CONTACT : DANIEL KAMUA NGANGA dankaihura@gmail.com 76 Abstract Antibiotic resistant bacteria and antibiotic residues are a serious safety problem for animal food products. Poultry products have been long recognized as a reservoir for antibiotic resistant commensals and pathogens. Antibiotic residues ingested via food animal products expose gut micro-flora to low concentrations of antibiotics, which promote antibiotic resistance. However, there is limited knowledge regarding the potential of chicken products to act as a transmission corridor for the spread of the antibiotic resistant bacteria and antibiotic residues. The present study aimed at profiling antibiotic resistant bacteria and antibiotic residues in raw chicken products sold around Kenyatta University, Kenya. A total of 32 meat and egg samples were randomly collected from two study sites; KM and KU. Antibiotic residues in the study samples were detected using two microbiological techniques with Bacillus subtilis , Staphylococcus aureus, Escherichia coli , and Salmonella typhi as test organisms. Selective and differential media were used to isolate Escherichia coli , Salmonella and Shigella from the samples. Antibiotic susceptibility testing of these isolates against commonly used antibiotics was done using Kirby-Bauer antibiotic discs diffusion method. Of the total samples tested, 87.50% meat and 100% egg samples showed presence of antibiotic residues. The mean colony forming units (CFUs) of meat samples from KM (190.25 x 10² CFU) was higher than that of KU (104.96 x 10² CFU). Eggs from KM showed contamination (158.88 x 10² CFU) than those sampled (108.29 x 10² CFU) within the university outlets. Escherichia coli , Salmonella and Shigella, were resistant to ampicillin. Escherichia coli showed intermediate resistance to tetracycline while Escherichia coli and Shigella showed intermediate resistance to amikacin. This study reveals the presence of antibiotic residues and antibiotic resistant bacteria in chicken meat and eggs sold in the study area. Knowledge generated from this study is helps to develops effective strategies to control antibiotic resistance. ISSN : 2580-2410 eISSN : 2580-2119 Profiling Antibiotic Resistant Bacteria and Antibiotic Residues in Raw Chicken Products Sold around Kenyatta University, Kenya Daniel Kamua Nganga1*, Harry Asena Musonye1, Patrick Kamau Kamande2, Lucy Muthoni Kamau 3 1 Department of Biochemistry, Microbiology and Biotechnology, Kenyatta University, Nairobi, Kenya 2 Department of Food Nutrition and Dietetics, Kenyatta University, Nairobi, Kenya 3 Department of Animal Health and Production, Kenyatta University, Nairobi, Kenya Introduction The burden of antibiotic resistance (ABR) has a negative impact on the health and socio-economic status of the people (Ferri et al., 2017). The emergency and spread of ABR is OPEN ACCESS International Journal of Applied Biology Keyword antibiotics; antibiotic resistance; antibiotic residues Article History Received 10 August 2020 Accepted 31 December 2020 International Journal of Applied Biology is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Applied Biology, 4(2), 2020 77 associated with the selective pressure exerted by antibiotic use in the community, farm agriculture, veterinary health, aquaculture, hospitals, and the environment. The World Health Organization has warned that inappropriate use of antibiotics in agriculture and food systems may lead to increased food insecurity and food safety hazard (WHO, 2017). The most substantial use of antibiotics worldwide is in the production of animals where they are used for therapeutics, prophylaxis or growth promoters in animal feed (Silbergeld et al., 2008; Cháfer-Pericás et al., 2010; Abdullahi et al., 2015). The practices risk exposure of human consumers to ABR food-borne pathogens and commensals via animal food products and the environment. Already, about 95% of the world food-borne infections are associated with ingestion of contaminated animal food products, poultry products and seafood products (Akbar and Anal, 2011; Jans et al. 2018; Sugrue et al., 2019). Furthermore, incorrect application of antibiotics has led to noticeable deposits of xenobiotics in meat, milk, eggs, cheese and other livestock products. Chronic exposure of antibiotics and their derivatives to commensal microorganisms has triggered the development of resistant strains of bacteria because of bioaccumulation of antibiotic residues in animal tissues (Pavlov et al., 2008). Although humans are not the target organism for these drugs, they consume copious amounts of them as residues in food, which potentially destabilizes and eliminate normal intestinal microflora. Other possible pathological effects of these xenobiotics include allergies, carcinogenicity, mutagenicity, teratogenicity, nephropathy, immunological disorders, hepatoxicity, and reproductive disorders (Panigrahi et al., 2017). Commensal bacteria like Escherichia coli and Salmonella found in livestock are frequently present in fresh meat products and they may serve as reservoirs for resistant genes that could potentially be transferred to pathogenic organisms in humans. In other cases, the intestinal microflora adapts to these antibiotics and their metabolites by developing resistance and consequently transfer antimicrobial resistance genes to clinically relevant pathogens (Manyi-Loh et al., 2018). The pathogens can be transferred from animals to humans, indirectly through food or directly during handling and processing, thereby posing a threat to public health (Nyamboya et al., 2013). Mathur and Singh (2005) noted that the food chain is a major route of antibiotic resistance transmission between animals and human populations. Subsequently, commensal bacteria are suspected of serving as resistance reservoirs as they can transfer resistance genes to pathogenic bacteria. Poultry farming is one of the most important small-scale agricultural businesses in sub-Sahara Africa. The population in this region depends partially on poultry farming for home consumption and monitory value from the sale of poultry and poultry products. In Kenya, small scale poultry farming is practiced in most cities with a per capita consumption of poultry meat and eggs at 1.1 Kg and 37.5 Kg per annum respectively (McCarron et al., 2015). Nonetheless, poultry production in Kenya is still constrained by ABR. Antibiotics are used indiscriminately on poultry treatment and in their feed, raising a lot of concern over their effects on the quality of poultry products. While antibiotic use in food animals may represent a risk to human health, the degree and relative efforts to combat this have not offered optimum solution (Darwish et al., 2013). Accurate information on the flow of ABR strains in food products is still lacks in Kenya. There is a need to carry out investigation whose results can help understand the local ABR and antibiotic residues in poultry food products to ensure food safety and food security. International Journal of Applied Biology, 4(2), 2020 78 Materials and Methods Study area Sample collection was carried out at Kenyatta University and adjacent Kiwanja market in Nairobi, Kenya (1°10'59.0"S; 36°55'34.0"E) (Geographic positioning system (GPS, eTrex, USA). These sites constitute the leading vendors of chicken meat and poultry products to the University community of more than 70,000 people. The chicken used for meat and other poultry products in the study area are outsourced from small scale farmers and other national suppliers across the country. Sample collection Overall, 32 samples of which 16 raw chicken meat and 16 eggs were collected from randomly selected outlets in KM open market and KU. The samples were collected in sterile zip-lock bags for two weeks. The samples were labelled and immediately placed in a cool box and transported to Kenyatta University Microbiology Research Laboratory for analysis. All the samples collected were within the required date for consumption. Detection of antibiotic residues in chicken meat First, 10 g of each meat sample was soaked in 100 ml ethyl acetate and crushed using a pestle in a sterile mortar. The solvent was then centrifuged at 6000 revolutions per minute (rpm) for 10 minutes. The supernatant was then transferred into a fresh sterile bijou bottle and sterile filter paper discs placed inside. The set up was left to stand until the solvent completely evaporated. These paper discs were then placed on spread plates of Salmonella typhi, Staphylococcus aureus, Bacillus subtilis and Escherichia coli and incubated for 18 hours at 37°C (Kehinde et al., 2012). Paper discs dipped in ethyl acetate and subsequently evaporated were used as a negative control. In the second method, 1 mg pieces of fresh meat samples were cut and placed directly on spread plates containing the test organisms and incubated at 37°C overnight. Inhibition zones were observed and measured in millimeters (Myllyniemi et al., 2001). Detection of antibiotic residues in chicken eggs Presence of antibiotic residues on eggs was done using two methods. First, pieces of the eggshells were placed on spread plates containing the test organisms and incubated at 37°C for 24 hours. In the second method, wells were punched into spread plates of Muller Hinton agar with the test organism. A 0.1ml of egg albumen from each egg sample was placed in the wells. The plates were then incubated at 37°C for 24 hours. Zones of inhibition around the agar wells on each plate were observed and the absence or presence of antimicrobial residues recorded (Idowu et al., 2010). Bacterial counts in raw chicken meat and eggs One gram of the meat sample was aseptically homogenized in 99 ml of 0.85% saline; this effected a 10-2 dilution which was further serial diluted to effect a 103 diluent. From the homogenate, 0.1 ml was spread plated on nutrient agar plates in triplicate and incubated at 37°C for 24 hours. The colony forming units (CFUs) were then counted from each plate using a colony counter (Akusu and Wemedo, 2016). For microbial contamination of eggs, each was placed in a separate sterile plastic bag. A 100 ml sterile isotonic saline was added and washed thoroughly. A 0.1 ml of the washing was then spread plated on nutrient agar plates and incubated at 37°C for 24 hours. Viable cell counts were determined by International Journal of Applied Biology, 4(2), 2020 79 counting the CFU. The washing was also used for microbiological isolation of other contaminating organisms (Englmaierová et al., 2014). Isolation and identification of Salmonella, Shigella and Escherichia coli The fresh meat and egg washing from samples were each pre-enriched in buffered peptone water at 370C for 16 hours. The pre-enriched samples were sub-cultured and incubated for 24 hours at 37°C in Selenite F broth for selective enrichment of Salmonella and Shigella. This was then followed by plate streaking on DCA and Salmonella agar (Oxoid, Basingstokes, UK) and incubation was done at 37°C for 24 hours. To isolate Escherichia coli, the samples were enriched in lactose broth and after incubation sub-cultured on EMB agar (Oxoid, Basingstokes, UK) at 37°C for 24 hours. Typical characteristic colonies of Escherichia coli, with a green metallic sheen on EMB agar (Oxoid, Basingstokes, UK) and those of Salmonella and Shigella on DCA were purified by sub-culturing on nutrient agar (HiMedia, Mumbai India). The identity of the isolates was further confirmed by subjecting them to biochemical tests; Urease test, Triple Sugar Iron and IMViC (Indole, Methyl red, Voges- Proskauer and Citrate). All the media were inoculated with suspected isolates and incubated at 37°C for 18-48 hours, and the result interpreted according to the manufacturer’s instructions (Kyung-Min et al., 2015). Antibiotic sensitivity testing Disc diffusion technique was used to determine the susceptibility of isolates to commonly used antibiotics in animal and human medicine as recommended by the Clinical and Laboratory Standards Institute guidelines (CLSI, 2015). The antibiotic-impregnated discs used include tetracycline (TE 30), chloramphenicol (C 50), ciprofloxacin (CIP 5) ampicillin (AX 10), Amikacin (AK 30) and gentamycin (GEN 10). Representatives of the isolates were spread plated on Muller Hinton agar and antibiotic discs placed on the agar and incubated at 370C for 24 hours. After incubation period, the diameter of the zones of inhibition was measured to the nearest millimeter using a digital calliper (0-150 mm).The isolates were classified as resistant, susceptible or intermediate. Data analyses Antibiotic susceptibility means of inhibition zones, values for the abundance of the isolated pathogen and bacterial load values were subjected to ANOVA with significant difference determination among means using Tukey’s Honesty Significant Difference test at P≤ 0.05. The analysis was done using Statistical Analysis System version 9.1 (SAS Institute, Cary, NC). Results Antibiotic residues in chicken meat Bacillus subtilis and Staphyloccocus aureus were inhibited (Plate 1). Of the total meat samples, 14 (87.50%) showed inhibition zones on Bacillus subtilis , 7 from each sampling site. This was observed on both meat pieces tested and the paper discs, although the paper discs showed more distinct and clear inhibition zones. Out of the total meat samples, 6 from KU and 5 from KM showed inhibition against Staphyloccocus aureus (Figure 1.1). International Journal of Applied Biology, 4(2), 2020 80 Figure 1.1 Percentage levels of inhibiting samples of antibiotic residues in chicken meat. Antibiotic residues in chicken egg albumen and shells Egg albumen of all the sampled eggs showed 100 % inhibition when tested against Bacillus subtilis and Staphyloccocus aureus (Plate 2). Of the total egg sampled, only two (6.25%), from different outlets, revealed inhibition against Escherichia coli. None of the samples inhibited the growth of Salmonella typhi (Figure 1.2). The eggshells did not show any inhibition when subjected to the test organisms. International Journal of Applied Biology, 4(2), 2020 81 Figure 1.2. Percentage levels of inhibiting samples of antibiotic residues in egg albumen/ Bacterial counts of meat samples The mean CFUs for chicken meat from KU and KM were 104.96 x 103/gm and 190.250 x103/gm, respectively (P≤0.05). Samples obtained from KM were significantly contaminated as compared to those obtained from KU. The highest bacterial load (288.00 x 103) was recorded in samples obtained from KM (P < 0.001). The highest bacterial load of the samples obtained from KU was recorded at 157.00 x 103 (P < 0.001). The least contaminated sample had a bacterial load of 57 x 103gm at P <0.001 and was from KU (Table 1.1). Table 1.1: Mean values breakdown of CFUs of meat samples for KU and KM KU KM Meat sample Mean ± SE Meat sample Mean ± SE MU1 111.667 x 103±3.28cdef MM1 127.333 x 103±4.67cde MU2 125.000 x 103±12.34cde MM2 288.000 x 103±5.03a MU3 70.000 x 103±7.10fg MM3 242.00 x 103±26.62ab MU4 100.667 x 103±2.19edfg MM4 286.333 X 103±5.93a MU5 157.000 x 103±6.43c MM5 142.667 x 103±3.76cd MU6 131.667 x 103±4.49cde MM6 210.333 x 103±3.53b MU7 57.000 x 103±2.52g MM7 86.333 x 103±7.62efg MU8 86.667 x 103±7.42efg MM8 138.333 x 103±4.67cd Site mean 104.96 x 103±6.77b Site mean 190.25 x 103±15.37a P-value< 0.001 P-value < 0.001 Key: MU; KU meat, MM; KM meat. Means values followed by the same letter within the columns are not significantly different according to Tukey’s Honest Significance Difference (HSD) at 5% level Bacterial counts on eggs There was a significant difference the between bacterial population of egg samples obtained from KU and KM at P≤0.05 (Table 1.2). The mean bacterial count from egg washing of samples obtained from KU was 108.292 x 103 while that from egg washing of samples International Journal of Applied Biology, 4(2), 2020 82 obtained from KM was 158.875 x 103 both at P < 0.001. The highest bacterial load (286.667 x 103) was recorded at KM while the lowest bacterial load (52.667 x 103) was recorded at KU (P < 0.001). Table 1.2: Mean values ± SE breakdown of CFUs of egg samples for the two study sites KU KM Egg sample Mean ± SE Egg sample Mean ± SE EU1 86.667 x 103±2.91hi EM1 286.667 x 103±6.36a EU2 145.333 x 103±6.36cde EM2 156.667 x 103±1,76cd EU3 117.667x 103±5.90efg EM3 93.000 x 103±6.56gh EU4 52.667 x 103±1.76j EM4 247.333 X 103±4.33b EU5 170.333x 103±3.84cd EM5 115.667 x 103±5.04fg EU6 150.667 x 103±7.69cd EM6 136.333 x 103±4.49def EU7 81.000 x 103±8.33hi EM7 156.000 x 103±6.43cd EU8 62.000 x 103±3.46ij EM8 79.333 x 103±3.53hij Site 108.292 x 103±8.74b Site 158.875 x 103±14.30a P-value < 0.001 P-value < 0.001 Key: EU; KU eggs, EM; KM eggs, 1-8 sampling points. Mean values followed by the same letter within the columns are not significantly different according to Tukey’s Honest Significance Difference (HSD) at 5% level. Total isolated pathogens Of the total samples analyzed, 14 (43.75%) were positive for Salmonella. Eight of these isolates were from chicken meat samples while six isolates were from the surfaces of eggs samples. About 6 (18.75%) of the samples were positive for Shigella. Three of the isolates were from chicken meat samples while the other three isolates were from eggs samples. Twenty-one samples (62.63%) were contaminated with Escherichia coli. Out of this, 12 were from chicken meat samples, while 9 were from egg samples (Table 1.3). Table 1.3: Bacterial pathogens isolated from both sites Site Sample Salmonella sp. Shigella sp. E. coli KU Meat (n = 8) 5 1 6 Eggs (n = 8) 3 2 4 KM Meat (n = 8) 3 2 6 Eggs (n = 8) 3 1 5 Percentage 43.75% 18.75% 62.63% The abundance of the isolated pathogen The detection level of the isolated bacteria varied across the sampled outlets within KU at P≤0.05. The abundance of pathogens isolated from KU was significantly different (P= 0.0282). Escherichia coli were the most abundant at an average mean of 15.6 followed by Salmonella and Shigella. The abundance trend of isolates from KM samples was similar to that of KM isolates with E. coli registering the highest abundance (17.19). Shigella was the least abundant in KM with a mean of 4.69. Overall, the abundance of the isolates between KU and KM was not significant with both having a mean of 8.203 and 8.984, respectively (Table 1.4). International Journal of Applied Biology, 4(2), 2020 83 Table 1.4: Abundance of pathogenic bacteria isolated from the study sites Key: Mean values followed by the same letter within a column are not significantly different according to Tukey’s Honest Significance Difference (HSD) at 5% level. Antibiotic sensitivity testing The results showed that within each isolated bacterial genus, the inhibition was significantly different (P≤0.05) against the tested antibiotics. All the isolates showed significant zones of inhibition against ciprofloxacin, followed by chloramphenicol (Table 1.5). The isolates were 100% susceptible to ciprofloxacin and gentamicin. None of the isolates was susceptible to ampicillin. All Escherichia coli species and Shigella species were resistant to ampicillin, while 83% Salmonella were resistant to ampicillin (Table 1.5) Table 1.5. Inhibition zones in mm of the isolates to the tested antibiotics Antibiotic Test organism Salmonella sp. E. coli Shigella sp. Amikacin (30) 17.5±0.55b 16.833±0.75b 17.333±1.53b Ampicillin (30) 7.33±3.27c 6.000±0.00c 8.67±4.62c Chloramphenicol (50) 28.667±1.12a 29.667±1.03a 30.667±0.58a Ciprofloxacin (30) 30.667±1.21a 29.33±4.23a 30.00±4.36a Gentamycin (10) 19.333±1.21b 19.5±1.23b 19.333±0.58b Tetracycline (50) 19.333±1.21b 19.17±4.26b 18.333±1.53b P-VALUE < 0.001 < 0.001 < 0.001 Key: Means followed by the same letter within a column are not significantly different according to Tukey’s Honest Significance Difference (HSD) at 5% level. Table 1.6. Antimicrobial susceptibility profiles of the isolates Key: S=sensitive; I=intermediate; R=resistant; n=number of tested isolates Isolate KU KM E. coli 15.63±3.13a 17.19±1.56a Salmonella sp 12.5±3.13ab 9.38±2.56ab Shigellasp 4.69±0.56ab 4.69±0.56ab P-value 0.0282 0.0312 SITE 8.203±2.50 8.984±2.16 P-value 0.0729 Percentage inhibition of all the isolates tested Antibiotic Salmonella sp. (n=6) Escherichia coli (n=6) Shigella sp. (n=6) S I R S I R S I R Ciprofloxacin(30) 100 0 0 100 0 0 100 0 0 Tetracycline (30) 100 0 0 84 16 0 100 0 0 Gentamicin (10) 100 0 0 100 0 0 100 0 0 Chloramphenicol (50) 100 0 0 100 0 0 100 0 0 amikacin (30) 100 0 0 67 33 0 67 33 0 Ampicillin (10) 0 17 83 0 0 100 0 0 100 International Journal of Applied Biology, 4(2), 2020 84 Discussion The current findings revealed Bacillus subtilis as the highly susceptible bacteria to most of the samples of chicken meat and egg albumen. These results correspond with those of a study carried out in Khartoum by Elnasri et al. (2014) who reported susceptibility of Bacillus subtilis to most of the samples of chicken tissue. These results also suggest that the test organisms were subjected to samples which contained unspecific inhibitory substances (Myllyniemi et al., 2001). The potential of chicken meat and egg albumen to inhibit the growth of Bacillus subtilis, Staphyloccocus aureus and Escherichia coli depicts poultry products as have unspecified antibiotic residues and therefore, potential reservoir of antibiotic resistance. Susceptibility of these bacteria to the chicken meat and egg albumen could be linked to the use of antibiotics in poultry production (Mund et al., 2017). Samples obtained from KM were significantly contaminated as compared to those obtained from KU. The samples evaluated in this study came from two different sites whose outlets serve almost the same clients. The difference in the contamination could be attributed to poor handling of poultry food products. According to Jans et al. (2018), transmission of bacteria between food animals and humans occurs during the handling of animal food products at production, distribution, and household levels. There is limited sanitary knowledge for most food handlers at KM as opposed to KU food handlers who have some level of knowledge in sanitation as trained caterers. In a related study carried out in Kumasi, Ghana, it was found that street food was more contaminated with bacteria, especially enteric bacteria caused by improper handling of food (Feglo and Sakyi, 2012). The most common bacterial contaminant in this study was Escherichia coli, followed by Salmonella, and Shigella, which gives evidence of the presence of enteric bacterial contamination in the tested samples within the study areas. Wong et al. (2009) documented that Salmonella mostly contaminate meat from faecal material during slaughter and processing. The authors added that Salmonella sometimes might be present internally in meat tissue of infected animals. The difference in bacterial contamination of samples in the current study can be associated with the preservation methods used. During sampling, most of the meat samples from KM were not refrigerated, while in KU all the samples were kept in a cold room. In a related study, Mensah et al. (2002) who investigated the microbial quality of foods sold on streets of Accra, Ghana and factors predisposing to their contamination reported similar findings. Rane, (2011) reported that in developing countries, food sold in open-air markets are mostly contaminated with Salmonella sp., Escherichia coli, and Shigella sp. The researcher associated this with poor handling techniques of food and lack of proper storage facilities. The results revealed a high percentage of Salmonella, Escherichia coli and Shigella resistance to ampicillin and high susceptibility to ciprofloxacin, gentamicin and chloramphenicol. This result corresponds with those of Nyamboya et al. (2013) who reported high resistance against ampicillin and susceptibility to chloramphenicol and gentamicin by bacteria isolates from different abattoirs in Nairobi. Tetracycline and amikacin were moderately resisted by Shigella and Escherichia coli. The existence of resistant bacteria in the chicken meat and egg albumen samples reflect the potential spread of these bacteria in human consumers as well as the transfer of resistant components among the bacteria in the environment (Singer et al., 2016). The incidence of antibiotic resistant bacteria in poultry products as shown in the current study is a threat to public health as the bacteria could disseminate antibiotic resistant genes to other bacteria of human clinical significance (Woolhouse et al., 2015). Poultry products could, therefore, act International Journal of Applied Biology, 4(2), 2020 85 as conduits for the dissemination of clinically relevant antibiotic resistance to the environment. Conclusions There is a high level of antibiotic residues in chicken meat and eggs sold in KU and KM. These residues are in contact with commensals and clinically relevant bacteria on the same chicken products. There is, therefore, a high risk of these microbes adapting to the presence of the antibiotics and thus developing resistance to them. The chicken products in the study area are contaminated with bacteria pathogens; Escherichia coli, Salmonella and Shigella, which pose a health risk to the population. The isolates were resistant to ampicillin and relatively resistant to tetracycline. This study findings form the basis upon which intervention tools for monitoring the influence of animal origin food on the development of antibiotic resistant bacteria in the environment can be developed. Consent for publication Not applicable Availability of data and materials The datasets analyzed during the current study are available from the corresponding author on reasonable request Competing interests The authors declare that they have no competing interests. Funding Not applicable Author contributions DKN and LMK conceived the idea for the study and designed the experiment. DKN and PKK collected data, performed the experiments and analyzed data. DKN and HAM prepared and reviewed the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors acknowledge meat and eggs vendors for agreeing to participate and consent for us to collect chicken meat and eggs. We thank the Department of Biochemistry, Microbiology and Biotechnology of Kenyatta University for allocating laboratory space for sample analysis.. References Abdullahi, M., S. Olonitola, V. Umoh, and I. Inabo. 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