• 1.

    Okeno TO, Kahi AK, Peters KJ, 2012. Characterization of indigenous chicken production systems in Kenya. Trop Anim Health Prod 44: 601608.

  • 2.

    Mack S, Hoffmann D, Otte J, 2005. The contribution of poultry to rural development. Worlds Poult Sci J 61: 714.

  • 3.

    Dibner JJ, Richards JD, 2005. Antibiotic growth promoters in agriculture: history and mode of action. Poult Sci 84: 634643.

  • 4.

    Levy S, FitzGeralad GB, Macone AB, 1976. Spread of antibiotic-resistant plasmids from chicken to chicken and from chicken to man. Nature 260: 4042.

    • Search Google Scholar
    • Export Citation
  • 5.

    Braykov NP 2016. Antibiotic resistance in animal and environmental samples associated with small-scale poultry farming in northwestern Ecuador. mSphere 1: 115.

    • Search Google Scholar
    • Export Citation
  • 6.

    Moser KA 2018. The role of mobile genetic elements in the spread of antimicrobial-resistant Escherichia coli from chickens to humans in small-scale production poultry operations in rural Ecuador. Am J Epidemol 187: 558567.

    • Search Google Scholar
    • Export Citation
  • 7.

    Bouallègue-Godet O, Salem BS, Fabre L, Demartin M, Grimont PAD, Mzoughi R, Weill F, 2005. Nosocomial outbreak caused by Salmonella enterica serotype livingstone producing CTX-M-27 extended-spectrum β-lactamase in a neonatal unit in Sousse, Tunisia. J Clin Microbiol 43: 10371044.

    • Search Google Scholar
    • Export Citation
  • 8.

    Barlow RS, Fegan N, Gobius KU, 2007. A comparison of antibiotic resistance integrons in cattle from separate beef meat production systems at slaughter. J App Microbiol 104: 65658.

    • Search Google Scholar
    • Export Citation
  • 9.

    Gebreyes WA, Thakur S, Morrow WEM, 2005. Campylobacter coli: prevalence and antimicrobial resistance in antimicrobial-free (ABF) swine production systems. J Antimicrob Chemoth 56: 765768.

    • Search Google Scholar
    • Export Citation
  • 10.

    Heur OE, Pedersen K, Andersen JS, Madsen M, 2001. Prevalence and antimicrobial susceptibility of thermophilic Campylobacter in organic and conventional broiler flocks. Lett Appl Microbiol 33: 269274.

    • Search Google Scholar
    • Export Citation
  • 11.

    Quintana-Hayashi MP, Thakur S, 2012. Longitudinal study of the persistence of antimicrobial-resistant campylobacter strains in distinct swine production systems on farms, at slaughter, and in the environment. Appl Environ Microbiol 78: 26982705.

    • Search Google Scholar
    • Export Citation
  • 12.

    Ray KA, Warnick LD, Mitchell RM, Kaneen JB, 2006. Antimicrobial susceptibility of salmonella from organic and conventional dairy farms. J Dairy Sci 89: 20382050.

    • Search Google Scholar
    • Export Citation
  • 13.

    Graham JP, Eisenberg JNS, Trueba G, Zhang L, Johnson TJ, 2017. Small-scale food animal production and antimicrobial resistance: mountain, molehill, or something in-between? Environ Health Perspect 125: 15.

    • Search Google Scholar
    • Export Citation
  • 14.

    Guo X, Stedtfeld RD, Hedman H, Eisenberg JNS, Trueba G, Yin D, Tiedje JM, Zhang L, 2018. Antibiotic resistome associated with small-scale poultry production in rural Ecuador. Environ Sci Tech 52: 81658172.

    • Search Google Scholar
    • Export Citation
  • 15.

    Lowenstein C, Waters WF, Roess A, Leibler JH, Graham JP, 2016. Animal husbandry practices and perceptions of zoonotic infectious disease risks among livestock keepers in a rural parish of Quito, Ecuador. Am J Trop Med Hyg 95: 4501458.

    • Search Google Scholar
    • Export Citation

 

 

 

 

 

High Prevalence of Extended-Spectrum Beta-Lactamase CTX-M–Producing Escherichia coli in Small-Scale Poultry Farming in Rural Ecuador

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  • 1 School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan;
  • 2 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan;
  • 3 Microbiology Institute, Universidad San Francisco de Quito, Quito, Ecuador;
  • 4 Department of Internal Medicine, Division of Infectious Diseases, University of Michigan Medical School, Ann Arbor, Michigan;
  • 5 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan;
  • 6 Department of Epidemiology and Biostatistics, Michigan State University, East Lancing, Michigan

Small-scale farming may have large impacts on the selection and spread of antimicrobial resistance to humans. We conducted an observational study to evaluate antibiotic-resistant Escherichia coli populations from poultry and humans in rural northwestern Esmeraldas, Ecuador. Our study site is a remote region with historically low resistance levels of third-generation antibiotics such cefotaxime (CTX), a clinically relevant antibiotic, in both poultry and humans. Our study revealed 1) high CTX resistance (66.1%) in farmed broiler chickens, 2) an increase in CTX resistance over time in backyard chicken not fed antibiotics (2.3–17.9%), and 3) identical blaCTX-M sequences from human and chicken bacteria, suggesting a spillover event. These findings provide evidence that small-scale meat production operations have direct impacts on the spread and selection of clinically important antibiotics among underdeveloped settings.

Small-scale agriculture is a growing practice throughout the world.1 It is estimated that more than 80% of the chicken population worldwide occurs in small-scale food systems, yielding up to 90% of the total poultry products in many low- and middle-income countries (LMICs).2 Antimicrobial agents are commonly administered in small-scale agricultural settings to maximize animal growth and survival,3 resulting in the potential for antimicrobial resistant (AMR) bacteria to spillover into other animals and humans. We examine this avian-to-human spillover potential in rural communities in northern coastal Ecuador.

To date, most of the work studying the transmission pathway of agricultural animal-to-human transmission of AMR focuses on concentrated animal feeding operations CAFOs3,4 and bacteria isolated under antibiotic pressure (media enriched with antibiotics). These large-scale food production facilities are defined by their high density of food production animals with high levels of subtherapeutic antibiotic use for growth promotion.5 Human exposure can occur through occupational handling or consumption of poultry products.4 By contrast, small-scale agricultural operations raise fewer animals, but often at the household setting, resulting in high risk for human exposure. Because of this proximity of humans to livestock, small-scale agriculture has the potential for animal-to-human spillover events.

Our present study in northwestern Ecuador is a model system for understanding the spread of antibiotic resistance in an underdeveloped, agricultural setting. In rural villages of Esmeraldas Province, Ecuador, small-scale poultry farming of broiler chickens co-occurs with farming of local backyard chicken breeds. Typically, broiler chickens are commercially bred within a CAFO and purchased as chicks by small-scale farming operations that either are based out of a single household or run by multiple households in a shared coop. These chickens have been exposed to high levels of antibiotics both at the CAFO and while being raised in the communities. Our prior analysis suggests that the CAFO environment compared with the community is the major driver of the antibiotic resistance observed in broiler chickens.5 By contrast, backyard chickens are a local variety of chicken that are free grazing around the household in the open environment and seldom prescribed chemotherapeutic agents. Foundational work from this study region has demonstrated higher phenotypic antibiotic resistance levels5 and higher levels of mobile genetic elements6 in broiler chickens compared with backyard chickens.

Between June and July 2015, we sampled chickens from households raising both broiler and backyard breeds within a community in the province of Esmeraldas, Ecuador. Specifically, our study design comprises two observational periods monitoring antibiotic resistance among chickens in 10 households at the time that the household received 30 broiler chickens to farm and 1 month later. We also sampled backyard chickens from 10 households in a control community (CC) where there was no broiler chicken farming (Table 1). In the farming community (FC), we collected a maximum of 10 samples from backyard chickens residing in the 10 households during both observation periods. If a household had fewer than 10 chickens, we sampled all backyard chickens. On the other hand, we sampled 10 broiler chickens from only two of the 10 households. We chose two households because of the close proximity of shared animal husbandry environment. In the CC, we sampled 10 households to fulfill a maximum of 10 backyard chickens but only during the first sample period. Antibiotic resistance data from children were collected 2 years later between February and May 2017 from the same FC that received broiler chickens. The chicken samples were collected via cloacal swabs, and children samples were provided by their parent guardian. All samples were placed in Cary Blair media and transported to Quito for analysis. Consent to participate was obtained from all households, and all study protocols were reviewed and approved by the University of Michigan Institutional Review Board and the Universidad San Francisco de Quito Bioethics Committee.

Table 1

Phenotypic resistance profiles for backyard chickens (sampled from the baseline and farming community [FC]) and broiler chickens both collected during sample periods one (S1) and two (S2)

AntibioticBackyard chickensBroiler chickens
Control community, S1 (n = 143)FC, S1 (n = 127)FC, S2 (n = 195)FC, S1 (n = 59)FC, S2 (n = 60)
Gentamicin0 (0)7 (5.5)12 (6.1)30 (50.8)7 (11.8)
Streptomycin20 (13.9)65 (51.1)42 (21.6)57 (96.6)42 (72.4)
Amoxicillin–clavulanate1 (0.7)0 (0)5 (2.5)12 (20.3)6 (10.0)
Ampicillin11 (7.6)40 (31.4)38 (19.4)52 (88.1)24 (40.0)
Cefotaxime2 (1.3)3 (2.3)35 (17.9)39 (66.1)44 (73.3)
Cephalothin43 (30.0)36 (28.3)56 (28.7)47 (79.6)24 (40.0)
Chloramphenicol14 (9.7)24 (18.8)38 (19.4)13 (22.0)27 (45.0)
Sulfisoxazole26 (20.8)57 (44.8)64 (35.7)51 (86.4)41 (74.5)
Ciprofloxacin2 (1.3)6 (4.7)14 (7.1)31 (52.5)12 (20.0)
Enrofloxacin2 (1.4)8 (6.4)3 (5.7)24 (40.6)13 (25.4)
Trimethoprim/sulfamethoxazole14 (9.7)47 (37.0)36 (18.4)51 (86.4)30 (50.8)
Tetracycline53 (37.0)73 (57.4)93 (48.1)56 (94.9)42 (76.3)

Each cell contains the number of antibiotic-resistant Escherichia coli isolates and the percentage resistant of those tested, n.

Bacteria were grown on selective media; Escherichia coli isolates were selected and analyzed for resistance to 12 antibiotics through Kirby Baur disc diffusion as described in detail in prior publications.5,6 We classified phenotypic resistance as resistant or sensitive (intermediate isolates were categorized as sensitive) and tested for differences between sample periods one and two using generalized linear mixed-effects models to control for repeated observations at the household level (Table 2). For all isolates that had phenotypic resistance to cefotaxime (CTX), we screened for the presence of the blaCTX gene and sequenced with Sanger sequencing. A phylogenetic tree was generated via maximum likelihood analysis in MEGA version 7.0 software (www.megasoftware.net).6

Table 2

Odds ratio and 95% CI comparing phenotypic resistance to 12 antibiotics among four comparison groups (P-value < 0.05*)

Antibiotic testedFC S1 (backyard chicken)FC S2 (backyard chicken)FC S2 (broiler chicken)FC S1 (broiler chicken)
CC S1 (backyard chicken)FC S1 (backyard chicken)FC S1 (broiler chicken)FC S1 (backyard chicken)
OR95% CIOR95% CIOR95% CIOR95% CI
Gentamicin0.420.17–1.022.390.98–5.838.011.05–61.24*
Streptomycin4.511.39–14.65*0.300.13–0.713.341.42–7.87*23.843.19–178.37*
Amoxicillin–clavulanate1.500.14–16.060.760.25–2.381.310.42–4.0618.805.02–70.46*
Ampicillin5.051.43–17.83*0.570.26–1.261.750.80–3.839.491.91–47.13*
Cefotaxime1.080.06–20.5625.653.56–185.0*0.040.01–0.281,12414.62–86,440.20*
Cephalothin1.020.34–3.060.710.32–1.541.410.75–3.8810.722.09–55.07*
Chloramphenicol1.890.57–6.311.740.82–3.690.570.27–1.223.200.78–13.10
Sulfisoxazole4.561.43–14.59*0.390.17–0.912.541.01–5.87*9.211.56–54.46*
Ciprofloxacin4.770.99–23.020.590.26–1.341.700.75–3.887.553.28–17.37*
Enrofloxacin4.820.99–23.580.500.19–1.352.000.74–5.347.953.05–20.73*
Trimethoprim/sulfamethoxazole7.591.83–31.42*0.290.12–0.723.411.39–8.33*19.342.28–164.32*
Tetracycline3.000.99–8.790.480.20–1.172.090.85–5.124.520.99–20.49

CC = control community; CI = confidence interval; FC = farming community; OR = odds ratio. We were unable to run a statistical model for gentamicin to compare backyard chickens of the CC with the FC because there were no phenotypic resistant isolates.

During the first sampling period, S1, backyard chickens had statistically lower phenotypic resistance levels than broiler chickens for all antibiotics tested except for chloramphenicol and tetracycline (Table 2). Cefotaxime, a clinically relevant third-generation cephalosporin, was the only antibiotic that significantly increased in the backyard chickens between S1 and S2 at 2.3% and 17.9%, respectively (Table 1).

When comparing the backyard chickens in the FC with the neighboring CC, we observed higher levels of phenotypic resistance in the FC for ampicillin, streptomycin, trimethoprim/sulfamethoxazole, ampicillin, and sulfisoxazole. We speculate that these higher levels of phenotypic resistance among backyard chickens are due to previous farming activity within the FC before the onset of this study.

When comparing the broiler chicken phenotypic resistance levels between the two sampling periods, we observed a statistically significant decline in streptomycin, trimethoprim/sulfamethoxazole, and sulfisoxazole (Table 2). This decline in phenotypic resistance followed the findings of Braykov et al.5 and is likely due to the high levels of antibiotics received at the CAFO while in the egg and shortly after hatching; after these chicks are purchased and moved to the community household setting, these resistance levels subsequently declined over time.

In our genetic analysis, we detected a substantially higher presence of blaCTX-M, a common gene associated with CTX resistance, in broiler chickens than in backyard chickens for both sample periods S1 (82.9% [n = 39] versus 0.0% [n = 0]) and S2 (68.8% [n = 31] versus 22.9% [n = 8]). Phylogenetic analysis clustered blaCTX-M genes into two clades, suggesting a shared evolutionary history among our chicken and human samples.

The intensive use of antibiotics in small-scale agriculture from LMICs is on the rise and has potential to develop and spread AMR to human populations. We observed initial high levels of CTX phenotypic resistance in broilers followed by fade out in several antibiotics; although broiler chickens received supplementary antibiotics via commercial feed, our data suggest that the greater selection pressure for resistance occurs within a CAFO setting.5 On the other hand, at the household level we detected a meaningful rise in CTX phenotypic resistance among backyard chickens (with no direct exposure to antibiotics) 1 month after the introduction of broiler chickens. Furthermore, sequenced blaCTX-M from our human and backyard chicken bacteria sources exhibited a shared evolutionary history embedded with broiler chickens, demonstrating that broiler chickens have greater blaCTX-M diversity than humans.

Cefotaxime extended-spectrum beta-lactamases (ESBLs) have globally emerged as the most common type of ESBL.7 In our study, CTX resistance was isolated without using antibiotic-selective plates, suggesting that these isolates were present at significant numbers and were not a minority strain. In our study region, however, CTX resistance in E. coli has been very low in the past decade and has not exceeded beyond approximately 0.5% among human isolates.6 We therefore speculate that this shared source of CTX resistance entry into backyard chicken and human populations originated from broiler chickens. These broiler chickens came from a regional producer of chickens that consistently administers a variety of antibiotics, including cephalosporins, to hens and eggs before selling to farmers.5

Most food-producing animals have exhibited higher resistance when comparing backyard and nonconventional animals.1,46,812 Our detection of a potential spillover event through the rise in CTX phenotypic resistance in backyard chickens (Table 1) expands on these studies. Typically, LMIC settings have poor sanitation infrastructure that can promote spillover because of the spread of these bacteria through the environment, and our data suggest this spread can occur even with no direct contact between backyard and broiler chickens.

Antibiotic use remains a pressing concern in LMICs, where small-scale intensive animal production farming is on the rise.1,7,13,14 In Ecuador, one study found that nearly half of the producers considered the use of antibiotics important for growth promotion, especially when animals are young.15 This intensive use of antibiotics has many implications, including greater AMR gene richness and lower taxonomic diversity compared with backyard chickens.14

Small-scale introductions of intensively raised food animals over a short duration may yield lasting effects on the surrounding environment. Development projects promoting these small-scale farms could inadvertently promote these spillover events of organisms and genes encoding resistance to antibiotics of clinical importance. Further longitudinal data and analysis is necessary to understand the effect that intensively farmed poultry introductions may have on other animals, humans, and the overall resistome.

Acknowledgments:

We thank the Ecología, Desarrollo, Salud y Sociedad (EcoDESS) project research team for their invaluable contributions to data collection. Also, we would like to thank Tom Duda, Sanchitha Meda, Danielle Autumn, Sophie Yu-Pun Chen, and Eric Krawczyk for their assistance with the data processing and analysis. All analyses were conducted using R Statistical Software version 3.4.1 (2018) with packages lme4 and plyr. The study protocol was approved by the Institutional Animal Care and Use Committee at the University of Michigan (Protocol: PRO00006200). Sequences from this study have been submitted to GenBank under accession nos. MG974110—MG974151.

REFERENCES

  • 1.

    Okeno TO, Kahi AK, Peters KJ, 2012. Characterization of indigenous chicken production systems in Kenya. Trop Anim Health Prod 44: 601608.

  • 2.

    Mack S, Hoffmann D, Otte J, 2005. The contribution of poultry to rural development. Worlds Poult Sci J 61: 714.

  • 3.

    Dibner JJ, Richards JD, 2005. Antibiotic growth promoters in agriculture: history and mode of action. Poult Sci 84: 634643.

  • 4.

    Levy S, FitzGeralad GB, Macone AB, 1976. Spread of antibiotic-resistant plasmids from chicken to chicken and from chicken to man. Nature 260: 4042.

    • Search Google Scholar
    • Export Citation
  • 5.

    Braykov NP 2016. Antibiotic resistance in animal and environmental samples associated with small-scale poultry farming in northwestern Ecuador. mSphere 1: 115.

    • Search Google Scholar
    • Export Citation
  • 6.

    Moser KA 2018. The role of mobile genetic elements in the spread of antimicrobial-resistant Escherichia coli from chickens to humans in small-scale production poultry operations in rural Ecuador. Am J Epidemol 187: 558567.

    • Search Google Scholar
    • Export Citation
  • 7.

    Bouallègue-Godet O, Salem BS, Fabre L, Demartin M, Grimont PAD, Mzoughi R, Weill F, 2005. Nosocomial outbreak caused by Salmonella enterica serotype livingstone producing CTX-M-27 extended-spectrum β-lactamase in a neonatal unit in Sousse, Tunisia. J Clin Microbiol 43: 10371044.

    • Search Google Scholar
    • Export Citation
  • 8.

    Barlow RS, Fegan N, Gobius KU, 2007. A comparison of antibiotic resistance integrons in cattle from separate beef meat production systems at slaughter. J App Microbiol 104: 65658.

    • Search Google Scholar
    • Export Citation
  • 9.

    Gebreyes WA, Thakur S, Morrow WEM, 2005. Campylobacter coli: prevalence and antimicrobial resistance in antimicrobial-free (ABF) swine production systems. J Antimicrob Chemoth 56: 765768.

    • Search Google Scholar
    • Export Citation
  • 10.

    Heur OE, Pedersen K, Andersen JS, Madsen M, 2001. Prevalence and antimicrobial susceptibility of thermophilic Campylobacter in organic and conventional broiler flocks. Lett Appl Microbiol 33: 269274.

    • Search Google Scholar
    • Export Citation
  • 11.

    Quintana-Hayashi MP, Thakur S, 2012. Longitudinal study of the persistence of antimicrobial-resistant campylobacter strains in distinct swine production systems on farms, at slaughter, and in the environment. Appl Environ Microbiol 78: 26982705.

    • Search Google Scholar
    • Export Citation
  • 12.

    Ray KA, Warnick LD, Mitchell RM, Kaneen JB, 2006. Antimicrobial susceptibility of salmonella from organic and conventional dairy farms. J Dairy Sci 89: 20382050.

    • Search Google Scholar
    • Export Citation
  • 13.

    Graham JP, Eisenberg JNS, Trueba G, Zhang L, Johnson TJ, 2017. Small-scale food animal production and antimicrobial resistance: mountain, molehill, or something in-between? Environ Health Perspect 125: 15.

    • Search Google Scholar
    • Export Citation
  • 14.

    Guo X, Stedtfeld RD, Hedman H, Eisenberg JNS, Trueba G, Yin D, Tiedje JM, Zhang L, 2018. Antibiotic resistome associated with small-scale poultry production in rural Ecuador. Environ Sci Tech 52: 81658172.

    • Search Google Scholar
    • Export Citation
  • 15.

    Lowenstein C, Waters WF, Roess A, Leibler JH, Graham JP, 2016. Animal husbandry practices and perceptions of zoonotic infectious disease risks among livestock keepers in a rural parish of Quito, Ecuador. Am J Trop Med Hyg 95: 4501458.

    • Search Google Scholar
    • Export Citation

Author Notes

Address correspondence to Joseph N. S. Eisenberg, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109. E-mail: jnse@umich.edu

Financial support: This work was supported by the National Institute of Allergy and Infectious Diseases (grant R01AI050038), the National Science Foundation, Ecology and Evolution of Infectious Diseases program (grant 08119234), the Courtney Wilson Memorial Award, the International Institute Fellowship at the University of Michigan, Rackham Graduate School at the University of Michigan, and the Tinker Field Research Grant through the Latin American and Caribbean Studies at the University of Michigan.

Authors’ addresses: Hayden D. Hedman, School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, E-mail: hedmanh@umich.edu. Joseph N. S. Eisenberg, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, E-mail: jnse@umich.edu. Karla A. Vasco and Gabriel Trueba, Institute of Microbiology, Universidad San Francisco de Quito, Quito, Ecuador, E-mails: kvasco@usfq.edu.ec and gtrueba@usfqedu.ec. Christopher N. Blair, Department of Internal Medicine, Division of Infectious Diseases, University of Michigan Medical School, Ann Arbor, MI, E-mail: chbl@med.umich.edu. Veronica J. Berrocal, Department of Biostatistics, University of Michigan, Ann Arbor, MI, E-mail: berrocal@umich.edu. Lixin Zhang, Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, E-mail: lxzhang@epi.msu.edu.

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