quinolone resistance is further supported by the pattern of increased odds ratios for the combined gyrA–parC mutation in this study.

However, the wide confidence intervals observed in several odds ratio estimates, particularly for the parC Glu84 mutation, indicate substantial statistical uncertainty in the magnitude of the association. This imprecision is likely influenced by the relatively small number of isolates included in the analysis, which limits the stability of effect size estimates. Therefore, the odds ratio values reported in this study should be interpreted cautiously as preliminary indicators of possible associations rather than precise estimates of the true effect size.

From a One Health perspective, the presence of fluoroquinolone-resistant E. coli in poultry production systems has implications beyond animal health. Resistant bacteria originating from poultry may be transmitted to humans through the food chain, environmental contamination, or direct occupational exposure (Li et al., 2019; Pitta et al., 2020). The detection of QRDR mutations associated with reduced enrofloxacin susceptibility highlights the importance of antimicrobial stewardship in livestock production. For poultry farmers, this finding emphasizes the need to use fluoroquinolones only under veterinary guidance and to avoid routine prophylactic use. For veterinarians, the results support the implementation of antimicrobial susceptibility testing before selecting treatment options. At the policy level, continued surveillance of antimicrobial resistance in food-producing animals and stricter regulation of critically important antimicrobials such as fluoroquinolones are essential to reduce the risk of resistance transmission across the animal human environment interface.

From a clinical perspective, fluoroquinolone resistance can lead to treatment failure, extended duration of infection, and increased mortality in poultry. Additionally, the potential for resistance in zoonotic bacteria, such as E. coli, to be transmitted to humans through the food chain, thus having public health implications. Mutations in the gyrA–parC combination associated with high MICs are a cause for concern, as they have the potential to diminish the efficacy of fluoroquinolone therapy in the field.

The results of this study in general confirm the hierarchical model of quinolone mutations, in which gyrA mutations are the initial phase and resistance is subsequently increased by additional mutations in parC. While statistical significance was not attained, the mutation pattern and odds ratio trend indicate a biological contribution of QRDR mutations to enrofloxacin resistance in E. coli isolates from layer chicken farms in West Java.

CONCLUSION

All isolates in this study were confirmed as Escherichia coli based on phenotypic characteristics, biochemistry, and detection of the uspA gene. The enrofloxacin resistance rate of 33.33% indicates the presence of fluoroquinolone selection pressure in layer chicken farms in West Java. QRDR mutations in the gyrA and parC genes may contribute to increased enrofloxacin resistance, particularly in isolates with combined mutations, which tended to show a greater probability of resistance and higher MIC values. Among the examined codons, mutations in parC Glu84 and gyrA Asp87 showed the highest odds ratios, suggesting a potential association with increased resistance.

Although the statistical association was not significant, the observed odds ratio pattern and MIC distribution suggest a possible biological contribution of QRDR mutation accumulation to resistance patterns. However, these findings should be interpreted cautiously due to the wide confidence intervals, lack of statistical significance, and the limited sample size. Resistant isolates lacking detectable QRDR mutations indicate that fluoroquinolone resistance may involve additional mechanisms such as efflux pump activity or plasmid-mediated quinolone resistance genes. These findings highlight the need for further molecular studies involving larger isolate collections and sequencing approaches to better understand resistance mechanisms and support antimicrobial stewardship in poultry production.

ACKNOWLEDGEMENT

Thank you for the educational scholarships provided by the Indonesian Quarantine Agency in 2024-2026 and the Ministry of Agriculture of the Republic of Indonesia in 2023. The authors gratefully acknowledge PT Medika Satwa Laboratoris and IPB University for their support and collaboration during this research. The authors thank Lusianawati Widjaja for assistance in improving the clarity of the manuscript.

NOVELTY STATEMENT

This study provides molecular insight into the relationship between QRDR mutations in the gyrA and parC genes and enrofloxacin resistance in Escherichia coli isolates from layer chicken farms in West Java, Indonesia. Data linking mutation patterns with phenotypic resistance in avian E. coli from this region remain limited. The identification of codon-specific mutation patterns and the observation of resistant isolates lacking detectable QRDR mutations highlight the multifactorial nature of fluoroquinolone resistance in poultry-associated bacteria. These findings contribute to antimicrobial resistance surveillance and support the development of more rational antibiotic use strategies within a One Health framework.

AUTHOR’S CONTRIBUTION

Difa Widyasari, Surachmi Setiyaningsih, Christian Marco Hadi Nugroho, Ryan Septa Kurnia, Muhammad Ade Putra, Agustin Indrawati were involved in conceptualizing the study, designing the experiments, gathering and analyzing the data, as well as drafting the manuscript. Difa Widyasari, Surachmi Setiyaningsih, Agustin Indrawati and Christian Marco Hadi Nugroho also provided oversight during the research process and contributed to the critical revision of the manuscript. All authors reviewed and approved the final version of the manuscript for submission.

generative AI and AI assisted technology statement

The authors declare that no generative AI and AI assisted

technology was used in the creation of this manuscript.

Conflict of interest

None of the authors have any conflicts of interest to disclose.

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