Long-term genomic surveillance unveils persistent contamination, clonal dissemination, and antimicrobial resistance dynamics of Salmonella in surface waters from Central Mexico

Long-term genomic surveillance unveils persistent contamination, clonal dissemination, and antimicrobial resistance dynamics of Salmonella in surface waters from Central Mexico | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term genomic surveillance unveils persistent contamination, clonal dissemination, and antimicrobial resistance dynamics of Salmonella in surface waters from Central Mexico Enrique Jesús Delgado-Suárez, Santiago Zapata-Ramírez, Luz del Carmen Sierra-Gómez Pedroso, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9216619/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We assessed the prevalence, persistence, genetic diversity, and AMR dynamics of Salmonella spp. (SAL) in surface waters (SuWa) of Central Mexico. On-site, 953 samples (10 L each) from 49 watersheds were collected in 24 sampling rounds (2019-2023) using the modified Moore swab technique. SAL was isolated and identified using conventional microbiological procedures and sequenced on Illumina platforms. Overall, the prevalence of SAL was 54%, with higher values (χ2=23.9, P<0.0001) observed in Tlaxcala and the State of Mexico (64%) than in the other regions (42-50%). The top 10 serovars (Agona, Anatum, Newport, London, Adelaide, Derby, Senftenberg, Infantis, Typhimurium, and Muenchen) represented nearly half of the population and were systematically isolated across sampling rounds and years. The most frequent antimicrobial resistance (AMR) phenotypes involved older antibiotics (e.g., tetracycline, streptomycin, and chloramphenicol: 36%, 33%, and 24%, respectively). Approximately 12% of the isolates showed resistance to azithromycin, whereas resistance to cephalosporins, carbapenems, amikacin, and fluoroquinolones was rarely observed (~0-5%). One-third of the isolates exhibited multidrug-resistant (MDR) phenotypes and genotypes. The overall pool of AMR determinants (62 genes and four gene point mutations) encoded 15 different resistance mechanisms at the population level. The prevalence of AMR phenotypes to older antibiotics and the abundance of AMR genes against all antimicrobial classes increased in the post-COVID-19 pandemic period (0.1-0.4 and 0.1-0.6 Log10-fold change, respectively). These results underscore the public health risks associated with agricultural SuWa and the need for further research to identify the sources of SAL contamination. Salmonella surface waters epidemiology antimicrobial resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Agricultural surface waters (SuWa) are essential for food production worldwide. Approximately 80% of Mexico’s total domestic water consumption is linked to agricultural activities (2015). Although critical for food security, SuWa are often contaminated with pathogens, such as Salmonella spp. (Bell et al., 2021) (from now on refer to as Salmonella ), posing a risk to food safety and public health. Increasing evidence has linked salmonellosis outbreaks to fresh produce irrigated with contaminated water (Callejón et al., 2015; Liu et al., 2018). In the USA alone, fresh produce was associated with 3,778 illnesses, with nearly one-third requiring hospitalization and 16 deaths between 2010 and 2017 (Carstens et al., 2019), highlighting the contribution of agricultural activities to disease burden. In Mexico, the source attribution of foodborne and waterborne diseases is limited. However, cross-sectional studies have demonstrated the circulation of Salmonella in SuWa used for food production (Castañeda-Ruelas and Jiménez Edeza, 2018; Jiménez et al., 2014), describing the serovar diversity and antimicrobial resistance (AMR) phenotypes of the circulating isolates. More recent studies have provided further insights into the important role of SuWa in the ecology and epidemiology Salmonella in several Latin American countries, including Mexico (Ballesteros-Nova et al., 2022; Chen et al., 2024a). However, comprehensive longitudinal studies at the population level are scarce. This information is critical considering that Salmonella has been included in the high priority group of the 2024 World Health Organization (WHO) Bacterial Priority Pathogens List (WHO, 2024). Therefore, we conducted a long-term surveillance study of agricultural SuWa in Central Mexico to assess the prevalence, epidemiology, and AMR dynamics of Salmonella at the population level. 2 Materials and Methods 2.1 Sampling design A longitudinal study was conducted to screen for Salmonella contamination in rivers, dams, ponds, and irrigation canals in the central states of Hidalgo, Morelos, State of Mexico, and Tlaxcala, as well as Mexico City. Sampling was conducted in consecutive rounds from July 2019 to November 2023 (Table 1). A sampling round consisted of taking samples from one region per week until all regions were sampled. The distribution of sampling rounds across years was uneven due to administrative constraints during university holidays or the implementation of restriction measures during the COVID-19 pandemic (March 2020-March 2021). A convenience-based sampling scheme was used, whereby the participating watersheds were selected based on previously described criteria (Ballesteros-Nova et al., 2022): wadeable streams with public access, proximity of food production areas, and proximity to the laboratory to allow sample processing in ≤24 h post collection. Table 1 Sample collection schedule (grey cells) throughout the sampling timeframe Year Sampling rounds January February March April May June July August September October November December 2019 1-5 2020 6-8 2021 8-12 2022 12-19 2023 17-24 A total of 49 watersheds were visited 24 times throughout the sampling period, and one sample was collected per site in each visit, unless the stream dried due to drought or landowners no longer permitted public access to it. An interactive map of the sampling sites is available in Google Maps (https://www.google.com/maps/d/viewer?mid=1Jde225H1TtycysUwDaOMfCaw_abfM7w&usp=sharing), which offers improved navigation via Google Earth. Overall, 953 samples were analyzed throughout the sampling timeframe (Table 2). Table 2 Surveyed sites and number of samples analyzed per Mexican region (July 2019-November 2023) Number of sites (number of samples) Mexican region Sites River Dam Pond Canal Total samples Hidalgo 8 2 (17) 6 (133) -- -- 150 Mexico City 10 1 (18) -- 1 (1) 8 (123) 142 Morelos 12 5 (93) 2 (42) 3 (70) 2 (39) 244 State of Mexico 12 10 (214) -- 1 (20) 1 (21) 255 Tlaxcala 7 3 (70) -- 2 (47) 2 (45) 162 Overall 49 21 (412) 8 (175) 7 (138) 13 (228) 953 2.2 Sample collection procedures SuWa samples were collected in situ using the modified Moore swab (MMS) technique (Sbodio et al., 2013), following the same procedures described in one of our previous publications (Ballesteros-Nova et al., 2022). A full description of this method is also available from protocols.io (doi: https://dx.doi.org/10.17504/protocols.io.bpw9mph6). 2.3 Salmonella analysis Salmonella isolation and confirmation procedures were conducted using a modified version of the US Food and Drug Administration (FDA) Bacteriological Analytical Manual methodology (FDA, 2021), as previously described (Ballesteros-Nova et al., 2022). A detailed description of these procedures is also available in protocols.io (doi: https://dx.doi.org/10.17504/protocols.io.bpybmpsn). 2.4 Antibiotic susceptibility testing (AST) To determine the AMR phenotypes of the isolates, we used the Kirby-Bauer disk diffusion method (Bauer et al., 1966) with a panel of 12 antibiotics included in the WHO list of critically important antimicrobials for human medicine (WHO, 2019). The tested antibiotics (Table 3) were selected based on their intensive use in human or veterinary medicine, or for treating invasive salmonellosis or other serious infections caused by enterobacteria, as previously described (Campos Granados et al., 2023). Table 3 List of antibiotics and criteria used to determine the resistance phenotype of isolates Antibiotics a Concentration (μg) Inhibition zone diameter (mm) b I R Ampicillin 10 14-16 ≤13 Amoxicillin-clavulanic acid 20/10 14-17 ≤13 Ceftriaxone 30 20-22 ≤19 Cefepime 30 19-24 ≤18 Meropenem 10 20-22 ≤19 Amikacin Streptomycin 30 10 15-16 13-14 ≤14 ≤12 Ciprofloxacin 5 21-30 ≤20 Azithromycin 15 - ≤12 Tetracycline 30 12-14 ≤11 Chloramphenicol 30 11-17 ≤10 Trimetoprim-sulfamethoxazole 1.25/23.75 11-15 ≤10 a Bencton Dickinson disks. AMP: ampicillin, AMC: amoxicillin-clavulanic acid, CRO: ceftriaxone, FEP: cefepime, MEM: meropenem, AMK: amikacin, STR: streptomycin, CIP: ciprofloxacin, AZM: azithromycin, TET: tetracycline, CHL: chloramphenicol, SXT: trimethoprim-sulfamethoxazole. b Criteria used to classify isolates as clinically resistant (R) or intermediate (I) (CLSI, 2024). Clinical cutoff values set by the Clinical and Laboratory Standards Institute (CLSI) (CLSI, 2024) were used to interpret the AST results. The Pseudomonas aeruginosa ATCC 27853 strain was used as a quality control organism. Isolates showing resistance to ≥3 antibiotic classes were classified as multidrug-resistant (MDR) (Magiorakos et al., 2012). A full description of the AST procedures is available in protocols.io (doi: dx.doi.org/10.17504/protocols.io.bpypmpvn). 2.5 Whole-genome sequencing, genome assembly, serovar prediction, and AMR genotypes We used roughly the same methodology described in our previous publication for these analyses (Ballesteros-Nova et al., 2022). In brief, genomic DNA was extracted from pure cultures using the Qiagen QIAsymphony system and the QIAsymphony DSP DNA kit (Qiagen N. V., Germantown, USA). Next, the DNA was subjected to fluorometric quantitation (Qubit 4, ThermoFisher Scientific, Waltham, USA), followed by DNA library preparation with ~300 ng of genomic DNA using the Illumina DNA Prep kit, according to the manufacturer’s instructions. The DNA libraries were then normalized and sequenced using the Illumina NextSeq Reagent High output v2.0 (paired end 2 x 150 bp insert size) in an Illumina NextSeq platform. The quality of the obtained raw reads was assessed using FastQC (Andrews, 2010), whereas Trimmomatic version 0.39 (Bolger et al., 2014) was used to remove Illumina adaptors and reads with low Phred quality score (Q≤30). Trimmed sequences were used for de novo genome assembly using SPAdes version 3.13.1 (Bankevich et al., 2012), and QUAST version 5.02 (Gurevich et al., 2013) was used to assess assembly quality. In silico serovar prediction was performed using SISTR version 1.1.1 (Yoshida et al., 2016), whereas AMR genotypes (genes and point mutations) of the assembled genomes were predicted using AMRFinderPlus version 3.10.1 (Feldgarden et al., 2019). 2.6 Phylogenetic analysis and genomic comparison with public isolates High-resolution phylogenetic analysis was conducted for each of the top 10 serovars. This analysis was based on single-nucleotide polymorphisms (SNP) using assembled genomes. The SNPs were located, filtered, and validated using CSI Phylogeny version 1.4 (Kaas et al., 2014), with the following default values: ≥10x depth at SNP positions, ≥10% relative depth at SNP positions, ≥10 bp distance between SNPs (prune), ≥30 SNP quality, ≥25 read mapping quality, and ≥1.96 Z-score. We used the closed genomes of each of the top 10 serovars to obtain the multiple genome alignment and the SNP matrix, with the following National Center for Biotechnology Information (NCBI) accessions: Agona (GCA_018339615.1), Anatum (CP068784), Derby (CP075036.1), Infantis (CP150499.1), London (CP074204.1), Muenchen (CP074332.1), Newport (CP147854.1), Senftenberg (CP029038.1), Typhimurium (NP_460230.1). Since there were no closed Adelaide genomes at NCBI, the reference genome of this serovar was downloaded from the European Nucleotide Archive (ENA) through the accession CP123777.1. The resulting genome alignments were analyzed using the maximum likelihood (ML) method in RAxML version 8.0 (Stamatakis et al., 2008). An ML tree was generated under the GTR+Γ model of nucleotide evolution with the following parameters: 1) let RAxML halt bootstrapping automatically; 2) sequence type: nucleotide; 3) do not estimate the proportion of invariable sites (GTRGAMMA+I); 4) find the best tree using maximum likelihood search; and 5) use the BFGS search algorithm to optimize branch lengths and GTR parameters simultaneously. A closed genome of S. enterica subsp. diarizonae (NCBI accession CP075128.1) was used as an outgroup. The resulting tree was edited using iTOL version 7.0 (Letunic and Bork, 2024). We also investigated the genetic relatedness among our major circulating serovars (the top 10) and public isolates by reviewing the SNP clusters of closely related genomes in the NCBI Pathogen Detection database (PD) as of June 20, 2025. For this purpose, we first accessed the PD SNP clusters to which our isolates belonged. Next, we recorded the country of origin and the isolation source of public isolates, including the presence of clinical strains in each cluster. 2.7 Statistical analysis A temporal stability assessment of the prevalence of Salmonella was performed based on relative differences (RD), as previously described (Kim et al., 2023). For this purpose, we computed the RD between the prevalence values in each region and the average over all regions for each sampling year using the following equation: where: RD ij : relative difference between the Salmonella prevalence in region “i" on the sampling year “j” (X ij ) and the average prevalence over all “i" regions on the sampling year “j” ( ). We also calculated the mean RD at the region “i" (MRD i ) as the average of RDij over all sampling years (n): Values of MRD i >0 indicate that the prevalence of Salmonella in region “i" is higher than the overall average. Consequently, MRD i <0 indicates that the prevalence in region “i" is lower, whereas MRD i ≈0 indicates that the prevalence of that region is close to the overall average. Moreover, we calculated the RD standard deviation at each “i" region (SDRD i ) to estimate the robustness of the temporal stability of Salmonella prevalence (lower SDRD values are indicative of stronger temporal stability): Chi-square tests and odds ratio calculations were also conducted to assess the association between the prevalence of Salmonella and geographical region, as well as between serovars and AMR phenotypes/genotypes. Moreover, we assessed the dynamics of AMR at the population level by calculating the log 10 -fold change in the proportion of isolates with resistant phenotypes and genotypes in the post COVID-19 pandemic (2021-2023) in relation to previous years (2019-2020). 3 Results 3.1 Salmonella contamination of surface waters: a virtually permanent phenomenon Salmonella was systematically isolated from SuWa throughout the sampling period (Fig. 1). The isolation rates varied across geographical regions (χ 2 =23.9, P<0.0001), with positive samples found in 75%, 83%, 88%, 96%, and 100% of the 24 sampling rounds in Hidalgo, Mexico City, Morelos, Tlaxcala, and the State of Mexico, respectively. The overall prevalence of Salmonella in SuWa was 42%, 47%, 50%, 64%, and 64% in Hidalgo, Mexico City, Morelos, State of Mexico, and Tlaxcala, respectively. Of the 953 samples analyzed, 515 tested positive for Salmonella , and 528 unique isolates were collected. Fig. 1 Prevalence of Salmonella spp. in surface waters across geographical regions and sampling rounds. The sampling timeframes are indicated at the bottom of the heatmap The temporal stability pattern of the prevalence of Salmonella further confirmed the existence of regional differences and the pathogen’s persistence (Fig. 2). Tlaxcala and the State of Mexico had positive mean relative difference (MRD) values, indicating that the prevalence of Salmonella in SuWa from these states is higher than that in the overall study region. The MRD was negative but close to zero in Mexico City and Morelos, indicating that these regions may provide a good estimate of the overall average prevalence. The lowest negative MRD was observed for Hidalgo state, indicating that this location is likely to have a lower Salmonella prevalence than the average of the whole region. Furthermore, the standard deviation of relative differences (SDRD) across regions showed that the temporal stability of Salmonella prevalence in the State of Mexico (SDRD=0.07) was more robust than that of the remaining locations: 0.17, 0.25, 0.26, and 0.33 in Tlaxcala, Morelos, Hidalgo, and Mexico City, respectively. Fig. 2 Mean relative difference (MRD) ± standard error of Salmonella prevalence across the five regions under study 3.2 High-diversity population and clonal expansion of major serovars Among the 528 strains, 61 Salmonella serovars were identified (Online Resource 1). The top 10 included epidemiologically relevant serovars, such as Typhimurium, Newport, and Infantis, among others, which collectively represented nearly 50% of the study population. These predominant isolates were detected across the sampling period and throughout the geographical regions (Fig. 3). Fig. 3 Geographical distribution and number of isolates belonging to each of the top 10 Salmonella serovars according to the year of isolation and percentage of the population represented by these serovars. The AMR phenotypes (pansusceptible, mono/bi resistance, and multidrug-resistant) are indicated on the map Phylogenetic analysis supported either Salmonella persistence or reintroduction events as shown by the observed clonality (≤20 SNP distance) among isolates collected in different sampling rounds/years from the same watersheds in the State of Mexico and Tlaxcala (Fig. 4A). Some examples include the Agona isolates collected from site MEX1 in sampling rounds 10 (2021) and 18 (2022), TLX7 in rounds 4 (2019) and 6 (2020), TLX5 in rounds 3 (2019) and 7 (2020), and MEX7 in rounds 1 and 2 (2019). Along the same lines, we observed clonality between London isolates collected from site MEX12 in rounds 22 and 23 (2023), as well as site MEX6 in rounds 19 and 23 (2023) (Fig. 4B). Fig. 4 SNP-based phylogenetic analysis illustrating the persistence of Salmonella in surface waters from Central Mexico. A) Serovar Agona isolates. B) Serovar London isolates. Clonal clusters (≤20 SNP distance) are highlighted in green. Tip labels indicate NCBI accession, serovar, sampling site name, sampling round, and year of collection, whereas the bootstrap support is indicated on the branches The phylogeny also provided evidence of clonal dissemination of isolates across sampling sites within regions, and across regions and sampling years. This pattern of dissemination was observed in isolates of the top 10 serovars except for Derby. In this regard, the most widespread were Adelaide, Typhimurium, and Senftenberg, with clonal isolates in 3-4 out of the five participating regions throughout the sampling period (Fig. 4 and Online Resource 2). Genomic comparison of our top 10 most abundant serovars with global public isolates showed that these isolates belonged to PD SNP clusters containing clinical isolates and food isolates originating mainly from meat and poultry: Agona: PDS000031492.413, Anatum: PDS000056615.40, Newport: PDS000007781.1362, London: PDS000027247.61, Adelaide: PDS000001382.408, Derby: PDS000053683.19, Senftenberg: PDS000031803.244, Infantis: PDS000248108.43, Typhimurium: PDS000013714.46, and Muenchen: PDS000027665.31. Most public isolates that clustered together with our study isolates were collected in the USA, Canada, and the United Kingdom, whereas those of serovars Agona, Infantis, and Newport originated from numerous countries. 3.3 Antimicrobial resistance dynamics Resistant Salmonella isolates were recovered from watersheds across all participating regions (Fig. 3), although variable proportions of MDR isolates were observed (χ 2 =17.6, P=0.0015). The odds of collecting MDR isolates was two times higher [95 confidence interval (95CI) 1.4-3.1] in Hidalgo and the State of Mexico than in Mexico City and Morelos. As shown in the map, MDR isolates were scattered within each region except for Morelos, where most MDR isolates were collected from its Western side. The most frequent AMR phenotypes affected older antibiotics, such as tetracycline (36%), streptomycin (33%), chloramphenicol (24%), trimethoprim-sulfamethoxazole (23%), and ampicillin (18%) (Fig. 5). Conversely, resistance to cephalosporins, meropenem, ciprofloxacin, and amikacin was rarely detected (≈0-5%), whereas resistance to azithromycin was observed in approximately 12% (62/528) of the isolates. Overall, nearly half of the isolates (256/528) showed resistance to at least one antibiotic, whereas 30% exhibited MDR phenotypes (Online Resource 1). AMR phenotypes were not uniformly distributed across serovars (χ 2 =103.8, P<0.0001). The proportion of MDR isolates was higher in the serovars Schwazergrund, Rissen, 1,4,[5],12:i:-, Typhimurium, Panama, Bredeney, Mbandaka, Derby, Albany, Hadar, Senftenberg, Newport, and Anatum (43-88%) than in the remaining serovars (0-33%) (Fig. 5, Online Resource 1). Fig. 5 Relative frequency of AMR phenotypes among isolates of Salmonella spp. (n=528) according to serovars and overall. AMP: ampicillin, AMC: amoxicillin+clavulanic acid, CRO: ceftriaxone, FEP: cefepime, MEM: meropenem, CIP: ciprofloxacin, AZM: azithromycin, AMK: amikacin, STR: streptomycin, TET: tetracycline, CHL: chloramphenicol, SXT: trimetoprim+sulfamethoxazole The observed AMR genotypes were generally consistent with the phenotypes, although disagreements were observed across all antibiotic classes. Of the 170 isolates that exhibited resistance to aminoglycosides, 44 had genotypes lacking all known aminoglycoside resistance genes. Likewise, no azithromycin resistance determinant was identified in 29 of the 62 isolates that showed resistance to azithromycin. This phenomenon was observed to a lesser extent in the remaining antibiotic classes. The opposite (resistance genotypes and susceptible phenotypes) also occurred across all antibiotic classes, with the highest frequencies observed in fluoroquinolones (63 isolates) and folate pathway inhibitors (35 isolates). As observed in AMR phenotypes, there was a higher abundance of AMR genes conferring resistance to older antibiotics, such as tetracycline ( tet ), folate pathway inhibitors ( sul / dfrA alleles), streptomycin ( aac / aadA / aph ), phenicols ( floR / cmlA ), and penicillins ( bla TEM/CARB ) (Fig. 6), whereas genes encoding cephalosporinases ( bla CTX-M /bla CMY ) were scarce. Similarly, few isolates (7%) carried the azithromycin resistance gene mph(A) and nearly 37% carried MDR genotypes (resistance alleles to ≥3 antimicrobial classes). The overall pool of AMR determinants (62 genes and 4 gene point mutations) encoded 15 different resistance mechanisms at the population level. Interestingly, the most abundant AMR determinants were plasmid-mediated quinolone resistance (PMQR) genes ( oqx / qnr alleles) (226/528). In contrast, few isolates (26/528) carried gyrA / parE mutations, which are also associated with quinolone resistance. Moreover, the isolates carried several AMR genes that confer resistance to other antibiotics that were not included in the AST panel. Among these, the most abundant was the fosA gene (97/528), which confers resistance to fosfomycin. Fig. 6 Relative abundance of antimicrobial resistance determinants (genes and point mutations) among 528 Salmonella spp. isolates from Central Mexico’s agricultural surface waters (2019-2023 timeframe) The number of AMR genes per genome varied among serovars. For instance, ≥75% of the Adelaide, Corvallis, Braenderup, Give, Montevideo, and Meleagridis isolates carried ≤2 AMR genes (Fig. 7). Conversely, in most of the remaining serovars, excluding Agona, Muenster, and the “Others” category, the number of AMR genes corresponding to the third quartile was in the 5-10 range. Intraserovar variability was also observed in multiple instances where isolates of the same serovar either lacked AMR genes or harbored up to 14 resistance alleles. Fig. 7 Boxplot distribution of antimicrobial resistance (AMR) genes per genome for the top 20 serovars. Values within boxes correspond to the first to third quartile range The distribution of AMR genes and MDR genotypes was also uneven among serovars (Fig. 8). Adelaide, Corvallis, Braenderup, Give, and Meleagridis serovar isolates had little contribution to the overall number of AMR genes, except for PMQR genes ( qnr/oqx alleles) in the first three serovars: Adelaide, Corvallis, and Braenderup. The remaining isolates harbored AMR genes to 5-7 antimicrobial classes, with the main contributors being the serovars of Typhimurium, Newport, Anatum, Senftenberg, Panama, and London. Interestingly, the macrolide resistance gene mph(A) was only present in three major serovars (Newport, Senftenberg, and London), and in the less represented serovars that were included under the “Other serovars” category: Schwarzengrund (7/8), and Havana (1/4) (Online Resource 1). Fig. 8 Sankey diagram showing the contribution of AMR genes of each antimicrobial class (right) by the study genomes according to serovar (left) Regarding the temporal dynamics of AMR, an increased prevalence of resistant phenotypes involving older antibiotics was observed in the post-pandemic period (after November 2020) (Fig. 9). The opposite was observed for meropenem, cephalosporins, ciprofloxacin, azithromycin, and amoxicillin-clavulanic acid, whereas the post-pandemic occurrence of all AMR determinants and MDR genotypes was higher than that in previous years. Fig. 9 Log 10 -fold change in the proportion of Salmonella spp. isolates exhibiting resistance phenotypes and carrying resistant determinants in the post-COVID-19 pandemic period (after November 2020, n=315) compared to the pre-pandemic period (July 2019-February 2020, n=213). AMP: ampicillin, AMC: amoxicillin+clavulanic acid, CRO: ceftriaxone, FEP: cefepime, MEM: meropenem, CIP: ciprofloxacin, AZM: azithromycin, AMK: amikacin, STR: streptomycin, TET: tetracycline, CHL: chloramphenicol, SXT: trimetoprim+sulfamethoxazole, MDR: multidrug-resistant 4 Discussion This comprehensive longitudinal study demonstrated that Salmonella contamination is virtually permanent in the agricultural SuWa in Central Mexico. The temporal stability of this phenomenon was supported both by the systematic isolation of the pathogen throughout the sampling period and the low standard deviation of the relative differences in the pathogen prevalence across regions (0.07-0.33). Despite the observed regional differences, the prevalence of Salmonella was considerably high (42-64% across regions), which is consistent with the results of multiple SuWa surveys conducted worldwide (Bell et al., 2021). The genetic proximity of the isolates of our top 10 serovars and clinical isolates collected globally underscores the role of SuWa as a Salmonella reservoir of public health importance. Moreover, some of the major Salmonella serovars circulating in the SuWa under study (e.g., Agona, Anatum, Newport, Typhimurium, Infantis, Muenchen) have been recently implicated in human illnesses in Mexico and globally (Alvarez et al., 2023; Ford et al., 2023; Godinez-Oviedo et al., 2020). Predominant isolates were also isolated throughout the study regions and across the 5-year sampling period, providing further evidence of Salmonella’s ability to successfully establish long-term environmental reservoirs. Interestingly, clonal isolates were rarely detected in the same site during consecutive sampling rounds. Therefore, their systematic isolation across years in some watersheds appears to be associated either with multiple re-introduction events or with a progressive decline in the concentration of these strains over time, making detection difficult. The observed clonality between isolates from different sites within the same region may be associated with water body interconnection. This was the case at sampling sites in Mexico City, which are located in the Xochilmico municipality’s canal network (Jiménez et al., 2020). Similarly, nearly half of the sampling sites in the State of Mexico (MEX8-MEX12) are located in the Toluca Valley, which hosts the Lerma Basin (Carreño de León et al., 2018). We also observed clonality among isolates collected across distant regions, highlighting the widespread dissemination of the predominant Salmonella serovars. Additionally, the high number of identified serovars (61 overall) indicates that SuWa contamination may also arise from multiple sources, such as runoff from adjacent farming or urban areas (Rocha et al., 2025). These findings underscore the role of SuWa from a one-health perspective, serving as an indicator of interconnected health at the human, animal, and environment interface. Hence, further one-health-based research is urgently needed to identify sources of SuWa contamination and develop effective intervention strategies. This long-term survey also revealed that AMR Salmonella isolates were scattered throughout the sampling region. Approximately one-third of the study population exhibited MDR phenotypes and genotypes despite the regional differences observed. The proportion of MDR isolates observed in this study was twice that observed in one of our previous studies involving SuWa isolates (≈15%, n=172) from the same region collected in 2019 (Ballesteros-Nova et al., 2022). Additionally, our results support the trend toward stronger AMR genotypes in SuWa isolates during the early post-COVID-19 pandemic. In the absence of data on antibiotic consumption, it is difficult to assess whether these findings are associated with the intensified use of certain antimicrobials during the COVID-19 pandemic. However, they underscore the increasing role of SuWa as an AMR pathogen reservoir. The observed disagreement between AMR phenotypes and genotypes suggests the presence of undescribed genetic determinants in the Salmonella resistome. Further research is needed to determine whether this phenomenon is linked to novel AMR genes or the activation of cryptic genes (e.g., those encoding stress response, cell membrane, and transporter proteins), as previously reported in Escherichia coli (Suarez and Martiny, 2021). In any case, these results reveal the occurrence of convergent evolution (i.e., different genotypes sustaining the same phenotype) in a context of selective pressure where certain AMR phenotypes are required for survival. The occurrence of susceptible phenotypes in the presence of AMR determinants was mainly observed in fluoroquinolones and folate pathway inhibitors, involving PMQR genes (i.e., qnr and oqx alleles), and sul / dfrA alleles, respectively. The PMQR genes confer resistance at levels below the clinical breakpoints (Strahilevitz et al., 2009). Therefore, ciprofloxacin susceptibility was expected in PMQR-positive isolates. Silent AMR genes involving several antibiotic classes have long been identified in E. coli (Enne et al., 2006). These silent genes may be expressed under suitable conditions, leading to therapeutic failure (Deekshit and Srikumar, 2022). Consequently, our results emphasize the potential clinical relevance of susceptible strains carrying AMR genotypes, granting further research in this field. Previous research has shown that an increasing proportion of SuWa and food isolates have acquired AMR genes against older antibiotics (e.g., tetracycline, streptomycin, and chloramphenicol) (Chen et al., 2024a; Chen et al., 2024b; Delgado-Suarez et al., 2021). Our results for AMR phenotypes further confirmed this trend and were consistent with the observed AMR genotypes. These findings highlight the importance of preventing the use of newer antibiotics in food production settings to compensate for the lack of effectiveness of older drugs. Such practices could lead to the emergence of resistance to critically important antimicrobials, as it appears to be happening already considering the unusual peak in azithromycin resistance observed in this study. Resistance to azithromycin has not been reported in environmental Salmonella isolates from Mexico until recently. According to a review covering the 2000-2017 period, this phenotype was observed in clinical isolates but not in food isolates, consistent with the restricted use of azithromycin in human health. However, a recent study by our research group appears to be the first Mexican report of emergent azithromycin resistance in beef Salmonella isolates (≈15%) (Campos Granados et al., 2023), which is comparable to that observed among our SuWa isolates (≈12%) and is associated with the presence of the macrolide phosphotransferase encoding gene mph(A) (Gomes et al., 2019). Taken together, these findings show a clear trend toward increasing azithromycin resistance in non-clinical isolates, which is apparently mediated by environmental dissemination of the mph(A) gene. The use of other macrolides (e.g., tylosine and tylvalosine) in veterinary medicine may induce cross-resistance to azithromycin, leading to the environmental dissemination of AMR determinants against this macrolide (Campos Granados et al., 2023). However, further research is required to validate this hypothesis. Our assessment of temporal AMR dynamics did not reveal a trend toward an increased proportion of resistance phenotypes to critically important antibiotic classes, such as third- and fourth-generation cephalosporins, carbapenems, or fluoroquinolones. However, the proportion of isolates harboring AMR genes against these antimicrobials did increase. Approximately 30% of the study isolates carried 5-14 AMR genes and exhibited MDR phenotypes, some of which were serovars of epidemiological importance. This pattern revealed the accumulation of AMR factors in the resistome of environmental Salmonella , highlighting the central role of SuWa in AMR ecology and epidemiology. Moreover, these findings underscore the public health risks of Salmonella circulating in SuWa, which is recognized as a vehicle for the introduction and dissemination of the pathogen along the food production continuum (Ballesteros-Nova et al., 2022; Bell et al., 2021; Delgado-Suarez et al., 2026; Rocha et al., 2022). 5 Conclusions This study unveiled the role of agricultural surface waters from Central Mexico as a critical and virtually permanent reservoir of multiple Salmonella serovars. The predominant circulating isolates belong to epidemiologically relevant serovars, with most of them exhibiting genetic proximity to clinical strains, as well as clonal geographical and temporal expansion across the study area and sampling timeframe. The resistome comprised a vast array of AMR determinants (>60) at the population level, with a moderate fraction of the isolates (~30%) exhibiting MDR profiles. The emergence of resistance to critically important antimicrobials, such as macrolides, is of particular interest as it further emphasizes the clinical significance of these environmental isolates. Moreover, the temporal dynamics of AMR showed a trend toward increasingly stronger AMR profiles, especially in older antibiotics. These findings have critical implications for food safety and public health and underscore the role of surface waters in the ecology and epidemiology of AMR Salmonella . Further research is required to identify the sources of Salmonella contamination in surface waters to effectively reduce the associated risks. Declarations Author contributions Conceptualization: EJ Delgado-Suárez, M Toro, J Meng, EW Brown, MW Allard Data curation: EJ Delgado-Suárez, S Zapata Ramírez, NE Ballesteros-Nova Formal analysis: EJ Delgado-Suárez, NE Ballesteros-Nova, LM Sánchez-Zamorano, Y Chavarin-Pineda. Funding acquisición: EJ Delgado-Suárez, M Toro, J Meng Research: S Zapata Ramírez, FA Ruíz López, LC Sierra Gómez-Pedroso, Z Chen, RL Bell, A Brover, M Balkey Methodology: EJ Delgado-Suárez, LC Sierra Gómez-Pedroso Project administración: EJ Delgado-Suárez, O Soberanis-Ramos, M Toro, J Meng Resources: O Soberanis-Ramos, C Grim, LM Sánchez-Zamorano, Y Chavarin-Pineda Supervision: EJ Delgado-Suárez, MS Rubio-Lozano, LC Sierra Gómez-Pedroso Writing – original draft: EJ Delgado-Suárez Writing – review and editing: EJ Delgado Suárez, S Zapata-Ramírez, LC Sierra Gómez-Pedroso, FA Ruíz-López, NE Ballesteros-Nova, O Soberanis-Ramos, MS Rubio-Lozano, LM Sánchez-Zamorano, Y Chavarin-Pineda, RL Bell, A Brover, M Balkey, C Grim, EW Brown, MW Allard, Z. Chen, M Toro, J Meng Funding This work was supported by the FDA of the U.S. Department of Health and Human Services (HHS) as part of the financial assistance award U01FDU001418 granted to the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) with 30% financed from nongovernmental sources. Data Availability Whole genome sequencing data (raw reads) derived from the current study were deposited in the NCBI repository (https://www.ncbi.nlm.nih.gov/bioproject) under Bioproject PRJNA560080. The individual accession numbers of isolates are provided in Online Resource 1. Ethics Approval Not applicable. Consent to Participate Not Applicable Consent to Publish Not Applicable Competing interests The authors have no relevant financial or non-financial interests to disclose. References Alvarez, D. M., Barron-Montenegro, R., Conejeros, J., Rivera, D., Undurraga, E. A. & Moreno-Switt, A. I. (2023). 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Allard","email":"","orcid":"","institution":"U.S. Food and Drug Administration","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"W.","lastName":"Allard","suffix":""},{"id":638476499,"identity":"45c8f5d4-f076-4380-9c6e-22d441ef4e4e","order_by":15,"name":"Zhao Chen","email":"","orcid":"","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Chen","suffix":""},{"id":638476502,"identity":"d879282f-6830-4959-a3e5-413fac6d8cd0","order_by":16,"name":"Magaly Toro","email":"","orcid":"","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Magaly","middleName":"","lastName":"Toro","suffix":""},{"id":638476506,"identity":"69c8fed1-6ab7-47de-b4be-9e70851bfcd4","order_by":17,"name":"Jianghong Meng","email":"","orcid":"","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Jianghong","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2026-03-25 00:23:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9216619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9216619/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109119886,"identity":"4138cd33-794d-4001-b546-73ec047b6fd9","added_by":"auto","created_at":"2026-05-12 17:02:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":181397,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of \u003cem\u003eSalmonella\u003c/em\u003espp. in surface waters across geographical regions and sampling rounds. The sampling timeframes are indicated at the bottom of the heatmap\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/842fe7aa3dd09f25ac2c36c7.png"},{"id":109119891,"identity":"bd96a4d6-ae63-435d-b6fb-0aaef6cf7701","added_by":"auto","created_at":"2026-05-12 17:02:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28480,"visible":true,"origin":"","legend":"\u003cp\u003eMean relative difference (MRD) ± standard error of \u003cem\u003eSalmonella\u003c/em\u003e prevalence across the five regions under study\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/9a824036b8643a818876c391.png"},{"id":109205115,"identity":"8e77c4b8-68ac-4f03-8f88-d6b884d70161","added_by":"auto","created_at":"2026-05-13 15:03:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129365,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution and number of isolates belonging to each of the top 10 \u003cem\u003eSalmonella\u003c/em\u003e serovars according to the year of isolation and percentage of the population represented by these serovars. The AMR phenotypes (pansusceptible, mono/bi resistance, and multidrug-resistant) are indicated on the map\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/0159798a67b49026188d3882.png"},{"id":109205117,"identity":"b1828730-a01d-45b4-860c-b3290e604170","added_by":"auto","created_at":"2026-05-13 15:03:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":263139,"visible":true,"origin":"","legend":"\u003cp\u003eSNP-based phylogenetic analysis illustrating the persistence of \u003cem\u003eSalmonella\u003c/em\u003e in surface waters from Central Mexico. A) Serovar Agona isolates. B) Serovar London isolates. Clonal clusters (≤20 SNP distance) are highlighted in green. Tip labels indicate NCBI accession, serovar, sampling site name, sampling round, and year of collection, whereas the bootstrap support is indicated on the branches\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/b5d5d9b5647c522dcb9110ee.png"},{"id":109119888,"identity":"44d9b3ac-1d3e-4d4c-aafc-d67a543fe4bd","added_by":"auto","created_at":"2026-05-12 17:02:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":195841,"visible":true,"origin":"","legend":"\u003cp\u003eRelative frequency of AMR phenotypes among isolates of \u003cem\u003eSalmonella\u003c/em\u003e spp. (n=528) according to serovars and overall. AMP: ampicillin, AMC: amoxicillin+clavulanic acid, CRO: ceftriaxone, FEP: cefepime, MEM: meropenem, CIP: ciprofloxacin, AZM: azithromycin, AMK: amikacin, STR: streptomycin, TET: tetracycline, CHL: chloramphenicol, SXT: trimetoprim+sulfamethoxazole\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/f24ad174c8c2136a02fd6ab2.png"},{"id":109119889,"identity":"8221d8b4-4fc1-4055-b4f0-4bee8016f2e3","added_by":"auto","created_at":"2026-05-12 17:02:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":37267,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of antimicrobial resistance determinants (genes and point mutations) among 528 \u003cem\u003eSalmonella \u003c/em\u003espp. isolates from Central Mexico’s agricultural surface waters (2019-2023 timeframe)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/6067e79a7ff74402a3f2774f.png"},{"id":109119894,"identity":"6c230829-0275-462b-b312-7a5835ce4b5b","added_by":"auto","created_at":"2026-05-12 17:02:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":186857,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot distribution of antimicrobial resistance (AMR) genes per genome for the top 20 serovars. Values within boxes correspond to the first to third quartile range\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/91aca1c93e3a9f8baa592a7c.png"},{"id":109119892,"identity":"71b2e71b-6c56-445b-be4f-0cf423869809","added_by":"auto","created_at":"2026-05-12 17:02:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1552756,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagram showing the contribution of AMR genes of each antimicrobial class (right) by the study genomes according to serovar (left)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/3efc4da7f0543a61bab9b5f2.png"},{"id":109204922,"identity":"c4910292-b043-45ad-a6dd-74a4d3ce9b1c","added_by":"auto","created_at":"2026-05-13 15:02:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":44017,"visible":true,"origin":"","legend":"\u003cp\u003eLog\u003csub\u003e10\u003c/sub\u003e-fold change in the proportion of \u003cem\u003eSalmonella\u003c/em\u003e spp. isolates exhibiting resistance phenotypes and carrying resistant determinants in the post-COVID-19 pandemic period (after November 2020, n=315) compared to the pre-pandemic period (July 2019-February 2020, n=213). AMP: ampicillin, AMC: amoxicillin+clavulanic acid, CRO: ceftriaxone, FEP: cefepime, MEM: meropenem, CIP: ciprofloxacin, AZM: azithromycin, AMK: amikacin, STR: streptomycin, TET: tetracycline, CHL: chloramphenicol, SXT: trimetoprim+sulfamethoxazole, MDR: multidrug-resistant\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/a939ac9b856f385c403bafb0.png"},{"id":109206682,"identity":"eb6eecd6-f596-4b19-8d65-368b9f5b118b","added_by":"auto","created_at":"2026-05-13 15:15:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2791230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9216619/v1/ef4490c5-4ba1-43f6-9c01-0e1e233b1b44.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term genomic surveillance unveils persistent contamination, clonal dissemination, and antimicrobial resistance dynamics of Salmonella in surface waters from Central Mexico","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAgricultural surface waters (SuWa) are essential for food production worldwide. Approximately 80% of Mexico\u0026rsquo;s total domestic water consumption is linked to agricultural activities (2015). Although critical for food security, SuWa are often contaminated with pathogens, such as \u003cem\u003eSalmonella\u003c/em\u003e spp. (Bell et al., 2021) (from now on refer to as \u003cem\u003eSalmonella\u003c/em\u003e), posing a risk to food safety and public health. Increasing evidence has linked salmonellosis outbreaks to fresh produce irrigated with contaminated water (Callej\u0026oacute;n et al., 2015; Liu et al., 2018). In the USA alone, fresh produce was associated with 3,778 illnesses, with nearly one-third requiring hospitalization and 16 deaths between 2010 and 2017 (Carstens et al., 2019), highlighting the contribution of agricultural activities to disease burden.\u003c/p\u003e\n\u003cp\u003eIn Mexico, the source attribution of foodborne and waterborne diseases is limited. However, cross-sectional studies have demonstrated the circulation of \u003cem\u003eSalmonella\u003c/em\u003e in SuWa used for food production (Casta\u0026ntilde;eda-Ruelas and Jim\u0026eacute;nez Edeza, 2018; Jim\u0026eacute;nez et al., 2014), describing the serovar diversity and antimicrobial resistance (AMR) phenotypes of the circulating isolates. More recent studies have provided further insights into the important role of SuWa in the ecology and epidemiology \u003cem\u003eSalmonella\u003c/em\u003e in several Latin American countries, including Mexico (Ballesteros-Nova et al., 2022; Chen et al., 2024a). However, comprehensive longitudinal studies at the population level are scarce. This information is critical considering that \u003cem\u003eSalmonella\u003c/em\u003e has been included in the high priority group of the 2024 World Health Organization (WHO) Bacterial Priority Pathogens List (WHO, 2024). Therefore, we conducted a long-term surveillance study of agricultural SuWa in Central Mexico to assess the prevalence, epidemiology, and AMR dynamics of \u003cem\u003eSalmonella\u003c/em\u003e at the population level.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e2.1 Sampling design\u003c/p\u003e\n\u003cp\u003eA longitudinal study was conducted to screen for \u003cem\u003eSalmonella\u003c/em\u003e contamination in rivers, dams, ponds, and irrigation canals in the central states of Hidalgo, Morelos, State of Mexico, and Tlaxcala, as well as Mexico City. Sampling was conducted in consecutive rounds from July 2019 to November 2023 (Table 1). A sampling round consisted of taking samples from one region per week until all regions were sampled. The distribution of sampling rounds across years was uneven due to administrative constraints during university holidays or the implementation of restriction measures during the COVID-19 pandemic (March 2020-March 2021).\u003c/p\u003e\n\u003cp\u003eA convenience-based sampling scheme was used, whereby the participating watersheds were selected based on previously described criteria (Ballesteros-Nova et al., 2022): wadeable streams with public access, proximity of food production areas, and proximity to the laboratory to allow sample processing in \u0026le;24 h post collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Sample collection schedule (grey cells) throughout the sampling timeframe\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse;border-width: medium;border-style: none;border-color: currentcolor;border-image: initial;width: 555px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:35.45pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0cm 5.4pt 0cm 5.4pt;height:55.65pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Aptos\",sans-serif;text-align:center;line-height:200%;'\u003e\u003cspan style='font-size:13px;line-height:200%;font-family:\"Arial\",sans-serif;'\u003eYear\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:2.0cm;border-top:solid windowtext 1.0pt;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:none;padding: 0cm 5.4pt 0cm 5.4pt;height:55.65pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Aptos\",sans-serif;text-align:center;line-height:200%;'\u003e\u003cspan style='font-size:13px;line-height:200%;font-family:\"Arial\",sans-serif;'\u003eSampling rounds\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.15pt;border-width: 1pt medium;border-style: solid none;border-color: windowtext currentcolor;padding: 0cm 5.4pt;writing-mode: sideways-lr;height: 55.65pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:5.65pt;margin-bottom: 0cm;margin-left:5.65pt;font-size:11.0pt;font-family:\"Aptos\",sans-serif;line-height:200%;'\u003e\u003cspan style='font-size:13px;line-height:200%;font-family:\"Arial\",sans-serif;'\u003eJanuary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.15pt;border-width: 1pt medium;border-style: solid none;border-color: windowtext currentcolor;padding: 0cm 5.4pt;writing-mode: sideways-lr;height: 55.65pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:5.65pt;margin-bottom: 0cm;margin-left:5.65pt;font-size:11.0pt;font-family:\"Aptos\",sans-serif;line-height:200%;'\u003e\u003cspan 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style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Aptos\",sans-serif;line-height:200%;'\u003e\u003cspan style='font-size:13px;line-height:200%;font-family:\"Arial\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.2pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor windowtext windowtext currentcolor;background: rgb(191, 191, 191);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Aptos\",sans-serif;line-height:200%;'\u003e\u003cspan style='font-size:13px;line-height:200%;font-family:\"Arial\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.15pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor windowtext windowtext currentcolor;background: rgb(191, 191, 191);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Aptos\",sans-serif;line-height:200%;'\u003e\u003cspan style='font-size:13px;line-height:200%;font-family:\"Arial\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.2pt;border-width: medium medium 1pt;border-style: none none solid;border-color: currentcolor currentcolor windowtext;border-image: initial;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Aptos\",sans-serif;line-height:200%;'\u003e\u003cspan style='font-size:13px;line-height:200%;font-family:\"Arial\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA total of 49 watersheds were visited 24 times throughout the sampling period, and one sample was collected per site in each visit, unless the stream dried due to drought or landowners no longer permitted public access to it. An interactive map of the sampling sites is available in Google Maps (https://www.google.com/maps/d/viewer?mid=1Jde225H1TtycysUwDaOMfCaw_abfM7w\u0026amp;usp=sharing), which offers improved navigation via Google Earth. Overall, 953 samples were analyzed throughout the sampling timeframe (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Surveyed sites and number of samples analyzed per Mexican region (July 2019-November 2023)\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eNumber of sites (number of samples)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMexican region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCanal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal samples\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHidalgo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMexico City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMorelos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eState of Mexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTlaxcala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2.2 Sample collection procedures\u003c/p\u003e\n\u003cp\u003eSuWa samples were collected \u003cem\u003ein situ\u003c/em\u003e using the modified Moore swab (MMS) technique (Sbodio et al., 2013), following the same procedures described in one of our previous publications (Ballesteros-Nova et al., 2022). A full description of this method is also available from protocols.io (doi: https://dx.doi.org/10.17504/protocols.io.bpw9mph6).\u003c/p\u003e\n\u003cp\u003e2.3 \u003cem\u003eSalmonella\u003c/em\u003e analysis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSalmonella\u0026nbsp;\u003c/em\u003eisolation and confirmation procedures were conducted using a modified version of the US Food and Drug Administration (FDA) Bacteriological Analytical Manual methodology (FDA, 2021), as previously described (Ballesteros-Nova et al., 2022). A detailed description of these procedures is also available in protocols.io (doi: https://dx.doi.org/10.17504/protocols.io.bpybmpsn).\u003c/p\u003e\n\u003cp\u003e2.4 Antibiotic susceptibility testing (AST)\u003c/p\u003e\n\u003cp\u003eTo determine the AMR phenotypes of the isolates, we used the Kirby-Bauer disk diffusion method (Bauer et al., 1966) with a panel of 12 antibiotics included in the WHO list of critically important antimicrobials for human medicine (WHO, 2019). The tested antibiotics (Table 3) were selected based on their intensive use in human or veterinary medicine, or for treating invasive salmonellosis or other serious infections caused by enterobacteria, as previously described (Campos Granados et al., 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e List of antibiotics and criteria used to determine the resistance phenotype of isolates\u003c/p\u003e\n\u003ctable style=\"width: 4.5e+2pt;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAntibiotics\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eConcentration (\u0026mu;g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eInhibition zone diameter (mm)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmpicillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmoxicillin-clavulanic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCeftriaxone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCefepime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMeropenem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmikacin\u003c/p\u003e\n \u003cp\u003eStreptomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15-16\u003c/p\u003e\n \u003cp\u003e13-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;14\u003c/p\u003e\n \u003cp\u003e\u0026le;12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCiprofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAzithromycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTetracycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChloramphenicol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrimetoprim-sulfamethoxazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.25/23.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eBencton Dickinson disks. AMP: ampicillin, AMC: amoxicillin-clavulanic acid, CRO: ceftriaxone, FEP: cefepime, MEM: meropenem, AMK: amikacin, STR: streptomycin, CIP: ciprofloxacin, AZM: azithromycin, TET: tetracycline, CHL: chloramphenicol, SXT: trimethoprim-sulfamethoxazole.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eCriteria used to classify isolates as clinically resistant (R) or intermediate (I) (CLSI, 2024).\u003c/p\u003e\n\u003cp\u003eClinical cutoff values set by the Clinical and Laboratory Standards Institute (CLSI) (CLSI, 2024) were used to interpret the AST results. The \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e ATCC 27853 strain was used as a quality control organism. Isolates showing resistance to \u0026ge;3 antibiotic classes were classified as multidrug-resistant (MDR) (Magiorakos et al., 2012). A full description of the AST procedures is available in protocols.io (doi: dx.doi.org/10.17504/protocols.io.bpypmpvn).\u003c/p\u003e\n\u003cp\u003e2.5 Whole-genome sequencing, genome assembly, serovar prediction, and AMR genotypes\u003c/p\u003e\n\u003cp\u003eWe used roughly the same methodology described in our previous publication for these analyses (Ballesteros-Nova et al., 2022). In brief, genomic DNA was extracted from pure cultures using the Qiagen QIAsymphony system and the QIAsymphony DSP DNA kit (Qiagen N. V., Germantown, USA). Next, the DNA was subjected to fluorometric quantitation (Qubit 4, ThermoFisher Scientific, Waltham, USA), followed by DNA library preparation with ~300 ng of genomic DNA using the Illumina DNA Prep kit, according to the manufacturer\u0026rsquo;s instructions. The DNA libraries were then normalized and sequenced using the Illumina NextSeq Reagent High output v2.0 (paired end 2 x 150 bp insert size) in an Illumina NextSeq platform. The quality of the obtained raw reads was assessed using FastQC (Andrews, 2010), whereas Trimmomatic version 0.39 (Bolger et al., 2014) was used to remove Illumina adaptors and reads with low Phred quality score (Q\u0026le;30). Trimmed sequences were used for \u003cem\u003ede novo\u003c/em\u003e genome assembly using SPAdes version 3.13.1 (Bankevich et al., 2012), and QUAST version 5.02 (Gurevich et al., 2013) was used to assess assembly quality. \u003cem\u003eIn silico\u003c/em\u003e serovar prediction was performed using SISTR version 1.1.1 (Yoshida et al., 2016), whereas AMR genotypes (genes and point mutations) of the assembled genomes were predicted using AMRFinderPlus version 3.10.1 (Feldgarden et al., 2019).\u003c/p\u003e\n\u003cp\u003e2.6 Phylogenetic analysis and genomic comparison with public isolates\u003c/p\u003e\n\u003cp\u003eHigh-resolution phylogenetic analysis was conducted for each of the top 10 serovars. This analysis was based on single-nucleotide polymorphisms (SNP) using assembled genomes. The SNPs were located, filtered, and validated using CSI Phylogeny version 1.4 (Kaas et al., 2014), with the following default values: \u0026ge;10x depth at SNP positions, \u0026ge;10% relative depth at SNP positions, \u0026ge;10 bp distance between SNPs (prune), \u0026ge;30 SNP quality, \u0026ge;25 read mapping quality, and \u0026ge;1.96 Z-score. We used the closed genomes of each of the top 10 serovars to obtain the multiple genome alignment and the SNP matrix, with the following National Center for Biotechnology Information (NCBI) accessions: Agona (GCA_018339615.1), Anatum (CP068784), Derby (CP075036.1), Infantis (CP150499.1), London (CP074204.1), Muenchen (CP074332.1), Newport (CP147854.1), Senftenberg (CP029038.1), Typhimurium (NP_460230.1). Since there were no closed Adelaide genomes at NCBI, the reference genome of this serovar was downloaded from the European Nucleotide Archive (ENA) through the accession CP123777.1.\u003c/p\u003e\n\u003cp\u003eThe resulting genome alignments were analyzed using the maximum likelihood (ML) method in RAxML version 8.0 (Stamatakis et al., 2008). An ML tree was generated under the GTR+\u0026Gamma; model of nucleotide evolution with the following parameters: 1) let RAxML halt bootstrapping automatically; 2) sequence type: nucleotide; 3) do not estimate the proportion of invariable sites (GTRGAMMA+I); 4) find the best tree using maximum likelihood search; and 5) use the BFGS search algorithm to optimize branch lengths and GTR parameters simultaneously. A closed genome of \u003cem\u003eS. enterica\u003c/em\u003e subsp. \u003cem\u003ediarizonae\u003c/em\u003e (NCBI accession CP075128.1) was used as an outgroup. The resulting tree was edited using iTOL version 7.0 (Letunic and Bork, 2024).\u003c/p\u003e\n\u003cp\u003eWe also investigated the genetic relatedness among our major circulating serovars (the top 10) and public isolates by reviewing the SNP clusters of closely related genomes in the NCBI Pathogen Detection database (PD) as of June 20, 2025. For this purpose, we first accessed the PD SNP clusters to which our isolates belonged. Next, we recorded the country of origin and the isolation source of public isolates, including the presence of clinical strains in each cluster.\u003c/p\u003e\n\u003cp\u003e2.7 Statistical analysis\u003c/p\u003e\n\u003cp\u003eA temporal stability assessment of the prevalence of \u003cem\u003eSalmonella\u003c/em\u003e was performed based on relative differences (RD), as previously described (Kim et al., 2023). For this purpose, we computed the RD between the prevalence values in each region and the average over all regions for each sampling year using the following equation:\u003c/p\u003e\n\u003cp\u003e\n \u003cv:shapetype id=\"_x0000_t75\" coordsize=\"21600,21600\" o:spt=\"75\" o:preferrelative=\"t\" path=\"m@4@5l@4@11@9@11@9@5xe\" filled=\"f\" stroked=\"f\"\u003e\u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1778603409.png\" width=\"199\" height=\"94\"\u003e\n \u003cv:stroke joinstyle=\"miter\"\u003e\u0026nbsp;\u003cv:formulas\u003e\u0026nbsp;\u003cv:f eqn=\"if lineDrawn pixelLineWidth 0\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @0 1 0\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum 0 0 @1\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @2 1 2\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @3 21600 pixelWidth\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @3 21600 pixelHeight\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @0 0 1\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @6 1 2\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @7 21600 pixelWidth\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @8 21600 0\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @7 21600 pixelHeight\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @10 21600 0\"\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:formulas\u003e\n \u003cv:path o:extrusionok=\"f\" gradientshapeok=\"t\" o:connecttype=\"rect\"\u003e\u0026nbsp;\u003c/v:path\u003e\u0026nbsp;\n \u003c/v:stroke\u003e\u0026nbsp;\n \u003c/v:shapetype\u003e\n \u003cv:shape id=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u0026nbsp;\u003cv:imagedata src=\"file:///C%3A/Users/muth05/AppData/Local/Temp/msohtmlclip1/01/clip_image001.png\" o:title=\"\" chromakey=\"white\"\u003e\u0026nbsp;\u003c/v:imagedata\u003e\u0026nbsp;\u003c/v:shape\u003e\u0026nbsp; where:\n\u003c/p\u003e\n\u003cp\u003eRD\u003csub\u003eij\u003c/sub\u003e: relative difference between the \u003cem\u003eSalmonella\u003c/em\u003e prevalence in region \u0026ldquo;i\u0026quot; on the sampling year \u0026ldquo;j\u0026rdquo; (X\u003csub\u003eij\u003c/sub\u003e) and the average prevalence over all \u0026ldquo;i\u0026quot; regions on the sampling year \u0026ldquo;j\u0026rdquo; (\u003cv:shape id=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u0026nbsp;\u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1778603472.png\" width=\"25\" height=\"46\"\u003e\n \u003cv:imagedata src=\"file:///C%3A/Users/muth05/AppData/Local/Temp/msohtmlclip1/01/clip_image002.png\" o:title=\"\" chromakey=\"white\"\u003e\u0026nbsp;\u003c/v:imagedata\u003e\u0026nbsp;\n \u003c/v:shape\u003e).\u003c/p\u003e\n\u003cp\u003eWe also calculated the mean RD at the region \u0026ldquo;i\u0026quot; (MRD\u003csub\u003ei\u003c/sub\u003e) as the average of RDij over all sampling years (n):\u003c/p\u003e\n\u003cp\u003e\n \u003cv:shape id=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1778603511.png\" width=\"268\" height=\"133\"\u003e\u003c/v:shape\u003e\u003cbr\u003e\n\u003c/p\u003e\n\u003cp\u003eValues of MRD\u003csub\u003ei\u003c/sub\u003e\u0026gt;0 indicate that the prevalence of \u003cem\u003eSalmonella\u003c/em\u003e in region \u0026ldquo;i\u0026quot; is higher than the overall average. Consequently, MRD\u003csub\u003ei\u003c/sub\u003e\u0026lt;0 indicates that the prevalence in region \u0026ldquo;i\u0026quot; is lower, whereas MRD\u003csub\u003ei\u003c/sub\u003e\u0026asymp;0 indicates that the prevalence of that region is close to the overall average. Moreover, we calculated the RD standard deviation at each \u0026ldquo;i\u0026quot; region (SDRD\u003csub\u003ei\u003c/sub\u003e) to estimate the robustness of the temporal stability of \u003cem\u003eSalmonella\u003c/em\u003e prevalence (lower SDRD values are indicative of stronger temporal stability):\u003c/p\u003e\n\u003cp\u003e\n \u003cv:shape id=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1778603549.png\" width=\"486\" height=\"178\"\u003e\u003c/v:shape\u003e\u003cbr\u003e\n\u003c/p\u003e\n\u003cp\u003eChi-square tests and odds ratio calculations were also conducted to assess the association between the prevalence of \u003cem\u003eSalmonella\u003c/em\u003e and geographical region, as well as between serovars and AMR phenotypes/genotypes. Moreover, we assessed the dynamics of AMR at the population level by calculating the log\u003csub\u003e10\u003c/sub\u003e-fold change in the proportion of isolates with resistant phenotypes and genotypes in the post COVID-19 pandemic (2021-2023) in relation to previous years (2019-2020).\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e3.1 \u003cem\u003eSalmonella\u003c/em\u003e contamination of surface waters: a virtually permanent phenomenon\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e was systematically isolated from SuWa throughout the sampling period (Fig. 1). The isolation rates varied across geographical regions (\u0026chi;\u003csup\u003e2\u003c/sup\u003e=23.9, P\u0026lt;0.0001), with positive samples found in 75%, 83%, 88%, 96%, and 100% of the 24 sampling rounds in Hidalgo, Mexico City, Morelos, Tlaxcala, and the State of Mexico, respectively. The overall prevalence of \u003cem\u003eSalmonella\u003c/em\u003e in SuWa was 42%, 47%, 50%, 64%, and 64% in Hidalgo, Mexico City, Morelos, State of Mexico, and Tlaxcala, respectively. Of the 953 samples analyzed, 515 tested positive for \u003cem\u003eSalmonella\u003c/em\u003e, and 528 unique isolates were collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 1\u003c/strong\u003e Prevalence of \u003cem\u003eSalmonella\u003c/em\u003e spp. in surface waters across geographical regions and sampling rounds. The sampling timeframes are indicated at the bottom of the heatmap\u003c/p\u003e\n\u003cp\u003eThe temporal stability pattern of the prevalence of \u003cem\u003eSalmonella\u003c/em\u003e further confirmed the existence of regional differences and the pathogen\u0026rsquo;s persistence (Fig. 2). Tlaxcala and the State of Mexico had positive mean relative difference (MRD) values, indicating that the prevalence of \u003cem\u003eSalmonella\u003c/em\u003e in SuWa from these states is higher than that in the overall study region. The MRD was negative but close to zero in Mexico City and Morelos, indicating that these regions may provide a good estimate of the overall average prevalence. The lowest negative MRD was observed for Hidalgo state, indicating that this location is likely to have a lower \u003cem\u003eSalmonella\u003c/em\u003e prevalence than the average of the whole region. Furthermore, the standard deviation of relative differences (SDRD) across regions showed that the temporal stability of \u003cem\u003eSalmonella\u003c/em\u003e prevalence in the State of Mexico (SDRD=0.07) was more robust than that of the remaining locations: 0.17, 0.25, 0.26, and 0.33 in Tlaxcala, Morelos, Hidalgo, and Mexico City, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 2\u003c/strong\u003e Mean relative difference (MRD) \u0026plusmn; standard error of \u003cem\u003eSalmonella\u003c/em\u003e prevalence across the five regions under study\u003c/p\u003e\n\u003cp\u003e3.2 High-diversity population and clonal expansion of major serovars\u003c/p\u003e\n\u003cp\u003eAmong the 528 strains, 61 \u003cem\u003eSalmonella\u003c/em\u003e serovars were identified (Online Resource 1). The top 10 included epidemiologically relevant serovars, such as Typhimurium, Newport, and Infantis, among others, which collectively represented nearly 50% of the study population. These predominant isolates were detected across the sampling period and throughout the geographical regions (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 3\u003c/strong\u003e Geographical distribution and number of isolates belonging to each of the top 10 \u003cem\u003eSalmonella\u003c/em\u003e serovars according to the year of isolation and percentage of the population represented by these serovars. The AMR phenotypes (pansusceptible, mono/bi resistance, and multidrug-resistant) are indicated on the map\u003c/p\u003e\n\u003cp\u003ePhylogenetic analysis supported either \u003cem\u003eSalmonella\u003c/em\u003e persistence or reintroduction events as shown by the observed clonality (\u0026le;20 SNP distance) among isolates collected in different sampling rounds/years from the same watersheds in the State of Mexico and Tlaxcala (Fig. 4A). Some examples include the Agona isolates collected from site MEX1 in sampling rounds 10 (2021) and 18 (2022), TLX7 in rounds 4 (2019) and 6 (2020), TLX5 in rounds 3 (2019) and 7 (2020), and MEX7 in rounds 1 and 2 (2019). Along the same lines, we observed clonality between London isolates collected from site MEX12 in rounds 22 and 23 (2023), as well as site MEX6 in rounds 19 and 23 (2023) (Fig. 4B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 4\u003c/strong\u003e SNP-based phylogenetic analysis illustrating the persistence of \u003cem\u003eSalmonella\u003c/em\u003e in surface waters from Central Mexico. A) Serovar Agona isolates. B) Serovar London isolates. Clonal clusters (\u0026le;20 SNP distance) are highlighted in green. Tip labels indicate NCBI accession, serovar, sampling site name, sampling round, and year of collection, whereas the bootstrap support is indicated on the branches\u003c/p\u003e\n\u003cp\u003eThe phylogeny also provided evidence of clonal dissemination of isolates across sampling sites within regions, and across regions and sampling years. This pattern of dissemination was observed in isolates of the top 10 serovars except for Derby. In this regard, the most widespread were Adelaide, Typhimurium, and Senftenberg, with clonal isolates in 3-4 out of the five participating regions throughout the sampling period (Fig. 4 and Online Resource 2).\u003c/p\u003e\n\u003cp\u003eGenomic comparison of our top 10 most abundant serovars with global public isolates showed that these isolates belonged to PD SNP clusters containing clinical isolates and food isolates originating mainly from meat and poultry: Agona: PDS000031492.413, Anatum: PDS000056615.40, Newport: PDS000007781.1362, London: PDS000027247.61, Adelaide: PDS000001382.408, Derby: PDS000053683.19, Senftenberg: PDS000031803.244, Infantis: PDS000248108.43, Typhimurium: PDS000013714.46, and Muenchen: PDS000027665.31. Most public isolates that clustered together with our study isolates were collected in the USA, Canada, and the United Kingdom, whereas those of serovars Agona, Infantis, and Newport originated from numerous countries.\u003c/p\u003e\n\u003cp\u003e3.3 Antimicrobial resistance dynamics\u003c/p\u003e\n\u003cp\u003eResistant \u003cem\u003eSalmonella\u003c/em\u003e isolates were recovered from watersheds across all participating regions (Fig. 3), although variable proportions of MDR isolates were observed (\u0026chi;\u003csup\u003e2\u003c/sup\u003e=17.6, P=0.0015). The odds of collecting MDR isolates was two times higher [95 confidence interval (95CI) 1.4-3.1] in Hidalgo and the State of Mexico than in Mexico City and Morelos. As shown in the map, MDR isolates were scattered within each region except for Morelos, where most MDR isolates were collected from its Western side.\u003c/p\u003e\n\u003cp\u003eThe most frequent AMR phenotypes affected older antibiotics, such as tetracycline (36%), streptomycin (33%), chloramphenicol (24%), trimethoprim-sulfamethoxazole (23%), and ampicillin (18%) (Fig. 5). Conversely, resistance to cephalosporins, meropenem, ciprofloxacin, and amikacin was rarely detected (\u0026asymp;0-5%), whereas resistance to azithromycin was observed in approximately 12% (62/528) of the isolates. Overall, nearly half of the isolates (256/528) showed resistance to at least one antibiotic, whereas 30% exhibited MDR phenotypes (Online Resource 1).\u003c/p\u003e\n\u003cp\u003eAMR phenotypes were not uniformly distributed across serovars\u0026nbsp;(\u0026chi;\u003csup\u003e2\u003c/sup\u003e=103.8, P\u0026lt;0.0001). The proportion of MDR isolates was higher in the serovars Schwazergrund, Rissen, 1,4,[5],12:i:-, Typhimurium, Panama, Bredeney, Mbandaka, Derby, Albany, Hadar, Senftenberg, Newport, and Anatum (43-88%) than in the remaining serovars (0-33%) (Fig. 5, Online Resource 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 5\u003c/strong\u003e Relative frequency of AMR phenotypes among isolates of \u003cem\u003eSalmonella\u003c/em\u003e spp. (n=528) according to serovars and overall. AMP: ampicillin, AMC: amoxicillin+clavulanic acid, CRO: ceftriaxone, FEP: cefepime, MEM: meropenem, CIP: ciprofloxacin, AZM: azithromycin, AMK: amikacin, STR: streptomycin, TET: tetracycline, CHL: chloramphenicol, SXT: trimetoprim+sulfamethoxazole\u003c/p\u003e\n\u003cp\u003eThe observed AMR genotypes were generally consistent with the phenotypes, although disagreements were observed across all antibiotic classes. Of the 170 isolates that exhibited resistance to aminoglycosides, 44 had genotypes lacking all known aminoglycoside resistance genes. Likewise, no azithromycin resistance determinant was identified in 29 of the 62 isolates that showed resistance to azithromycin. This phenomenon was observed to a lesser extent in the remaining antibiotic classes. The opposite (resistance genotypes and susceptible phenotypes) also occurred across all antibiotic classes, with the highest frequencies observed in fluoroquinolones (63 isolates) and folate pathway inhibitors (35 isolates).\u003c/p\u003e\n\u003cp\u003eAs observed in AMR phenotypes, there was a higher abundance of AMR genes conferring resistance to older antibiotics, such as tetracycline (\u003cem\u003etet\u003c/em\u003e), folate pathway inhibitors (\u003cem\u003esul\u003c/em\u003e/\u003cem\u003edfrA\u0026nbsp;\u003c/em\u003ealleles), streptomycin (\u003cem\u003eaac\u003c/em\u003e/\u003cem\u003eaadA\u003c/em\u003e/\u003cem\u003eaph\u003c/em\u003e), phenicols (\u003cem\u003efloR\u003c/em\u003e/\u003cem\u003ecmlA\u003c/em\u003e), and penicillins (\u003cem\u003ebla\u003csub\u003eTEM/CARB\u003c/sub\u003e\u003c/em\u003e) (Fig. 6), whereas genes encoding cephalosporinases (\u003cem\u003ebla\u003csub\u003eCTX-M\u003c/sub\u003e/bla\u003csub\u003eCMY\u003c/sub\u003e\u003c/em\u003e) were scarce. Similarly, few isolates (7%) carried the azithromycin resistance gene \u003cem\u003emph(A)\u003c/em\u003e and nearly 37% carried MDR genotypes (resistance alleles to \u0026ge;3 antimicrobial classes). The overall pool of AMR determinants (62 genes and 4 gene point mutations) encoded 15 different resistance mechanisms at the population level.\u003c/p\u003e\n\u003cp\u003eInterestingly, the most abundant AMR determinants were plasmid-mediated quinolone resistance (PMQR) genes (\u003cem\u003eoqx\u003c/em\u003e/\u003cem\u003eqnr\u003c/em\u003e alleles) (226/528). In contrast, few isolates (26/528) carried \u003cem\u003egyrA\u003c/em\u003e/\u003cem\u003eparE\u003c/em\u003e mutations, which are also associated with quinolone resistance. Moreover, the isolates carried several AMR genes that confer resistance to other antibiotics that were not included in the AST panel. Among these, the most abundant was the \u003cem\u003efosA\u003c/em\u003e gene (97/528), which confers resistance to fosfomycin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 6\u003c/strong\u003e Relative abundance of antimicrobial resistance determinants (genes and point mutations) among 528 \u003cem\u003eSalmonella\u003c/em\u003e spp. isolates from Central Mexico\u0026rsquo;s agricultural surface waters (2019-2023 timeframe)\u003c/p\u003e\n\u003cp\u003eThe number of AMR genes per genome varied among serovars. For instance, \u0026ge;75% of the Adelaide, Corvallis, Braenderup, Give, Montevideo, and Meleagridis isolates carried \u0026le;2 AMR genes (Fig. 7). Conversely, in most of the remaining serovars, excluding Agona, Muenster, and the \u0026ldquo;Others\u0026rdquo; category, the number of AMR genes corresponding to the third quartile was in the 5-10 range. Intraserovar variability was also observed in multiple instances where isolates of the same serovar either lacked AMR genes or harbored up to 14 resistance alleles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 7\u003c/strong\u003e Boxplot distribution of antimicrobial resistance (AMR) genes per genome for the top 20 serovars. Values within boxes correspond to the first to third quartile range\u003c/p\u003e\n\u003cp\u003eThe distribution of AMR genes and MDR genotypes was also uneven among serovars (Fig. 8). Adelaide, Corvallis, Braenderup, Give, and Meleagridis serovar isolates had little contribution to the overall number of AMR genes, except for PMQR genes (\u003cem\u003eqnr/oqx\u003c/em\u003e alleles) in the first three serovars: Adelaide, Corvallis, and Braenderup. The remaining isolates harbored AMR genes to 5-7 antimicrobial classes, with the main contributors being the serovars of Typhimurium, Newport, Anatum, Senftenberg, Panama, and London. Interestingly, the macrolide resistance gene \u003cem\u003emph(A)\u003c/em\u003e was only present in three major serovars (Newport, Senftenberg, and London), and in the less represented serovars that were included under the \u0026ldquo;Other serovars\u0026rdquo; category: Schwarzengrund (7/8), and Havana (1/4) (Online Resource 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e Sankey diagram showing the contribution of AMR genes of each antimicrobial class (right) by the study genomes according to serovar (left)\u003c/p\u003e\n\u003cp\u003eRegarding the temporal dynamics of AMR, an increased prevalence of resistant phenotypes involving older antibiotics was observed in the post-pandemic period (after November 2020) (Fig. 9). The opposite was observed for meropenem, cephalosporins, ciprofloxacin, azithromycin, and amoxicillin-clavulanic acid, whereas the post-pandemic occurrence of all AMR determinants and MDR genotypes was higher than that in previous years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 9\u003c/strong\u003e Log\u003csub\u003e10\u003c/sub\u003e-fold change in the proportion of \u003cem\u003eSalmonella\u003c/em\u003e spp. isolates exhibiting resistance phenotypes and carrying resistant determinants in the post-COVID-19 pandemic period (after November 2020, n=315) compared to the pre-pandemic period (July 2019-February 2020, n=213). AMP: ampicillin, AMC: amoxicillin+clavulanic acid, CRO: ceftriaxone, FEP: cefepime, MEM: meropenem, CIP: ciprofloxacin, AZM: azithromycin, AMK: amikacin, STR: streptomycin, TET: tetracycline, CHL: chloramphenicol, SXT: trimetoprim+sulfamethoxazole, MDR: multidrug-resistant\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis comprehensive longitudinal study demonstrated that \u003cem\u003eSalmonella\u003c/em\u003e contamination is virtually permanent in the agricultural SuWa in Central Mexico. The temporal stability of this phenomenon was supported both by the systematic isolation of the pathogen throughout the sampling period and the low standard deviation of the relative differences in the pathogen prevalence across regions (0.07-0.33). Despite the observed regional differences, the prevalence of\u003cem\u003e\u0026nbsp;Salmonella\u003c/em\u003e was considerably high (42-64% across regions), which is consistent with the results of multiple SuWa surveys conducted worldwide (Bell et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe genetic proximity of the isolates of our top 10 serovars and clinical isolates collected globally underscores the role of SuWa as a \u003cem\u003eSalmonella\u003c/em\u003e reservoir of public health importance. Moreover, some of the major \u003cem\u003eSalmonella\u003c/em\u003e serovars circulating in the SuWa under study (e.g., Agona, Anatum, Newport, Typhimurium, Infantis, Muenchen) have been recently implicated in human illnesses in Mexico and globally (Alvarez et al., 2023; Ford et al., 2023; Godinez-Oviedo et al., 2020). Predominant isolates were also isolated throughout the study regions and across the 5-year sampling period, providing further evidence of \u003cem\u003eSalmonella\u0026rsquo;s\u003c/em\u003e ability to successfully establish long-term environmental reservoirs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, clonal isolates were rarely detected in the same site during consecutive sampling rounds. Therefore, their systematic isolation across years in some watersheds appears to be associated either with multiple re-introduction events or with a progressive decline in the concentration of these strains over time, making detection difficult.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe observed clonality between isolates from different sites within the same region may be associated with water body interconnection. This was the case at sampling sites in Mexico City, which are located in the Xochilmico municipality\u0026rsquo;s canal network (Jim\u0026eacute;nez et al., 2020). Similarly, nearly half of the sampling sites in the State of Mexico (MEX8-MEX12) are located in the Toluca Valley, which hosts the Lerma Basin (Carre\u0026ntilde;o de Le\u0026oacute;n et al., 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also observed clonality among isolates collected across distant regions, highlighting the widespread dissemination of the predominant \u003cem\u003eSalmonella\u003c/em\u003e serovars. Additionally, the high number of identified serovars (61 overall) indicates that SuWa contamination may also arise from multiple sources, such as runoff from adjacent farming or urban areas (Rocha et al., 2025). These findings underscore the role of SuWa from a one-health perspective, serving as an indicator of interconnected health at the human, animal, and environment interface. Hence, further one-health-based research is urgently needed to identify sources of SuWa contamination and develop effective intervention strategies.\u003c/p\u003e\n\u003cp\u003eThis long-term survey also revealed that AMR \u003cem\u003eSalmonella\u003c/em\u003e isolates were scattered throughout the sampling region. Approximately one-third of the study population exhibited MDR phenotypes and genotypes despite the regional differences observed. The proportion of MDR isolates observed in this study was twice that observed in one of our previous studies involving SuWa isolates (\u0026asymp;15%, n=172) from the same region collected in 2019 (Ballesteros-Nova et al., 2022). Additionally, our results support the trend toward stronger AMR genotypes in SuWa isolates during the early post-COVID-19 pandemic. In the absence of data on antibiotic consumption, it is difficult to assess whether these findings are associated with the intensified use of certain antimicrobials during the COVID-19 pandemic. However, they underscore the increasing role of SuWa as an AMR pathogen reservoir.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe observed disagreement between AMR phenotypes and genotypes suggests the presence of undescribed genetic determinants in the \u003cem\u003eSalmonella\u003c/em\u003e resistome. Further research is needed to determine whether this phenomenon is linked to novel AMR genes or the activation of cryptic genes (e.g., those encoding stress response, cell membrane, and transporter proteins), as previously reported in \u003cem\u003eEscherichia coli\u0026nbsp;\u003c/em\u003e(Suarez and Martiny, 2021). In any case, these results reveal the occurrence of convergent evolution (i.e., different genotypes sustaining the same phenotype) in a context of selective pressure where certain AMR phenotypes are required for survival.\u003c/p\u003e\n\u003cp\u003eThe occurrence of susceptible phenotypes in the presence of AMR determinants was mainly observed in fluoroquinolones and folate pathway inhibitors, involving PMQR genes (i.e., \u003cem\u003eqnr\u003c/em\u003e and \u003cem\u003eoqx\u003c/em\u003e alleles), and \u003cem\u003esul\u003c/em\u003e/\u003cem\u003edfrA\u003c/em\u003e alleles, respectively. The PMQR genes confer resistance at levels below the clinical breakpoints (Strahilevitz et al., 2009). Therefore, ciprofloxacin susceptibility was expected in PMQR-positive isolates. Silent AMR genes involving several antibiotic classes have long been identified in \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003e(Enne et al., 2006). These silent genes may be expressed under suitable conditions, leading to therapeutic failure (Deekshit and Srikumar, 2022). Consequently, our results emphasize the potential clinical relevance of susceptible strains carrying AMR genotypes, granting further research in this field.\u003c/p\u003e\n\u003cp\u003ePrevious research has shown that an increasing proportion of SuWa and food isolates have acquired AMR genes against older antibiotics (e.g., tetracycline, streptomycin, and chloramphenicol) (Chen et al., 2024a; Chen et al., 2024b; Delgado-Suarez et al., 2021). Our results for AMR phenotypes further confirmed this trend and were consistent with the observed AMR genotypes. These findings highlight the importance of preventing the use of newer antibiotics in food production settings to compensate for the lack of effectiveness of older drugs. Such practices could lead to the emergence of resistance to critically important antimicrobials, as it appears to be happening already considering the unusual peak in azithromycin resistance observed in this study.\u003c/p\u003e\n\u003cp\u003eResistance to azithromycin has not been reported in environmental \u003cem\u003eSalmonella\u003c/em\u003e isolates from Mexico until recently. According to a review covering the 2000-2017 period, this phenotype was observed in clinical isolates but not in food isolates, consistent with the restricted use of azithromycin in human health. However, a recent study by our research group appears to be the first Mexican report of emergent azithromycin resistance in beef \u003cem\u003eSalmonella\u003c/em\u003e isolates (\u0026asymp;15%) (Campos Granados et al., 2023), which is comparable to that observed among our SuWa isolates (\u0026asymp;12%) and is associated with the presence of the macrolide phosphotransferase encoding gene \u003cem\u003emph(A)\u0026nbsp;\u003c/em\u003e(Gomes et al., 2019). Taken together, these findings show a clear trend toward increasing azithromycin resistance in non-clinical isolates, which is apparently mediated by environmental dissemination of the\u003cem\u003e\u0026nbsp;mph(A)\u003c/em\u003e gene. The use of other macrolides (e.g., tylosine and tylvalosine) in veterinary medicine may induce cross-resistance to azithromycin, leading to the environmental dissemination of AMR determinants against this macrolide (Campos Granados et al., 2023). However, further research is required to validate this hypothesis.\u003c/p\u003e\n\u003cp\u003eOur assessment of temporal AMR dynamics did not reveal a trend toward an increased proportion of resistance phenotypes to critically important antibiotic classes, such as third- and fourth-generation cephalosporins, carbapenems, or fluoroquinolones. However, the proportion of isolates harboring AMR genes against these antimicrobials did increase. Approximately 30% of the study isolates carried 5-14 AMR genes and exhibited MDR phenotypes, some of which were serovars of epidemiological importance. This pattern revealed the accumulation of AMR factors in the resistome of environmental \u003cem\u003eSalmonella\u003c/em\u003e, highlighting the central role of SuWa in AMR ecology and epidemiology. Moreover, these findings underscore the public health risks of \u003cem\u003eSalmonella\u003c/em\u003e circulating in SuWa, which is recognized as a vehicle for the introduction and dissemination of the pathogen along the food production continuum (Ballesteros-Nova et al., 2022; Bell et al., 2021; Delgado-Suarez et al., 2026; Rocha et al., 2022).\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study unveiled the role of agricultural surface waters from Central Mexico as a critical and virtually permanent reservoir of multiple \u003cem\u003eSalmonella\u003c/em\u003e serovars. The predominant circulating isolates belong to epidemiologically relevant serovars, with most of them exhibiting genetic proximity to clinical strains, as well as clonal geographical and temporal expansion across the study area and sampling timeframe. The resistome comprised a vast array of AMR determinants (\u0026gt;60) at the population level, with a moderate fraction of the isolates (~30%) exhibiting MDR profiles. The emergence of resistance to critically important antimicrobials, such as macrolides, is of particular interest as it further emphasizes the clinical significance of these environmental isolates. Moreover, the temporal dynamics of AMR showed a trend toward increasingly stronger AMR profiles, especially in older antibiotics. These findings have critical implications for food safety and public health and underscore the role of surface waters in the ecology and epidemiology of AMR \u003cem\u003eSalmonella\u003c/em\u003e. Further research is required to identify the sources of \u003cem\u003eSalmonella\u003c/em\u003e contamination in surface waters to effectively reduce the associated risks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: EJ Delgado-Su\u0026aacute;rez, M Toro, J Meng, EW Brown, MW Allard\u003c/p\u003e\n\u003cp\u003eData curation: EJ Delgado-Su\u0026aacute;rez, S Zapata Ram\u0026iacute;rez, NE Ballesteros-Nova\u003c/p\u003e\n\u003cp\u003eFormal analysis: EJ Delgado-Su\u0026aacute;rez, NE Ballesteros-Nova, LM S\u0026aacute;nchez-Zamorano, Y Chavarin-Pineda.\u003c/p\u003e\n\u003cp\u003eFunding acquisici\u0026oacute;n: EJ Delgado-Su\u0026aacute;rez, M Toro, J Meng\u003c/p\u003e\n\u003cp\u003eResearch: S Zapata Ram\u0026iacute;rez, FA Ru\u0026iacute;z L\u0026oacute;pez, LC Sierra G\u0026oacute;mez-Pedroso, Z Chen, RL Bell,\u0026nbsp;A Brover, M Balkey\u003c/p\u003e\n\u003cp\u003eMethodology: EJ Delgado-Su\u0026aacute;rez, LC Sierra G\u0026oacute;mez-Pedroso\u003c/p\u003e\n\u003cp\u003eProject administraci\u0026oacute;n: EJ Delgado-Su\u0026aacute;rez, O Soberanis-Ramos, M Toro, J Meng\u003c/p\u003e\n\u003cp\u003eResources: O Soberanis-Ramos, C Grim, LM S\u0026aacute;nchez-Zamorano, Y Chavarin-Pineda\u003c/p\u003e\n\u003cp\u003eSupervision: EJ Delgado-Su\u0026aacute;rez, MS Rubio-Lozano, LC Sierra G\u0026oacute;mez-Pedroso\u003c/p\u003e\n\u003cp\u003eWriting\u0026nbsp;\u0026ndash;\u0026nbsp;original draft: EJ Delgado-Su\u0026aacute;rez\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review and editing: EJ Delgado Su\u0026aacute;rez, S Zapata-Ram\u0026iacute;rez, LC Sierra G\u0026oacute;mez-Pedroso, FA Ru\u0026iacute;z-L\u0026oacute;pez, NE Ballesteros-Nova, O Soberanis-Ramos, MS Rubio-Lozano, LM S\u0026aacute;nchez-Zamorano, Y Chavarin-Pineda, RL Bell, A Brover, M Balkey, C Grim, EW Brown, MW Allard, Z. Chen, M Toro, J Meng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the FDA of the U.S. Department of Health and Human Services (HHS) as part of the financial assistance award U01FDU001418 granted to the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) with 30% financed from nongovernmental sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole genome sequencing data (raw reads) derived from the current study were deposited in the NCBI repository (https://www.ncbi.nlm.nih.gov/bioproject) under Bioproject PRJNA560080. The individual accession numbers of isolates are provided in Online Resource 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlvarez, D. M., Barron-Montenegro, R., Conejeros, J., Rivera, D., Undurraga, E. A. \u0026amp; Moreno-Switt, A. I. (2023). 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(2024). \u003cem\u003eWHO bacterial priority pathogens list, 2024: Bacterial pathogens of public health importance to guide research, development and strategies to prevent and control antimicrobial resistance\u003c/em\u003e. World Health Organization. Retrieved February 4, 2025, from https://www.who.int/publications/i/item/9789240093461\u003c/li\u003e\n \u003cli\u003eYoshida, C. E., Kruczkiewicz, P., Laing, C. R., Lingohr, E. J., Gannon, V. P., Nash, J. H. \u0026amp; Taboada, E. N. (2016). The \u003cem\u003eSalmonella\u003c/em\u003e \u003cem\u003ein silico\u003c/em\u003e typing resource (SISTR): an open web-accessible tool for rapidly typing and subtyping draft \u003cem\u003eSalmonella\u003c/em\u003e genome assemblies. \u003cem\u003ePLoS One,\u003c/em\u003e (\u003cem\u003e11)\u003c/em\u003e, e0147101. https://doi.org/10.1371/journal.pone.0147101\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Salmonella, surface waters, epidemiology, antimicrobial resistance","lastPublishedDoi":"10.21203/rs.3.rs-9216619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9216619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We assessed the prevalence, persistence, genetic diversity, and AMR dynamics of Salmonella spp. (SAL) in surface waters (SuWa) of Central Mexico. On-site, 953 samples (10 L each) from 49 watersheds were collected in 24 sampling rounds (2019-2023) using the modified Moore swab technique. SAL was isolated and identified using conventional microbiological procedures and sequenced on Illumina platforms. Overall, the prevalence of SAL was 54%, with higher values (χ2=23.9, P\u003c0.0001) observed in Tlaxcala and the State of Mexico (64%) than in the other regions (42-50%). The top 10 serovars (Agona, Anatum, Newport, London, Adelaide, Derby, Senftenberg, Infantis, Typhimurium, and Muenchen) represented nearly half of the population and were systematically isolated across sampling rounds and years. The most frequent antimicrobial resistance (AMR) phenotypes involved older antibiotics (e.g., tetracycline, streptomycin, and chloramphenicol: 36%, 33%, and 24%, respectively). Approximately 12% of the isolates showed resistance to azithromycin, whereas resistance to cephalosporins, carbapenems, amikacin, and fluoroquinolones was rarely observed (~0-5%). One-third of the isolates exhibited multidrug-resistant (MDR) phenotypes and genotypes. The overall pool of AMR determinants (62 genes and four gene point mutations) encoded 15 different resistance mechanisms at the population level. The prevalence of AMR phenotypes to older antibiotics and the abundance of AMR genes against all antimicrobial classes increased in the post-COVID-19 pandemic period (0.1-0.4 and 0.1-0.6 Log10-fold change, respectively). These results underscore the public health risks associated with agricultural SuWa and the need for further research to identify the sources of SAL contamination.","manuscriptTitle":"Long-term genomic surveillance unveils persistent contamination, clonal dissemination, and antimicrobial resistance dynamics of Salmonella in surface waters from Central Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 17:02:52","doi":"10.21203/rs.3.rs-9216619/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b55d500-d4c7-49d7-bb18-215d0851b23c","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"11574237848592350420483930481760682121","date":"2026-05-13T13:28:36+00:00","index":29,"fulltext":""},{"type":"reviewersInvited","content":"16","date":"2026-05-04T12:48:19+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T17:02:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 17:02:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9216619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9216619","identity":"rs-9216619","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}