Molecular Characterisation of Carbapenemase-Producing Multi-Drug Resistant Klebsiella pneumoniae from Human, Animal, and Environmental Samples from Ekiti and Ondo State, Nigeria

Molecular Characterisation of Carbapenemase-Producing Multi-Drug Resistant Klebsiella pneumoniae from Human, Animal, and Environmental Samples from Ekiti and Ondo State, Nigeria | 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 Molecular Characterisation of Carbapenemase-Producing Multi-Drug Resistant Klebsiella pneumoniae from Human, Animal, and Environmental Samples from Ekiti and Ondo State, Nigeria Leonard Ighodalo Uzairue, Olufunke Bolatito Shittu, Tolulope M. Obuotor, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9059978/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background The escalating global threat of human infections caused by multi-drug-resistant (MDR) Enterobacteriaceae, particularly Klebsiella pneumoniae , poses a serious challenge to public health. The study investigated the prevalence and molecular characteristics of carbapenemase-producing Klebsiella pneumoniae in human, animal, and environmental samples from Ekiti and Ondo States, Nigeria. Methods A total of 1329 samples comprising 399 clinical human samples, 404 non-clinical human samples, 393 animal samples, and 133 environmental samples were examined. The samples were processed using standard microbiological techniques, and antibiotic susceptibility testing was performed in accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines. Antibiotic resistance genes ( OXA-48, NDM-1, VIM , and KPC ) were determined in all K. pneumoniae isolates that were phenotypically positive for carbapenemase production. Whole-genome sequencing (WGS) was performed on two K. pneumoniae isolates from the same household, which exhibited similar resistance patterns. Data analysis was performed in Excel and the Statistical Package for Social Sciences (SPSS) version 23. Results The overall prevalence of K. pneumoniae was 105 (7.9%), with higher rates in animal samples 38 (9.4%) than in human samples 59 (7.3%) and in the environmental sample 8 (6.0%). Antibiotic resistance was significantly higher in human isolates than in animal and environmental isolates (p < 0.05). All isolates were susceptible to colistin and amikacin. Multidrug resistance was observed in 65 (61.9%) isolates, which was significantly more common in human samples (p < 0.05). Carbapenemase production was detected in 9.2% of MDR isolates, predominantly in human samples. The OXA-48, VIM , and KPC genes were identified in Carbapenemase-producing K. pneumoniae . Whole-genome sequencing of K. pneumoniae isolates NGEK23 and NGEK25 revealed distinct resistomes and mobile genetic elements between the two isolates, suggesting distinct origins and differing diversity of resistance genes. Conclusion The high frequency of multidrug resistance and the presence of carbapenemase-producing K. pneumoniae across human, animal, and environmental sources in the study samples highlight an urgent need for robust infection control and antibiotic stewardship programmes. AMR One-Health Carbapenemases Oxa-48 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Antimicrobial resistance (AMR) is a significant public health challenge [ 1 ], of concern is the rise in multidrug-resistant bacteria, particularly Carbapenem-Resistant Bacteria (CRB) [ 2 ]. Carbapenem-resistant Enterobacterales (CRE) and other carbapenem-resistant Gram-negatives are now reported on every continent, with CRE accounting for a substantial share of the global AMR burden that contributed to an estimated 1.27 million deaths directly attributable to AMR and 4.95 million associated deaths in 2019 [ 3 , 4 ]. Numerous systematic reviews, meta-analyses, and surveillance studies conducted across regions have reported varying estimates of the prevalence of carbapenem-resistant Klebsiella pneumoniae . For instance, pooled data from parts of West Africa indicate a notably high burden of carbapenem-resistant strains K. pneumoniae and E. coli with dominant blaNDM , blaVIM and blaOXA-48 carbapenemases [ 5 ]; in Latin American paediatric cohorts, carbapenem-resistant K. pneumoniae infections have reported mortality as high as 52.6% [ 6 ]; and in US surveillance, carbapenemase-producing CRE clinical cultures rose by about 18% between 2019 and 2023, with a roughly fivefold increase in NDM-producing CRE [ 7 ]. Hospital-based series from Asia and Africa often report carbapenem resistance rates in Enterobacterales and other major Gram-negatives in the 20–50% range, while colonisation surveys in African hospitals have found CRE carriage prevalences around 12%, indicating large unrecognised reservoirs that fuel ongoing transmission [ 8 , 9 ]. There has been a global increase in the reporting of carbapenem-resistant bacteria. However, studies exploring the transmission dynamics of carbapenem resistance genotypes across one-health are limited and significantly low, especially in Africa [ 3 , 10 – 12 ]. The global rise in human infections caused by multi-drug resistant (MDR) Enterobacteriaceae, including Klebsiella pneumoniae , seriously threatens public health [ 13 , 14 ]. The emergence and spread of carbapenem resistance in clinical settings leads to high healthcare costs, long hospital stays, and an increase in mortalities associated with such pathogen [ 15 – 17 ]. Furthermore, carbapenem resistance can be harboured on both the chromosome and the plasmid, but most often, plasmid-encoded β-lactamases usually enhance their transmissible activities, leading to the spread of these genes among Enterobacteriaceae, contributing to the rapid dissemination of carbapenem resistance traits to other Enterobacteriaceae genera and species [ 18 , 19 ]. Evidence from the literature shows that carbapenem resistance in Enterobacteriaceae is common, with the highest prevalence reported in Klebsiella pneumoniae [ 7 ]. In developed countries, surveillance systems are in place to track the spread and transmission dynamics across the human, animal, and environmental sectors. However, surveillance systems in developing countries are limited and not fully optimised, particularly for tracking the spread of infections within health systems. Limited evidence on CR Klebsiella pneumoniae surveillance in developing countries is usually focused solely on human health, without data from animal and environmental isolates. In particular, there is limited epidemiological data on the genes responsible for carbapenem resistance in Klebsiella pneumoniae , with most studies focusing on antibiotic susceptibility testing with imipenem and meropenem. Equally, the mechanisms underlying the spread of carbapenem-resistant bacterial infections remain incompletely elucidated in the Nigerian population. Studies exploring the transmission dynamics of carbapenem-resistant bacteria among humans, animals, and the environment in Nigeria are limited [ 20 – 22 ]. In Nigeria, infection prevention and control (IPC) strategies are not holistic, as they are most often focused on healthcare settings, with no active IPC and biosecurity programmes in the animal, agriculture, and environmental sectors. The problem of quality surveillance data is further worsened in developing countries due to poor access to laboratory facilities for accurate identification and molecular characterisation of MDR pathogens, thereby enhancing clinical diagnosis and the detection of transmissible resistance determinants [ 23 – 27 ]. Therefore, surveillance studies of carbapenem-resistant bacteria, particularly in Klebsiella pneumoniae , in humans, animals, and their environment are needed in these settings, as no such study has been conducted to our knowledge. A quick search in two databases (PubMed and Africa Online Journals (AJOLs)) using the keywords (carbapenem, Klebsiella pneumoniae, One-Health, Ondo, and Ekiti) in the study area did not return any studies that used a One-Health approach to explore the prevalence of carbapenem-resistant Klebsiella pneumoniae . Studies of this nature are necessary to understand the transmission dynamics of resistant bacteria and to inform public health strategies and interventions. There are data gaps in the molecular epidemiology of MDR pathogens and ARGs across sectors, including human, animal, and environmental samples, in developing countries and, in particular, in study areas [ 12 , 28 , 29 ]. This study bridged this gap by providing insights into the prevalence of multidrug-resistant K. pneumoniae through a one-health approach. In addition, identified carbapenemase-producing K. pneumoniae , detected carbapenemase genes, and conducted whole-genome sequencing on two isolates with similar antimicrobial profiles from the same household in the study settings. METHODS Study Area Study Design and study time frame The study employed a cross-sectional design and was conducted over 18 months (April 2022–September 2023) in Ekiti and Ondo States, Nigeria. Sample collection points The study was carried out at the Federal Medical Centre, Owo, Federal Teaching Hospitals, Ido-Ekiti, The Ekiti State University Teaching Hospital, Ado-Ekiti, Hospital Management Board Hospitals in Ikere-Ekiti, Ikole-Ekiti and Oye-Ekiti, while Mother and Children Hospital, General Hospital Igarra Oke, Ondo State, animal farms in Akure and Ado-Ekiti respectively, Slaughterhouses in Owo, Akure, Ido-Ekiti and Ado-Ekiti and the home and animals of agreed hospitalised participants. Sample Size The study analysed a total of 1,329 samples, including 399 clinical human samples, 404 non-clinical human samples, 393 animal samples, and 133 environmental samples. Specifically, environmental samples totalled 91 from Ekiti and 42 from Ondo; animal samples, 253 from Ekiti and 140 from Ondo; clinical samples, 263 from Ekiti and 136 from Ondo; and samples from animal workers, 251 in Ekiti and 153 in Ondo (Table 1 ). Sample collections Samples were collected from three cohorts (Hospitals, farms, and slaughterhouses). Human, animal, and environmental samples were collected from farms, households, and hospital-admitted patients. The residents of each household/farm, their domestic animals, and the water source (well or borehole) used for drinking and washing purposes were sampled. The distribution of the samples collected from each sample cohort in each state is presented in Table 1 . In the hospital cohort, clinical samples (blood, urine, stool and rectal swabs) were collected from inpatients after obtaining consent, and the procedures were explained to patients or their guardians who were under the age of consent or vulnerable. For those who agreed, further samples were collected from participants' homes and, for those who own animals, from their animals. The samples collected were water samples, rectal swabs from their animals, tables, and bench swabs from their residents. In the farm cohort, samples were collected from animal farms and from their handlers, with consent. Also, water samples and swabbing of tables, benches, and pegs from the farms were collected. For the slaughter cohort, rectal swabs were collected from animals to be killed and from butchers after consent was obtained from them. Water from their water sources was collected, and benches and tables used for meat processing were also swabbed. Each participant was randomly assigned a code to maintain confidentiality. All the samples were processed for K. pneumoniae isolates. Table 1 Sample collected from the three sampling cohorts Sample Collection Settings and Sample Types Ekiti Ondo Clinical Sample Blood 89 67 Urine 146 63 Stool 2 0 Non-Clinical Sample (Animal Worker) (Rectal Swabs) 238 186 Animals (Rectal Swabs) Poultry 113 66 Cow 94 44 Dogs 51 30 Environment Water 45 15 Table, animal peg, Bench Swab 46 27 Bacteriology of the Isolates The study involved processing human blood and urine samples. The blood samples were collected and inoculated aseptically into the appropriate blood culture bottles, then incubated at 37°C for up to 5 days. Those that gave a growth signal were cultured on solid media (blood agar, MacConkey agar, and chocolate agar) (Oxoid Ltd, Basingstoke, Hampshire, England). Urine samples were inoculated onto cystine-lactose-electrolyte-deficient (CLED) agar (Oxoid Ltd, Basingstoke, Hampshire, England) and incubated at 37°C. The rectal and environmental swabbed samples were enriched in buffered peptone water for about 3 hours, then plated on MacConkey lactose agar (Oxoid Ltd, Basingstoke, Hampshire, England) and incubated at 37°C for 24 hours. Water samples were processed using membrane filtration to isolate and identify K. pneumoniae . Individual sterile filter discs with 0.45 µm pores were used in a filtration apparatus to filter 100 mL of the water sample. The filter membranes were then placed on MacConkey lactose agar plates and incubated at 37°C for 24 hours. In all the sample cohorts, colonies resembling Klebsiella species were further processed. Gram reactions were carried out on the bacterial isolates according to the Distinct Laboratory Practices in Tropical Countries [ 32 ]. The glass slide was smeared with the test isolate, air-dried, heat-fixed, flooded with crystal violet (the primary stain) for 60 seconds, and rinsed with distilled water. It was mordanted with Lugol’s iodine for 30 seconds, rinsed again, decolourised with acetone, counterstained with safranin for 60 seconds, rinsed once more, and air-dried. A drop of immersion oil was added to the stained slide, and the slide was examined under an oil-immersion (X100) objective lens on a microscope. Bacterial isolates were identified using morphology, Gram staining and biochemical tests using API 20E (BioMérieux SA, Lyon, France). The API 20E system (BioMérieux SA, Lyon, France), a plastic strip with 20 microtubes for various biochemical and substrate utilisation tests, was used to characterise and confirm K. pneumoniae . Bacterial isolates were stored in 15% glycerol peptone water at − 30°C for further study. Antibiotic Susceptibility Testing Antibiotic susceptibility testing was performed using the provided antibiotics according to CLSI guideline [ 33 ]. These antibiotics were obtained from Oxoid Ltd, Basingstoke, Hampshire, England. The antibiotic used include tetracycline (TET, 30 µg), ceftazidime (CFZ, 30 µg), cefuroxime (CFX, 30 µg), cefotaxime (CTX, 30 µg), ceftriaxone (CFT, 30 µg), meropenem (MER, 10 µg), trimethoprim-sulfamethoxazole (TRM-SXT, 1.25/23.75 µg), chloramphenicol (CHL, 30 µg), ciprofloxacin (CIP, 5 µg), amoxicillin-clavulanate (AMC-CLA, 30 µg), colistin (COL, 25 µg), gentamicin (10 µg), amikacin (AMK, 30 µg), cephalothin (CEP, 30 µg), ceftazidime-clavulanate (CFZ-CLA, 30/10 µg), and cefotaxime-clavulanate (CTX-CLA, 30/10 µg). The control strain used for comparison was E. coli ATCC 25922. The breakpoint was interpreted by the CLSI (2021) guideline (M100, 31st edition) Carbapenemase Phenotypic Detection Carbapenemase production was assessed using a Carba NP/CarbAcineto NP-based assay as previously described by Shinde et al. [ 34 ]. Briefly, two solutions (A and B) were prepared. Solution A consisted of phenol red (0.05%) and ZnSO₄·7H₂O (0.1 mmol/L) in distilled water, adjusted to pH 7.8 ± 0.1 and stored at 4°C, while solution B was prepared by adding imipenem–cilastatin injectable to Solution A to achieve an imipenem concentration of 6 mg/mL. For each isolate, 2–3 loops of overnight growth were suspended in NaCl and aliquoted into two tubes: tube A (Solution A, no imipenem) and tube B (Solution B, with imipenem) and incubated for 2 hours at 35°C, carbapenemase production was defined as a colour change from red to yellow/orange in tube B, while tube A remained red, in line with acceptable Carba NP interpretive criteria provided by Shinde et al. [ 34 ]. DNA extraction DNA extraction was performed using the QIAamp DNA Mini Kit (Qiagen, Venlo, Netherlands) for genomic DNA. The modified protocol for Gram-negative bacteria DNA extraction, as provided by Yang [ 35 ]. was used. The quality of the extracted DNA was assessed using a NanoDrop (Thermo Fisher Scientific, Waltham, Massachusetts, USA) as described by Yang [ 35 ]. The eluted DNA was pipetted into storage tubes and stored in a freezer at -30 degrees Celsius until PCR amplification. Amplification of Carbapenem Antimicrobial Resistance Gene MDR K. pneumoniae Isolates with Positive Carbapenem Phenotypic Result Real-time PCR (RT-PCR) was used to detect resistance genes in K. pneumoniae using specific primers and probes. Specific primers and probes targeting these genes (supplementary table 1 ) were incorporated into RT-PCRs to potentially identify resistance genes (OXA-48, NDM-1, VIM, and KPC). Primers and probes for RT-PCR were obtained from Biosearch LGC (Novato, California, USA). The primer and probe sequences ( supplementary table 1 ). The reaction mixture utilised PerfeCTa MultiPlex qPCR ToughMix(Quanta Biosciences, Gaithersburg, MD, USA). Amplification was performed using an AriaMx RT-PCR cycler (Agilent Inc., Santa Clara, California, USA). The cycle thresholds were determined from fluorescence signals obtained from amplification plots in the AriaMx system software version 3.1 after the completion of the amplification experiment, following the protocol for real-time PCR, as utilised by the authors in another study [ 36 ]. Whole-Genome Sequencing The DNA extractions and library preparations for whole-genome sequencing (WGS) were performed as previously described by Cookson et al. [ 37 ]. Briefly, the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) was used for DNA extraction, and libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA). WGS was undertaken by Novogene Limited (Helios, Singapore) using the Illumina HiSeq paired-end v4 platform (2 by 150 bp). The Nullarbor pipeline was used, including adapter-trimmed read processing and examination of WGS reads for de novo genome assembly using SKESA (v.2.2.1), annotation using Prokka (v.1.13.3), and phylogenetic analysis using Snippy (v.4.2.1). Bioinformatic analysis of WGS data for the antimicrobial Resistance determinants of E. coli isolates The identification and categorisation of genes that confer resistance were carried out using ResFinder 4.1, a bioinformatics tool developed by the Centre for Genomics Epidemiology (CGE) ( http://www.genomicepidemiology.org/ ) (accessed on 2024/03/2). Each isolate's genes were compared to an annotated resistance gene using a threshold of 95–100% identity [ 38 ]. The plasmid replicon types of each E. coli isolate were identified using PlasmidFinder 2.1. As earlier documented by Joensen et al [ 39 ], the strain's O and H serotypes were determined by analysing the generated FASTA files using the Centre for Genomic Epidemiology (CGE) platform ( http://www.genomicepidemiology.org/ accessed on 2024/03/2). Additionally, the CARD pipeline ( https://card.mcmaster.ca/analyze/rgi/ accessed on 2024/06/04) was used to determine the AROs from the sequence data. Table 2 Basis for Performing Whole Genome Sequencing SAMPLE ID Sources Isolates Resistance pattern Remarks NGEK23 Human (Blood) Klebsiella pneumoniae AMC, CIP, CTX, CTM, CRO and TET WGS DONE NGEK25 Environment (Water) (home of GNEK 23) Klebsiella pneumoniae AMC, CIP, CTX, CTM, CRO and TET WGS DONE Data Analysis Data were entered and validated in Excel 2020 version. Frequencies and descriptive statistics were carried out on all data. Differences in proportions of virulence and resistance genes among humans, animals, and environmental samples were evaluated using two-sample t-tests, ANOVA, and chi-square tests, which were appropriate. Continuous variables were analysed using ANOVA, while categorical variables were analysed using chi-square and Fisher's exact test. P 0.05 was accepted as statistically insignificant in all cases. Statistical software for Social Science (SPSS) (IBM, version 27) was used for the analysis. Results were presented in Tables and Figures. Ethical Considerations Ethical Clearance from the National Health Research Ethics Committee of Nigeria (NHREC) (NHREC/01/01/2007-21/03/2023), the Federal Teaching Hospital, Ido-Ekiti (ERC/2023/09/20/1034B) and the Ondo State Ministry of Agriculture (MNR/V.384/64) were obtained for this study. Results Prevalence of E. coli and K. pneumoniae in the study The overall prevalence of K. pneumoniae in the study was 7.9% (105/1329). Animal samples had the highest prevalence at 9.4% (38/404), followed by human samples at 7.3% (59/804), and environmental samples at 6.0% (8/121). Among clinical human samples, the prevalence was 8.8% (35/399), compared with 5.7% (24/408) in non-clinical samples. Among animal samples, cows had the highest rate at 13.2% (18/136), followed by poultry at 8.9% (16/180) and dogs at 3.8% (3/80). Environmental isolates were most commonly found in swab samples from surfaces in slaughterhouses, poultry, and patients' homes, 9.8% (6/61), whereas water samples showed a lower prevalence, 3.3% (2/60) (Table 3 ). Antimicrobial Susceptibility Pattern of the Isolated K. pneumoniae from Human, Animal and the Environment The resistance profile of the K. pneumoniae from this study is shown in Table 4 . The resistance profile of the 59 K. pneumoniae from humans shows highest resistance to amoxicillin-clavulanate 49 (83.1%), the resistance to other antibiotics were as follows: 32 (54.2%), 20 (33.9%), 35 (59.3%), 47 (79.7%), 36 (61.0%), 38 (64.4), 46 (77.9%), 35 (59.3%), 30 (50.8%) and 7 (11.9%) to trimethoprim-sulfamethoxazole, gentamicin, chloramphenicol, cefuroxime, ceftriaxone, cefotaxime, ceftazidime, tetracycline, ciprofloxacin and meropenem, with one isolate of K. pneumoniae from humans having intermediate susceptibility to colistin. All 59 K. pneumoniae isolates were susceptible to amikacin (Table 4 ). The resistance profile of the 38 K. pneumoniae from animal samples showed highest resistance to cefuroxime 29 (76.3%), while resistance to other antibiotic were as follows; 13 (34.2%), 5 (13.2%), 5(13.2%), 6 (15.8%), 12 (31.6%), 8 (21.1%), 23 (60.5%), 14 (36.8%), 4(10.5%) and 1 (2.6%) resistant to amoxicillin-clavulanate, trimethoprim-sulfamethoxazole, gentamicin, chloramphenicol,, ceftriaxone, cefotaxime, ceftazidime, tetracycline, ciprofloxacin and meropenem. Of the 8 K. pneumoniae isolates from the environment, 2 (12.5%), 3 (37.5%), 1 (12.5%), 2 (25.0%) and 1 (12.5%) were resistant to amoxicillin-clavulanate, cefuroxime, ceftriaxone, ceftazidime and tetracycline, respectively. MDR and Carbapenemase and Gene Detection The overall occurrence of MDR K. pneumoniae in the study was 65 (61.9%), as shown in Fig. 2 a. The occurrence of MDR K. pneumoniae in the human samples was 94.9% among the 59 K. pneumoniae isolates. The occurrence of MDR K. pneumoniae in the animal and environmental samples was 23.7% and 12.5% , respectively. The occurrence of MDR K. pneumoniae was significantly higher in the K. pneumoniae from human samples compared to those from animals and the environment, as shown in Fig. 2 b. The phenotypic detection of carbapenemase among the 65 MDR K. pneumoniae was 9.2%, as shown in Fig. 2 c. Of the 6 MDR K. pneumoniae that were carbapenemase-producing, 5 were from human samples and 1 from animal samples; none were from environmental samples. Of the 5 MDR K. pneumoniae that were carbapenemase-producing, 3 (60.0%) harboured the 0XA-48 gene, 1 (20.0%) had the VIM genes, and 2 (40.0%) had the KPC gene, as shown in Table 5 . Only one of the three genes ( OXA-48 , VIM , and KPC ) was detected in a single MDR K. pneumoniae carbapenemase producer from the animal sample. Gene Mapping and Phylogenetic Analysis of K. pneumoniae isolates Whole-genome sequencing was carried out on two MDR K. pneumoniae isolates from human blood culture and from water samples collected from the patients' homes. The two samples exhibited phenotypic similarities in resistance patterns, as shown in Table 2 . Figure 3 a-f shows the genome maps for the two K. pneumoniae strains (NGEK23 and NGEK25). The gene mapping of the two sequenced K. pneumoniae strains shows different gene intensities across various sections of the genome, as shown in the figure. The gene mapping of the two genes revealed their gene makeup. Looking at the first one, you can see that the intensity was higher in certain regions than in the second genome. Figure 3 b shows the coding sequences (CDS) and genome indel length distribution mapping of the two genes. The indel length distribution CDS in the NGEK25 was indicated to peak at 3, 12, 15, 18, and 21 base pairs, as against the indel length distribution CDS in NGEK23 at 8, 9, and 12 base pairs. The structure variation (SV) length in base pairs found in the sequenced K. pneumoniae NGEK23 and 25 showed that NGEK23 had a higher SV > 1200 base pairs than that of NGEK25 using the reference genome. However, SNPs with base pairs between 100 and 300 were more common in the K. pneumoniae NGEK25 than in the K. pneumoniae NGEK23 isolates. The SNV type showed that the deletion, ITX, and insertion were higher in the isolate K. pneumoniae NGEK25 than in the isolate NGEK23. However, SNV inversion was higher in the isolate K. pneumoniae NGEK23 than in the isolate NGEK25, as shown in Fig. 4 c. The phylogenetic analysis showed the evolutionary relationship between the two isolates and other K. pneumoniae isolates available in the gene bank, as shown in Fig. 4 . The nearest four isolates, the whole genome isolate that came up, were K. pneumoniae strain SH209, K. pneumoniae strain KP8701, K. pneumoniae sub-species K. pneumoniae HS11286, and K. pneumoniae strain KP294. The phylogenetic analysis results indicate that the two isolates ( K. pneumoniae NGEK23 and NGEK25) are not of the same origin. Isolates NGEK23 were more related to K. pneumoniae strain NH209 and K. pneumoniae strain KP8701. Resistance Genes, MLST, and Mobile Genetic Element Table 6 summarises the bioinformatic analysis of the two sequence isolates ( K. pneumoniae NGEK23 and NGEK25). From the Insilco analysis, the resistance genes found in isolate NGEK23 were aph (6)id, aph(3)ib, OqxB, blaSHV-30, blaSHV-189, blaSHV-14, sul2, fosA5, tetA, blaTEM-1B, OqxA, qnrS1, dfrA14, while blaSHV-11, blaSHV-110, sul2, fosA6, catA2, OmpK36, pmrB, catll2 genes were found in the isolate. The multilocus sequence (MLST) analysis of the two isolates shows that isolate NGEK23 is of serotype (ST)3157, while isolate NGEK23 is of a new strain, ST 5ee4. The isolates NGEK23 were KL30 K-locus and 01/02 vs2 O-locus, whereas isolates NGEK25 were KL12 and OL103 O-locus. Three (IncFIB(K_1), IncY_1 and IncFII(K)_1) mobile genetic elements were found in K. pneumoniae NGEK23, while two (IncFIB(K)_1 and col(phAD28_1) mobile genetic elements were found in the isolate. Based on the results, the two isolates were from different origins, as they had distinct resistomes, virulomes, and mobilomes. The resistant genes found in K. pneumoniae NGEK23 were more diverse than those in NGEK25. The antimicrobial resistance ontology (ARO) data from the WGS analysis indicate that K. pneumoniae NGEK23 has 32 ARO outcomes, whereas K. pneumoniae NGEK25 has 30, with 25 similar outcomes (Fig. 5 ). Table 3 Occurrence of K. pneumoniae in the Study Sample Sources Klebsiella pneumoniae n(%) No Growth n(%) Total n(%) Human 59 (7.3) 744 (92.7) 803 (60.4) Clinical 35 (8.8) 364 (91.2) 399 (49.7) Non-Clinical 24 (5.9) 380 (94.1) 404 (50.3) Animal 38 (9.4) 355 (90.6) 393 (29.6) Cow 18 (13.2) 118 (86.8) 136 (34.6) Dog 3 (3.8) 75 (96.2) 78 (19.8) Poultry 16 (8.9) 163(91.1) 179 (50.4) Environment 8 (6.0) 125(94.0) 133 (10.0) Water 2 (2.8) 70 (97.2) 72 (54.1) Table Swab 6 (9.8) 55 (90.2) 61 (45.9) Total 105 (7.9 ) 1223 (92.0) 1329 (100.0) Distribution of Klebsiella pneumoniae isolates and samples with no bacterial growth across human, animal, and environmental sources. Data are presented as numbers (percentages) of isolates or samples within each category, with clinical and non-clinical subgroups detailed for human samples, and specific sources indicated for animal and environmental samples. Table 4 Antibiotic Susceptibility Pattern of Isolated K. pneumoniae from Humans, Animals and their Environment Antibiotics Human n = 59 Animal n = 38 Environmental Sample n = 8 Sensitive n (%) Immediate n (%) Resistant n (%) Sensitive n (%) Immediate n (%) Resistant n (%) Sensitive n (%) Immediate e n (%) Resistant Amoxicillin-clavulanate 10 (16.9) 0 (0.0) 49 (83.1) 25 (65.8) 0(0.0) 13 (34.2) 7 (87.5) 0 (0.0) 1 (12.5) Trimethoprim-Sulfamethoxazole 17 (28.9) 10 (16.9) 32 (54.2) 31 (81.6) 2 (5.3) 5 (13.2) 8 (100.0) 0 (0.0) 0 (0.0) Gentamicin 20 (33.9) 19 (32.2) 20 (33.9) 26 (68.4) 7 (18.4) 5 (13.2) 7 (87.5) 1 (12.5) 0 (0.0) Chloramphenicol 18 (30.5) 6 (10.2) 35 (59.3) 32 (84.2) 0 (0.0) 6 (15.8) 8 (100.0) 0 (0.0) 0 (0.0) Cefuroxime 4 (6.8) 8 (13.6) 47 (79.7) 3 (7.9) 6 (15.8) 29 (76.3) 3 (37.5) 2 (25.0) 3 (37.5) Ceftriaxone 15 (25.4) 8 (13.6) 36 (61.0) 22 (57.9) 4 (10.5) 12 (31.6) 7 (87.5) 0 (0.0) 1 (12.5) Cefotaxime 9 (15.3) 12 (20.3) 38 (64.4) 5 (13.2) 25 (65.7) 8 (21.1) 2 (25.0) 6 (75 .0) 0 (0.0) Ceftazidime 5 (8.5) 8 (13.6) 46 (77.9) 4 (10.5) 11 (29.0) 23 (60.5) 2 (25.0) 4 (50.0) 2 (25.0) Tetracycline 5 (8.5) 19 (32.2) 35 (59.3) 14 (36.8) 10 (26.4) 14 (36.8) 4 (50.0) 3 (37.5) 1 (12.5) Amikacin 59 (100.0) 0 (0.0) 0 (0.0) 38 (0.0) 0 (0.0) 0 (0.0) 8 (100.0) 0 (0.0) 0 (0.0) Ciprofloxacin 27 (45.8) 2 (3.4) 30 (50.8) 34 (89.5) 0 (0.0) 4 (10.5) 8 (100.0) 0 (0.0) 0 (0.0) Meropenem 43 (72.9) 9 (15.2) 7 (11.9) 37 (97.4) 0 (0.0) 1 (2.6) 8 (100.0) 0 (0.0) 0 (0.0) Colistin 58 (98.3) 1 (1.7) 0 (0.0) 37 (97.4) 1 (2.6) 0 (0.0) 8 (100.0) 0 (0.0) 0 (0.0) Antimicrobial susceptibility profiles of Klebsiella pneumoniae isolates from human, animal, and environmental samples. The table presents the number and percentage of isolates classified as sensitive, intermediate, or resistant to each antibiotic tested, across all three sources. Human (n = 59), animal (n = 38), and environmental (n = 8) data are shown. Results are expressed as n (%). Table 5 Detection of Carbapenem Genes from Carbapenemase-Producing MDR K. pneumoniae Genes MDR K. pneumoniae Human n = 5 Animal n = 1 Environment n = 0 Total n = 6 OXA-48 3 (60.0) 0 (0.0) 0 (0.0) 3 (50.0) VIM 1 (20.0) 0 (0.0) 0 (0.0) 1 (16.7) KPC 2 (40.0) 0 (0.0) 0 (0.0) 2 (33.3) Distribution of carbapenemase genes ( OXA-48 , VIM , KPC ) among multidrug-resistant ( MDR ) Klebsiella pneumoniae isolates sourced from humans (n = 5), animals (n = 1), and the environment (n = 0). Values are presented as counts with percentages in parentheses, representing the proportion of positive isolates within each source category and overall. Table 6 Summary of the Bioinformatic results for K. pneumoniae isolates Parameter NGEK23 NGEK25 Resistance genes aph (6)id, aph(3)ib, OqxB, blaSHV-30, blaSHV-189, blaSHV-14, sul2, fosA5, tetA, blaTEM-1B, OqxA, qnrS1, dfrA14 blaSHV-11, blaSHV-110, sul2, fosA6, catA2, OmpK36, pmrB, catll2 Clonal -group ST 3157 New (5ee4) K-locus KL30 KL12 O locus 01/02vs2 OL103 Plasmid Inc types IncFIB(K)_1, IncY_1 and IncFII (K)_1 IncFIB(K)_1 and col(phAD28)_1 The table compares the genotypic and plasmid profiles of multidrug-resistant Klebsiella pneumoniae isolates NGEK23 (human blood) and NGEK25 (environmental water). This table shows the key molecular features of two K. pneumoniae isolates, highlighting differences in resistance genes, sequence types, capsular and O-antigen loci, and plasmid types. The findings illustrate the genetic diversity and varied resistance profiles between clinical and environmental strains, emphasising the importance of integrated surveillance. Note: these results are based on the analysis from the pipeline of http://www.genomicepidemiology.org/ ) DISCUSSION This study aimed to isolate and characterise MDR K. pneumoniae from humans, animals, and their environment in the study area using a one-health approach. Understanding the transmission of MDR K. pneumoniae through One Health surveillance across human, animal, and environmental sources is vital for identifying reservoirs, tracking transmission, and guiding targeted interventions against antimicrobial resistance. The overall prevalence of K. pneumoniae (7.9%) and its distribution across animal (9.4%), human (7.3%), and environmental (6.0%) samples indicate its ubiquitous nature. The prevalence found in our study closely matches the 8.6% reported in a 2025 meta-analysis in sub-Saharan Africa using the one-health approach [ 40 ]. In the Olaitan et al. [ 40 ], the pooled prevalence estimates of 12.1%, 8.6%, and 6.2% were reported for animal, human, and environmental samples of ESBL-producing K. pneumoniae, respectively. The country-specific pooled prevalence of K. pneumoniae ranged from 8.1% in Tanzania to 23.3% in South Africa, with K. pneumoniae from Nigeria showing a pooled prevalence of 11.1%. The finding of a higher prevalence of K. pneumoniae in clinical isolates (8.5%) versus 6.4% in non-clinical human samples shows a trend in line with the evidence documented in the previous studies in Nigeria [ 22 , 41 – 44 ].The higher prevalence in clinical human samples (8.5%) compared to non-clinical samples 6.4%) aligns with the established role of K. pneumoniae as a leading opportunistic nosocomial pathogen. This disparity suggests that the healthcare environment exerts selective pressure that favours the colonisation and persistence of this bacterium, likely exacerbated by compromised host immunity and frequent exposure to antimicrobial agents. However, the higher prevalence in animals in cows (13.2%) supports the reservoir hypothesis, as shown recently by Hetland et al. [ 45 ] and previously by Hu et al. [ 46 ] and Wareth and Neubauer [ 47 ] which posits that livestock may serve as significant natural reservoirs for Klebsiella species, potentially facilitating zoonotic spillover to humans through the food chain, direct contact, or environmental contamination. The finding of high resistance to amoxicillin–clavulanate and second/third-generation cephalosporins in our study supports recent One Health and clinical studies, which show high resistance to amoxicillin–clavulanate and second/third-generation cephalosporins in human K. pneumoniae isolates, reflecting widespread ESBL production and high multidrug resistance (MDR) in humans compared to lower MDR in animals. For example, Marzouk et al. [ 48 ] reported 97% resistance to cefotaxime, ceftazidime, and amoxicillin–clavulanate in ESBL-producing K. pneumoniae , which is consistent with the 83.1% resistance to amoxicillin–clavulanate and similarly high resistance to other cephalosporins observed in our human isolates. In addition, Bayaba et al [ 49 ], in Cameroon reported that 82% of Enterobacterales isolates were MDR, similar to the high MDR burden identified in our study. Olaitan et al [ 40 ], also found that ESBL-producing K. pneumoniae is widespread, with MDR rates markedly higher in humans (94.9%) compared to animals (23.7%), reinforcing our observation of greater selection pressure from antibiotic use in human clinical settings. The animal K. pneumoniae isolates in our study showed substantial resistance to certain cephalosporins, particularly cefuroxime (76.3%), which was lower than the 95% resistance reported by Aslam et al [ 50 ] for the 115 K. pneumoniae isolates in Pakistan. These further demonstrated the high resistance to cephalosporin associated with K. pneumoniae isolates globally. The 11.9% meropenem resistance in K. pneumoniae from clinical human isolates in this study further aligns with recent WHO and global reports, which highlight the rise of hypervirulent, carbapenem-resistant K. pneumoniae and raise global concerns about resistance to last-line antibiotics [ 51 ]. The report on meropenem resistance from CHINET Surveillance networks reveals a sharp increase in carbapenem resistance in K. pneumoniae , with meropenem resistance rising from about 3% in 2005 to over 26% by 2018 in China [ 52 ], which show a gradual increase in meropenem resistance over the years. The detection of blaOXA-48 , blaKPC , and blaVIM among MDR K. pneumoniae isolates in this study provides molecular confirmation of diverse carbapenem resistance mechanisms, encompassing class D, A, and B carbapenemases, respectively. The predominance of blaOXA-48, frequently located on conjugative IncL/M-type plasmids, is consistent with reports of efficient horizontal transfer of these plasmids between Enterobacterales in clinical and experimental models [ 53 – 55 ].​ The identification of a carbapenemase-producing isolate in livestock, in the absence of such isolates from the sampled environment, raises the hypothesis of reverse zoonosis (anthropozoonosis), whereby resistant strains or mobile genetic elements move from humans into animal populations [ 56 ]. However, without genomic comparisons of human and animal isolates, temporal linkage, and more extensive environmental sampling, the direction of transmission remains uncertain, and this interpretation should be regarded as speculative [ 56 , 57 ]. OXA-48-producing K. pneumoniae are associated with reduced susceptibility to ertapenem and meropenem and have been linked in multiple cohorts to delayed effective therapy, prolonged hospital stay, and increased mortality compared with carbapenem-susceptible infections[ 55 , 58 ]. VIM metallo-β-lactamases confer resistance to most β-lactams [ 59 , 60 ]. They are often detected in high-risk patients, including those in intensive care or with significant immunosuppression, where they are associated with severe invasive infections [ 61 ].​ KPC enzymes mediate resistance to a broad spectrum of β-lactams, including carbapenems, and infections due to KPC-producing K. pneumoniae have historically required prolonged courses of toxic agents such as colistin or aminoglycosides [ 62 – 64 ]. Although newer β-lactam/β-lactamase inhibitor combinations have partially improved outcomes, there remains a need to develop a new antibiotic to treat emerging infections caused by carbapenem-resistant pathogens, including those caused by MDR K. pneumoniae . Whole-genome sequencing of K. pneumoniae NGEK23 from human blood and K. pneumoniae NGEK25 obtained from a water sample from the home of the patient NGEK23 environmental water) provides insights into K. pneumoniae genomic plasticity, including differences in core-genome phylogeny, structural variation, and resistome composition. Despite similar multidrug-resistant phenotypes (Table 2 ), phylogenetic analysis indicates that the two isolates are not closely related. This is consistent with previous work demonstrating that similar resistance profiles can emerge in distinct K. pneumoniae lineages through horizontal acquisition of shared mobile genetic elements [ 65 – 67 ].​ Differences in Indel length distribution and structural variation between K. pneumoniae NGEK23 and K. pneumoniae NGEK25 indicate substantial genome remodelling. The higher number of insertions and deletions in the environmental isolate ( K. pneumoniae NGEK25) may reflect recent recombination and mobile element activity, potentially linked to adaptation to fluctuating environmental conditions, as previously documented in the studies by Thorpe et al [ 67 ] and Rocha et al [ 68 ]. However, this remains speculative and could have also been driven by other lineage-specific evolutionary phenomena.​ K. pneumoniae NGEK23 harbours a broader resistome than K. pneumoniae NGEK25, with more annotated resistance determinants. The above finding in our study agrees with large-scale studies showing that clinical K. pneumoniae isolates often carry expanded AMR gene repertoires compared with environmental counterparts, likely due to intensive antibiotic selection in healthcare settings [ 69 , 70 ]. This pattern supports the widely described model in which clinical isolates accumulate resistance genes through the acquisition of plasmids and other mobile genetic elements[ 65 ]. However, it should be interpreted with caution, given that resistome size is also influenced by sequence type and plasmid background[ 69 ].​ The detection of ST3157 and a putatively novel sequence type (ST 5ee4) highlights the high genetic diversity of K. pneumoniae in the study area and mirrors international genomic surveillance data that report numerous STs, including many rare or newly described lineages [ 71 , 72 ]. While the identification of a novel ST underscores the dynamic local emergence of previously undocumented lineages, its epidemiological significance and potential to become a high-risk clone will depend on evidence of dissemination, convergence of resistance and virulence, and association with outbreaks, which require ongoing genomic and epidemiological surveillance [ 70 ]. The presence of multiple serotypes and unique combinations of K- and O-loci suggest variations in virulence factors and distinct interactions with hosts, indicating that they are not of the same lineage or origin. Afolayan et al [ 73 ] asserted that performing K- and O-locus typing of K. pneumoniae is essential for public health and clinical management. It aids in identifying clonal lineages, monitoring transmission, comprehending virulence and pathogenesis, and predicting antibiotic resistance profiles. K-loci are responsible for encoding capsular polysaccharide (CPS), which aids the bacteria in evading the immune defences of the host and facilitating infection [ 73 – 76 ]. Various K-loci are associated with varying levels of virulence and may affect the types of infections. Specific antibiotic resistance profiles are influenced by certain K-loci, which in turn affect treatment choices. K-locus typing is crucial for identifying precise capsular antigens that vaccines can selectively target against K. pneumoniae [ 12 , 73 ]. It has also been found to aid in understanding the evolutionary process of K. pneumoniae . It provides valuable insights for public health policies regarding infection control, antibiotic management, and vaccine advancement [ 75 ]. Differences among mobile genetic elements indicate varying capacities for horizontal gene transfer, enabling the rapid dissemination of resistance genes and virulence factors among bacterial populations. Monitoring the dissemination of these components among various ecosystems and host populations is crucial. The resistome, virulome, and mobilome profiles found in the two isolates indicate the potential for the transmission and evolution of bacteria resistant to antibiotics and highly infectious across species. This finding is consistent with the literature, which reports diversity in the mobilome of K. pneumoniae and other Gram-negative bacteria [ 77 – 79 ]. Conclusion The study demonstrates high levels of antibiotic resistance among K. pneumoniae isolates from humans and environmental samples in the study area. At the same time, the detection of carbapenemase genes in human and animal samples suggests that resistance is not confined within hospital boundaries but extends into surrounding ecosystems. The high diversity of mobile genetic elements and the identification of new sequence types indicate a dynamic, evolving resistome that can traverse human, animal, and environmental reservoirs, underscoring the risk of wider dissemination. These findings highlight the urgent need for integrated antimicrobial stewardship that explicitly targets the hospital–livestock interface, including prudent antibiotic use in clinical care and animal husbandry, coordinated infection-prevention practices, and control of clinical and faecal waste. Sustained genomic and epidemiological surveillance within a One Health framework is essential to detect emerging high-risk lineages early and to inform targeted interventions before they become entrenched in human and animal populations. Declarations DATA AVAILABILITY AND WGS DATA-GENERATED INFORMATION . The WGS data generated in this study have been deposited at NCBI under the project accession PRJNA1295602. An additional request can be made to the corresponding author. Acknowledgements We appreciate the assistance provided by the laboratory staff at the Department of Microbiology, Faculty of Life Sciences, and the Department of Pharmaceutical Microbiology and Biotechnology, Faculty of Pharmacy, Federal University, Oye-Ekiti, Ekiti State, Nigeria. Author Contributions ULI-Conceptualisation, investigation, software, formal analysis, write first draft, OBS-Supervision, data validation, Editing, Writing. OTM-Supervision, data validation, Editing, Writing, OOE-Supervision, data validation, Editing, Writing, AFA-investigation, formal analysis, writing, project administration, OB-investigation, formal analysis, writing, project administration, FOS-investigation, formal analysis, writing and editing, AEC-Supervision, data validation, Editing, Writing and OSK-Supervision, data validation, Editing, Writing. Funding Declaration There was no external funding associated with this study. 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Proteome analysis, genetic characterization, and antibiotic resistance patterns of Klebsiella pneumoniae clinical isolates. AMB Express. 2024;14:54. https://doi.org/10.1186/S13568-024-01710-7 . Bayaba S, Founou RC, Tchouangueu FT, Dimani BD, Mafo LD, Nkengkana OA, et al. High prevalence of multidrug resistant and extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae isolated from urinary tract infections in the West region, Cameroon. BMC Infect Dis. 2025;25:115. https://doi.org/10.1186/S12879-025-10483-8 . Aslam B, Chaudhry TH, Arshad MI, Muzammil S, Siddique AB, Yasmeen N, et al. Distribution and genetic diversity of multi-drug-resistant Klebsiella pneumoniae at the human–animal–environment interface in Pakistan. Front Microbiol. 2022;13:898248. https://doi.org/10.3389/FMICB.2022.898248/BIBTEX . WHO, Antimicrobial Resistance. Hypervirulent Klebsiella pneumoniae - Global situation. 2026. https://www.who.int/emergencies/disease-outbreak-news/item/2024-DON527 . Accessed 2 Jan 2026. Hou B, Niu X, Yu Q, Wang W. Epidemiological Trends and Drug Resistance Patterns of Carbapenem-Resistant Gram-Negative Bacteria: A Retrospective Study in a Tertiary Hospital in China (2019–2024). Infect Drug Resist. 2025;18:2867. https://doi.org/10.2147/IDR.S518461 . Göttig S, Gruber TM, Stecher B, Wichelhaus TA, Kempf VAJ. In Vivo Horizontal Gene Transfer of the Carbapenemase OXA-48 During a Nosocomial Outbreak. Clin Infect Dis. 2015;60:1808–15. https://doi.org/10.1093/CID/CIV191 . Boyd SE, Holmes A, Peck R, Livermore DM, Hope W. OXA-48-Like β-Lactamases: Global Epidemiology, Treatment Options, and Development Pipeline. Antimicrob Agents Chemother. 2022. https://doi.org/10.1128/AAC.00216-22 . ;REQUESTEDJOURNAL:JOURNAL:AAC;ISSUE:ISSUE:DOI. 66. Hamprecht A, Sommer J, Willmann M, Brender C, Stelzer Y, Krause FF, et al. Pathogenicity of Clinical OXA-48 Isolates and Impact of the OXA-48 IncL Plasmid on Virulence and Bacterial Fitness. Front Microbiol. 2019;10:483491. https://doi.org/10.3389/FMICB.2019.02509/BIBTEX . Menezes J, Frosini SM, Weese S, Perreten V, Schwarz S, Amaral AJ, et al. Transmission dynamics of ESBL/AmpC and carbapenemase-producing Enterobacterales between companion animals and humans. Front Microbiol. 2024;15:1432240. https://doi.org/10.3389/FMICB.2024.1432240/BIBTEX . Ramírez-Castillo FY, Guerrero-Barrera AL, Avelar-González FJ. An overview of carbapenem-resistant organisms from food-producing animals, seafood, aquaculture, companion animals, and wildlife. Front Vet Sci. 2023;10:1158588. https://doi.org/10.3389/FVETS.2023.1158588/FULL . Cañada-García JE, Moure Z, Sola-Campoy PJ, Delgado-Valverde M, Cano ME, Gijón D, et al. CARB-ES-19 Multicenter Study of Carbapenemase-Producing Klebsiella pneumoniae and Escherichia coli From All Spanish Provinces Reveals Interregional Spread of High-Risk Clones Such as ST307/OXA-48 and ST512/KPC-3. Front Microbiol. 2022;13:918362. https://doi.org/10.3389/FMICB.2022.918362/BIBTEX . Abouelfetouh A, Torky AS, Aboulmagd E. Phenotypic and genotypic characterization of carbapenem-resistant Acinetobacter baumannii isolates from Egypt. Antimicrob Resist Infect Control. 2019;8. https://doi.org/10.1186/s13756-019-0611-6 . Pfeifer Y, Cullik A, Witte W. Resistance to cephalosporins and carbapenems in Gram-negative bacterial pathogens. Int J Med Microbiol. 2010;300:371–9. https://doi.org/10.1016/J.IJMM.2010.04.005 . Karnmueng P, Montakantikul P, Paiboonvong T, Plongla R, Chatsuwan T, Chumnumwat S. Mortality factors and antibiotic options in carbapenem-resistant Enterobacterales bloodstream infections: Insights from a high-prevalence setting with co-occurring NDM-1 and OXA-48. Clin Transl Sci. 2024;17. https://doi.org/10.1111/CTS.13855 . Queenan AM, Bush K, Carbapenemases. The versatile β-lactamases. Clin Microbiol Rev. 2007. https://doi.org/10.1128/CMR.00001-07 . Leal HF, Azevedo J, Silva GEO, Amorim AML, De Roma LRC, Arraes ACP, et al. Bloodstream infections caused by multidrug-resistant gram-negative bacteria: Epidemiological, clinical and microbiological features. BMC Infect Dis. 2019;19:1–11. https://doi.org/10.1186/s12879-019-4265-z . Uzairue LI, Rabaan AA, Adewumi FA, Okolie OJ, Folorunso JB, Bakhrebah MA et al. Global Prevalence of Colistin Resistance in Klebsiella pneumoniae from Bloodstream Infection: A Systematic Review and Meta-Analysis. Pathogens. 2022;:1–13. Kumar V, Sun P, Vamathevan J, Li Y, Ingraham K, Palmer L, et al. Comparative Genomics of Klebsiella pneumoniae Strains with Different Antibiotic Resistance Profiles. Antimicrob Agents Chemother. 2011;55:4267. https://doi.org/10.1128/AAC.00052-11 . Kumar S, Anwer R, Azzi A. Molecular typing methods & resistance mechanisms of MDR Klebsiella pneumoniae. AIMS Microbiol. 2023;9:112. https://doi.org/10.3934/MICROBIOL.2023008 . Thorpe HA, Booton R, Kallonen T, Gibbon MJ, Couto N, Passet V, et al. A large-scale genomic snapshot of Klebsiella spp. isolates in Northern Italy reveals limited transmission between clinical and non-clinical settings. Nat Microbiol 2022 712. 2022;7:2054–67. https://doi.org/10.1038/s41564-022-01263-0 . Rocha J, Henriques I, Gomila M, Manaia CM. Common and distinctive genomic features of Klebsiella pneumoniae thriving in the natural environment or in clinical settings. Sci Rep 2022 121. 2022;12:10441. https://doi.org/10.1038/s41598-022-14547-6 . Hussain A, Mazumder R, Ahmed A, Saima U, Phelan JE, Campino S, et al. Genome dynamics of high-risk resistant and hypervirulent Klebsiella pneumoniae clones in Dhaka, Bangladesh. Front Microbiol. 2023;14:1184196. https://doi.org/10.3389/FMICB.2023.1184196/BIBTEX . Olund-Matos E, Franco-Duarte R, Santa-Cruz A, Nogueira M, Correia-Neves M, Lopes D, et al. Hospital-Based Genomic Surveillance of Klebsiella pneumoniae: Trends in Resistance and Infection. Biology (Basel). 2025;14:1795. https://doi.org/10.3390/BIOLOGY14121795/S1 . Liu Y, Bai J, Kang J, Song Y, Yin D, Wang J, et al. Three Novel Sequence Types Carbapenem-Resistant Klebsiella pneumoniae Strains ST5365, ST5587, ST5647 Isolated from Two Tertiary Teaching General Hospitals in Shanxi Province, in North China: Molecular Characteristics, Resistance and Virulence Factors. Infect Drug Resist. 2022;15:2551. https://doi.org/10.2147/IDR.S366480 . Bonnin RA, Jousset AB, Chiarelli A, Emeraud C, Glaser P, Naas T, et al. Emergence of New Non–Clonal Group 258 High-Risk Clones among Klebsiella pneumoniae Carbapenemase–Producing K. pneumoniae Isolates, France - 26, Number 6—June 2020 - Emerging Infectious Diseases journal - CDC. Emerg Infect Dis. 2020;26:1212–20. https://doi.org/10.3201/EID2606.191517 . Afolayan AO, Oaikhena AO, Aboderin AO, Olabisi OF, Amupitan AA, Abiri OV et al. Clones and Clusters of Antimicrobial-Resistant Klebsiella from Southwestern Nigeria. bioRxiv. 2021;:2021.06.21.449255. https://doi.org/10.1101/2021.06.21.449255 Ajayi AO, Egbebi AO. Antibiotic sucseptibility of Salmonella typhi and Klebsiella pneumoniae from poultry and local birds in Ado-Ekiti, Ekiti-State, Nigeria. Ann Biol Res. 2011. Duru C, Olanipekun G, Odili V, Kocmich N, Rezac A, Ajose TO, et al. Molecular characterization of invasive Enterobacteriaceae from pediatric patients in Central and Northwestern Nigeria. PLoS ONE. 2020;15(10):165. https://doi.org/10.1371/journal.pone.0230037 . Osei Sekyere J, Reta MA. Genomic and Resistance Epidemiology of Gram-Negative Bacteria in Africa: a Systematic Review and Phylogenomic Analyses from a One Health Perspective. mSystems. 2020;5. https://doi.org/10.1128/mSystems.00897-20 . Ludden C, Raven KE, Jamrozy D, Gouliouris T, Blane B, Coll F et al. One health genomic surveillance of escherichia coli demonstrates distinct lineages and mobile genetic elements in isolates from humans versus livestock. MBio. 2019. https://doi.org/10.1128/mBio.02693-18 Harris M, Fasolino T, Ivankovic D, Davis NJ, Brownlee N. Genetic Factors That Contribute to Antibiotic Resisactance through Intrinsic and Acquired Bacterial Genes in Urinary Tract Infections. Microorg 2023, Vol 11, Page 1407. 2023;11:1407. https://doi.org/10.3390/MICROORGANISMS11061407 Van Hoek AHAM, Mevius D, Guerra B, Mullany P, Roberts AP, Aarts HJM. Acquired antibiotic resistance genes: An overview. Front Microbiol. 2011. https://doi.org/10.3389/fmicb.2011.00203 . Additional Declarations No competing interests reported. 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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-9059978","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605200185,"identity":"6c528d47-5613-411a-8312-1d8eb1f55a63","order_by":0,"name":"Leonard Ighodalo Uzairue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYJCCAyCCD8JmlgOLPCBGCxtUizFYJIEYq2BaEhtAFD4tBucPPzxc2WbDwMbefOzBzz3W6fPDDj8E2mInp9uAQ8uBYwYHz7alMbDxHEs37HmWnrvxdpoBUEuysdkBHFoONhgcbGw7zMAmkWMmwXPgcO7G2QkgLQcSt+HScpj9A1DLfwY2+Tdmkn8OHE43nJ3+Ab+WYzwgWw4AbeExkwbakiAvnYPfFskzPAUHG84l87DxpKVJyxxIN9wgnVNwIMEAt1/4zh/f/LGhzE6On/3wMck3B6zl5Wenb/7wocJODpcWGOBBOBWs0gC/clQg30CK6lEwCkbBKBgJAADLXGFP8kg8eAAAAABJRU5ErkJggg==","orcid":"","institution":"OLGNova","correspondingAuthor":true,"prefix":"","firstName":"Leonard","middleName":"Ighodalo","lastName":"Uzairue","suffix":""},{"id":605200186,"identity":"921d6383-4219-416f-89e5-b8ba2b9f1e71","order_by":1,"name":"Olufunke Bolatito Shittu","email":"","orcid":"","institution":"Federal University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Olufunke","middleName":"Bolatito","lastName":"Shittu","suffix":""},{"id":605200187,"identity":"cf555e34-131f-4d77-bef4-4fc2361b1495","order_by":2,"name":"Tolulope M. Obuotor","email":"","orcid":"","institution":"Federal University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Tolulope","middleName":"M.","lastName":"Obuotor","suffix":""},{"id":605200188,"identity":"9dc63be5-07c9-4c92-804c-ced70242e46a","order_by":3,"name":"Olufemi Ernest Ojo","email":"","orcid":"","institution":"Federal University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Olufemi","middleName":"Ernest","lastName":"Ojo","suffix":""},{"id":605200189,"identity":"23260285-78ab-4955-a9ab-57b7dcf083ba","order_by":4,"name":"Funmilayo Ajoke Adewumi","email":"","orcid":"","institution":"Ekiti State University","correspondingAuthor":false,"prefix":"","firstName":"Funmilayo","middleName":"Ajoke","lastName":"Adewumi","suffix":""},{"id":605200190,"identity":"e6de1822-469e-4276-bf79-6198e37e9342","order_by":5,"name":"Faith Osamoka-Samuel","email":"","orcid":"","institution":"Federal University Oye Ekiti","correspondingAuthor":false,"prefix":"","firstName":"Faith","middleName":"","lastName":"Osamoka-Samuel","suffix":""},{"id":605200191,"identity":"96f5692b-c7ee-4adf-940a-168a2165f590","order_by":6,"name":"Bola Oluwatosin Ojo","email":"","orcid":"","institution":"Federal Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bola","middleName":"Oluwatosin","lastName":"Ojo","suffix":""},{"id":605200195,"identity":"483962c0-62f6-49d0-a842-b9b8dd82a328","order_by":7,"name":"Emmanuel C. Adukwu","email":"","orcid":"","institution":"University of the West of England","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"C.","lastName":"Adukwu","suffix":""},{"id":605200196,"identity":"ae095e9b-a3ae-4d7c-9adc-9c0a673c3779","order_by":8,"name":"Stephen K. Obaro","email":"","orcid":"","institution":"International Foundation Against Infectious Disease in Nigeria (IFAIN)","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"K.","lastName":"Obaro","suffix":""}],"badges":[],"createdAt":"2026-03-07 17:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9059978/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9059978/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104705577,"identity":"14de0b32-1a91-4c7c-bf72-1925540c6574","added_by":"auto","created_at":"2026-03-16 09:18:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":255969,"visible":true,"origin":"","legend":"\u003cp\u003eMap Diagram of Nigeria, highlighting Ekiti and Ondo States Google Map, 2019).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9059978/v1/651a7568c7208d9e99083cb4.png"},{"id":104705581,"identity":"5c029caa-e626-4f6a-9fa7-0cc79e621481","added_by":"auto","created_at":"2026-03-16 09:18:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147663,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of multidrug resistance and carbapenemase phenotypes among isolates. (a) Pie chart showing the overall proportion of multidrug-resistant (MDR) versus non-MDR isolates. (b) Bar chart depicting percentages of MDR and non-MDR \u003cem\u003eK. pneumoniae\u003c/em\u003eisolates stratified by human, animal, and environmental sample sources. (c) Pie chart illustrating the proportion of phenotypically carbapenemase-positive and carbapenemase-negative isolates among the MDR-\u003cem\u003eK. pneumoniae\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9059978/v1/e464c6ab51b86be3dc5661bc.png"},{"id":104705580,"identity":"aeb28d97-404e-4ff6-add1-546158c9839f","added_by":"auto","created_at":"2026-03-16 09:18:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":585298,"visible":true,"origin":"","legend":"\u003cp\u003eComparative genome analysis of \u003cem\u003eK. pneumoniae \u003c/em\u003e\u0026nbsp;NGEK23 and NGEK25. Heatmaps show the distribution of insertion sequences and integron cassettes across individual genomes a=NGEK23 and b=NGEK25. Genomic island and chromosomal cassette length distributions are presented for (c) intact and (d) truncated elements. (e) Stacked bar plots compare the proportional abundance of mobile element categories between carbapenem-resistant and susceptible isolates. (f) The bar plot shows the percentage contribution of each insertion sequence family to the total mobile element content.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9059978/v1/66e16c571b124ba229f51af1.png"},{"id":104705578,"identity":"f223acb2-3490-483c-9527-1c5e20a39753","added_by":"auto","created_at":"2026-03-16 09:18:58","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20587,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic Analysis of the \u003cem\u003eTwo K. pneumoniae \u003c/em\u003ewith reference genomes\u003cem\u003e: \u003c/em\u003ePhylogenetic tree illustrating the genetic relationships between the two \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates (NGEK23 and NGEK25) and selected reference genomes.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9059978/v1/fb7778a475b6e66c5e468c08.jpeg"},{"id":104783124,"identity":"68135528-6016-4b05-acf2-c27b1207824c","added_by":"auto","created_at":"2026-03-17 07:58:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100036,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of antimicrobial resistance gene profiles using the CARD pipeline for \u003cem\u003e\u003cstrong\u003eKlebsiella pneumoniae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e isolates\u0026nbsp;NGEK23 (human blood) and NGEK25 (environmental water\u003c/strong\u003e).\u0026nbsp;\u003cem\u003e\u003cstrong\u003epneumoniae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;isolates\u0026nbsp;NGEK23 (human blood) and NGEK25 (environmental water\u003c/strong\u003e.\u0026nbsp;\u0026nbsp;Venn diagram illustrating the distribution and overlap of antimicrobial resistance (AMR) gene profiles identified in \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e isolates NGEK23 (recovered from human blood) and NGEK25 (recovered from environmental water) using the CARD pipeline. Unique and shared AMR genes between the two isolates are depicted, highlighting both isolate-specific resistance determinants and common elements, thereby providing insight into the genetic basis of resistance in clinical versus environmental sources. The Venn diagram was created from http://www.bioinformatics.com.cn./\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9059978/v1/06baf22b984feaf5a9a9fc66.png"},{"id":104808582,"identity":"8b340cc5-369f-4e60-84c7-e682ee80f854","added_by":"auto","created_at":"2026-03-17 12:38:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2674305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9059978/v1/57357c85-540e-4397-afd8-f7bd0a90e839.pdf"},{"id":104782729,"identity":"6a7e8287-9d02-43de-a760-e9f5ce2ff9c7","added_by":"auto","created_at":"2026-03-17 07:57:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1375336,"visible":true,"origin":"","legend":"","description":"","filename":"SuppementaryFiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-9059978/v1/b54dca308c246e0048aef174.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular Characterisation of Carbapenemase-Producing Multi-Drug Resistant Klebsiella pneumoniae from Human, Animal, and Environmental Samples from Ekiti and Ondo State, Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntimicrobial resistance (AMR) is a significant public health challenge [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], of concern is the rise in multidrug-resistant bacteria, particularly Carbapenem-Resistant Bacteria (CRB) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Carbapenem-resistant Enterobacterales (CRE) and other carbapenem-resistant Gram-negatives are now reported on every continent, with CRE accounting for a substantial share of the global AMR burden that contributed to an estimated 1.27\u0026nbsp;million deaths directly attributable to AMR and 4.95\u0026nbsp;million associated deaths in 2019 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Numerous systematic reviews, meta-analyses, and surveillance studies conducted across regions have reported varying estimates of the prevalence of carbapenem-resistant \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e. For instance, pooled data from parts of West Africa indicate a notably high burden of carbapenem-resistant strains \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e with dominant \u003cem\u003eblaNDM\u003c/em\u003e, \u003cem\u003eblaVIM\u003c/em\u003e and \u003cem\u003eblaOXA-48\u003c/em\u003e carbapenemases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; in Latin American paediatric cohorts, carbapenem-resistant K. pneumoniae infections have reported mortality as high as 52.6% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; and in US surveillance, carbapenemase-producing CRE clinical cultures rose by about 18% between 2019 and 2023, with a roughly fivefold increase in NDM-producing CRE [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hospital-based series from Asia and Africa often report carbapenem resistance rates in Enterobacterales and other major Gram-negatives in the 20\u0026ndash;50% range, while colonisation surveys in African hospitals have found CRE carriage prevalences around 12%, indicating large unrecognised reservoirs that fuel ongoing transmission [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. There has been a global increase in the reporting of carbapenem-resistant bacteria. However, studies exploring the transmission dynamics of carbapenem resistance genotypes across one-health are limited and significantly low, especially in Africa [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The global rise in human infections caused by multi-drug resistant (MDR) Enterobacteriaceae, including \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, seriously threatens public health [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe emergence and spread of carbapenem resistance in clinical settings leads to high healthcare costs, long hospital stays, and an increase in mortalities associated with such pathogen [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, carbapenem resistance can be harboured on both the chromosome and the plasmid, but most often, plasmid-encoded β-lactamases usually enhance their transmissible activities, leading to the spread of these genes among Enterobacteriaceae, contributing to the rapid dissemination of carbapenem resistance traits to other Enterobacteriaceae genera and species [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence from the literature shows that carbapenem resistance in Enterobacteriaceae is common, with the highest prevalence reported in \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In developed countries, surveillance systems are in place to track the spread and transmission dynamics across the human, animal, and environmental sectors. However, surveillance systems in developing countries are limited and not fully optimised, particularly for tracking the spread of infections within health systems. Limited evidence on CR \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e surveillance in developing countries is usually focused solely on human health, without data from animal and environmental isolates.\u003c/p\u003e \u003cp\u003eIn particular, there is limited epidemiological data on the genes responsible for carbapenem resistance in \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, with most studies focusing on antibiotic susceptibility testing with imipenem and meropenem. Equally, the mechanisms underlying the spread of carbapenem-resistant bacterial infections remain incompletely elucidated in the Nigerian population. Studies exploring the transmission dynamics of carbapenem-resistant bacteria among humans, animals, and the environment in Nigeria are limited [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In Nigeria, infection prevention and control (IPC) strategies are not holistic, as they are most often focused on healthcare settings, with no active IPC and biosecurity programmes in the animal, agriculture, and environmental sectors. The problem of quality surveillance data is further worsened in developing countries due to poor access to laboratory facilities for accurate identification and molecular characterisation of MDR pathogens, thereby enhancing clinical diagnosis and the detection of transmissible resistance determinants [\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, surveillance studies of carbapenem-resistant bacteria, particularly in \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, in humans, animals, and their environment are needed in these settings, as no such study has been conducted to our knowledge. A quick search in two databases (PubMed and Africa Online Journals (AJOLs)) using the keywords (carbapenem, Klebsiella pneumoniae, One-Health, Ondo, and Ekiti) in the study area did not return any studies that used a One-Health approach to explore the prevalence of carbapenem-resistant \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e. Studies of this nature are necessary to understand the transmission dynamics of resistant bacteria and to inform public health strategies and interventions. There are data gaps in the molecular epidemiology of MDR pathogens and ARGs across sectors, including human, animal, and environmental samples, in developing countries and, in particular, in study areas [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study bridged this gap by providing insights into the prevalence of multidrug-resistant K. \u003cem\u003epneumoniae\u003c/em\u003e through a one-health approach. In addition, identified carbapenemase-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e, detected carbapenemase genes, and conducted whole-genome sequencing on two isolates with similar antimicrobial profiles from the same household in the study settings.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and study time frame\u003c/h3\u003e\n\u003cp\u003eThe study employed a cross-sectional design and was conducted over 18 months (April 2022\u0026ndash;September 2023) in Ekiti and Ondo States, Nigeria.\u003c/p\u003e\n\u003ch3\u003eSample collection points\u003c/h3\u003e\n\u003cp\u003e The study was carried out at the Federal Medical Centre, Owo, Federal Teaching Hospitals, Ido-Ekiti, The Ekiti State University Teaching Hospital, Ado-Ekiti, Hospital Management Board Hospitals in Ikere-Ekiti, Ikole-Ekiti and Oye-Ekiti, while Mother and Children Hospital, General Hospital Igarra Oke, Ondo State, animal farms in Akure and Ado-Ekiti respectively, Slaughterhouses in Owo, Akure, Ido-Ekiti and Ado-Ekiti and the home and animals of agreed hospitalised participants.\u003c/p\u003e\n\u003ch3\u003eSample Size\u003c/h3\u003e\n\u003cp\u003eThe study analysed a total of 1,329 samples, including 399 clinical human samples, 404 non-clinical human samples, 393 animal samples, and 133 environmental samples. Specifically, environmental samples totalled 91 from Ekiti and 42 from Ondo; animal samples, 253 from Ekiti and 140 from Ondo; clinical samples, 263 from Ekiti and 136 from Ondo; and samples from animal workers, 251 in Ekiti and 153 in Ondo (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSample collections\u003c/h3\u003e\n\u003cp\u003eSamples were collected from three cohorts (Hospitals, farms, and slaughterhouses). Human, animal, and environmental samples were collected from farms, households, and hospital-admitted patients. The residents of each household/farm, their domestic animals, and the water source (well or borehole) used for drinking and washing purposes were sampled. The distribution of the samples collected from each sample cohort in each state is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the hospital cohort, clinical samples (blood, urine, stool and rectal swabs) were collected from inpatients after obtaining consent, and the procedures were explained to patients or their guardians who were under the age of consent or vulnerable. For those who agreed, further samples were collected from participants' homes and, for those who own animals, from their animals. The samples collected were water samples, rectal swabs from their animals, tables, and bench swabs from their residents. In the farm cohort, samples were collected from animal farms and from their handlers, with consent. Also, water samples and swabbing of tables, benches, and pegs from the farms were collected. For the slaughter cohort, rectal swabs were collected from animals to be killed and from butchers after consent was obtained from them. Water from their water sources was collected, and benches and tables used for meat processing were also swabbed. Each participant was randomly assigned a code to maintain confidentiality. All the samples were processed for \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates.\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\u003eSample collected from the three sampling cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSample Collection Settings and Sample Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEkiti\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOndo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eClinical Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNon-Clinical Sample (Animal Worker) (Rectal Swabs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAnimals (Rectal Swabs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePoultry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDogs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTable, animal peg, Bench Swab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\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=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBacteriology of the Isolates\u003c/h2\u003e \u003cp\u003eThe study involved processing human blood and urine samples. The blood samples were collected and inoculated aseptically into the appropriate blood culture bottles, then incubated at 37\u0026deg;C for up to 5 days. Those that gave a growth signal were cultured on solid media (blood agar, MacConkey agar, and chocolate agar) (Oxoid Ltd, Basingstoke, Hampshire, England). Urine samples were inoculated onto cystine-lactose-electrolyte-deficient (CLED) agar (Oxoid Ltd, Basingstoke, Hampshire, England) and incubated at 37\u0026deg;C. The rectal and environmental swabbed samples were enriched in buffered peptone water for about 3 hours, then plated on MacConkey lactose agar (Oxoid Ltd, Basingstoke, Hampshire, England) and incubated at 37\u0026deg;C for 24 hours. Water samples were processed using membrane filtration to isolate and identify \u003cem\u003eK. pneumoniae\u003c/em\u003e. Individual sterile filter discs with 0.45 \u0026micro;m pores were used in a filtration apparatus to filter 100 mL of the water sample. The filter membranes were then placed on MacConkey lactose agar plates and incubated at 37\u0026deg;C for 24 hours. In all the sample cohorts, colonies resembling \u003cem\u003eKlebsiella\u003c/em\u003e species were further processed. Gram reactions were carried out on the bacterial isolates according to the Distinct Laboratory Practices in Tropical Countries [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The glass slide was smeared with the test isolate, air-dried, heat-fixed, flooded with crystal violet (the primary stain) for 60 seconds, and rinsed with distilled water. It was mordanted with Lugol\u0026rsquo;s iodine for 30 seconds, rinsed again, decolourised with acetone, counterstained with safranin for 60 seconds, rinsed once more, and air-dried. A drop of immersion oil was added to the stained slide, and the slide was examined under an oil-immersion (X100) objective lens on a microscope. Bacterial isolates were identified using morphology, Gram staining and biochemical tests using API 20E (BioM\u0026eacute;rieux SA, Lyon, France). The API 20E system (BioM\u0026eacute;rieux SA, Lyon, France), a plastic strip with 20 microtubes for various biochemical and substrate utilisation tests, was used to characterise and confirm \u003cem\u003eK. pneumoniae\u003c/em\u003e. Bacterial isolates were stored in 15% glycerol peptone water at \u0026minus;\u0026thinsp;30\u0026deg;C for further study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAntibiotic Susceptibility Testing\u003c/h3\u003e\n\u003cp\u003eAntibiotic susceptibility testing was performed using the provided antibiotics according to CLSI guideline [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These antibiotics were obtained from Oxoid Ltd, Basingstoke, Hampshire, England. The antibiotic used include tetracycline (TET, 30 \u0026micro;g), ceftazidime (CFZ, 30 \u0026micro;g), cefuroxime (CFX, 30 \u0026micro;g), cefotaxime (CTX, 30 \u0026micro;g), ceftriaxone (CFT, 30 \u0026micro;g), meropenem (MER, 10 \u0026micro;g), trimethoprim-sulfamethoxazole (TRM-SXT, 1.25/23.75 \u0026micro;g), chloramphenicol (CHL, 30 \u0026micro;g), ciprofloxacin (CIP, 5 \u0026micro;g), amoxicillin-clavulanate (AMC-CLA, 30 \u0026micro;g), colistin (COL, 25 \u0026micro;g), gentamicin (10 \u0026micro;g), amikacin (AMK, 30 \u0026micro;g), cephalothin (CEP, 30 \u0026micro;g), ceftazidime-clavulanate (CFZ-CLA, 30/10 \u0026micro;g), and cefotaxime-clavulanate (CTX-CLA, 30/10 \u0026micro;g). The control strain used for comparison was \u003cem\u003eE. coli\u003c/em\u003e ATCC 25922. The breakpoint was interpreted by the CLSI (2021) guideline (M100, 31st edition)\u003c/p\u003e\n\u003ch3\u003eCarbapenemase Phenotypic Detection\u003c/h3\u003e\n\u003cp\u003eCarbapenemase production was assessed using a Carba NP/CarbAcineto NP-based assay as previously described by Shinde et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Briefly, two solutions (A and B) were prepared. Solution A consisted of phenol red (0.05%) and ZnSO₄\u0026middot;7H₂O (0.1 mmol/L) in distilled water, adjusted to pH 7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 and stored at 4\u0026deg;C, while solution B was prepared by adding imipenem\u0026ndash;cilastatin injectable to Solution A to achieve an imipenem concentration of 6 mg/mL. For each isolate, 2\u0026ndash;3 loops of overnight growth were suspended in NaCl and aliquoted into two tubes: tube A (Solution A, no imipenem) and tube B (Solution B, with imipenem) and incubated for 2 hours at 35\u0026deg;C, carbapenemase production was defined as a colour change from red to yellow/orange in tube B, while tube A remained red, in line with acceptable Carba NP interpretive criteria provided by Shinde et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction\u003c/h2\u003e \u003cp\u003eDNA extraction was performed using the QIAamp DNA Mini Kit (Qiagen, Venlo, Netherlands) for genomic DNA. The modified protocol for Gram-negative bacteria DNA extraction, as provided by Yang [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. was used. The quality of the extracted DNA was assessed using a NanoDrop (Thermo Fisher Scientific, Waltham, Massachusetts, USA) as described by Yang [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The eluted DNA was pipetted into storage tubes and stored in a freezer at -30 degrees Celsius until PCR amplification.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAmplification of Carbapenem Antimicrobial Resistance Gene MDR\u003c/b\u003e \u003cb\u003eK. pneumoniae\u003c/b\u003e \u003cb\u003eIsolates with Positive Carbapenem Phenotypic Result\u003c/b\u003e\u003c/p\u003e \u003cp\u003eReal-time PCR (RT-PCR) was used to detect resistance genes in K. pneumoniae using specific primers and probes. Specific primers and probes targeting these genes (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were incorporated into RT-PCRs to potentially identify resistance genes (OXA-48, NDM-1, VIM, and KPC). Primers and probes for RT-PCR were obtained from Biosearch LGC (Novato, California, USA). The primer and probe sequences (\u003cb\u003esupplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/b\u003e). The reaction mixture utilised PerfeCTa MultiPlex qPCR ToughMix(Quanta Biosciences, Gaithersburg, MD, USA). Amplification was performed using an AriaMx RT-PCR cycler (Agilent Inc., Santa Clara, California, USA). The cycle thresholds were determined from fluorescence signals obtained from amplification plots in the AriaMx system software version 3.1 after the completion of the amplification experiment, following the protocol for real-time PCR, as utilised by the authors in another study [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWhole-Genome Sequencing\u003c/h2\u003e \u003cp\u003eThe DNA extractions and library preparations for whole-genome sequencing (WGS) were performed as previously described by Cookson et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Briefly, the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) was used for DNA extraction, and libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA). WGS was undertaken by Novogene Limited (Helios, Singapore) using the Illumina HiSeq paired-end v4 platform (2 by 150 bp). The Nullarbor pipeline was used, including adapter-trimmed read processing and examination of WGS reads for de novo genome assembly using SKESA (v.2.2.1), annotation using Prokka (v.1.13.3), and phylogenetic analysis using Snippy (v.4.2.1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBioinformatic analysis of WGS data for the antimicrobial Resistance determinants of\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003eisolates\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe identification and categorisation of genes that confer resistance were carried out using ResFinder 4.1, a bioinformatics tool developed by the Centre for Genomics Epidemiology (CGE) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genomicepidemiology.org/\u003c/span\u003e\u003cspan address=\"http://www.genomicepidemiology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 2024/03/2). Each isolate's genes were compared to an annotated resistance gene using a threshold of 95\u0026ndash;100% identity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The plasmid replicon types of each \u003cem\u003eE. coli\u003c/em\u003e isolate were identified using PlasmidFinder 2.1. As earlier documented by Joensen et al [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], the strain's O and H serotypes were determined by analysing the generated FASTA files using the Centre for Genomic Epidemiology (CGE) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genomicepidemiology.org/\u003c/span\u003e\u003cspan address=\"http://www.genomicepidemiology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed on 2024/03/2). Additionally, the CARD pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://card.mcmaster.ca/analyze/rgi/\u003c/span\u003e\u003cspan address=\"https://card.mcmaster.ca/analyze/rgi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed on 2024/06/04) was used to determine the AROs from the sequence data.\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\u003eBasis for Performing Whole Genome Sequencing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAMPLE ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsolates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResistance pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGEK23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman (Blood)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAMC, CIP, CTX, CTM, CRO and TET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWGS DONE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGEK25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironment (Water) (home of GNEK 23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAMC, CIP, CTX, CTM, CRO and TET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWGS DONE\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData were entered and validated in Excel 2020 version. Frequencies and descriptive statistics were carried out on all data. Differences in proportions of virulence and resistance genes among humans, animals, and environmental samples were evaluated using two-sample t-tests, ANOVA, and chi-square tests, which were appropriate. Continuous variables were analysed using ANOVA, while categorical variables were analysed using chi-square and Fisher's exact test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was accepted as statistically significant, while p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 was accepted as statistically insignificant in all cases. Statistical software for Social Science (SPSS) (IBM, version 27) was used for the analysis. Results were presented in Tables and Figures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e Ethical Clearance from the National Health Research Ethics Committee of Nigeria (NHREC) (NHREC/01/01/2007-21/03/2023), the Federal Teaching Hospital, Ido-Ekiti (ERC/2023/09/20/1034B) and the Ondo State Ministry of Agriculture (MNR/V.384/64) were obtained for this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003ePrevalence of\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eK. pneumoniae\u003c/b\u003e \u003cb\u003ein the study\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe overall prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e in the study was 7.9% (105/1329). Animal samples had the highest prevalence at 9.4% (38/404), followed by human samples at 7.3% (59/804), and environmental samples at 6.0% (8/121). Among clinical human samples, the prevalence was 8.8% (35/399), compared with 5.7% (24/408) in non-clinical samples. Among animal samples, cows had the highest rate at 13.2% (18/136), followed by poultry at 8.9% (16/180) and dogs at 3.8% (3/80). Environmental isolates were most commonly found in swab samples from surfaces in slaughterhouses, poultry, and patients' homes, 9.8% (6/61), whereas water samples showed a lower prevalence, 3.3% (2/60) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAntimicrobial Susceptibility Pattern of the Isolated\u003c/b\u003e \u003cb\u003eK. pneumoniae from Human, Animal and the Environment\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe resistance profile of the \u003cem\u003eK. pneumoniae\u003c/em\u003e from this study is shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The resistance profile of the 59 \u003cem\u003eK. pneumoniae\u003c/em\u003e from humans shows highest resistance to amoxicillin-clavulanate 49 (83.1%), the resistance to other antibiotics were as follows: 32 (54.2%), 20 (33.9%), 35 (59.3%), 47 (79.7%), 36 (61.0%), 38 (64.4), 46 (77.9%), 35 (59.3%), 30 (50.8%) and 7 (11.9%) to trimethoprim-sulfamethoxazole, gentamicin, chloramphenicol, cefuroxime, ceftriaxone, cefotaxime, ceftazidime, tetracycline, ciprofloxacin and meropenem, with one isolate of \u003cem\u003eK. pneumoniae\u003c/em\u003e from humans having intermediate susceptibility to colistin. All 59 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates were susceptible to amikacin (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The resistance profile of the 38 \u003cem\u003eK. pneumoniae\u003c/em\u003e from animal samples showed highest resistance to cefuroxime 29 (76.3%), while resistance to other antibiotic were as follows; 13 (34.2%), 5 (13.2%), 5(13.2%), 6 (15.8%), 12 (31.6%), 8 (21.1%), 23 (60.5%), 14 (36.8%), 4(10.5%) and 1 (2.6%) resistant to amoxicillin-clavulanate, trimethoprim-sulfamethoxazole, gentamicin, chloramphenicol,, ceftriaxone, cefotaxime, ceftazidime, tetracycline, ciprofloxacin and meropenem. Of the 8 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates from the environment, 2 (12.5%), 3 (37.5%), 1 (12.5%), 2 (25.0%) and 1 (12.5%) were resistant to amoxicillin-clavulanate, cefuroxime, ceftriaxone, ceftazidime and tetracycline, respectively.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMDR and Carbapenemase and Gene Detection\u003c/h2\u003e \u003cp\u003eThe overall occurrence of MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e in the study was 65 (61.9%), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. The occurrence of MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e in the human samples was 94.9% among the 59 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates. The occurrence of MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e in the animal and environmental samples was \u003cem\u003e23.7% and 12.5%\u003c/em\u003e, respectively. The occurrence of MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e was significantly higher in the \u003cem\u003eK. pneumoniae\u003c/em\u003e from human samples compared to those from animals and the environment, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. The phenotypic detection of carbapenemase among the 65 MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e was 9.2%, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec. Of the 6 MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e that were carbapenemase-producing, 5 were from human samples and 1 from animal samples; none were from environmental samples. Of the 5 MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e that were carbapenemase-producing, 3 (60.0%) harboured the 0XA-48 gene, 1 (20.0%) had the VIM genes, and 2 (40.0%) had the \u003cem\u003eKPC\u003c/em\u003e gene, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Only one of the three genes (\u003cem\u003eOXA-48\u003c/em\u003e, \u003cem\u003eVIM\u003c/em\u003e, and \u003cem\u003eKPC\u003c/em\u003e) was detected in a single MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e carbapenemase producer from the animal sample.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene Mapping and Phylogenetic Analysis of\u003c/b\u003e \u003cb\u003eK. pneumoniae\u003c/b\u003e \u003cb\u003eisolates\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhole-genome sequencing was carried out on two MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates from human blood culture and from water samples collected from the patients' homes. The two samples exhibited phenotypic similarities in resistance patterns, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-f shows the genome maps for the two \u003cem\u003eK. pneumoniae\u003c/em\u003e strains (NGEK23 and NGEK25). The gene mapping of the two sequenced \u003cem\u003eK. pneumoniae\u003c/em\u003e strains shows different gene intensities across various sections of the genome, as shown in the figure. The gene mapping of the two genes revealed their gene makeup. Looking at the first one, you can see that the intensity was higher in certain regions than in the second genome. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the coding sequences (CDS) and genome indel length distribution mapping of the two genes. The indel length distribution CDS in the NGEK25 was indicated to peak at 3, 12, 15, 18, and 21 base pairs, as against the indel length distribution CDS in NGEK23 at 8, 9, and 12 base pairs.\u003c/p\u003e \u003cp\u003eThe structure variation (SV) length in base pairs found in the sequenced \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 and 25 showed that NGEK23 had a higher SV\u0026thinsp;\u0026gt;\u0026thinsp;1200 base pairs than that of NGEK25 using the reference genome. However, SNPs with base pairs between 100 and 300 were more common in the \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK25 than in the \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 isolates. The SNV type showed that the deletion, ITX, and insertion were higher in the isolate \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK25 than in the isolate NGEK23. However, SNV inversion was higher in the isolate \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 than in the isolate NGEK25, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec.\u003c/p\u003e \u003cp\u003eThe phylogenetic analysis showed the evolutionary relationship between the two isolates and other \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates available in the gene bank, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The nearest four isolates, the whole genome isolate that came up, were \u003cem\u003eK. pneumoniae\u003c/em\u003e strain SH209, \u003cem\u003eK. pneumoniae\u003c/em\u003e strain KP8701, \u003cem\u003eK. pneumoniae\u003c/em\u003e sub-species \u003cem\u003eK. pneumoniae\u003c/em\u003e HS11286, and \u003cem\u003eK. pneumoniae\u003c/em\u003e strain KP294. The phylogenetic analysis results indicate that the two isolates (\u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 and NGEK25) are not of the same origin. Isolates NGEK23 were more related to \u003cem\u003eK. pneumoniae\u003c/em\u003e strain NH209 and \u003cem\u003eK. pneumoniae\u003c/em\u003e strain KP8701.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eResistance Genes, MLST, and Mobile Genetic Element\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarises the bioinformatic analysis of the two sequence isolates (\u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 and NGEK25). From the Insilco analysis, the resistance genes found in isolate NGEK23 were aph (6)id, aph(3)ib, OqxB, blaSHV-30, blaSHV-189, blaSHV-14, sul2, fosA5, tetA, blaTEM-1B, OqxA, qnrS1, dfrA14, while blaSHV-11, blaSHV-110, sul2, fosA6, catA2, OmpK36, pmrB, catll2 genes were found in the isolate. The multilocus sequence (MLST) analysis of the two isolates shows that isolate NGEK23 is of serotype (ST)3157, while isolate NGEK23 is of a new strain, ST 5ee4. The isolates NGEK23 were KL30 K-locus and 01/02 vs2 O-locus, whereas isolates NGEK25 were KL12 and OL103 O-locus. Three (IncFIB(K_1), IncY_1 and IncFII(K)_1) mobile genetic elements were found in \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23, while two (IncFIB(K)_1 and col(phAD28_1) mobile genetic elements were found in the isolate. Based on the results, the two isolates were from different origins, as they had distinct resistomes, virulomes, and mobilomes. The resistant genes found in \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 were more diverse than those in NGEK25. The antimicrobial resistance ontology (ARO) data from the WGS analysis indicate that K. pneumoniae NGEK23 has 32 ARO outcomes, whereas K. pneumoniae NGEK25 has 30, with 25 similar outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\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\u003eOccurrence of \u003cem\u003eK. pneumoniae\u003c/em\u003e in the Study\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=\"left\" 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\u003eSample Sources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Growth\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (7.3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e744 (92.7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e803 (60.4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364 (91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399 (49.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Clinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380 (94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404 (50.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnimal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e38 (9.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e355 (90.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e393 (29.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (34.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (19.8)\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163(91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (50.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8 (6.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e125(94.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e133 (10.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTable Swab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (45.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (7.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1223 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1329 (100.0)\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\u003eDistribution of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e isolates and samples with no bacterial growth across human, animal, and environmental sources. Data are presented as numbers (percentages) of isolates or samples within each category, with clinical and non-clinical subgroups detailed for human samples, and specific sources indicated for animal and environmental samples.\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\u003eAntibiotic Susceptibility Pattern of Isolated \u003cem\u003eK. pneumoniae\u003c/em\u003e from Humans, Animals and their Environment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntibiotics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;59\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAnimal\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;38\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eEnvironmental Sample\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;8\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitive\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmediate\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResistant\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitive\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImmediate\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResistant\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSensitive\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eImmediate e\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResistant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmoxicillin-clavulanate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrimethoprim-Sulfamethoxazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (81.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGentamicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloramphenicol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefuroxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (79.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftriaxone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefotaxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6 (75 .0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftazidime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTetracycline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmikacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCiprofloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColistin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAntimicrobial susceptibility profiles of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e isolates from human, animal, and environmental samples. The table presents the number and percentage of isolates classified as sensitive, intermediate, or resistant to each antibiotic tested, across all three sources. Human (n\u0026thinsp;=\u0026thinsp;59), animal (n\u0026thinsp;=\u0026thinsp;38), and environmental (n\u0026thinsp;=\u0026thinsp;8) data are shown. Results are expressed as n (%).\u003c/p\u003e \u003cp\u003e \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\u003eDetection of Carbapenem Genes from Carbapenemase-Producing MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMDR K. pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnimal\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnvironment n\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOXA-48\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVIM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKPC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (33.3)\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\u003eDistribution of carbapenemase genes (\u003cem\u003eOXA-48\u003c/em\u003e, \u003cem\u003eVIM\u003c/em\u003e, \u003cem\u003eKPC\u003c/em\u003e) among multidrug-resistant (\u003cem\u003eMDR\u003c/em\u003e) \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e isolates sourced from humans (n\u0026thinsp;=\u0026thinsp;5), animals (n\u0026thinsp;=\u0026thinsp;1), and the environment (n\u0026thinsp;=\u0026thinsp;0). Values are presented as counts with percentages in parentheses, representing the proportion of positive isolates within each source category and overall.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\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\u003eSummary of the Bioinformatic results for \u003cem\u003eK. pneumoniae\u003c/em\u003e 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\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNGEK23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNGEK25\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\u003eResistance genes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaph (6)id, aph(3)ib, OqxB, blaSHV-30, blaSHV-189, blaSHV-14, sul2, fosA5, tetA, blaTEM-1B, OqxA, qnrS1, dfrA14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eblaSHV-11, blaSHV-110, sul2, fosA6, catA2, OmpK36, pmrB, catll2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClonal -group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST 3157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew (5ee4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eK-locus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKL30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKL12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eO locus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01/02vs2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOL103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasmid Inc types\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncFIB(K)_1, IncY_1 and IncFII (K)_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncFIB(K)_1 and col(phAD28)_1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eThe table compares the genotypic and plasmid profiles of multidrug-resistant\u003c/b\u003e \u003cb\u003eKlebsiella pneumoniae\u003c/b\u003e \u003cb\u003eisolates NGEK23 (human blood) and NGEK25 (environmental water).\u003c/b\u003e This table shows the key molecular features of two \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates, highlighting differences in resistance genes, sequence types, capsular and O-antigen loci, and plasmid types. The findings illustrate the genetic diversity and varied resistance profiles between clinical and environmental strains, emphasising the importance of integrated surveillance. Note: these results are based on the analysis from the pipeline of \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genomicepidemiology.org/\u003c/span\u003e\u003cspan address=\"http://www.genomicepidemiology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to isolate and characterise MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e from humans, animals, and their environment in the study area using a one-health approach. Understanding the transmission of MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e through One Health surveillance across human, animal, and environmental sources is vital for identifying reservoirs, tracking transmission, and guiding targeted interventions against antimicrobial resistance.\u003c/p\u003e \u003cp\u003eThe overall prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e (7.9%) and its distribution across animal (9.4%), human (7.3%), and environmental (6.0%) samples indicate its ubiquitous nature. The prevalence found in our study closely matches the 8.6% reported in a 2025 meta-analysis in sub-Saharan Africa using the one-health approach [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In the Olaitan et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], the pooled prevalence estimates of 12.1%, 8.6%, and 6.2% were reported for animal, human, and environmental samples of ESBL-producing K. pneumoniae, respectively. The country-specific pooled prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e ranged from 8.1% in Tanzania to 23.3% in South Africa, with \u003cem\u003eK. pneumoniae\u003c/em\u003e from Nigeria showing a pooled prevalence of 11.1%. The finding of a higher prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e in clinical isolates (8.5%) versus 6.4% in non-clinical human samples shows a trend in line with the evidence documented in the previous studies in Nigeria [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].The higher prevalence in clinical human samples (8.5%) compared to non-clinical samples 6.4%) aligns with the established role of \u003cem\u003eK. pneumoniae\u003c/em\u003e as a leading opportunistic nosocomial pathogen. This disparity suggests that the healthcare environment exerts selective pressure that favours the colonisation and persistence of this bacterium, likely exacerbated by compromised host immunity and frequent exposure to antimicrobial agents.\u003c/p\u003e \u003cp\u003eHowever, the higher prevalence in animals in cows (13.2%) supports the reservoir hypothesis, as shown recently by Hetland et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and previously by Hu et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and Wareth and Neubauer [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] which posits that livestock may serve as significant natural reservoirs for \u003cem\u003eKlebsiella\u003c/em\u003e species, potentially facilitating zoonotic spillover to humans through the food chain, direct contact, or environmental contamination. The finding of high resistance to amoxicillin\u0026ndash;clavulanate and second/third-generation cephalosporins in our study supports recent One Health and clinical studies, which show high resistance to amoxicillin\u0026ndash;clavulanate and second/third-generation cephalosporins in human \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates, reflecting widespread ESBL production and high multidrug resistance (MDR) in humans compared to lower MDR in animals. For example, Marzouk et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] reported 97% resistance to cefotaxime, ceftazidime, and amoxicillin\u0026ndash;clavulanate in ESBL-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e, which is consistent with the 83.1% resistance to amoxicillin\u0026ndash;clavulanate and similarly high resistance to other cephalosporins observed in our human isolates. In addition, Bayaba et al [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], in Cameroon reported that 82% of Enterobacterales isolates were MDR, similar to the high MDR burden identified in our study. Olaitan et al [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], also found that ESBL-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e is widespread, with MDR rates markedly higher in humans (94.9%) compared to animals (23.7%), reinforcing our observation of greater selection pressure from antibiotic use in human clinical settings. The animal \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates in our study showed substantial resistance to certain cephalosporins, particularly cefuroxime (76.3%), which was lower than the 95% resistance reported by Aslam et al [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] for the 115 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates in Pakistan. These further demonstrated the high resistance to cephalosporin associated with \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates globally. The 11.9% meropenem resistance in \u003cem\u003eK. pneumoniae\u003c/em\u003e from clinical human isolates in this study further aligns with recent WHO and global reports, which highlight the rise of hypervirulent, carbapenem-resistant \u003cem\u003eK. pneumoniae\u003c/em\u003e and raise global concerns about resistance to last-line antibiotics [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The report on meropenem resistance from CHINET Surveillance networks reveals a sharp increase in carbapenem resistance in \u003cem\u003eK. pneumoniae\u003c/em\u003e, with meropenem resistance rising from about 3% in 2005 to over 26% by 2018 in China [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], which show a gradual increase in meropenem resistance over the years.\u003c/p\u003e \u003cp\u003eThe detection of \u003cem\u003eblaOXA-48\u003c/em\u003e, \u003cem\u003eblaKPC\u003c/em\u003e, and \u003cem\u003eblaVIM\u003c/em\u003e among MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates in this study provides molecular confirmation of diverse carbapenem resistance mechanisms, encompassing class D, A, and B carbapenemases, respectively. The predominance of blaOXA-48, frequently located on conjugative IncL/M-type plasmids, is consistent with reports of efficient horizontal transfer of these plasmids between Enterobacterales in clinical and experimental models [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].​ The identification of a carbapenemase-producing isolate in livestock, in the absence of such isolates from the sampled environment, raises the hypothesis of reverse zoonosis (anthropozoonosis), whereby resistant strains or mobile genetic elements move from humans into animal populations [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However, without genomic comparisons of human and animal isolates, temporal linkage, and more extensive environmental sampling, the direction of transmission remains uncertain, and this interpretation should be regarded as speculative [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. OXA-48-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e are associated with reduced susceptibility to ertapenem and meropenem and have been linked in multiple cohorts to delayed effective therapy, prolonged hospital stay, and increased mortality compared with carbapenem-susceptible infections[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. \u003cem\u003eVIM\u003c/em\u003e metallo-β-lactamases confer resistance to most β-lactams [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. They are often detected in high-risk patients, including those in intensive care or with significant immunosuppression, where they are associated with severe invasive infections [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].​ KPC enzymes mediate resistance to a broad spectrum of β-lactams, including carbapenems, and infections due to KPC-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e have historically required prolonged courses of toxic agents such as colistin or aminoglycosides [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Although newer β-lactam/β-lactamase inhibitor combinations have partially improved outcomes, there remains a need to develop a new antibiotic to treat emerging infections caused by carbapenem-resistant pathogens, including those caused by MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eWhole-genome sequencing of \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 from human blood and \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK25 obtained from a water sample from the home of the patient NGEK23 environmental water) provides insights into \u003cem\u003eK. pneumoniae\u003c/em\u003e genomic plasticity, including differences in core-genome phylogeny, structural variation, and resistome composition. Despite similar multidrug-resistant phenotypes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), phylogenetic analysis indicates that the two isolates are not closely related. This is consistent with previous work demonstrating that similar resistance profiles can emerge in distinct \u003cem\u003eK. pneumoniae\u003c/em\u003e lineages through horizontal acquisition of shared mobile genetic elements [\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].​\u003c/p\u003e \u003cp\u003eDifferences in Indel length distribution and structural variation between \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 and \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK25 indicate substantial genome remodelling. The higher number of insertions and deletions in the environmental isolate (\u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK25) may reflect recent recombination and mobile element activity, potentially linked to adaptation to fluctuating environmental conditions, as previously documented in the studies by Thorpe et al [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] and Rocha et al [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. However, this remains speculative and could have also been driven by other lineage-specific evolutionary phenomena.​ \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK23 harbours a broader resistome than \u003cem\u003eK. pneumoniae\u003c/em\u003e NGEK25, with more annotated resistance determinants. The above finding in our study agrees with large-scale studies showing that clinical \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates often carry expanded AMR gene repertoires compared with environmental counterparts, likely due to intensive antibiotic selection in healthcare settings [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. This pattern supports the widely described model in which clinical isolates accumulate resistance genes through the acquisition of plasmids and other mobile genetic elements[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. However, it should be interpreted with caution, given that resistome size is also influenced by sequence type and plasmid background[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].​\u003c/p\u003e \u003cp\u003eThe detection of ST3157 and a putatively novel sequence type (ST 5ee4) highlights the high genetic diversity of \u003cem\u003eK. pneumoniae\u003c/em\u003e in the study area and mirrors international genomic surveillance data that report numerous STs, including many rare or newly described lineages [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. While the identification of a novel ST underscores the dynamic local emergence of previously undocumented lineages, its epidemiological significance and potential to become a high-risk clone will depend on evidence of dissemination, convergence of resistance and virulence, and association with outbreaks, which require ongoing genomic and epidemiological surveillance [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The presence of multiple serotypes and unique combinations of K- and O-loci suggest variations in virulence factors and distinct interactions with hosts, indicating that they are not of the same lineage or origin. Afolayan et al [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] asserted that performing K- and O-locus typing of \u003cem\u003eK. pneumoniae\u003c/em\u003e is essential for public health and clinical management. It aids in identifying clonal lineages, monitoring transmission, comprehending virulence and pathogenesis, and predicting antibiotic resistance profiles. K-loci are responsible for encoding capsular polysaccharide (CPS), which aids the bacteria in evading the immune defences of the host and facilitating infection [\u003cspan additionalcitationids=\"CR74 CR75\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Various K-loci are associated with varying levels of virulence and may affect the types of infections. Specific antibiotic resistance profiles are influenced by certain K-loci, which in turn affect treatment choices. K-locus typing is crucial for identifying precise capsular antigens that vaccines can selectively target against \u003cem\u003eK. pneumoniae\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. It has also been found to aid in understanding the evolutionary process of \u003cem\u003eK. pneumoniae\u003c/em\u003e. It provides valuable insights for public health policies regarding infection control, antibiotic management, and vaccine advancement [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Differences among mobile genetic elements indicate varying capacities for horizontal gene transfer, enabling the rapid dissemination of resistance genes and virulence factors among bacterial populations. Monitoring the dissemination of these components among various ecosystems and host populations is crucial. The resistome, virulome, and mobilome profiles found in the two isolates indicate the potential for the transmission and evolution of bacteria resistant to antibiotics and highly infectious across species. This finding is consistent with the literature, which reports diversity in the mobilome of \u003cem\u003eK. pneumoniae\u003c/em\u003e and other Gram-negative bacteria [\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study demonstrates high levels of antibiotic resistance among \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates from humans and environmental samples in the study area. At the same time, the detection of carbapenemase genes in human and animal samples suggests that resistance is not confined within hospital boundaries but extends into surrounding ecosystems. The high diversity of mobile genetic elements and the identification of new sequence types indicate a dynamic, evolving resistome that can traverse human, animal, and environmental reservoirs, underscoring the risk of wider dissemination. These findings highlight the urgent need for integrated antimicrobial stewardship that explicitly targets the hospital\u0026ndash;livestock interface, including prudent antibiotic use in clinical care and animal husbandry, coordinated infection-prevention practices, and control of clinical and faecal waste. Sustained genomic and epidemiological surveillance within a One Health framework is essential to detect emerging high-risk lineages early and to inform targeted interventions before they become entrenched in human and animal populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY AND WGS DATA-GENERATED INFORMATION\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe WGS data generated in this study have been deposited at NCBI under the project accession PRJNA1295602. An additional request can be made to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the assistance provided by the laboratory staff at the Department of Microbiology, Faculty of Life Sciences, and the Department of Pharmaceutical Microbiology and Biotechnology, Faculty of Pharmacy, Federal University, Oye-Ekiti, Ekiti State, Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eULI-Conceptualisation, investigation, software, formal analysis, write first draft, OBS-Supervision, data validation, Editing, Writing. \u0026nbsp; OTM-Supervision, data validation, Editing, Writing, OOE-Supervision, data validation, Editing, Writing, AFA-investigation, formal analysis, writing, project administration, OB-investigation, formal analysis, writing, project administration, FOS-investigation, formal analysis, writing and editing, AEC-Supervision, data validation, Editing, Writing and OSK-Supervision, data validation, Editing, Writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no external funding associated with this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics \u0026nbsp;and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical Clearance from the National Health Research Ethics Committee of Nigeria (NHREC) (NHREC/01/01/2007-21/03/2023), the Federal Teaching Hospital, Ido-Ekiti (ERC/2023/09/20/1034B) and the Ondo State Ministry of Agriculture (MNR/V.384/64) were obtained for this study. Consent was gotten from the participants to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarston HD, Dixon DM, Knisely JM, Palmore TN, Fauci AS, Antimicrobial Resistance. 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MBio. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/mBio.02693-18\u003c/span\u003e\u003cspan address=\"10.1128/mBio.02693-18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarris M, Fasolino T, Ivankovic D, Davis NJ, Brownlee N. Genetic Factors That Contribute to Antibiotic Resisactance through Intrinsic and Acquired Bacterial Genes in Urinary Tract Infections. Microorg 2023, Vol 11, Page 1407. 2023;11:1407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/MICROORGANISMS11061407\u003c/span\u003e\u003cspan address=\"10.3390/MICROORGANISMS11061407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Hoek AHAM, Mevius D, Guerra B, Mullany P, Roberts AP, Aarts HJM. Acquired antibiotic resistance genes: An overview. Front Microbiol. 2011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2011.00203\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2011.00203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AMR, One-Health, Carbapenemases, Oxa-48","lastPublishedDoi":"10.21203/rs.3.rs-9059978/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9059978/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe escalating global threat of human infections caused by multi-drug-resistant (MDR) Enterobacteriaceae, particularly \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, poses a serious challenge to public health. The study investigated the prevalence and molecular characteristics of carbapenemase-producing \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e in human, animal, and environmental samples from Ekiti and Ondo States, Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 1329 samples comprising 399 clinical human samples, 404 non-clinical human samples, 393 animal samples, and 133 environmental samples were examined. The samples were processed using standard microbiological techniques, and antibiotic susceptibility testing was performed in accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines. Antibiotic resistance genes (\u003cem\u003eOXA-48, NDM-1, VIM\u003c/em\u003e, and \u003cem\u003eKPC\u003c/em\u003e) were determined in all K. pneumoniae isolates that were phenotypically positive for carbapenemase production. Whole-genome sequencing (WGS) was performed on two \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates from the same household, which exhibited similar resistance patterns. Data analysis was performed in Excel and the Statistical Package for Social Sciences (SPSS) version 23.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e was 105 (7.9%), with higher rates in animal samples 38 (9.4%) than in human samples 59 (7.3%) and in the environmental sample 8 (6.0%). Antibiotic resistance was significantly higher in human isolates than in animal and environmental isolates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All isolates were susceptible to colistin and amikacin. Multidrug resistance was observed in 65 (61.9%) isolates, which was significantly more common in human samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Carbapenemase production was detected in 9.2% of MDR isolates, predominantly in human samples. The \u003cem\u003eOXA-48, VIM\u003c/em\u003e, and \u003cem\u003eKPC\u003c/em\u003e genes were identified in Carbapenemase-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e. Whole-genome sequencing of \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates NGEK23 and NGEK25 revealed distinct resistomes and mobile genetic elements between the two isolates, suggesting distinct origins and differing diversity of resistance genes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe high frequency of multidrug resistance and the presence of carbapenemase-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e across human, animal, and environmental sources in the study samples highlight an urgent need for robust infection control and antibiotic stewardship programmes.\u003c/p\u003e","manuscriptTitle":"Molecular Characterisation of Carbapenemase-Producing Multi-Drug Resistant Klebsiella pneumoniae from Human, Animal, and Environmental Samples from Ekiti and Ondo State, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 09:18:53","doi":"10.21203/rs.3.rs-9059978/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-12T15:59:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T02:44:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T02:43:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-03-07T16:53:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b734a5a7-3bf8-4e06-8425-928e91601420","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T14:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 09:18:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9059978","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9059978","identity":"rs-9059978","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}