Next Article in Journal
Novel Reoviruses of Waterfowl Origin in Northern Vietnam: A Laboratory Investigation
Previous Article in Journal
Risk Factors and Seroprevalence of Infection by Corynebacterium pseudotuberculosis in Goats from Espírito Santo State, Southeastern Brazil
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microbiology Research
Volume 16
Issue 8
10.3390/microbiolres16080186
microbiolres-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of Antibiotic Resistance in Escherichia coli Isolated from a Watershed Section of Ameca River in Mexico

by
Mariana Díaz-Zaragoza
1,†,
Sergio Yair Rodriguez-Preciado
1,†,
Lizeth Hernández-Ventura
1,
Alejandro Ortiz-Covarrubias
1,
Gustavo Castellanos-García
1,
Sonia Sifuentes-Franco
1,
Ana Laura Pereira-Suárez
2,
José Francisco Muñoz-Valle
2,
Margarita Montoya-Buelna
3 and
Jose Macias-Barragan
1,*
1
Laboratorio de Sistemas Biológicos, Departamento de Ciencias de la Salud, Centro Universitario de los Valles, Universidad de Guadalajara, Carretera Guadalajara-Ameca Km. 45.5, C.P., Ameca 46600, Jalisco, Mexico
2
Instituto de Investigación en Ciencias Biomédicas, Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Sierra Mojada 950, Independencia Oriente, Guadalajara 44340, Jalisco, Mexico
3
Laboratorio de Inmunología, Departamento de Fisiología, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Sierra Mojada 950, Independencia Oriente, Guadalajara 44340, Jalisco, Mexico
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microbiol. Res. 2025, 16(8), 186; https://doi.org/10.3390/microbiolres16080186
Submission received: 30 May 2025 / Revised: 20 July 2025 / Accepted: 29 July 2025 / Published: 12 August 2025
Browse Figures
Versions Notes

Abstract

Antibiotic resistance (AR) in environmental Escherichia coli represents a growing public health challenge. This study evaluated the prevalence of AR among E. coli isolates recovered from surface water bodies within the Ameca River basin in Jalisco, Mexico, and examined associations with anthropogenic influence and seasonal variation. Over a 1-year period, water samples were collected monthly from 16 sites, including tributaries, wetlands, and main river channels with differing degrees of urban impact. E. coli isolates were confirmed by malB gene PCR and tested for susceptibility to six antibiotics using the Kirby–Bauer disk diffusion method. High resistance frequencies were observed for ampicillin (93.9%), tetracycline (92.4%), and streptomycin (89.6%), while gentamicin exhibited the lowest resistance rate (48.1%). Resistance prevalence was significantly higher at sites adjacent to urban settlements and during the rainy season (p < 0.05). These findings underscore the influence of land use and seasonal dynamics on AR dissemination in aquatic environments and highlight the need for improved wastewater management strategies to mitigate the spread of resistant bacteria.

Graphical Abstract

1. Introduction

Antibiotics are chemical substances produced by microorganisms that have the capacity to inhibit bacteria and other microorganisms [1,2]. Inappropriate exposure of bacteria to antibiotics has led to the selection of strains capable of surviving antimicrobial treatments, making infections more difficult to treat and contributing to the emergence of antibiotic resistance (AR) [3,4]. AR is currently one of the main public health challenges worldwide. The World Health Organization (WHO) estimates that antibiotic-resistant infections cause approximately 700,000 deaths per year [5,6,7,8].
The WHO’s Global Antimicrobial Resistance Surveillance System (GLASS) has revealed a pervasive presence of AR in more than 500,000 individuals with suspected bacterial infections across 22 countries. The most commonly reported resistant bacteria include E. coli, Klebsiella pneumoniae, Staphylococcus aureus, and Streptococcus pneumoniae, followed by Salmonella spp. [5,9]. Some of these organisms belong to the so-called ESKAPE antibiotic-resistant bacteria: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. [10]. These pathogens have developed multidrug resistance and can cause severe or fatal infections, particularly in immunocompromised patients [11,12,13].
In Mexico, approximately 50% of enterobacteria exhibit resistance to extended-spectrum beta-lactams [14,15]. Moreover, a national surveillance network involving 47 health centers across 20 states has reported high resistance rates among enterobacteria to fluoroquinolones (63.2% for ciprofloxacin and 66.2% for levofloxacin), while E. coli has shown resistance rates of 61.5% to trimethoprim-sulfamethoxazole [16].
The increase in antibiotic-resistant bacteria in recent decades is attributed to the misuse and overuse of antimicrobials, improper prescriptions, incomplete treatment courses, and limited access to appropriate care [17,18]. Hospitals also contribute through the excessive use of antibiotics to treat both community-acquired and nosocomial infections [19,20]. Moreover, the extensive use of antibiotics in livestock for human consumption [21], together with the inadequate disposal of antibiotics in municipal waste and wastewater [22], creates conditions conducive to horizontal gene transfer [23,24,25]. Wastewater discharges originating from industries, hospitals, agriculture, livestock, and domestic activities are major contributors to the contamination of surface water and groundwater [26,27,28]. These discharges often contain pharmaceutical residues, including various antibiotics, which promote the proliferation and dissemination of antibiotic-resistant bacteria and resistance genes [22,29,30].
Inadequate management of aquatic environments leads to the establishment of reservoirs of resistant bacteria. Studies in different regions have documented antibiotic-resistant bacteria in surface waters near urban wastewater discharges and agricultural runoff [31,32,33,34,35,36]. Once introduced into waterways, these bacteria can be ingested by animals or humans and become part of the transient microbiota [37,38]. In Mexico, the presence of antibiotic-resistant bacteria has been reported in surface water [39,40,41,42], groundwater [43], and irrigation water used for crops [44]. Different strains of E. coli have been isolated from freshwater and irrigation sources, revealing the widespread occurrence of resistance phenotypes [42,43,45,46,47].
The Ameca River watershed, located in western Mexico, is characterized by diverse land uses, including agriculture, livestock farming, and urban settlements, all of which contribute potential sources of antibiotic contamination. However, to our knowledge, no previous studies have systematically investigated the prevalence of AR in E. coli isolates from surface water bodies in this region. Thus, this study was performed to evaluate the prevalence of AR in E. coli isolates recovered from surface waters and to assess its association with anthropogenic activities, land use, and seasonal variation within a section of the Ameca River watershed in Mexico.

2. Materials and Methods

2.1. Study Site

A section of the Ameca River watershed, located in the Valles Region of Jalisco State, western Mexico, was selected for sampling. Sixteen sampling points were established to represent different types of aquatic environments (tributaries, wetlands, and main river channels) with varying degrees of anthropogenic influence (Figure 1). Detailed characteristics of each sampling site, including location, type of water body, and primary land use, are provided in Table S1 as Supplementary Information.

2.2. Microbiological Assessments

Water samples (single grab sample of 1200 mL) were collected monthly at each site over 1 year, starting in winter and ending in autumn, in accordance with the Mexican Standard NOM-230-SSA1-2002 [49]. Sampling was conducted approximately 20–30 cm below the water surface using sterile containers.
Microbiological analyses were performed within 24 h of collection. E. coli and total coliform concentrations (CFU/100 mL) were quantified using the spread plate method on CHROMagar™ CCA (CHROMagar™, Paris, France) as per the USEPA [50] and Mexican Standard NMX-AA-102-SCFI-2006 protocols [51]. For each sample, a single plate was inoculated with 100 µL of undiluted water and incubated at 37 °C for 18 h. Colonies were counted using a digital colony counter (Model J-2, LUZEREN, Singapore), following NOM-113-SSA1-1994 [52]. In samples with higher bacterial loads, this procedure allowed the isolation of several colonies for further analyses. It is acknowledged that the use of a single plate per sample and direct inoculation without membrane filtration may limit the statistical representativeness of colony counts in samples with low bacterial concentrations.
Six presumptive E. coli colonies per plate were randomly selected and subcultured in tryptic soy broth at 37 °C for 18 h. Molecular confirmation was performed by endpoint PCR targeting the malB gene, using primers ECO-1 GACCTCGGTTTAGTTCACAGA and ECO-2 CACACGCTGACGCTGACCA (Thermo Scientific, Waltham, MA, USA), which have been extensively applied as reliable confirmatory markers in combination with chromogenic media in environmental studies [53,54,55,56]. Amplification was carried out in a UVP MultiDoc-It thermocycler (Analytik Jena, Jena, Germany) according to the manufacturer’s protocol. Only colonies testing positive for malB were retained for further analysis.
Confirmed E. coli isolates were tested for antibiotic susceptibility to six antibiotics: ampicillin (10 µg), gentamicin (10 µg), streptomycin (10 µg), tetracycline (30 µg), amoxicillin/clavulanic acid (20 µg), and sulfamethoxazole/trimethoprim (25 µg), using the Kirby–Bauer disk diffusion method [57] on Mueller–Hinton agar. Plates were incubated at 37 °C for 24 h, and inhibition zones were measured. Results were interpreted according to Clinical and Laboratory Standards Institute (CLSI) guidelines [58]. E. coli ATCC 25922 and ATCC 11229 were used as reference strains [59].

2.3. Statistical Analysis

Seasonality was classified as follows: rainy season (June–January) and dry–hot season (March–May). Descriptive statistics were calculated as mean ± standard deviation for quantitative variables and frequencies with percentages for qualitative variables. Comparisons across groups were assessed using Kruskal–Wallis or chi-square tests, as appropriate. Pearson correlation was used to explore associations between variables. Statistical analyses were performed using SPSS version 20.0 (IBM Corp., Chicago, IL, USA), with p-values ≤ 0.05 considered statistically significant.
Multivariate analyses were conducted to identify patterns in resistance profiles and sampling sites. Principal component analysis (PCA) and hierarchical clustering (heatmaps and dendrograms) were generated using RStudio version 2022.07.1 + 554 and Heatmapper [60].

3. Results

3.1. AR in E. coli

To determine the presence and distribution of E. coli carrying AR throughout the year, including both dry and rainy seasons, sampling sites were selected based on several characteristics: the presence of human settlements, domestic and industrial wastewater discharges, and areas distant from human settlements but surrounded by agricultural crops, free-range animal farming (horses, cattle, and chickens), and wild-type zones (wetlands with sustainable resource use, such as tourist areas, fishing zones, and water storage for crop irrigation). Based on these features, three general zones were established, each containing several sampling points: First, Tala, an urban area forming the upper section of the sampled points. Second, the Ameca River stream, which includes a few tributaries—the most representative being the Salado, Seco, and Zarco streams. Third, La Vega wetland, with points located in La Vega dam, a water body designated as a RAMSAR site [61] (Table S1 in Supplementary Information).
In total, 1123 E. coli strains were isolated and their identity confirmed by amplifying the malB gene. Only colonies testing positive for the malB gene were retained for further analysis (Table S2 in Supplementary Information). The proportion of presumptive colonies that did not amplify the malB gene was not systematically recorded because the objective of this step was confirmatory rather than quantitative.
Table 1 summarizes the frequency of E. coli resistance to six antibiotics across the three sampling zones. High resistance rates (>75%) were observed for ampicillin, streptomycin, amoxicillin/clavulanic acid, sulfamethoxazole/trimethoprim, and tetracycline in all areas. Statistically significant differences were detected for streptomycin (p = 0.050), gentamicin (p < 0.001), and tetracycline (p = 0.024), indicating geographic variation in resistance patterns. In particular, gentamicin resistance was substantially lower in La Vega (30.1%) than in Tala (51.0%) and Ameca (55.4%).
Table 2 shows the distribution of resistance according to the season. Overall, resistance frequencies remained high throughout the year. Significant seasonal differences were observed for streptomycin (p < 0.001), gentamicin (p < 0.001), sulfamethoxazole/trimethoprim (p = 0.006), and tetracycline (p = 0.050). Resistance to gentamicin and streptomycin was markedly higher during the rainy season (62.6% and 95.6%, respectively) compared with the dry season (38.7% and 85.1%). These findings suggest that seasonal factors may influence the prevalence of resistance to certain antibiotics.
Table S3, in the Supplementary Information, shows monthly total coliform concentrations (CFU/100 mL) from 16 sampling sites along the Ameca River basin over one year. Higher counts, including TNTC (Too Numerous To Count), were common in urban-impacted sites, especially during the rainy season, while lower values (<10 CFU/100 mL) were recorded in less impacted or remote areas. Some entries are marked as N/A due to inaccessibility. These data reflect seasonal and spatial variations in microbial water quality, supporting this study’s findings on the relationship between contamination levels and anthropogenic influence.

3.2. Antibiotic Resistance (AR) Profile by Zone

Following sample collection, antibiograms were performed to assess resistance to six antibiotics. Three general zones were studied and coded as mentioned above. Two matrices were developed in each area, as follows:
  • Ameca: 6 × 12 (six antibiotics for each month over 12 months) and 5 × 12 (five sampling points for each month over 12 months);
  • Presa la Vega: 6 × 12 (six antibiotics for each month over 12 months) and 6 × 12 (six sampling points for each month over 12 months);
  • Tala: 6 × 12 (six antibiotics for each month over 12 months) and 5 × 12 (five sampling points for each month over 12 months).
The AR profile was studied over the course of 1 year, and samples were collected at different points (sampling point codes and further information are provided in Tables S1 and S3 in Supplementary Information) across the three general zones, as described below.

3.2.1. Tala

The first area analyzed was Tala, where the AR profile exhibited trends comparable to those observed in the other study zones. The highest frequencies of resistance were recorded for TE, AM, and S, with 221 (18.4 ± 4.9), 220 (18.3 ± 5.0), and 211 (17.6 ± 5.1) resistant isolates, respectively. In contrast, GE displayed the lowest level of resistance, with 118 positive colonies (9.8 ± 7.1) (Figure 2A). PCA revealed that two components accounted for 97.3% of the variance, with AM, S, TE, AMC, and STX being the most strongly correlated variables, while GE contributed the least to the overall variability (Figure 2B).
Regarding the spatial distribution within Tala, sampling sites T1, T4, and T2 had the highest counts of resistant isolates, with 66 (5.5 ± 1.2), 57 (4.8 ± 2.1), and 56 (4.7 ± 1.9) colonies, respectively. In terms of temporal patterns, December, September, and June were the months with the most frequent detection of resistance, with 28 (5.6 ± 0.9), 24 (4.8 ± 2.7), and 24 (4.8 ± 2.6) resistant isolates (Figure 2C). A second PCA explained 70% of the variance across sampling sites, indicating that sites T1 and T3 were the most strongly correlated, while the remaining vectors showed lower degrees of association (Figure 2D).

3.2.2. La Vega Wetland

In this area, the months exhibiting the highest levels of AR were July, August, and September, with a combined total of 236 resistant colonies (mean 78.6 ± 11.9). AM, TE, and S were the antibiotics with the most frequent resistance profiles, accounting for 121 (10.1 ± 4.9), 106 (8.8 ± 5.6), and 103 (8.5 ± 5.5) resistant isolates, respectively. Conversely, GE showed the lowest occurrence of resistance, with 37 positive colonies (3.1 ± 3.1) (Figure 3A). PCA revealed that two components explained 86.7% of the observed variance, with GE, S, TE, AMC, and STX displaying the strongest correlations (Figure 3B).
Regarding spatial distribution, sampling points P6 and P5 yielded the highest counts of resistant strains, with 42 (3.5 ± 2.3) and 30 (2.5 ± 2.6) colonies, respectively, while P1 and P2 exhibited the lowest frequencies (9 and 11 isolates). Resistance was predominantly observed in September and July, which recorded 20 (3.3 ± 2.4) and 17 (2.8 ± 2.8) colonies, whereas January and February had the lowest counts, with four and five isolates, respectively. A second PCA accounted for 57.8% of the variance, illustrating distinct vector trajectories across sampling sites. Notably, vectors corresponding to P1 and P6 were moderately aligned, as were those of P2 and P4 (Figure 3D). Sampling in April was not possible because of adverse field conditions associated with the onset of the rainy season.

3.2.3. Ameca

In this zone, resistance peaked during August, September, October, and November, with an overall count of 1102 resistant colonies (mean 275.5 ± 5.3). The most prevalent resistance profiles corresponded to AM (272 isolates; 22.6 ± 6.4), S (261; 21.7 ± 6.5), and TE (234; 19.5 ± 8.4). In contrast, GE exhibited the lowest resistance frequency, with 159 positive colonies (18 ± 8.4) (Figure 4A). PCA performed on AR patterns identified two principal components that together explained 82.3% of the variance, with AMC, AM, and S being the most strongly associated variables (Figure 4B).
In terms of spatial patterns, site A3 displayed the highest resistance counts (70 colonies; 5.8 ± 0.6), followed by A4, A2, and A1, which recorded 58, 57, and 55 resistant isolates, respectively (Figure 4C). The second PCA accounted for 69.6% of the variance and showed that most vectors (A1, A2, A4, and A5) had similar orientations, whereas A3 exhibited a distinct trajectory (Figure 4D).
Moreover, multiresistant strains were detected, with a significant proportion of isolates showing resistance to four, five, or all six antibiotics assessed. Strains that were sensitive to all six antibiotics are also shown, although their percentage was not substantial (Figure 5).

4. Discussion

AR represents a major global public health threat, aggravated by socioeconomic inequality and the widespread misuse of antibiotics. E. coli is commonly used as an indicator of fecal contamination and has demonstrated a remarkable capacity to acquire and disseminate resistance genes [4,62]. In our study, high resistance frequencies were observed, particularly for ampicillin, tetracycline, and streptomycin, while gentamicin exhibited the lowest proportion of resistant isolates. These findings are consistent with previous reports indicating that ampicillin and tetracycline are among the most commonly used antibiotics worldwide and therefore show a higher resistance prevalence [35,63,64].
The elevated resistance rates found at sampling points close to urban settlements and wastewater discharge areas support the hypothesis that anthropogenic influence plays a significant role in shaping resistance patterns [63,65,66]. Specifically, sites such as Arroyo Zarco, located downstream from wastewater inputs, showed the highest levels of resistant E. coli strains. This observation aligns with the findings of Zhang et al. [65], who reported an increased dissemination of resistance genes in aquatic systems near urban and hospital discharges.
Seasonal variation was also evident, with higher numbers of resistant isolates detected during the rainy season. This pattern likely reflects increased runoff of fecal and organic matter into surface water bodies during periods of heavy precipitation [35,66,67]. Similar trends have been reported in other studies, including that by Boehm et al. [63], who documented higher coliform counts on Californian beaches during rainy months.
Although our study did not quantify antibiotic concentrations in water samples or directly measure resistance genes, the literature suggests that sub-lethal levels of antibiotics in wastewater can contribute to the selection and persistence of resistant bacteria in aquatic environments [68,69,70,71,72]. Additionally, it has been demonstrated that sediments act as reservoirs of resistance genes, facilitating their spread through the food web [73].
Lower resistance frequencies were observed in regions dominated by agriculture and livestock than in urban-impacted areas. However, it is important to note that animal farming can still introduce resistant bacteria into water bodies through manure and runoff [74,75,76]. The relatively lower resistance levels at these sites may be due to the reduced presence of pharmaceutical waste compared with urban wastewater.
Our results regarding the higher resistance to ampicillin and lower resistance to gentamicin agree with prior observations [35,68,77]. Ampicillin has been widely prescribed since the 1960s [77], contributing to long-term selective pressure, whereas gentamicin, despite its earlier introduction [78], has been used less extensively.
Overall, this study highlights the association between anthropogenic influence, seasonality, and the prevalence of antibiotic-resistant E. coli in surface water bodies of the Valles region. While we recognize the limitations arising from the lack of quantitative measurements of antibiotic residues or resistance genes, our findings contribute to understanding the spatial and temporal variability of AR and underscore the need for improved wastewater management to mitigate the spread of resistant bacteria [65,72].

5. Conclusions

This study has demonstrated that E. coli strains isolated from surface water bodies in the Ameca River watershed exhibit high levels of resistance to commonly used antibiotics, particularly ampicillin, tetracycline, and streptomycin. Resistance frequencies varied significantly between sampling sites, with higher levels observed in locations adjacent to urban settlements and wastewater discharge points, while sites predominantly influenced by agricultural activities showed comparatively lower resistance rates. Seasonal patterns were also evident, with an increased prevalence of resistant isolates during the rainy season, likely reflecting enhanced runoff of contaminants into water bodies. These findings highlight the influence of anthropogenic activities and seasonal dynamics on the distribution of antibiotic-resistant E. coli and underscore the importance of implementing effective wastewater management practices to mitigate the environmental dissemination of resistant bacteria.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/microbiolres16080186/s1, Table S1: Surface water sampling site features; Table S2: Frequency of resistance to antibiotics in E. coli; Table S3: Frequency of resistance to antibiotics in E. coli.

Author Contributions

J.M.-B., S.Y.R.-P., and M.D.-Z. designed this study. J.M.-B., M.D.-Z., S.Y.R.-P., L.H.-V., A.O.-C., and G.C.-G. collected the samples. M.D.-Z., and S.Y.R.-P. performed microbiological analysis. M.M.-B., S.S.-F., A.L.P.-S., and J.F.M.-V. contributed with data analysis, direction, and guidance. J.M.-B., M.D.-Z., and S.Y.R.-P. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Program for the Improvement in Production Conditions of SNII and SNCA Members (Programa de Apoyo a la Mejora en las Condiciones de Producción de los Miembros del SNII y SNCA: PROSNII) from the University of Guadalajara granted to Macias-Barragan J., Díaz-Zaragoza M., and Rodriguez-Preciado S. Some supplies were provided by the Intermunicipal Board for Environmental Management in the Valley Region (JIMAV, Junta Intermunicipal para la Gestión Integral del Medio Ambiente de la Región Valles). Díaz-Zaragoza M. received a fellowship through the Support Program for the Incorporation of New Full-Time Professors (Apoyo Incorporación de Nuevos Profesores de Tiempo Completo) (grant number 511-6/2020-8586 PTC-1528) under the Teacher Professional Development Program for Higher Education (Programa para el Desarrollo Profesional Docente para el Tipo Superior, PRODEP) of the Ministry of Public Education (Secretaría de Educación Pública, SEP).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Acknowledgments

All authors have consented to the acknowledgement to JIMAV for its support of field work and technical advice in sampling, and also PRODEP of the Ministry of Public Education (Secretaría de Educación Pública, SEP). Plus, we are grateful to Edén Oceguera Contreras for his valuable contribution to the statistical analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ARAntibiotic resistance
WHOWorld Health Organization
GLASSGlobal Antimicrobial Surveillance System
ESKAPEEnterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.
CFU/mLColony-forming units per milliliter
PCRPolymerase chain reaction
CLSIClinical and Laboratory Standards Institute
ATCCAmerican Type Culture Collection
PCAPrincipal component analysis
SPSSStatistical Package for the Social Sciences
AMPAmpicillin
AMCAmoxicillin/clavulanic acid
GEGentamicin
TETetracycline
STXSulfamethoxazole/Trimethoprim
SStreptomycin
NMXNorma Mexicana
NOMNorma Oficial Mexicana
USEPAUnited States Environmental Protection Agency
SNIISistema Nacional de Investigadores e Investigadoras/National System of Researchers
SNCASistema Nacional de Creadores de Arte/National System of Art Creators
PROSNIIPrograma de Apoyo a la Mejora en las Condiciones de Producción de los Miembros del SNII y SNCA/Improvement in Production Conditions of SNII and SNCA Members
JIMAVJunta Intermunicipal para la Gestión Integral del Medio Ambiente de la Región Valles/Intermunicipal Board for Environmental Management in the Valley Region
PRODEPPrograma para el Desarrollo Profesional Docente para el Tipo Superior/Support Program for the Incorporation of New Full-Time Professors
SEPSecretaría de Educación Pública/Ministry of Public Education

References

  1. Andersson, D.I.; Hughes, D. Selection and Transmission of Antibiotic-Resistant Bacteria. Microb. Transm. 2017, 5, 117–137. [Google Scholar] [CrossRef]
  2. Patel, P.; Wermuth, H.R.; Calhoun, C.; Hall, G.A. Antibiotics. In StatPearls [Internet]; StatPearls Publishing: Treasure Island, FL, USA, 2025. Available online: https://www.ncbi.nlm.nih.gov/books/NBK535443/ (accessed on 10 May 2025).
  3. Munita, J.M.; Arias, C.A. Mechanisms of Antibiotic Resistance. Microbiol. Spectr. 2016, 4, 1–24. [Google Scholar] [CrossRef]
  4. World Health Organization. High Levels of Antibiotic Resistance Found Worldwide, New Data Shows. Available online: https://www.who.int/news/item/29-01-2018-high-levels-of-antibiotic-resistance-found-worldwide-new-data-shows (accessed on 10 May 2025).
  5. World Health Organization. Antimicrobial Resistance. Available online: https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance (accessed on 10 May 2025).
  6. Ventola, C.L. The antibiotic resistance crisis: Part 1: Causes and threats. P T 2015, 40, 277–283. [Google Scholar]
  7. European Comission. A European One Health Action Plan against Antimicrobial Resistance (AMR) European Comission; European Comission: Brussels, Belgium, 2017; pp. 3–5. [Google Scholar]
  8. Laxminarayan, R. The overlooked pandemic of antimicrobial resistance. Lancet 2022, 399, 606–607. [Google Scholar] [CrossRef]
  9. Hadi, Z.; Goodarzi, F.; Dalir, A.; Sholeh, M. Comments on the published meta-analysis of antibiotic resistance in hospital-acquired ESKAPE-E infections in low- and lower-middle-income countries. Emerg. Microbes Infect. 2022, 11, 1602–1603. [Google Scholar] [CrossRef] [PubMed]
  10. Jadimurthy, R.; Mayegowda, S.B.; MNayak, S.C.; Mohan, C.D.; Rangappa, K.S. Escaping mechanisms of ESKAPE pathogens from antibiotics and their targeting by natural compounds. Biotechnol. Rep. 2022, 34, e00728. [Google Scholar] [CrossRef]
  11. Gould, I.M.; Bal, A.M. New antibiotic agents in the pipeline and how they can help overcome microbial resistance. Virulence 2013, 4, 185–191. [Google Scholar] [CrossRef] [PubMed]
  12. Karkman, A.; Do, T.T.; Walsh, F.; Virta, M.P.J. Antibiotic-Resistance Genes in Waste Water. Trends Microbiol. 2018, 26, 220–228. [Google Scholar] [CrossRef]
  13. Shankar, P.R.; Balasubramanium, R. Antimicrobial Resistance: Global Report on Surveillance; Report WHO/HSE/PED/AIP/2014.2; World Health Organization (WHO): Geneva, Switzerland, 2014; pp. 9–34. [Google Scholar]
  14. Silva, J.; Aguilar, C.; Estrada, M.A.; Echániz, G.; Carnalla, N.; Soto, A.; López-Antuñano, F.J. Susceptibility to new beta-lactams of enterobacterial extended-spectrum beta-lactamase (ESBL) producers and penicillin-resistant Streptococcus pneumoniae in Mexico. J. Chemother. 1998, 10, 102–107. [Google Scholar] [CrossRef]
  15. Robles-Torres, J.I.; Ocaña-Munguía, M.A.; Madero Morales, P.A.; Ruiz-Galindo, E.; Garza-González, E.; Gómez-Guerra, L. Antimicrobial resistance and extended spectrum beta-lactamases in urinary tract infections: A serious problem in Northern Mexico. Rev. Mex. De Urol. 2020, 80, e02. [Google Scholar] [CrossRef]
  16. Garza-González, E.; Morfín-Otero, R.; Mendoza-Olazarán, S.; Bocanegra-Ibarias, P.; Flores-Treviño, S.; Rodríguez-Noriega, E.; Ponce-de-León, A.; Sanchez-Francia, D.; Franco-Cendejas, R.; Arroyo-Escalante, S.; et al. A snapshot of antimicrobial resistance in Mexico. Results from 47 centers from 20 states during a six-month period. PLoS ONE 2019, 14, e0209865. [Google Scholar] [CrossRef]
  17. Kiratisin, P.; Chongthaleong, A.; Tan, T.Y.; Lagamayo, E.; Roberts, S.; Garcia, J.; Davies, T. Comparative in vitro activity of carbapenems against major Gram-negative pathogens: Results of Asia-Pacific surveillance from the COMPACT II study. Int. J. Antimicrob. Agents 2012, 39, 311–316. [Google Scholar] [CrossRef]
  18. Kraemer, S.A.; Ramachandran, A.; Perron, G.G. Antibiotic Pollution in the Environment: From Microbial Ecology to Public Policy. Microorganisms 2019, 7, 180. [Google Scholar] [CrossRef] [PubMed]
  19. Larsson, D.G.; de Pedro, C.; Paxeus, N. Effluent from drug manufactures contains extremely high levels of pharmaceuticals. J. Hazard. Mater. 2007, 148, 751–755. [Google Scholar] [CrossRef] [PubMed]
  20. Breathnach, A.S.; Cubbon, M.D.; Karunaharan, R.N.; Pope, C.F.; Planche, T.D. Multidrug-resistant Pseudomonas aeruginosa outbreaks in two hospitals: Association with contaminated hospital waste-water systems. J. Hosp. Infect. 2012, 82, 19–24. [Google Scholar] [CrossRef]
  21. Price, L.B.; Graham, J.P.; Lackey, L.G.; Roess, A.; Vailes, R.; Silbergeld, E. Elevated risk of carrying gentamicin-resistant Escherichia coli among U.S. poultry workers. Environ. Health Perspect 2007, 115, 1738–1742. [Google Scholar] [CrossRef] [PubMed]
  22. Bielen, A.; Šimatović, A.; Kosić-Vukšić, J.; Senta, I.; Ahel, M.; Babić, S.; Jurina, T.; González Plaza, J.J.; Milaković, M.; Udiković-Kolić, N. Negative environmental impacts of antibiotic-contaminated effluents from pharmaceutical industries. Water Res. 2017, 126, 79–87. [Google Scholar] [CrossRef]
  23. Lushniak, B.D. Antibiotic resistance: A public health crisis. Public Health Rep. 2014, 129, 314–316. [Google Scholar] [CrossRef]
  24. Rossolini, G.M.; Arena, F.; Pecile, P.; Pollini, S. Update on the antibiotic resistance crisis. Curr. Opin. Pharmacol. 2014, 18, 56–60. [Google Scholar] [CrossRef]
  25. Alm, E.W.; Zimbler, D.; Callahan, E.; Plomaritis, E. Patterns and persistence of antibiotic resistance in faecal indicator bacteria from freshwater recreational beaches. J. Appl. Microbiol. 2014, 117, 273–285. [Google Scholar] [CrossRef]
  26. Rizzo, L.; Manaia, C.; Merlin, C.; Schwartz, T.; Dagot, C.; Ploy, M.C.; Michael, I.; Fatta-Kassinos, D. Urban wastewater treatment plants as hotspots for antibiotic resistant bacteria and genes spread into the environment: A review. Sci. Total Environ. 2013, 447, 345–360. [Google Scholar] [CrossRef]
  27. Diario Oficial de la Federación. Norma Mexicana NMX-AA-034-SCFI-2015. Available online: https://www.dof.gob.mx/normasOficiales/5959/seeco12_C/seeco12_C.html (accessed on 10 May 2025).
  28. Yang, Y.; Song, W.; Lin, H.; Wang, W.; Du, L.; Xing, W. Antibiotics and antibiotic resistance genes in global lakes: A review and meta-analysis. Environ. Int. 2018, 116, 60–73. [Google Scholar] [CrossRef] [PubMed]
  29. Luo, Y.; Guo, W.; Ngo, H.H.; Nghiem, L.D.; Hai, F.I.; Zhang, J.; Liang, S.; Wang, X.C. A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment. Sci. Total Environ. 2014, 473–474, 619–641. [Google Scholar] [CrossRef]
  30. Andersson, D.; Hughes, D. Microbiological effects of sublethal levels of antibiotics. Nat. Rev. Microbiol. 2014, 12, 465–478. [Google Scholar] [CrossRef]
  31. Kim, T.W.; Joung, Y.; Han, J.H.; Jung, W.; Kim, S.B. Antibiotic resistance among aquatic bacteria in natural freshwater environments of Korea. J. Water Health 2015, 13, 1085–1097. [Google Scholar] [CrossRef]
  32. Knapp, C.W.; Dolfing, J.; Ehlert, P.A.; Graham, D.W. Evidence of increasing antibiotic resistance gene abundances in archived soils since 1940. Environ. Sci. Technol. 2010, 44, 580–587. [Google Scholar] [CrossRef]
  33. Pruden, A.; Arabi, M.; Storteboom, H.N. Correlation between upstream human activities and riverine antibiotic resistance genes. Environ. Sci. Technol. 2012, 46, 11541–11549. [Google Scholar] [CrossRef]
  34. Czekalski, N.; Gascón Díez, E.; Bürgmann, H. Wastewater as a point source of antibiotic-resistance genes in the sediment of a freshwater lake. ISME J. 2014, 8, 1381–1390. [Google Scholar] [CrossRef]
  35. Sanderson, C.E.; Fox, J.T.; Dougherty, E.R.; Cameron, A.D.S.; Alexander, K.A. The Changing Face of Water: A Dynamic Reflection of Antibiotic Resistance Across Landscapes. Front. Microbiol. 2018, 9, 1894. [Google Scholar] [CrossRef] [PubMed]
  36. Cho, S.; Jackson, C.R.; Frye, J.G. The prevalence and antimicrobial resistance phenotypes of Salmonella, Escherichia coli and Enterococcus sp. in surface water. Lett. Appl. Microbiol. 2020, 71, 3–25. [Google Scholar] [CrossRef] [PubMed]
  37. Bouki, C.; Venieri, D.; Diamadopoulos, E. Detection and fate of antibiotic resistant bacteria in wastewater treatment plants: A review. Ecotoxicol. Environ. Saf. 2013, 91, 1–9. [Google Scholar] [CrossRef]
  38. Nnadozie, C.F.; Odume, O.N. Freshwater environments as reservoirs of antibiotic resistant bacteria and their role in the dissemination of antibiotic resistance genes. Environ. Pollut. 2019, 254, 113067. [Google Scholar] [CrossRef]
  39. Mondragón, V.A.; Llamas-Pérez, D.F.; González-Guzmán, G.E.; Márquez-González, A.R.; Padilla-Noriega, R.; Durán-Avelar, M.d.J.; Franco, B. Identification of Enterococcus faecalis bacteria resistant to heavy metals and antibiotics in surface waters of the Mololoa River in Tepic, Nayarit, Mexico. Environ. Monit. Assess. 2011, 183, 329–340. [Google Scholar] [CrossRef] [PubMed]
  40. Castillo-Rojas, G.; Mazari-Hiríart, M.; Ponce de León, S.; Amieva-Fernández, R.I.; Agis-Juárez, R.A.; Huebner, J.; López-Vidal, Y. Comparison of Enterococcus faecium and Enterococcus faecalis Strains isolated from water and clinical samples: Antimicrobial susceptibility and genetic relationships. PLoS ONE 2013, 8, e59491. [Google Scholar] [CrossRef]
  41. Delgado-Gardea, M.C.; Tamez-Guerra, P.; Gomez-Flores, R.; Zavala-Díaz de la Serna, F.J.; Eroza-de la Vega, G.; Nevárez-Moorillón, G.V.; Pérez-Recoder, M.C.; Sánchez-Ramírez, B.; Del González-Horta, M.C.; Infante-Ramírez, R. Multidrug-Resistant Bacteria Isolated from Surface Water in Bassaseachic Falls National Park, Mexico. Int. J. Environ. Res. Public Health 2016, 13, 597. [Google Scholar] [CrossRef] [PubMed]
  42. Magaña-Lizárraga, J.A.; Gómez-Gil, B.; Rendón-Maldonado, J.G.; Delgado-Vargas, F.; Vega-López, I.F.; Báez-Flores, M.E. Genomic Profiling of Antibiotic-Resistant Escherichia coli Isolates from Surface Water of Agricultural Drainage in North-Western Mexico: Detection of the International High-Risk Lineages ST410 and ST617. Microorganisms 2022, 10, 662. [Google Scholar] [CrossRef]
  43. Moore, A.; Lenczewski, M.; Leal-Bautista, R.M.; Duvall, M. Groundwater microbial diversity and antibiotic resistance linked to human population density in Yucatan Peninsula, Mexico. Can. J. Microbiol. 2020, 66, 46–58. [Google Scholar] [CrossRef] [PubMed]
  44. Lugo-Melchor, Y.; Quiñones, B.; Amézquita-López, B.A.; León-Félix, J.; García-Estrada, R.; Chaidez, C. Characterization of tetracycline resistance in Salmonella enterica strains recovered from irrigation water in the Culiacan Valley, Mexico. Microb. Drug Resist. 2010, 16, 185–190. [Google Scholar] [CrossRef]
  45. Rosas, I.; Salinas, E.; Martínez, L.; Cruz-Córdova, A.; González-Pedrajo, B.; Espinosa, N.; Amábile-Cuevas, C.F. Characterization of Escherichia coli Isolates from an Urban Lake Receiving Water from a Wastewater Treatment Plant in Mexico City: Fecal Pollution and Antibiotic Resistance. Curr. Microbiol. 2015, 71, 490–495. [Google Scholar] [CrossRef]
  46. Corzo-Ariyama, H.A.; García-Heredia, A.; Heredia, N.; García, S.; León, J.; Jaykus, L.; Solís-Soto, L. Phylogroups, pathotypes, biofilm formation and antimicrobial resistance of Escherichia coli isolates in farms and packing facilities of tomato, jalapeño pepper and cantaloupe from Northern Mexico. Int. J. Food Microbiol. 2019, 290, 96–104. [Google Scholar] [CrossRef]
  47. Canizalez-Roman, A.; Velazquez-Roman, J.; Valdez-Flores, M.A.; Flores-Villaseñor, H.; Vidal, J.E.; Muro-Amador, S.; Guadrón-Llanos, A.M.; Gonzalez-Nuñez, E.; Medina-Serrano, J.; Tapia-Pastrana, G.; et al. Detection of antimicrobial-resistance diarrheagenic Escherichia coli strains in surface water used to irrigate food products in the northwest of Mexico. Int. J. Food Microbiol. 2019, 304, 1–10. [Google Scholar] [CrossRef] [PubMed]
  48. Google Maps. Available online: https://www.google.com/maps (accessed on 30 June 2022).
  49. Diario Oficial de la federación. Norma Oficial Mexicana NOM-230-SSA1-2002. Available online: https://www.dof.gob.mx/nota_detalle.php?codigo=2081772&fecha=12/07/2005#gsc.tab=0 (accessed on 10 May 2025).
  50. EPA 821-R-02-024; Method 1604: Total Coliforms and Escherichia coli in Water by Membrane Filtration Using a Simultaneous Detection Technique (MI Medium). USEPA—United States Environmental Protection Agency, Office of Water: Washington, DC, USA, 2002.
  51. Diario Oficial de la Federación. Norma Mexicana NMX-AA-102-SCFI-2006. Available online: https://www.dof.gob.mx/normasOficiales/8303/seeco14_C/seeco14_C.html (accessed on 10 May 2025).
  52. Diario Oficial de la Federación. Norma Oficial Mexicana NOM-113-SSA1-1994. Available online: https://dof.gob.mx/nota_detalle.php?codigo=4880115&fecha=25/08/1995#gsc.tab=0 (accessed on 10 May 2025).
  53. Rekha, R.; Alam-Rizvi, M.; Jaishree, P. Designing and validation of genus-specific primers for human gut flora study. Electron. J. Biotechnol. 2006, 9, 505–511. [Google Scholar] [CrossRef]
  54. Candrian, U.; Furrer, B.; Höfelein, C.; Meyer, R.; Jermini, M.; Lüthy, J. Detection of Escherichia coli and identification of enterotoxigenic strains by primer-directed enzymatic amplification of specific DNA sequences. Int. J. Food Microbiol. 1991, 12, 339–351. [Google Scholar] [CrossRef]
  55. Pesapane, R.; Ponder, M.; Alexander, K.A. Tracking pathogen transmission at the human–wildlife interface: Banded mongooses (Mungos mungo) and Escherichia coli. EcoHealth 2013, 10, 115–128. [Google Scholar] [CrossRef]
  56. My, T.T.; Thien, L.V.; Manh, V.D.; My, B.T.P.; Lan, D.T.M.; Binh, D.X.; Duc, V.M. Antimicrobial resistance and molecular characterization of Escherichia coli isolated from bovine mastitis samples in Nghe An province, Vietnam. Vet. World 2023, 16, 743–751. [Google Scholar] [CrossRef]
  57. Biemer, J.J. Antimicrobial susceptibility testing by the Kirby-Bauer disc diffusion method. Ann. Clin. Lab. Sci. 1973, 3, 135–140. [Google Scholar]
  58. CLSI M100-S24; Performance Standards for Antimicrobial Susceptibility Testing; Twenty-fourth Informational Supplement. Clinical and Laboratory Standards Institute: Wayne, PA, USA, 2014.
  59. Skof, A.; Koller, M.; Baumert, R.; Hautz, J.; Treiber, F.; Kittinger, C.; Zarfel, G. Comparison of the Antibiotic Resistance of Escherichia coli Populations from Water and Biofilm in River Environments. Pathogens 2024, 13, 171. [Google Scholar] [CrossRef]
  60. Babicki, S.; Arndt, D.; Marcu, A.; Liang, Y.; Grant, J.R.; Maciejewski, A.; Wishart, D.S. Heatmapper: Web-enabled heat mapping for all. Nucleic Acids Res. 2016, 44, W147–W153. [Google Scholar] [CrossRef]
  61. Ramsar Sites Information Service. Available online: https://rsis.ramsar.org/ris/2026 (accessed on 10 May 2025).
  62. Holcomb, D.A.; Stewart, J.R. Microbial Indicators of Fecal Pollution: Recent Progress and Challenges in Assessing Water Quality. Curr. Environ. Health Rep. 2020, 7, 311–324. [Google Scholar] [CrossRef] [PubMed]
  63. Boehm, A.B.; Grant, S.B.; Kim, J.H.; Mowbray, S.L.; McGee, C.D.; Clark, C.D.; Foley, D.M.; Wellman, D.E. Decadal and shorter period variability of surf zone water quality at Huntington Beach, California. Environ. Sci. Technol. 2002, 36, 3885–3892. [Google Scholar] [CrossRef] [PubMed]
  64. Li, X.; Watanabe, N.; Xiao, C.; Harter, T.; McCowan, B.; Liu, Y.; Atwill, E.R. Antibiotic-resistant E. coli in surface water and groundwater in dairy operations in Northern California. Environ. Monit. Assess. 2014, 186, 1253–1260. [Google Scholar] [CrossRef] [PubMed]
  65. Zhang, S.H.; Lv, X.; Han, B.; Gu, X.; Wang, P.F.; Wang, C.; He, Z. Prevalence of antibiotic resistance genes in antibiotic-resistant Escherichia coli isolates in surface water of Taihu Lake Basin, China. Environ. Sci. Pollut. Res. Int. 2015, 22, 11412–11421. [Google Scholar] [CrossRef]
  66. Reeves, R.L.; Grant, S.B.; Mrse, R.D.; Copil Oancea, C.M.; Sanders, B.F.; Boehm, A.B. Scaling and management of fecal indicator bacteria in runoff from a coastal urban watershed in southern California. Environ. Sci. Technol. 2004, 38, 2637–2648. [Google Scholar] [CrossRef]
  67. McLellan, S.L.; Hollis, E.J.; Depas, M.M.; Van Dyke, M.; Harris, J.; Scopel, C.O. Distribution and fate of Escherichia coli in Lake Michigan following contamination with urban stormwater and combined sewer overflows. J. Great Lakes Res. 2007, 33, 566–580. [Google Scholar] [CrossRef]
  68. Manaia, C.M.; Vaz-Moreira, I.; Nunes, O.C. Antibiotic Resistance in Waste Water and Surface Water and Human Health Implications. In Emerging Organic Contaminants and Human Health; Barceló, D., Ed.; Springer: Heidelberg/Berlin, Germany, 2011; Volume 20, pp. 173–212. [Google Scholar] [CrossRef]
  69. Samrot, A.V.; Wilson, S.; Sanjay Preeth, R.S.; Prakash, P.; Sathiyasree, M.; Saigeetha, S.; Shobana, N.; Pachiyappan, S.; Rajesh, V.V. Sources of Antibiotic Contamination in Wastewater and Approaches to Their Removal—An Overview. Sustainability 2023, 15, 12639. [Google Scholar] [CrossRef]
  70. Rayan, R.A. Pharmaceutical effluent evokes superbugs in the environment: A call to action. Biosaf. Health 2023, 5, 363–371. [Google Scholar] [CrossRef]
  71. Akiyama, T.; Savin, M.C. Populations of antibiotic-resistant coliform bacteria change rapidly in a wastewater effluent dominated stream. Sci. Total Environ. 2010, 408, 6192–6201. [Google Scholar] [CrossRef]
  72. Ibekwe, A.M.; Obayiuwana, A.C.; Murinda, S.E. Enterococcus Species and Their Antimicrobial Resistance in an Urban Watershed Affected by Different Anthropogenic Sources. Water 2024, 16, 116. [Google Scholar] [CrossRef]
  73. Singh, A.; Pratap, S.G.; Raj, A. Occurrence and dissemination of antibiotics and antibiotic resistance in aquatic environment and its ecological implications: A review. Environ. Sci Pollut. Res. Int. 2024, 31, 47505–47529. [Google Scholar] [CrossRef]
  74. Homeier-Bachmann, T.; Heiden, S.E.; Lübcke, P.K.; Bachmann, L.; Bohnert, J.A.; Zimmermann, D.; Schaufler, K. Antibiotic-Resistant Enterobacteriaceae in Wastewater of Abattoirs. Antibiotics 2021, 10, 568. [Google Scholar] [CrossRef] [PubMed]
  75. Ibekwe, A.M.; Murinda, S.E.; Graves, A.K. Genetic diversity and antimicrobial resistance of Escherichia coli from human and animal sources uncovers multiple resistances from human sources. PLoS ONE 2011, 6, e20819. [Google Scholar] [CrossRef] [PubMed]
  76. Rothrock, M.J.; Min, B.R.; Castleberry, L.; Waldrip, H.; Parker, D.; Brauer, D.; Pitta, D.; Indugu, N. Antibiotic resistance, antimicrobial residues, and bacterial community diversity in pasture-raised poultry, swine, and beef cattle manures. J. Anim. Sci. 2021, 99, skab144. [Google Scholar] [CrossRef] [PubMed]
  77. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Pharmaceutical Drugs; International Agency for Research on Cancer: Lyon, France, 1990; (IARC Monographs on the Evaluation of the Carcinogenic Risks to Humans, No. 50) Ampicillin. Available online: https://www.ncbi.nlm.nih.gov/books/NBK526244/ (accessed on 10 May 2025).
  78. Chaves, B.J.; Tadi, P. Gentamicin. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2025. Available online: https://www.ncbi.nlm.nih.gov/books/NBK557550/ (accessed on 3 May 2025).
Microbiolres 16 00186 g001
Figure 1. Sampling points were located in the Valles Region of Jalisco State, Mexico, in Tala municipality sites (T1—downtown Tala; T2—Ingenio; T3—El Refugio; T4—Acacias; T5—Cuisillos), bordering the wetland Presa La Vega (P1—Fishermen’s wharf; P2—El Amarillo; P3—dam curtain; P4—Pacana; P5—La Estanzuela; P6—Teuchitlán), and in Ameca River’s first 40 km downstream of the dam curtain (A1—La Vega; A2—La Esperanza; A3—downtown Ameca; A4—Higuera; A5—El Cuis). Red arrows: stream direction. Blue lines: rivers, brooks, and wetland outline. Purple points: sampling sites at Ameca River. Yellow points: sampling sites of Tala municipality effluents. Orange points: sampling sites at Presa La Vega wetland (Adapted with permission from Google Maps [48]. ©2022 Google).
Figure 1. Sampling points were located in the Valles Region of Jalisco State, Mexico, in Tala municipality sites (T1—downtown Tala; T2—Ingenio; T3—El Refugio; T4—Acacias; T5—Cuisillos), bordering the wetland Presa La Vega (P1—Fishermen’s wharf; P2—El Amarillo; P3—dam curtain; P4—Pacana; P5—La Estanzuela; P6—Teuchitlán), and in Ameca River’s first 40 km downstream of the dam curtain (A1—La Vega; A2—La Esperanza; A3—downtown Ameca; A4—Higuera; A5—El Cuis). Red arrows: stream direction. Blue lines: rivers, brooks, and wetland outline. Purple points: sampling sites at Ameca River. Yellow points: sampling sites of Tala municipality effluents. Orange points: sampling sites at Presa La Vega wetland (Adapted with permission from Google Maps [48]. ©2022 Google).
Microbiolres 16 00186 g001
Microbiolres 16 00186 g002
Figure 2. Tala antibiotic profile pattern. (A) Heatmap with dendrogram analysis for the 6 × 12 matrix; (B) principal component analysis for the correlation of the antibiotic resistance profile and the months; (C) heatmap with dendrogram analysis for the 5 × 12 matrix; (D) principal component analysis for the correlation of the antibiotic resistance profile and the collection points. AMP: ampicillin; AMC: amoxicillin/clavulanic acid; GE: gentamicin; TE: tetracycline; STX: sulfamethoxazole/trimethoprim; S: streptomycin. T1—downtown Tala; T2—Ingenio; T3—El Refugio; T4—Acacias; T5—Cuisillos.
Figure 2. Tala antibiotic profile pattern. (A) Heatmap with dendrogram analysis for the 6 × 12 matrix; (B) principal component analysis for the correlation of the antibiotic resistance profile and the months; (C) heatmap with dendrogram analysis for the 5 × 12 matrix; (D) principal component analysis for the correlation of the antibiotic resistance profile and the collection points. AMP: ampicillin; AMC: amoxicillin/clavulanic acid; GE: gentamicin; TE: tetracycline; STX: sulfamethoxazole/trimethoprim; S: streptomycin. T1—downtown Tala; T2—Ingenio; T3—El Refugio; T4—Acacias; T5—Cuisillos.
Microbiolres 16 00186 g002
Microbiolres 16 00186 g003
Figure 3. La Vega wetland antibiotic profile pattern. (A) Heatmap with dendrogram analysis for the 6 × 12 matrix; (B) principal component analysis for the correlation of the antibiotic resistance profile and the months; (C) heatmap with dendrogram analysis for the 6 × 12 matrix; (D) principal component analysis for the correlation of the antibiotic resistance profile and the collection points. AMP: ampicillin; AMC: amoxicillin/clavulanic acid; GE: gentamicin; TE: tetracycline; STX: sulfamethoxazole/trimethoprim; S: streptomycin. P1—Fishermen’s wharf; P2—El Amarillo; P3—dam curtain; P4—Pacana; P5—La Estanzuela; P6—Teuchitlán.
Figure 3. La Vega wetland antibiotic profile pattern. (A) Heatmap with dendrogram analysis for the 6 × 12 matrix; (B) principal component analysis for the correlation of the antibiotic resistance profile and the months; (C) heatmap with dendrogram analysis for the 6 × 12 matrix; (D) principal component analysis for the correlation of the antibiotic resistance profile and the collection points. AMP: ampicillin; AMC: amoxicillin/clavulanic acid; GE: gentamicin; TE: tetracycline; STX: sulfamethoxazole/trimethoprim; S: streptomycin. P1—Fishermen’s wharf; P2—El Amarillo; P3—dam curtain; P4—Pacana; P5—La Estanzuela; P6—Teuchitlán.
Microbiolres 16 00186 g003
Microbiolres 16 00186 g004
Figure 4. Ameca antibiotic profile pattern. (A) Heatmap with dendrogram analysis for the 6 × 12 matrix. (B) PCA showing the correlation between the AR profile and the months. (C) Heatmap with dendrogram analysis for the 5 × 12 matrix. (D) PCA showing the correlation between the AR profile and the collection points. AMP: ampicillin; AMC: amoxicillin/clavulanic acid; GE: gentamicin; TE: tetracycline; STX: sulfamethoxazole/trimethoprim; S: streptomycin. A1—La Vega; A2—La Esperanza; A3—downtown Ameca; A4—Higuera; A5—El Cuis.
Figure 4. Ameca antibiotic profile pattern. (A) Heatmap with dendrogram analysis for the 6 × 12 matrix. (B) PCA showing the correlation between the AR profile and the months. (C) Heatmap with dendrogram analysis for the 5 × 12 matrix. (D) PCA showing the correlation between the AR profile and the collection points. AMP: ampicillin; AMC: amoxicillin/clavulanic acid; GE: gentamicin; TE: tetracycline; STX: sulfamethoxazole/trimethoprim; S: streptomycin. A1—La Vega; A2—La Esperanza; A3—downtown Ameca; A4—Higuera; A5—El Cuis.
Microbiolres 16 00186 g004
Microbiolres 16 00186 g005
Figure 5. Percentages of E. coli strains resistant to different numbers of antibiotics.
Figure 5. Percentages of E. coli strains resistant to different numbers of antibiotics.
Microbiolres 16 00186 g005
Table 1. Antibiotic resistance profile for Escherichia coli by zone.
Table 1. Antibiotic resistance profile for Escherichia coli by zone.
TalaLa VegaAmecap
AntibioticE. coli
n = 231		E. coli
n = 123		E. coli
n = 287
Ampicillin220 (95.2%)114 (92.7%)272 (94.8%)0.586
Streptomycin211 (91.3%)103 (83.7%)261 (90.9%)0.050 *
Amoxicillin/clavulanic acid200 (86.5%)94 (76.4%)229 (79.7%)0.103
Gentamicin118 (51.0%)37 (30.1%)159 (55.4%)˂0.001 *
Sulfamethoxazole/trimethoprim192 (83.1%)99 (80.5%)239 (83.3%)0.800
Tetracycline221 (95.7%)106 (86.2%)261 (90.9%)0.024 *
Data are presented as frequencies (percentages). The chi-square test was used, and * p-values ≤ 0.05 were considered statistically significant.
Table 2. Antibiotic resistance profile for Escherichia coli by season.
Table 2. Antibiotic resistance profile for Escherichia coli by season.
Rainy SeasonDry Seasonp
AntibioticE. coli
n = 273		E. coli
n = 362
Ampicillin261 (95.6%)339 (93.6%)0.471
Streptomycin261 (95.6%)308 (85.1%)˂0.001 *
Amoxicillin/clavulanic acid234 (85.7%)283 (78.2%)0.086
Gentamicin171 (62.6%)140 (38.7%)˂0.001 *
Sulfamethoxazole/trimethoprim238 (87.3%)286 (79.0%)0.006 *
Tetracycline259 (94.8%)323 (89.2%)0.050 *
Months of the rainy season: July–January. Months of the dry season: March–May. * p-values ≤ 0.05 were considered statistically significant.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Díaz-Zaragoza, M.; Rodriguez-Preciado, S.Y.; Hernández-Ventura, L.; Ortiz-Covarrubias, A.; Castellanos-García, G.; Sifuentes-Franco, S.; Pereira-Suárez, A.L.; Muñoz-Valle, J.F.; Montoya-Buelna, M.; Macias-Barragan, J. Evaluation of Antibiotic Resistance in Escherichia coli Isolated from a Watershed Section of Ameca River in Mexico. Microbiol. Res. 2025, 16, 186. https://doi.org/10.3390/microbiolres16080186

AMA Style

Díaz-Zaragoza M, Rodriguez-Preciado SY, Hernández-Ventura L, Ortiz-Covarrubias A, Castellanos-García G, Sifuentes-Franco S, Pereira-Suárez AL, Muñoz-Valle JF, Montoya-Buelna M, Macias-Barragan J. Evaluation of Antibiotic Resistance in Escherichia coli Isolated from a Watershed Section of Ameca River in Mexico. Microbiology Research. 2025; 16(8):186. https://doi.org/10.3390/microbiolres16080186

Chicago/Turabian Style

Díaz-Zaragoza, Mariana, Sergio Yair Rodriguez-Preciado, Lizeth Hernández-Ventura, Alejandro Ortiz-Covarrubias, Gustavo Castellanos-García, Sonia Sifuentes-Franco, Ana Laura Pereira-Suárez, José Francisco Muñoz-Valle, Margarita Montoya-Buelna, and Jose Macias-Barragan. 2025. "Evaluation of Antibiotic Resistance in Escherichia coli Isolated from a Watershed Section of Ameca River in Mexico" Microbiology Research 16, no. 8: 186. https://doi.org/10.3390/microbiolres16080186

APA Style

Díaz-Zaragoza, M., Rodriguez-Preciado, S. Y., Hernández-Ventura, L., Ortiz-Covarrubias, A., Castellanos-García, G., Sifuentes-Franco, S., Pereira-Suárez, A. L., Muñoz-Valle, J. F., Montoya-Buelna, M., & Macias-Barragan, J. (2025). Evaluation of Antibiotic Resistance in Escherichia coli Isolated from a Watershed Section of Ameca River in Mexico. Microbiology Research, 16(8), 186. https://doi.org/10.3390/microbiolres16080186

Article Metrics

Back to TopTop