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. 2026 Jan 23;23(2):223–232. doi: 10.1007/s10393-026-01775-9

Diverse Reservoirs of Third-Generation Cephalosporin-Resistant Escherichia coli in Guatemala

Gabriela Perez-Garcia 1,✉, Danilo Alvarez 2, Andrea Gomez Barillas 2, Renata Mendizabal-Cabrera 2, Brooke M Ramay 2,3, Tin Ho 4, Nikolina Walas 4, Jay P Graham 4
PMCID: PMC13287249  PMID: 41577943

Abstract

In Guatemala, there is a significant gap in our understanding of the prevalence of antibiotic resistance across different reservoirs likely to affect human exposures. We conducted a One Health study of produce, poultry, water, and humans within peri-urban and urban Guatemalan markets to characterize the prevalence of third-generation cephalosporin-resistant Escherichia coli (3GCR-EC). We screened a total of 132 samples for 3GCR-EC using selective media. Samples were collected across three markets in Guatemala and included produce (n = 32), public tap water (n = 30), commercial and non-commercial poultry (n = 40), and human fecal matter (n = 30). We measured phenotypic and genotypic resistance using culture-based methods and whole-genome sequencing. Among the 132 samples, 87.1% (n = 115) were positive for E. coli, and 67.4% (n = 43) were positive for 3GCR-EC. After removing duplicates, a total of 40 3GCR-EC isolates were sequenced from commercial chicken carcasses (n = 8), non-commercial chicken carcasses (n = 16), water (n = 4), and human feces (n = 12). Out of 11 unique extended-spectrum beta-lactamase genes, the most prevalent gene was blaCTX-M-55. There was high clonal diversity, with 32 distinct sequence types (ST) detected. Clinically important pandemic lineages of E. coli were identified: ST10, ST648, and ST410. This study identified a high prevalence of antibiotic resistance across diverse samples within public Guatemalan markets. The frequent detection of 3GCR-EC, their resistance genes, and epidemiologically important lineages of E. coli identified from humans, produce, water, and poultry samples highlight the importance of implementing more extensive surveillance using a One Health approach to develop coordinated strategies to mitigate the spread of antibiotic resistance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10393-026-01775-9.

Keywords: one Health, antibiotic resistance, Guatemala, Escherichia coli, third-generation cephalosporin resistance, extended-spectrum β-lactamases

Introduction and Purpose

Antibiotic resistance (ABR) is a growing threat to public health. Although projections differ by source, some have estimated that 10 million people will die of antibiotic-resistant infections every year by 2050 (O’Neill, 2014). The drivers of ABR are varied, but non-clinical drivers, including the misuse of antibiotics in livestock production and human medicine, as well as the lack of human and animal wastewater collection and treatment, are important factors in the rising prevalence of ABR (Martin et al., 2015).

Escherichia coli (E. coli) is a fecal indicator bacterial species that is widespread in humans, animals, and the environment (Leonard et al., 2018). Though E. coli can be a commensal organism, there are certain pathotypes, including diarrheagenic E. coli (DEC) and extraintestinal pathogenic E. coli (ExPEC), that primarily cause most extraintestinal diseases, including urinary tract and bloodstream infections in humans (Russo & Johnson, 2000). There are many ExPEC strains, but certain lineages characterized by ExPEC sequence types, such as ST131 and ST10, are commonly found when genotyping E. coli from extraintestinal infections (Manges et al., 2019). These ExPEC sequence types are known as pandemic lineages because of their global abundance and frequency in causing disease.

Resistance in E. coli populations to extended-spectrum beta-lactam drugs has been increasing and is largely mediated by resistance genes known as extended-spectrum beta-lactamases (ESBLs). The spread of these resistance genes can be complex, through both vertical and horizontal gene transfer, and ESBL-producing E. coli (ESBL-EC) are typically resistant to broad-spectrum antibiotics, including penicillin and cephalosporins (Hussain et al., 2021; Knothe et al., 1983). ESBL-EC is commonly used as an indicator in ABR national surveillance programs (Anjum et al., 2021). ESBL-EC has been considered a serious threat by the US Centers for Disease Control and Prevention (CDC), which has introduced programs, such as the Multi-site Gram-negative Surveillance Initiative, to surveil ESBL-Enterobacterales (Duffy et al., 2023). The World Health Organization has also initiated the Tricycle protocol in 2018 to surveil ESBL-EC infections in humans, livestock, and water (WHO Integrated Global Surveillance on ESBL-Producing E. coli Using a One Health Approach, 2021). Third-generation cephalosporin-resistant E. coli (3GCR-EC) is an emerging dominant phenotype and has been associated with more severe clinical outcomes and has been shown to increase the risk of mortality (Camilleri et al., 2023; Pitout & Laupland, 2008).

A “One Health” approach focuses on the study of disease arising from the interaction between humans, the environment, and animals (Mackenzie & Jeggo, 2019). A benefit of this framework in ABR surveillance is the potential to elucidate exposure pathways and assess the risk of particular reservoirs of ABR (Lebov et al., 2017). Previous literature has shown that limited studies are using the One Health framework. In a systematic review of drug-resistant E. coli in India, only two of the 38 studies assessed all three aspects of the One Health framework (Rajagopal et al., 2021). In Central America, most studies have focused primarily on human isolates (O’Neal et al., 2020). Evidence on the importance of the environment in the spread of AMR is growing, but gaps remain (Fletcher, 2015).

In Guatemala and other Central American countries, there are currently no studies to our knowledge that have investigated multiple reservoirs of ABR, including humans. Additionally, it is unknown which sectors, such as livestock and poultry production, may play a key role in the spread of ESBL-EC in Guatemala, which is the largest broiler chicken-producing country in Central America (Jarquin et al., 2015). In this study, we investigated the prevalence of 3GCR-EC and assessed genotypic and phenotypic resistance. We hypothesized that human and non-human 3GCR-EC would be highly related in terms of the ABR genes and sequence types circulating.

Methods

Study Area and Design

We conducted a cross-sectional study utilizing randomly selected convenience samples at three Guatemalan markets located less than 44 miles from the Universidad del Valle de Guatemala (UVG). The markets were in Ciudad de Guatemala, Guatemala; Santa Catarina Pinula, Guatemala; and San Martín Jilotepeque, Chimaltenango.

This study involved environmental and human samples collected from two peri-urban Guatemalan markets and one urban market, from June to August 2022. In order to assess the prevalence of ABR across different reservoirs of interest, the following sources were sampled: non-commercial poultry, commercial poultry, produce, soiled toilet paper from bathroom stalls, and tap water. The final sample consisted of non-commercial chicken carcasses (n = 20), commercial chicken carcasses (n = 20) sold by vendors, produce (strawberries, cilantro, watercress, lettuce) (n = 32) sold by vegetable and fruit vendors, toilet paper used for anal cleansing, found in the trash receptacles of market bathrooms (n = 30), and public tap water samples from the markets (n = 30). Non-commercial chicken carcasses were categorized as those sold without packaging and without an identifiable brand. We used toilet paper samples as a proxy for human fecal samples and to estimate the prevalence of antimicrobial resistance within the human population.

Sample Collection

A randomly generated list of market stalls that sold produce or poultry was created using the RAND function in Excel. This list assigned a market stall for all produce samples and a market stall for all poultry samples that would be visited on each sampling day. If the assigned market stall or the products that needed to be purchased were not available, the next market stall on the list was used. Water samples were collected from publicly available tap water sources using Aquagenx® sampling bags according to the manufacturer’s instructions (Young et al., 2022). Pieces of toilet paper where fecal matter was observed were collected from the bathroom stall trash bins within the markets. Stalls in each bathroom were sampled for toilet paper once per collection day to ensure that no samples were duplicated (Perez-Garcia et al., 2024). The soiled toilet paper was aseptically placed in a sterile Whirl–Pak® bag (55 oz) after collection. All samples were transported to UVG in coolers using ice packs (4–8°C) and analyzed within 6 h of collection.

Detection of Escherichia coli

For commercial and non-commercial poultry samples, whole chicken carcasses were transferred from the sample collection bag into a Whirl–Pak® bag (55 oz). Four hundred milliliters (mL) of peptone buffer was added to the bag and was then shaken at 200 rotations per minute (rpm) for 15 min at room temperature (20–22°C) (Jarquin et al., 2015). After the shaking was complete, the peptone buffer was poured from the bag into three 50-mL conical tubes, each tube having approximately the same volume of the solution. Ten grams of the produce sample was measured on a laboratory balance and then placed into a Whirl–Pak® bag (55 oz). Peptone buffer (90 mL) was added to the bag and shaken for 15 min at 37°C (Montero et al., 2021). Next, the peptone buffer was poured from the bag and distributed evenly into two 50-mL conical tubes. For water sample processing, two milliliters from each positive compartment of the Aquagenx® bag were collected using a disposable pipette. The media were transferred to a 50-mL conical tube.

The tubes with the solution collected from the poultry, produce, and water samples were centrifuged at 5000 rpm for 10 min at 20°C. The supernatant was disposed from each tube, and the bacterial pellet was resuspended using 500 µL (µL) of Tryptic Soy Broth medium (Difco, USA). The media from the tubes respective to each sample were combined into one 50-mL conical tube and then vortexed for 5 s to prepare for culturing. The media was then streaked onto MacConkey agar (Difco, Maryland) absent of antibiotics and incubated at 37°C for 24 h, after which ten lactose-positive colonies appearing dark pink or red were selected from each MacConkey plate for ESBL testing (Lautenbach et al., 2008).

Toilet paper samples were removed from the Whirl–Pak® sampling bag (55 oz) in a biosafety cabinet. A sterile cotton swab saturated with Tryptic Soy Broth medium (Difco, USA) was rubbed on the areas of the toilet paper with the most observed fecal matter and streaked onto a MacConkey plate (Difco, Maryland) and incubated at 37°C for 24 h using the same microbiology protocols as above (Perez-Garcia et al., 2024).

Isolation of 3GCR-EC

Ten putative E. coli colonies selected from the MacConkey plate were plated on CHROMagar™ ESBL and incubated at 37°C for 24 h (Hornsey et al., 2013). Up to two dark pink or red isolates were identified as putative 3GCR-EC and selected from plates. Isolates were frozen at −80°C in Tryptic Soy Broth medium (Difco, USA) with 15% glycerol. We used a negative control (non-ESBL-producing E. coli, ATCC 25922) and a positive control (ESBL-producing E. coli, ATCC 13476) when culturing each batch of samples for quality assurance.

DNA Sequencing and Bioinformatic Analysis

Genomic DNA sequence analysis was completed using the following methods. Briefly, DNA from the selected isolates was extracted using the DNeasy Blood and Tissue kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Whole genome sequencing data from the isolates were generated using an Illumina NovaSeq 6000 platform with a paired-end protocol (Nextera XT library; Illumina) to create ~150-bp length reads. The quality of the reads was assessed using FastQC version 0.11.9 (Babraham Institute, 2019). De novo assemblies of the paired short reads were generated using Unicycler version 0.4.9 (Wick et al., 2017). Computation was carried out using the Savio high-performance cluster (HPC) provided by the University of California, Berkeley. Assembled genome sequences were evaluated based on quality metrics using QUAST version 5.2 (Mikheenko et al., 2018). Contigs below 500 bp were excluded from the final draft assemblies.

Antibiotic resistance genes were identified using the ABRicate tool version 1.0 (Zankari et al., 2012). The ResFinder database, one of the pre-downloaded databases in ABRicate, was used to detect resistance genes in the sequences. The assembled sequences were then genotyped using multilocus sequence typing (MLST) with the MLST package version 2.23.0 (https://github.com/tseemann/mlst) to identify unique clonal groups based on highly conserved bacterial genes (Jolley et al., 2018). Isolates determined to have identical MLST and ARG profiles and that originated from the same sample as another isolate were considered duplicates and excluded from the analysis.

Data visualization and cleaning was conducted using the R programming language (R Core Team, 2021). The R package ‘ggplot2’ was used to visualize the presence of beta-lactamase genes among the isolates.

PHYLOViZ 2 was used to generate the phylogeny tree, utilizing the goeBURST Full MST algorithm (Feil et al., 2004; Francisco et al., 2009, 2012; Nascimento et al., 2017). To improve readability and appearance, a slight visual touch-up of the diagram was performed using the GNU Image Manipulation Program (GIMP).

Results

A total of 132 samples were collected and analyzed during the study period from June to August 2022. One hundred and fifteen samples (87.1%) were positive for putative E. coli, including 50% of water samples and 93.8% of produce samples. Forty-three samples (32.6%) tested positive for 3GCR-EC. A total of eighteen non-commercial poultry samples (90%), nine commercial poultry samples (45%), one produce sample (3.1%), ten human samples (33.3%), and five water samples (16.7%) tested positive for 3GCR-EC (Table 1).

Table 1.

Distribution of E. coli and third-generation cephalosporin-resistant E. coli (3GCR-Ec) positivity by sample type and market, among environmental samples from peri-urban and urban markets in Guatemala City, Guatemala.

E. coli-negative
n (%)		 E. coli-positive
n (%)		 3GCR-Ec-negative
n (%)		 3GCR-Ec-positive
n (%)
Total (n = 132) 17 (12.9) 115 (87.1) 89 (67.4) 43 (32.6)
Sample type
Non-commercial poultry (n = 20) 0 (0.0) 20 (100.0) 2 (10.0) 18 (90.0)
Commercial poultry (n = 20) 0 (0.0) 20 (100.0) 11 (55.0) 9 (45.0)
Produce (n = 32) 2 (6.2) 30 (93.8) 31 (96.9) 1 (3.1)
Human (n = 30) 0 (0.0) 30 (100.0) 20 (66.7) 10 (33.3)
Water (n = 30) 15 (50.0) 15 (50.0) 25 (83.3) 5 (16.7)
Market
San Martín Jilotepeque (n = 46) 0 (0.0) 46 (100.0) 29 (63.0) 17 (37.0)
Guatemala City (n = 10) 0 (0.0) 10 (100.0) 5 (50.0) 5 (50.0)
Santa Catarina Pinula (n = 76) 17 (22.4) 59 (77.6) 55 (72.4) 21 (27.6)
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Seventy-two isolates were identified from 43 samples positive for 3GCR-EC. Sixty-nine isolates (96%) were successfully sequenced, and 40 isolates remained after the removal of duplicates. The sample type distribution of the isolates positive for at least one ESBL gene consisted of 8 out of 20 commercial chicken carcass samples, 16 out of 20 non-commercial chicken carcass samples, 4 out of 30 water samples, 12 out of 30 human samples, and no produce samples (see Supplemental Table S1).

Prevalence and Distribution of ESBL Genes Among Reservoirs

There was variation in the number and type of beta-lactamase (bla) genes present in isolates that contained at least one ESBL gene. Non-commercial poultry isolates had the highest amount of detected blaCTX-M genes, followed by human, commercial poultry, and water isolates. blaTEM genes were the second-most common bla gene detected, with all sample types having isolates that carried these genes. Additionally, non-commercial poultry isolates were the only reservoir type that had isolates detected from each bla gene group analyzed (blaCTX-M, blaOXA, blaSHV, blaTEM) (Table 2).

Table 2.

Prevalence of beta-lactamase resistance genes among sequenced third-generation cephalosporin-resistant E. coli (3GCR-Ec) isolates (n = 40), stratified by sample type and market location*.

CTX-Ma OXAb SHVc TEMd
n (%) n (%) n (%) n (%)
Total 37 1 2 20
Sample type
Non-commercial poultry (n = 16) 14 (37.9) 1 (100.0) 1 (50.0) 8 (40.0)
Commercial poultry (n = 8) 8 (21.6) 0 (0.0) 0 (0.0) 3 (15.0)
Human (n = 12) 11 (29.7) 0 (0.0) 1 (50.0) 7 (35.0)
Water (n = 4) 4 (10.8) 0 (0.0) 0 (0.0) 2 (10.0)
Market
San Martín Jilotepeque (n = 18) 17 (46.0) 0 (0.0) 1 (50.0) 7 (35.0)
Guatemala City (n = 5) 5 (13.5) 0 (0.0) 0 (0.0) 3 (15.0)
Santa Catarina Pinula (n = 17) 15 (40.5) 1 (100.0) 1 (50.0) 10 (50.0)
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*Many isolates had more than one ARG found.

aCTX-M (cefotaximase-Munich) genes.

bOXA (oxacillinase) genes.

cSHV (sulfhydryl variable) genes.

dTEM (Temoniera) genes.

blaCTX-M-55 was the most frequently detected allele among the isolates, found in the highest number of isolates and from isolates selected from non-commercial poultry, human feces, and commercial poultry (Fig. 1). Certain genes were only found in certain reservoir types. For example, blaTEM-1A, blaOXA-10, and blaSHV-2 were alleles only detected in non-commercial poultry, and blaTEM-1B, blaSHV-2, blaTEM-104, and blaCTX-M-27 were only carried by human isolates (Fig. 1).

Figure 1.

Figure 1

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Frequency of allelic variants of ESBL genes identified in third-generation cephalosporin-resistant Escherichia coli (3GCR-Ec) isolates from different sample types.

A reference distribution was created to demonstrate how the number of isolates from each sampling type would be represented if the number of isolates per sample type was proportional to the amount found in each bla gene (Fig. 2). We found that the number of observed isolates was not proportional to the sample size within each sampling type. For example, only 30% of all isolates were from humans, but these represented 100% of the blaTEM-104 gene, and no human isolates carried the blaOXA-10 gene.

Figure 2.

Figure 2

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Proportion of allelic variants of ESBL genes in third-generation cephalosporin-resistant Escherichia coli (3GCR-Ec) isolates from sampling sites. The expected count based on the sample size of each sample type is in the bottom row.

The only gene shared among all four sources was blaTEM-1B (Fig. 1). However, certain bla genes were detected in three of the four sample types. blaTEM-141 was found in non-commercial poultry, commercial poultry, and human fecal samples. blaCTX-M-15 was found in non-commercial poultry, water, and human fecal isolates. blaCTX-M-14 was found in non-commercial poultry, commercial poultry, and water samples (Fig. 1).

Detection of Clinically Important Sequence Types

Among the 40 isolates that contained an ESBL gene, there were 31 known E. coli sequence types and one undefined E. coli sequence type. The most common sequence type was ST10, appearing in human, commercial poultry, non-commercial poultry, and water isolates. There were only 3 other sequence types comprised of isolates from multiple sources: ST2973, ST93, and ST410. ST2973 was shared between non-commercial poultry and commercial poultry isolates. ST93 was detected only in non-commercial poultry and commercial poultry isolates as well. ST410 was found among commercial poultry and human isolates (Fig. 3).

Figure 3.

Figure 3

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Minimum spanning tree analysis of 40 third-generation cephalosporin-resistant Escherichia coli (3GCR-Ec) isolates based on Multi-Locus Sequence Type (MLST) profile (number inside each circle) and according to source (color of the circles, represented by pie chart when multiple sources were present). The size of the circle indicates the relative prevalence of the isolate. Line weight (numbered) indicates phylogenetic distances. A hyphen (-) indicates an undefined sequence type.

The diversity of sequence types varied between sources. Commercial poultry isolates had the highest number of distinct STs (n = 14), followed by human isolates (n = 12 STs), non-commercial poultry isolates (n = 7 STs), and water isolates (n = 4 STs). The human and water samples had different sequence types between each of the isolates, and no sequence types were shared between isolates.

The identified sequence types known to be pandemic lineages (i.e., sequence types frequently associated with infections globally) included ST10 (n = 4), ST648 (n = 2), ST410 (n = 2), ST69 (n = 1), ST23 (n = 1), ST117 (n = 1), ST90 (n = 1), ST224 (n = 1) (see Supplemental Table S2).

Discussion

Our study showed that one-third of the human samples contained E. coli resistant to third-generation cephalosporins. The study findings indicated that non-human sources of 3GCR-EC may play an important role in the dissemination of drug resistance in surveyed markets. The high detection rate of 3GCR-EC identified in non-commercial poultry may indicate that these products have an elevated chance of containing antibiotic-resistant E. coli, and this presents a potential target for further investigation. The high prevalence of 3GCR-EC among non-commercial poultry is also concerning, given recent studies have shown that E. coli from poultry is associated with the development of UTIs in humans (Aziz et al., 2024).

This study had various limitations. Our study was comprised of three markets with a limited geographic range that may not be representative of all Guatemalan markets. Additionally, our identification of non-large-scale industry chickens was dependent on evident branding and asking market vendors about the origin of the product. Due to the subjective nature of this categorization, there is a possibility that there may have been some misclassification for non-large-scale chickens. We were also unable to complete susceptibility testing on the isolates and were limited to using phenotypic testing with a third-generation cephalosporin to determine the presence of 3GCR-EC. Additionally, the presence of the beta-lactamase genes does not necessarily mean that the genes are being expressed. Though there are other pathogens associated with antibiotic resistance, the scope of this study was only focused on E. coli. Finally, our study had a small sample size of isolates from different sources. These limitations should be taken into account when interpreting the results of the study.

We showed that there is an overlap in the resistance genes identified across different sample types. Eleven unique ESBL genes were identified among the reservoir types and humans. Differences were displayed in the genes present between environmental sources and humans, with certain genes only detected in one reservoir type or humans. The most prevalent antibiotic resistance gene among isolates was blaCTX-M-55. There has been a documented spread of blaCTX-M-55-positive E. coli around the world, with China having a particularly high prevalence, where clonal transmission was frequently found between humans, animals, and the environment (Yang et al., 2023). The endemic nature of blaCTX-M-55 and its propensity to be transmitted to humans make it a resistance gene of particular concern in terms of surveillance of antibiotic resistance. The second-most prevalent gene and the gene most frequently detected among human fecal samples was blaCTX-M-15. blaCTX-M-15 has been found worldwide and is one of the most common genotypes among CTX-M beta-lactamase genes associated with drug-resistant UTIs (Pitout & Laupland, 2008).

This study illuminated potential transmission pathways of antibiotic resistance genes by identifying shared genes between reservoir types and humans. The genes carried by multiple reservoir types included blaTEM-1B, blaTEM-141, blaCTX-M-14, blaCTX-M-15, and blaCTX-M-55. These ESBL genes were most frequently found among human samples and were also detected in water and non-commercial poultry. This indicates that water and non-commercial poultry may potentially be important sources of exposure to 3GCR-EC in humans within the surveyed markets.

This study confirmed the presence of antibiotic resistance across a diverse set of reservoirs. These findings are in alignment with similar studies in low- and middle-income countries. For example, in Nairobi, Kenya, a large amount of ABR carriage in humans and livestock was shown, with 47.6% of study isolates displaying resistance to three or more antibiotic classes (Muloi et al., 2019). A study based in Antananarivo, Madagascar, detected a prevalence ranging from 17 to 42% for ESBL-EC in water, human, and animal sources (Gay et al., 2023). blaCTX-M carrying E. coli were present among livestock and humans in Hanoi, Vietnam (Nguyen et al., 2021).

There are no studies to our knowledge that assess the prevalence of antibiotic resistance in multiple reservoirs in Guatemala. There has been detection of community-acquired antimicrobial-resistant Enterobacteriaceae (CA-ARE) studied separately in humans, animals, and the environment in Central America (O’Neal et al., 2020). It has also been shown that in western Guatemala, the prevalence of colonization with extended-spectrum cephalosporin-resistant Enterobacterales was 46% among individuals in the community and 67% among hospitalized patients (Ramay et al., 2023).

Conclusion

Here, we provided evidence of antibiotic resistance across diverse sample types in community markets, with non-commercial poultry being of particular concern. We also found that many ESBL genes are shared across different sequence types and reservoir types, indicating that horizontal gene transfer could be taking place between bacterial populations from the environment, livestock, and humans. Given the exploratory data found in this study, there is a demonstrated need to further characterize transmission of antibiotic resistance through future studies with a “One Health” scope in peri-urban areas of Guatemala.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 17 kb) (17.4KB, docx)

Acknowledgements

We would like to thank the staff of Universidad del Valle de Guatemala for their time, resources, and participation in this research project. We would like to thank the Center for Global Public Health for the additional funding used for research materials. We would also like to thank Nazario Lopez for his assistance in the field and his hard work completing sample collection. Finally, we would like to acknowledge the UC Berkeley Chancellor, Vice Chancellor for Research, and Chief Information Officer for making available the Berkeley Research's Savio high-performance cluster for the computationally heavy tasks of this project.

Funding

Research reported in this publication was supported in part by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH) under Award Number R01AI135118 and R01AI167989. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethics Declaration

This project did not involve any interaction with humans or the collection of confidential health data from individuals. This study also did not involve the collection of samples from live animals. This study was approved as non-human subjects research by the Ethics Committee of the Center for Health Studies of the Universidad del Valle de Guatemala (Protocol 267-05-2022).

Footnotes

Publisher's Note

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References

  1. Anjum MF, Schmitt H, Börjesson S, Berendonk TU, Donner E, Stehling EG, Boerlin P, Topp E, Jardine C, Li X, Li B, Dolejska M, Madec J-Y, Dagot C, Guenther S, Walsh F, Villa L, Veldman K, Sunde M, Pedersen K (2021) The potential of using E. coli as an indicator for the surveillance of antimicrobial resistance (AMR) in the environment. Current Opinion in Microbiology 64:152–158. 10.1016/j.mib.2021.09.011 [DOI] [PubMed] [Google Scholar]
  2. Aziz M, Davis GS, Park DE, Idris AH, Sariya S, Wang Y, Zerbonne S, Nordstrom L, Weaver B, Statham S, Johnson TJ, Campos J, Castro-Nallar E, Crandall KA, Wu Z, Liu CM, DeBiasi RL, Price LB (2024) Pediatric urinary tract infections caused by poultry-associated Escherichia coli. Microbiology Spectrum 12(7):e03415-e3423. 10.1128/spectrum.03415-23 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Babraham Institute. (2019). FastQC. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
  4. Camilleri S, Tsai D, Langham F, Ullah S, Chiong F (2023) Epidemiology, clinical outcomes and risk factors of third-generation cephalosporin-resistant Escherichia coli hospitalized infections in remote Australia: A case–control study. JAC-Antimicrobial Resistance 5(6):dlad138. 10.1093/jacamr/dlad138 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Duffy N, Li R, Czaja CA, Johnston H, Janelle SJ, Jacob JT, Smith G, Wilson LE, Vaeth E, Lynfield R, O’Malley S, Vagnone PS, Dumyati G, Tsay R, Bulens SN, Grass JE, Pierce R, Cassidy PM, Hertzel H, Guh AY (2023) Trends in incidence of carbapenem-resistant enterobacterales in 7 US Sites, 2016–2020. Open Forum Infectious Diseases 10(12):ofad609. 10.1093/ofid/ofad609 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Feil EJ, Li BC, Aanensen DM, Hanage WP, Spratt BG (2004) eBURST: Inferring Patterns of Evolutionary Descent among Clusters of Related Bacterial Genotypes from Multilocus Sequence Typing Data. Journal of Bacteriology 186(5):1518–1530. 10.1128/JB.186.5.1518-1530.2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Fletcher S (2015) Understanding the contribution of environmental factors in the spread of antimicrobial resistance. Environmental Health and Preventive Medicine 20(4):243–252. 10.1007/s12199-015-0468-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Francisco AP, Bugalho M, Ramirez M, Carriço JA (2009) Global optimal eBURST analysis of multilocus typing data using a graphic matroid approach. BMC Bioinformatics 10(1):152. 10.1186/1471-2105-10-152 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Francisco AP, Vaz C, Monteiro PT, Melo-Cristino J, Ramirez M, Carriço JA (2012) PHYLOViZ: Phylogenetic inference and data visualization for sequence based typing methods. BMC Bioinformatics 13(1):87. 10.1186/1471-2105-13-87 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gay N, Rabenandrasana MAN, Panandiniaina HP, Rakotoninidrina MF, Ramahatafandry IT, Enouf V, Roger F, Collard J-M, Cardinale E, Rieux A, Loire E (2023) One Health compartment analysis of ESBL-producing Escherichia coli reveals multiple transmission events in a rural area of Madagascar. Journal of Antimicrobial Chemotherapy 78(8):1848–1858. 10.1093/jac/dkad125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Hornsey M, Phee L, Woodford N, Turton J, Meunier D, Thomas C, Wareham DW (2013) Evaluation of three selective chromogenic media, CHROMagar ESBL, CHROMagar CTX-M and CHROMagar KPC, for the detection of Klebsiella pneumoniae producing OXA-48 carbapenemase. Journal of Clinical Pathology 66(4):348–350. 10.1136/jclinpath-2012-201234 [DOI] [PubMed] [Google Scholar]
  12. Hussain HI, Aqib AI, Seleem MN, Shabbir MA, Hao H, Iqbal Z, Kulyar MF-A, Zaheer T, Li K (2021) Genetic basis of molecular mechanisms in β-lactam resistant gram-negative bacteria. Microbial Pathogenesis 158:105040. 10.1016/j.micpath.2021.105040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Jarquin C, Alvarez D, Morales O, Morales AJ, López B, Donado P, Valencia MF, Arévalo A, Muñoz F, Walls I, Doyle MP, Alali WQ (2015) Salmonella on raw poultry in retail markets in guatemala: levels, antibiotic susceptibility, and serovar distribution. Journal of Food Protection 78(9):1642–1650. 10.4315/0362-028X.JFP-15-117 [DOI] [PubMed] [Google Scholar]
  14. Jolley KA, Bray JE, Maiden MCJ (2018) Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open Research 3:124. 10.12688/wellcomeopenres.14826.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Knothe H, Shah P, Krcmery V, Antal M, Mitsuhashi S (1983) Transferable resistance to cefotaxime, cefoxitin, cefamandole and cefuroxime in clinical isolates of Klebsiella pneumoniae and Serratia marcescens. Infection 11(6):315–317. 10.1007/BF01641355 [DOI] [PubMed] [Google Scholar]
  16. Lautenbach E, Bilker WB, Tolomeo P, Maslow JN (2008) Impact of diversity of colonizing strains on strategies for sampling Escherichia coli from fecal specimens. Journal of Clinical Microbiology 46(9):3094–3096. 10.1128/JCM.00945-08 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Lebov J, Grieger K, Womack D, Zaccaro D, Whitehead N, Kowalcyk B, MacDonald PDM (2017) A framework for One Health research. One Health 3:44–50. 10.1016/j.onehlt.2017.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Leonard AFC, Zhang L, Balfour AJ, Garside R, Hawkey PM, Murray AK, Ukoumunne OC, Gaze WH (2018) Exposure to and colonisation by antibiotic-resistant E. coli in UK coastal water users: Environmental surveillance, exposure assessment, and epidemiological study (Beach Bum Survey). Environment International 114:326–333. 10.1016/j.envint.2017.11.003 [DOI] [PubMed] [Google Scholar]
  19. Mackenzie JS, Jeggo M (2019) The One Health approach—Why is it so important? Tropical Medicine and Infectious Disease 4(2):88. 10.3390/tropicalmed4020088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Manges AR, Geum HM, Guo A, Edens TJ, Fibke CD, Pitout JDD (2019) Global Extraintestinal pathogenic Escherichia coli (ExPEC) Lineages. Clinical Microbiology Reviews 32(3):e00135-e218. 10.1128/CMR.00135-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Martin MJ, Thottathil SE, Newman TB (2015) Antibiotics overuse in animal agriculture: A call to action for health care providers. American Journal of Public Health 105(12):2409–2410. 10.2105/AJPH.2015.302870 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Mikheenko A, Prjibelski A, Saveliev V, Antipov D, Gurevich A (2018) Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34(13):i142–i150. 10.1093/bioinformatics/bty266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Montero L, Irazabal J, Cardenas P, Graham JP, Trueba G (2021) Extended-spectrum beta-lactamase producing-Escherichia coli isolated from irrigation waters and produce in Ecuador. Frontiers in Microbiology 12:709418. 10.3389/fmicb.2021.709418 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Muloi D, Kiiru J, Ward MJ, Hassell JM, Bettridge JM, Robinson TP, Van Bunnik BAD, Chase-Topping M, Robertson G, Pedersen AB, Fèvre EM, Woolhouse MEJ, Kang’ethe EK, Kariuki S (2019) Epidemiology of antimicrobial-resistant Escherichia coli carriage in sympatric humans and livestock in a rapidly urbanizing city. International Journal of Antimicrobial Agents 54(5):531–537. 10.1016/j.ijantimicag.2019.08.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Nascimento M, Sousa A, Ramirez M, Francisco AP, Carriço JA, Vaz C (2017) PHYLOViZ 2.0: Providing scalable data integration and visualization for multiple phylogenetic inference methods. Bioinformatics 33(1):128–129. 10.1093/bioinformatics/btw582 [DOI] [PubMed] [Google Scholar]
  26. Nguyen MN, Hoang HTT, Xavier BB, Lammens C, Le HT, Hoang NTB, Nguyen ST, Pham NT, Goossens H, Dang AD, Malhotra-Kumar S (2021) Prospective One Health genetic surveillance in Vietnam identifies distinct blaCTX-M-harbouring Escherichia coli in food-chain and human-derived samples. Clinical Microbiology and Infection 27(10):1515.e1-1515.e8. 10.1016/j.cmi.2021.01.006 [DOI] [PubMed] [Google Scholar]
  27. O’Neal L, Alvarez D, Mendizábal-Cabrera R, Ramay BM, Graham J (2020) Community-acquired antimicrobial resistant enterobacteriaceae in Central America: A One Health systematic review. International Journal of Environmental Research and Public Health 17(20):7622. 10.3390/ijerph17207622 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. O’Neill, J. (2014). Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations. Review on Antimicrobial Resistance. https://amr-review.org/
  29. Perez-Garcia G, Gomez Barillas A, Mendizábal-Cabrera R, Alvarez D, Ramay BM, Walas N, Graham JP (2024) Soiled toilet paper for community-based surveillance of antibiotic resistance and enteric pathogens: A pilot study. Field Methods. 10.1177/1525822X241287030 [Google Scholar]
  30. Pitout JD, Laupland KB (2008) Extended-spectrum β-lactamase-producing Enterobacteriaceae: An emerging public-health concern. The Lancet Infectious Diseases 8(3):159–166. 10.1016/S1473-3099(08)70041-0 [DOI] [PubMed] [Google Scholar]
  31. R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/.
  32. Rajagopal K, Chandy SJ, Graham JP (2021) A One Health review of community-acquired antimicrobial-resistant Escherichia coli in India. International Journal of Environmental Research and Public Health 18(22):12089. 10.3390/ijerph182212089 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ramay BM, Castillo C, Grajeda L, Santos LF, Romero JC, Lopez MR, Gomez A, Caudell M, Smith RM, Styczynski A, Herzig CTA, Bollinger S, Ning MF, Horton J, Omulo S, Palmer GH, Cordon-Rosales C, Call DR (2023) Colonization with antibiotic-resistant bacteria in a hospital and associated communities in Guatemala: An Antibiotic Resistance in Communities and Hospitals (ARCH) Study. Clinical Infectious Diseases 77(Supplement_1):S82–S88. 10.1093/cid/ciad222 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Russo TA, Johnson JR (2000) Proposal for a new inclusive designation for extraintestinal pathogenic isolates of Escherichia coli: ExPEC. The Journal of Infectious Diseases 181(5):1753–1754. 10.1086/315418 [DOI] [PubMed] [Google Scholar]
  35. WHO Integrated Global Surveillance on ESBL-Producing E. Coli Using a One Health Approach: Implementation and Opportunities (1st ed). (2021). World Health Organization.
  36. Wick RR, Judd LM, Gorrie CL, Holt KE (2017) Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLOS Computational Biology 13(6):e1005595. 10.1371/journal.pcbi.1005595 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Yang J-T, Zhang L-J, Lu Y, Zhang R-M, Jiang H-X (2023) Genomic insights into global blaCTX-M-55-positive Escherichia coli epidemiology and transmission characteristics. Microbiology Spectrum 11(4):e01089-e1123. 10.1128/spectrum.01089-23 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Young CCW, Karmacharya D, Bista M, Sharma AN, Goldstein T, Mazet JAK, Johnson CK (2022) Antibiotic resistance genes of public health importance in livestock and humans in an informal urban community in Nepal. Scientific Reports 12(1):13808. 10.1038/s41598-022-14781-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup FM, Larsen MV (2012) Identification of acquired antimicrobial resistance genes. Journal of Antimicrobial Chemotherapy 67(11):2640–2644. 10.1093/jac/dks261 [DOI] [PMC free article] [PubMed] [Google Scholar]

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