Unraveling Antimicrobial Resistance Dynamics in Mycoplasma gallisepticum: Insights Into Antibiotic and Disinfectant Interactions

ABSTRACT

A major risk to the poultry industry is antimicrobial resistance (AMR), specifically with regard to Mycoplasma gallisepticum (MG) infections. The sensitivity patterns of 100 MG isolates to biocides and antibiotics were examined in this study to clarify the interactions between antimicrobial agents and resistance mechanisms. The antimicrobial activity against MG was assessed using broth microdilution, and the results are shown as the minimum inhibitory concentration (MIC) for each strain, the MIC distribution (range), the MIC₅₀, and/or the MIC₉₀. The statistical associations between the MICs of the antibiotics and biocides were investigated using regression model analysis and correlation coefficients. The absence of a cell wall in MG inherently confers resistance to beta‐lactams, thereby necessitating the utilization of enrofloxacin, difloxacin, flumequine, oxytetracycline, chlortetracycline, doxycycline, tylosin, tilmicosin, tylvalosin, erythromycin, spiramycin, tiamulin, lincomycin, spectinomycin and dihydrostreptomycin. These antibiotics exhibited MIC₅₀ values of 0.5, 0.5, 0.12, 0.062, 0.12, 0.031, 0.016, 0.016, 0.062, 16, 1, 0.008, 2, 0.5 and 32, respectively. In addition to antibiotics, disinfectants have garnered attention for their contribution to the development of AMR in MG. Notably, formalin, phenol, NADES, Halamid, Virkon‐S, MicroSet and SteriSet exhibited MIC₅₀ values of 125, 500, 31.25, 15.63, 15.63, 7.81 and 62.5, respectively. Significant positive correlations and direct associations were identified between various biocides and the development of antibiotic resistance, with coefficients ranging from 0.098 to 1.176. This research highlights the intricate nature of resistance profiles in MG and underscores the necessity for a thorough understanding of antimicrobial interactions. This finding emphasizes the importance of managing emerging AMR stemming from disinfectant misuse in the poultry farms to prevent additional constraints on antibiotic treatment options.

The threat of antimicrobial resistance in Mycoplasma gallisepticum poses significant challenges to poultry health and industry. The resistance mechanisms of Mycoplasma gallisepticum to antibiotics, coupled with potential cross‐resistance to disinfectants, demand urgent attention. The evolution of resistance in Mycoplasma gallisepticum strains emphasizes the need for strict surveillance programmes to monitor patterns. Addressing antimicrobial resistance in Mycoplasma gallisepticum requires collaborative efforts to implement effective surveillance, prudent antimicrobial use and the exploration of alternative therapies. The complexity of cross‐resistance necessitates thorough research to understand the underlying mechanisms involved and enforce rigorous biosecurity measures in poultry farming. A comprehensive approach is vital for combating antimicrobial resistance in Mycoplasma gallisepticum, ensuring poultry health and industry sustainability. Further investigations are recommended to determine the antibacterial effect of natural antibiotic alternatives (herbal extracts, essential oils, phytobiotics, etc.) that were developed to limit the spread of antimicrobial resistance to Mycoplasma gallisepticum among poultry houses.

1. Introduction

Poultry is highly susceptible to Mycoplasma gallisepticum (MG) infections, which often initiate within eggs and persist as a potential source of contagion for other birds within the farm (Marouf et al. 2022; Kamal et al. 2019). This bacterium colonizes various avian secretions, including the respiratory, ocular, and cloacal areas, as well as eggs and semen, utilizing multiple pathways for avian entry, such as the trachea, conjunctiva and oral cavity (Kleven 2008; Hu et al. 2021). Despite aerosol spread being confined to short distances, aerosols contribute significantly to MG transmission within a flock (Feberwee et al. 2005). Contaminated surfaces, litter and feathers pose substantial risks, especially when disseminated within the farm or inadequately managed during the shed cleanup, potentially perpetuating a Mycoplasma‐free poultry environment at risk (Prajapati et al. 2018). Transmission through fomites and workers underscores the role of external factors in disease transmission (Ayim‐Akonor et al. 2018). The poultry industry implements various approaches to ensure healthy flocks (Mehdi et al. 2018). Disease freedom and stringent biosecurity measures are primary strategies for disease control (Ferguson‐Noel et al. 2020; Behboudi 2022). However, interventions employing antimicrobial agents, often used for the therapy and control of infections, constitute an integral part of disease control on poultry farms. These antibacterial agents, comprising antibiotics and disinfectants, are vital for limiting pathogen proliferation. The Centre Européen d'Etudes pour la Santé Animale (CEESA) proposed the MycoPath programme in 2010, aiming to evaluate antimicrobial susceptibility in four veterinary Mycoplasma species from pigs, cattle and poultry (De Jong et al. 2013). This initiative focused on monitoring the occurrence of antimicrobial resistance (AMR), ensuring judicious and targeted antimicrobial use for effective therapy (Klein et al. 2017). Within the poultry industry, there is an urgent need to address bacterial resistance, especially resistance to commonly used biocides, such as antibiotics and disinfectants (Muhammad et al. 2021). The surge in AMR over the last decade has posed a significant challenge in combatting bacterial infections (Rozman et al. 2020). The decreasing sensitivity to disinfectants and the concurrent increase in antibiotic resistance among bacterial strains underscores the imperative of employing available and effective disinfectants responsibly (Rozman et al. 2020). At present, standardized test methods for evaluating Mycoplasma sensitivity to disinfectants are inadequate (Nagatomo et al. 2001; Wilson and Margolin 2003; Mazzola et al. 2009). Because Mollicutes lack a cell wall, they are immune to betalactams, glycopeptide antibiotics and bacitracin. Additionally, because they lack an enzyme that produces folic acid, they are not susceptible to sulphonamides or trimethoprim. Finally, conservative mutations in RNA polymerase have made Mollicutes resistant to rifampicin. These limitations on the range of antimicrobials can be used to treat MG and multiple sclerosis (MS) infections (Shakal, Qoraa, and Salem 2024). Nonetheless, it is well known that MG and MS have demonstrated susceptibility to a range of antimicrobial drugs that penetrate cells, including macrolides, tetracyclines and quinolones (Shakal, Qoraa, and Salem 2024). Determining the minimum inhibitory concentration (MIC) is crucial for identifying the most efficacious commercial agents (Shakal et al. 2024). Additionally, the D value, which measures the time of decimal decrease, plays a pivotal role in evaluating disinfectant performance (Nagatomo et al. 2001). Despite advancements, a knowledge gap persists regarding Mycoplasma susceptibility to these compounds, with certain studies highlighting associations between disinfectant resistance and antibiotic resistance. Therefore, this study aimed to assess and update the data on the anti‐Mycoplasma sensitivity of 100 MG‐isolated strains from Egypt compared to our previous isolate (recorded in GenBank as MZ826700, a 26 bp DNA linear BCT dated 30‐September‐2021). This goal will be achieved by determining new MIC data for commonly used anti‐Mycoplasma drugs and common disinfectants. Moreover, this study aimed to explore co and cross‐resistance through statistical correlation and regression models.

2. Materials and Methods

2.1. Gathering of MG Isolates

Mycoplasma isolates were obtained from poultry farms in the Al‐Sharqia governorate, Egypt and were specifically chosen based on predefined criteria. The collection spanned from 2019 to 2023 and included 300 field cases. The samples were collected from the trachea, lungs and air sacs of freshly dead birds from flocks suffering from respiratory signs, and tracheal swabs were also collected from diseased birds with respiratory manifestations. These samples were subsequently sent to the National Laboratory to undergo culture, isolation and characterization procedures to confirm the presence of MG by using pleuropneumonia‐like organism (PPLO) broth and agar (Oxoid), which were fortified with a specific supplement for MG growth and incubated for 48 h in PPLO broth and 12–21 days on PPLO agar. The positive samples were identified via changes in the colour of the PPLO broth from red to yellow with broth turbidity and the detection of fried egg‐shaped colonies on PPLO agar under a light microscope (Olympus). During the survey period, multiple isolates per farm per clinical episode were permitted, indicating potential epidemiological connections among the strains. The isolates were obtained from different geographic locations within the Al‐Sharqia governorate and from chickens that had not received any antimicrobial medication 15 days before sample collection. To ensure uniformity, each participating farm was targeted to provide three MG isolates. The contributing laboratories adhered to their standard procedures for Mycoplasma culture isolation, filter cloning and molecular characterization, as outlined in previous studies (Kreizinger et al. 2017; Cisneros‐Tamayo et al. 2020). To confirm the purity of the cultures, the laboratories employed various methods, including the digitonin test, which distinguishes Mycoplasma colonies from Acholeplasma; growth inhibition tests, which use specific antisera (BioChek) following the protocol outlined by Khalifa et al. (2013); universal PCR and sequencing, as previously described (Lauerman et al. 1995); or universal PCR after denaturing gradient gel electrophoresis fingerprinting, according to McAuliffe et al. (2003). Molecular testing was performed on the isolated strains in order to categorize them, verify whether a single strain was tested more than once on a farm and exclude the vaccine strains (Qoraa, Salem, and Shakal 2023). The isolates were lyophilized and stored at temperatures less than −20°C before being transported to the Veterinary Hygiene and Management (VHM) laboratory at Cairo University, Egypt. Upon arrival, the VHM laboratory assessed the cultured isolates for typical MG growth characteristics on PPLO agar and broth (Oxoid). Moreover, the VHM laboratory conducted additional identity verification on a randomly chosen subset of 100 field isolates. For confirmation, these selected MG isolates were reidentified using PCR methods employing primer pairs targeting the mgc2 gene specific to these species (Buim et al. 2009). The identities of all 100 isolates were subsequently confirmed.

2.2. Antimicrobial Testing

At the VHM laboratory, antimicrobial susceptibility was assessed for all the MG isolates. Viability assessments of the isolates were performed using culture media optimized for MG growth in Frey media supplemented with phenol red as an indicator, excluding any antimicrobial agents (Frey, Hanson, and Andrson 1968). Each isolate was cultured in a broth medium until a discernible colour alteration occurred. After this, the isolates were divided into separate aliquots and preserved by freezing at −20°C. The number of viable bacteria in one of these aliquots was determined through a process of serial dilution and plating onto agar media. Broth microdilution, a validated method, was used to determine antimicrobial activity against animal Mycoplasmas, and the results are presented as the final MIC, MIC per strain, MIC distribution (range), MIC₅₀ and/or MIC₉₀ (Gautier‐Bouchardon 2018). The MIC is the lowest concentration of an antimicrobial that hampers microorganism growth after an appropriate incubation period and serves as the gold standard for determining sensitivity and prescribing antimicrobial dosages. Breakpoints, determined via the MIC, establish specific antimicrobial concentration values to categorize microorganisms as susceptible, intermediate or resistant to a drug (Gnanadhas, Marathe, and Chakravortty 2013). For subsequent MIC tests, thawed aliquots were diluted to a density of 10⁶ colony‐forming units (CFU)/mL, resulting in an end inoculum density of approximately 5 × 10⁵ CFU/mL on the MIC plates, following protocols outlined by Hannan (2000) and Kreizinger et al. (2017). To monitor the MIC, a previously isolated MG strain (accession number MZ826700) from GenBank with a 26 bp DNA linear BCT sequence dated 30 September 2021 was utilized as a quality control strain instead of ATCC strain which was not available at the experiment time due to logistic issues.

The antimicrobial diluent (used to prepare antimicrobial stock solutions) exhibited no bacterial growth when the effect of the sterile detailed water that was used as a diluent was tested for bacterial growth, confirming that there was no conflict in the MIC values.

The broth microdilution method described by Hannan, Windsor, and Ripley (1997) was utilized for determining the minimum inhibitory concentrations (MICs). A total of 15 antimicrobial agents from 7 distinct classes of fluoroquinolones, tetracyclines, macrolides, pleuromutilins, lincosamides, aminocyclitols and aminoglycosides were evaluated for their effectiveness against MG field isolates. The antibiotics used in this study included enrofloxacin base (ACS Chemicals), difloxacin (Sigma), flumequine (Sigma), oxytetracycline dihydrate (Sigma), chlortetracycline (Sigma), doxycycline (Sigma), tylosin tartrate (Sigma), tilmicosin (Sigma), tylvalosin (Aivlosin tartarate, E.Co.A.H.), erythromycin thiocyanate (Sigma), spiramycin (Sigma), tiamulin base (ACS Chemicals), lincomycin hydrochloride (Sigma), spectinomycin dihydrochloride (Sigma) and dihydrostreptomycin (Sigma) were used. The antibiotic solutions were filtered through a 450 nm pore‐size membrane filter from Millipore, USA, followed by aseptic dispensing. Stock solutions were prepared for each antimicrobial agent at a concentration of 1280 µg/mL by applying specified solvents and diluents as per Clinical and Laboratory Standards Institute (CLSI) guidelines (CLSI 2018). Dilutions were then made in Frey's medium to achieve final test concentrations ranging from 0.001 to 64 µg/mL. To assess the MIC for each isolate, a standardized procedure was followed. Initially, 100 µL of the specific antibacterial solution was dispensed into the conical wells of polystyrene 96‐well microtitre plates from Greiner Bio‐One, Frickenhausen, Germany. Then, 100 µL of the cultured material (thawed, preincubated for an hour, and appropriately diluted as detailed earlier) was added to each well, for a terminal cell count of approximately 5 × 10⁵ CFU/mL. The following control wells were included in the trial: a positive control well without antimicrobial agents but with 100 µL of sterile medium and a negative control well containing 200 µL of sterile medium without inoculation. The plates were covered with polystyrene lids, immediately placed in a humidified environment and kept at 35°C ± 1°C. Regular checks were conducted every 24 h, with extended incubation for up to 5 days if no growth was observed in the positive control wells. The MIC was assessed that once clear growth (defined as a colour alteration) was detected in the positive control wells. Observations were made against a white background to clearly distinguish among colour changes in the medium, from red (indicating no development) to orange/yellow (indicating culture development), as detailed in de Jong et al. (2021). The MIC for each antimicrobial agent was calculated as the minimal concentration that entirely stopped growth. The validity of the test required an evident colour change in the positive control well and no change in the negative control well. Three independent experiments were conducted on different days, and the median MIC obtained from these experiments was considered the final MIC for each isolate. The MIC ranges (distributions), MIC₅₀s and MIC₉₀s were assessed for each antimicrobial agent and Mycoplasma species. MIC₅₀ and MIC₉₀ values represent percentiles derived from the entire set of MIC outcomes for a specific substance versus a designated group of Mycoplasma isolates. The MIC₅₀ denotes the minimum level of an antimicrobial that halts the growth of 50% of the strains tested, whereas the MIC₉₀ represents the minimal level of an antimicrobial that inhibits growth in 90% of the strains tested (Morante et al. 2021).

2.3. Disinfectant Susceptibility Testing

The study employed various disinfectants commonly used in poultry settings: formalin (37% w/v), phenol (10% w/v), chlorocresol (p‐chloro‐m‐cresol, PCMC, Sigma Aldrich), slightly acidic electrolyzed oxidizing (EO) water (pH 6.1, free chlorine 200 ppm, ORP 910 mV) in NADES, sodium p‐toluenesulfonchloramide trihydrate 100% in Halamid, potassium peroxymonosulfate (20.4%)/NaCl (1.5%) in Virkon‐S, polyhexamethylene biguanide hydrochloride (3%)/benzalkonium chloride (BC) (4%) in MicroSet spray and polyhexamethylene biguanide hydrochloride (0.2%)/BC (0.16%) in SteriSet fog. These disinfectants were selected due to their frequent use in poultry settings (LaBreck et al. 2020). The MIC of each disinfectant was assessed for all the MG isolates following the methodology outlined by Barry (1999).

The determination of microorganism susceptibility to biocides does not typically use the term ‘breakpoint’. Instead, it relies on the concept of an epidemiological cutoff (ECOFF) to classify microorganisms into susceptible, intermediate or resistant categories (Gnanadhas, Marathe, and Chakravortty 2013). The MICs of the biocides were assessed in triplicate using the broth microdilution technique. The ECOFF values (with a 95% cutoff) were derived using the ECOFF finder XL 2010 programme (https://clsi.org/meetings/microbiology/ecoffinder/). However, this programme is not currently available for MG (Amsalu et al. 2020). In the absence of standardized breakpoints and ECOFFs for disinfectants versus MG, concentrations were modified from data recorded by Vijayakumar and Sandle (2019).

The MICs for antimicrobial agents followed the EUCAST guidelines and were adapted for biocides using the 2020 EUCAST ECOFF value (http://www.eucast.org/). For biocide MIC testing, the concentrations of all disinfectants varied from 0.1 to 1000 µg/mL (Chen et al. 2021). Stock solutions (1 mg/mL) were prepared in warm distilled water. Using 96‐well microtitre plates, doubling dilutions of disinfectants were prepared in Frey broth medium. The bacterial suspension was set in 5 mL of sterile saline, adjusted to a 0.5 McFarland standard turbidity and diluted 1:100 (Sarwar et al. 2023). The inoculum was prepared as described previously, with the same final concentration in each well. A fixed volume of this bacterial suspension was added to all the wells with doubling dilutions of the disinfectants and to the positive control development wells without disinfectant. Negative controls, with distilled water in one well and Frey's broth in another well, were included in each test. The plates were incubated at 35°C, after which growth and colour changes were observed to determine the MIC, MIC₅₀ and MIC₉₀ after 24 h, revealing that the minimal concentrations of disinfectant stopped visible development. Reports have indicated instances where no relationship exists between MIC and resistance phenomena (Suller and Russell 1999; Meyer 2006; Rose et al. 2009). When a microorganism is naturally resistant to a biocide, it is termed ‘insusceptible’. Conversely, if bacteria are minimally influenced by biocide concentrations, potent or susceptible strains are called ‘tolerant’ or ‘resistant’, respectively (Gnanadhas, Marathe, and Chakravortty 2013). The term ‘resistance’ is applied in discussions related to the killing effect, whereas ‘tolerance’ is utilized when referring to adaptation to inhibitory concentrations (Cerf, Carpentier, and Sanders 2010). Isolates that are resistant to three or more antimicrobial agents are classified as multidrug‐resistant (MDR) (Magiorakos et al. 2021).

2.4. Statistical Analysis

The data were gathered, and subsequent findings were organized and processed with Microsoft Excel 2016. To perform the analysis, the Statistical Package for Social Sciences software, version 25.0 (SPSS, Inc., Chicago, IL), was utilized. First, all the gathered data were coded into variables for systematic examination. The normality of the data was evaluated using the Kolmogorov–Smirnov test. Descriptive and inferential statistical methods, such as the Mann–Whitney U test, Spearman's correlation and linear regression, were employed to present and interpret the outcomes. A significance level of less than 0.05 indicated statistical significance (Campbell and Swinscow 2011).

3. Results

The evaluation of 7 biocides and 15 antibiotics against 100 MG isolates of chicken origin revealed varied susceptibility patterns, providing insights into the effectiveness of these agents against the bacterial population.

3.1. Antibiotic Susceptibility Patterns

All antibiotics tested against MG isolates had MICs that were distributed in a nonnormal, multimodal manner, as Figure 1 illustrates. The most variable range of susceptibility was shown for tilmicosin, whose MIC values varied from 0.004 to >64 µg/mL. Dihydrostreptomycin, on the other hand, has extremely high MICs ranging from 8 to >64 µg/mL, indicating a restricted range of susceptibility. Table 1 details the MIC₅₀, MIC₉₀ and MICc for different classes of antibiotics against chicken MG isolates. The results showed that some antibiotics, such as tiamulin, had the lowest MIC₅₀ (0.008 µg/mL) and MIC₉₀ (0.031 µg/mL), which suggests that they are more effective against the bacterial population. Tiamulin, on the other hand, displayed a MIC range of <0.001–0.12 µg/mL. However, dihydrostreptomycin had the highest MIC₅₀ (32 µg/mL) and MIC₉₀ (>64 µg/mL) values, indicating reduced effectiveness against the tested isolates.

FIGURE 1.

Minimal inhibitory concentration distribution (µg/mL) of antibiotics for Mycoplasma gallisepticum isolates of chicken origin.

TABLE 1.

MIC₅₀, MIC₉₀, and minimal inhibitory concentration (MIC) range (µg/mL) of antibiotics for chicken Mycoplasma gallisepticum isolates.

Class	Antibiotic	MIC50 (µg/mL)	MIC90 (µg/mL)	MICc (µg/mL)	Range
Fluoroquinolones	Enrofloxacin	0.5	4	0.25	0.062–16
	Difloxacin	0.5	4	0.12	0.016–8
	Flumequine	0.12	4	0.062	0.008–8
Tetracyclines	Oxytetracycline	0.062	2	0.062	0.008–16
	Chlortetracycline	0.12	2	0.062	0.016–32
	Doxycycline	0.031	1	0.016	0.004–4
Macrolides	Tylosin	0.016	2	0.016	0.004–8
	Tilmicosin	0.016	64	0.25	0.004–64
	Tylvalosin	0.062	0.25	0.008	0.001–1
	Erythromycin	16	>64	16	2–64
	Spiramycin	1	16	0.5	0.031–64
Pleuromutilins	Tiamulin	0.008	0.031	0.002	<0.001–0.12
Lincosamides	Lincomycin	2	32	4	0.12–64
Aminocyclitols	Spectinomycin	0.5	8	1	0.031–16
Aminoglycosides	Dihydrostreptomycin	32	>64	16	8–64

Note: The table includes the MIC for the control strain (MICc). MIC₅₀ = lowest concentration inhibiting the growth of 50% of the bacterial population. MIC₉₀ = lowest concentration inhibiting the growth of 90% of the bacterial population. MICc = the minimal inhibitory concentration of the control isolates.

Table 2 presents the frequency of antibiotic resistance in the MG isolates. Antibiotics such as enrofloxacin showed a split sensitivity pattern among the isolates, with 50% of the isolates being sensitive (MIC 0.312 ± 0.084 µg/mL) and the remaining demonstrating resistance (MIC 6 ± 2.53 µg/mL). However, flumequine and tiamulin showed sensitivity patterns among the isolates, with 100% sensitivity (MICs of 2.42 ± 0.879 and 0.021 ± 0.011 µg/mL, respectively).

TABLE 2.

Frequency of antibiotic resistance in Mycoplasma gallisepticum isolates.

Antibiotic	Sensitivity	Isolates (%)	MIC (Mean ± SE)	p value	Break points *
Enrofloxacin	Sensitive	50	0.312 ± 0.084	0.008	≤0.5, (≥2)
	Resistant	50	6 ± 2.53
Flumequine	Sensitive	100	2.42 ± 0.879		≤4, (≥16)
	Resistant	0
Oxytetracycline	Sensitive	90	0.376 ± 0.214	0.115	≤4, (≥16)
	Resistant	10	16 ± 0
Tylosin	Sensitive	90	0.380 ± 0.229	0.115	≤1, (≥4)
	Resistant	10	8 ± 0
Erythromycin	Sensitive	30	3.33 ± 0.667	0.015	≤1, (>4)
	Resistant	70	64 ± 18.14
Spiramycin	Sensitive	80	3.21 ± 1.41	0.034	≤2, (>8)
	Resistant	20	40 ± 24
Tiamulin	Sensitive	100	0.021 ± 0.011		≤8, (≥16)
	Resistant	0
Lincomycin	Sensitive	70	2.91 ± 1.33	0.016	<8, (>8)
	Resistant	30	64 ± 32

Notes: The table includes the antibiotics that have reference breakpoints.

^*(µg/mL) without parentheses indicates the sensitivity level; the number within parentheses indicates the resistance level (Hannan 2000).

3.2. Biocide Susceptibility Patterns

As shown in Figure 2, the MIC distributions of the biocides exhibited a diverse range of susceptibilities among the tested isolates. Formalin demonstrated consistent effectiveness, with MIC values ranging from 31.25 to >1000 µg/mL across different isolates, indicating variability in sensitivity. Phenol exhibited a narrow range of susceptibility, with MIC values ranging from 250 to >1000 µg/mL. NADES, Halamid, Virkon‐S, MicroSet and SteriSet also demonstrated nonnormal, multimodal distributions with varying degrees of effectiveness according to differences in MIC values across isolates.

FIGURE 2.

Minimal inhibitory concentration distribution (µg/mL) of biocides for Mycoplasma gallisepticum isolates of chicken origin.

Table 3 outlines the MIC₅₀, MIC₉₀ and MIC range of biocides against chicken MG isolates. Notably, among the biocides tested, MicroSet exhibited the lowest MIC₅₀ (7.81 µg/mL), indicating its greater ability to inhibit bacterial growth in 50% of the isolates. Conversely, phenol exhibited the highest MIC₅₀ of 500 µg/mL, indicating a comparatively lower inhibitory effect. The MIC₉₀ values further indicated the range of concentrations required to inhibit 90% of the bacterial population, with NADES, Halamid and MicroSet demonstrating the same effectiveness.

TABLE 3.

MIC₅₀, MIC₉₀, and minimal inhibitory concentration (MIC) range (µg/mL) of biocides for chicken Mycoplasma gallisepticum isolates. The table includes the MIC for the control strain (MICc).

Biocide	MIC50 (µg/mL)	MIC90 (µg/mL)	MICc (µg/mL)	Range (µg/mL)
Formalin	125	500	62.5	31.25–1000
Phenol	500	>1000	500	250–1000
NADES	31.25	125	15.63	3.91–250
Halamid	15.63	125	15.63	0.49–250
Virkon‐S	15.63	62.5	3.91	0.49–125
MicroSet	7.81	125	1.95	0.24–250
SteriSet	62.5	500	31.25	7.81–1000

Note: MIC₅₀ = lowest concentration inhibiting the growth of 50% of the bacterial population. MIC₉₀ = lowest concentration inhibiting the growth of 90% of the bacterial population. MICc =  the minimal inhibitory concentration of the control isolate.

3.3. The Interplay Between Antibiotics and Disinfectants

The mean ± SE of the MICs of the isolates that exhibited sensitivity or resistance to antibiotics with reference breakpoints are displayed in Table 4. Statistically significant differences were detected among those testing sensitive or resistant to enrofloxacin with formalin, spiramycin with three biocides (formalin, phenol, and SteriSet) and lincomycin with formalin.

TABLE 4.

Minimum inhibitory concentrations (MICs) of biocides (mean ± SE) by antibiotics for which reference breakpoints were present.

Antimicrobial agents	MIC (µg/mL)
	Formalin		Phenol		NADES		Halamid		Virkon‐S		MicroSet		SteriSet
	Mean ± SE	p	Mean ± SE	p	Mean ± SE	p	Mean ± SE	p	Mean ± SE	p	Mean ± SE	p	Mean ± SE	p
Enrofloxacin
Sensitive Resistant	81.25 ± 18.75 700 ± 329.8	0.008 *	800 ± 329.8 1000 ± 273.9	0.389	40.63 ± 23.84 96.88 ± 42.62	0.171	51.86 ± 49.54 59.38 ± 18.75	0.115	37.89 ± 24.85 22.66 ± 5.58	0.596	78.22 ± 48.88 8.01 ± 3.39	0.750	576.6 ± 365.1 59.38 ± 18.75	0.141
Flumequine
Sensitive Resistant	390.6 ± 186.8		900 ± 204.8		68.75 ± 24.86		55.62 ± 25		30.28 ± 12.27		43.12 ± 25.89		318 ± 192.7
Oxytetracycline
Sensitive Resistant	406.3 ± 208.1 250 ± 0	0.724	972.2 ± 214.3 250 ± 0	0.151	62.5 ± 26.9 125 ± 0	0.292	47.91 ± 26.59 125 ± 0	0.220	30.17 ± 13.72 31.25 ± 0	0.596	46.17 ± 28.74 15.63 ± 0	0.595	131.1 ± 52.47 2000 ± 0	0.115
Tylosin
Sensitive Resistant	378.5 ± 208.4 500 ± 0	0.290	777.8 ± 183.7 2000 ± 0	0.151	62.5 ± 26.9 125 ± 0	0.292	34.02 ± 14.08 250 ± 0	0.115	31.9 ± 13.6 15.63 ± 0	0.860	46.17 ± 28.74 15.63 ± 0	0.595	131.1 ± 52.47 2000 ± 0	0.115
Erythromycin
Sensitive Resistant	333.3 ± 83.3 415.2 ± 271	0.249	333.3 ± 83.3 1142.9 ± 236.9	0.025	9.12 ± 3.45 94.31 ± 30.97	0.066	16.93 ± 7.92 72.2 ± 34.32	0.647	6.84 ± 4.48 40.32 ± 16.24	0.298	7.98 ± 4.37 58.18 ± 36.16	0.816	83.33 ± 20.83 418.5 ± 271.8	0.819
Spiramycin
Sensitive Resistant	175.8 ± 54.75 1250 ± 750	0.047 *	625 ± 115.7 2000 ± 0	0.031 *	54.69 ± 29.18 125 ± 0	0.114	46.08 ± 30.08 93.75 ± 31.25	0.148	30.03 ± 15.56 31.25 ± 0	0.426	49.99 ± 32.3 15.63 ± 0	0.425	84.96 ± 28.38 1250 ± 750	0.036 *
Tiamulin
Sensitive Resistant	390.6 ± 186.8		900 ± 204.8		68.75 ± 24.86		55.62 ± 25		30.28 ± 12.27		43.12 ± 25.89		318 ± 192.7
Lincomycin
Sensitive Resistant	129.5 ± 33.70 1000 ± 500	0.015 *	714.3 ± 234.2 1333.3 ± 333.3	0.100	62.5 ± 35.22 83.33 ± 20.83	0.250	57.13 ± 36.37 52.08 ± 10.42	0.302	32.09 ± 17.81 26.04 ± 5.21	0.563	56.01 ± 36.65 13.02 ± 2.61	0.562	382.8 ± 277.5 166.7 ± 41.67	0.302

*p value < 0.05.

The MICs of disinfectants and antibiotics were compared using correlation coefficient analysis to determine whether there was a positive or negative relationship. Strong correlations are demonstrated by correlation coefficients above 0.75, as shown in Table 5, which shows strong positive correlations between different MICs of biocides and antibiotics against MG isolates. Notably, significant (p < 0.05) strong positive correlations were observed between specific biocides and antibiotics, indicating potential relationships among their mechanisms of action or bacterial response, except for those involving dihydrostreptomycin with three biocides (formalin, MicroSet and SteriSet).

TABLE 5.

Correlation coefficients (r_s ) between MICs of biocides and antibiotics in 100 Mycoplasma gallisepticum isolates.

Antibiotics	Formalin	Phenol	NADES	Halamid	Virkon‐S	MicroSet	SteriSet
Fluoroquinolones
Enrofloxacin	0.989 **	0.794 **	0.948 **	0.968 **	0.961 **	0.929 **	0.976 **
Difloxacin	0.958 **	0.907 **	0.968 **	1.000 **	0.991 **	0.973 **	0.956 **
Flumiquine	0.851 **	0.884 **	0.937 **	0.901 **	0.922 **	0.804 **	0.808 **
Tetracyclines
Oxytetracycline	0.978 **	0.685 *	0.860 **	0.916 **	0.908 **	0.936 **	0.991 **
Chlortetracycline	0.977 **	0.651 *	0.854 **	0.897 **	0.892 **	0.911 **	0.984 **
Doxycycline	0.995 **	0.771 **	0.944 **	0.959 **	0.944 **	0.948 **	0.989 **
Macrolides
Tylosin	0.990 **	0.764 *	0.914 **	0.956 **	0.944 **	0.968 **	0.999 **
Tilmicosin	0.935 **	0.850 **	0.892 **	0.964 **	0.950 **	0.998 **	0.962 **
Tylvalosin	0.999 **	0.787 **	0.954 **	0.964 **	0.962 **	0.945 **	0.989 **
Erythromycin	0.775 **	0.979 **	0.905 **	0.915 **	0.904 **	0.857 **	0.762 *
Spiramycin	0.992 **	0.788 **	0.925 **	0.964 **	0.963 **	0.961 **	0.995 **
Pleuromutilins
Tiamulin	0.992 **	0.787 **	0.925 **	0.964 **	0.965 **	0.961 **	0.995 **
Lincosamides
Lincomycin	0.997 **	0.777 **	0.947 **	0.959 **	0.950 **	0.948 **	0.991 **
Aminocyclitols
Spectinomycin	0.938 **	0.864 **	0.982 **	0.958 **	0.940 **	0.915 **	0.915 **
Aminoglycosides
Dihydrostreptomycin	0.621	0.843 **	0.813 **	0.759 *	0.746 *	0.610	0.563

^*Correlation is significant at the 0.05 level.

^**Correlation is significant at the 0.01 level.

3.4. Predictive Models

The bivariate regression analysis in Table 6 highlights the predictive capacity of biocide MICs for antibiotic MICs. A significant direct association was detected between several biocides and resistance to antibiotics, either with positivity or negativity, with standardized coefficient beta values ranging from 0.098 to 1.176.

TABLE 6.

Regression analysis for the MICs of the biocides as a predictor of antibiotic MICs.

Antimicrobials	Formalin		Phenol		NADES		Halamid		Virkon‐S		MicroSet		SteriSet		R 2
	Beta	p value	Beta	p value	Beta	p value	Beta	p value	Beta	p value	Beta	p value	Beta	p value
Enrofloxacin	0.98 9	<0.001 **	0.071	0.429	0.133	0.442	0.246	0.200	0.193	0.300	−0.038	0.828	−0.364	0.431	0.978
Difloxacin	−0.001	0.518	0.000	0.922	0.001	0.589	1.000	<0.001 **	0.002	0.455	−0.001	0.450	−0.001	0.358	1
Flumequine	−0.304	0.455	0.227	0.453	0.937	<0.001 **	−0.091	0.868	0.226	0.685	−0.275	0.390	−0.291	0.375	0.877
Oxytetracycline	−0.219	0.270	−0.182	0.001 *	−0.143	0.087	0.005	0.983	0.104	0.606	0.055	0.716	1.131	<0.001 **	0.996
Chlortetracycline	0.263	0.294	−0.251	0.001 *	0.046	0.701	0.051	0.855	0.338	0.149	−0.302	0.066	1.176	<0.001 **	0.994
Doxycycline	0.995	<0.001 **	0.003	0.966	0.042	0.729	0.071	0.612	−0.059	0.661	0.079	0.512	0.123	0.706	0.989
Tylosin	−0.046	0.772	−0.003	0.913	0.008	0.872	0.015	0.815	−0.037	0.540	0.042	0.573	0.999	<0.001 **	0.998
Tilmicosin	0.003	0.789	0.002	0.801	0.004	0.841	0.183	<0.001 **	−0.336	<0.001 **	1.144	<0.001 **	0.003	0.849	1
Tylvalosin	0.907	<0.001 **	−0.015	0.695	0.098	0.047 *	0.014	0.847	0.048	0.481	0.000	0.998	0.017	0.908	0.999
Erythromycin	0.044	0.727	0.979	<0.001 **	0.138	0.430	0.153	0.405	0.055	0.779	0.034	0.831	0.029	0.817	0.959
Spiramycin	0.308	0.316	0.063	0.284	0.095	0.306	0.154	0.224	0.197	0.075	0.004	0.979	0.995	<0.001 **	0.99
Tiamulin	−0.071	0.684	−0.252	0.009 *	−0.085	0.244	−0.112	0.528	0.684	0.002 **	−0.058	0.633	0.539	0.001 *	0.998
Lincomycin	0.997	<0.001 **	0.013	0.778	0.054	0.524	0.045	0.651	−0.021	0.823	0.061	0.468	0.057	0.806	0.995
Spectinomycin	0.098	0.657	−0.081	0.621	0.982	<0.001 **	0.118	0.684	−0.217	0.459	0.129	0.454	0.106	0.552	0.965
Dihydrotreptomycin	−0.078	0.814	0.843	0.002 *	0.294	0.529	−0.029	0.954	−0.148	0.775	−0.482	0.227	−0.201	0.533	0.71

Note: Beta, linear regression coefficient at 95% confidence interval. R ², R‐square: indicates the proportion of the variance explained by the regression model.

^* p value < 0.05;.

^** p value < 0.001.

4. Discussion

An increase in AMR poses a significant threat to poultry well‐being and productivity, necessitating a deep understanding of resistance patterns and the interplay between antibiotics and disinfectants, particularly concerning MG (Uemura, Sueyoshi, and Nagatomo 2010). In poultry, controlling MG largely depends on antibiotics, leading to challenges such as resistance development and potential cross‐resistance to common antibiotics (Gharaibeh and Al‐Rashdan 2011). Frequent blind antibiotic use for MG eradicates susceptible strains, increasing the prevalence of resistant bacterial strains (Khalil, Becker, and Tardy 2017; Kraupner et al. 2018). The global poultry trade heightens the risk of introducing MG strains with varying antimicrobial susceptibilities, complicating standard treatment protocols (Gharaibeh and Al‐Rashdan 2011).

Antimicrobial usage in diverse settings escalates resistance, particularly through extended subtherapeutic doses, hastening resistance development (Taylor‐Robinson and Bebear 1997; Kamal et al. 2019; Amsalu et al. 2020). High mutation rates of Mycoplasma increase susceptibility to AMR (Kibeida 2011). However, the understanding of the mechanisms underlying AMR in veterinary Mycoplasmas is limited (Gautier‐Bouchardon et al. 2002). Concerns have emerged regarding disinfectant resistance and potential cross‐resistance to antibiotics, raising significant concerns (Köhler et al. 2019). This investigation aimed to analyse the susceptibility patterns of MG isolates to antibiotics and disinfectants, revealing insights into their cross‐resistance, effectiveness and implications for poultry health management and treatment approaches.

4.1. Antibiotic Susceptibility Patterns

Several studies have evaluated the susceptibility of MG to antibiotics and the occurrence of cross‐resistance. To be clear, variations in MIC methods can make it difficult to compare data from different laboratories. Owing to the absence of standardized testing methods, researchers have opted for a microbroth dilution procedure due to its efficiency and minimal resource requirements. The determination of breakpoints using MIC testing categorizes microorganisms into susceptibility groups. However, it is critical to recognize the gap between MIC values that are determined under controlled laboratory conditions and those that may not encompass drug pharmacokinetics (Bi, Li, and Nekka 2009; Cerf, Carpentier, and Sanders 2010). Antimicrobial susceptibility testing for MG revealed diverse susceptibility patterns to different antibiotics, particularly fluoroquinolones such as enrofloxacin, difloxacin and flumequine, which exhibited varying MIC values among the MG isolates. Enrofloxacin and difloxacin exhibited higher MIC₅₀ values (0.5 µg/mL) than did flumequine (0.12 µg/mL) (Figure 1 and Table 1). Although MG isolates had 50% sensitivity to enrofloxacin, flumequine demonstrated 100% sensitivity (Table 2), consistent with prior studies highlighting variable responses to fluoroquinolones within MG isolates (Cisneros‐Tamayo et al. 2020). Enrofloxacin, a widely used fluoroquinolone, showed moderate sensitivity among isolates, supporting evidence of emerging resistance in poultry pathogens (Olsen et al. 2012), which aligns with the findings of previous research (Behbahan et al. 2008; Gerchman et al. 2011, 2008; Gharaibeh and Al‐Rashdan 2011). Earlier studies identified mutations in genes encoding DNA gyrase and topoisomerase IV, indicating resistance to quinolones (Gautier‐Bouchardon et al. 2002; Reinhardt, Bébéar et al. 2002; Reinhardt, Kempf et al. 2002; Lysnyansky et al. 2008; Gautier‐Bouchardon 2018). A stepwise process, usually starting with mutations in the gyrA gene, may result in cross‐resistance to various fluoroquinolones (Wolfson and Hooper 1989; Hooper 2000). Recent findings also suggested that low‐level resistance can occur through efflux pumps, which can potentially be carried in plasmids (Jacoby 2005). In contrast, several earlier studies reported lower MICs for quinolones (Jordan et al. 1989; Pakpinyo and Sasipreeyajan 2007; Lysnyansky et al. 2012), mainly enrofloxacin (Hinz and Rottmann 1990; Zanella et al. 1998; Hannan 2000; Behbahan et al. 2008), than did the findings presented in this study.

Tetracyclines, such as oxytetracycline and chlortetracycline, demonstrated varying levels of sensitivity and resistance, reflecting the intricate nature of tetracycline resistance mechanisms in Mycoplasmas spp., which are attributed to efflux mechanisms and ribosomal protection proteins (Kreizinger et al. 2017; Emam et al. 2020). Among these antibiotics, chlortetracycline exhibited a greater MIC₅₀ value (0.12 µg/mL) than oxytetracycline (0.062 µg/mL) (Figure 1 and Table 1). Notably, the MG isolates showed 90% sensitivity to oxytetracycline (Table 2). These findings are consistent with prior research indicating the superior efficacy of oxytetracycline over chlortetracycline (Tanner and Wu 1992; Bradbury, Yavari, and Giles 1994; Behbahan et al. 2008; de Jong et al. 2021). In contrast, doxycycline had a relatively lower MIC₅₀ (0.031 µg/mL), suggesting its potential as an effective therapeutic option against MG. These results are consistent with previous studies emphasizing the effectiveness of doxycycline against Mycoplasmas spp., as well as its ability to enhance tissue penetration and prolong the half‐life (Newnham 1963; Kleven and Anderson 1971; Pakpinyo and Sasipreeyajan 2007; Gharaibeh and Al‐Rashdan 2011; Magiorakos et al. 2021). Increased resistance to one tetracycline within the family typically corresponds to increased resistance to other members (Gharaibeh and Al‐Rashdan 2011). Homozygous mutations at positions 965 and 967 of the rrs gene have crucial functions in elevating the MIC of oxytetracycline and characterizing the isolates as resistant (Gautier‐Bouchardon 2018). According to Kibeida (2011), tetracycline resistance often occurs due to the overexpression of bacterial membrane efflux pumps. However, contrasting findings by Lin et al. (1994) reported high MIC₅₀ values for oxytetracycline (>32 mg/L) in Taiwanese MG isolates. Additionally, Gautier‐Bouchardon (2018) noted an increase in the maximum MIC of oxytetracycline from 0.5 to 4 mg/L in MG.

Furthermore, within the macrolide category, tylosin, tilmicosin, tylvalosin, spiramycin and erythromycin had MIC₅₀ values varying (0.016, 0.016, 0.062, 1 and 16 µg/mL, respectively) (Figure 1 and Table 1), indicating a mix of sensitivity and resistance among isolates, with 90%, 30% and 80% sensitivity to tylosin, erythromycin and spiramycin, respectively (Table 2). These findings echo previous reports on the heterogeneous susceptibility of MG isolates to macrolides due to the extensive use of different MG treatment protocols (Wu et al. 2005; Kreizinger et al. 2017). Tylvalosin had the lowest MIC₉₀ (0.25 µg/mL) (range 0.001–1 µg/mL), which indicates its robust effectiveness against MG, as mentioned previously by many researchers who mentioned that tylvalosin is a newer antibiotic that was recently used in many countries (Zanella et al. 1998; Hannan 2000; Behbahan et al. 2008). According to Gharaibeh and Al‐Rashdan (2011), there was a statistically significant increase in resistance to all macrolides, notably erythromycin, where the MIC₅₀ for the 2007–2008 group was ≥64 mg/mL, whereas it was ≤0.031 mg/mL for the 2004–2005 group. In addition to Europe, Lin et al. (1994) reported high MIC₅₀ values of spiramycin (>32 mg/L) from Taiwanese MG isolates as early as 1994. In line with the findings of this study, erythromycin had higher MIC values than did the other antibiotics, indicating the reduced effectiveness of erythromycin, consistent with the documented emergence of macrolide resistance in Mycoplasma species (Bradbury, Yavari, and Giles 1994; Behbahan et al. 2008; Yang et al. 2020). Tylosin resistance in MG has been reported to extend more slowly than erythromycin resistance, and resistance to tylosin can result in cross‐resistance to erythromycin but not vice versa, as reported by Zanella et al. (1998) and Wu et al. (2005). Therapeutic challenges in treating MG infections in chickens have been documented worldwide, particularly concerning macrolides (Reinhardt, Kempf et al. 2002). Although MG is typically not intrinsically resistant to 14‐membered macrolides such as erythromycin, most strains have been historically susceptible to MG. However, elevated MICs of erythromycin, tylosin and tilmicosin have been observed for strains isolated before and after 2000 across different countries (Levisohn 1981; Whithear et al. 1983; Jordan et al. 1989; Tanner and Wu 1992; Hannan et al. 1997; Gerchman et al. 2011; Gharaibeh and Al‐Rashdan 2011; Ammar et al. 2016). Multiple point mutations in domain V of the 23S rRNA gene have been identified in macrolide‐resistant MG isolates from Egypt (Ammar et al. 2016). Lu et al. (2010) reported that no efflux mechanism associated with macrolide resistance was identified in Mycoplasma species. However, they reported the existence of an ermB methylase gene and three subtypes of an active efflux msr gene in a macrolide‐ and lincosamide‐resistant Ureaplasma urealyticum strain, a member of the Mycoplasmataceae family. In contrast to our findings, Gharaibeh and Al‐Rashdan (2011) noted that tilmicosin had exceptional effects on most of the MG isolates in their study. Although tylosin was used extensively before its introduction, tilmicosin displayed superior in vitro activity against MG isolates. Previous studies have reported that high resistance to erythromycin and tilmicosin can develop rapidly, possibly due to mutations that decrease the affinity of macrolides for ribosomes (Lucier et al. 1995; Gautier‐Bouchardon et al. 2002; Wu et al. 2005).

In the present study, all the isolates exhibited high susceptibility to tiamulin, with an MIC₅₀ of 0.008 µg/mL and a MIC range of <0.001–0.12 (Figure 1 and Table 1), aligning with 100% sensitivity based on a breakpoint of ≤8 µg/mL. However, Gharaibeh and Al‐Rashdan (2011) reported a tiamulin MIC ≤0.031 µg/mL for their isolates. This difference could be attributed to the limited use of tiamulin in broiler chickens due to concerns about its lethal interaction with the ionophore antibiotic feed additives utilized to manage coccidia (Horrox 1980; Islam, Klein, and Burch 2009). Nonetheless, the observed high susceptibility of MG isolates to tiamulin suggests its potential as a potent medication for breeders and layers that do not receive ionophore drugs. Like our findings, previous studies have shown that tiamulin is effective against MG isolates (Baughn et al. 1978; Jordan and Knight 1984; Jordan et al. 1989; Zanella et al. 1998; Hannan 2000; Behbahan et al. 2008; Ammar et al. 2016). Conversely, other researchers have associated decreased susceptibility to pleuromutilins (tiamulin or valnemulin) with point mutations in the 23S rRNA gene and L3 protein in various bacterial species (Li et al. 2010; Gautier‐Bouchardon 2018).

Lincomycin displayed an MIC₅₀ of 2 µg/mL (Figure 1 and Table 1), with 70% of the isolates categorized as sensitive (below the breakpoint of <8 µg/mL). Similarly, Bradbury, Yavari, and Giles (1994) reported that tylosin exhibited the highest activity, followed closely by lincomycin, oxytetracycline and spectinomycin. The MIC₅₀ of spectinomycin was 0.5 µg/mL (Figure 1 and Table 1). However, Behbahan et al. (2008) reported contrasting results, stating that lincomycin was generally more effective than spectinomycin against field isolates. Pleuromutilin‐resistant mutants displayed cross‐resistance to lincomycin, chloramphenicol and florfenicol (Sulyok et al. 2017). Mutants harbouring specific mutations, such as A2058G or A2059G, demonstrated cross‐resistance to lincomycin and macrolides, such as erythromycin, tilmicosin and tylosin (Gautier‐Bouchardon 2018). In another investigation, mutants specifically chosen for tiamulin resistance exhibited cross‐resistance to florfenicol and increased lincomycin MICs (Gautier‐Bouchardon 2018). Additionally, in vitro‐generated tetracycline‐resistant mutants exhibited cross‐resistance to tetracycline and spectinomycin (Sulyok et al. 2017).

The MIC₅₀ of dihydrostreptomycin was greatest at 32 µg/mL, ranging from 8 to more than 64 µg/mL (as presented in Figure 1 and Table 1). Lee, Miles and Inal (1987) documented resistance mutations to aminoglycosides across different Mycoplasma species, indicating resistance that could follow either a multistep or high‐level‐step pattern. The recorded MIC values highlight the diverse susceptibilities to antimicrobial agents among MG populations, revealing intricate resistance mechanisms involving factors such as efflux pumps, alterations in target sites and the genetic diversity of the bacteria (Gnanadhas, Marathe, and Chakravortty 2013; Morante et al. 2021). These varying patterns of antibiotic susceptibility stress the importance of considering distinct antibiotic classes and their specific effectiveness against MG strains when planning treatments.

4.2. Biocide Susceptibility Patterns

The improper haphazard use of antimicrobial agents can foster resistance development, especially for most poultry producers in Egypt who use heavy doses of antimycoplasma drugs during the first few days of life. Unlike antibiotics, biocides act on less specific target sites, potentially leading to nonspecific resistance mechanisms, and bacteria developing resistance against biocides is unlikely due to the covalent interaction between biocides and bacterial targets. Bacterial resistance to biocides may stem from intrinsic factors or acquired traits through genetic mutations or horizontal gene transfer (Gnanadhas, Marathe, and Chakravortty 2013). However, some studies have shown no clear relationship between MIC and the observed resistance phenomena (Moken, McMurry, and Levy 1997; Suller and Russell 1999; Meyer 2006). The MIC distributions of various biocides highlighted a range of susceptibilities among the tested isolates. Formalin consistently displayed effectiveness across isolates, as indicated by its MIC₅₀ of 125 µg/mL (Figure 2 and Table 3), which is consistent with its historical use in disinfection due to its broad‐spectrum antimicrobial properties (Dee et al. 2004). In contrast, phenol exhibited a broader range of MIC values, with an MIC₅₀ of 500 µg/mL (Figure 2 and Table 3), suggesting variable sensitivity among isolates, likely influenced by its diverse mechanism of action and potential for resistance development (Barry 1999; Verma et al. 2022). At present, there is a lack of established breakpoints for differentiating between biocide‐susceptible and biocide‐resistant strains. The comparison of MIC values for biocides remains challenging due to the absence of standardized experimental conditions across studies (Roedel et al. 2021). Microbial inactivation of biocides, including phenols, aldehydes and triclosan, represents an inherent resistance property observed in certain microbes (Klingeren 1993; Hay, Dees, and Sayler 2001; Meade, Waddell, and Callahan 2001). Researchers have previously linked the increased MIC of formalin to the presence of the transmissible plasmid adhC, which encodes a glutathione‐dependent formaldehyde dehydrogenase enzyme (Kaulfers and Brandt 1987; Kaulfers and Marquardt 1991; Kümmerle, Feucht, and Kaulfers 1996; Dorsey and Actis 2004; Rozman et al. 2021). Resistance mechanisms against phenol include target mutations, heightened target expression (the overexpressed genes mufA1 and mufM), active cell excretion and enzyme inactivation or degradation (Rozman et al. 2021). NADES, Halamid, Virkon‐S, MicroSet and SteriSet showed differing levels of effectiveness against MG. NADES had a lower MIC₅₀ value (31.25 µg/mL) (Figure 2 and Table 3), indicating its potential as an efficient biocide against MG. Halamid and Virkon‐S exhibited intermediate effectiveness, both with MIC₅₀ values of 15.63 µg/mL (Figure 2 and Table 3), suggesting that these agents are suitable disinfectants, although their efficacy might vary among different MG isolates. These susceptibility patterns highlight the importance of carefully choosing biocides based on their efficacy against specific MG strains. MicroSet demonstrated the lowest MIC₅₀ of 7.81 µg/mL (Figure 2 and Table 3), whereas SteriSet showed a higher MIC₅₀ of 62.5 µg/mL.

Certain strains may exhibit reduced sensitivity to biocides due to phenotypic alterations, such as the development of an outer membrane and specific outer membrane proteins (OMPs). These changes can impede the diffusion and uptake of biocides such as BC and chlorhexidine, contributing to increased insensitivity in these strains (Russell, Furr, and Maillard 1997; Murtough et al. 2001).

The overexpression of specific plasmid‐borne genes (qacA, qacB, smr qacH and qacJ) serves as a significant mechanism contributing to reduced sensitivity to biocides such as cetrimide, chlorhexidine gluconate, triclosan and BC (Nikaido 1998; Correa et al. 2008; Longtin et al. 2011). Microorganisms can develop resistance to biocides such as phenol, BC and PHMB through target alteration, which involves mutations in the genome leading to changes in biocide susceptibility. These alterations can impact various processes, such as inhibiting bacterial fatty acid synthesis, modifying the membrane lipid composition, decreasing membrane fluidity, altering the physicochemical features of the cell surface and modifying lipid membrane permeability (Heath et al. 1999; Bisbiroulas et al. 2011; Frenzel et al. 2011; Zhang et al. 2011). Since the 1960s, the transfer of genetic elements and the increase in resistant bacteria have been widely recognized phenomena. Plasmids, in particular, are known as carriers of biocide resistance and are specifically associated with phenols (Townsend et al. 1983; Townsend, Grubb, and Ashdown 1983; Townsend et al. 1984) and QACs (Wright, Berghuis, and Mobashery 1998; Kqcken, Feucht, and Kaulfers 2000; Rodríguez‐Blanco, Lemos, and Osorio 2012) in various bacteria (Cooksey 1987; Russell 1997). The bacterial resistance mechanisms against biguanides are incompletely understood (Gnanadhas, Marathe, and Chakravortty 2013). SteriSet, which shares a similar composition with MicroSet, displayed higher MIC values, possibly attributed to lower concentrations of biocidal active elements in SteriSet. The expression of stress response genes, such as those encoding Msn2/4 transcription factors after PHMB treatment, may contribute to the resistance mechanism against SteriSet (Elsztein, de Lucena, and de Morais 2011). Whole‐genome transcriptional profiling of PHMB‐treated cells revealed associations between specific genes responsible for nucleic acid metabolism and DNA repair, which contribute to PHMB tolerance (Allen, White, and Morby 2006). Virkon‐S, which contains peroxygen and chlorine‐releasing compounds such as Halamid, affects multiple bacterial targets, including the membrane (Russell and Day 1996), proteins (Suller and Russell 1999), nucleic acids (Russell and Day 1996) and cell wall (Moken, McMurry, and Levy 1997), potentially explaining the lower MICs observed in this study (Scientific Committee on Emerging and Newly Identified Health Risks [SCENIHR] 2009; Gnanadhas, Marathe, and Chakravortty 2013).

4.3. The Interplay Between Antibiotics and Disinfectants

There was a significant difference (p ≤ 0.05) in the MICs of the biocides among the antibiotic‐sensitive and antibiotic‐resistant strains (Table 4). Formalin had significantly (p ≤ 0.05) different MICs against isolates sensitive and resistant to three antibiotics (enrofloxacin, spiramycin and lincomycin), phenol against spiramycin and SteriSet against spiramycin. Previous studies often used correlation analysis to explore such associations (Lerma et al. 2015; Oggioni et al. 2015; Roedel et al. 2021). This study explored the correlation between antibiotic and disinfectant MICs against MG isolates and revealed a strong positive correlation between certain biocides and antibiotic MICs (Table 5). These correlations suggest potential interactions or shared resistance mechanisms between specific antimicrobial agents. The observed relationships underscore the need for further investigations into the molecular mechanisms underlying these associations. These findings echo recent studies illustrating how biocides and antibiotics interact to shape microbial susceptibility profiles (Kümmerer 2004). Cross‐resistance and co‐resistance mechanisms play roles in the biocide‐induced emergence and spread of AMR. For instance, cross‐resistance has been observed in multiple bacteria due to the use of PCMC and chlorhexidine (SCENIHR 2009; Fernando et al. 2014; Wand et al. 2017; Stein et al. 2019; Rezasoltani et al. 2020; Ansari et al. 2021; Rizvi and Ahammad 2022). Coresistance involves the coexistence of two or more resistance genes that encode independent resistance methods; these genes are subsequently transmitted together and detected jointly in a new bacterial cell, such as those linked to QAC (SCENIHR 2009; Partridge et al. 2018; Roedel et al. 2021).

4.4. Predictive Models and Future Perspectives

It is essential to acknowledge studies documenting disinfectant‐induced AMR in bacterial strains (Caselli et al. 2018; Kampf 2018a). This potential scenario might have contributed to the antibiotic resistance of our strains if they had been subjected to disinfectants earlier. The regression analysis in our study highlighted the varying predictive ability of biocides for antibiotic susceptibility (Table 6), revealing specific biocides with significant predictive value for certain antibiotics. These findings suggest potential coresistance or shared mechanisms among MG isolates, emphasizing the need for a comprehensive understanding of how antibiotics and disinfectants interact to shape MG resistance profiles. Previous research has suggested that reduced chlorhexidine susceptibility might be accompanied by resistance to several antibiotics, potentially due to elevated efflux of both substances through the same route (Dopcea et al. 2020; da Silva et al. 2023). Exposure to subinhibitory concentrations of QAC, biguanides and phenolic compounds has been related to decreased biocide susceptibility and the development of bacterial AMR (Langsrud, Sundheim, and Holck 2004; Soumet et al. 2012, Soumet et al. 2016; Fernando et al. 2014; Wand et al. 2017). Previous studies have noted that the colocalization of biocide and AMR genes, often on the same plasmids, might offer bacteria a selective advantage, particularly at higher biocide concentrations (da Silva et al. 2023). Additionally, multiple gene cassettes can be organized in sequence within elements, providing added resistance to various antibiotics, such as ß‐lactams, tetracycline, gentamicin and aminoglycosides (Zou et al. 2014; Zhang et al. 2016; Deus et al. 2017). Our findings align with those of a study by Chen et al. (2020), which reported that carbapenem‐resistant Klebsiella pneumoniae strains with a MIC of 32 µg/mL exhibited reduced susceptibility to chlorhexidine, which is close to the ecological cutoff limit of 64 µg/mL (Morante et al. 2021). Efflux pump overexpression has been identified as a major mechanism linked to antimicrobial cross‐resistance, as observed in various studies (Chuanchuen et al. 2001; Poole 2002; Mc Cay, Ocampo‐Sosa, and Fleming 2010; Kampf 2018b; Kim, Weigand et al. 2018; Kim, Hatt et al. 2018; Tetard et al. 2019). However, conflicting reports exist regarding the correlation between antibiotic resistance and decreased susceptibility to disinfectants, with studies providing inconsistent findings (Russell 2003; Morrissey et al. 2014; Schwaiger et al. 2014; Kampf 2018b; Feßler et al. 2022b, 2022a). Future studies could explore the underlying mechanisms behind the observed correlations between biocides and antibiotics. Understanding the interrelationships between these agents at the molecular level could help elucidate cross‐resistance or coresistance mechanisms, aiding in the design of effective intervention strategies.

4.5. Clinical Implications and Limitations

Understanding the interplay between biocides and antibiotics is pivotal for mitigating AMR and ensuring effective disease control in poultry production. The variability in resistance patterns necessitates constant surveillance and periodic updates of antimicrobial stewardship programmes on poultry farms. This study has several limitations. First, the absence of standardized breakpoints and ECOFFs for disinfectants against MG poses challenges in interpreting biocide susceptibility. Additionally, the study did not investigate the molecular procedures underlying the recorded resistance, warranting future research in this domain.

5. Conclusion

The threat of AMR in MGs poses significant challenges to poultry health and industry. The resistance mechanisms of MG to antibiotics, coupled with potential cross‐resistance to disinfectants, demand urgent attention. The evolution of resistance in MG strains emphasizes the need for strict surveillance programmes to monitor patterns. Addressing AMR in MGs requires collaborative efforts to implement effective surveillance, prudent antimicrobial use and the exploration of alternative therapies. The complexity of cross‐resistance necessitates thorough research to understand the underlying mechanisms involved and enforce rigorous biosecurity measures in poultry farming. A comprehensive approach is vital for combating AMR in MGs, ensuring poultry health and industry sustainability. Further investigations are recommended to determine the antibacterial effect of natural antibiotic alternatives (herbal extracts, essential oils, phytobiotics, etc.) that were developed to limit the spread of AMR to MG among poultry houses.

Author Contributions

All the authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by K.M.A., H.M.S., A.A.S., and H.A.K. The first draft of the manuscript was written by K.M.A., H.M.S., R.A.A., E.O.H., B.G., A.A.S., and H.A.K., and all the authors commented on previous versions of the manuscript. All the authors have read and approved the final manuscript.

Ethics Statement

The experimental protocol was approved by the Institutional Animal Care and Use Committee, Faculty of Veterinary Medicine, Cairo University. Approval number: Vet CU 25122023850.

Consent

All the authors provided informed consent for this submission.

Conflicts of Interest

The authors declare no conflicts of interest.

A Randomized Controlled Trial

Not applicable

Data Availability Statement

The data and materials will be made available upon reasonable request.