Antimicrobial Resistance Gene Distribution and Population Structure ofEscherichia coliisolated from Humans, Livestock, and the Environment: Insights from a One Health Approach

Antimicrobial Resistance Gene Distribution and Population Structure of Escherichia coli isolated from Humans, Livestock, and the Environment: Insights from a One Health Approach | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Antimicrobial Resistance Gene Distribution and Population Structure of Escherichia coli isolated from Humans, Livestock, and the Environment: Insights from a One Health Approach View ORCID Profile Beatus Lyimo , Valery Sonola doi: https://doi.org/10.1101/2025.04.07.25325408 Beatus Lyimo 1 School of Life Sciences and Bioengineering, Nelson Mandela African Institution of Science and Technology , Arusha Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Beatus Lyimo For correspondence: beatus.lyimo{at}nm-aist.ac.tz Valery Sonola 2 Livestock Training Agency (LITA) , Buhuri Campus, P. O. Box 1483, Tanga, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Antimicrobial resistance (AMR) is an escalating public health threat, with evidence highlighting the exchange of resistance genes among humans, animals, and the environment. Whole-genome sequence (WGS) offers high-resolution pathogen subtyping and provides extensive insights into AMR’s early emergence and spread. This study investigates the distribution of AMR genes, plasmid types, and the population structure of Escherichia coli isolates from humans, livestock, fish, and the environment. A total of 244 WGS datasets of E. coli isolates were pulled from a public database from multiple studies and analyzed to characterize AMR gene distribution, plasmid diversity, and population structure across humans, livestock and the environment. The findings reveal widespread dissemination of AMR genes across all sources. Aminoglycoside resistance genes (aac(3)-IId, aph(3’’)-Ib, aph(6)-Id, aadA1, aadA5) and β-lactam resistance genes (bla TEM-1 , bla OXA-1 , bla CTX-M-15 ) were prevalent across all environments. Quinolone resistance mutations (gyrA_S83L, gyrA_D87N, parC_S80I) were also shared among human, livestock, fish, and environmental isolates, indicating cross-species transmission. Tetracycline resistance genes (tet(A), tet(B), tet(D)) were found in humans, livestock, and fish. Plasmid types IncFIA, IncI1, and IncFII exhibited extensive cross-source sharing, with strong connectivity between humans and livestock. Principal Component Analysis (PCA) revealed that E. coli isolates from Kenya formed a tight, distinct cluster, while others were more dispersed. The Minimum Spanning Tree (MST) network showed the clusters where human and livestock isolates were closely connected, it further showed some human isolates cluster with fish and environmental isolates. The MST network demonstrated close clustering of human and livestock isolates, indicating possible cross-species transmission. These findings showed the interconnected nature of AMR across human, animal, and environmental sectors and underscored the need for integrated surveillance under a One Health framework to monitor and control the spread of clinically significant AMR genes. Background Antimicrobial Resistance (AMR) is one of the biggest global public health threats as it is the leading cause of death globally, however, its magnitude is not well known ( Murray et al., 2022a ). In the year 2019, it’s estimated that over 4.95 million deaths have been associated with AMR, including 1.27 million deaths attributed to bacterial infections ( Murray et al., 2022a ). The impact of AMR-related morbidity and mortality is particularly severe in low- and middle-income countries (LMICs), where individuals are 1.5 times more likely to die from AR infectious organisms compared to those in high-income countries. This disparity is even more pronounced among young children. Among children under the age of five who die from AR infections, 99.65% are in low- or middle-income countries ( Baker et al., 2018 ; UNICEF, 2023 ). In 2019, the United Republic of Tanzania reported an estimated 12,500 deaths directly attributable to AMR and an additional 54,000 deaths associated with AMR-related infections ( Murray et al., 2022a ). Moreover, Tanzania was ranked as the 30th highest globally in terms of age-standardized mortality rates associated with AMR, among 204 countries analyzed in a global study (Murray et al., 2022). In 2016, Tanzania formulated a National Action Plan for AMR (2017–2022) in response to recommendations from the WHO and the Global Health Security Agenda Joint External Evaluation ( Neema et al., 2023 ; WHO, 2015 ). Following this, a comprehensive One Health AMR Surveillance Framework was developed to facilitate the establishment of AMR surveillance systems across human, animal, and environmental health sectors ( United Republic of Tanzania, 2018 ). The 2022 Global Burden of Disease study highlighted Escherichia coli , Staphylococcus aureus , Klebsiella pneumoniae , Streptococcus pneumoniae , Acinetobacter baumannii , and Pseudomonas aeruginosa as the six primary contributors to AMR-related deaths, accounting for 73% of such fatalities in 2019 ( Murray et al., 2022b ). Among these, E. coli is often used as a standard indicator of water contamination, reflecting faecal pollution and the potential presence of other harmful microorganisms. While many strains of E. coli are harmless and part of the normal flora, however under favorable conditions, they can cause infections such as gastroenteritis, urinary tract infections (UTIs), and sepsis when conditions allow. The burden of infections caused by multidrug-resistant E. coli is further exacerbated by the combination of factors, including the widespread misuse of antibiotics, poor sanitation infrastructure, and limited access to adequate healthcare services ( Lyimo, et al., 2016a ; Subbiah et al., 2020 ). E. coli can persist in the gut or other body sites without causing disease and often serve as a reservoir for potential infection. Under certain conditions, such as immune suppression or body microbiome disruptions due to antibiotics overuse, the colonized E. coli strains can change from a harmless state to pathogenic ones. Additionally, the presence of multidrug resistant E. coli in the environment particularly in water sources, food, and hospital settings contribute to the infections risk. Global collective action is required to strengthen AMR surveillance, promote the development of rapid diagnostic tools, and integrate advanced technologies such as WGS to minimize the unnecessary use of antibiotics. The environmental microbiology approach is critical in this effort, as AMR spreads across human, animal, and environmental interfaces, necessitating coordinated interventions. WGS has transformed the study of bacterial pathogens by providing comprehensive insights into phylogenetic relationships, the presence of antimicrobial resistance genes (ARGs), and the detection of virulence ( Hazen et al., 2018 ; Manyahi et al., 2014 ). The most common extended-spectrum beta-lactamase (ESBL) genes, including bla CTX-M , bla TEM , and bla SHV , confer resistance to third-generation cephalosporins and are frequently associated with mobile genetic elements such as plasmids, enabling their rapid dissemination across different hosts and environments ( Nordmann et al., 2011 ). Integrating One Health principles into AMR surveillance enhances the ability to track resistance patterns, identify transmission pathways, and develop targeted strategies to mitigate its impact on public health, veterinary medicine, and environmental ecosystems ( Accogli et al., 2013 ; Bailey et al., 2011 ; Peirano et al., 2010 ; Wu et al., 2014 ). Although carbapenems are considered last-resort antibiotics, molecular surveillance within a One Health framework has revealed the widespread presence of carbapenemase-producing strains across human, animal, and environmental reservoirs. Studies have identified strains carrying carbapenemase genes, such as bla NDM and bla KPC ( Hazen et al., 2018 ; Manyahi et al., 2022 ; Nordmann & Poirel, 2005 ) highlighting the urgent need for integrated AMR monitoring and control strategies. The detection of these resistance genes ( Table 1 ) emphasizes the necessity of a coordinated approach across public health, veterinary medicine, and environmental sectors to track transmission dynamics and develop effective mitigation strategies ( Cella et al., 2023 ; McEwen & Collignon, 2018 ; Velazquez-Meza et al., 2022 ; Welch et al., 2007 ). One of the most significant contributions of WGS and bioinformatics tools is the ability to trace the movement of antibiotic resistance genes between different bacterial populations. The WGS analysis has shown that resistance genes are often carried on plasmids, integrons, and transposons, which can be transferred between commensal and pathogenic strains. This transfer increases the genetic diversity of resistant E. coli strains, complicating efforts to control their spread. View this table: View inline View popup Download powerpoint Table 1: Summary of major classes of antibiotics, AMR-related genes, and proteins associated with E. coli This diversity is driven by the exchange of genetic material between human, animal, and environmental strains. In rural areas, zoonotic transmission plays a key role in shaping the genetic landscape of E. coli , as livestock are frequently in close contact with humans, allowing for the exchange of bacterial strains and resistance genes. This paper explores the use of the One Health approach to WGS data available in public repositories, focusing on E. coli strains circulating in East Africa to examine the distribution of antibiotic resistance genes and genetic diversity of E. coli strains across human, animal, and environmental reservoirs. Methods A literature search was conducted across various scientific journals and databases using key terms such as “antibiotic resistance in Tanzania,” “antibiotic resistance in humans in Tanzania,” “antibiotic resistance in livestock in Tanzania,” and “antibiotic resistance in the environment (soil and water).” In each relevant paper, accession number(s) were identified and used to download whole genome sequences (WGS) from public repositories for analysis. All raw WGS data, along with the reference E. coli strain K-12 substr. MG1655 were retrieved from the NCBI GenBank database using the accession numbers listed in Table 2 . View this table: View inline View popup Download powerpoint Table 2 presents the list of accession numbers for the genomic sequences included in this study. These accession numbers correspond to the E. coli isolates analyzed across various sources, including human, livestock, and environmental samples. Data Preprocessing All data were inspected for quality using fastaqc v0.12.1 to generate individual reports and MultiQC to put all reports together, then trimmed to remove low-quality sequences and adapters using Trimmomati( Bolger et al., 2014 )c before downstream analysis. Genome Assembly Paired-end reads were assembled into contigs using SPAdes. Genome annotation was performed by the Prokka software ( Seemann, 2014 ). Single nucleotide polymorphism (SNP) variant calling was performed using bcftools software ( Danecek et al., 2021 ). In brief, the sequences were mapped to the reference genome E. coli str. K-12 substr.MG1655 to generate SAM files and then converted to BAM files and then sorted to produce sorted BAM file, then generate the mpileup. The bcftools was then used for variant calling to generate VCF files of each sample. The individual VCF files were merged to produce one merged vcf file using bcftools. AMR Gene Identification The spade software was used to generate a contig sequence from each pared end sequence. The ResFinder plus software ( Florensa et al., 2022 ) was used to identify AMR genes. In-house Python script followed by pheatmap function in R software was used to generate heatmap plots. The ggVennDiagram function in R was used to show the distribution of shared and unique antimicrobial resistance genes, ARGs) among five different sources. Population structure To assess gene flow between E. coli isolated from humans, livestock and environment (soil and water), genetic differentiation was first estimated using the Wright Fixation index (FST) using Vcftools v0.1.5( Danecek et al., 2011 ) and population structure was determined using principal component analysis (PCA) as implemented in PLINK1.9( Purcell et al., 2007 ). Analysis of Genetic Similarity Among Antimicrobial-Resistant Strains Isolated from Humans, Livestock, Fish, and the Environment The genetic similarity of antimicrobial-resistant isolates was analyzed using Multilocus Sequence Typing (MLST) v2.23.0, which was downloaded and installed in a Linux environment. The results were then plotted in R using the ggraph and tidygraph packages to visualize genetic relationships among the isolates. Results AMR Gene Identification and Distributions A total of 244 WGS sequences were obtained from the NCBI database and analyzed, results showed the distribution of AMR genes across four key sources with several resistance genes found in multiple sources, indicating possible horizontal gene transfer and the movement of resistant bacteria between ecosystems ( Table 3 ). Aminoglycoside resistance genes ( aac(3)-IId , aph(3’’)-Ib , aph(6)-Id , aadA1 , aadA5 ) were frequently detected across environmental, fish, human and livestock samples. Similarly, β-lactam resistance genes (bla TEM-1 , bla OXA- 1 , bla CTX-M-15 ) were identified in all sources, reflecting the widespread dissemination of β-lactamase-producing bacteria. Notably, bla CTX-M-15 , an extended-spectrum β-lactamase (ESBL) gene, was present in humans, livestock, fish and the environment, raising concerns about its impact on clinical treatment options. Quinolone resistance genes ( gyrA_S83L , gyrA_D87N , parC_S80I ) were also shared across all sources, suggesting cross-species transmission and potential selection pressure from extensive fluoroquinolone use. Sulfonamide resistance genes ( sul1 , sul2 ) were consistently found in all environments, indicating sustained exposure, likely driven by veterinary and agricultural practices. Tetracycline resistance genes ( tet(A) , tet(B) , tet(D) ) were detected in humans, livestock, and fish, emphasising the global influence of tetracycline use in animal husbandry and its role in environmental contamination. View this table: View inline View popup Table 3: Table showing the distribution of detected resistance genes in selected antibiotics among E. coli isolates from humans, livestock, fish, and the environment Heatmap and hierarchical Clustering of AMR Profiles A Heatmap of the presence and absence of key AMR genes across the sample set ( Figure 1 ), showed the hierarchical clustering of isolates based on their resistance profiles reveals distinct groupings that align with their sources. Isolates from clinical settings form a separate cluster from those obtained from environmental and animal sources, suggesting source-specific patterns in the acquisition and dissemination of resistance genes. Additionally, isolates from livestock in Kenya cluster separately from other isolates, indicating potential geographic or host-specific variations in AMR gene distribution. These findings underscore the influence of both sample origin and geographical location on the structure of AMR profiles and suggest possible transmission pathways across different environments. Download figure Open in new tab Figure 1: Heatmap plot of the relative abundance of ARGs in humans, livestock, Nile perch, environment and fish from Tanzania and livestock from Kenya. Clustered each group ARGs in rows columns and source in columns. The Venn diagram illustrates the distribution of antimicrobial resistance genes (ARGs) across four ecological sources: humans, livestock (Tanzania and Kenya), fish, and the environment ( Figure 3 ). The numbers within each circle represent the count of unique and shared ARGs across these sources, with percentages indicating their relative proportions. Human isolates show the highest number of unique ARGs (44, 40%), suggesting significant ARG diversity in human-associated E. coli . Environmental samples contain 16 unique ARGs (14%), reflecting the role of the environment as a reservoir for diverse resistance genes. Shared ARGs among the four sources are limited, with only 11 ARGs (10%) found across all groups, highlighting niche-specific genetic signatures. Download figure Open in new tab Figure 2: Venn Diagram of ARG distribution across different sources. Numbers in circles represent the count of unique and shared ARGs among sources, with percentages indicating relative proportions Download figure Open in new tab Figure 3: Plasmid Network of E. coli Isolates Across Humans, Livestock, Fish, and Environmental Sources. Which showed evidence of Cross-Source Horizontal Gene Transfer The plasmid network The plasmid network visualization depicts the relationships between plasmid replicon types detected in E. coli isolates from humans, livestock, fish (Nile perch), and the environment ( Figure 3 ). Node colours represent sources (human = green, livestock = blue, fish = red, environment = black), while edges indicate shared plasmid types, with thickness reflecting connection strength. Plasmid types IncFIA, IncI1, and IncFII show the most extensive cross-source sharing, indicating their role in horizontal gene transfer. Human and livestock isolates exhibit the most significant connectivity, suggesting potential zoonotic transmission. Environmental nodes show widespread but weaker connections, reflecting their role as passive reservoirs. Population structure The PCA analysis of E. coli isolates from humans, livestock, fish and the environment ( Figure 4 ) showed isolates from Kenya (grey) form a tight and distinct cluster, possibly due to geographical factors or antibiotic use policies. Other isolates from humans, livestock, and the environment are more dispersed across the PCA space, implying greater genetic diversity. This variability may result from different ecological pressures, host-specific adaptations, and regional variations in antimicrobial use and environmental exposure. widely dispersed across the PCA plot, suggesting a higher degree of variability. This distribution highlights potential differences in E. coli populations across ecological niches and geographical regions. The observed patterns may provide insights into transmission dynamics, antimicrobial resistance dissemination, and environmental reservoirs of E. coli in Tanzania Download figure Open in new tab Figure 4 presents the principal component analysis (PCA) of E. coli isolates collected from various sources and locations. The isolates from Kenya (grey) formed a distinct cluster, indicating genetic or phenotypic similarities. In contrast, isolates from humans, livestock, and the environment were more. Minimum Spanning Tree of cgMLST Results showed the pie chart which displays the proportion of ST from different sources. The sources include the environment, fish, humans, livestock, and livestock (Kenya). The distribution appears relatively even, with all sources contributing significantly. Livestock and human constitute a major proportion, highlighting their role in AMR transmission. Fish and environmental sources also contribute, reinforcing the multi-sectoral AMR dissemination. Fig 5 (A). Results showed also clustering patterns reveal that human isolates (red nodes) are closely related to livestock isolates (green, orange), suggesting potential transmission events. Environmental isolates (grey nodes) are distributed throughout the tree, implying their role as reservoirs or intermediates in pathogen transmission. Fish isolates (light blue) are interspersed, suggesting interactions between aquatic and terrestrial ecosystems. Fig 5 (B) Download figure Open in new tab Figure 5 (A): The pie chart presents the proportional distribution based on allelic profiles from E. coli across different sources. (B) Minimum Spanning Tree (MST) based on core genome Multilocus Sequence Typing (cgMLST). Each node represents ST, and the edges indicate genetic relatedness. Nodes are colour-coded according to the source of isolation. The branching structure highlights potential evolutionary trajectories, with some clusters showing a dominant source while others appear mixed Discussion Bacterial resistance toward broad-spectrum antibiotics has become a major concern in recent years. The One Health approach has been widely advocated to enhance the understanding of the transmission dynamics of antibiotic bacteria from different sources ( Cella et al., 2023 ; McEwen & Collignon, 2018 ). Additionally, advancements in sequencing platforms and bioinformatics tools have significantly revolutionized the ability to trace AMR transmission between humans, livestock, and the environment ( Quitmeyer, 2024 ; Satam et al., 2023 ). Furthermore, comparative analyses of different sequencing platforms and databases have been conducted to provide comprehensive analysis pipelines for defining AMR gene occurrence ( Soni et al., 2021 ). These studies highlight the critical role of secondary data analysis in accurately identifying and characterizing AMR genes, thereby informing strategies to combat the spread of resistant pathogens. Collectively, these advancements demonstrate how secondary analysis of available sequence data could revolutionize our capacity to trace AMR transmission across various reservoirs. Therefore, this study leverages existing WGS data to elucidate the distribution and genetic relationships of antibiotic-resistant E. coli isolated from diverse sources within the One Health framework, encompassing humans, livestock, and environmental reservoirs, and the role of each source in the transmission of multidrug-resistant E. coli . Results indicated a significant burden of antibiotic resistance genes across all sources. Among all antibiotic classes, aminoglycosides and β-lactams exhibited the highest prevalence across all samples. This finding is consistent with studies showing that resistance to β-lactams and aminoglycosides is increasingly common in human, animal, aquatic ecosystems and environmental reservoirs which, underlining the interconnectedness of human, animal, and environmental reservoirs in AMR transmission ( Gaşpar et al., 2021 ; Gemeda et al., 2023 ; Kiiti et al., 2021 ; Pormohammad et al., 2019 ; Sonola et al., 2022 ). Compared to studies in sub-Saharan Africa, our findings align with reports from Tanzania and Kenya, which have documented high prevalence rates of ESBL-producing E. coli in livestock and humans ( Kemp, 2020 ; Mwakyoma et al., 2023 ). Notably, the blaCTX-M-15 gene was detected in nearly all samples, including isolates from fish and the environment. This gene encodes a β-lactamase enzyme that confers resistance to third-generation cephalosporins, particularly ceftriaxone, cefotaxime, and ceftazidime. Reports from other studies have reported the occurrence of bla CTX -M-15 in E. coli isolated from different sources ranging from humans, livestock and the environment ( Lyimo, Buza, Subbiah, Smith, et al., 2016b ; Minja et al., 2021 ; Moremi et al., 2016 ; Shawa et al., 2021 ). The widespread presence of bla CTX-M-15 and aminoglycoside resistance genes suggests high levels of antibiotic pressure in both clinical and agricultural settings. The detection of these genes in environmental samples highlights the potential role of wastewater and agricultural runoff in spreading AMR. This is very alarming for the future of antibiotics under the cephalosporins group. Resistance genes, such as aph(3’’)-Ib , aph(6)-Id , aadA1 , and aadA2 , which are responsible for resistance to the aminoglycoside class of antibiotics, particularly streptomycin, have been shown to exhibit high prevalence across all sources( Shi et al., 2013 ; Zhang et al., 2023 ). These resistance genes are important markers of the broader issue of AMR, which is increasingly complicating the treatment of infections caused by bacteria. The presence of these resistance genes in clinical isolates, as well as environmental and agricultural isolates, suggests a widespread dissemination of resistance mechanisms across various settings, including hospitals, communities, and agricultural environments. In some isolates, the study found the occurrence of more than five different resistance genes occurring together. This contributes to the spreading of the genes to the environment including soil and water through manure back to humans by direct contact with farm animals, through exposure to animal manure, wastewater, or aerosol, and by consumption of uncooked animal products such as meat, eggs, milk, etc ( Jaja et al., 2020 ; Zhang et al., 2023 ). This information is very critical in applying one-health in the control of AMR transmission. The Venn diagram results showed humans exhibit the highest number of unique antimicrobial resistance genes (44, 40%), suggesting that medical antibiotic use plays a dominant role in resistance selection. These resistance determinants could be driven by excessive antibiotic prescriptions, self-medication, or hospital-acquired infections. The resistance-determinant genes can find their way back to animals or the environment. The environment category contains a substantial number of unique ARGs (16, 14%), emphasizing the importance of wastewater contamination, aquaculture antibiotic use, and the environmental persistence of resistance genes. Since many resistance genes in the environment are derived from human and livestock sources, pollution control and water treatment strategies are critical for AMR mitigation. The overlapping ARGs between this category and others suggest the potential for ARG spillover into aquatic ecosystems, contributing to horizontal gene transfer (HGT) between bacterial communities. Livestock samples from Tanzania and Kenya exhibit fewer unique ARGs and relatively low overlap with Nile perch. This suggests that direct transmission between these reservoirs is limited. However, ARG exchange may still occur through shared environmental exposure, such as contaminated water sources, runoff from farms, or the use of animal waste in aquaculture. The presence of some common ARGs (e.g., 11 shared ARGs, 10%) indicates potential indirect transmission pathways. Differences between livestock in Tanzania and Kenya indicate potential geographical, management, and antibiotic use policy variations. These findings reinforce the need for harmonized antimicrobial use policies in East Africa, focusing on standardized surveillance and interventions across borders. They were further confirmed by heatmap results which illustrated the distribution of antimicrobial AMR genes across various sample sources, including livestock from Kenya, human, livestock, fish and environmental (water and soil) and fish samples from Tanzania. The hierarchical clustering provides insights into the similarities between AMR gene profiles across sample categories and the co-occurrence of resistance genes. This result aligns with other studies which showed the co-occurrence of antibiotic-resistance genes across different sources ( Altayb et al., 2022 ; Inda-Díaz et al., 2023 ). The highest AMR gene abundance was observed in human samples, particularly for aminoglycoside, beta-lactam, and quinolone resistance genes. The observed result aligns with previous findings where human-associated bacteria harbour a diverse and extensive repertoire of resistance genes, likely due to frequent antibiotic exposure in clinical and community settings. The plasmid network visualization reveals important insights into the dynamics of horizontal gene transfer (HGT) in E. coli populations from different sources, including humans, livestock, fish, and the environment. The plasmids IncFIA , IncI1 , and IncFII are notably widespread across multiple sources, indicating their pivotal role in facilitating horizontal gene transfer in E. coli . These plasmids are known to carry genes related to antibiotic resistance, virulence factors, and other traits that enhance bacterial survival ( Chen et al., 2024 ; Lyimo, Buza, Subbiah, Temba, et al., 2016; Pankok et al., 2022 ; Rozwandowicz et al., 2018a , 2018b ). Their extensive sharing between isolates from diverse sources suggests that they serve as key vectors for gene transfer, allowing for the spread of potentially harmful traits across species boundaries. The high connectivity between human and livestock isolates observed in the plasmid network suggests a significant role for zoonotic transmission in the spread of resistant E. coli strains. This sharing of plasmids has also been observed in other studies. For example, a study by Ibekwe et al . showed that E. coli isolated from swine and dairy manure carried plasmids such as IncFIA(B), IncFII, IncX1, IncX4, and IncQ( Ibekwe et al., 2021 ). The frequent interactions between human and livestock plasmid types imply that certain practices in livestock farming, such as the use of antibiotics, could be facilitating the spread of resistant strains. Additionally, the proximity of humans to livestock— whether through direct contact, consumption of contaminated meat, or environmental contamination might create opportunities for the exchange of plasmids carrying resistance genes ( Graham et al., 2019 ; Velazquez-Meza et al., 2022 ). This is particularly concerning when considering the role of livestock as a reservoir for pathogenic bacteria that can affect human health. Furthermore, the presence of IncFIA, IncI1, and IncFII plasmids in both humans and livestock suggests that E. coli may be evolving to adapt to the selective pressures exerted by human and animal health systems. Understanding these zoonotic transmission pathways ( Koutsoumanis et al., 2021 ) is essential for developing more effective public health policies and strategies for controlling the spread of resistant E. coli . The environmental nodes in the network could represent passive reservoirs where E. coli strains persist in a dormant state or without direct pathogenic impact but contribute to the continuous introduction of plasmid-borne genes into different ecological niches ( Koutsoumanis et al., 2021 ). Additionally, environmental conditions like contamination from agricultural runoff or sewage could lead to the spread of resistant strains in the broader ecosystem, indirectly affecting both animal and human populations ( Koutsoumanis et al., 2021 ; Pormohammad et al., 2019 ). The use of plasmid network visualization provides a powerful tool for understanding the complex relationships between E. coli isolates from different sources. The PCA results offer valuable insights into the complex interactions between humans, livestock, and the environment, particularly concerning E. coli transmission and AMR dynamics ( McEwen & Collignon, 2018 ; Velazquez-Meza et al., 2022 ). The distinct clustering of the Kenyan isolates suggests that these isolates share a common genetic signature, possibly reflecting specific local factors that influence E. coli populations, such as regional environmental conditions, agricultural practices, or unique microbial reservoirs. The broader dispersion of the isolates from humans, livestock, and the environment across the PCA plot indicates significant variability within these groups, pointing to a heterogeneous nature of E. coli populations in different ecological niches. This could reflect a variety of factors, including the diversity of E. coli strains in different host species, environmental influences such as water quality or soil conditions, and the role of human behavior (e.g., antibiotic use, hygiene practices) in shaping microbial populations. The results highlight the need for integrated One Health surveillance strategies to monitor and mitigate the spread of AMR across human, animal, and environmental reservoirs. The presence of clinically relevant resistance genes in non-human sources raises concerns about zoonotic transmission and the role of environmental compartments as AMR reservoirs. Conclusion and recommendations This study highlights the extensive distribution of AMR genes across multiple ecological niches, emphasizing the urgent need for a coordinated One Health response. Key resistance genes were detected across all sources, with plasmid types IncFIA, IncI1, and IncFII showing extensive cross-source sharing, suggesting their critical role in facilitating horizontal gene transfer. Strong connectivity between human and livestock isolates indicates potential zoonotic transmission. PCA revealed a distinct cluster of Kenyan isolates, likely driven by localized antibiotic use and environmental conditions, while other isolates were more dispersed, reflecting greater genetic diversity influenced by different ecological and host-specific pressures. Addressing these challenges requires a multidisciplinary approach integrating enhanced surveillance such as establishing regional AMR surveillance networks integrating human, veterinary, and environmental sectors. improved wastewater management, antimicrobial stewardship, and advanced genomic and bioinformatics techniques. Implementing these strategies within the One Health framework is essential to mitigate the spread of antimicrobial resistance across ecosystems. Future research should include targeted epidemiological studies to directly link transmission pathways and assess the impact of antimicrobial stewardship interventions. Study Strengths and Limitations This study leveraged publicly available datasets compiled from multiple independent studies, enabling access to a large and diverse sample set. While this approach enhances the breadth of analysis and potential for general insights, it also introduces variability due to differences in sampling frames, definitions, and methodologies used across the original studies. Care was taken to account for these differences during analysis; however, some residual heterogeneity may influence data comparability and interpretation. Data Availability All raw WGS data, along with the reference E. coli strain K-12 substr. MG1655 were retrieved from the NCBI GenBank database using the following accession numbers: PRJEB12361, PRJEB12376, PRJEB71714, PRJEB32607 https://www.ncbi.nlm.nih.gov/bioproject/PRJEB12361/ https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB12376 https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB71714 https://www.ncbi.nlm.nih.gov/bioproject/PRJEB32607/ Author Contributions BL and VS conceived and designed the study. BL analyzed and interpreted the data. BL and VS wrote the manuscript, and submitted it for publication. The authors are driven by a commitment to advancing the integration of genomic surveillance into routine healthcare systems. Conflict of Interest The authors declare no conflict of interest. Ethical Clearance No ethical clearance was required, as the data were downloaded from the public database. Funding No funds were allocated for the analysis or preparation of this manuscript. View this table: View inline View popup S1: Supplement materials of Antimicrobial resistance (AMR) genes detected Acknowledgements The authors acknowledge all research teams and study participants involved in generating the Whole Genome Sequences and submitting them to public repositories. References ↵ Accogli , M. , Fortini , D. , Giufrè , M. , Graziani , C. , Dolejska , M. , Carattoli , a , & Cerquetti , M. ( 2013 ). IncI1 plasmids associated with the spread of CMY-2, CTX-M-1 and SHV-12 in Escherichia coli of animal and human origin . Clinical Microbiology and Infection : The Official Publication of the European Society of Clinical Microbiology and Infectious Diseases , 19 ( 5 ), E238 – 40 . doi: 10.1111/1469-0691.12128 OpenUrl CrossRef PubMed ↵ Altayb , H. N. , Elbadawi , H. S. , Alzahrani , F. A. , Baothman , O. , Kazmi , I. , Nadeem , M. S. , Hosawi , S. , & Chaieb , K . ( 2022 ). Co-Occurrence of β-Lactam and Aminoglycoside Resistance Determinants among Clinical and Environmental Isolates of Klebsiella pneumoniae and Escherichia coli: A Genomic Approach . Pharmaceuticals , 15 ( 8 ). doi: 10.3390/ph15081011 OpenUrl CrossRef ↵ Bailey , J. K. , Pinyon , J. L. , Anantham , S. , & Hall , R. M . ( 2011 ). Distribution of the blaTEM gene and blaTEM-containing transposons in commensal Escherichia coli . The Journal of Antimicrobial Chemotherapy , 66 ( January ), 745 – 751 . doi: 10.1093/jac/dkq529 OpenUrl CrossRef PubMed Web of Science ↵ Baker , S. J. , Payne , D. J. , Rappuoli , R. , & De Gregorio , E. ( 2018 ). Technologies to address antimicrobial resistance . In Proceedings of the National Academy of Sciences of the United States of America (Vol. 115 , Issue 51 , pp. 12887 – 12895 ). National Academy of Sciences. doi: 10.1073/pnas.1717160115 OpenUrl Abstract / FREE Full Text ↵ Bolger , A. M. , Lohse , M. , & Usadel , B . ( 2014 ). Trimmomatic: A flexible trimmer for Illumina sequence data . Bioinformatics , 30 ( 15 ), 2114 – 2120 . doi: 10.1093/bioinformatics/btu170 OpenUrl CrossRef PubMed Web of Science Bush , K. , Courvalin , P. , Dantas , G. , Davies , J. , Eisenstein , B. , Huovinen , P. , Jacoby , G. A. , Kishony , R. , Kreiswirth , B. N. , Kutter , E. , Lerner , S. A. , Levy , S. , Lewis , K. , Lomovskaya , O. , Miller , J. H. , Mobashery , S. , Piddock , L. J. V. , Projan , S. , Thomas , C. M. , … Zgurskaya , H. I . ( 2011 ). Tackling antibiotic resistance . In Nature Reviews Microbiology (Vol. 9 , Issue 12 , pp. 894 – 896 ). doi: 10.1038/nrmicro2693 OpenUrl CrossRef PubMed ↵ Cella , E. , Giovanetti , M. , Benedetti , F. , Scarpa , F. , Johnston , C. , Borsetti , A. , Ceccarelli , G. , Azarian , T. , Zella , D. , & Ciccozzi , M . ( 2023 ). Joining Forces against Antibiotic Resistance: The One Health Solution . In Pathogens (Vol. 12 , Issue 9 ). Multidisciplinary Digital Publishing Institute (MDPI) . doi: 10.3390/pathogens12091074 OpenUrl CrossRef ↵ Chen , R. , Li , C. , Ge , H. , Qiao , J. , Fang , L. , Liu , C. , Gou , J. , & Guo , X . ( 2024 ). Difference analysis and characteristics of incompatibility group plasmid replicons in gram-negative bacteria with different antimicrobial phenotypes in Henan, China . BMC Microbiology , 24 ( 1 ). doi: 10.1186/s12866-024-03212-9 OpenUrl CrossRef PubMed ↵ Danecek , P. , Auton , A. , Abecasis , G. , Albers , C. A. , Banks , E. , DePristo , M. A. , Handsaker , R. E. , Lunter , G. , Marth , G. T. , Sherry , S. T. , McVean , G. , & Durbin , R . ( 2011 ). The variant call format and VCFtools . Bioinformatics , 27 ( 15 ), 2156 – 2158 . doi: 10.1093/bioinformatics/btr330 OpenUrl CrossRef PubMed Web of Science ↵ Danecek , P. , Bonfield , J. K. , Liddle , J. , Marshall , J. , Ohan , V. , Pollard , M. O. , Whitwham , A. , Keane , T. , McCarthy , S. A. , & Davies , R. M . ( 2021 ). Twelve years of SAMtools and BCFtools . GigaScience , 10 ( 2 ). doi: 10.1093/gigascience/giab008 OpenUrl CrossRef Doi , Y. , & Paterson , D. L . ( 2007 ). Detection of plasmid-mediated class C beta-lactamases . International Journal of Infectious Diseases : IJID : Official Publication of the International Society for Infectious Diseases , 11 ( 3 ), 191 – 197 . doi: 10.1016/j.ijid.2006.07.008 OpenUrl CrossRef PubMed Web of Science ↵ Florensa , A. F. , Kaas , R. S. , Clausen , P. T. L. C. , Aytan-Aktug , D. , & Aarestrup , F. M . ( 2022 ). ResFinder – an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes . Microbial Genomics , 8 ( 1 ). doi: 10.1099/mgen.0.000748 OpenUrl CrossRef ↵ Gaşpar , C. M. , Cziszter , L. T. , Lăzărescu , C. F. , Ţibru , I. , Pentea , M. , & Butnariu , M. ( 2021 ). Antibiotic resistance among escherichia coli isolates from hospital wastewater compared to community wastewater . Water (Switzerland ) , 13 ( 23 ). doi: 10.3390/w13233449 OpenUrl CrossRef ↵ Gemeda , B. A. , Wieland , B. , Alemayehu , G. , Knight-Jones , T. J. D. , Wodajo , H. D. , Tefera , M. , Kumbe , A. , Olani , A. , Abera , S. , & Amenu , K . ( 2023 ). Antimicrobial Resistance of Escherichia coli Isolates from Livestock and the Environment in Extensive Smallholder Livestock Production Systems in Ethiopia . Antibiotics , 12 ( 5 ). doi: 10.3390/antibiotics12050941 OpenUrl CrossRef ↵ Graham , D. W. , Bergeron , G. , Bourassa , M. W. , Dickson , J. , Gomes , F. , Howe , A. , Kahn , L. H. , Morley , P. S. , Scott , H. M. , Simjee , S. , Singer , R. S. , Smith , T. C. , Storrs , C. , & Wittum , T. E. ( 2019 ). Complexities in understanding antimicrobial resistance across domesticated animal, human, and environmental systems . In Annals of the New York Academy of Sciences (Vol. 1441 , Issue 1 , pp. 17 – 30 ). Blackwell Publishing Inc . doi: 10.1111/nyas.14036 OpenUrl CrossRef PubMed ↵ Hazen , T. H. , Mettus , R. , McElheny , C. L. , Bowler , S. L. , Nagaraj , S. , Doi , Y. , & Rasko , D. A . ( 2018 ). Diversity among bla KPC-containing plasmids in Escherichia coli and other bacterial species isolated from the same patients . Scientific Reports , 8 ( 1 ). doi: 10.1038/s41598-018-28085-7 OpenUrl CrossRef PubMed ↵ Ibekwe , A. , Durso , L. , Ducey , T. F. , Oladeinde , A. , Jackson , C. R. , Frye , J. G. , Dungan , R. , Moorman , T. , Brooks , J. P. , Obayiuwana , A. , Karathia , H. , Fanelli , B. , & Hasan , N . ( 2021 ). Diversity of plasmids and genes encoding resistance to extended-spectrum β-lactamase in escherichia coli from different animal sources . Microorganisms , 9 ( 5 ). doi: 10.3390/microorganisms9051057 OpenUrl CrossRef ↵ Inda-Díaz , J. S. , Lund , D. , Parras-Moltó , M. , Johnning , A. , Bengtsson-Palme , J. , & Kristiansson , E . ( 2023 ). Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes . Microbiome , 11 ( 1 ). doi: 10.1186/s40168-023-01479-0 OpenUrl CrossRef PubMed ↵ Jaja , I. F. , Oguttu , J. , Jaja , C. J. I. , & Green , E . ( 2020 ). Prevalence and distribution of antimicrobial resistance determinants of Escherichia coli isolates obtained from meat in South Africa . PLoS ONE , 15 ( 5 ). doi: 10.1371/journal.pone.0216914 OpenUrl CrossRef Kanje , L. E. , Kumburu , H. , Kuchaka , D. , Shayo , M. , Juma , M. A. , Kimu , P. , Beti , M. , van Zwetselaar , M. , Wadugu , B. , Mmbaga , B. T. , Mkumbaye , S. I. , & Sonda , T. ( 2024 ). Short reads-based characterization of pathotype diversity and drug resistance among Escherichia coli isolated from patients attending regional referral hospitals in Tanzania . BMC Medical Genomics , 17 ( 1 ). doi: 10.1186/s12920-024-01882-y OpenUrl CrossRef PubMed ↵ Kiiti , R. W. , Komba , E. V. , Msoffe , P. L. , Mshana , S. E. , Rweyemamu , M. , & Matee , M. I. N . ( 2021 ). Antimicrobial Resistance Profiles of Escherichia coli Isolated from Broiler and Layer Chickens in Arusha and Mwanza, Tanzania . International Journal of Microbiology , 2021 . doi: 10.1155/2021/6759046 OpenUrl CrossRef ↵ Koutsoumanis , K. , Allende , A. , Álvarez-Ordóñez , A. , Bolton , D. , Bover-Cid , S. , Chemaly , M. , Davies , R. , De Cesare , A. , Herman , L. , Hilbert , F. , Lindqvist , R. , Nauta , M. , Ru , G. , Simmons , M. , Skandamis , P. , Suffredini , E. , Argüello , H. , Berendonk , T. , Cavaco , L. M. , … Peixe , L. ( 2021 ). Role played by the environment in the emergence and spread of antimicrobial resistance (AMR) through the food chain . EFSA Journal , 19 ( 6 ). doi: 10.2903/j.efsa.2021.6651 OpenUrl CrossRef Leclercq , R. , & Courvalin , P . ( 2002 ). Resistance to macrolides and related antibiotics in Streptococcus pneumoniae . In Antimicrobial Agents and Chemotherapy (Vol. 46 , Issue 9 , pp. 2727 – 2734 ). doi: 10.1128/AAC.46.9.2727-2734.2002 OpenUrl FREE Full Text ↵ Lyimo , B. , Buza , J. , Subbiah , M. , Smith , W. , & Call , D. R . ( 2016a ). Comparison of antibiotic resistant Escherichia coli obtained from drinking water sources in northern Tanzania: a cross-sectional study . BMC Microbiology , 16 ( 1 ). doi: 10.1186/s12866-016-0870-9 OpenUrl CrossRef PubMed ↵ Lyimo , B. , Buza , J. , Subbiah , M. , Smith , W. , & Call , D. R . ( 2016b ). Comparison of antibiotic resistant Escherichia coli obtained from drinking water sources in northern Tanzania: a cross-sectional study . BMC Microbiology , 16 ( 1 ), 1 – 10 . doi: 10.1186/s12866-016-0870-9 OpenUrl CrossRef PubMed Lyimo , B. , Buza , J. , Subbiah , M. , Temba , S. , Kipasika , H. , Smith , W. , & Call , D. R . ( 2016 ). IncF plasmids are commonly carried by antibiotic resistant Escherichia coli isolated from drinking water sources in northern Tanzania . International Journal of Microbiology , 2016 . doi: 10.1155/2016/3103672 OpenUrl CrossRef PubMed ↵ Manyahi , J. , Matee , M. I. , Majigo , M. , Moyo , S. , Mshana , S. E. , & Lyamuya , E. F . ( 2014 ). Predominance of multi-drug resistant bacterial pathogens causing surgical site infections in Muhimbili national hospital, Tanzania . BMC Research Notes , 7 ( 1 ), 500 . doi: 10.1186/1756-0500-7-500 OpenUrl CrossRef PubMed ↵ Manyahi , J. , Moyo , S. J. , Kibwana , U. , Goodman , R. N. , Allman , E. , Hubbard , A. T. M. , Blomberg , B. , Langeland , N. , & Roberts , A. P . ( 2022 ). First identification of blaNDM-5producing Escherichia coli from neonates and a HIV infected adult in Tanzania . Journal of Medical Microbiology , 71 ( 2 ). doi: 10.1099/jmm.0.001513 OpenUrl CrossRef ↵ McEwen , S. A. , & Collignon , P. J . ( 2018 ). Antimicrobial Resistance: a One Health Perspective . Microbiology Spectrum , 6 ( 2 ). doi: 10.1128/microbiolspec.arba-0009-2017 OpenUrl CrossRef ↵ Minja , C. A. , Shirima , G. , & Mshana , S. E . ( 2021 ). Conjugative plasmids disseminating ctx-m-15 among human, animals and the environment in Mwanza Tanzania: A need to intensify one health approach . Antibiotics , 10 ( 7 ). doi: 10.3390/antibiotics10070836 OpenUrl CrossRef ↵ Moremi , N. , Manda , E. V. , Falgenhauer , L. , Ghosh , H. , Imirzalioglu , C. , Matee , M. , Chakraborty , T. , & Mshana , S. E . ( 2016 ). Predominance of CTX-M-15 among ESBL producers from environment and fish gut from the shores of Lake Victoria in Mwanza, Tanzania . Frontiers in Microbiology , 7 ( DEC ). doi: 10.3389/fmicb.2016.01862 OpenUrl CrossRef Mshana , S. E. , Falgenhauer , L. , Mirambo , M. M. , Mushi , M. F. , Moremi , N. , Julius , R. , Seni , J. , Imirzalioglu , C. , Matee , M. , & Chakraborty , T . ( 2016 ). Predictors of blaCTX-M-15 in varieties of Escherichia coli genotypes from humans in community settings in Mwanza, Tanzania . BMC Infectious Diseases , 16 ( 1 ). doi: 10.1186/s12879-016-1527-x OpenUrl CrossRef PubMed ↵ Murray , C. J. , Ikuta , K. S. , Sharara , F. , Swetschinski , L. , Robles Aguilar , G. , Gray , A. , Han , C. , Bisignano , C. , Rao , P. , Wool , E. , Johnson , S. C. , Browne , A. J. , Chipeta , M. G. , Fell , F. , Hackett , S. , Haines-Woodhouse , G. , Kashef Hamadani , B. H. , Kumaran , E. A. P. , McManigal , B. , … Naghavi , M . ( 2022a ). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis . The Lancet , 399 ( 10325 ), 629 – 655 . doi: 10.1016/S0140-6736(21)02724-0 OpenUrl CrossRef PubMed ↵ Murray , C. J. , Ikuta , K. S. , Sharara , F. , Swetschinski , L. , Robles Aguilar , G. , Gray , A. , Han , C. , Bisignano , C. , Rao , P. , Wool , E. , Johnson , S. C. , Browne , A. J. , Chipeta , M. G. , Fell , F. , Hackett , S. , Haines-Woodhouse , G. , Kashef Hamadani , B. H. , Kumaran , E. A. P. , McManigal , B. , … Naghavi , M . ( 2022b ). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis . The Lancet , 399 ( 10325 ), 629 – 655 . doi: 10.1016/S0140-6736(21)02724-0 OpenUrl CrossRef PubMed ↵ Neema , C. , Nyambura , M. , Janneth , M. , Eliudi , E. , Edwin , S. , Pascale , O. , & Beverly , E . ( 2023 ). Surveillance of antimicrobial resistance in human health in Tanzania: 2016-2021 . In African Journal of Laboratory Medicine (Vol. 12 , Issue 1 ). AOSIS (Pty) Ltd. doi: 10.4102/ajlm.v12i1.2053 OpenUrl CrossRef ↵ Nordmann , P. , Naas , T. , & Poirel , L . ( 2011 ). Global spread of carbapenemase producing Enterobacteriaceae . Emerging Infectious Diseases , 17 ( 10 ), 1791 – 1798 . doi: 10.3201/eid1710.110655 OpenUrl CrossRef PubMed ↵ Nordmann , P. , & Poirel , L . ( 2005 ). Emergence of plasmid-mediated resistance to quinolones in Enterobacteriaceae . The Journal of Antimicrobial Chemotherapy , 56 ( 3 ), 463 – 469 . doi: 10.1093/jac/dki245 OpenUrl CrossRef PubMed Web of Science ↵ Pankok , F. , Taudien , S. , Dekker , D. , Thye , T. , Oppong , K. , Akenten , C. W. , Lamshöft , M. , Jaeger , A. , Kaase , M. , Scheithauer , S. , Tanida , K. , Frickmann , H. , May , J. , & Loderstädt , U . ( 2022 ). Epidemiology of Plasmids in Escherichia coli and Klebsiella pneumoniae with Acquired Extended Spectrum Beta-Lactamase Genes Isolated from Chronic Wounds in Ghana . Antibiotics , 11 ( 5 ). doi: 10.3390/antibiotics11050689 OpenUrl CrossRef ↵ Peirano , G. , Richardson , D. , Nigrin , J. , McGeer , A. , Loo , V. , Toye , B. , Alfa , M. , Pienaar , C. , Kibsey , P. , & Pitout , J. D. D . ( 2010 ). High prevalence of ST131 isolates producing CTX-M-15 and CTX-M-14 among extended-spectrum-beta-lactamase-producing Escherichia coli isolates from Canada . Antimicrobial Agents and Chemotherapy , 54 ( 3 ), 1327 – 1330 . doi: 10.1128/AAC.01338-09 OpenUrl Abstract / FREE Full Text ↵ Pormohammad , A. , Nasiri , M. J. , & Azimi , T. ( 2019 ). Prevalence of antibiotic resistance in escherichia coli strains simultaneously isolated from humans, animals, food, and the environment: A systematic review and meta-analysis . In Infection and Drug Resistance (Vol. 12 , pp. 1181 – 1197 ). Dove Medical Press Ltd . doi: 10.2147/IDR.S201324 OpenUrl CrossRef PubMed ↵ Purcell , S. , Neale , B. , Todd-Brown , K. , Thomas , L. , Ferreira , M. A. R. , Bender , D. , Maller , J. , Sklar , P. , De Bakker , P. I. W. , Daly , M. J. , & Sham , P. C. ( 2007 ). PLINK: A tool set for whole-genome association and population-based linkage analyses . American Journal of Human Genetics , 81 ( 3 ), 559 – 575 . doi: 10.1086/519795 OpenUrl CrossRef PubMed ↵ Quitmeyer , A . ( 2024 ). Advancements in Bioinformatics: From Research to Clinical Application Correspondence to . 20 ( 3 ). doi: 10.24105/ejbi.2024.20.4.264-265 OpenUrl CrossRef Ramirez , M. S. , & Tolmasky , M. E . ( 2010 ). Aminoglycoside modifying enzymes . Drug Resistance Updates , 13 ( 6 ), 151 – 171 . doi: 10.1016/j.drup.2010.08.003 OpenUrl CrossRef PubMed Web of Science Roberts , M. C . ( 2005 ). Update on acquired tetracycline resistance genes . In FEMS Microbiology Letters (Vol. 245 , Issue 2 , pp. 195 – 203 ). Elsevier. doi: 10.1016/j.femsle.2005.02.034 OpenUrl CrossRef PubMed Web of Science ↵ Rozwandowicz , M. , Brouwer , M. S. M. , Fischer , J. , Wagenaar , J. A. , Gonzalez-Zorn , B. , Guerra , B. , Mevius , D. J. , & Hordijk , J . ( 2018a ). Plasmids carrying antimicrobial resistance genes in Enterobacteriaceae . Journal of Antimicrobial Chemotherapy , 73 ( 5 ), 1121 – 1137 . doi: 10.1093/jac/dkx488 OpenUrl CrossRef PubMed ↵ Rozwandowicz , M. , Brouwer , M. S. M. , Fischer , J. , Wagenaar , J. A. , Gonzalez-Zorn , B. , Guerra , B. , Mevius , D. J. , & Hordijk , J . ( 2018b ). Plasmids carrying antimicrobial resistance genes in Enterobacteriaceae . Journal of Antimicrobial Chemotherapy , 73 ( 5 ), 1121 – 1137 . doi: 10.1093/jac/dkx488 OpenUrl CrossRef PubMed Ruiz , J . ( 2003 ). Mechanisms of resistance to quinolones: Target alterations, decreased accumulation and DNA gyrase protection . In Journal of Antimicrobial Chemotherapy (Vol. 51 , Issue 5 , pp. 1109 – 1117 ). doi: 10.1093/jac/dkg222 OpenUrl CrossRef PubMed Web of Science ↵ Satam , H. , Joshi , K. , Mangrolia , U. , Waghoo , S. , Zaidi , G. , Rawool , S. , Thakare , R. P. , Banday , S. , Mishra , A. K. , Das , G. , & Malonia , S. K . ( 2023 ). Next-Generation Sequencing Technology: Current Trends and Advancements . In Biology (Vol. 12 , Issue 7 ). Multidisciplinary Digital Publishing Institute (MDPI) . doi: 10.3390/biology12070997 OpenUrl CrossRef Schwarz , S. , Kehrenberg , C. , Doublet , B. , & Cloeckaert , A . ( 2004 ). Molecular basis of bacterial resistance to chloramphenicol and florfenicol . In FEMS Microbiology Reviews (Vol. 28 , Issue 5 , pp. 519 – 542 ). doi: 10.1016/j.femsre.2004.04.001 OpenUrl CrossRef PubMed Web of Science ↵ Seemann , T . ( 2014 ). Prokka: Rapid prokaryotic genome annotation . Bioinformatics , 30 ( 14 ), 2068 – 2069 . doi: 10.1093/bioinformatics/btu153 OpenUrl CrossRef PubMed Web of Science ↵ Shawa , M. , Furuta , Y ., Mulenga , G. , Mubanga , M. , Mulenga , E. , Zorigt , T. , Kaile , C. , Simbotwe , M. , Paudel , A. , Hang’ombe , B. , & Higashi , H. ( 2021 ). Novel chromosomal insertions of ISEcp1-bla CTX-M-15 and diverse antimicrobial resistance genes in Zambian clinical isolates of Enterobacter cloacae and Escherichia coli . Antimicrobial Resistance and Infection Control , 10 ( 1 ). doi: 10.1186/s13756-021-00941-8 OpenUrl CrossRef ↵ Shi , K. , Caldwell , S. J. , Fong , D. H. , & Berghuis , A. M . ( 2013 ). Prospects for circumventing aminoglycoside kinase mediated antibiotic resistance . Frontiers in Cellular and Infection Microbiology , 3 ( June ), 22 . doi: 10.3389/fcimb.2013.00022 OpenUrl CrossRef Sköld , O . ( 2000 ). Sulfonamide resistance: Mechanisms and trends . Drug Resistance Updates , 3 ( 3 ), 155 – 160 . doi: 10.1054/drup.2000.0146 OpenUrl CrossRef PubMed Web of Science ↵ Soni , T. , Pandit , R. , Blake , D. , Joshi , C. , & Joshi , M . ( 2021 ). Comparative analysis of two NGS platforms and different databases for analysis of AMR genes . doi: 10.1101/2021.12.27.474239 OpenUrl Abstract / FREE Full Text ↵ Subbiah , M. , Caudell , M. A. , Mair , C. , Davis , M. A. , Matthews , L. , Quinlan , R. J. , Quinlan , M. B. , Lyimo , B. , Buza , J. , Keyyu , J. , & Call , D. R . ( 2020 ). Antimicrobial resistant enteric bacteria are widely distributed amongst people, animals and the environment in Tanzania . Nature Communications , 11 ( 1 ). doi: 10.1038/s41467-019-13995-5 OpenUrl CrossRef PubMed ↵ UNICEF . ( 2023 ). THE URGENT THREAT OF DRUG-RESISTANT INFECTIONS PROTECTING CHILDREN WORLDWIDE A UNICEF Guidance Note on Antimicrobial Resistance . ↵ United Republic of Tanzania . ( 2018 ). NATIONAL ANTIMICROBIAL RESISTANCE SURVEILLANCE FRAMEWORK THE UNITED REPUBLIC OF TANZANIA . ↵ Velazquez-Meza , M. E. , Galarde-López , M. , Carrillo-Quiróz , B. , & Alpuche-Aranda , C. M . ( 2022 ). Antimicrobial resistance: One Health approach . In Veterinary World (Vol. 15 , Issue 3 , pp. 743 – 749 ). Veterinary World . doi: 10.14202/vetworld.2022.743-749 OpenUrl CrossRef PubMed ↵ Welch , T. J. , Fricke , W. F. , McDermott , P. F. , White , D. G. , Rosso , M. L. , Rasko , D. a. , Mammel , M. K. , Eppinger , M. , Rosovitz , M. J. , Wagner , D. , Rahalison , L. , LeClerc , J. E. , Hinshaw , J. M. , Lindler , L. E. , Cebula , T. a. , Carniel , E. , & Ravel , J. ( 2007 ). Multiple antimicrobial resistance in plague: An emerging public health risk . PLoS ONE , 2 ( 3 ). doi: 10.1371/journal.pone.0000309 OpenUrl CrossRef ↵ WHO . ( 2015 ). Global action plan on antimicrobial resistance . www.paprika-annecy.com ↵ Wu , N. , Chen , B. , Tian , S. , & Chu , Y. ( 2014 ). The inoculum effect of antibiotics against CTX-M-extended-spectrum β-lactamase-producing Escherichia coli . Annals of Clinical Microbiology and Antimicrobials , 13 ( 1 ), 45 . doi: 10.1186/s12941-014-0045-1 OpenUrl CrossRef PubMed ↵ Zhang , Y. , Zhang , N. , Wang , M. , Luo , M. , Peng , Y. , Li , Z. , Xu , J. , Ou , M. , Kan , B. , Li , X. , & Lu , X . ( 2023 ). The prevalence and distribution of aminoglycoside resistance genes . In Biosafety and Health (Vol. 5 , Issue 1 , pp. 14 – 20 ). Elsevier B.V. doi: 10.1016/j.bsheal.2023.01.001 OpenUrl CrossRef ↵ Mwakyoma , A. A. , Kidenya , B. R. , Minja , C. A. , Mushi , M. F. , Sandeman , A. , Sabiti , W. , … & Mshana , S. E. ( 2023 ). Allele distribution and phenotypic resistance to ciprofloxacin and gentamicin among extended-spectrum β-lactamase-producing Escherichia coli isolated from the urine, stool, animals, and environments of patients with presumptive urinary tract infection in Tanzania . Frontiers in Antibiotics , 2 , 1164016 . OpenUrl PubMed ↵ Sonola , V. S. , Katakweba , A. , Misinzo , G. , & Matee , M. I . ( 2022 ). Molecular epidemiology of antibiotic resistance genes and virulence factors in multidrug-resistant Escherichia coli isolated from rodents, humans, chicken, and household soils in Karatu, Northern Tanzania . International Journal of Environmental Research and Public Health , 19 ( 9 ), 5388 . OpenUrl ↵ Kemp , S. A. ( 2020 ). Patterns of Antimicrobial Resistant E. coli and Genetic Interplay Between Livestock, Humans and Their Shared Environment in a High-Density Livestock-Human Population in Western Kenya . The University of Liverpool (United Kingdom) . View the discussion thread. Back to top Previous Next Posted April 09, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Antimicrobial Resistance Gene Distribution and Population Structure of Escherichia coli isolated from Humans, Livestock, and the Environment: Insights from a One Health Approach Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Antimicrobial Resistance Gene Distribution and Population Structure of Escherichia coli isolated from Humans, Livestock, and the Environment: Insights from a One Health Approach Beatus Lyimo , Valery Sonola medRxiv 2025.04.07.25325408; doi: https://doi.org/10.1101/2025.04.07.25325408 Share This Article: Copy Citation Tools Antimicrobial Resistance Gene Distribution and Population Structure of Escherichia coli isolated from Humans, Livestock, and the Environment: Insights from a One Health Approach Beatus Lyimo , Valery Sonola medRxiv 2025.04.07.25325408; doi: https://doi.org/10.1101/2025.04.07.25325408 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9220) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffd2bc6ad68dfa9',t:'MTc3OTQ2NzA2NQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();