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. 2026 Jan 14;8(1):001006.v3. doi: 10.1099/acmi.0.001006.v3

Genomic surveillance of multidrug-resistant Escherichia coli and Klebsiella in clinical and wastewater isolates from a paediatric hospital in Lima, Peru

Brenda Ayzanoa 1,†, Camila Castillo-Vilcahuaman 1,2,*,†, Guillermo Salvatierra 1,3, Alejandra Dávila-Barclay 1, Diego Cuicapuza 1,4, Janet Huancachoque 1, Maritza Calderón 5, Emiliana Rizo-Patrón 6, Carlos Santillán-Salas 6,7, Robert H Gilman 8, Pablo Tsukayama 1,9,10
PMCID: PMC12804347  PMID: 41551538

Abstract

The environmental spread of antibiotic-resistant bacteria is a growing global health concern, particularly in low- and middle-income countries where limited wastewater treatment infrastructure may facilitate the dissemination of multidrug-resistant (MDR) organisms. Escherichia coli and Klebsiella spp. are clinically significant MDR pathogens commonly associated with healthcare-associated infections and known to carry diverse antimicrobial resistance genes (ARGs). In this study, we conducted genomic and phenotypic analyses of E. coli and Klebsiella spp. isolated from hospital wastewater and paediatric patient samples at a tertiary hospital in Lima, Peru, between 2017 and 2019. A total of 157 isolates were collected (E. coli, n=113; Klebsiella spp., n=44). Whole-genome sequencing was performed to identify ARGs and assess sequence types (STs). MDR phenotypes were more prevalent among wastewater isolates (73.5%) compared to clinical isolates (56.8%, P=0.014), while extended-spectrum β-lactamase production was more frequent in clinical isolates (52.9 % vs. 13.9 %, P<0.001). Carbapenemase-producing isolates were found only in wastewater, whereas colistin resistance was restricted to a subset of clinical E. coli isolates from urine. Genomic analysis revealed greater sequence type diversity among wastewater isolates, including high-risk STs such as ST10, ST131 and ST405. The Shannon diversity index was higher for wastewater-derived isolates (H=3.45) compared to clinical isolates (H=2.95), indicating a more heterogeneous resistance reservoir. In total, 1,302 resistance gene hits were identified, with clinical isolates carrying significantly more ARGs per genome than wastewater isolates. A small number of shared STs were detected in both sources, suggesting possible overlap in bacterial populations. Our findings highlight the potential role of hospital wastewater as a reservoir of antimicrobial resistance and support the value of integrating environmental and clinical genomic surveillance. Wastewater-based monitoring may inform infection control efforts and guide interventions to curb the spread of AMR within healthcare settings and their surrounding environments.

Keywords: antimicrobial resistance, enterobacteria, wastewater, whole-genome sequencing

Data Summary

RAW sequencing reads (.fastq) generated in this study have been deposited in the NCBI database under BioProject ID PRJNA1168566. Individual sequence accession numbers are available in the supplementary table.

Introduction

The increasing prevalence of antimicrobial resistance (AMR) is a significant global public health concern, particularly in low- and middle-income countries (LMICs), where limited wastewater treatment and infection control measures contribute to the persistence and dissemination of multidrug-resistant (MDR) bacteria [1,2]. Among the most clinically relevant MDR pathogens are Escherichia coli and Klebsiella spp., which frequently cause healthcare-associated infections such as urinary tract infections, bloodstream infections and pneumonia [3,5]. These pathogens are of particular concern due to their ability to acquire and disseminate resistance determinants, including extended-spectrum β-lactamases (ESBLs) and carbapenemases, which render many frontline antibiotics ineffective [6,8].

Hospital wastewater represents a potential reservoir for MDR bacteria and resistance genes, as it contains a mixture of human-associated microbes, antimicrobial residues and disinfectants that exert selective pressure, favouring the persistence of resistant strains [9,10]. Studies have shown that hospital effluents harbour clinically significant MDR Enterobacteriaceae, including carbapenem-resistant Klebsiella spp. and ESBL-producing E. coli, which can enter municipal sewage systems and contribute to the environmental dissemination of AMR [11,13]. In LMICs, where wastewater treatment infrastructure is often inadequate, these reservoirs may serve as a pathway for reintroducing resistant bacteria into clinical and community settings [14]. Despite this recognized risk, there is a lack of genomic studies comparing resistance profiles between hospital wastewater and clinical isolates in Peru, limiting our understanding of how hospital effluents contribute to the transmission of MDR bacteria.

Traditional molecular typing methods, such as PFGE and MLST, have been widely used to characterize bacterial strains in AMR surveillance but lack the resolution to fully capture the complexity of resistance gene transfer and bacterial evolution [15]. Whole-genome sequencing (WGS) provides a higher-resolution approach by enabling the comprehensive identification of antimicrobial resistance genes (ARGs), mobile genetic elements and phylogenetic relationships between strains [16,18]. WGS has become an essential tool for outbreak investigations and the characterization of emerging MDR clones in hospital settings, offering insights into the evolutionary dynamics of resistant pathogens [19,22]. Despite increasing recognition of hospital wastewater as a reservoir of AMR, few genomic studies in LMICs have systematically compared clinical and wastewater isolates. Understanding these genetic links is crucial for designing surveillance and intervention strategies.

This study provides a genomic and phenotypic characterization of E. coli and Klebsiella spp. isolated from hospital wastewater and clinical samples in a paediatric hospital in Lima, Peru, over a 2-year period over a 2-year period. By integrating WGS with antimicrobial susceptibility testing, we aim to evaluate the extent to which wastewater contributes to the dissemination of MDR bacteria and resistance genes within hospital and community settings. Our findings will contribute to the growing body of evidence supporting the use of genomic surveillance as a tool for AMR monitoring in both clinical and environmental contexts, particularly in resource-limited settings where AMR control strategies remain a critical public health challenge [23].

Methods

Study site and sample collection

This study was conducted at the Instituto Nacional de Salud del Niño San Borja (INSN-SB), a national referral paediatric hospital in Lima, Peru. The hospital provides specialized care in neonatal surgery, cardiology and burn treatment and has implemented an Antibiotic Stewardship Programme to mitigate antimicrobial resistance [24]. Despite these efforts, there is limited data on resistant bacteria in hospital wastewater. Hospital wastewater samples (n=26) were collected from the central sewer system at two locations between October 2017 and May 2019 over 14 sampling dates. Samples were collected in sterile 50-ml conical centrifuge tubes, stored at 4 °C, and transported to the laboratory following national sanitary guidelines [25]. Clinical isolates of E. coli (n=34) and Klebsiella spp. (n=15) were obtained from urine and blood cultures per paediatric patient admitted to the intensive care unit (ICU) at INSN-SB between December 2018 and May 2019. These isolates were identified using the BD Phoenix automated identification system (Becton Dickinson, Franklin Lakes, NJ) as part of routine clinical diagnostics.

Bacterial isolation and antimicrobial susceptibility testing

Wastewater samples were centrifuged at 10,000 r.p.m. for 10 min, and the pellet was streaked onto CHROMagar Orientation medium and incubated at 37 °C for 24 h. Colonies were subcultured on MacConkey agar for lactose fermentation confirmation. Biochemical identification was performed using triple sugar iron agar, motility indole, ornithine, lysine, iron agar and Simmons’ citrate agar tests [3]. Antimicrobial susceptibility testing was conducted according to the Clinical and Laboratory Standards Institute 2018 guidelines [26]. Twenty-one antibiotics were tested using the disc diffusion method (Table 1). Isolates resistant to at least three antibiotic classes were classified as MDR [27]. ESBL production was confirmed using the combination disc test with cefotaxime, aztreonam, ceftazidime and cefepime placed around an amoxicillin-clavulanic acid disc, following EUCAST 2024 guidelines [28]. AmpC β-lactamase production was determined using cefoxitin as an inducer, while carbapenemase activity was assessed using imipenem and meropenem discs with EDTA and phenylboronic acid inhibitors to differentiate between metallo-β-lactamases and serine carbapenemases [29]. E. coli ATCC 25922 was used as a quality control strain throughout susceptibility testing.

Table 1. Resistance profiles and bivariate analysis of E. coli isolates from clinical and wastewater samples.

Result E. coli (n=113)
Total Wastewater (n=79) Clinical samples (n=34) Total P-value*
Urine (n=30) Blood (n=4)
Multidrug resistance
Positive 64 (56.6) 39 (69.9) 22 (73.3) 3 (75.0) 25 (73.5) 0.014
ESBL
Positive 29 (25.6) 11 (13.9) 14 (46.7) 4 (100) 18 (52.9) <0.001
AmpC
Positive 3 (2.7) 3 (3.8) 0 (0) 0 (0) 0 (0) 0.338
Carbapenemases
Positive 1 (0.9) 1 (1.26) 0 (0) 0 (0) 0 (0) 0.699
Polymyxins
Colistin 5 (4.4) 0 (0) 5 (16.6) 0 (0) 5 (14.7) 0.002
Phenicols
Chloramphenicol 22 (19.5) 13 (16.5) 8 (26.7) 1 (25.0) 9 (26.5) 0.165
Tetracyclines
Tetracycline 53 (46.9) 32 (40.5) 18 (60.0) 3 (75.0) 21 (61.8) 0.031
Sulphonamides
Trimethoprim/sulfamethoxazole 52 (46.0) 33 (41.7) 18 (60.0) 1 (25.0) 19 (55.9) 0.120
Aminoglycosides
Gentamicin 18 (15.9) 14 (17.7) 3 (10.0) 1 (25.0) 4 (11.8) 0.311
Amikacin 2 (1.8) 2 (2.5) 0 (0) 0 (0) 0 (0) 0.487
Macrolides
Azithromycin 27 (23.9) 16 (20.3) 11 (36.7) 0 (0) 11 (32.4) 0.127
Penicillins
Amoxicillin 69 (61.1) 41 (51.9) 24 (80.0) 4 (100) 28 (82.4) 0.002
Amoxicillin with clavulonic acid 8 (7.1) 7 (8.9) 1 (3.3) 0 (0) 1 (2.9) 0.244
Aztreonam 17 (15.0) 7 (8.9) 7 (23.3) 3 (75.0) 10 (29.4) 0.007
Piperacillin 63 (55.8) 36 (45.6) 23 (76.7) 4 (100) 27 (79.4) 0.001
Cephalosporins
Cefalotin 37 (32.7) 16 (20.3) 18 (60.0) 3 (75.0) 21 (61.8) <0.001
Cefotaxime 31 (27.4) 11 (13.9) 17 (56.7) 3 (75.0) 20 (58.8) <0.001
Cefepime 18 (15.9) 6 (7.6) 9 (30.0) 3 (75.0) 12 (35.3) 0.001
Ceftazidime 13 (11.5) 6 (7.6) 4 (13.33) 3 (75.0) 7 (20.6) 0.052
Ceftriaxone 30 (26.5) 11 (13.9) 16 (53.33) 3 (75.0) 19 (55.9) <0.001
Cefoxitin 4 (3.5) 4 (5.1) 0 (0) 0 (0) 0 (0) 0.233
Carbapenemases
Meropenem 1 (0.9) 1 (1.3) 0 (0) 0 (0) 0 (0) 0.699
Imipenem 1 (0.9) 1 (1.3) 0 (0) 0 (0) 0 (0) 0.699
Quinolones
Nalidixic acid 57 (50.4) 31 (39.2) 22 (73.3) 4 (100) 26 (76.5) <0.001
Ciprofloxacin 25 (22.1) 8 (10.1) 13 (43.3) 4 (100) 17 (50.0) <0.001
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P-value: calculated using Fisher’s exact test with a 95% confidence level

AmpC, AmpC β-lactamase production; Carbapenemases, carbapenemase production; ESBL, extended-spectrum Beta-Lactamase production.

WGS and bioinformatic analysis

Genomic DNA was extracted from 1 ml of overnight cultures using the GeneJET Genomic DNA Purification Kit (Thermo Scientific, Waltham, MA). Libraries were prepared using the Illumina Nextera XT kit with 1 ng of DNA and sequenced on an Illumina MiSeq platform (2×250 paired-end reads) using the MiSeq V2 Reagents Kit (500 cycles), yielding an average sequencing depth of 84× (range: 17×–163×). The average sequencing coverage was 84× (range: 17×–163×). Raw sequencing reads were assessed for quality using FastQC v0.72 and adapters were removed with Fastp v0.23.4 (https://github.com/OpenGene/fastp). Reads were assembled de novo using SPAdes v3.12.0 [30] and evaluated with Quast v5.2.0 [31], with genomes below an N50 threshold of 16,000 bp excluded. Genome quality was evaluated with CheckM v1.2.4 (https://github.com/Ecogenomics/CheckM), and only assemblies with completeness >95% and contamination <5% were retained; in addition, genomes with coverage below 20× were excluded. MLST was performed using MLST v2.22.0 using the E. coli ecoli_achtman_4 scheme, while Klebsiella spp. were typed with Kleborate v2.2.0 (https://github.com/klebgenomics/Kleborate) [32,33]. Novel alleles were submitted to EnteroBase (https://enterobase.warwick.ac.uk/) for the correct assignment of new sequence types, and ARGs were identified using Abricate v1.0.1 with the National Center for Biotechnology Information (NCBI) database (release downloaded: 27 March 2021) [34], and genome annotation was performed with Prokka v1.14.6. The resulting GFF files were then used for the pangenome reconstruction and the core genome alignment using Roary v3.13 [35], and phylogenetic trees were inferred using RAxML v8.2.12 [36] under the GTR+GAMMA substitution model with 100 bootstrap replicates. Finally, trees were visualized and annotated with iTOL v7 [37].

Diversity and statistical analysis

Genomic diversity among E. coli isolates was assessed using the Shannon diversity index, a standard metric for evaluating phylogenetic richness within bacterial populations implemented with the vegan package (v2.7–1) in R v4.5.0 [38,39]. Statistical comparisons of resistance rates were performed using Fisher’s exact test in STATA 16 (StataCorp, College Station, TX), applying a 95% confidence level. For all statistical comparisons, P-values lower than 0.05 were considered significant.

Results

Antimicrobial susceptibility and resistance profiles

One hundred fifty-seven isolates were analysed, including E. coli (n=113) and Klebsiella spp. (n=44), recovered from hospital wastewater (n=113) and clinical samples (urine, n=44; blood, n=9). Among E. coli isolates, the prevalence of MDR was significantly higher in clinical isolates (73.5%) compared to wastewater isolates (69.9%) (P=0.014, Fisher’s exact test). ESBL production was also more frequent in clinical isolates (52.9%) than in wastewater isolates (13.9%, P<0.001). Carbapenemase production was observed exclusively in wastewater E. coli isolates (1.26%, n=1), whereas colistin resistance was only detected in clinical urine isolates (16.6%, n=5) (Table 1). Among Klebsiella spp. isolates, MDR phenotypes were detected more frequently in clinical isolates (60.0%) than in wastewater isolates (48.3%), though this difference was not statistically significant (P=0.338). ESBL production was significantly higher in clinical isolates (60.0%) than in wastewater isolates (24.1%) (P=0.023). Notably, carbapenemase production (17.2%) and AmpC β-lactamase production (3.4%) were only detected in wastewater isolates. Resistance to amikacin, cefoxitin, meropenem and imipenem was found exclusively in wastewater isolates, whereas colistin resistance (9.1%) was observed only in one clinical urine isolate (Table 2). Resistance rates for key antibiotics varied between E. coli and Klebsiella spp. isolates from clinical and wastewater sources, with a higher prevalence of ESBL production in clinical isolates and carbapenem resistance detected exclusively in wastewater isolates (Fig. 1).

Table 2. Resistance profiles and bivariate analysis of Klebsiella spp. isolates from clinical and wastewater samples.

Results Klebsiella sp. (n=44)
Total Wastewater (n=29) Clinical samples (n=15) Total P-value*
Urine (n=11) Blood (n=4)
Multidrug resistance
Positive 23 (52.3) 14 (48.3) 6 (54.5) 3 (75.0) 9 (60.0) 0.338
ESBL
Positive 16 (36.4) 7 (24.1) 6 (54.5) 3 (75.0) 9 (60.0) 0.023
AmpC
Positive 1 (2.2) 1 (3.4) 0 (0) 0 (0) 0 (0) 0.659
Carbapenemases
Positive 5 (11.4) 5 (17.2) 0 (0) 0 (0) 0 (0) 0.109
Polymyxins
Colistin 1 (2.2) 0 (0) 1 (9.1) 0 (0) 1 (6.7) 0.341
Phenicols
Chloramphenicol 6 (13.6) 5 (17.2) 1 (9.1) 0 (0) 1 (6.7) 0.320
Tetracyclines
Tetracycline 14 (31.8) 4 (13.8) 6 (54.5) 4 (100) 10 (66.7) 0.001
Sulphonamides
Trimethoprim/sulfamethoxazole 18 (40.9) 8 (27.6) 7 (63.6) 3 (75.0) 10 (66.7) 0.015
Aminoglycosides
Gentamicin 6 (13.6) 3 (10.3) 1 (9.1) 2 (50.0) 3 (20.0) 0.327
Amikacin 1 (2.2) 1 (3.4) 0 (0) 0 (0) 0 (0) 0.659
Macrolides
Azithromycin 16 (36.4) 11 (37.9) 4 (36.4) 1 (25.0) 5 (33.3) 0.516
Penicillins
Amoxicillin 36 (81.8) 22 (75.8) 10 (90.9) 4 (100) 14 (93.3) 0.156
Amoxicillin with clavulonic acid 6 (13.6) 6 (20.7) 0 (0) 0 (0) 0 (0) 0.067
Aztreonam 12 (27.3) 10 (34.5) 1 (9.1) 1 (25.0) 2 (13.3) 0.127
Piperacillin 18 (40.9) 8 (27.6) 7 (6.4) 3 (75.0) 10 (66.7) 0.015
Cephalosporins
Cefalotin 20 (45.5) 11 (37.9) 6 (54.5) 3 (75.0) 9 (60.0) 0.141
Cefotaxime 18 (40.9) 9 (31.0) 6 (54.5) 3 (75.0) 9 (60.0) 0.063
Cefepime 10 (22.7) 5 (17.2) 4 (36.4) 1 (25.0) 5 (33.3) 0.202
Ceftazidime 10 (22.7) 8 (27.6) 1 (9.1) 1 (25.0) 2 (13.3) 0.250
Ceftriaxone 18 (40.9) 8 (27.6) 7 (6.4) 3 (75.0) 10 (66.7) 0.015
Cefoxitin 3 (6.8) 3 (10.3) 0 (0) 0 (0) 0 (0) 0.276
Carbapenems
Meropenem 3 (6.8) 3 (10.3) 0 (0) 0 (0) 0 (0) 0.276
Imipenem 3 (6.8) 3 (10.3) 0 (0) 0 (0) 0 (0) 0.276
Quinolones
Nalidixic acid 21 (47.7) 14 (48.3) 5 (45.5) 2 (50.0) 7 (46.7) 0.586
Ciprofloxacin 16 (36.4) 10 (34.5) 4 (36.4) 2 (50.0) 6 (40.0) 0.484
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P-value: calculated using Fisher’s exact test with a 95% confidence level.

AmpC, AmpC β-lactamase production ; Carbapenemases, carbapenemase production; ESBL, extended-spectrum Beta-Lactamase production.

Fig. 1. Antibiotic Antimicrobial resistance rates in Klebsiella spp. (a) and E. coli (b) isolates from hospital wastewater and clinical samples. Resistance rates in hospital wastewater isolates (n=113) are compared with clinical isolates from urine (n=11) and blood (n=4) collected from the same hospital. Asterisks (*) indicate statistically significant differences (P<0.05, Fisher’s exact test) between blood/urine isolates and wastewater isolates. Abbreviations: TET, tetracycline; AMX, amoxicillin; SXT, trimethoprim/sulfamethoxazole; NA, nalidixic acid; CLO, chloramphenicol; CF, cefalotin; CIP, ciprofloxacin; CTX, cefotaxime; AZT, azithromycin; GN, gentamicin; AMC, amoxicillin–clavulanic acid; FEP, cefepime; CAZ, ceftazidime; C, colistin; FOX, cefoxitin; M, meropenem; I, imipenem; A, aztreonam; PIP, piperacillin; CRO, ceftriaxone; MK, amikacin.

Fig. 1.

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Genomic diversity and shared sequence types

Following bioinformatic quality control, 39 Klebsiella and 110 E. coli isolates were retained for analysis. Whole-genome sequencing identified 59 distinct sequence types (STs) among E. coli and 16 among Klebsiella, reflecting substantial genetic diversity across clinical and wastewater sources. Among clinical E. coli, the most frequent STs were ST131 (n=5), ST101 (n=3), ST1193 (n=3) and ST457 (n=3), consistent with their global association with hospital-acquired infections. In contrast, wastewater isolates were dominated by ST10 (n=10), ST216 (n=7) and ST88 (n=4). ST88 and ST101 were recovered from both clinical and wastewater samples, suggesting overlapping reservoirs of potentially pathogenic strains (Fig. 2). Many Klebsiella isolates from wastewater could not be confidently assigned to a definitive ST; the predominant species within this group were Klebsiella michiganensis (n=10), Klebsiella grimontii (n=6) and Klebsiella pasteurii (n=1). Among clinical Klebsiella pneumoniae isolates, the most common ST was ST15 (n=5).

Fig. 2. Distribution of STs among sequenced E. coli and Klebsiella spp. isolates. The upper panel represents E. coli sequence types, while the lower panel shows Klebsiella spp. sequence types. Colours indicate different sample sources (clinical or wastewater), highlighting the diversity of sequence types in each group.

Fig. 2.

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Phylogenetic analysis showed that clinical and wastewater isolates generally clustered separately (Fig. 3), although some overlapping STs formed closely related clusters irrespective of origin, supporting possible bacterial flow between environments. The Shannon diversity index was higher in wastewater isolates (H=3.45) compared to clinical isolates (H=2.95), consistent with wastewater representing a more diverse reservoir of resistance determinants, likely shaped by continuous bacterial input from hospital and environmental sources. Seven novel STs were identified, six from wastewater [including ST18047 and ST18048 within clonal complex (CC) 10, and ST18051 (CC165) and ST18049 (CC226)] and one from clinical isolates (ST18050, CC95).

Fig. 3. Core genome phylogeny of E. coli isolates from hospital wastewater and clinical sources. Isolates are labelled by sequence identifier (black text), and STs are colour-coded. Coloured strips adjacent to the tree indicate sample origin (wastewater vs. clinical). The tree shows distinct clustering of most clinical and wastewater isolates, while certain shared STs formed closely related clusters regardless of source, consistent with potential overlap between hospital- and wastewater-associated bacterial populations.

Fig. 3.

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Resistome analysis

Nine hundred ninety-four ARG hits representing 109 unique genes were identified across all isolates. Clinical E. coli isolates carried a significantly higher average number of ARGs (7.84, 95 % CI 6.53–9.14) compared to wastewater isolates (4.85, 95 % CI 3.91–5.79) (P=0.0006). Similarly, clinical Klebsiella isolates harboured more ARGs (11.2, 95 % CI 8.58–13.81) than wastewater isolates (8.58, 95 % CI 6.28–10.88) (P=0.1463).

In E. coli, the mphB gene (85.84%, n=94) was the most prevalent ARG, while ST-131 isolates carried multiple resistance genes, including blaOXA−1 (n=4),blaCTX−M−15 (n=3) and blaTEM−1 (n=1). Colistin resistance genes (mcr-1) were detected exclusively in urine isolates of ST-1421 (n=1), ST-457 (n=2) and ST-354 (n=1) (Fig. 4). Among Klebsiella isolates, oqxA (n=36, 92.3%) was present in almost all genomes, with K. pneumoniae ST-15 frequently carrying blaSHV−106 (n=5), blaTEM−1 (n=5), blaOXA−1 (n=5) and mcr-1 (n=2).

Fig. 4. Prevalence of ARGs across STs in E. coli (a) and Klebsiella spp. (b).

Fig. 4.

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The distribution of ARGs varied between clinical and wastewater isolates, with wastewater samples harbouring a broader diversity of ARGs across E. coli and Klebsiella spp., including genes associated with β-lactam resistance (blaCTX−M−14 and blaCTX−M−15), aminoglycoside resistance (aac3-IId), and colistin resistance (mcr-1) (Fig. 5). As shown in the Sankey diagram, clinical isolates exhibited a higher prevalence of β-lactamase genes, while wastewater isolates displayed a more diverse array of resistance genes, suggesting distinct selective pressures between hospital and environmental settings.

Fig. 5. Distribution of the most frequent ARGs by source and species. A Sankey diagram illustrates the associations between sample origin (hospital wastewater vs. clinical isolates), bacterial species (E. coli and Klebsiella spp.) and the most frequently detected ARGs. The width of the connections represents the frequency of ARGs within each species and source.

Fig. 5.

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Discussion

This study provides a genomic and phenotypic characterization of E. coli and Klebsiella spp. recovered from hospital wastewater and clinical samples at a paediatric hospital in Lima, Peru. We integrated WGS and phenotypic resistance testing to identify diverse resistance profiles and STs across both environments. These findings contribute to understanding how hospital effluents may act as reservoirs of AMR with implications for surveillance strategies in LMICs.

ESBL production was significantly more frequent in clinical E. coli isolates than in wastewater isolates, consistent with the strong selective pressures of antibiotic use in healthcare settings [40]. Conversely, carbapenemase-producing isolates were detected only in wastewater, underscoring the potential risk of highly resistant organisms entering the environment through hospital effluents [41]. The detection of the mcr-1 gene, associated with colistin resistance, in clinical isolates, although limited in number, highlights the circulation of resistance to last-resort antibiotics [42]. Together, these observations reinforce the need for both hospital-based infection control and environmental mitigation strategies.

The identification of 59 STs among E. coli and 16 STs among Klebsiella spp. highlights the high genetic diversity present in clinical and environmental samples. E. coli ST-10, one of the most frequently reported STs in hospital wastewater, was among the dominant sequence types identified in this study [43,44]. In contrast, E. coli ST-131 and ST-101 were predominant in clinical isolates, consistent with their global association with hospital-acquired infections [45,46]. Notably, the shared presence of ST-88 and ST-101 in both wastewater and clinical isolates suggests a potential link between hospital environments and wastewater, reinforcing the utility of wastewater surveillance in tracking antimicrobial resistance. Among Klebsiella isolates, the predominance of K. pneumoniae ST-15 and its association with multiple resistance genes (blaSHV−106, blaTEM−1 and blaOXA−1) raises concerns about the persistence of highly resistant clones within hospital settings. ST-15 has been implicated in outbreaks of carbapenem-resistant Klebsiella in healthcare facilities, emphasizing the importance of ongoing monitoring for this sequence type [47,49].

The presence of overlapping sequence types, such as E. coli ST88 and ST101, in both wastewater and clinical isolates suggests potential overlap between bacterial populations in these settings. While no high-resolution SNP analysis was performed, these findings support the hypothesis that hospital effluents may contribute to maintaining or amplifying AMR diversity. Phylogenetic clustering further showed that specific wastewater and clinical isolates grouped closely despite different origins, consistent with possible bacterial flow between the two environments. Moreover, the higher Shannon diversity index observed in wastewater isolates compared to clinical isolates emphasizes the complexity of AMR reservoirs outside direct clinical settings [10,14].

Several limitations must be considered when interpreting these findings. Clinical isolates were restricted to diagnostic urine and blood cultures from paediatric ICU patients, excluding carriage isolates that may represent additional resistance reservoirs. The lack of contemporaneous sampling between wastewater and clinical isolates limits temporal comparisons and precludes direct inference of transmission. Furthermore, the single-hospital focus constrains generalizability to other healthcare or community settings in Peru. Future studies incorporating longitudinal sampling, carriage isolates from patients and healthcare workers, and high-resolution genomic analyses will be critical to clarify potential transmission dynamics.

Despite these limitations, our study demonstrates the value of combining clinical and environmental genomic surveillance to monitor AMR. Wastewater-based monitoring is particularly relevant in LMICs, where routine clinical sequencing may be limited but effluent sampling can provide a broader view of ARG circulation. Establishing integrated surveillance systems that include hospital wastewater could enable earlier detection of high-risk lineages, inform infection control interventions and ultimately help reduce the spread of MDR organisms within healthcare facilities and into surrounding communities.

Supplementary material

Uncited Table S1.
acmi-8-01006-s001.xlsx (20.3KB, xlsx)
DOI: 10.1099/acmi.0.001006.v3

Acknowledgements

We thank the clinical, administrative and laboratory staff at the Instituto Nacional de Salud del Niño – San Borja for their invaluable support in facilitating access to hospital facilities, assisting with sample collection and providing clinical cultures for this study.

Abbreviations

AMR

antimicrobial resistance

ARGs

antimicrobial resistance genes

CC

clonal complex

ESBL

extended-spectrum β-lactamase

ICU

intensive care unit

LMICs

low- and middle-income countries

MDR

multidrug-resistant

NCBI

National Center for Biotechnology Information

STs

sequence types

WGS

whole-genome sequencing

Footnotes

Funding: This research was funded by the Instituto Nacional de Salud del Niño – San Borja 2018 Research Grant, Innóvate Perú (contract no. 289-2017) and Prociencia (contract no. 088-2018). The funding agencies had no role in the study’s design, data collection, analysis, interpretation or publication. The views expressed in this article are solely those of the authors and do not necessarily reflect the positions or policies of the funding institutions.

Author contributions: Conceptualization: C.C.-V., B.A. and P.T. Investigation: C.C.-V, B.A.C. and J.H. Methodology: C.C.-V., B.A., D.C., G.S. and A.D.-B. Software and data curation: C.C.-V. and B.A. Resources: P.T., M.C., C.S.-S., and E.R.-P. Formal analysis: C.C.-V., B.A.C. and G.S. Visualization: C.C.-V., B.A.C., D.C. and G.S. Funding acquisition: P.T. Supervision: C.C.-V., B.A.C., P.T., and R.H.G.

Ethical statement: This study was approved by the Institutional Ethics Committee of the Instituto Nacional de Salud del Niño – San Borja (project code PI-2018-21, approval 052-2018-CIEI-INSN-SAN BORJA). The procedures and techniques used in this study are subject to the ethical principles of good clinical practice dictated by the Declaration of Helsinki and the Code of Ethics and Deontology of the Peruvian Medical Association. The investigators did not have access to private patient data since each sample evaluated was coded by numbers to protect the patient. No additional patient data was recorded in the epidemiological records, nor were previous culture results taken.

Contributor Information

Brenda Ayzanoa, Email: brenda.ayzanoa@upch.pe.

Camila Castillo-Vilcahuaman, Email: castillo.vilcahuaman@gmail.com;camila.castillo.v@upch.pe.

Guillermo Salvatierra, Email: guillermo.salvatierra@upch.pe.

Alejandra Dávila-Barclay, Email: alejandra.davila.b@upch.pe.

Diego Cuicapuza, Email: diego.cuicapuza@upch.pe.

Janet Huancachoque, Email: janet.huancachoque@upch.pe.

Maritza Calderón, Email: maritza.calderon.s@upch.pe.

Emiliana Rizo-Patrón, Email: emirizop@hotmail.com.

Carlos Santillán-Salas, Email: santillansalas.carlos@gmail.com.

Robert H. Gilman, Email: gilmanbob@gmail.com.

Pablo Tsukayama, Email: pablo.tsukayama@upch.pe.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Uncited Table S1.
acmi-8-01006-s001.xlsx (20.3KB, xlsx)
DOI: 10.1099/acmi.0.001006.v3

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