https://orcid.org/0000-0002-0936-6653
Figures
Abstract
Background
Aeromonas species are ubiquitous aquatic bacteria and opportunistic pathogens associated with motile Aeromonas septicemia (MAS) in freshwater fish. MAS is characterized by hemorrhagic lesions and septicemia and can cause severe economic losses in aquaculture. The increasing occurrence of antimicrobial-resistant Aeromonas strains has raised concerns for fish health, aquaculture sustainability, and public health.
Aim
This study aimed to determine the prevalence, species distribution, virulence gene profiles, and antimicrobial resistance patterns of Aeromonas spp. isolated from farm-raised Oreochromis niloticus and Labeo rohita collected from local markets in Noakhali, Bangladesh.
Methods
A total of 22 Aeromonas isolates were obtained from intestinal samples and characterized using biochemical assays and PCR amplification of the gyrB gene. Species-level identification and phylogenetic relationships were determined by 16S rRNA gene sequencing. Antimicrobial susceptibility was evaluated using the disc diffusion method following CLSI guidelines, and multiple antibiotic resistance (MAR) was calculated for each isolate. PCR-based screening of nine virulence-associated genes was performed to assess pathogenic potential.
Results
Phylogenetic analysis identified five Aeromonas species, with A. veronii as the predominant species. Several virulence genes, particularly act, alt, and ast, were frequently detected among the isolates. High levels of antimicrobial resistance were observed against β-lactam antibiotics, and MAR index analysis indicated that many isolates were multidrug resistant.
Citation: Siddiquee NH, Hossain I, Devnath P, Islam F, Akter R, Topu MG, et al. (2026) Prevalence, potential virulence genes, and antimicrobial resistance of Aeromonas spp. in farm-raised Oreochromis niloticus and Labeo rohita in Noakhali, Bangladesh. PLoS One 21(4): e0347577. https://doi.org/10.1371/journal.pone.0347577
Editor: Mohammed Fouad El Basuini, Tanta University Faculty of Agriculture, EGYPT
Received: October 27, 2025; Accepted: April 2, 2026; Published: April 28, 2026
Copyright: © 2026 Siddiquee 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 paper and its Supporting information file.
Funding: This work is financially supported by the Noakhali Science and Technology University Research Cell, Noakhali 3814, Bangladesh.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: CLSI, Clinical and Laboratory Standards Institute; EUS, Epizootic Ulcerative Syndrome; MAS, Motile Aeromonas Septicemia; MDR, Multidrug-Resistant; MHA, Mueller Hinton Agar; MIU, Motility Indole Urea; PBS, Phosphate Buffered Saline; PCR, Polymerase Chain Reaction; TAE, Tris-acetate-EDTA; TSB, Tryptic Soy Broth; TSI, Triple Sugar Iron
The graphical abstract of this study is illustrated in Fig 1.
1. Introduction
Aquaculture has expanded rapidly worldwide in recent decades, driven by the depletion of wild seafood stocks and increasing demand for sustainable animal protein, making it indispensable for economic development and food security [1,2]. Bangladesh is the fourth-largest inland fish producer globally after China, India, and Myanmar. The country’s aquaculture sector continues to expand rapidly, with an annual production growth rate of approximately 8%, and supports the livelihoods of over 17 million people [3–5]. Labeo rohita (Rohu) and Oreochromis niloticus (Tilapia) are the most widely consumed fish species in Bangladesh, accounting for 25% and 8.95% of total production, respectively [4,6]. Despite these socioeconomic benefits, the aquaculture industry in Bangladesh faces persistent threats from infectious diseases caused by a diverse array of pathogens [7]. These pathogens include bacteria (e.g., Aeromonas spp., Vibrio spp.), viruses (e.g., iridoviruses and herpesviruses), fungi and oomycetes (e.g., Saprolegnia spp.), and parasites [8–12]. Among these, bacterial infections, particularly those due to Aeromonas spp., are especially devastating, causing mass mortalities and severe economic losses in freshwater aquaculture systems [13,14]. Multiple Aeromonas species—including A. hydrophila, A. salmonicida, A. caviae, A. sobria, A. veronii, and A. jandaei—infect fish and humans, causing diseases such as Motile Aeromonas Septicemia (MAS) and Epizootic Ulcerative Syndrome (EUS) [15,16]. In fish, these diseases commonly cause loss of appetite, skin hemorrhages, exophthalmia, and abnormal swimming [17–21]. Intensive farming practices, poor water quality, overcrowding, and unhygienic handling exacerbate the transmission and severity of these diseases [1]. To mitigate infections, antibiotics such as β-lactams, quinolones, and sulfonamides are routinely administered [2]. However, the indiscriminate and excessive application of these drugs has contributed to the emergence of multidrug-resistant (MDR) Aeromonas spp., posing significant threats to aquatic ecosystems, aquaculture productivity, and public health through the dissemination of resistance genes [2–4,16,22,23]. Effective management requires ongoing surveillance of resistance patterns and virulence genes [24].
Aeromonas spp. are opportunistic pathogens in aquaculture and possess numerous virulence factors that contribute to disease development in fish and humans. These bacteria produce extracellular toxins and enzymes, including enterotoxins, hemolysins, proteases, and cytotoxins, which disrupt epithelial barriers and damage host tissues [25–27]. Infection typically occurs through the skin, gills, or gastrointestinal tract, leading to systemic disease such as hemorrhagic septicemia in fish [21]. Several virulence genes, including those encoding toxins (act, ast, alt), cytotoxins (aerA, hlyA), and adhesion or motility factors (laf), have been widely used as molecular markers to evaluate the pathogenic potential of Aeromonas isolates [28–32]. Therefore, monitoring virulence gene profiles alongside antimicrobial resistance is important for understanding the pathogenic risk of Aeromonas in aquaculture systems [33,34].
This study aimed to investigate the prevalence, species distribution, virulence gene profiles, and antimicrobial resistance patterns of Aeromonas spp. isolated from farm-raised Oreochromis niloticus and Labeo rohita collected from local markets in Noakhali, Bangladesh. Understanding these characteristics is essential for assessing the potential risks posed by Aeromonas to aquaculture sustainability and public health.
2. Materials & methods
2.1. Sample collection and ethical approval
In this study, twenty-two random farm-raised healthy O. niloticus (n = 11) and L. rohita (n = 11) fish samples were collected from local vendors in the Noakhali region of Bangladesh (Fig 2 and Table 1). No signs or symptoms of infection were observed at the time of sampling, as only healthy appearing fish were available in the markets (Figure 1 in S1 File). The entire procedure was executed in compliance with the guidelines specified in the Guide for the Care and Use of Laboratory Animals by the National Institutes of Health, and ethical approval was also taken from the Noakhali Science and Technology University ethical committee with reference number NSTU/SCI/EC/2025/330.
The map was created using ArcGIS Pro (Esri, Redlands, CA, USA). Basemap data were obtained from Esri World Ocean Base (https://www.arcgis.com/home/item.html?id=6348e67824504fc9a62976434bf0d8d5), and administrative boundary layers were obtained from Esri World Administrative Divisions (https://www.arcgis.com/home/item.html?id=1b243539f4514b6ba35e7d995890db1d) via ArcGIS Online.
2.2. Sample preparation & bacterial isolation
Fish surfaces were disinfected with 70% ethanol, and intestinal samples were aseptically collected [35] and samples were homogenized in sterile PBS [36]. Then, 100 μL of homogenized aliquots were plated on MacConkey agar plate and incubated at 37 °C for 24 h [37,38]. Although MacConkey agar is not a selective medium specifically designed for Aeromonas isolation, it allows preliminary differentiation of Gram-negative bacteria based on lactose fermentation. Therefore, suspected non-lactose-fermenting colonies were further confirmed using biochemical and molecular identification methods [39].
2.3. Biochemical characterization of isolated bacteria
Non-lactose-fermenting bacterial colonies on MacConkey’s agar plates were selected and purified. Then, isolated pure colonies were subjected to a series of biochemical tests, such as triple sugar iron (TSI), motility indole urea (MIU), hydrogen sulfide production (H2S), citrate, and oxidase [40], according to Bergey’s manual, 9th Edition [41], to identify the suspected Aeromonas.
2.4. Bacterial characterization based on gyrB gene
PCR (Polymerase Chain Reaction) was used for molecular identification of all suspected Aeromonas isolates. All Aeromonas isolates identified by biochemical tests were cultivated overnight in Tryptic Soy Broth (TSB) at 37 °C for 24 h [40]. Subsequently, bacterial DNA was extracted from these cultures using the AddPrep bacterial genomic DNA extraction kit (addbio, Korea), following the manufacturer’s provided protocol. The isolated DNA was stored at −20 °C. A particular segment of the DNA gyrase B (gyrB) gene [42], with a PCR product size of around 1100 base pairs, was selected for the molecular identification of these isolates. The PCR reactions were conducted in the following manner: The final volume of each PCR reaction was 25 μL, comprising 12.5 μL of Promega 2X PCR mastermix, 0.6 μL of each forward and reverse primer (10 pmol/μL), 2 μL of the extracted DNA template, and 9.3 μL of nuclease-free water. The master mix contained MgCl₂ at a final concentration of 1.5 mM. The nucleotide sequences of the primers are shown in Table 1 in S1 File.
The PCR reaction was performed for 35 amplification cycles consisting of denaturation at 94 °C for 30 seconds, annealing at 62 °C for 30 seconds, and extension at 72 °C for 1 minute, using BioRad T100 Thermal Cycler [43]. The PCR products were electrophoresed on a 1.5% agarose gel in 1X Tris-acetate EDTA (TAE) buffer and stained with EtBr. Vivantis’ 100-bp DNA ladder was used as a molecular marker, and the Bio-Rad GelDoc EZ Gel imaging device visualised DNA bands under UV light.
2.5. 16S rRNA sequence analysis
Species identification of isolated Aeromonas was achieved using PCR amplification with the following primers: 27F: 5′-AGAGTTTGATCCTGGCTCAG-3′; 1392R: 5’-GGTTACCTTGTTACGACTT-3′ supplied by Macrogen Oligo. Each PCR reaction was prepared for 20 μL, including 10 μL of Add Taq 2X PCR mastermix (addbio, Korea), 0.25 μL of forward and reverse primers (10 pmol/μL), 2 μL of DNA template, and 7.5 μL of nuclease-free water. Amplification was performed on a Bio-Rad T100 thermal cycler for 35 cycles, denaturation was at 95 ºC, annealing at 55 ºC for 30 seconds, and extension at 72 ºC for 1 minute 30 seconds [44]. ExoSAP-ITTM PCR Product Cleanup Reagent, made by Thermo Fisher Scientific, was used to remove excess primers and dNTPs from the PCR products. Then, purified products were cycle sequenced using the BigDyeTM Terminator v3.1 Cycle Sequencing Kit (Thermo Fisher Scientific) following the manufacturer’s instructions. The sequencing reaction consisted of 25 cycles of denaturation at 96 ºC for 10 seconds, annealing at 50 ºC for 5 seconds, and extension at 60 ºC for 4 minutes. These products were further purified using the D-Pure™ DyeTerminator Cleanup Kit. Sequencing runs were conducted in a 3500 Dx Genetic Analyzer, and raw sequencing data were analyzed using Sequencing Analysis Software v6.0 (Thermo Fisher Scientific). The quality of the obtained sequences was assessed using the Phred quality score, and high-quality sequences were aligned and compared to reference sequences in the NCBI GenBank database using the BLASTn tool for species identification.
After analysis, all the sequences were submitted to the NCBI GenBank. The accession numbers for the full nucleotide sequence of 16S rRNA from the fish Aeromonas spp. strains that were submitted to GenBank are displayed in Table 2 and Table 2 in S1 File.
The obtained 16S rRNA gene sequences were compared with reference sequences available in the NCBI GenBank database using the BLASTn algorithm to determine species identity. Taxonomic identification was assigned based on the highest sequence similarity, query coverage, and E-value obtained from BLAST results. The murkiness surrounding the taxonomic categorization of enrichment culture clones, uncultured bacteria, and unclassified bacteria led to their exclusion.
2.6. Phylogenetic data analysis
The quality of the 16S rRNA gene sequences from Aeromonas isolates was assessed, and a consensus sequence was computed from the forward and reverse sequences using MEGA version 11. Muscle, a method found in version 11 of the software Molecular Evolutionary Genetics Analysis (MEGA), was used to align the 16S rRNA gene sequences of the Aeromonas isolates with the strains that scored highest in the BLAST results. A neighbor-joining phylogenetic analysis of the aligned sequences was conducted with the neighbour-joining method and 1000 iterations for bootstrap support using MEGA version 11 software [45–47].
2.7. Antibiotic susceptibility testing
All the isolates of Aeromonas (n = 22) were tested for antimicrobial drug resistance. Isolates were cultivated overnight in TSB at 37 °C for 24 h. Then a standardized bacterial inoculum was used for antimicrobial susceptibility testing, representing a bacterial suspension of approximately 1.5 x 10^8 CFU/mL, adjusting the turbidity of the broth to a 0.5 McFarland standard [48,49].
The inoculum was spread onto Mueller-Hinton agar (MHA) using a sterile swab stick. Then we used standard antimicrobial disks (Oxoid, UK) contained a range of antimicrobials at specific concentrations, which included Ampicillin (10 μg), Amoxicillin-clavulanic acid (30 μg), Piperacillin-Tazobactam (110 μg), Amikacin (30 μg), Ciprofloxacin (5 μg), Cefotaxime (30 μg), Cefepime (30 μg), Ceftazidime (30 μg), Ceftriaxone (30 μg), Chloramphenicol (30 μg), Imipenem (10 μg), Meropenem (10 μg), and Sulfamethoxazole/Trimethoprim (25 μg) on MHA plates. Then the plates were incubated for 24 h at 37 °C (Fig 3). Following incubation, the zones of inhibition around each antimicrobial disk were interpreted according to the Clinical and Laboratory Standards Institute (CLSI M100, 30th edition, 2020) guidelines using breakpoints established for Enterobacterales, as CLSI does not provide specific interpretive criteria for Aeromonas spp. This study used first-generation, second-generation, third-generation, and fourth-generation antimicrobials with narrow and broad-spectrum activity to observe the variety of organisms’ susceptibility to the antimicrobials of different generations, which are mostly used.
2.8. Detection of virulence genes
Amplification of the genes encoding cytotoxic enterotoxin (act), hemolysin (hlyA), heat-labile cytotoxic enterotoxin (alt), flagellin (laf), Shiga toxins (stx-1 and stx-2), type III secretion system (ascF-ascG), allatostatin (ast), and aerolysin (aerA) was performed using polymerase chain reaction (PCR) from each bacterial isolate. The primer sets specific to these genes are detailed in Table 1 in S1 File. Amplification of the nine virulence genes was carried out using identical thermocycling techniques as the gyrB gene, except for the use of different annealing temperatures. The gel electrophoresis process was similar to section 2.4.
2.9. Statistical analysis
All the statistical analyses were done using R version 4.4.1. The multiple antibiotic resistance (MAR) index was computed per isolate as the number of resistant outcomes divided by the number of antibiotics tested. Multidrug resistance (MDR) was defined as non-susceptibility to ≥3 antimicrobial classes. Differences in resistance burden among fish groups were tested using a binomial GLM (logit) with p < 0.05. Antibiotic susceptibility outcomes were recorded as susceptible (S), intermediate (I), or resistant (R). Thus, for regression analysis, resistance burden was summarized for each isolate as the number of resistant events (“R”) out of 13 antibiotics and analyzed using GLM with a binomial error distribution and a logit link. Confidence intervals (95% CI) for proportions were in R using the exact binomial (Clopper–Pearson) method in R programming (R codes are available in S1 File).
3. Results
3.1. Isolation and identification of the Aeromonas isolates
Sixty-four out of 94 isolates (64/94, 68%) were presumptively identified as belonging to the genus Aeromonas based on phenotypic and biochemical tests. Of these, 22 (22/64, 34.4%) isolates were confirmed as Aeromonas spp. by gyrB gene detection, producing a distinct ~1100 bp amplicon on gel electrophoresis (Fig 4).
3.2. Taxonomic analysis
16S rRNA gene sequences were analyzed for phylogeny and homology, which determined the molecular taxonomy of Aeromonas strains. Species-level identification was assigned based on ≥98% sequence identity, 100% query coverage, and an E-value of 0 (zero) obtained from BLASTn comparison with reference sequences in the NCBI GenBank database (Table 2). A 16S rRNA gene sequence identity above 98% is commonly used as an initial criterion for species-level identification within the genus Aeromonas, supporting the preliminary taxonomic assignments made in this study [50].
3.3. Prevalence of isolates and phylogenetic analysis
A total of 22 Aeromonas strains were identified using 16S rRNA gene sequencing, of which 32% were isolated from O. niloticus and 68% from L. rohita. These 22 presumed Aeromonas isolates were divided into five species based on an unrooted Tamura-Nei neighbor-joining (NJ) tree, which included 22 reference strains with partial sequences (Fig 5). A. veronii was the predominant species (72.73%, 95% CI: 49.8–89.3), followed by A. hydrophila (9.09%, 95% CI: 1.1–29.2), A. jandaei (9.09%, 95% CI: 1.1–29.2), A. caviae (4.55%, 95% CI: 0.1–22.8), and A. bivalvium (4.55%, 95% CI: 0.1–22.8), as shown in Fig 6.
Bootstrap values were calculated from 1000 replicates and are shown at branch nodes.
Phylogenetic analysis of the 16S rRNA gene sequences showed that the isolates clustered with their corresponding reference strains, confirming species-level identification (Fig 5). The phylogenetic tree formed two main clusters: Cluster 1 contained all Aeromonas isolates and reference sequences, whereas Cluster 2 consisted of the outgroup Klebsiella pneumoniae. Within Cluster 1, A. veronii isolates formed subcluster 1A, while A. bivalvium, A. caviae, A. jandaei, and A. hydrophila grouped within subcluster 1B.
3.4. Antimicrobial susceptibility profiling and MAR indexing
All the Aeromonas isolates were highly resistant to ampicillin (95%) and amoxicillin-clavulanic acid (86%). Additionally, approximately 27% of the isolates demonstrated resistance to the β-lactam antibiotic ceftazidime (Table 3). On the other hand, the remaining isolates were found to be sensitive to ciprofloxacin, cefepime, ceftriaxone, chloramphenicol, and sulfamethoxazole/trimethoprim. The multiple antibiotic resistance (MAR) index (Table 4) was calculated for each isolate, and isolates with a MAR index >0.2 are considered to originate from environments with high antibiotic exposure [51]. The MAR index values ranged from 0.07 to 0.69; 16 isolates (16/22, 68.2%) were found to be positive for MAR status. Among L. rohita -derived isolates, resistance was observed to 1–5 antibiotics, with MAR index ranging from 0.07 to 0.38. In contrast, isolates derived from the O. niloticus showed higher resistance burden, showing resistance to 3–9 antibiotics and MAR index values ranging from 0.23 to 0.69. Overall, MAR-positive isolates were more frequent among O. niloticus (85.7%) than L. rohita (60.0%), indicating a greater exposure of Tilapia-associated isolates to antimicrobial selective pressure.
3.5. Fish species–specific differences in antibiotic resistance
A binomial generalized linear model (GLM) was used to evaluate the burden of antibiotic resistance in Aeromonas isolated from two fish species. We used L. rohita isolates as the reference group; the binomial GLM intercept (β = −1.177) represents the baseline resistance burden across the 13 antibiotics tested, corresponding to a resistance probability of ~3.1 resistant antibiotics per L. rohita isolate. In comparison, O. niloticus isolates had significantly higher odds of resistance burden than those from L. rohita (β = 0.707 ± 0.284 SE; z = 2.487; p < 0.05) (Table 5), indicating approximately a two-fold increase in the likelihood of resistance among O. niloticus isolates. The probability of ~5.0 resistant antibiotics out of 13 antibiotics per isolate.
3.6. Assessment of virulence genes
All the isolates were screened for the presence of nine specific genes (aerA, act, ast, hlyA, alt, laf, stx-1, stx-2, and ascF-ascG) through PCR. The distribution of virulence genes within fish-derived Aeromonas isolates is as follows: The cytotoxic enterotoxin gene act was the most frequently detected virulence gene, found in 72.72% (16/22) of the isolates. The ast gene, linked to a heat-labile cytotonic enterotoxin, and the alt gene, associated with a secreted heat-stable enterotoxin, were present in 50% (11/22) of the isolates. The stx-2 gene was identified in 18.18% of the isolates, and the ascF-ascG gene in 22.73% (5/22) of the isolates. Meanwhile, the laf gene, the stx-1 gene, and the hlyA gene were detected in 9.09% of the isolates. Notably, the aerA gene was not found in any of the Aeromonas isolates after PCR (Fig 7).
4. Discussion
The expansion of aquaculture and the intensification of farming practices can increase the risk of bacterial diseases in farmed fish populations [52,53]. Notably, bacterial hemorrhagic septicemia and epizootic ulcerative syndrome (EUS) caused by Aeromonas spp. has become a significant cause of disease outbreaks and mortality in tilapia farming worldwide [54–57].
Assessment of Aeromonas spp. prevalence is challenged by ongoing taxonomic revisions and variability in diagnostic methodologies. Reliance on 16S rRNA gene sequencing alone frequently fails to resolve closely related species, such as A. veronii and A. sobria [58]. Although the gyrB gene was used for molecular confirmation of Aeromonas isolates in this study, the phylogenetic tree was constructed using 16S rRNA gene sequences alone. A concatenated phylogenetic analysis using multiple housekeeping genes would provide stronger taxonomic resolution for closely related Aeromonas species. Thus, advanced molecular approaches, including multilocus sequence typing (MLST) and sequencing of housekeeping genes, are recommended for more accurate species identification.
In the present study, five Aeromonas species were identified, namely A. veronii, A. jandaei, A. hydrophila, A. caviae, and A. bivalvium. Out of these, A. veronii was the most prevalent (72.72%) in consistent with previous findings from Bangladesh [59]. This finding is noteworthy because severe Aeromonas infections, especially bacteremia caused by A. veronii and A. hydrophila, have been linked to high case fatality rates (33–56%) in humans [60]. Potential routes of human exposure include the consumption of contaminated fish or water and contact of open wounds with contaminated aquatic environments. The predominance of A. veronii in the present study also supported by reports from Tanzania, China and Malaysia showing similarly high prevalence of A. veronii in healthy fish [61–63]. Moreover, the widespread distribution of A. veronii across diverse hosts, including humans, aquatic animals, food, and the environment, underscores its significance as an emerging pathogen in Bangladeshi aquaculture [16]. By contrast, the comparatively low prevalence of A. hydrophila differs from reports in Vietnam, Malaysia, and Egypt, which may reflect geographic variation in Aeromonas species distribution [64,65].
Pathogenicity in Aeromonas spp. is contributed by several virulence factors, including enterotoxins (act, alt, ast) and hemolysins (hlyA, aerA) [66]. Interestingly, the aerA gene, which is commonly associated with highly virulent A. hydrophila strains and severe disease, was not detected in any of the isolates [67–70]. Previous studies have reported high prevalence of the hlyA gene among Aeromonas isolates [71–73]; however, in the present study, hlyA was detected exclusively in A. hydrophila isolates and was absent in all other species. The absence of these virulence-associated genes may be attributed to the fact that isolates were obtained from clinically healthy fish.
The high prevalence of the act gene among A. veronii isolates in this study is consistent with previous reports highlighting its association with Aeromonas pathogenicity, whereas the lower detection of ast, alt, and other virulence genes may reflect strain-specific variation reported in earlier studies [31,74–77]. However, because all fish sampled in this study were clinically healthy, the detection of virulence-associated genes should not be interpreted as evidence of active disease. These genes only indicate the potential pathogenic capability of the isolates, and further phenotypic or infection studies would be required to confirm their role in disease development. Flagellar genes are essential for motility and colonization; however, only 9.1% of isolates carried the laf gene, much lower than rates reported elsewhere [78–80]. This variability highlights the complex interactions among environmental, genetic, and methodological factors influencing Aeromonas spp. pathogenicity.
Antimicrobial resistance (AMR) among Aeromonas isolates presents a significant concern. The majority of the isolates exhibited resistance to ampicillin (95%) and amoxicillin-clavulanic acid (82%), consistent with the prevalence of widespread occurrence of chromosomally encoded β-lactamase genes [81]. No resistance to chloramphenicol was observed, indicating its continued potential as a therapeutic agent, though ongoing monitoring is warranted. Resistance to ciprofloxacin was low (9%), but resistance rates to carbapenems, imipenem (36%), and meropenem (45%) were much higher than previously reported (0.5–7.7%) [82]. This study also demonstrated a high percentage of Aeromonas isolates (68%) with a MAR index of more than 0.2, which indicates that the Aeromonas strains originated from a high-risk source of contamination [83]. Overall, the antimicrobial resistance patterns and multidrug resistance observed in this study highlight the presence of resistant Aeromonas isolates in aquaculture-associated fish. This study focused on phenotypic antimicrobial resistance patterns; however, specific antimicrobial resistance genes such as blaTEM, blaSHV, blaCTX-M, cphA, qnr genes, tet genes, or sul genes were not investigated. The coexistence of virulence-associated genes and antimicrobial resistance in the same isolates may increase overall public health concern; however, no direct relationship between these traits was investigated in this study.
In summary, our findings underscore the necessity for enhanced surveillance and monitoring of Aeromonas spp. in aquaculture, judicious antimicrobial use, and the development of preventive strategies such as vaccination and improved management practices. Addressing these challenges is crucial for sustainable aquaculture, food safety, and public health in Bangladesh and other developing countries.
5. Limitations
This study provides important epidemiological insights into Aeromonas spp. in Bangladesh; however, several limitations should be acknowledged. Sampling was limited to a single region and season, which may not reflect broader geographic or temporal variation. Isolation was performed using MacConkey agar, a non-selective medium that may affect recovery efficiency. Antimicrobial susceptibility was interpreted using Enterobacterales breakpoints due to the lack of CLSI standards for Aeromonas, which may introduce interpretive limitations. Phylogenetic analysis relied solely on 16S rRNA gene sequences, which have limited discriminatory power for closely related species. PCR detection of virulence genes indicates only their presence and does not confirm gene expression or toxin production; moreover, all fish sampled were clinically healthy, so these findings do not indicate disease. Additionally, antimicrobial resistance genes were not investigated, limiting insights into resistance mechanisms. Future studies should include broader sampling, incorporate multilocus or whole-genome approaches, and include both healthy and diseased fish to better understand pathogenicity and resistance.
6. Conclusion
This study molecularly identified Aeromonas spp. from farm-raised O. niloticus and L. rohita in Noakhali, Bangladesh, revealing A. veronii as the predominant species. High prevalence of key virulence genes and multidrug resistance, especially against β-lactams, including carbapenems, indicates significant threats to aquaculture biosecurity and public health. These findings highlight the need for strengthened surveillance, responsible antimicrobial use, and improved farm management practices.
Supporting information
S2 File. Supplementary S2- Sensitivity data of all isolates.
https://doi.org/10.1371/journal.pone.0347577.s002
(XLSX)
Acknowledgments
We are grateful to the faculty of the Department of Microbiology, Noakhali Science and Technology University, for providing immense support and assistance during this work.
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