Introduction

Globally, fish farming has the potential to alleviate nutritional deficiencies and reduce poverty. Egypt and Nigeria are the leading aquaculture countries in Africa because of their comparable production methods (1). Egypt's aquaculture industry produces approximately 1.8 million tons of fish annually (2), with Nile tilapia accounting for approximately 65.15% of the country's fish production (3). Mullets are the second most farmed fish species after Nile tilapia in brackish water (4), where tilapia, carp, and mullets are commonly raised together in polycultured earthen ponds (5). The aquatic environment of Egypt is frequently contaminated by agrochemicals, sewage discharge, and declining water quality standards, all of which contribute to outbreaks of bacterial illnesses among fish species (6,7). Fish mortality hinders industrial growth and reduces fish production, severely impacting aquaculture (6). Three key factors facilitate disease outbreaks in fish farms: A) fish are raised at higher densities than in the wild, increasing fish-to-fish contact; B) infected fish are not removed as efficiently as they would be by natural predators; and C) farmed fish are more accessible for observation than are wild fish (8). Consequently, pathogens are typically present in small quantities and are nonpathogenic in the wild, which may induce illness in aquaculture systems (9). Significant interconnections exist among hosts, pathogens, and environmental variables. In aquaculture, unfavorable environmental conditions and insufficient abiotic stress mitigation weaken organisms' immune systems, increasing their vulnerability to disease outbreaks (10). Co-infecting pathogens may interact synergistically or antagonistically (5). Abdel-Moneam et al. (6) and Khalefa et al. (10) noted that synergistic interactions occur when one pathogen suppresses the host's immune response or establishes a point of entry, allowing another pathogen to invade. Such concurrent infections exacerbate disease severity, particularly bacterial infections, and delay diagnosis and treatment, increasing mortality rates (11). Nair et al. (12) identified Escherichia coli, Pseudomonas putida, Salmonella sp., Shigella sp., Staphylococcus aureus, Enterobacter, and Enterococcus faecalis as the most common bacterial isolates from infected fish skin samples. Several factors contribute to fish mortality, including overcrowding, abrupt temperature fluctuations, poor water quality, and other environmental factors (13). The disease can be attributed primarily to inadequate management and environmental stressors (6). The presence of antibiotic-resistant pathogenic bacteria in fish samples poses a significant threat to food safety, underscoring the need for public health interventions to enhance hygiene and food safety practices in fish markets (14). Water quality degradation and deterioration facilitate the invasion and propagation of a diverse array of opportunistic and facultative bacterial pathogens, including Acinetobacter baumannii, Aeromonas hydrophila, Klebsiella pneumoniae, and Pseudomonas fluorescens (15). Maintaining water quality standards in commercial fish farms prioritizes the immediate resolution of reduced dissolved oxygen levels. Deviations from optimal water parameters result in the persistence and intensification of infectious disease prevalence in fish (16). Additional abiotic factors, such as pH, salinity, ammonia, nitrite, and free CO₂, must be addressed because of their compelling influence on fish health (15). Effective management practices can mitigate these stressors by maintaining fish-friendly thresholds, as these are predominantly environmental factors (6). Significant interrelations exist between hosts, pathogens, and environmental conditions (17). Aquaculture ponds are treated with a variety of disinfectants to reduce disease-causing parasites and pathogenic bacteria (18). However, limited research has demonstrated the effectiveness of CuSO₄ and KMnO₄ as bactericides for treating bacterial illnesses in fish (19). KMnO₄ is frequently used in aquaculture to increase fish health, with its application in baths being a common treatment for aquatic diseases (20,21). According to Darwish et al. (22), CuSO₄ at 2.1 mg/L (1% total alkalinity) effectively prevented experimental columnaris infections in channel catfish during the early stages of the disease.

The aim of this study was to identify bacterial pathogens responsible for summer mortality syndrome in farmed fish during the summer season in Wadi El-Natrun, El Menoufia, Egypt, via molecular techniques. Additionally, this study aimed to evaluate the efficacy of KMnO₄ and CuSO₄ treatments in controlling mixed bacterial infections in O. niloticus through a clinical field trial.

Materials and methods

Ethics statement

The Research Ethics Committee of Cairo University's Faculty of Veterinary Medicine (Vet CU 18042024939) approved the study protocol following the relevant regulations and guidelines.

Study area

Wadi El-Natrun is an elongated depression located 23 m below sea level in the western desert of Egypt and is situated between latitudes 30°17′N and 30°19′N and longitudes 30°10′E and 30°25′E. Extending approximately 60 km from north to south. During the summer season of 2023, a field survey was conducted at five fish farms in Wadi El-Natrun, El Menoufia, Egypt, with five fish farms beginning in August and persisting through October (Figure 1).

Figure 1: Satellite map from Google Earth showing various fish farms located in the study area. Wadi El- Natrun, El- Menoufia, Governorate, Egypt (red circles).

Water quality measurements and sampling:

Three water samples were taken from each investigated farm. The samples were collected in sterile 500-mL glass containers for bacteriological examination and in 2-L bottles for physiochemical analysis. The parameters (temperature, pH, and dissolved oxygen) were analyzed in situ via a precalibrated probe (Yellow Springs Comp., Ohio, model 33, and oximeter model DO200A EcoSense®), whereas the total hardness and concentration of various ions present were measured following the standard protocol from the American Public Health Association (23). EDTA titrimetric, argentometric, phenate, and calorimetric methods were employed to assess the total hardness and chloride (Cl), ammonia (NH₃), nitrate (NO₃), and nitrite (NO₂) levels. The gravimetric method was used to measure the sulfate (SO₄) concentration. The stannous chloride method was used to detect phosphorus.

Microbiological analyses of water samples

The microbiological testing of the water samples was conducted following the guidelines of the APHA (24). The standard presumptive examination employed the multiple-tube fermentation technique with lauryl tryptone broth (Biolife, Italy) to determine the most likely number (MPN/100 mL water) of total coliforms. Positive presumptive tubes (gas production within 24-48 hours) were transferred to 2% brilliant green bile lactose broth (Difco, Thermo Fisher Scientific) for the MPN confirmation procedure, and gas production was monitored within 48 hours at 35°C. The total bacterial count (TBC) of the water samples was determined via the poured plate method. Following serial dilutions of the water samples, 1 mL was transferred in triplicate to sterile glass Petri dishes. Approximately 15 mL of liquefied nutrient agar medium was added to each plate, mixed, and allowed to solidify. The dishes were incubated at 30°C for 48 hours. The mean value was calculated by counting the number of colonies produced per plate at the same dilution after incubation, and the results are reported as the mean (log10) (25). Further isolation of a representative number of the different colony types detected on the media was achieved by streaking 0.1 mL of each of the last three dilutions of water samples onto selective media. These included Eosin Methylene Blue (EMB; HIMEDIA Laboratories, India), Xylose Lysine Deoxycholate (XLD; Oxoid), and Aeromonas-selective agar supplemented with rehydrated ampicillin (HIMEDIA, India) (6). KF streptococcal agar media (26), and Pseudomonas agar (Oxoid) with Pseudomonas CN supplement (Oxoid) (6). These media were used to isolate E. coli, Salmonella spp., Aeromonas spp., Enterococcus spp., and Pseudomonas spp., respectively. The plates were incubated at 37°C for 24 hours for the detection of bacterial colonies. To obtain pure colonies, the dominant colonies were selected and streaked again on tryptic soy agar (TSA) plates. For further molecular identification, the pure culture was stored at -20°C as a glycerol stock.

Fish sampling and clinical examination

A cross-sectional survey approach was used to investigate the prevalence of pathogenic bacteria in five private fish farms in Wadi El-Natrun, El Menoufia, Egypt, and their associations with fish diseases. A total of 114 fish were randomly selected from these farms: 40 M. capito (200-220 g) and 74 O. niloticus (150-200 g). These farms recorded daily mortality cases (approximately 50-100 fish per day) during the summer of 2023. Diseased fishes displayed patterns of symptoms such as pronounced eye opacity, edemas or hemorrhagic skin irritation. Postmortem (PM) lesions and clinical indicators were analyzed via techniques previously published by Austin and Austin (27). Postmortem examination of the infected fish revealed congestion in the liver and kidney. The fish muscles, internal organs, and tissues were carefully dissected to isolate the microorganisms. The moribund fish were recently housed in tanks with aerators before being transferred to the laboratory.

Bacteriological examination of fish

Serological identification of E. coli and Salmonella

At the serology unit of the Animal Health Research Institute in Egypt, the isolates were serologically identified following the methods of Kok et al. (29) via quick diagnostic E. coli antisera sets (Serological identification of E. coli was performed by slide agglutination test by using o polyvalent sera and corresponding monovalent one sifin diagnostic gmbh Berlin Germany) for the diagnosis of enteropathogenic types. Salmonella species were serologically identified via Salmonella Mast Assure antisera (Mast group Ltd., UK) according to ISO6579:2014 part 3 via the Kauffman le minor scheme (30) for the determination of somatic (O) and flagellar (H) antigens.

Bacterial DNA extraction

The bacterial DNA was extracted via a QIAamp DNA Micro Kit (Qiagen, Catalog no. 51304, USA) following the manufacturer's instructions. In brief, 200 µL of the sample suspension was incubated with 10 µL of proteinase K and 200 µL of lysate buffer at 56°C for 10 minutes. After incubation, the lysate was rinsed and centrifuged, and 200 µL of 100% ethanol was added. The nucleic acid was then eluted with 100 µL of elution buffer and stored at ~20°C for subsequent use in the PCR steps (31).

16S rRNA PCR, sequencing, and phylogenetic analysis

The universal primers designed by Hall et al. (32) were used for analysis: F: 5′-AGAGTTTGATCCTGGCTCAG-3′ and R: 5′-GGTTACCTTGTTACGACTT-3′. A 50 µL reaction mixture containing Maxima Hot Start PCR Master Mix (Thermo Fisher Scientific, Waltham, MA) was used for PCR. The cycling conditions were detailed by Abdelsalam et al. (6). A GeneJET PCR Purification Kit (Thermo K0701) was used to purify the amplicons. The amplified 16S rRNA gene segments of four selected bacterial isolates were analyzed via the ABI 3730xl DNA sequencing machine. After the sequences were obtained, editing and assembly were performed via BioEdit version 7.0 (32). Each isolate's final 16S rRNA gene sequence was compared to previously published sequences via the National Center for Biotechnology Information (NCBI) BLAST tool.

Antibiogram testing of the pathogenic bacterial strains

The in vitro antimicrobial sensitivity of bacteria to six different classes of antibacterial agents was determined via the Kirby-Bauer disc diffusion method, according to Bauer et al. (33). The organisms were incubated at 30 ± 2°C for 24 hours. Then, according to the Clinical and Laboratory Standards Institute (CLSI) (34), the strains were categorized as sensitive, moderate, or resistant on the basis of the widths of the inhibitory zones around the discs. The selected antimicrobial discs, obtained from Bioanalyze® (Ankara, Turkey), contained the following: amoxicillin (AML, 25 µg), ciprofloxacin (CIP, 5 µg), norfloxacin (NOR, 10 µg), doxycycline (DO, 30 µg), streptomycin (S, 10 µg), and trimethoprim (Ts, 5 µg).

Fish treatment trial and experimental design

Sixty diseased O. niloticus (100 g ± 5.0) were randomly selected from the private farms under investigation and used as an experimental model to determine the inhibitory effects of KMnO₄ and CuSO₄ on the isolated bacteria. The fish were kept in aquarium tanks measuring 70 × 40 × 40 cm. The fish were randomly divided into three treatment groups, with two replicates for each treatment: (i) the untreated control group, (ii) the KMnO₄-treated group, and (iii) the CuSO₄-treated group, as shown in Figure 2. The dose was administered over seven days, as described by Darwish et al. (22), to achieve final concentrations of 2.1 mg/L copper sulfate and 2.5 mg/L KMnO₄ in the respective treatment tanks. The experiment was conducted in tanks with adequate aeration, the water temperature was standardized to approximately 27°C, the dissolved oxygen concentration was approximately 7.00 mg/L, and half of the water was exchanged twice a week to maintain the water quality. The fish were examined daily for signs of infection, and those that appeared moribund were removed for necropsy. During the treatment period, the fish were fed twice daily.

Figure 2: Diagram illustrating the setup for the study.

Treatment effectiveness assessment

On days 1, 4, and 7, water and fish samples were collected from each group via a 20-mL sterile glass container and sterile polyethylene bags. The first, second, and third samplings were assigned to these samples in that order. After the last treatment, five fish were randomly selected from each aquaria to assess the impact of the treatment on the general health of the fish. As previously described, the bacteria were reisolated on specific media and counted via the drop-plate method in triplicate to determine the total bacterial count. The mortality rate of the fish was evaluated throughout the treatment period.

 Statistical analysis

 A chi-square test (SAS software) was applied to examine the null hypothesis of multiple farms, with p values less than 0.05 indicating a significant difference between the observed parameters. The Shapiro‒Wilk test was used to determine whether the data were normally distributed or evenly distributed. One-way ANOVA with Tukey's post hoc test was used to analyze the data for the in vitro experiment in which KMnO₄ and CuSO_{4 were used} to control the isolated mixed bacteria. In addition to the Kruskal‒Wallis test, which evaluates the density of bacteria in fish muscle tissue and the water quality variables, the Mann‒Whitney U test was used to evaluate differences between the groups. SPSS version 18.0 (SPSS Inc., Chicago, IL) was used to analyze all the data, with a statistical threshold set at p< 0.05 for all the tests. The findings are expressed as the mean ± SE. Significant differences were determined via the least significant difference test.

Results

Field investigation, clinical examination, and necropsy findings

During the study, a field assessment revealed substantial fish mortality rates in the fish farms in Wadi El-Natrun, El Menoufia, Egypt. Figure 3 depicts the mortality of the investigated fish farms due to bacterial pathogen coinfection. Clinical examination of O. niloticus revealed signs of disease, including rot and patches in the gills. Following necropsy, the postmortem lesions revealed congested gills. Moreover, the abdominal cavity contained a significant volume of red ascitic fluid (Figure 4, A-B). Similarly, most M. capito infections (Figure 4C) presented exophthalmia, detached scales, ascites, and mild fin erosion. Additionally, postmortem examination of the infected fish revealed serous fluid accumulation in the stomach cavity and congestion in the gills, liver, and kidney (Figure 4D).

Figure 3: (A-B) Mass mortality in the fish farms under investigation due to coinfection with bacterial pathogens. (C) Diseased O. niloticus displayed patterns of symptoms such as pronounced eye opacity, edemas or hemorrhagic skin irritation.

Figure 4. (A-B) Naturally, infected O. niloticus presented hemorrhagic and necrotic gills with ulcerative skin lesions. Infected M. capito (Figure 4 C) displayed congested gills, ascites, and detached scales (Figure 4 D). Postmortem examination of the infected fish revealed congestion in the liver and kidney.

Water quality

Table 1 summarizes the quality parameters of the pond water collected from the fish farms during the study period. The water parameters evaluated were all within the acceptable range except for a decrease in the dissolved oxygen concentration (2.8-3.25 mg/L) and an increase in the ammonia (0.7-1.15 mg/L) and nitrite (7-9 mg/L) concentrations. Farms 4 and 5 also had phosphate, sulfate, hardness, and chloride levels that exceeded the recommended acceptable limits.

Table 1: Physical-chemical water analysis (mean ±SE) of five studied fish farms in Wadi El-Natrun, El Menoufia, Egypt, during summer 2023

*Permissible limits according to Boyd (35)

The samples obtained from farms 4 and 5 had the highest bacterial counts, as indicated by the bacterial water analysis in Table 2. In this investigation, Pseudomonas spp. were isolated from farms 2, 4, and 5. The isolation of E. coli and Enterococcus spp. was limited to farms 4 and 5. Salmonella was isolated from only one farm. Farm 5 had the highest total bacterial count (TBC) concentration (7.38 log₁₀ CFU/100 mL) of all the water samples. Total coliforms were detected only on farms 4 and 5, with numbers ranging from 12 to 15 MPN/100 mL. The MPN values of the coliforms in the water samples were within the permissible range for freshwater that can be used for irrigation and fish farming (1000 MPN/100 mL).

Table 2: Microbiological analysis of water samples of five studied fish farms in Wadi El-Natrun, El Menoufia, Egypt, during summer 2023

^a CFU: colony-forming unit. ᵇ MPN: most probable number.

Bacteriological examination of fish

Table 3 displays the total bacterial count (CFU/g) and total coliform count (TCC/g) of the fish muscle samples from the farms under investigation, with samples from muscular tissue from farms 4 and 5 being the highest. Table 4 shows the analysis of the fish samples, with most of the samples being contaminated. One pure isolate was identified from both fish species (O. niloticus and M. capito) via the biochemical profile of the recovered isolates. These isolates included 11 E. coli, 5 Enterobacter, 5 Klebsiella, 3 Salmonella, 12 Pseudomonas, 2 Aeromonas, and 13 Enterococcus spp.; E. coli and Pseudomonas were the most common bacteria in both fish species. Farms 4 and 5 had significantly more Pseudomonas spp. and Enterobacteriaceae infections in fish showing symptoms and E. coli (χ² = 6.02, p = 0.031) than did the other fish farms. Significant differences were observed between farms (χ² = 9.37, p = 0.024). The fish samples from the fishponds presented several illnesses or a high degree of contamination.

Table 3. Total Bacterial Count TBC (CFU/g) and Total Coliform Count TCC (CFU/g) of fish-muscle samples in the studied fish farms (Mean ± SE) in Wadi El-Natrun, El Menoufia, Egypt, during summer 2023

SE: standard error *The result is significant at p <.05. ^{a, b, c} Different letters in the same column indicate a significant difference in count between different farms.

Table 4: Occurrence of the different bacteria genera in the studied fish farms in Wadi El-Natrun, El Menoufia, Egypt, during summer 2023

χ2= chi-square. The result is significant at p <.05.

Serotyping E. coli and Salmonella isolates

Table 5 shows the serotyping of the E. coli and Salmonella isolates. Serotyping was performed for the E. coli isolates, which included O26:K60, O86:K61, O55:59, O103:K-, O114:K90, autoagglutination, O128:K67, O126:K71, O44:K74, and O25:K11. The Salmonella isolates were all identified as Salmonella enteritidis.

Table 5: Serotyping of E. coli and Salmonella isolates obtained from studied fish farms in Wadi El-Natrun, El Menoufia, Egypt, during summer 2023.

Molecular identification of bacterial pathogens

BLAST analysis of the 16S rRNA gene sequences from the four bacterial strains responsible for fish diseases revealed the isolation of E. coli, E. faecium, E. faecalis, and Pseudomonas from water samples and fish. The E. coli strain exhibited 100% identity with accession number ON126162. The E. faecium strains presented 99% identity with MK332163, which was isolated from Qualifiers, and 89% identity with other strains from fermented food and meat in China, including MK332163, MF424510, MF108290, and MZ229662. The E. faecalis strain (PP759381), which was isolated from infected fish, showed 100% identity with two Enterococcus species (OK271787 and MZ229662) identified from human feces in South Korea and rotten crab meat in China, respectively. Moreover, it exhibited 100% identity with other E. faecium strains (MT882700, MG557608, and MW856655) and 99% identity with PQ065633 from fish and water samples in this study. The E. coli strain (PP188022), recovered from a fish aquaria, demonstrated 100% similarity with KR148984 from a wetland in China and KC254646 obtained from tap water in India. This strain was 96% similar to ON054416, which was isolated from water in Colombia, and MT177009, which was isolated from Hunan, China. Additionally, it shares 100% similarity with both ON054416 and MT177009.

The neighbor-joining phylogenetic trees constructed for the sequenced 16S rRNA genes of E. faecium, E. faecalis, and E. coli were grouped with their corresponding sequences, distinguishing them from other related species (Figure 5). The Pseudomonas strain (PP187982), recovered from the water of fish aquaria, demonstrated 100% similarity with Pseudomonas sp. (MN490878) isolated from water samples from lakes in Austria and JQ308618 obtained from environmental samples in China. The strain showed 99% resemblance to KX714291, which was isolated from soil in Antarctica, and OR841090, which was isolated in China. Furthermore, it shares 100% similarity with MW047419, JQ995152 (sea mud of Yantai), MT177009 (food contact in Pennsylvania), HF548404 (water reservoir in Russia), and KR095629 (tannery effluent in Pakistan) (Figure 6).

Figure 5: Phylogenetic tree constructed on the basis of the partial 16S rRNA gene sequences of E. coli, E. faecalis, and E. faecium and other related partial 16S rRNA gene sequences isolated from bacterial pathogens.

Figure 6. A phylogenetic tree was constructed on the basis of the partial 16S rRNA gene sequence of the Pseudomonas strain (PP187982), which was recovered from the water of fish ponds, and other related partial 16S rRNA gene sequences.

Antibiotic susceptibility of the pathogenic bacterial strains

Each isolate's selected antimicrobial sensitivity test revealed distinct levels of resistance and sensitivity. The 11 E. coli isolates presented 100% resistance to tetracycline, 90% resistance to amoxicillin, 90% resistance to trimethoprim, and 54.5% sensitivity to streptomycin. All three Salmonella isolates were 100% susceptible to norfloxacin and 100% resistant to amoxicillin, streptomycin, and trimethoprim. The ten Pseudomonas isolates were 70% susceptible to ciprofloxacin but exhibited high resistance, with 70% susceptible to trimethoprim, 60% susceptible to tetracycline, and 50% susceptible to amoxicillin. The two Aeromonas isolates showed 100% resistance to ciprofloxacin, 50% resistance to trimethoprim, and 100% sensitivity to amoxicillin and doxycycline. The 13 Enterococcus isolates displayed high levels of resistance to norfloxacin, doxycycline (30.7%), trimethoprim (15.3%), and ciprofloxacin (61.5%), as shown in Table 6.

Table 6: Antibiogram profile of isolated bacteria obtained from studied fish farms in Wadi El-Natrun, El Menoufia, Egypt, during summer 2023.

Fish treatment trial and experimental design

Figure 7 shows the bacterial load of the aquarium water at three different time points: pretreatment and days 1, 4, and 7. The quantitative analysis of the TBCs revealed that the number of bacteria in the water decreased from 3.95 ± 0.05 to 3.14 ± 0.30 and 3.27 ± 0.21 before and after the third day of treatment with KMnO₄ and CuSO_4, respectively. A significant difference (p < 0.05) was observed between the untreated and treated groups on days 4 and 7 of treatment.

Figure 7: The effects of Cuso₄ and Kmno₄ on the bacterial load (TBC) of treated aquarium water (mean ± SE) are expressed as CFU/ml at three different time points: day 1 (pretreatment), day 4, and day 7. A significant difference (p < 0.05) was observed between the untreated and treated groups on days 4 and 7 of treatment.

*n=3 water samples each time

The total bacteria count revealed that the untreated fish samples had an average of 7.82 ± 0.026 CFUs per gram of skin at the beginning of the trial. When the TBCs of the fish treated with KMnO₄ and CuSO₄ were compared with those of the untreated control fish, the log count analysis on day 7 revealed a statistically significant decrease. Figure 8 displays the average log counts/g (4.50 ± 0.020 and 3.96 ± 0.124, respectively) in comparison with the control group (8.60 ± 0.33 CFU/g) (P < 0.05) at the end of treatment (on day 7). The TBC of the fish treated with CuSO₄ and KMnO₄ was four times lower than that of the untreated control group. On day seven, the untreated control fish had a mean percent survival of 43% ± 3.5%. The survival rate of the fish treated with CuSO4 was 80.0% ± 2.4%, whereas that of the KMnO4-treated fish was 70.2% ± 4.3% (Figure 9). The survival rate of the fish treated with CuSO4 was 36% greater than that of the untreated control fish. Similarly, the survival rate of the fish treated with KMnO₄ was 26% greater than that of the untreated control fish. Survival curve analysis revealed a statistically significant difference in survival rates between treated and untreated control fish (P < 0.05).

Figure 8: The effects of Cuso₄ and Kmno₄ on the bacterial load (TBC) are expressed as the mean ± SE of treated fish (CFU/g of skin) at three different time points: day 1, day 4, and day 7. A significant difference (p < 0.05) was observed between the untreated and treated groups at the end of treatment (day 7).

*n=5 fish samples each time

Figure 9: Survival percentage of Oreochromis niloticus naturally infected with coinfected bacteria and subjected to one of two treatments: (i) the control, (ii) KMNO₄, or (iii) CUSO₄ for 7 successive days.

Discussion

Field assessment findings indicate that fish farms in Wadi El-Natrun face significant health challenges, especially concerning mortality rates associated with bacterial coinfections. Symptoms such as gill rot, pale patches, and congested gills are consistent with severe infections caused by pathogens such as Aeromonas spp., Vibrio spp., or Pseudomonas, all of which are commonly encountered in aquaculture environments under stressful conditions (6,35,36). There are similar clinical signs in M. capito, which suggests widespread bacterial infection, potentially caused by environmental stressors such as low water quality, high stocking densities, or temperature changes (15,37). Early disease detection, effective water management, and targeted therapeutic interventions are essential for mitigating bacterial infections, enhancing fish health, and minimizing economic losses in aquaculture systems (18,38).

The overall health and productivity of an aquaculture system may be affected by the water quality parameters of its ponds. Most of the parameters assessed in this study remained within acceptable limits, which is essential for maintaining optimal fish growth and preventing disease outbreaks (39). Nevertheless, the significantly reduced dissolved oxygen (DO) level, reaching 2.8 mg/L, poses a serious concern considering that oxygen is an essential component of fish respiration and metabolism (40). Low DO levels can result in hypoxia, stress, and increased susceptibility to infection, which may explain the high mortality rates observed (6,15). In addition to elevated levels of ammonia to 1.15 mg/L and nitrite to 9 mg/L, in fish, ammonia is toxic at elevated levels, affecting gill function and causing gill erosion. These compounds increase the susceptibility of farmed fish to bacterial diseases, corroborating findings from previous studies (41). In the bacterial analysis of pond water samples, there were significant differences among farms, especially between farms 4 and 5, which may have implications for fish health and water quality management. The elevated TBC, particularly at farm 5, suggests a high level of microbial contamination, possibly as a result of poor sanitation, an organic load, or an excess of nutrients (6,15,26). There is evidence that fecal contamination originates from wastewater runoff or insufficient effluent management on farms 4 and 5, posing health risks to fish and humans (42). The detection of Salmonella on one farm raises concerns that pathogenic bacteria are present, which can cause severe infections in fish and pose zoonotic risks (43). Although total coliforms were detected only on farms 4 and 5, with counts of 12 to 15 MPN/100 mL, they remain within the permissible range for freshwater irrigation and aquaculture, suggesting that contamination may not be immediately dangerous on the basis of current standards (44). Microbiological analyses of fish muscle samples collected from farms 4 and 5 revealed high levels of contamination, with the highest TBC (CFU/g) and total coliform counts (TCC/g) observed on these farms (Table 3). Pathogenic bacteria can contaminate fish through polluted water, supporting findings by Kim and Lee (45), who reported total bacterial counts ranging from 2.3 to 3.5 log CFU/mL in water and 2.2 to 4.0 log CFU/g in fish. The significantly greater prevalence of Pseudomonas spp. and Enterobacteriaceae in fish from farms 4 and 5, confirmed by statistical analysis (χ2 = 6.02, p = 0.031; χ2 = 9.37, p = 0.024), indicates a strong association between bacterial contamination and disease manifestation, which is likely exacerbated by environmental stressors. The presence of these bacteria in fish tissues highlights a serious health concern, implying that poor water quality, sanitation, or handling practices contribute to disease outbreaks and contamination within these aquaculture systems (6,15,41).

This study confirmed the phenotypic and biochemical identification of bacteria isolated from water and fish through 16S rRNA sequence analysis. The 16S rRNA gene sequences of the bacterial isolates in this analysis were more than 98% similar to those of their related bacteria. On the basis of the 16S rRNA gene sequences of the retrieved isolates, the phylogenetic tree in Figure 5 illustrates the results of the phylogenetic analysis. Among the three isolates identified from water and fish (E. coli (PP188022), E. faecalis (PP759381), and E. faecium (ON126162)) some exhibited 100% identity (in sequence BLAST analysis) with bacterial isolates from the experimental fish, indicating their potential role in causing infections, which aligns with findings by Abdelsalam et al. (46). In our study, Pseudomonas was the bacterium most frequently isolated from water and fish on fish farms. Similarly, Li et al. (47) reported that Pseudomonas spp. is the most prevalent and dominant bacterial pathogen associated with various freshwater fish. Figure 6 shows that our identified Pseudomonas isolate had 100% identity (in sequence BLAST analysis) with bacterial isolates obtained from the experimental fish, further indicating its role in causing infections.

Antimicrobial sensitivity testing indicates diverse resistance patterns among bacterial isolates, emphasizing antibiotic stewardship. E. coli showed high resistance to tetracycline, amoxicillin, and trimethoprim, but some sensitivity to streptomycin was observed. Salmonella isolates are susceptible to norfloxacin but resistant to amoxicillin, streptomycin, and trimethoprim. There is susceptibility to ciprofloxacin in Pseudomonas but resistance to trimethoprim, tetracycline, and amoxicillin in Pseudomonas. Despite being resistant to ciprofloxacin, Aeromonas is sensitive to amoxicillin and doxycycline. Enterococcus shows high resistance to norfloxacin and ciprofloxacin. These findings clearly indicate that continuous monitoring and judicious use of antibiotics are key to preventing multidrug-resistant bacteria. These findings align with prior research showing high resistance to amikacin, streptomycin, trimethoprim, and azithromycin in various genera (48). However, earlier studies reported lower resistance rates: 29.4% for E. coli, 46.7% for Salmonella spp., and 42.9% for S. aureus (49). According to Wang et al. (50), antibiotics can alter bacterial abundance and blue-green algae populations in aquaculture systems, indirectly affecting the food web.

Many chemicals have been used in aquaculture for the treatment of diseased animals and, to a lesser extent, to enhance water quality in culture facilities. Chemical applications have the major advantage of quick results (51). KMnO₄ and CuSO₄ have been shown to significantly reduce bacterial loads in water and on fish skin. This led to improved survival rates in this study. This finding is in line with previous research indicating their antimicrobial efficacy in aquaculture (20-22). In water, the pathogenic microorganism concentration decreased from approximately 3.95 to 3.14-3.27 log CFU/mL. The significant differences on days 4 and 7 (p < 0.05) support their efficacy over time. Similar findings were reported by Kumar et al. (52), who noted that water treatment primarily reduced the bacterial counts from sources without significant immediate effects. By the end of the experiment on day 7, the bacterial loads in the water of the control group increased, likely because dead fish serve as nutrient-rich substrates for microorganisms. In contrast, the bacterial loads in the treated group decreased rapidly, which is consistent with findings of Faruk et al. (53), who investigated the use of chemicals such as salt, lime, formalin, and potassium permanganate for aquarium water disinfection.

The number of bacteria in the skin of the fish decreased to approximately 4.5 log CFU/g. The TBC of the fish treated with KMnO₄ and CuSO₄ was fourfold lower than that of the untreated fish. This aligns with previous studies indicating that KMnO₄ is an effective bactericide that eradicates skin and gill infections through its potent oxidizing properties. Similarly, Jenkins (54) reported a bacterial load of 7.73 ± 0.023 CFU/g in fish skin from the untreated control group, with a significant decrease in the treated groups. Additionally, Moshtaghi et al. (55) demonstrated that exposure to copper sulfate and potassium permanganate solutions reduced bacterial loads in the gills, intestines, skin, and surrounding water of sturgeon fries. Interestingly, CuSO₄ demonstrated slightly superior results in reducing pathogenic microorganisms, resulting in enhanced fish survival and health. In addition, the significant increase in survival rates-up to 80% with CuSO₄-further supports their protective role, although further research on optimal dosing is needed. These findings confirm that CuSO₄ and KMnO₄ are effective disinfectants and prophylactic agents in aquaculture.

Conclusion

This study revealed that the elevated mortality rates in polycultured O. niloticus and M. capito during the summer of 2023 are attributed to substandard water quality and bacterial infections. CuSO₄ treatment significantly decreased the bacterial load and mortality rates, whereas KMnO₄ improved the clinical signs and survival rates. These findings emphasize the importance of implementing fish health management practices and biosecurity controls to increase productivity and sustainability in aquaculture systems.

Acknowledgments

Not applicable

Conflict of interest

The authors declare that there are no potential conflicts of interest that could influence the work reported in this paper.