https://orcid.org/0000-0001-7575-5377
Figures
Abstract
Objective
The purpose of this study is to isolate and characterize bacteria from corneal ulcers and screen them for multiple antibiotic resistance, contributing to understanding patterns of resistance and identifying effective treatment strategies.
Subjects, participants and/or controls
The study involved the isolation of pathogenic bacterial strains from 15 corneal ulcer samples.
Methods, intervention, or testing
We isolated and characterized bacterial strains from corneal ulcer samples, which were routinely collected at an ophthalmology clinic for suspected infectious corneal ulcers and examined their resistance to twenty-six routinely prescribed antibiotics. The bacterial species included methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa and E. coli among others. Antibiotic resistance patterns were assessed, with a focus on commonly used antibiotics such as ciprofloxacin, gentamycin, and vancomycin.
Main outcome measures
The study measured the healing rates of corneal ulcer patients, antibiotic resistance levels across bacterial strains, and specific treatment responses to various antibiotics.
Results
Thirteen out of fifteen corneal ulcer patients healed completely, while two patients developed full corneal opacity due to infection with P. aeruginosa. Significant differences in resistance were observed among the bacterial strains. MRSA exhibited the highest resistance levels, particularly to multiple antibiotics. Ciprofloxacin and gentamycin were less effective against the isolated strains, while vancomycin showed reduced resistance, against Gram-positive bacteria. Both ciprofloxacin and co-trimoxazole exhibited strong connections with multiple bacterial strains, indicating high resistance.
Conclusion
This study underscores the need for ongoing surveillance of antibiotic resistance patterns to guide treatment approaches and slow the spread of resistant bacteria. It also highlights the importance of developing new antibiotics and alternative therapies, with an emphasis on understanding the molecular mechanisms behind resistance to combat the global health posed by antibiotic-resistant infections.
Citation: Obaid N, Saeed A, Abbas S, Perveen S, Younas H (2025) Resistance trend in bacteria isolated from corneal ulcers: A retrospective analysis from Pakistan. PLoS One 20(6): e0325157. https://doi.org/10.1371/journal.pone.0325157
Editor: Mengistu Hailemariam Zenebe, Hawassa University College of Medicine and Health Sciences, ETHIOPIA
Received: November 18, 2024; Accepted: May 7, 2025; Published: June 5, 2025
Copyright: © 2025 Obaid et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: NO authors have competing interests.
Introduction
Corneal ulcers are disruption of corneal epithelium, often resulting from collagenases produced by regenerating epithelial cells and inflammatory cells, which contribute to ulcer formation. Once healed, corneal ulcers may lead to corneal scarring, a significant cause of blindness [1]. Epidemiological data indicates that corneal ulcers are primarily caused by trauma, often followed by corneal infections. Men are affected more frequently than women, with the majority of cases occurring in individuals between the age of 21 and 40, typically within the productive working age. Farming is the most common occupation associated with these cases [2,3]. Corneal ulcers can result from bacterial, fungal and viral infections as well as non-infectious causes such as chemical burns, radiation, and extreme temperatures. The most common bacterial pathogens responsible for corneal ulcers include Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus pneumoniae, Streptococcus pyogenes, Moraxella spp, Pseudomonas aeruginosa, Proteus spp, Klebsiella pneumoniae, and Escherichia coli [4,5]. The most common fungi responsible for corneal infection include Culvularia sp, Aspergillus sp, Paecilomyces sp, Cladosporium spp, Bipolaris sp, Altenaria spp, and Candida spp [6,7]. Among these, Aspergillus species are predominant in cases associated with trauma, while Candida infections are more frequently seen in individuals with predisposing factors such as contact lens use, ocular surface diseases and immunosuppression [8,9]. Fungal infections often present with slow-progression ulcers with feathery edges, satellite lesions, and hypopyon formation, making early diagnosis and appropriate antifungal therapy crucial to prevent complications [10,11]. Corneal infections can also be caused by viruses such as Herpes Simplex, Papillomavirus, Retrovirus, Rubella virus, and Adenoviruses [12], or parasites like Acanthamoeba [13]. Non-infectious causes of corneal ulcers include trauma from acidic or alkaline substances, radiation or extreme temperature.
The empirical use of antibiotics is common in ophthalmic infections in Pakistan, often due to limited access to microbiological diagnostic and self-medication practices [14]. Fluoroquinolones, particularly ciprofloxacin are widely prescribed for bacterial keratitis, while fortified aminoglycosides and cephalosporins are used for severe infections [15]. However, inappropriate antibiotic use, including over-the-counter availability, prolonged dosing and the use of broad-spectrum antibiotics without culture-based selection, has contributed to antibiotic resistance [16]. These factors drive the selection of resistant bacterial strains, complicating treatment outcomes and increasing the risk of recurrent or refractory infection. Over the past decade, studies conducted in Pakistan’s Sindh region particularly in Hyderabad and Karachi, have identified the highest level (median resistance – 90.5%) of resistance of Salmonella species to ciprofloxacin [17]. In Punjab, a recent study demonstrated highest fluoroquinolone resistance in Staphylococcus aureus, with prevalence rate of 91% to norfloxacin, 83.9% to ciprofloxacin, 78% to azithromycin, 77% to erythromycin and 69.8% to moxifloxacin [18]. Similarly, Gondal et al. [19] reported a surge in carbapenem-resistance Enterobacterales in a Lahore tertiary care hospital, exceeding earlier recorded levels, i.e., 21.84% in another tertiary care hospital [20] and 25.55% Rawalpindi’s community settings [21].
Early diagnosis and timely administration of appropriate antimicrobial therapy is crucial for preventing permanent visual loss and enhancing patient’s quality of life [22,23]. The choice of antimicrobial treatment is guided by the results of corneal smear, culture and antibiotic sensitivity. Some infections in corneal ulcers are caused by resistant bacteria. Fluroquinolone resistance is particularly observed in cases with prior antibiotic use. P. aeruginosa has been reported to exhibit resistance to moxifloxacin [24,25] as well as other fluoroquinolones [26].
This study aims to investigate the etiology and antibiotic sensitivity of pathogens causing corneal ulcers at Fauji Foundation Hospital. The findings will contribute to improving the diagnosis and treatment of this potentially blinding condition, while also addressing the growing issue of microbial antibiotic resistance.
Materials and methods
Ethical approval statement
The study obtained ethical approval from the Ethical Committee of the Fauji Foundation Hospital.
Ethics reference number: 804/RC/FFH/RWP.
Study design
This retrospective study was conducted at the Ophthalmology and Microbiology department of Fauji Foundation Hospital, Rawalpindi, Pakistan Rawalpindi from January 2024 to August 2024. The inclusion criteria comprised all adult patients (age ≥18 years) clinically diagnosed with infectious bacterial who underwent corneal scraping for microbiological analysis during the study period.
Exclusion criteria were: (1) patients with non-infectious keratitis (e.g., herpetic keratitis, neurotrophic keratitis with reduced corneal sensitivity); (2) those with concomitant ocular comorbidities that could confound healing assessment (uncontrolled glaucoma, uveitis, or non-healing ulcers from other etiologies); and (3) systemically immunocompromised patients (uncontrolled diabetes [HbA1c >8%], active malignancy, HIV/AIDS, or chronic immunosuppressive therapy). These criteria ensured homogeneous evaluation of antimicrobial resistance patterns in typical infectious keratitis cases while excluding confounding factors.
Sample collection, isolation, and identification of strains
In accordance with the Helsinki Declaration of 2013, samples were collected as part of the routine practice of ophthalmology clinic for suspected infectious corneal ulcers [27]. A comprehensive external and slit-lamp biomicroscopic examination was conducted on all suspected corneal ulcers by an ophthalmology resident [28].
Corneal scraping was performed under slit-lamp magnification, after administrating a drop of 0.5% proparacaine hydrochloride (Alcaine, Alcon laboratories) for topical anesthesia. Under sterile conditions, a sterile No. 15 Bard Parker blade was used to collect samples from the leading edge and base of the ulcer. The material obtained was then inoculated onto blood agar and Pronadisa medium (Pronadisa Lab. Conda, Madrid, Spain), chocolate agar (Pronadisa Lab. Conda, Madrid, Spain), MacConkey agar (Pronadisa Lab. Conda, Madrid, Spain), and Sabouraud dextrose agar (Pronadisa Lab. Conda, Madrid, Spain) for culture. The procedures followed the guidelines of the American Society for Microbiology [29]. A smear was also prepared for Gram staining to identify bacterial and fungal morphology.
Antibiotic susceptibility testing
The disc diffusion method, as described by Bauer et al. [30], was employed by placing antibiotic-impregnated discs onto Mueller-Hinton agar plates inoculated with the test bacteria. For cultured isolates, antibiotic susceptibility testing was conducted using Mast Discs (Mast Group, Merseyside, UK). The antimicrobial susceptibility patterns were interpreted using by CLSI M45-A3 (2015), (CLSI, 2015) and the 2022 CLSI M100 standard (CLSI, 2022) by measuring inhibition zone diameters (to the nearest 0.1 mm using calibrated calipers) and comparing them to CLSI breakpoints. For vancomycin and polymyxin B, minimum inhibitory concentration (MIC) testing was performed via broth microdilution (BMD) per CLSI guidelines. Susceptibility to the antibiotics listed in S1 Table was determined.
An antibiotic disc was selected based on Gram staining results and the distinct characteristics cultured colony. The microbiological report provided information on the sensitivity or resistance of each sample to the chosen antibiotic.
Statistical analysis
Microsoft office excel 2016v was used for the descriptive analysis of antibiotic resistance data. The significance of the data was further validated through a Two Way Analysis of Variance (ANOVA), with and without replication, where a p-value of less than 0.05 was considered significant. Other statistical analysis included estimating the frequency and percentage of interaction among selected antibiotics and bacterial strains. The clades were established based on the similarity of biochemical tests (S2 Table). Strains exhibiting similar results were grouped together using R software version 4.4.1.
Results and discussion
Sampling site
The Fauji Foundation Hospital (FFH), which is situated in Punjab, Pakistan, provided the study’s sample. The attached map shows the sampling site’s geographic context and gives a clear visual depiction of its location within Pakistan’s larger geography (Fig 1). Pakistan’s province borders, including those of Baluchistan, Sindh, Punjab, Khyber Pakhtunkhwa, Gilgit Baltistan, and Azad Kashmir, are depicted on the main map. A closer look at the sampling site is provided by the expanded depiction of the Punjab province in the inset map. The inset map shows the location of the sampling site, which is Fauji Foundation Hospital (FFH). The map indicates that this hospital is situated in the city of Rawalpindi. Grid coordinates are included in the inset map to aid in precisely locating FFH inside Punjab.
https://data.humdata.org/dataset/cod-ab-pak). The map was created in R using open-source mapping packages, ensuring accurate spatial representation of the selected regions.
The collection of the sample from FFH in Punjab is significant due to several reasons. Punjab is one of the most populous and agriculturally important provinces in Pakistan [31]. The data collected from this region can provide valuable insights into local environmental and health conditions [32]. Fauji Foundation Hospital is a well-established medical and research facility, ensuring the reliability and accuracy of the collected samples. The hospital’s infrastructure supports advanced medical research, making it an ideal location for such studies. The hospital’s central location within the province ensures that the sample is representative of the region’s demographic and environmental conditions.
Patient history and sample collection
Samples were obtained from 15 patients aged 10 and 69 years, during the period from January 2024 to August 2024, from the female eye ward at Fauji Foundation Hospital. These samples were collected from suspected infectious corneal ulcers as part of the routine practice in the ophthalmology clinic.
Patient management and treatment protocol
Each corneal ulcer follows a specific pattern, and treatment in initiated accordingly at the time of presentation, even before the Gram-staining report. Upon presentation, corneal ulcer scrapings were collected and sent for Gram-staining, culture and sensitivity testing. However, treatment of all the patients began immediately after they were diagnosed with corneal ulcers. If the culture and sensitivity report later indicate a change is necessary, treatment can be adjusted accordingly. All the patients were admitted for 5–7 days in the eye ward of the hospital. In recent years, there has been more emphasis on culture and sensitivity testing, as the number of resistant corneal ulcers cases has risen.
Isolation of strains and morphological characteristics
The colony morphology of the bacterial isolates was assessed based on their appearance. All colonies were circular with raised elevation and smooth margins. Of the fifteen isolates, five were cocci, and the remaining were classified as bacilli. All the isolates were mesophiles, exhibiting growth at pH 7–9. Isolates EW8, EW9, EW11, EW12, EW13, EW15 had mucilaginous, while other displayed a creamy texture.
Pathogen-specific responses in corneal ulcer treatment
Out of the 15 patients, 13 experienced complete healing of the infection following treatment. However, two patients developed full corneal opacity, which occurred because they sought medical attention almost one week after the onset of the ulcer. In both cases, the causative agent was P. aeruginosa. The corneal ulcer (as shown in Fig 2a) was taken after two weeks of treatment and shows the ulcer fully healed. The ulcer was caused by Staphylococcus aureus, a common bacterium known to cause various infections. The patient sought medical help within 24 hours, allowing for prompt treatment, which led to complete healing. In contrast, the corneal ulcer shown in the Fig 2b, was presented at much late stage, one week after the onset of ulcer. Due to the delayed presentation, the infection had progressed, and the patient developed a corneal opacity. The identified bacterium in this case was Pseudomonas aeruginosa, an opportunistic pathogen (Fig 2a and b).
Biochemical characterization of the bacterial isolates
The bacterial Isolates were identified and grouped into distinct clusters based on their phenotypic and biochemical characteristics. It was observed that the majority of the isolates were unable to metabolize different sugars (S2 Table). Based on the biochemical characterization strains EW1, EW2, EW4 and EW10 were identified as Staphylococcus aureus, of which three were methicillin resistant Staphylococcus aureus (MRSA). Staphylococcus aureus were catalase, coagulase and DNase positive, and two staphylococci appeared to be coagulase negative (Fig 3). Strains EW5 and EW6 were clustered together, identified as Escherichia coli (EC6, EC7) and utilized same sugars. Strains EW8, EW9, EW11, EW12, EW13, EW15 were grouped together in a single clade and were identified as Pseudomonas species. A few other bacterial isolates, including Streptococcus spp., (STC), Stenotrophomonas maltophilia (STM), as well as EC6 and EC7 demonstrated ability to utilize D-glucose, nitrate reductase. Only one isolate, Haemophilis spp (HAE), showed hydrogen sulphide gas production.
Similarity between isolates was assessed through 10 biochemical sugar fermentation tests using the API 10 S strips, with results converted into binary data (0 for negative and 1 for positive) using R software. The Jacquard coefficient was employed to estimate the similarity between the isolates, and clustering was performed using the unweighted average linkage method. This approach grouped bacterial isolates with highest similarity, providing a clear visualization of their relatedness.
Antibiotic resistance profile of bacterial species
Methicillin-resistant coagulase-negative staphylococci (MR-CoNS) exhibited a multidrug-resistant profile, showing resistance to ceftriaxone (zone <24 mm), cloxacillin (zone ≤10 mm), co-trimoxazole (zone ≤10 mm), imipenem (zone ≤16 mm), cephradine (zone ≤14 mm), augmentin, and ampicillin (zone ≤10 mm). This resistance pattern is consistent with typical MR-CoNS phenotypes, where mecA-mediated β-lactam resistance frequently coexists with resistance to other antibiotic classes due to selective pressures in healthcare settings [33,34]. Studies from India have reported widespread dissemination of the mecA gene, contributing to the rise of MR-CoNS. Accordingly, this study sought to assess the prevalence of mecA and its correlation with antimicrobial resistance [33,35]. Vancomycin susceptibility testing revealed MICs of ≤2 µg/mL for the MRSA isolates, classifying them as sensitive according to CLSI guidelines. Streptococcus spp. isolates were resistant to ceftriaxone and penicillin (both zones <24 mm) but remained susceptible to linezolid (zone ≥21 mm). This resistance may be attributed to mutations in penicillin-binding proteins (PBPs) or efflux mechanisms, while sustained susceptibility to linezolid likely reflects its limited clinical use and slow resistance development [36].
E. coli isolates were resistant to ceftriaxone (zone <23 mm), co-trimoxazole (zone ≤10 mm), gentamicin (zone ≤12 mm), and ampicillin (zone ≤13 mm) but remained susceptible to amikacin (zone ≥17 mm), doxycycline (zone ≥14 mm), and imipenem (zone ≥23 mm). This profile is consistent with extended-spectrum β-lactamase (ESBL)-producing strains [37]. Among Pseudomonas isolates (n = 6), one strain exhibited resistance to both amikacin (zone ≤14 mm) and gentamicin (zone ≤12 mm), while all isolates were uniformly resistant to ciprofloxacin (zone ≤15 mm). This resistance pattern suggests the presence of upregulated efflux pumps conferring fluoroquinolone resistance, along with sporadic occurrence of aminoglycoside-modifying enzymes [38].
Species-specific antibiotic resistance trends
The distribution of resistance levels across various antibiotics for different bacterial species can be seen in Fig 4. The antibiotic ciprofloxacin shows the highest cumulative resistance level, with a notable contribution from various bacterial species. Methicillin-resistant, coagulase-negative staphylococci (MR-CoNS) and EC6 show significant resistance, indicating these bacteria are particularly resistant to ciprofloxacin. This suggests ciprofloxacin is less effective against these species. Both gentamicin and co-trimoxazole show high cumulative resistance levels. For gentamicin, a significant portion of resistance comes from methicillin resistant Staphylococcus aureus (MRSA) strains. Co-trimoxazole also shows high resistance levels, primarily contributed by EC6 and MR-CoNS. This indicates these antibiotics face substantial resistance from multiple bacterial species, suggesting limited effectiveness. Whereas amikacin shows lower resistance levels compared to ciprofloxacin and gentamicin. The resistance is primarily from methicillin resistant Staphylococcus aureus strains, with minor contributions from other species. This suggests amikacin is more effective against a broader range of bacteria. Vancomycin shows relatively overall lower resistance levels, suggesting that vancomycin remains effective against many bacterial species, except MRSA still poses a challenge. Overall, ciprofloxacin, gentamicin, and co-trimoxazole exhibit high resistance levels from multiple bacterial species, indicating the need for alternative treatments or combination therapies to manage infections caused by these resistant strains. Antibiotics like vancomycin and amikacin show relatively lower resistance levels, suggesting they are more effective against a broader range of bacterial species. The high resistance observed in MRSA strains across multiple antibiotics highlights the need for targeted interventions and continuous monitoring to manage and mitigate antibiotic resistance in these species.
The x-axis represents the antibiotics, while the y-axis indicates the cumulative resistance levels. Each color in the bars corresponds to a specific bacterial species, as denoted in the legend on the right.
The distribution of resistance levels among various bacteria is shown in Fig 5. Each bacterial species is represented by a distinct color, allowing for a clear comparison of their resistance profiles. MR-CoNS (shown in pink) demonstrates a notable density at resistance level 3, signifying a significant (P < 0.05) proportion of this bacteria type being resistant to antibiotics. This high level of resistance is concerning and underscores the need for targeted interventions to manage and mitigate resistance in MR-CoNS. Coagulase-negative Staphylococci (depicted in orange) peaks around level 1, indicating that these bacteria are generally sensitive to antibiotics. This suggests that standard antibiotic treatments remain effective for Coagulase-negative Staphylococci. Both EC6 and EC7 (represented in shades of yellow and light green, respectively) exhibit a bimodal distribution with peak at 1 and 3, indicating substantial variability in resistance levels. This suggests the presence of both low and high resistance strains within these types, complicating treatment efforts and highlighting the need for personalized treatment strategies based on specific resistance profiles [39]. The significant (P < 0.05) variability suggests the need for personalized treatment strategies based on specific resistance profiles. This implies sensitivity to the antibiotics used.
The y-axis indicates the density, and the x-axis represents the resistance levels. The density plot illustrates the distribution of resistance levels for various bacterial species, classified into four categories: 0 (antibiotic not used), 1 (sensitive), 2 (intermediate), and 3 (resistant).
MRSA11 and MRSA5 (depicted in shades of blue) display significant (P < 0.05) peaks at resistance level 3, similar to MR-CoNS. MRSA strains showed resistance to imipenem, co-trimoxazole, cloxacillin, cephradine, ceftriaxone, augmentin, ampicillin, cloxacillin, penicillin, ciprofloxacin, gentamicin, meropenem. This highlights the high resistance in these MRSA strains, pointing towards a critical need for alternative therapeutic strategies and resistance management practices [40]. The P. aeruginosa (PA) groups, represented in various shades of purple) show high resistance to some commonly used antibiotics such as (gentamicin, amikacin, ciprofloxacin, polymyxin B, colistin, aztreonam, ceftazidime, imipenem, meropenem, tigecycline), suggesting continuous monitoring [41]. Lastly, Streptococcus spp., (STC) and Stenotrophomonas maltophilia (STM) primarily peak at level 1, indicating that these bacteria are generally sensitive to antibiotics, which is a positive indication for current treatment efficacy. This density plot underscores the diverse responses of bacterial species to antibiotics, with particular concern for those exhibiting high resistance levels like MRSA and P. aeruginosa strains. The variability in antibiotic sensitivity and resistance highlights the necessity for continuous surveillance, prudent antibiotic use, and the development of new treatment strategies to effectively manage and reduce antibiotic resistance in these bacterial populations [42]. The wide distribution of resistance levels among bacteria in clinical settings underscores the challenges in treating infections caused by these bacteria.
Comparative resistance trends
Our study documented concerning resistance patterns to empiric antibiotics for corneal ulcers, revealing both regional consistencies and global divergences. P. aeruginosa isolates demonstrated particularly alarming resistance to ciprofloxacin (85%), substantially exceeding global averages of 50–65% [41] while matching South Asian reports of 75–90% resistance from India [25,26,43] and 80–90% from Pakistan [24]. This regional epidemic of fluoroquinolone resistance likely stems from widespread empiric use and unregulated antibiotic access [14], compelling consideration of alternative therapies like fortified aminoglycosides or combination regimens in South Asian clinical practice.
MRSA isolates mirrored this troubling pattern, exhibiting 65–85% ciprofloxacin resistance comparable to South Asian reports [26,44] and global averages of 50–70% [41,45,46], further implicating Pakistan’s fluoroquinolone misuse [14] in undermining this drug’s empirical utility. While gentamicin resistance in MRSA (67%) surpassed global rates (50–60%), amikacin retained efficacy against E. coli vs. studies showed about 15–25% resistance regionally [2,47]. This suggests amikacin’s potential as a first-line alternative in settings with high gentamicin resistance, though local surveillance is critical given variability in P. aeruginosa susceptibility (33% resistant to gentamicin in our study.
Carbapenem resistance in P. aeruginosa (40%) was double global rates (10–20%) [48,49], reflecting regional dissemination of metallo-β-lactamase genes. Conversely, vancomycin resistance in MRSA remained low, consistent with Pakistan’s restricted use [23,47], affirming its role for Gram-positive infections despite rising VRSA reports elsewhere. These findings collectively mandate urgent implementation of antimicrobial stewardship programs, enhanced diagnostic capacity, and regionally tailored treatment guidelines to address this mounting public health crisis.
Frequency of interactions between each antibiotic and bacterial strain
The frequency of interactions between each antibiotic and bacterial strain is visualized using chord diagram (Fig 6). The significant variability in resistance levels across antibiotics underscores the challenge of treating bacterial infections effectively. Some antibiotics face widespread resistance, making them less reliable, while others still show potential effectiveness [50]. Each antibiotic and bacterial strain is represented by a segment along the circumference of the circle. The connections (chords) between segments represent the resistance levels. The width of each chord indicates the resistance level, with thicker chords representing higher resistance levels and thinner chords representing lower resistance levels [51]. Antibiotics like ciprofloxacin and co-trimoxazole have multiple thick connections to various bacterial strains, indicating high resistance levels [48,52]. Conversely, antibiotics like linezolid and meropenem have fewer and thinner chords, indicating lower resistance levels. Amikacin shows interactions with several bacterial strains, indicating its broad usage. ceftriaxone also shows multiple interactions, suggesting its extensive use across different strains. Bacterial Strains including Coagulase-Negative Staphylococci and methicillin-resistant Coagulase-Negative staphylococci (MR-CoNS) show interactions with multiple antibiotics, indicating a pattern of multi-drug usage possibly due to resistance issues. Coagulase-Negative Staphylococci’s interactions with amikacin, ceftriaxone, and clindamycin highlight its multi-drug resistance profile, which is a significant concern in clinical settings. Streptococcus spp., and MRSA also show significant interactions, suggesting challenges in treating these strains effectively. By understanding these interactions, healthcare providers can make informed decisions to optimize treatment protocols and mitigate the development of antibiotic resistance.
The relationships between various bacterial categories based on their antibiotic resistance profiles are shown in correlation matrix plot (Fig 7). Strong positive correlations, indicated by dark blue circles, are observed within certain groups, such as between MRSA categories such as MRSA11 and MRSA5 and within the P. aeruginosa categories. These strong correlations suggest that these bacteria might share similar resistance mechanisms, possibly due to genetic similarities or horizontal gene transfer. Conversely, negative correlations, indicated by lighter red circles, are less frequent but present, such as between P. aeruginosa and S. aureus strains, suggesting inverse resistance patterns possibly due to competitive interactions or different ecological niches [54].
The axes of the plot represent the same bacterial categories, and the color and size of the circles denote the strength and direction of the correlations.
Conclusion
The emergence and spread of antibiotic resistance represent a critical global health challenge, contributing significantly to increased morbidity and mortality across all regions. This study characterized antibiotic resistance patterns in a select group of clinical isolates, revealing key trends that warrant further investigation in larger cohorts. Most notably, we observed complete resistance to ciprofloxacin across all Pseudomonas isolates and high-level multidrug resistance in MR-CoNS strains—findings consistent with established regional resistance patterns. These results align with surveillance data suggesting declining fluoroquinolone efficacy against Gram-negative pathogens and persistent β-lactam resistance in staphylococci. Understanding these resistance dynamics is essential for guiding clinical decision-making and implementing effective infection control measures. While these preliminary findings demonstrate the value of local susceptibility monitoring for detecting emerging trends, future studies with expanded isolate collection and molecular characterization of resistance mechanisms would strengthen the evidence base.
Supporting information
S1 Table. Antibiotic resistant spectra of bacterial isolates from corneal ulcer samples.
https://doi.org/10.1371/journal.pone.0325157.s001
(DOCX)
S2 Table. Biochemical characters of the bacterial isolates obtained from corneal ulcer samples.
https://doi.org/10.1371/journal.pone.0325157.s002
(DOCX)
References
- 1. Rose-Nussbaumer J, Prajna NV, Krishnan KT, Mascarenhas J, Rajaraman R, Srinivasan M, et al. Vision-related quality-of-life outcomes in the mycotic ulcer treatment trial I: a randomized clinical trial. JAMA Ophthalmol. 2015;133(6):642–6. pmid:25764482
- 2. Chidambaram JD, Venkatesh Prajna N, Srikanthi P, Lanjewar S, Shah M, Elakkiya S, et al. Epidemiology, risk factors, and clinical outcomes in severe microbial keratitis in South India. Ophthalmic Epidemiol. 2018;25(4):297–305. pmid:29580152
- 3. Tewari A, Sood N, Vegad MM, Mehta DC. Epidemiological and microbiological profile of infective keratitis in Ahmedabad. Indian J Ophthalmol. 2012;60(4):267–72. pmid:22824594
- 4. Zare M, Torbati PM, Asadi-Amoli F, Talebnejad M, Parvizi M, Nasiri Z, et al. Microbiological profile of corneal ulcers at a tertiary referral center. Med Hypothesis Discov Innov Ophthalmol. 2019;8(1):16–21. pmid:30923718
- 5. Suwal S, Bhandari D, Thapa P, Shrestha MK, Amatya J. Microbiological profile of corneal ulcer cases diagnosed in a tertiary care ophthalmological institute in Nepal. BMC Ophthalmol. 2016;16(1):209. pmid:27899093
- 6. Ho JW, Fernandez MM, Rebong RA, Carlson AN, Kim T, Afshari NA. Microbiological profiles of fungal keratitis: a 10-year study at a tertiary referral center. J Ophthalmic Inflamm Infect. 2016;6(1):5. pmid:26897131
- 7. Naiker S, Odhav B. Mycotic keratitis: profile of Fusarium species and their mycotoxins. Mycoses. 2004;47(1–2):50–6. pmid:14998400
- 8. Brown L, Leck AK, Gichangi M, Burton MJ, Denning DW. The global incidence and diagnosis of fungal keratitis. Lancet Infect Dis. 2021;21(3):e49–57. pmid:33645500
- 9. Khambati A, Wright RE 3rd, Das S, Pasula S, Sepulveda A, Hernandez F, et al. Aspergillus endophthalmitis: epidemiology, pathobiology, and current treatments. J Fungi (Basel). 2022;8(7):656. pmid:35887412
- 10. Woodland RM, Darougar S, Thaker U, Cornell L, Siddique M, Wania J, et al. Causes of conjunctivitis and keratoconjunctivitis in Karachi, Pakistan. Trans R Soc Trop Med Hyg. 1992;86(3):317–20. pmid:1412664
- 11. Awad R, Ghaith AA, Awad K, Mamdouh Saad M, Elmassry AA. Fungal keratitis: diagnosis, management, and recent advances. Clin Ophthalmol. 2024;85–106.
- 12.
Kim TI, Azar DT, Pavan-Langston D. Viral disease of the cornea and external eye. In: Albert and Jakobiec’s Principles and Practice of Ophthalmology. Cham: Springer International Publishing; 2022. p. 187–279.
- 13. Bagga B, Sharma S, Gour RPS, Mohamed A, Joseph J, M Rathi V, et al. A randomized masked pilot clinical trial to compare the efficacy of topical 1% voriconazole ophthalmic solution as monotherapy with combination therapy of topical 0.02% polyhexamethylene biguanide and 0.02% chlorhexidine in the treatment of Acanthamoeba keratitis. Eye (Lond). 2021;35(5):1326–33. pmid:32719525
- 14. Iqbal A, Khan S. Outbreak of acute conjunctivitis in Karachi: a perfect ground for misuse of topical antibiotics. J Pak Med Assoc. 2024;74(7):1409. pmid:39028096
- 15. Dikmetaş Ö, Deniz Y, Kocabeyoğlu S, Başol M, İrkeç M. The value of fortified aminoglycoside/cephalosporin treatment as first-line treatment and in fluoroquinolone-resistant bacterial keratitis. Turk J Ophthalmol. 2020;50(5):258–63. pmid:33342191
- 16. Habib A, Nazir Z, Ali T, Akilimali A. Rising rates of conjunctivitis in Pakistan: epidemic, challenges, solutions, and recommendations. IJS Glob Health. 2024;7(2):e0422.
- 17. Bilal H, Khan MN, Rehman T, Hameed MF, Yang X. Antibiotic resistance in Pakistan: a systematic review of past decade. BMC Infect Dis. 2021;21(1):244. pmid:33676421
- 18. Idrees MM, Saeed K, Shahid MA, Akhtar M, Qammar K, Hassan J, et al. Prevalence of mecA- and mecC-associated methicillin-resistant Staphylococcus aureus in clinical specimens, Punjab, Pakistan. Biomedicines. 2023;11(3):878. pmid:36979857
- 19. Gondal AJ, Choudhry N, Bukhari H, Rizvi Z, Jahan S, Yasmin N. Estimation, evaluation and characterization of carbapenem resistance burden from a tertiary care hospital, Pakistan. Antibiotics (Basel). 2023;12(3):525. pmid:36978392
- 20. Uddin F, Imam SH, Khan S, Khan TA, Ahmed Z, Sohail M, et al. NDM Production as a dominant feature in carbapenem-resistant Enterobacteriaceae isolates from a Tertiary Care Hospital. Antibiotics (Basel). 2021;11(1):48. pmid:35052925
- 21. Habib A, Lo S, Villageois-Tran K, Petitjean M, Malik SA, Armand-Lefèvre L, et al. Dissemination of carbapenemase-producing enterobacterales in the community of Rawalpindi, Pakistan. PLoS One. 2022;17(7):e0270707. pmid:35802735
- 22. O’Brien KS, Byanju R, Kandel RP, Poudyal B, Gautam M, Gonzales JA, et al. Village-integrated eye worker trial (view): rationale and design of a cluster-randomised trial to prevent corneal ulcers in resource-limited settings. BMJ Open. 2018;8(8):e021556.
- 23. Abidi S, Sherwani S, Siddiqui T, Bashir A, Kazmi S. Drug resistance profile and biofilm forming potential of Pseudomonas aeruginosa isolated from contact lenses in Karachi-Pakistan. BMC Ophthalmol. 2013;13:1–6.
- 24. Abbas S, Yasmin A, Maqbool N, Shah AA, Fariq A. Insights into the microbiological and virulence characteristics of bacteria in orthopaedic implant infections: A study from Pakistan. PLoS One. 2023;18(10):e0292956. pmid:37847701
- 25. Oldenburg CE, Lalitha P, Srinivasan M, Rajaraman R, Ravindran M, Mascarenhas J, et al. Emerging moxifloxacin resistance in Pseudomonas aeruginosa keratitis isolates in South India. Ophthalmic Epidemiol. 2013;20(3):155–8. pmid:23662986
- 26. Borkar D, Acharya N, Leong C, Lalitha P, Srinivasan M, Oldenburg C, et al. Cytotoxic clinical isolates of Pseudomonas aeruginosa identified during the steroids for corneal ulcers trial show elevated resistance to fluoroquinolones. BMC Ophthalmol. 2014;14:1–6.
- 27. Shrestha B, Dunn L. The declaration of Helsinki on medical research involving human subjects: a review of seventh revision. J Nepal Health Res Counc. 2019;17(4):548–52.
- 28.
Kaur K, Gurnani B. Slit-lamp biomicroscope. Treasure Island (FL): StatPearls Publishing; 2022.
- 29.
Versalovic J, editor. Manual of clinical microbiology. Vol. 1. American Society for Microbiology Press; 2011.
- 30. Bauer AW, Kirby WM, Sherris JC, Turck M. Antibiotic susceptibility testing by a standardized single disk method. Am J Clin Pathol. 1966;45(4):493–6. pmid:5325707
- 31. Rahman KU, Hussain A, Ejaz N, Shang S, Balkhair KS, Khan KUJ, et al. Analysis of production and economic losses of cash crops under variable drought: A case study from Punjab province of Pakistan. Int J Disaster Risk Reduction. 2023;85:103507.
- 32. Nasar-u-Minallah M, Parveen N, Jabbar M. Assessing air quality dynamics in Punjab, Pakistan: Pre, during, and post COVID-19 lockdown and evaluating strategies for mitigating. GeoJournal. 2024;89(4):125.
- 33. Mohammed MM, Isa MA, Abubakar MB, Dikwa ASB, Kappo AP. Molecular detection of mecA gene from methicillin-resistant Staphylococcus aureus isolated from clinical and environmental samples and its potential inhibition by phytochemicals using in vitro and in silico approach. In Silico Pharmacol. 2025;13(1):26. pmid:39936169
- 34. Vilar LC, Rego ACS, Miguel MAL, Paranhos RP da R, Laport MS, Rossi CC, et al. Staphylococcus spp. and methicillin-resistance gene mecA dispersion in seawater: A case study of Guanabara Bay’s recreational and touristic waters. Comp Immunol Microbiol Infect Dis. 2025;118:102326. pmid:39954386
- 35. Tluanpuii V, Mahajan R. Detection of the mecA gene and its association with antimicrobial resistance among coagulase-negative staphylococci isolated from clinical samples in a tertiary care hospital: A cross-sectional study. Cureus. 2025;17(4).
- 36. Marsland P, Jain R, Tverdek F, Hendrie P, Liu C, Pergam SA, et al. Ceftriaxone-resistant viridans streptococci bacteraemia among patients treated at a large comprehensive cancer care centre: a retrospective eighteen-year study. JAC Antimicrob Resist. 2024;6(4):dlae126. pmid:39104770
- 37. Zeynudin A, Degefa T, Belay T, Mumicha JB, Husen A, Yasin J, et al. Detections of antimicrobial resistance phenotypes and extended-spectrum beta-lactamase (ESBL)- producing Salmonella spps and Escherichia coli O157:H7 in raw vegetables and fruits from open markets in Jimma town, Ethiopia and evaluation of hygiene and handling practices of vendors. One Health Outlook. 2025;7(1):2. pmid:39891306
- 38. Yu X-Q, Yang H, Feng H-Z, Hou J, Tian J-Q, Niu S-M, et al. Targeting efflux pumps prevents the multi-step evolution of high-level resistance to fluoroquinolone in Pseudomonas aeruginosa. Microbiol Spectr. 2025;13(4):e0298124. pmid:39982069
- 39. Ali T, Ahmed S, Aslam M. Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation. Antibiotics (Basel). 2023;12(3):523. pmid:36978390
- 40. Abebe A, Birhanu A. Methicillin resistant Staphylococcus aureus: molecular mechanisms underlying drug resistance development and novel strategies to combat. Infect Drug Resist. 2023;16:7641–62.
- 41. Díaz Santos E, Mora Jiménez C, Del Río-Carbajo L, Vidal-Cortés P. Treatment of severe multi-drug resistant Pseudomonas aeruginosa infections. Med Intensiva (Engl Ed). 2022;46(9):508–20. pmid:35840495
- 42. Yamin D, Uskoković V, Wakil AM, Goni MD, Shamsuddin SH, Mustafa FH, et al. Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria. Diagnostics (Basel). 2023;13(20):3246. pmid:37892067
- 43. Naik P, Pandey S, Gagan S, Biswas S, Joseph J. Virulence factors in multidrug (MDR) and Pan-drug resistant (XDR) Pseudomonas aeruginosa: a cross-sectional study of isolates recovered from ocular infections in a high-incidence setting in southern India. J Ophthalmic Inflamm Infect. 2021;11(1):36. pmid:34585284
- 44. Khor W-B, Lakshminarayanan R, Periayah MH, Prajna VN, Garg P, Sharma N, et al. The antibiotic resistance profiles of Pseudomonas aeruginosa in the Asia Cornea Society Infectious Keratitis Study. Int Ophthalmol. 2024;44(1):361. pmid:39215853
- 45. Hassanzadeh S, Mashhadi R, Yousefi M, Askari E, Saniei M, Pourmand MR. Frequency of efflux pump genes mediating ciprofloxacin and antiseptic resistance in methicillin-resistant Staphylococcus aureus isolates. Microb Pathog. 2017;111:71–4. pmid:28826767
- 46. Harford DA, Greenan E, Knowles SJ, Fitzgerald S, Murphy CC. The burden of methicillin-resistant Staphylococcus aureus in the delivery of eye care. Eye (Lond). 2022;36(7):1368–72. pmid:34172947
- 47. Cabrera-Aguas M, Chidi-Egboka N, Kandel H, Watson SL. Antimicrobial resistance in ocular infection: A review. Clin Exp Ophthalmol. 2024;52(3):258–75. pmid:38494451
- 48. Gil-Gil T, Martínez JL, Blanco P. Mechanisms of antimicrobial resistance in Stenotrophomonas maltophilia: a review of current knowledge. Expert Rev Anti Infect Ther. 2020;18(4):335–47. pmid:32052662
- 49. AlBahrani S, Alqazih TQ, Aseeri AA, Al Argan R, Alkhafaji D, Alrqyai NA, et al. Pattern of cephalosporin and carbapenem-resistant Pseudomonas aeruginosa: a retrospective analysis. IJID Reg. 2023;10:31–4. pmid:38076026
- 50. Muteeb G, Rehman MT, Shahwan M, Aatif M. Origin of Antibiotics and Antibiotic Resistance, and Their Impacts on Drug Development: A Narrative Review. Pharmaceuticals (Basel). 2023;16(11):1615. pmid:38004480
- 51. Ojkic N, Serbanescu D, Banerjee S. Antibiotic resistance via bacterial cell shape-shifting. mBio. 2022;13:e00659-22.
- 52. Knoll KE, Lindeque Z, Adeniji AA, Oosthuizen CB, Lall N, Loots DT. Elucidating the antimycobacterial mechanism of action of ciprofloxacin using metabolomics. Microorganisms. 2021;9(6):1158. pmid:34071153
- 53. Gu Z, Gu L, Eils R, Schlesner M, Brors B. Circlize implements and enhances circular visualization in R. Bioinformatics. 2014;30(19):2811–2.
- 54. Pajon C, Fortoul MC, Diaz-Tang G, Marin Meneses E, Kalifa AR, Sevy E, et al. Interactions between metabolism and growth can determine the co-existence of Staphylococcus aureus and Pseudomonas aeruginosa. Elife. 2023;12:e83664. pmid:37078696