Total bacteria load, selected pathogens (vibrio spp., EHEC, salmonella spp. and shigella spp.) and their antibiotic susceptibility patterns from bio-slurry of selected biodigesters in Wakiso District, Uganda

Total bacteria load, selected pathogens (vibrio spp., EHEC, salmonella spp. and shigella spp.) and their antibiotic susceptibility patterns from bio-slurry of selected biodigesters in Wakiso District, Uganda | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Total bacteria load, selected pathogens (vibrio spp., EHEC, salmonella spp. and shigella spp.) and their antibiotic susceptibility patterns from bio-slurry of selected biodigesters in Wakiso District, Uganda Senyonga Emmanuel, Twinamatsiko Robert, Tumwine Gabriel, Namukomazi Lydia, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6358337/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The utilization of bio-slurry as a nutrient-rich organic fertilizer in sustainable agricultural practices has gained significant attention. However, the presence of pathogenic bacteria in bio-slurry poses a potential risk to human health and the environment. This study aimed at understanding the total bacteria load, prevalence of selected pathogens ( Vibrio , EHEC, Salmonella , and Shigella ), and their antibiotic susceptibility patterns, including multi-drug resistance, for assessing the safety and effectiveness of bio-slurry application in agriculture. A cross-sectional study was conducted in Wakiso District with a total of 40 samples collected from 12 villages, representing different biodigesters with various feedstock materials, including cow dung, poultry fecal products, pig and rabbit urine. The collected samples were analyzed to determine the distribution of materials used in the biodigesters, the prevalence and distribution of selected bacterial pathogens ( Shigella Salmonella, Vibrio spp and Enteroheamarrhic Ecoli strains). The antimicrobial susceptibility patterns of isolated bacterial pathogens were assessed using the disk diffusion method, testing multiple commonly used antibiotics to identify multi-drug resistance among the bacterial isolates. Results The prevalence of selected pathogens revealed Enteroheamorrhagic Escherichia coli (EHEC) as the most prevalent pathogen (31%), followed by Shigella spp. and Vibrio spp, all at (25%) while Salomenella spp were the least prevalent (19.0%). The isolated bacterial pathogens showed varying levels of resistance against different antibiotics with EHEC displaying high resistance to Amoxicillin and moderate resistance to Ciprofloxacin. Salmonella spp demonstrated high resistance to Amoxicillin and moderate resistance to Azithromycin and Ampicillin, highlighting potential Multi-Drug Resistance concerns. Conclusions The distribution of selected pathogens highlighted the importance of implementing stringent safety protocols during bio-slurry handling and management to mitigate potential health risks. Moreover, prudent antibiotic use in animal production systems is essential in addressing Multi-Drug Resistance in bacterial pathogens found in bio-slurry. Further research on treatment technologies and potential risks associated with bio-slurry application is recommended to develop efficient and safe strategies for bio-slurry management in agriculture. Bio-slurry pathogenic bacteria antibiotic susceptibility patterns multi-drug resistance Background Bio-slurry, a byproduct of anaerobic digestion in biogas plants, has potential as an organic fertilizer but also poses risks due to pathogenic bacteria. The presence of pathogenic bacteria in bio-slurry poses potential risks to human health and the environment. Studies have shown that anaerobic digestion can significantly reduce microbial loads in bio-slurry, with longer digestion times and higher temperatures leading to greater pathogen reduction ( 1 ). However, complete pathogen inactivation is unlikely, and antibiotic-resistant bacteria remain a concern ( 2 ). The lack of standardization in anaerobic digestion processes and varying feedstocks necessitates case-by-case risk assessments and calls for a unified methodology to evaluate the safety of bio-slurry application in agriculture ( 2 ). The assessment of the total bacterial load in bio-slurry is fundamental for understanding the microbial composition and potential risks associated with its application. Several studies have focused on quantifying bacterial populations in various agricultural waste materials, including bio-slurry. For instance, ( 3 ) employed culture-dependent methods to estimate the total bacterial count in cattle manure, a primary component of bio-slurry. They reported a wide range of bacterial concentrations, ranging from 10^6 to 10^9 colony-forming units per gram (CFU/g). Similarly, ( 4 ) investigated the bacterial load in pig slurry and found bacterial counts ranging from 10^7 to 10^10 CFU/g using molecular techniques such as quantitative polymerase chain reaction (qPCR). A study by ( 1 ) reported total viable counts ranging from 10^7 to 10^10 CFU/g in manure and bio-slurry samples, with significant reductions observed after anaerobic digestion. ( 5 ) found bacterial and fungal colony-forming units varying by up to five orders of magnitude (10^5–10^10 and 0–10^5 CFU/g, respectively) across different anaerobic digestates. ( 6 ) developed a probabilistic model to assess human exposure to fecal indicator bacteria from bio-based fertilizers, finding very low to moderate risks of illness from E. coli. Identifying and characterizing bacterial pathogens in bio-slurry is essential for assessing the potential risks associated with their presence and understanding their prevalence in agricultural waste. In a study conducted by ( 7 ), Salmonella spp. isolates were successfully recovered from bio-slurry samples collected from pig farms. The isolates were further characterized using serotyping and antimicrobial susceptibility testing. Similarly, ( 8 ) isolated and identified Vibrio spp. strains from bio-slurry samples obtained from aquaculture systems. The isolates were then characterized using molecular techniques, including 16S rRNA sequencing and virulence gene detection. Studies have detected Salmonella spp. in sewage sludge and bio-slurry samples, with Salmonella Infantis being the most prevalent serotype ( 9 ), ( 1 ). Salmonella isolates often possess virulence genes and exhibit antibiotic resistance ( 9 ). Similarly, Vibrio spp have been isolated from aquaculture systems, showing antibiotic resistance patterns ( 10 ). The persistence of Salmonella spp in piggeries and agricultural soil amended with contaminated slurry has been observed, highlighting transmission risks ( 11 ). These findings emphasize the importance of proper waste management practices in agriculture and aquaculture to mitigate potential health hazards. Antibiotic resistance in agriculture is a growing public health concern, with potential transmission to humans through food chains and environmental dissemination ( 12 ), ( 13 ). The study by ( 14 ) revealed the presence of antibiotics in swine slurries which has been correlated with antibiotic resistance genes, raising concerns about their use as fertilizers. The use of sewage sludge and bio-slurry as fertilizers may contribute to this problem, as they can contain antibiotic-resistant bacteria and resistance genes ( 15 ). To address this issue, there is an urgent need for risk assessment and improved surveillance of antibiotic use and resistance in agriculture, particularly in developing countries where regulations may be less stringent ( 13 ), ( 15 ). Assessing the antimicrobial susceptibility profile of bacterial isolates from bio-slurry is crucial to understand their potential resistance to commonly used antibiotics and the implications for public health. In Uganda, there has been increasing installation of biodigesters, in collaboration with the German Cooperation of Biogas, which has generated bio-slurry as a byproduct. However, the potential public health risks associated with the utilization of this bio-slurry have not received sufficient attention. There is limited research assessing the prevalence of antibiotic-resistant bacteria, their antimicrobial susceptibility, and the safety and effectiveness of bio-slurry application in agriculture, with a focus on public health and environmental sustainability. Additionally, the rapid implementation of biodigesters in Wakiso District, Uganda, raises concerns about the risks associated with bio-slurry utilization as an organic fertilizer. There is a lack of comprehensive research on the total bacteria load, prevalence of selected bacterial pathogens ( Salmonella spp., Vibrio spp., Shigella spp., and enterohemorrhagic E. coli ), and their antibiotic susceptibility patterns in bio-slurry derived from biodigesters in Wakiso District. Therefore, there is a critical need to understand these microbial aspects to assess the safety and effectiveness of bio-slurry application in agriculture and develop appropriate guidelines and management strategies for safeguarding public health and environmental sustainability. This research aimed to investigate the aforementioned aspects in bio-slurry obtained from selected bio-digester plants in Wakiso district. Methods Study Design This research employed a cross-sectional study design, incorporating both quantitative and qualitative approaches to achieve the research objectives. Qualitative data included the type of materials used in the biodigesters, location of the biodigesters and the distribution of selected bacteria pathogens in the bio-slurry samples while Quantitative data included the microbial load and the percentage of anti-microbial resistance of the selected pathogens against commonly used antibiotics. The total bacteria load, presence of selected pathogens ( Vibrio , EHEC, Salmonella and Shigella ) plus their antibiotic susceptibility patterns were determined at a point in time in the laboratory. Study area Bio-slurry samples were collected from selected biogas plants across Wakiso district. The district was chosen because it is a strategic area for many organizations and extension workers for biogas technology, has the best methods of cattle grazing and reasonable access to information concerning the modernization of agriculture. Since cow dung is the major feedstock used for biogas in Uganda, these areas had the required raw materials to support the technology and therefore have a significant number of biogas plants and also being that it is part of central Uganda and densely populated. The high population makes it a high-risk community from the health threats of bio-slurry bacteria micro flora which would easily spread due to its nearness to the main towns including Kampala (The capital city). The study population The study population included individual households and farms which operated the biogas plants then and those that were actively producing bio-slurry. This group was selected as they were a first line risk group to potential health risks of bio-slurry poor handling, use and disposal. On farms, the pathogens in bio-slurry would endanger animal health, growth as well the total productivity and the presence of antibiotic resistant strains in slurry would result into its spread in animals and possibly humans in case of zoonotic pathogens. Inclusion criteria Only fresh bio-slurry was sampled and only for the owners who consented to the sampling. Exclusion criteria Bio-slurry waste which was soiled was excluded as well as that for the farmers who failed to consent to the sampling plus those redundant biodigesters for a long time (abandoned). Sample collection and transportation A total of 40 bio-slurry samples were collected aseptically from the selected sites. To ensure representativeness, a composite sample was obtained by collecting multiple sub-samples with a sterile spoon from various depths and locations of the bio-digester. The sub-samples were carefully transferred into a sterile sampling container and tightly sealed to prevent sample leakage and contamination. Each container was clearly labeled with a unique identifier, which included the sampling site, date, village and biomaterial used. Immediately after collection, the samples were recorded in a sample log sheet, placed in a cool box with ice packs, and transported to the laboratory within a few hours to minimize alterations in microbial composition. If immediate transport was not possible, samples were stored at 4°C. Throughout the process, all safety precautions, ethical considerations, and relevant regulatory guidelines were strictly followed to preserve sample integrity and ensure the validity of laboratory analyses. Microbiological investigations were conducted in the College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB) microbiology laboratory. Experiments Determination of the total bacteria load A series of serial dilutions (10 ־1 , 10 − 2 , 10 − 3 and 10 − 4 ) were prepared by aseptic transfer of 1 mL of well-agitated bio-slurry samples to 9 ml of sterile dilution blanks (peptone water) and subsequently transferred 1ml of diluted samples, at every dilution into separate peptone blanks in well labeled pre-sterised test tubes followed by gentle swirling. Once a 10 − 4 dilution was achieved, the serial dilution was stopped 0.1 mL of the undiluted bio-slurry sample was transferred into a labeled Plate Count Agar (PCA) agar plate. A sterile glass spreader was used to surface spread the sample evenly over the surface of the plate count agar plate. The plated sample was the neat. This was repeated for each dilution tube only 10 − 2 and 10 − 4 dilutions were plated. The plates were then incubated at 37°C for 24 hours to allow bacterial growth. After incubation, the visible colonies on plates were counted with a suitable range of colony counts (e.g., 30–300 colonies) using a colony counter. The number of colony-forming units per milliliter (CFU/ml) was calculated by multiplying the colony count by the dilution factor and the reciprocal of the sample volume plated. Isolation and identification of selected bacteria pathogens from bio-slurry Vibrio spp., Preparation of TCBS Media TCBS agar was prepared by weighing and dissolving the powder in distilled water according to the manufacturer's instructions. The media was then sterilized by autoclaving at 121°C for 15 minutes and then cooled to approximately 45–50°C before casting and setting into sterile Petri dishes. Principle of TCBS Media TCBS agar is a selective and differential media specifically designed for the isolation and identification of Vibrio spp. The selective components of TCBS media, including bile salts and high salt concentration, inhibit the growth of most other bacterial species while allowing the growth of Vibrio . The differential component of TCBS media is sucrose. Vibrio spp are able to ferment sucrose, resulting in the production of acid, which leads to a pH drop and a color change of the media from green to yellowish Isolation of Vibrio spp from Bio-slurry using TCBS Media A loopful of the liquid bio-slurry was inoculated onto the Nutrient agar plate and then used a sterile spreader to spread the sample evenly over the surface of the agar plate and then incubated the plate at a temperature of 37°C for 24 hours for the growth of Vibrio species. After incubation, the suspected colonies were then incubated on the TCBS agar plate for 24 hours at 37°C. Colony Morphology Vibrio colonies appeared as smooth, round, convex colonies with a translucent or yellowish coloration. The colonies had a well-defined edge and varied in size from small to large, depending on the species and growth conditions and the texture of the colonies was usually moist and slightly mucoid. Staining characteristics Gram Staining Vibrio species were typically Gram-negative bacteria and appeared pink or red after performing the Gram staining procedure. Biochemical Tests Oxidase Test An oxidase test was performed by applying a drop of oxidase reagent (e.g., N,N,N',N'-Tetramethyl-p- phenylenediamine dihydrochloride) onto a piece of filter paper and then transferring a small portion of suspected colony onto the filter paper using a wooden stick and then observed for the appearance of a blue-purple color within 10–30 seconds. The result was recorded as positive if a color change occurred and as negative if no color change was observed. Indole Production An indole production test was performed by inoculating a selected colony into a tube containing tryptone broth and then incubating the tube at a temperature of 37°C for 24 hours. After 24 hours, 0.1mls of Kovac's reagent was added to the tube and then observed for the development of red coloration in the upper layer of the broth. The result was recorded as positive if a red color appeared and as negative if no color change occurred. Citrate Utilization The pure tentative Vibrio suspects were selected and streaked onto a citrate agar slant and then incubated the slant at 37°C for 24–48 hours and observed for the growth and color change of the slant. The result was recorded as positive if the slant turned blue, indicating citrate utilization, and as negative if the slant remained green. Carbohydrate Fermentation Tests A loopful of a pure culture of Vibrio species was transferred into a test tube containing Triple Sugar Iron (TSI) agar medium. The test tube was inoculated by stabbing the agar with the inoculation loop, ensuring the depth of the stab reaches the bottom of the tube and the slant was streaked and after the TSI agar tube was incubated at an appropriate temperature at 37°C for 24 hours to allow for bacterial growth and metabolic activity. After incubation, the TSI agar slant was observed for characteristic changes in the medium. Isolation and identification of Salmonella and Shigella For isolation of Salmonella , pre-enrichment was done by aseptically transferring 1ml of bio-slurry samples into 9 mls of Rapaport broth and the tubes were incubated for 24 hours at 40ºC. This selectively inhibited other bacteria other than Salmonella species. After incubation, the Rapaport that had changed color to greenish was inoculated aseptically on Salmonella - Shigella Agar (SSA). For the isolation of Shigella spp., the minus 1 dilution of the bio-slurry sample was streaked on SSA agar plate. The plates were then incubated for 24 hours at 37ºC. Shigella spp. presented as colorless colonies with no blackening whereas Salmonella colonies were colorless with black centers due to their ability to produce hydrogen sulfide. The tentative isolates were then sub-cultured for biochemical identification. Biochemical identification and confirmation Triple Sugar Iron (TSI) Agar Test A TSI agar slant was streaked and the butt stubbed with the bacterial pure colony using an inoculation loop and then incubated at 37°C for 24 hours. After incubation, observations were made for changes in the butt and slant for gas production and acid production. Results were interpreted based on the color change, gas production, and acid production in the slant and butt of the agar in the test tube. On TSI, Salmonella spp showed an alkaline slant and acid butt with H₂S with gas production, whereas Shigella showed an alkaline slant and acid butt without H₂S or gas. Indole Test Bacterial cultures were inoculated into tryptone broth and incubated at 37°C for 24–48 hours. After incubation, Kovac's reagent was added to the broth and the development of a red color indicated a positive reaction for indole production. Both Shigella and Salmonella isolates were negative for Indole production (no red coloration upon addition of Kovac’s reagent) Methyl Red (MR) Test Bacterial cultures were inoculated into MR-VP broth and incubated at 37°C for 24 hours. After incubation, methyl red indicator was added to the broth and the development of a red color indicated a positive reaction for acid production. Salmonella isolates were methyl red (MR) positive while the Shigella spp were negative (no color change) Voges-Proskauer (VP) Test Bacterial cultures were inoculated into MR-VP broth and incubated at 37°C. After incubation, Barritt's reagents A and B were added to the broth. The development of a red color indicated a positive reaction for acetoin production. Both organisms were Voges-Proskauer (VP) negative. Citrate Utilization Test Bacterial cultures were inoculated into Simmons citrate in test tubes and then incubated at 37°C for 24 hours. The color change of the agar from green to blue indicated a positive reaction for citrate utilization. Salmonella isolates utilized Citrate while Shigella spp were negative for citrate utilization. Urease Test Bacterial cultures were streaked onto the slant and stubbed in the butt of urea agar tubes and then incubated at 37°C for 24 hours. Development of a pink or magenta color indicated a positive reaction for urease production and yellow for negative. Both Salmonella and Shigella isolates were urease negative. Biochemical Characteristics of Salmonella and Shigella Isolates Table 1 Biochemical characteristics of Salmonella and Shigella isolates Biochemical Test Salmonella spp. Shigella spp. Triple Sugar Iron (TSI) Alkaline slant / acid butt, H₂S positive , gas positive Alkaline slant / acid butt, H₂S negative , no gas Indole Test Negative (no red ring) Negative (no red ring) Methyl Red (MR) Test Positive (red color) Negative (no color change) Voges-Proskauer (VP) Test Negative Negative Citrate Utilization Test Positive (blue color) Negative (no color change) Urease Test Negative (yellow color) Negative (yellow color) Isolation and identification of Enteroheamorhagic E. coli For isolation of the organism, 0.1 mL dilution of the samples was aseptically transferred and streaked onto MacConkey Sorbitol agar plates using a sterile loop and then incubated the plates aerobically at 37°C for 24 to 48 hours. After incubation, the MacConkey Sorbitol plates were examined for bacterial growth indicated by colorless colonies as the strain is a non-lactose fermenter. The tentative positive isolates were selected and subcultured and the following biochemical tests were performed. Indole Test Inoculated tryptone broth and added Kovac's reagent. Observed for a red color, indicating a positive result. Methyl Red (MR) Test Inoculated MR-VP broth and added methyl red indicator. Observed for a red color, indicating a positive result. Voges-Proskauer (VP) Test Inoculated MR-VP broth and added Barritt's reagents A and B and then observed for a red color after incubation, which indicated a positive result. Citrate Utilization Streaked the pure colonies onto Simmons citrate agar and incubated. Observed the tubes for growth where a blue color change on the agar indicated a positive result. Urease Test Streaked the suspect pure colonies onto urea agar and incubated the tubes for growth where a pink/magenta color change indicated a positive result while no change for a negative isolate. The positive isolates were identified by the presence of colorless colonies on MacConkey Sorbitol Agar, due to non-sorbitol fermentation. Biochemical tests showed positive reactions for indole, Voges-Proskauer and citrate utilization while urease and methyl red were negative. Antimicrobial susceptibility testing of the positive isolates Susceptibility patterns of E. coli was determined using Kirby Bauer disc diffusion method. The antibiotics used included; gentamicin (10µg), Ciprofloxacin (5µg), Tetracycline (30µg), Ampicillin (30µg), ciprofloxacin (5 µg), azithromycin (30 µg) and ceftriaxone of HiMedia. Two to five pure colonies were uniformly mixed with 5ml of sterile physiological saline and the turbidity of the resultant mixture was compared with 0.5 McFarland standard (Beckton and Dickson) to estimate the seeding density. The bacterial suspension was inoculated on sterile Mueller Hinton agar by surface spreading, and drug discs with the known volume and concentration of each one of the selected antibiotics were placed on the Mueller Hinton agar and incubated at 37 o C for 24 hours. The susceptibilities were determined by measuring the diameter of growth inhibition around the antibiotic discs and the measured diameters compared to the standard in order to qualify the isolates as resistant, Intermediate or susceptible to the antibiotics used according to the standards of the Clinical and Laboratory Standards Institute Data management and analysis Data was entered in MS Excel version 2013 and further transferred to STATA 15 analytical software for analysis. Data presentation was by line graphs and tables and statistical significance was at P ≤ 0.05. Results Distribution of samples according to location of biodigesters A total of 40 bio-slurry samples were collected from 12 different villages within Wakiso District, Uganda. The number and proportion of samples collected from each village are presented in Table 2 . The highest proportion of samples was collected from Namayumba (17.5%, n = 7), followed by Nabalanga and Sseganga, each contributing 12.5% (n = 5). The lowest number of samples (5.0%, n = 2) were obtained from several villages, including Buyale, Kabulengwa, Kyampisi, Lukoma, Mattugga, and Namosera. Out of the 40 samples, 31(77.5%) were derived from cow dung, 2(5%) were from pig feacal + rabbit urine and 2(17.5%) were from poultry bio digesters (Table 3 ). The results indicated that cow dung was the most commonly used material in the biodigester plants, followed by poultry fecal matter, while pig and rabbit urine were the least used materials. Table 2 Distribution of samples according to village location of bio digester plants Village Frequency (Percentage Buyale 2 5.0 Kabulengwa 2 5.0 Kisimbiri 4 10.0 Kyampisi 2 5.0 Lukoma 2 5.0 Lwadda 13 3 7.5 Mattugga 2 5.0 Mpunga 4 10.0 Nabalanga 5 12.5 Namayumba 7 17.5 Namosera 2 5.0 Sseganga 5 12.5 Total 40 100.0 Table 3 Distribution of the samples by materials used in the biodigesters Material used Frequency Percentage (%) Cowdung 31 77.5 Pig Feacal + Rabbit Urine 2 5.0 Poultry 7 17.5 Total 40 100.0 Mean log CFU/ml of bio-slurry The results showed that biodigesters fed with poultry had the highest mean log CFU/ml bacteria load (6.6) with a standard deviation of ± 0.53, followed by pig feacal plus Rabbit urine with a mean bacteria load of (6.5) with a standard deviation of ± 0.34 and cow dung had the least mean bacteria load (6.1) with a standard deviation of ± 0.64. The total mean bacteria load for all the samples was (6.1) with a corresponding standard deviation of ± 0.63 (Table 4 ). Table 4 The mean log CFU/ml of bio-slurry MATERIAL USED Mean log cfu/ml N Std. Deviation Cowdung 6.096790 31 .6350274 Pig Feacal + Rabbit Urine 6.493833 2 .3373757 Poutry 6.595978 7 .5267110 Total 6.145116 40 .6346778 The occurrence of the selected bacteria pathogens in bio-slurry samples A total of 16 bacterial isolates were identified from the bio-slurry samples (Table 5 ). Of these, 5 isolates (31%) were identified as Enteroheamorrhagic Escherichia coli (EHEC), 3 isolates (19%) as Salmonella spp., 4 isolates (25%) as Shigella spp., and 4 isolates (25%) as Vibrio spp. Among the identified pathogens, Enteroheamorrhagic Escherichia coli (EHEC) was the most frequently isolated, while Salmonella spp. had the lowest occurrence. Out of the 40 bio-slurry samples analyzed, 5 samples (12.5%) tested positive for Enteroheamorrhagic Escherichia coli (EHEC), while 3 samples (7.5%) were positive for Salmonella spp. In addition, Shigella spp. and Vibrio spp. were each detected in 4 samples (10.0%). The remaining samples did not yield isolates of these selected pathogens. Table 5 The occurrence of the selected bacteria pathogens in bio-slurry samples Organism Frequency Percentage EHEC s pp. 5 31.0 Salmonella spp. 3 19.0 Shigella spp. 4 25.0 Vibrio spp. 4 25.0 Total 16 100.0 The antimicrobial susceptibility patterns of the isolates The antimicrobial susceptibility patterns of the isolates were evaluated, and the results suggested varying levels of resistance to different antibiotics among the studied bacteria isolates. Regarding Enteroheamorrhagic Escherichia coli (EHEC), all isolates exhibited high resistance to Amoxicillin, while resistance to Ciprofloxacin was moderate. Tetracycline, Ceftriaxone, Azithromycin, Ampicillin, and Gentamycin showed lower levels of resistance. In the case of Vibrio , moderate resistance was observed against Amoxicillin, Ceftriaxone, and Azithromycin, while Tetracycline, Ciprofloxacin, Ampicillin, and Gentamycin had relatively low resistance. Shigella isolates displayed moderate resistance to Tetracycline, while the resistance to Amoxicillin, Ceftriaxone, Azithromycin, Ciprofloxacin, Ampicillin, and Gentamycin was low. There were variations in resistance patterns among the Shigella isolates. Salmonella isolates showed high resistance to Amoxicillin, while resistance to Azithromycin and Ampicillin was moderate. Tetracycline, Ceftriaxone, Ciprofloxacin, and Gentamycin demonstrated low resistance (Table 6 ). Table 6 The antimicrobial susceptibility patterns of the bacteria isolates Isolate Percentage Resistance AMOX TET CEF AZI CIPRO AMP GENTA EHEC(n = 5) Vibrio (n = 4) Shigella (n = 4) 3(60) 3(75) 1(25) 1( 20 ) 2(50) 0(0) 0(0) 0(0) 0(0) 0(0) 2(50) 1(25) 0(0) 0(0) 1(25) 4(80) 1(25) 2(50) 0(0) 0(0) 0(0) Salmonella (n = 3) 3(100) 0(0) 0(0) 1(33.3) 0(0) 2(50) 0(0) AMOX = Amoxicllin, TET = Tetracycline, CEF = Ceftriaxone, AZI = Azithromycin, CIPRO = Ciproflaxacin, AMP = Ampicilin, GENTA = Gentamycin, EHEC = Enteroheamorrhagic Escherichia Discussion The analysis of the bio-slurry samples revealed a wide range of bacterial concentrations. The mean log CFU/ml of the samples was 6.145116, with a standard deviation of 0.6346778. Significant variations in bacterial load were observed based on the type of material used for the biodigester. Poultry waste showed the highest mean bacterial load (6.595978), followed by pig fecal plus rabbit urine (6.493833), while cow dung had the lowest mean bacterial load (6.096790). These findings suggest that the biomass used in biodigesters has a substantial impact on the bacterial load in the resulting bio-slurry. The higher bacterial load in poultry waste may be attributed to the nature of the waste and its composition, which provides a favorable environment for bacterial growth. On the other hand, cow dung, being relatively less conducive to bacterial growth, exhibited a lower bacterial load. These results align with previous studies emphasizing the influence of feedstock on the microbial composition and load in bio-slurry ( 1 ). Studies by ( 16 ), ( 17 ) have shown that manure from different livestock sources results in diverse bacterial communities, with Firmicutes, Bacteroidetes, Proteobacteria, and Chloroflexi being dominant phyla. Spatial variations within biodigesters also affect bacterial diversity and composition ( 17 ). The presence of bacterial pathogens in bio-slurry raises concerns regarding potential health risks and the need for appropriate management strategies. The analysis revealed the presence of Enteroheamorrhagic Escherichia coli (EHEC), Salmonella spp., Shigella spp., and Vibrio spp. Among the 16 isolates characterized, EHEC was the most prevalent pathogen, accounting for 31% of the isolates. Shigella spp. and Vibrio spp. showed equal prevalence (25%), while Salmonella spp. had the lowest prevalence (19%). These results align with previous studies that have identified various pathogens, including Escherichia coli , Salmonella spp., Shigella spp., and Vibrio cholerae in these materials ( 1 ). While anaerobic digestion can significantly reduce bacterial loads, some pathogens may persist, particularly in shorter digestion periods or at lower temperatures ( 1 ), ( 18 ). The application of digestate as fertilizer can introduce these pathogens into agroecosystems, although soil microorganisms may inhibit their resurgence ( 19 ). These findings emphasize the need to address the potential risks associated with the presence of bacterial pathogens in bio-slurry derived from biodigesters. The prevalence of EHEC, known for its association with foodborne illnesses, emphasizes the need for proper handling and treatment of bio-slurry to prevent potential contamination of crops and the food chain. The evaluation of antimicrobial susceptibility patterns in bacterial isolates from various sources suggested significant antibiotic resistance concerns. The isolates exhibited varying levels of resistance to different antibiotics. EHEC isolates demonstrated high resistance to Amoxicillin and moderate resistance to Ciprofloxacin. Vibrio spp. displayed moderate resistance to Amoxicillin, Ceftriaxone, and Azithromycin, while Shigella spp. showed moderate resistance only to Tetracycline. Salmonella spp. exhibited high resistance to Amoxicillin and moderate resistance to Azithromycin and Ampicillin. Studies on biogas digestates, food pathogens, and environmental effluents have demonstrated widespread resistance among diverse bacterial species. Isolates from biogas digestates showed multi-resistance to several antibiotics, with food waste digestates exhibiting higher resistance levels ( 20 ). Emerging foodborne pathogens, including Vibrio spp . and Salmonella enterica , displayed resistance to β-lactams, sulfonamides, tetracyclines, and fluoroquinolones ( 21 ). Hospital and slaughterhouse effluents contained E. coli and Salmonella isolates with high resistance to multiple antibiotics, including ampicillin, ciprofloxacin, and erythromycin ( 22 ). Similarly, abattoir wastewater in Nairobi, Kenya, harbored faecal bacteria indicators and pathogens with significant antibiotic resistance, posing potential environmental and health risks ( 23 ). These findings highlight the widespread occurrence of antibiotic resistance in various environmental sources including in bio-slurry. The high resistance observed in EHEC and Salmonella spp., which are commonly associated with foodborne infections, underscores the importance of prudent antibiotic use in livestock production systems and the need for effective waste management practices to mitigate the spread of antibiotic resistance. Conclusions The analysis of bio-slurry from biodigesters in Wakiso District revealed significant variations in total bacterial load, with poultry waste exhibiting the highest levels and cow dung the lowest. The detection of enteric bacterial pathogens, including Enteroheamorrhagic Escherichia coli (EHEC), Salmonella spp., Shigella spp., and Vibrio spp., highlights potential public health risks associated with the use of untreated or inadequately treated bio-slurry in agricultural settings. Considering bacterial load, pathogen presence, and antimicrobial resistance is crucial when designing biodigesters and managing bio-slurry to ensure public health and safety. Notably, the isolated pathogens exhibited varying levels of resistance to commonly used antibiotics, raising concerns about the potential role of bio-slurry in the dissemination of antimicrobial resistance (AMR) within the environment and food chain. These findings underscore the importance of prudent antibiotic use in livestock systems and the need for integrated biosecurity and waste management strategies. It is essential to establish and enforce safety protocols during the handling and management of bio-slurry. This should include using appropriate personal protective equipment (PPE) such as gloves and masks, ensuring proper hygiene practices, and following guidelines for storage, transportation, and application of bio-slurry. Routine monitoring of bio-slurry for microbial contaminants and resistance profiles is recommended to support informed decision-making on its safe utilization. In addition, further research into treatment technologies and pathogen inactivation methods is warranted to ensure the safe and sustainable application of bio-slurry in agriculture, thereby minimizing potential risks to public and environmental health. Limitations of the study The findings, though representative of the biodigesters in Wakiso District, may not be generalizable to all regions in Uganda due to variations in climate, livestock systems, and biodigester management practices. Although a representative number of biodigesters were selected based on accessibility and operational status, the sample size was relatively small, which may limit statistical inference. Although antibiotic resistance was observed, the absence of data on prior antibiotic use in the host farms limited our ability to draw correlations between usage patterns and resistance profiles. Despite these limitations, the study provides important baseline data on pathogen presence and resistance in bio-slurry from biodigesters, highlighting the need for integrated surveillance and future studies incorporating broader spatial coverage. Declarations Ethics approval and consent to participate This study did not involve human subjects or animal experimentation. However, samples were collected from biodigesters located on private property, and the owners provided verbal informed consent prior to participation. All procedures were conducted in accordance with relevant ethical standards and fieldwork guidelines. Although formal approval from an institutional review board was not sought, ethical considerations including voluntary participation, confidentiality and non-invasive sampling were strictly followed. The study was approved by the Department of Biomolecular Resources & Biolab Sciences, College of Veterinary Medicine, Animal Resources and Bio-security, Makerere University. The department provided an introductory letter to the authors, clearly explaining the purpose and procedures of the study to the District leadership and biodigester owners. The letter was submitted to the Chief Administrative Officer (CAO) who forwarded it to the District Production Officer (DPO) and then the District Veterinary Officer (DVO). Consent for publication Not applicable Data availability (where applicable) All data produced or analyzed in this study are provided in the article or can be obtained from the corresponding author upon request. Competing interests The authors report no conflicts of interest related to this study. Funding The study received partial funding from Makerere University Research and Innovation Fund (MAK-RIF) supported by the Government of Uganda, which facilitated field data collection and analysis. Author Contributions All authors made a significant contribution to the work reported, whether in the conception, study design, execution, acquisition of data, analysis and interpretation or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. SE, TR and TG conceived the study. SE, TR, TG and NL developed the study protocol including data collection tool, data collection strategy. SE, TR, TG, LE and NL were involved in field data collection activities. SE, TR and TG conducted data analysis and interpretation. SE, TR, TG, NL, LE and TA were involved in preparation of the manuscript. All authors contributed to the article and approved the submitted article. Acknowledgments Our appreciation goes to the Government of Uganda's support through Makerere University Research and Innovation Fund (MAK-RIF) for funding field data collection and analysis. We thank the extension staff, local leaders and farmers in the study District of Wakiso for mobilization and participation in the study. Authors and Affiliations Department of Biomolecular Resources & Biolab Sciences, College of Veterinary Medicine, Animal Resources and Bio-security, Makerere University, Kampala, Uganda Senyonga Emmanuel and Tumwine Gabriel Department of Livestock and Industrial Resources, College of Veterinary Medicine, Animal Resources and Bio-security, Makerere University, Kampala, Uganda Twinamatsiko Robert A frica Institute for Strategic Animal Resource Services and Development (AF RISA), College of Veterinary Medicine, Animal Resources and Bio-security, Makerere University, Kampala, Uganda Namukomazi Lydia Department of bio-slurry extension, Biogas Solutions Uganda LTD, Kampala, Uganda Luwemba Emmanuel Department of Health Policy, Planning and Management, College of Health Sciences, Makerere University, Kampala, Uganda Tukamuhebwa Agatha References Islam MA, Biswas P, Sabuj AAM, Haque ZF, Saha CK, Alam MM, et al. Microbial load in bio-slurry from different biogas plants in Bangladesh. J Adv veterinary Anim Res. 2019;6(3):376. Nag R, Auer A, Markey BK, Whyte P, Nolan S, O'Flaherty V, et al. Anaerobic digestion of agricultural manure and biomass–critical indicators of risk and knowledge gaps. Sci Total Environ. 2019;690:460–79. Tomar A, Choudhary S, Kumar L, Singh M, Dhillon N, Arya S. Screening of Bacteria Present in Cow Dung. Int J Curr Microbiol App Sci. 2020;9(2):584–91. Kumar H, Jang YN, Kim K, Park J, Jung MW, Park J-E. Compositional and functional characteristics of swine slurry microbes through 16S rRNA metagenomic sequencing approach. Animals. 2020;10(8):1372. Coelho JJ, Prieto ML, Hennessy A, Casey I, Woodcock T, Kennedy N. Determination of microbial numbers in anaerobically digested biofertilisers. Environ Technol. 2021;42(5):753–63. Nag R, Nolan S, O'Flaherty V, Fenton O, Richards KG, Markey BK, et al. Quantitative microbial human exposure model for faecal indicator bacteria and risk assessment of pathogenic Escherichia coli in surface runoff following application of dairy cattle slurry and co-digestate to grassland. J Environ Manage. 2021;299:113627. Huong LQ, Forslund A, Madsen H, Dalsgaard A. Survival of Salmonella spp. and fecal indicator bacteria in Vietnamese biogas digesters receiving pig slurry. Int J Hyg Environ Health. 2014;217(7):785–95. Rahman MM, Alam Tumpa MA, Zehravi M, Sarker MT, Yamin M, Islam MR, et al. An overview of antimicrobial stewardship optimization: the use of antibiotics in humans and animals to prevent resistance. Antibiotics. 2022;11(5):667. Krzyzanowski F, Zappelini L, Martone-Rocha S, Dropa M, Matté MH, Nacache F, et al. Quantification and characterization of Salmonella spp. isolates in sewage sludge with potential usage in agriculture. BMC Microbiol. 2014;14:1–9. Banerjee S, Ooi MC, Shariff M, Khatoon H. Antibiotic resistant Salmonella and Vibrio associated with farmed Litopenaeus vannamei. Sci World J. 2012;2012(1):130136. Baloda SB, Christensen L, Trajcevska S. Persistence of a Salmonella enterica serovar Typhimurium DT12 clone in a piggery and in agricultural soil amended with Salmonella-contaminated slurry. Appl Environ Microbiol. 2001;67(6):2859–62. Iwu CD, Korsten L, Okoh AI. The incidence of antibiotic resistance within and beyond the agricultural ecosystem: A concern for public health. Microbiologyopen. 2020;9(9):e1035. Manyi-Loh C, Mamphweli S, Meyer E, Okoh A. Antibiotic use in agriculture and its consequential resistance in environmental sources: potential public health implications. Molecules. 2018;23(4):795. Sanz C, Casado M, Navarro-Martin L, Tadić Đ, Parera J, Tugues J, et al. Antibiotic and antibiotic-resistant gene loads in swine slurries and their digestates: implications for their use as fertilizers in agriculture. Environ Res. 2021;194:110513. Bondarczuk K, Markowicz A, Piotrowska-Seget Z. The urgent need for risk assessment on the antibiotic resistance spread via sewage sludge land application. Environ Int. 2016;87:49–55. Li J, Rui J, Yao M, Zhang S, Yan X, Wang Y, et al. Substrate type and free ammonia determine bacterial community structure in full-scale mesophilic anaerobic digesters treating cattle or swine manure. Front Microbiol. 2015;6:1337. García-Lozano M, Hernández-De Lira IO, Huber DH, Balagurusamy N. Spatial variations of bacterial communities of an anaerobic lagoon-type biodigester fed with dairy manure. Processes. 2019;7(7):408. Russell L, Whyte P, Zintl A, Gordon S, Markey B, de Waal T, et al. A small study of bacterial contamination of anaerobic digestion materials and survival in different feed stocks. Bioengineering. 2020;7(3):116. Gong J, Liu B, Liu P, Zhang L, Chen C, Wei Y, et al. Changes in bacterial diversity, co-occurrence pattern, and potential pathogens following digestate fertilization: Extending pathogen management to field for anaerobic digestion of livestock manure. Waste Manag. 2023;158:107–15. Sun H, Bjerketorp J, Levenfors JJ, Schnürer A. Isolation of antibiotic-resistant bacteria in biogas digestate and their susceptibility to antibiotics. Environ Pollut. 2020;266:115265. Grudlewska-Buda K, Bauza-Kaszewska J, Wiktorczyk-Kapischke N, Budzyńska A, Gospodarek-Komkowska E, Skowron K. Antibiotic Resistance in selected emerging bacterial foodborne Pathogens—An issue of concern? Antibiotics. 2023;12(5):880. Hassan M, Ahaduzzaman M, Alam M, Bari M, Amin K, Faruq A. Antimicrobial resistance pattern against E. coli and Salmonella spp. in environmental effluents. Int J Nat Sci. 2015;5(2):52–8. Atieno R, Okemo P, Ombori O. Antibiotic resistance of faecal bacteria indicators and pathogens isolated from sludge and wastewaters of Abattoirs in Nairobi, Kenya. J Biol. 2013;1(5):106–11. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6358337","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454915751,"identity":"00af6f29-783a-4648-a824-970e74b0774b","order_by":0,"name":"Senyonga Emmanuel","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Senyonga","middleName":"","lastName":"Emmanuel","suffix":""},{"id":454915752,"identity":"9502cdfb-889f-4a62-8dca-4f8de5a4f3c2","order_by":1,"name":"Twinamatsiko Robert","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACHgjFxsDA+ICB4QCxWhIMQFqYDUjTArJIgigt5u29Bx9X/viT2Cfd/Kyap+aOHD8D88NHN/BokTlzLtnwTIJBYpvMMbPbPMeeGUs2sBkb5+DRIiGRYybZkGCQ2yaRANTCdjhxwwEeNmkCWsx/QrSkfyvm+UecFjNGiJYcM2beNmK08JxLlmxIM64HaimWnNt32FiymZBf2HsPfmywkTOWn5G+8cObb4fl+NmbHz7GpwUFMIFTAjOxykGA8QcpqkfBKBgFo2DEAAD/WkkAopTs0QAAAABJRU5ErkJggg==","orcid":"","institution":"Makerere University","correspondingAuthor":true,"prefix":"","firstName":"Twinamatsiko","middleName":"","lastName":"Robert","suffix":""},{"id":454915753,"identity":"35883ddc-13e4-4a0b-b654-ac764ab85311","order_by":2,"name":"Tumwine Gabriel","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Tumwine","middleName":"","lastName":"Gabriel","suffix":""},{"id":454915754,"identity":"ebe92174-711e-41c3-8a4d-c2525e00c467","order_by":3,"name":"Namukomazi Lydia","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Namukomazi","middleName":"","lastName":"Lydia","suffix":""},{"id":454915755,"identity":"8518ee77-6757-4001-bf5d-98d1edef4557","order_by":4,"name":"Luwemba Emmanuel","email":"","orcid":"","institution":"Biogas Solutions Uganda LTD","correspondingAuthor":false,"prefix":"","firstName":"Luwemba","middleName":"","lastName":"Emmanuel","suffix":""},{"id":454915756,"identity":"6f39f5d4-88dc-4320-8b2a-e770144fa0ab","order_by":5,"name":"Tukamuhebwa Agatha","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Tukamuhebwa","middleName":"","lastName":"Agatha","suffix":""}],"badges":[],"createdAt":"2025-04-02 06:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6358337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6358337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91702289,"identity":"39c39317-23c4-4039-850d-920252c7f38d","added_by":"auto","created_at":"2025-09-19 10:47:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1514466,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6358337/v1/4c7b321e-705d-4a2b-ac46-d0b64bc8c6e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Total bacteria load, selected pathogens (vibrio spp., EHEC, salmonella spp. and shigella spp.) and their antibiotic susceptibility patterns from bio-slurry of selected biodigesters in Wakiso District, Uganda","fulltext":[{"header":"Background","content":"\u003cp\u003eBio-slurry, a byproduct of anaerobic digestion in biogas plants, has potential as an organic fertilizer but also poses risks due to pathogenic bacteria. The presence of pathogenic bacteria in bio-slurry poses potential risks to human health and the environment. Studies have shown that anaerobic digestion can significantly reduce microbial loads in bio-slurry, with longer digestion times and higher temperatures leading to greater pathogen reduction (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, complete pathogen inactivation is unlikely, and antibiotic-resistant bacteria remain a concern (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The lack of standardization in anaerobic digestion processes and varying feedstocks necessitates case-by-case risk assessments and calls for a unified methodology to evaluate the safety of bio-slurry application in agriculture (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe assessment of the total bacterial load in bio-slurry is fundamental for understanding the microbial composition and potential risks associated with its application. Several studies have focused on quantifying bacterial populations in various agricultural waste materials, including bio-slurry. For instance, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) employed culture-dependent methods to estimate the total bacterial count in cattle manure, a primary component of bio-slurry. They reported a wide range of bacterial concentrations, ranging from 10^6 to 10^9 colony-forming units per gram (CFU/g). Similarly, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) investigated the bacterial load in pig slurry and found bacterial counts ranging from 10^7 to 10^10 CFU/g using molecular techniques such as quantitative polymerase chain reaction (qPCR). A study by (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) reported total viable counts ranging from 10^7 to 10^10 CFU/g in manure and bio-slurry samples, with significant reductions observed after anaerobic digestion. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) found bacterial and fungal colony-forming units varying by up to five orders of magnitude (10^5\u0026ndash;10^10 and 0\u0026ndash;10^5 CFU/g, respectively) across different anaerobic digestates. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) developed a probabilistic model to assess human exposure to fecal indicator bacteria from bio-based fertilizers, finding very low to moderate risks of illness from E. coli.\u003c/p\u003e \u003cp\u003eIdentifying and characterizing bacterial pathogens in bio-slurry is essential for assessing the potential risks associated with their presence and understanding their prevalence in agricultural waste. In a study conducted by (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), \u003cem\u003eSalmonella\u003c/em\u003e spp. isolates were successfully recovered from bio-slurry samples collected from pig farms. The isolates were further characterized using serotyping and antimicrobial susceptibility testing. Similarly, (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) isolated and identified \u003cem\u003eVibrio\u003c/em\u003e spp. strains from bio-slurry samples obtained from aquaculture systems. The isolates were then characterized using molecular techniques, including 16S rRNA sequencing and virulence gene detection. Studies have detected \u003cem\u003eSalmonella\u003c/em\u003e spp. in sewage sludge and bio-slurry samples, with \u003cem\u003eSalmonella Infantis\u003c/em\u003e being the most prevalent serotype (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). \u003cem\u003eSalmonella\u003c/em\u003e isolates often possess virulence genes and exhibit antibiotic resistance (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Similarly, \u003cem\u003eVibrio\u003c/em\u003e spp have been isolated from aquaculture systems, showing antibiotic resistance patterns (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The persistence of \u003cem\u003eSalmonella\u003c/em\u003e spp in piggeries and agricultural soil amended with contaminated slurry has been observed, highlighting transmission risks (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These findings emphasize the importance of proper waste management practices in agriculture and aquaculture to mitigate potential health hazards.\u003c/p\u003e \u003cp\u003eAntibiotic resistance in agriculture is a growing public health concern, with potential transmission to humans through food chains and environmental dissemination (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The study by (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) revealed the presence of antibiotics in swine slurries which has been correlated with antibiotic resistance genes, raising concerns about their use as fertilizers. The use of sewage sludge and bio-slurry as fertilizers may contribute to this problem, as they can contain antibiotic-resistant bacteria and resistance genes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To address this issue, there is an urgent need for risk assessment and improved surveillance of antibiotic use and resistance in agriculture, particularly in developing countries where regulations may be less stringent (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Assessing the antimicrobial susceptibility profile of bacterial isolates from bio-slurry is crucial to understand their potential resistance to commonly used antibiotics and the implications for public health.\u003c/p\u003e \u003cp\u003eIn Uganda, there has been increasing installation of biodigesters, in collaboration with the German Cooperation of Biogas, which has generated bio-slurry as a byproduct. However, the potential public health risks associated with the utilization of this bio-slurry have not received sufficient attention. There is limited research assessing the prevalence of antibiotic-resistant bacteria, their antimicrobial susceptibility, and the safety and effectiveness of bio-slurry application in agriculture, with a focus on public health and environmental sustainability. Additionally, the rapid implementation of biodigesters in Wakiso District, Uganda, raises concerns about the risks associated with bio-slurry utilization as an organic fertilizer. There is a lack of comprehensive research on the total bacteria load, prevalence of selected bacterial pathogens (\u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eVibrio\u003c/em\u003e spp., \u003cem\u003eShigella\u003c/em\u003e spp., and enterohemorrhagic \u003cem\u003eE. coli\u003c/em\u003e), and their antibiotic susceptibility patterns in bio-slurry derived from biodigesters in Wakiso District. Therefore, there is a critical need to understand these microbial aspects to assess the safety and effectiveness of bio-slurry application in agriculture and develop appropriate guidelines and management strategies for safeguarding public health and environmental sustainability. This research aimed to investigate the aforementioned aspects in bio-slurry obtained from selected bio-digester plants in Wakiso district.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis research employed a cross-sectional study design, incorporating both quantitative and qualitative approaches to achieve the research objectives. Qualitative data included the type of materials used in the biodigesters, location of the biodigesters and the distribution of selected bacteria pathogens in the bio-slurry samples while Quantitative data included the microbial load and the percentage of anti-microbial resistance of the selected pathogens against commonly used antibiotics. The total bacteria load, presence of selected pathogens (\u003cem\u003eVibrio\u003c/em\u003e, EHEC, \u003cem\u003eSalmonella\u003c/em\u003e and \u003cem\u003eShigella\u003c/em\u003e) plus their antibiotic susceptibility patterns were determined at a point in time in the laboratory.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eBio-slurry samples were collected from selected biogas plants across Wakiso district. The district was chosen because it is a strategic area for many organizations and extension workers for biogas technology, has the best methods of cattle grazing and reasonable access to information concerning the modernization of agriculture. Since cow dung is the major feedstock used for biogas in Uganda, these areas had the required raw materials to support the technology and therefore have a significant number of biogas plants and also being that it is part of central Uganda and densely populated. The high population makes it a high-risk community from the health threats of bio-slurry bacteria micro flora which would easily spread due to its nearness to the main towns including Kampala (The capital city).\u003c/p\u003e\n\u003ch3\u003eThe study population\u003c/h3\u003e\n\u003cp\u003eThe study population included individual households and farms which operated the biogas plants then and those that were actively producing bio-slurry. This group was selected as they were a first line risk group to potential health risks of bio-slurry poor handling, use and disposal. On farms, the pathogens in bio-slurry would endanger animal health, growth as well the total productivity and the presence of antibiotic resistant strains in slurry would result into its spread in animals and possibly humans in case of zoonotic pathogens.\u003c/p\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eOnly fresh bio-slurry was sampled and only for the owners who consented to the sampling.\u003c/p\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003eBio-slurry waste which was soiled was excluded as well as that for the farmers who failed to consent to the sampling plus those redundant biodigesters for a long time (abandoned).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and transportation\u003c/h2\u003e \u003cp\u003eA total of 40 bio-slurry samples were collected aseptically from the selected sites. To ensure representativeness, a composite sample was obtained by collecting multiple sub-samples with a sterile spoon from various depths and locations of the bio-digester. The sub-samples were carefully transferred into a sterile sampling container and tightly sealed to prevent sample leakage and contamination.\u003c/p\u003e \u003cp\u003eEach container was clearly labeled with a unique identifier, which included the sampling site, date, village and biomaterial used. Immediately after collection, the samples were recorded in a sample log sheet, placed in a cool box with ice packs, and transported to the laboratory within a few hours to minimize alterations in microbial composition. If immediate transport was not possible, samples were stored at 4\u0026deg;C. Throughout the process, all safety precautions, ethical considerations, and relevant regulatory guidelines were strictly followed to preserve sample integrity and ensure the validity of laboratory analyses. Microbiological investigations were conducted in the College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB) microbiology laboratory.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperiments\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of the total bacteria load\u003c/h2\u003e \u003cp\u003eA series of serial dilutions (10\u003csup\u003e־1\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) were prepared by aseptic transfer of 1 mL of well-agitated bio-slurry samples to 9 ml of sterile dilution blanks (peptone water) and subsequently transferred 1ml of diluted samples, at every dilution into separate peptone blanks in well labeled pre-sterised test tubes followed by gentle swirling. Once a 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e dilution was achieved, the serial dilution was stopped\u003c/p\u003e \u003cp\u003e0.1 mL of the undiluted bio-slurry sample was transferred into a labeled Plate Count Agar (PCA) agar plate. A sterile glass spreader was used to surface spread the sample evenly over the surface of the plate count agar plate. The plated sample was the neat. This was repeated for each dilution tube only 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e dilutions were plated.\u003c/p\u003e \u003cp\u003eThe plates were then incubated at 37\u0026deg;C for 24 hours to allow bacterial growth. After incubation, the visible colonies on plates were counted with a suitable range of colony counts (e.g., 30\u0026ndash;300 colonies) using a colony counter. The number of colony-forming units per milliliter (CFU/ml) was calculated by multiplying the colony count by the dilution factor and the reciprocal of the sample volume plated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIsolation and identification of selected bacteria pathogens from bio-slurry\u003c/h2\u003e \u003cp\u003e \u003cb\u003eVibrio\u003c/b\u003e \u003cb\u003espp., Preparation of TCBS Media\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTCBS agar was prepared by weighing and dissolving the powder in distilled water according to the manufacturer's instructions. The media was then sterilized by autoclaving at 121\u0026deg;C for 15 minutes and then cooled to approximately 45\u0026ndash;50\u0026deg;C before casting and setting into sterile Petri dishes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrinciple of TCBS Media\u003c/h2\u003e \u003cp\u003eTCBS agar is a selective and differential media specifically designed for the isolation and identification of \u003cem\u003eVibrio\u003c/em\u003e spp. The selective components of TCBS media, including bile salts and high salt concentration, inhibit the growth of most other bacterial species while allowing the growth of \u003cem\u003eVibrio\u003c/em\u003e. The differential component of TCBS media is sucrose. \u003cem\u003eVibrio\u003c/em\u003e spp are able to ferment sucrose, resulting in the production of acid, which leads to a pH drop and a color change of the media from green to yellowish\u003c/p\u003e \u003cp\u003e \u003cb\u003eIsolation of\u003c/b\u003e \u003cb\u003eVibrio\u003c/b\u003e \u003cb\u003espp from Bio-slurry using TCBS Media\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA loopful of the liquid bio-slurry was inoculated onto the Nutrient agar plate and then used a sterile spreader to spread the sample evenly over the surface of the agar plate and then incubated the plate at a temperature of 37\u0026deg;C for 24 hours for the growth of \u003cem\u003eVibrio\u003c/em\u003e species. After incubation, the suspected colonies were then incubated on the TCBS agar plate for 24 hours at 37\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eColony Morphology\u003c/h2\u003e \u003cp\u003e \u003cem\u003eVibrio\u003c/em\u003e colonies appeared as smooth, round, convex colonies with a translucent or yellowish coloration. The colonies had a well-defined edge and varied in size from small to large, depending on the species and growth conditions and the texture of the colonies was usually moist and slightly mucoid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStaining characteristics\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eGram Staining\u003c/strong\u003e \u003cp\u003e \u003cem\u003eVibrio\u003c/em\u003e species were typically Gram-negative bacteria and appeared pink or red after performing the Gram staining procedure.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical Tests\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eOxidase Test\u003c/h2\u003e \u003cp\u003eAn oxidase test was performed by applying a drop of oxidase reagent (e.g., N,N,N',N'-Tetramethyl-p- phenylenediamine dihydrochloride) onto a piece of filter paper and then transferring a small portion of suspected colony onto the filter paper using a wooden stick and then observed for the appearance of a blue-purple color within 10\u0026ndash;30 seconds. The result was recorded as positive if a color change occurred and as negative if no color change was observed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIndole Production\u003c/h2\u003e \u003cp\u003eAn indole production test was performed by inoculating a selected colony into a tube containing tryptone broth and then incubating the tube at a temperature of 37\u0026deg;C for 24 hours. After 24 hours, 0.1mls of Kovac's reagent was added to the tube and then observed for the development of red coloration in the upper layer of the broth. The result was recorded as positive if a red color appeared and as negative if no color change occurred.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCitrate Utilization\u003c/h2\u003e \u003cp\u003eThe pure tentative \u003cem\u003eVibrio\u003c/em\u003e suspects were selected and streaked onto a citrate agar slant and then incubated the slant at 37\u0026deg;C for 24\u0026ndash;48 hours and observed for the growth and color change of the slant. The result was recorded as positive if the slant turned blue, indicating citrate utilization, and as negative if the slant remained green.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCarbohydrate Fermentation Tests\u003c/h2\u003e \u003cp\u003eA loopful of a pure culture of \u003cem\u003eVibrio\u003c/em\u003e species was transferred into a test tube containing Triple Sugar Iron (TSI) agar medium. The test tube was inoculated by stabbing the agar with the inoculation loop, ensuring the depth of the stab reaches the bottom of the tube and the slant was streaked and after the TSI agar tube was incubated at an appropriate temperature at 37\u0026deg;C for 24 hours to allow for bacterial growth and metabolic activity. After incubation, the TSI agar slant was observed for characteristic changes in the medium.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIsolation and identification of\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eShigella\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFor isolation of \u003cem\u003eSalmonella\u003c/em\u003e, pre-enrichment was done by aseptically transferring 1ml of bio-slurry samples into 9 mls of Rapaport broth and the tubes were incubated for 24 hours at 40\u0026ordm;C. This selectively inhibited other bacteria other than \u003cem\u003eSalmonella\u003c/em\u003e species. After incubation, the Rapaport that had changed color to greenish was inoculated aseptically on \u003cem\u003eSalmonella\u003c/em\u003e-\u003cem\u003eShigella\u003c/em\u003e Agar (SSA). For the isolation of \u003cem\u003eShigella\u003c/em\u003e spp., the minus 1 dilution of the bio-slurry sample was streaked on SSA agar plate. The plates were then incubated for 24 hours at 37\u0026ordm;C.\u003c/p\u003e \u003cp\u003e \u003cem\u003eShigella\u003c/em\u003e spp. presented as colorless colonies with no blackening whereas \u003cem\u003eSalmonella\u003c/em\u003e colonies were colorless with black centers due to their ability to produce hydrogen sulfide. The tentative isolates were then sub-cultured for biochemical identification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical identification and confirmation\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eTriple Sugar Iron (TSI) Agar Test\u003c/strong\u003e \u003cp\u003eA TSI agar slant was streaked and the butt stubbed with the bacterial pure colony using an inoculation loop and then incubated at 37\u0026deg;C for 24 hours. After incubation, observations were made for changes in the butt and slant for gas production and acid production. Results were interpreted based on the color change, gas production, and acid production in the slant and butt of the agar in the test tube. On TSI, \u003cem\u003eSalmonella\u003c/em\u003e spp showed an alkaline slant and acid butt with H₂S with gas production, whereas \u003cem\u003eShigella\u003c/em\u003e showed an alkaline slant and acid butt without H₂S or gas.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIndole Test\u003c/strong\u003e \u003cp\u003eBacterial cultures were inoculated into tryptone broth and incubated at 37\u0026deg;C for 24\u0026ndash;48 hours. After incubation, Kovac's reagent was added to the broth and the development of a red color indicated a positive reaction for indole production. Both \u003cem\u003eShigella\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e isolates were negative for Indole production (no red coloration upon addition of Kovac\u0026rsquo;s reagent)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMethyl Red (MR) Test\u003c/strong\u003e \u003cp\u003eBacterial cultures were inoculated into MR-VP broth and incubated at 37\u0026deg;C for 24 hours. After incubation, methyl red indicator was added to the broth and the development of a red color indicated a positive reaction for acid production. \u003cem\u003eSalmonella\u003c/em\u003e isolates were methyl red (MR) positive while the \u003cem\u003eShigella\u003c/em\u003e spp were negative (no color change)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVoges-Proskauer (VP) Test\u003c/strong\u003e \u003cp\u003eBacterial cultures were inoculated into MR-VP broth and incubated at 37\u0026deg;C. After incubation, Barritt's reagents A and B were added to the broth. The development of a red color indicated a positive reaction for acetoin production. Both organisms were Voges-Proskauer (VP) negative.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCitrate Utilization Test\u003c/strong\u003e \u003cp\u003eBacterial cultures were inoculated into Simmons citrate in test tubes and then incubated at 37\u0026deg;C for 24 hours. The color change of the agar from green to blue indicated a positive reaction for citrate utilization. \u003cem\u003eSalmonella\u003c/em\u003e isolates utilized Citrate while \u003cem\u003eShigella\u003c/em\u003e spp were negative for citrate utilization.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eUrease Test\u003c/strong\u003e \u003cp\u003eBacterial cultures were streaked onto the slant and stubbed in the butt of urea agar tubes and then incubated at 37\u0026deg;C for 24 hours. Development of a pink or magenta color indicated a positive reaction for urease production and yellow for negative. Both \u003cem\u003eSalmonella\u003c/em\u003e and \u003cem\u003eShigella\u003c/em\u003e isolates were urease negative.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBiochemical Characteristics of\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eShigella\u003c/b\u003e \u003cb\u003eIsolates\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiochemical characteristics of Salmonella and Shigella isolates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalmonella spp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShigella spp.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriple Sugar Iron (TSI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlkaline slant / acid butt, \u003cb\u003eH₂S positive\u003c/b\u003e, \u003cb\u003egas positive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlkaline slant / acid butt, \u003cb\u003eH₂S negative\u003c/b\u003e, \u003cb\u003eno gas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndole Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e (no red ring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e (no red ring)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMethyl Red (MR) Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e (red color)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e (no color change)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVoges-Proskauer (VP) Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCitrate Utilization Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e (blue color)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e (no color change)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrease Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e (yellow color)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e (yellow color)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIsolation and identification of Enteroheamorhagic\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFor isolation of the organism, 0.1 mL dilution of the samples was aseptically transferred and streaked onto MacConkey Sorbitol agar plates using a sterile loop and then incubated the plates aerobically at 37\u0026deg;C for 24 to 48 hours. After incubation, the MacConkey Sorbitol plates were examined for bacterial growth indicated by colorless colonies as the strain is a non-lactose fermenter. The tentative positive isolates were selected and subcultured and the following biochemical tests were performed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIndole Test\u003c/strong\u003e \u003cp\u003eInoculated tryptone broth and added Kovac's reagent. Observed for a red color, indicating a positive result.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMethyl Red (MR) Test\u003c/strong\u003e \u003cp\u003eInoculated MR-VP broth and added methyl red indicator. Observed for a red color, indicating a positive result.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVoges-Proskauer (VP) Test\u003c/strong\u003e \u003cp\u003eInoculated MR-VP broth and added Barritt's reagents A and B and then observed for a red color after incubation, which indicated a positive result.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCitrate Utilization\u003c/strong\u003e \u003cp\u003eStreaked the pure colonies onto Simmons citrate agar and incubated. Observed the tubes for growth where a blue color change on the agar indicated a positive result.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eUrease Test\u003c/strong\u003e \u003cp\u003eStreaked the suspect pure colonies onto urea agar and incubated the tubes for growth where a pink/magenta color change indicated a positive result while no change for a negative isolate.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe positive isolates were identified by the presence of colorless colonies on MacConkey Sorbitol Agar, due to non-sorbitol fermentation. Biochemical tests showed positive reactions for indole, Voges-Proskauer and citrate utilization while urease and methyl red were negative.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eAntimicrobial susceptibility testing of the positive isolates\u003c/h2\u003e \u003cp\u003eSusceptibility patterns of \u003cem\u003eE. coli\u003c/em\u003e was determined using Kirby Bauer disc diffusion method. The antibiotics used included; gentamicin (10\u0026micro;g), Ciprofloxacin (5\u0026micro;g), Tetracycline (30\u0026micro;g), Ampicillin (30\u0026micro;g), ciprofloxacin (5 \u0026micro;g), azithromycin (30 \u0026micro;g) and ceftriaxone of HiMedia. Two to five pure colonies were uniformly mixed with 5ml of sterile physiological saline and the turbidity of the resultant mixture was compared with 0.5 McFarland standard (Beckton and Dickson) to estimate the seeding density. The bacterial suspension was inoculated on sterile Mueller Hinton agar by surface spreading, and drug discs with the known volume and concentration of each one of the selected antibiotics were placed on the Mueller Hinton agar and incubated at 37\u003csup\u003eo\u003c/sup\u003eC for 24 hours. The susceptibilities were determined by measuring the diameter of growth inhibition around the antibiotic discs and the measured diameters compared to the standard in order to qualify the isolates as resistant, Intermediate or susceptible to the antibiotics used according to the standards of the Clinical and Laboratory Standards Institute\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eData management and analysis\u003c/h2\u003e \u003cp\u003eData was entered in MS Excel version 2013 and further transferred to STATA 15 analytical software for analysis. Data presentation was by line graphs and tables and statistical significance was at P\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of samples according to location of biodigesters\u003c/h2\u003e \u003cp\u003eA total of 40 bio-slurry samples were collected from 12 different villages within Wakiso District, Uganda. The number and proportion of samples collected from each village are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The highest proportion of samples was collected from Namayumba (17.5%, n\u0026thinsp;=\u0026thinsp;7), followed by Nabalanga and Sseganga, each contributing 12.5% (n\u0026thinsp;=\u0026thinsp;5). The lowest number of samples (5.0%, n\u0026thinsp;=\u0026thinsp;2) were obtained from several villages, including Buyale, Kabulengwa, Kyampisi, Lukoma, Mattugga, and Namosera.\u003c/p\u003e \u003cp\u003eOut of the 40 samples, 31(77.5%) were derived from cow dung, 2(5%) were from pig feacal\u0026thinsp;+\u0026thinsp;rabbit urine and 2(17.5%) were from poultry bio digesters (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results indicated that cow dung was the most commonly used material in the biodigester plants, followed by poultry fecal matter, while pig and rabbit urine were the least used materials.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of samples according to village location of bio digester plants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVillage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Percentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuyale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKabulengwa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKisimbiri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKyampisi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLukoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLwadda 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMattugga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMpunga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNabalanga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNamayumba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNamosera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSseganga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of the samples by materials used in the biodigesters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCowdung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePig Feacal\u0026thinsp;+\u0026thinsp;Rabbit Urine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMean log CFU/ml of bio-slurry\u003c/h2\u003e \u003cp\u003eThe results showed that biodigesters fed with poultry had the highest mean log CFU/ml bacteria load (6.6) with a standard deviation of \u0026plusmn;\u0026thinsp;0.53, followed by pig feacal plus Rabbit urine with a mean bacteria load of (6.5) with a standard deviation of \u0026plusmn;\u0026thinsp;0.34 and cow dung had the least mean bacteria load (6.1) with a standard deviation of \u0026plusmn;\u0026thinsp;0.64. The total mean bacteria load for all the samples was (6.1) with a corresponding standard deviation of \u0026plusmn;\u0026thinsp;0.63 (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mean log CFU/ml of bio-slurry\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMATERIAL USED\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean log cfu/ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCowdung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.096790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.6350274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePig Feacal\u0026thinsp;+\u0026thinsp;Rabbit Urine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.493833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.3373757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoutry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.595978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.5267110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6.145116\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.6346778\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eThe occurrence of the selected bacteria pathogens in bio-slurry samples\u003c/h2\u003e \u003cp\u003eA total of 16 bacterial isolates were identified from the bio-slurry samples (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Of these, 5 isolates (31%) were identified as Enteroheamorrhagic \u003cem\u003eEscherichia coli\u003c/em\u003e (EHEC), 3 isolates (19%) as \u003cem\u003eSalmonella\u003c/em\u003e spp., 4 isolates (25%) as \u003cem\u003eShigella\u003c/em\u003e spp., and 4 isolates (25%) as \u003cem\u003eVibrio\u003c/em\u003e spp. Among the identified pathogens, Enteroheamorrhagic \u003cem\u003eEscherichia coli\u003c/em\u003e (EHEC) was the most frequently isolated, while \u003cem\u003eSalmonella\u003c/em\u003e spp. had the lowest occurrence.\u003c/p\u003e \u003cp\u003eOut of the 40 bio-slurry samples analyzed, 5 samples (12.5%) tested positive for Enteroheamorrhagic \u003cem\u003eEscherichia coli\u003c/em\u003e (EHEC), while 3 samples (7.5%) were positive for \u003cem\u003eSalmonella\u003c/em\u003e spp. In addition, \u003cem\u003eShigella\u003c/em\u003e spp. and \u003cem\u003eVibrio\u003c/em\u003e spp. were each detected in 4 samples (10.0%). The remaining samples did not yield isolates of these selected pathogens.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe occurrence of the selected bacteria pathogens in bio-slurry samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOrganism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEHEC s\u003c/em\u003epp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVibrio\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eThe antimicrobial susceptibility patterns of the isolates\u003c/h2\u003e \u003cp\u003eThe antimicrobial susceptibility patterns of the isolates were evaluated, and the results suggested varying levels of resistance to different antibiotics among the studied bacteria isolates. Regarding Enteroheamorrhagic \u003cem\u003eEscherichia coli\u003c/em\u003e (EHEC), all isolates exhibited high resistance to Amoxicillin, while resistance to Ciprofloxacin was moderate. Tetracycline, Ceftriaxone, Azithromycin, Ampicillin, and Gentamycin showed lower levels of resistance. In the case of \u003cem\u003eVibrio\u003c/em\u003e, moderate resistance was observed against Amoxicillin, Ceftriaxone, and Azithromycin, while Tetracycline, Ciprofloxacin, Ampicillin, and Gentamycin had relatively low resistance. \u003cem\u003eShigella\u003c/em\u003e isolates displayed moderate resistance to Tetracycline, while the resistance to Amoxicillin, Ceftriaxone, Azithromycin, Ciprofloxacin, Ampicillin, and Gentamycin was low. There were variations in resistance patterns among the \u003cem\u003eShigella\u003c/em\u003e isolates. \u003cem\u003eSalmonella\u003c/em\u003e isolates showed high resistance to Amoxicillin, while resistance to Azithromycin and Ampicillin was moderate. Tetracycline, Ceftriaxone, Ciprofloxacin, and Gentamycin demonstrated low resistance (Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe antimicrobial susceptibility patterns of the bacteria isolates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003ePercentage Resistance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAMOX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAZI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCIPRO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGENTA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEHEC(n\u0026thinsp;=\u0026thinsp;5)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eVibrio (n\u0026thinsp;=\u0026thinsp;4)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eShigella\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(60)\u003c/p\u003e \u003cp\u003e3(75)\u003c/p\u003e \u003cp\u003e1(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e2(50)\u003c/p\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e2(50)\u003c/p\u003e \u003cp\u003e1(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e1(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4(80)\u003c/p\u003e \u003cp\u003e1(25)\u003c/p\u003e \u003cp\u003e2(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSalmonella\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eAMOX\u0026thinsp;=\u0026thinsp;Amoxicllin, TET\u0026thinsp;=\u0026thinsp;Tetracycline, CEF\u0026thinsp;=\u0026thinsp;Ceftriaxone, AZI\u0026thinsp;=\u0026thinsp;Azithromycin, CIPRO\u0026thinsp;=\u0026thinsp;Ciproflaxacin, AMP\u0026thinsp;=\u0026thinsp;Ampicilin, GENTA\u0026thinsp;=\u0026thinsp;Gentamycin, EHEC\u0026thinsp;=\u0026thinsp;Enteroheamorrhagic Escherichia\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis of the bio-slurry samples revealed a wide range of bacterial concentrations. The mean log CFU/ml of the samples was 6.145116, with a standard deviation of 0.6346778. Significant variations in bacterial load were observed based on the type of material used for the biodigester. Poultry waste showed the highest mean bacterial load (6.595978), followed by pig fecal plus rabbit urine (6.493833), while cow dung had the lowest mean bacterial load (6.096790). These findings suggest that the biomass used in biodigesters has a substantial impact on the bacterial load in the resulting bio-slurry. The higher bacterial load in poultry waste may be attributed to the nature of the waste and its composition, which provides a favorable environment for bacterial growth. On the other hand, cow dung, being relatively less conducive to bacterial growth, exhibited a lower bacterial load. These results align with previous studies emphasizing the influence of feedstock on the microbial composition and load in bio-slurry (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Studies by (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) have shown that manure from different livestock sources results in diverse bacterial communities, with Firmicutes, Bacteroidetes, Proteobacteria, and Chloroflexi being dominant phyla. Spatial variations within biodigesters also affect bacterial diversity and composition (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe presence of bacterial pathogens in bio-slurry raises concerns regarding potential health risks and the need for appropriate management strategies. The analysis revealed the presence of Enteroheamorrhagic \u003cem\u003eEscherichia coli\u003c/em\u003e (EHEC), \u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eShigella\u003c/em\u003e spp., and \u003cem\u003eVibrio\u003c/em\u003e spp. Among the 16 isolates characterized, EHEC was the most prevalent pathogen, accounting for 31% of the isolates. \u003cem\u003eShigella\u003c/em\u003e spp. and \u003cem\u003eVibrio\u003c/em\u003e spp. showed equal prevalence (25%), while \u003cem\u003eSalmonella\u003c/em\u003e spp. had the lowest prevalence (19%). These results align with previous studies that have identified various pathogens, including \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eShigella\u003c/em\u003e spp., and \u003cem\u003eVibrio cholerae\u003c/em\u003e in these materials (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). While anaerobic digestion can significantly reduce bacterial loads, some pathogens may persist, particularly in shorter digestion periods or at lower temperatures (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The application of digestate as fertilizer can introduce these pathogens into agroecosystems, although soil microorganisms may inhibit their resurgence (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These findings emphasize the need to address the potential risks associated with the presence of bacterial pathogens in bio-slurry derived from biodigesters. The prevalence of EHEC, known for its association with foodborne illnesses, emphasizes the need for proper handling and treatment of bio-slurry to prevent potential contamination of crops and the food chain.\u003c/p\u003e \u003cp\u003eThe evaluation of antimicrobial susceptibility patterns in bacterial isolates from various sources suggested significant antibiotic resistance concerns. The isolates exhibited varying levels of resistance to different antibiotics. EHEC isolates demonstrated high resistance to Amoxicillin and moderate resistance to Ciprofloxacin. \u003cem\u003eVibrio\u003c/em\u003e spp. displayed moderate resistance to Amoxicillin, Ceftriaxone, and Azithromycin, while \u003cem\u003eShigella\u003c/em\u003e spp. showed moderate resistance only to Tetracycline. \u003cem\u003eSalmonella\u003c/em\u003e spp. exhibited high resistance to Amoxicillin and moderate resistance to Azithromycin and Ampicillin. Studies on biogas digestates, food pathogens, and environmental effluents have demonstrated widespread resistance among diverse bacterial species. Isolates from biogas digestates showed multi-resistance to several antibiotics, with food waste digestates exhibiting higher resistance levels (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Emerging foodborne pathogens, including \u003cem\u003eVibrio spp\u003c/em\u003e. and \u003cem\u003eSalmonella enterica\u003c/em\u003e, displayed resistance to β-lactams, sulfonamides, tetracyclines, and fluoroquinolones (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Hospital and slaughterhouse effluents contained \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eSalmonella isolates\u003c/em\u003e with high resistance to multiple antibiotics, including ampicillin, ciprofloxacin, and erythromycin (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Similarly, abattoir wastewater in Nairobi, Kenya, harbored faecal bacteria indicators and pathogens with significant antibiotic resistance, posing potential environmental and health risks (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). These findings highlight the widespread occurrence of antibiotic resistance in various environmental sources including in bio-slurry. The high resistance observed in EHEC and \u003cem\u003eSalmonella\u003c/em\u003e spp., which are commonly associated with foodborne infections, underscores the importance of prudent antibiotic use in livestock production systems and the need for effective waste management practices to mitigate the spread of antibiotic resistance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe analysis of bio-slurry from biodigesters in Wakiso District revealed significant variations in total bacterial load, with poultry waste exhibiting the highest levels and cow dung the lowest. The detection of enteric bacterial pathogens, including Enteroheamorrhagic \u003cem\u003eEscherichia coli\u003c/em\u003e (EHEC), \u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eShigella\u003c/em\u003e spp., and \u003cem\u003eVibrio\u003c/em\u003e spp., highlights potential public health risks associated with the use of untreated or inadequately treated bio-slurry in agricultural settings.\u003c/p\u003e \u003cp\u003eConsidering bacterial load, pathogen presence, and antimicrobial resistance is crucial when designing biodigesters and managing bio-slurry to ensure public health and safety.\u003c/p\u003e \u003cp\u003eNotably, the isolated pathogens exhibited varying levels of resistance to commonly used antibiotics, raising concerns about the potential role of bio-slurry in the dissemination of antimicrobial resistance (AMR) within the environment and food chain. These findings underscore the importance of prudent antibiotic use in livestock systems and the need for integrated biosecurity and waste management strategies.\u003c/p\u003e \u003cp\u003eIt is essential to establish and enforce safety protocols during the handling and management of bio-slurry. This should include using appropriate personal protective equipment (PPE) such as gloves and masks, ensuring proper hygiene practices, and following guidelines for storage, transportation, and application of bio-slurry. Routine monitoring of bio-slurry for microbial contaminants and resistance profiles is recommended to support informed decision-making on its safe utilization. In addition, further research into treatment technologies and pathogen inactivation methods is warranted to ensure the safe and sustainable application of bio-slurry in agriculture, thereby minimizing potential risks to public and environmental health.\u003c/p\u003e\n\u003ch3\u003eLimitations of the study\u003c/h3\u003e\n\u003cp\u003eThe findings, though representative of the biodigesters in Wakiso District, may not be generalizable to all regions in Uganda due to variations in climate, livestock systems, and biodigester management practices. Although a representative number of biodigesters were selected based on accessibility and operational status, the sample size was relatively small, which may limit statistical inference. Although antibiotic resistance was observed, the absence of data on prior antibiotic use in the host farms limited our ability to draw correlations between usage patterns and resistance profiles. Despite these limitations, the study provides important baseline data on pathogen presence and resistance in bio-slurry from biodigesters, highlighting the need for integrated surveillance and future studies incorporating broader spatial coverage.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human subjects or animal experimentation. However, samples were collected from biodigesters located on private property, and the owners provided verbal informed consent prior to participation. All procedures were conducted in accordance with relevant ethical standards and fieldwork guidelines. Although formal approval from an institutional review board was not sought, ethical considerations including voluntary participation, confidentiality and non-invasive sampling were strictly followed. The study was approved by the Department of Biomolecular Resources \u0026amp; Biolab Sciences, College of Veterinary Medicine, Animal Resources and Bio-security, Makerere University. The department provided an introductory letter to the authors, clearly explaining the purpose and procedures of the study to the District leadership and biodigester owners. The letter was submitted to the Chief Administrative Officer (CAO) who forwarded it to the District Production Officer (DPO) and then the District Veterinary Officer (DVO).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability (where applicable)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data produced or analyzed in this study are provided in the article or can be obtained from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received partial funding from Makerere University Research and Innovation Fund (MAK-RIF) supported by the Government of Uganda, which facilitated field data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the work reported, whether in the conception, study design, execution, acquisition of data, analysis and interpretation or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eSE, TR and TG conceived the study. SE, TR, TG and NL developed the study protocol including data collection tool, data collection strategy. SE, TR, TG, LE and NL were involved in field data collection activities. SE, TR and TG conducted data analysis and interpretation. SE, TR, TG, NL, LE \u0026nbsp; and TA were involved in preparation of the manuscript. All authors contributed to the article and approved the submitted article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur appreciation goes to the Government of Uganda's support through Makerere University Research and Innovation Fund (MAK-RIF) for funding field data collection and analysis. We thank the extension staff, local leaders and farmers in the study District of Wakiso for mobilization and participation in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Biomolecular Resources \u0026amp; Biolab Sciences, College of Veterinary Medicine, Animal Resources and Bio-security, Makerere University, Kampala, Uganda\u003c/p\u003e\n\u003cp\u003eSenyonga Emmanuel and Tumwine Gabriel\u003c/p\u003e\n\u003cp\u003eDepartment of Livestock and Industrial Resources, College of Veterinary Medicine, Animal Resources and Bio-security, Makerere University, Kampala, Uganda\u003c/p\u003e\n\u003cp\u003eTwinamatsiko Robert\u003c/p\u003e\n\u003cp\u003eA\u003cem\u003efrica Institute for Strategic Animal Resource Services and Development (AF\u003c/em\u003eRISA), College of Veterinary Medicine, Animal Resources and Bio-security, Makerere University, Kampala, Uganda\u003c/p\u003e\n\u003cp\u003eNamukomazi Lydia\u003c/p\u003e\n\u003cp\u003eDepartment of bio-slurry extension, Biogas Solutions Uganda LTD, Kampala, Uganda\u003c/p\u003e\n\u003cp\u003eLuwemba Emmanuel\u003c/p\u003e\n\u003cp\u003eDepartment of Health Policy, Planning and Management, College of Health Sciences, Makerere University, Kampala, Uganda\u003c/p\u003e\n\u003cp\u003eTukamuhebwa Agatha\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIslam MA, Biswas P, Sabuj AAM, Haque ZF, Saha CK, Alam MM, et al. 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Animals. 2020;10(8):1372.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoelho JJ, Prieto ML, Hennessy A, Casey I, Woodcock T, Kennedy N. Determination of microbial numbers in anaerobically digested biofertilisers. Environ Technol. 2021;42(5):753\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNag R, Nolan S, O'Flaherty V, Fenton O, Richards KG, Markey BK, et al. Quantitative microbial human exposure model for faecal indicator bacteria and risk assessment of pathogenic Escherichia coli in surface runoff following application of dairy cattle slurry and co-digestate to grassland. J Environ Manage. 2021;299:113627.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuong LQ, Forslund A, Madsen H, Dalsgaard A. Survival of Salmonella spp. and fecal indicator bacteria in Vietnamese biogas digesters receiving pig slurry. Int J Hyg Environ Health. 2014;217(7):785\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MM, Alam Tumpa MA, Zehravi M, Sarker MT, Yamin M, Islam MR, et al. An overview of antimicrobial stewardship optimization: the use of antibiotics in humans and animals to prevent resistance. Antibiotics. 2022;11(5):667.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrzyzanowski F, Zappelini L, Martone-Rocha S, Dropa M, Matt\u0026eacute; MH, Nacache F, et al. Quantification and characterization of Salmonella spp. isolates in sewage sludge with potential usage in agriculture. BMC Microbiol. 2014;14:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee S, Ooi MC, Shariff M, Khatoon H. Antibiotic resistant Salmonella and Vibrio associated with farmed Litopenaeus vannamei. Sci World J. 2012;2012(1):130136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaloda SB, Christensen L, Trajcevska S. Persistence of a Salmonella enterica serovar Typhimurium DT12 clone in a piggery and in agricultural soil amended with Salmonella-contaminated slurry. Appl Environ Microbiol. 2001;67(6):2859\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwu CD, Korsten L, Okoh AI. The incidence of antibiotic resistance within and beyond the agricultural ecosystem: A concern for public health. Microbiologyopen. 2020;9(9):e1035.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManyi-Loh C, Mamphweli S, Meyer E, Okoh A. Antibiotic use in agriculture and its consequential resistance in environmental sources: potential public health implications. Molecules. 2018;23(4):795.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanz C, Casado M, Navarro-Martin L, Tadić Đ, Parera J, Tugues J, et al. Antibiotic and antibiotic-resistant gene loads in swine slurries and their digestates: implications for their use as fertilizers in agriculture. Environ Res. 2021;194:110513.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBondarczuk K, Markowicz A, Piotrowska-Seget Z. The urgent need for risk assessment on the antibiotic resistance spread via sewage sludge land application. Environ Int. 2016;87:49\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Rui J, Yao M, Zhang S, Yan X, Wang Y, et al. Substrate type and free ammonia determine bacterial community structure in full-scale mesophilic anaerobic digesters treating cattle or swine manure. Front Microbiol. 2015;6:1337.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Lozano M, Hern\u0026aacute;ndez-De Lira IO, Huber DH, Balagurusamy N. Spatial variations of bacterial communities of an anaerobic lagoon-type biodigester fed with dairy manure. Processes. 2019;7(7):408.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell L, Whyte P, Zintl A, Gordon S, Markey B, de Waal T, et al. A small study of bacterial contamination of anaerobic digestion materials and survival in different feed stocks. Bioengineering. 2020;7(3):116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong J, Liu B, Liu P, Zhang L, Chen C, Wei Y, et al. Changes in bacterial diversity, co-occurrence pattern, and potential pathogens following digestate fertilization: Extending pathogen management to field for anaerobic digestion of livestock manure. Waste Manag. 2023;158:107\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Bjerketorp J, Levenfors JJ, Schn\u0026uuml;rer A. Isolation of antibiotic-resistant bacteria in biogas digestate and their susceptibility to antibiotics. Environ Pollut. 2020;266:115265.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrudlewska-Buda K, Bauza-Kaszewska J, Wiktorczyk-Kapischke N, Budzyńska A, Gospodarek-Komkowska E, Skowron K. Antibiotic Resistance in selected emerging bacterial foodborne Pathogens\u0026mdash;An issue of concern? Antibiotics. 2023;12(5):880.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassan M, Ahaduzzaman M, Alam M, Bari M, Amin K, Faruq A. Antimicrobial resistance pattern against E. coli and Salmonella spp. in environmental effluents. Int J Nat Sci. 2015;5(2):52\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtieno R, Okemo P, Ombori O. Antibiotic resistance of faecal bacteria indicators and pathogens isolated from sludge and wastewaters of Abattoirs in Nairobi, Kenya. J Biol. 2013;1(5):106\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bio-slurry, pathogenic bacteria, antibiotic susceptibility patterns, multi-drug resistance","lastPublishedDoi":"10.21203/rs.3.rs-6358337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6358337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe utilization of bio-slurry as a nutrient-rich organic fertilizer in sustainable agricultural practices has gained significant attention. However, the presence of pathogenic bacteria in bio-slurry poses a potential risk to human health and the environment. This study aimed at understanding the total bacteria load, prevalence of selected pathogens (\u003cem\u003eVibrio\u003c/em\u003e, EHEC, \u003cem\u003eSalmonella\u003c/em\u003e, and \u003cem\u003eShigella\u003c/em\u003e), and their antibiotic susceptibility patterns, including multi-drug resistance, for assessing the safety and effectiveness of bio-slurry application in agriculture. A cross-sectional study was conducted in Wakiso District with a total of 40 samples collected from 12 villages, representing different biodigesters with various feedstock materials, including cow dung, poultry fecal products, pig and rabbit urine. The collected samples were analyzed to determine the distribution of materials used in the biodigesters, the prevalence and distribution of selected bacterial pathogens (\u003cem\u003eShigella Salmonella, Vibrio spp\u003c/em\u003e and \u003cem\u003eEnteroheamarrhic Ecoli\u003c/em\u003e strains). The antimicrobial susceptibility patterns of isolated bacterial pathogens were assessed using the disk diffusion method, testing multiple commonly used antibiotics to identify multi-drug resistance among the bacterial isolates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of selected pathogens revealed Enteroheamorrhagic \u003cem\u003eEscherichia coli\u003c/em\u003e (EHEC) as the most prevalent pathogen (31%), followed by \u003cem\u003eShigella\u003c/em\u003e spp. and \u003cem\u003eVibrio\u003c/em\u003e spp, all at (25%) while \u003cem\u003eSalomenella\u003c/em\u003e spp were the least prevalent (19.0%). The isolated bacterial pathogens showed varying levels of resistance against different antibiotics with EHEC displaying high resistance to Amoxicillin and moderate resistance to Ciprofloxacin. \u003cem\u003eSalmonella\u003c/em\u003e spp demonstrated high resistance to Amoxicillin and moderate resistance to Azithromycin and Ampicillin, highlighting potential Multi-Drug Resistance concerns.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe distribution of selected pathogens highlighted the importance of implementing stringent safety protocols during bio-slurry handling and management to mitigate potential health risks. Moreover, prudent antibiotic use in animal production systems is essential in addressing Multi-Drug Resistance in bacterial pathogens found in bio-slurry. Further research on treatment technologies and potential risks associated with bio-slurry application is recommended to develop efficient and safe strategies for bio-slurry management in agriculture.\u003c/p\u003e","manuscriptTitle":"Total bacteria load, selected pathogens (vibrio spp., EHEC, salmonella spp. and shigella spp.) and their antibiotic susceptibility patterns from bio-slurry of selected biodigesters in Wakiso District, Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 16:47:30","doi":"10.21203/rs.3.rs-6358337/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f031614-d603-4ed9-9d90-bcb0368e93d8","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-19T10:39:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 16:47:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6358337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6358337","identity":"rs-6358337","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}