Lance Matthew F. Farinas
Laurice Beatrice Raphaelle O. dela Peña
Windell L. Rivera- Pathogen-Host-Environment Interactions Research Laboratory, Institute of Biology, College of Science, University of the Philippines Diliman, Quezon City, Philippines
Laguna Lake, the largest freshwater lake in the Philippines, has been reported to harbor antibiotic-resistant bacteria, posing health risks to the millions who depend on it. However, limited knowledge of antibiotic resistance genes (ARGs) in the lake highlights the need for a comprehensive assessment of its resistome. In line with this, we characterized ARGs in the West Bay of Laguna Lake using shotgun metagenomic sequencing based on six metagenomes collected from three stations across two sampling months at a single depth. ARGs were quantified from short reads, and assembled contigs containing these genes—antibiotic-resistant contigs (ARCs)—were analyzed to assess mobility through associations with plasmids and mobile genetic elements (MGEs). β-lactam resistance genes (0.023–0.048 copies per cell) were the most prevalent, corroborating previous reports. Meanwhile, the detection of bacitracin (0.013–0.028 cpc) and polymyxin (0.009–0.011 cpc) resistance genes raises new concerns, as resistance to these antibiotic classes has not been previously reported in the lake. Furthermore, 44.8 and 30.4% of ARCs were associated with plasmids and MGEs, respectively. ARCs carrying genes for resistance to β-lactams, chloramphenicol, and tetracyclines were frequently identified as mobile, indicating a high potential for horizontal gene transfer and suggesting possible antibiotic contamination in the lake. Overall, this study provides the first metagenomic insight into the resistome of Laguna Lake using short-read sequencing and highlights its role as an environmental reservoir of mobile ARGs. The findings underscore the need for expanded ARG surveillance to improve antimicrobial resistance risk prediction.
1 Introduction
Antimicrobial resistance (AMR) is a growing public health concern as drug-resistant strains continue to appear and cause millions of deaths worldwide each year (Zhang A. N. et al., 2021; Parmanik et al., 2022). The widespread use of antibiotics by humans introduced unprecedented selection pressures that sped up the evolution and spread of AMR among microbial populations, overtaking the development of antibiotics used to combat bacterial infections (Edwards et al., 2021; Larsson and Flach, 2022). With the conception of the One Health approach, the role of the environment in the emergence of AMR is given importance. Current research shows that the environment significantly contributes to the rise of antibiotic-resistant bacteria (ARB) by serving as reservoirs for antibiotic resistance genes (ARGs) that can be acquired by previously non-resistant bacteria through horizontal gene transfer (Larsson and Flach, 2022). Hence, environmental monitoring of ARGs to assess AMR threat and inform mitigation policies is a viable strategy to combat the emergence of resistant pathogens.
Freshwater ecosystems are areas of interest for environmental monitoring because they are highly susceptible to contamination from sources such as urban wastewater and agricultural runoff (Nnadozie and Odume, 2019). Contaminants such as antibiotic residues, heavy metals, and organic nutrients can create selection pressures that favor ARBs and lead to the enrichment of ARGs in the ecosystem (Li et al., 2019; Wang et al., 2020; Zhang Y. et al., 2021). Additionally, human and animal wastes can introduce ARBs that evolved endogenously within hosts as a result of widespread antibiotic use in both human and veterinary medicine (Karkman et al., 2019; Czatzkowska et al., 2022; Larsson and Flach, 2022).
Laguna Lake, the largest inland freshwater lake in the Philippines, is currently classified as Class C by the Department of Environment and Natural Resources (DENR), indicating suitability for fisheries, irrigation, and recreational use (DENR, n.d.). The lake is reportedly used for aquaculture, agriculture, recreation, domestic water supply, and as a potable water source (Salvador-Membreve and Rivera, 2021; dela Peña et al., 2022). Under the regular monitoring of the Laguna Lake Development Authority (LLDA), Laguna Lake and its tributaries have been reported with various levels of contamination with heavy metals and fecal coliform (LLDA, 2024). Recent studies also provide evidence of the presence of ARBs and ARGs in Laguna Lake (Ntabugi et al., 2021, 2023; Salvador-Membreve and Rivera, 2021; dela Peña et al., 2022; Castro et al., 2024), subsequently posing health risks to the 16 million people living in its vicinity (dela Peña et al., 2022).
Existing studies on ARGs in Laguna Lake and neighboring bodies of water have primarily utilized PCR-based methods targeting a limited set of genes that confer resistance to sulfonamides, aminoglycosides, β-lactams, and tetracyclines (Suzuki et al., 2013; Vital et al., 2018; Salvador-Membreve and Rivera, 2021; Mamawal et al., 2023). Moreover, current ARG monitoring strategies rely on isolation-based methods using representative species, usually Escherichia coli (Salvador-Membreve and Rivera, 2021; Mamawal et al., 2023). The aforementioned strategies, while valued for their accurate estimation of genotypic resistance, often miss many ARGs (Bai et al., 2022; Burch et al., 2022). Thus, the limitations in methods used present a likely underestimated AMR threat within Laguna Lake, calling for a broader surveillance strategy.
With the developments in and increased accessibility to next-generation sequencing (NGS), comprehensive sampling of all genes present in an environmental sample is now possible. Specifically, shotgun metagenomic pipelines combine environmental genetic data with computational techniques to allow taxonomic, functional, and genetic profiling of the microbiome (Quince et al., 2017). And yet, despite evidence of AMR in Laguna Lake and the emergence of metagenomic analyses, evaluations of its overall ARG profile remain limited. Hence, we sought to contribute new insights into the state of AMR in the lake by utilizing short-read shotgun metagenomics to elucidate the diversity, abundance, and mobility of ARGs. The findings can guide future research and the development of monitoring and mitigation strategies within the lake and similar freshwater environments in the Philippines.
2 Materials and methods
2.1 Study area and sample collection
The study focused on the West Bay, the largest distinct section of Laguna Lake and the area closest to Metro Manila, a major source of urban wastewater. This proximity makes the West Bay a highly relevant study site, as urban centers often contribute to the persistence of ARGs in aquatic environments through high rates of antibiotic use and substantial wastewater discharge (Díaz-Torres et al., 2024).
To investigate the ARG profile of the West Bay, three lake stations (LS) were chosen to represent the whole area, namely LS1 (Central West Bay; 14.415556°N, 121.173611°E), LS5 (Northern West Bay; 14.483844°N, 121.134035°E), and LS16 (Sta. Rosa; 14.370446°N, 121.095687°E) as shown in Figure 1. To make the sampling more representative of the West Bay, two water samples were collected from each station during the rainy season, specifically, in August and September 2024. We collected samples in coordination with LLDA’s monthly water quality monitoring. Grab samples from the water surface were temporarily stored in 1 L sterile PET bottles and were transported to the laboratory on ice. The water samples were filtered using 0.45 μm nitrocellulose membranes (Pall Corp., USA) within 24 h of sample collection. Membranes were then placed in DNA/RNA Shield (Zymo Research, USA) and stored at 4 °C until DNA extraction.
Figure 1. Sampling sites in the West Bay of Laguna Lake selected using data from the Laguna Lake Development Authority (LLDA) lake monitoring stations.
2.2 DNA extraction and metagenomic shotgun sequencing
Total eDNA was extracted using the Omega E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, USA). Filter membranes were cut into small pieces and transferred into disruptor tubes with the SLX-Mlus Buffer. To remove sediments and lyse cells from the membranes, the tubes were vortexed and then incubated at 70 °C for 10 min with the DS Buffer, followed by another 10 min on a bead beater. The tubes were then centrifuged at 10,000×g for 2 min, and the supernatants were transferred into microcentrifuge tubes. Another round of centrifugation was performed to remove any remaining solid particles. After this, the extraction protocol of the kit was followed.
DNA concentrations were assessed using a Qubit Fluorometer. The DNA samples were then temporarily stored at 4 °C before being sent to a commercial sequencing facility for next-generation sequencing (Macrogen, South Korea). Library preparation was performed using the Illumina Nextera XT DNA Kit. Sequencing was conducted on the Illumina NovaSeq platform to generate 2 × 150 bp paired-end reads, which were deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1381765, with run accessions SRR36471021–SRR36471026. Raw reads were then pre-processed for downstream analyses using Fastp v0.24.0 (Chen, 2023) to trim adapter sequences, remove poly-G and poly-X tails, perform paired-end base correction, and filter reads based on quality (Phred >15).
2.3 ARG profiling
Antibiotic resistance genes (ARGs) were detected and quantified using ARGs-OAP v3.2.4 (Yin et al., 2023). Annotation and quantification were performed using the default parameters. In ARGs-OAP, ARG types refer to the antibiotics against which the encoded proteins confer resistance, comparable to antibiotic or drug classes used in other studies, while ARG subtypes refer to the specific gene genotypes. In addition, we grouped ARG subtypes into ARG families based on the Comprehensive Antibiotic Resistance Database (CARD) classification (Alcock et al., 2023). The abundance of each ARG type or subtype was calculated as the sum of all reads classified within the same category, normalized to the ARG reference sequence length and the estimated number of prokaryotic cells, as shown in the following equation:
where n is the number of ARGs belonging to the same category, Nimapped reads is the number of reads mapped to an ARG, Lreads is the length of the reads, LARG reference sequence is the length of the reference ARG sequence, and Ncell is the cell number for each sample as calculated in stage one of the ARGs-OAP pipeline. The correction factor k was introduced in v3.0 to account for ARG subtypes that utilize two- or three-component systems to encode functional resistance. It is set to 0.5 for ARGs with two-component systems, 0.33 for those with three-component systems, and 1.0 for all other ARGs (Yin et al., 2023).
2.4 Assembly of short reads and identification of antibiotic-resistant contigs (ARCs)
To obtain a more comprehensive profile of the ARGs, de novo assembly of the clean, short reads into longer contigs was performed using metaSPAdes v3.15.5 on the Galaxy EU platform1 using default parameters (Nurk et al., 2017). Each sample was assembled individually.
Antibiotic-resistant contigs (ARCs) were then identified from the assembled contigs following the methods of recently published works (Zhang et al., 2019; Chen et al., 2022). The lengths of the assembled contigs were first determined using SeqKit v2.9.0 (Shen et al., 2024), and only contigs ≥500 bp were retained for downstream analyses (Chen et al., 2022). Open reading frames (ORFs) within contigs were predicted using Prodigal v2.6.3 in meta mode, which is optimized for metagenomic datasets (Hyatt et al., 2010).
The translated ORFs were aligned with the SARG v3.2.1-S database (Yin et al., 2023) using DIAMOND v2.1.11.165 (Buchfink et al., 2021) with the BLASTP command, with cutoffs set at 10−7 e-value, 70% identity, and 70% query cover. A contig was considered an ARC if it contained at least one ORF that matched to an ARG (Zhang et al., 2019; Chen et al., 2022).
2.5 Characterization of the mobility of ARCs
Plasmid prediction for the identified ARCs was performed using PlasFlow v1.1 (Krawczyk et al., 2018) on the Galaxy EU server. Contigs confidently classified as plasmid-derived are reported as “Plasmid” in this paper, while others are labeled “Non-Plasmid,” indicating potential mobility for those identified as plasmid-like.
All translated ORFs from the identified ARCs were also queried against the mobile orthologous genes (MobileOG) database (v2.0.1–90 [pre-release])2 (Brown et al., 2022) using DIAMOND with the same alignment parameters used for ARG detection. ARCs that were identified to harbor mobile genetic element (MGE)-associated genes were classified as mobile ARCs (Zhang et al., 2019; Chen et al., 2022).
2.6 Quantification of ARCs
The trimmed short reads from each sample were individually mapped to the contigs using BWA v0.7.19 (Li and Durbin, 2009). The resulting SAM files were processed using SAMtools v1.21 (Danecek et al., 2021) for sorting and coverage estimation. The SAMtools coverage command was used to quantify the number of reads mapped to the contigs and to assess contig quality. The abundance of each ARC was then calculated using the following equation, normalized to cell number:
where Nimapped reads is the number of reads mapped to an ARC, Lreads is the length of the short reads, LARC is the length of the ARC, and Ncell is the estimated cell number for each sample, as determined in stage one of the ARGs-OAP pipeline.
2.7 Data analysis
Data analyses were primarily conducted using R v4.4.3. The pheatmap package (Kolde, 2019) was used to visualize the distribution of ARG types across the samples. Stacked bar plots were generated using the ggplot2 package (Wickham, 2009) to visualize relative abundances. The complete analysis workflow and code are documented on GitHub at https://github.com/lmfarinas/laguna-lake-ARG-Analysis.
3 Results
3.1 Extracted eDNA and pre-processed shotgun metagenomes
The extracted eDNA samples had concentrations ranging from 14.1 to 24.0 ng/μL (Table 1). Illumina paired-end sequencing yielded 47.08 M to 52.74 M reads containing 7.11 G to 7.96 G bases, and 93.82–94.98% of the bases had a Phred quality score ≥ Q20. After Fastp processing, 44.82–50.54 M reads containing 6.69–7.58 G bases passed the quality control, and the Q20 bases increased to 95.40–95.85%. Moreover, 0.009–0.042% of the bases were corrected, and the GC content of the samples ranged from 49.78 to 54.59%.
Table 1. Summary of eDNA extraction, metagenomic sequencing, and pre-processing results.
3.2 Prevalent ARG types and subtypes
A wide range of ARG types was detected across the six water samples (Figure 2A). Based on overall abundance across samples, β-lactam resistance genes were the most abundant overall (0.030 cpc; range across samples: 0.023–0.048 cpc), followed by bacitracin (0.022 cpc; 0.013–0.028 cpc). Multidrug (0.015 cpc; 0.013–0.020 cpc), chloramphenicol (0.011 cpc; 0.008–0.020 cpc), and polymyxin (0.010 cpc; 0.009–0.011 cpc) resistance genes were also consistently detected. Tetracycline (0.009 cpc), mupirocin (0.005 cpc), macrolide-lincosamide-streptogramin (0.005 cpc), and sulfonamide (0.004 cpc) resistance genes were also consistently detected, while the rest were present at negligible levels (<0.001 cpc).
Figure 2. Abundance of ARG types and subtypes in the West Bay of Laguna Lake. (A) Heatmap of the abundance (in cpc) of ARG types across the six metagenomic samples. (B,C) Relative abundance of ARG families and subtypes, respectively, within the six most prevalent ARG types (overall abundance >0.009 cpc). “OVERALL” represents the cumulative abundance of ARG types from all samples normalized to the total cell copy number. MLS: macrolide-lincosamide-streptogramin. Subtypes or families with less than 5% relative abundance were grouped into “Other.”
Moreover, analysis at the ARG subtype level showed that a few gene families or specific genes dominated each resistance type (Figures 2B,C). For β-lactam resistance genes, TEM β-lactamases (blaTEM) accounted for 88.5%. Bacitracin resistance was largely attributed to the undecaprenyl pyrophosphate phosphatase gene bacA, which comprised 90.6%. Among multidrug resistance genes, major facilitator superfamily (MFS), ATP-binding cassette (ABC), and resistance-nodulation-cell division (RND) efflux families contributed 35.8, 28.3, and 26.9%, respectively. For chloramphenicol, polymyxin, and tetracycline resistance genes, the most prevalent genes were the chloramphenicol acetyltransferase gene catI (93.0%), the UDP-glucose dehydrogenase gene ugd (71.2%), and the tetracycline efflux pump gene tet(C) (87.2%), respectively.
Complete abundance data for all detected ARG types and subtypes are available in Supplementary Data S1.
3.3 Potential mobility of ARCs
The potential mobility of ARCs in this study was determined based on whether the contigs were plasmid-associated and whether they contained genes associated with MGEs. As shown in Figure 3A, 44.8% of all ARCs were predicted to be plasmid-associated. Notably, certain ARGs are predominantly carried by plasmid-associated ARCs, including those conferring resistance to β-lactams (100%), chloramphenicol (85.3%), tetracycline (96.9%), and sulfonamides (78.0%). Meanwhile, Figure 3B shows that 30.4% of all ARCs also contain MGEs. ARGs conferring resistance to β-lactams, chloramphenicol, and tetracycline were predominantly found to colocalize with MGE genes. Specifically, β-lactam ARGs were mostly associated with a combination of RRR (replication, recombination, or nucleic acid repair) and IE (integration and excision)-type MGE genes (93.8%). Chloramphenicol resistance genes were primarily associated with IE-type MGEs (85.3%), while tetracycline resistance genes were predominantly colocalized with RRR-IE MGEs (95.5%).
Figure 3. Relative abundance of potentially mobile ARCs with an overall abundance >0.001 cpc. (A) Mobility based on plasmid association. (B) Mobility based on the presence of MGE-related genes. ARC classification is based on the ARG type they carry. MGE gene types: RRR—replication, recombination, or repair, IE—integration or excision, T—interorganism transfer, P—phage-specific processes.
Detailed coverage statistics, abundance profiles (in cpc), ARG and MGE annotations, and plasmid classifications of ARCs are provided in Supplementary Data S2.
4 Discussion
This study provides an exploratory snapshot of the resistome of Laguna Lake, and the conclusions should be interpreted in the context of the study’s limited sampling design. The analysis is based on six metagenomes derived from three sampling stations across two sampling months within a single season (wet season), with samples collected at a single depth and without biological replicates. Accordingly, the findings are intended to characterize the diversity, abundance, and mobility of ARGs in the West Bay rather than to assess spatial or temporal variation across the lake. Future studies incorporating expanded spatial coverage, multiple depths, seasonal sampling, and biological replication will be valuable for validating and extending the ARG profiles and mobility patterns observed here.
4.1 Genotypic antimicrobial resistance of the lake
We determined the prevalent ARGs in the lake, shedding light on the potential emerging resistance of primary concern. The findings can be used to identify which classes of antibiotics to focus on and which genes to target for environmental monitoring and future research. However, PCR-based quantification of target genes is needed to accurately assess ARG contamination, and antibacterial susceptibility testing is required to confirm phenotypic resistance before formulating monitoring and management policies.
4.1.1 β-Lactam resistance genes
The prevalence of β-lactam resistance genes in the lake is supported by the observed phenotypic resistance in bacterial isolates against β-lactams and the detection of related genes—particularly Extended-Spectrum β-Lactamase (ESBL) genes—in previous studies (Salvador-Membreve and Rivera, 2021; Mamawal et al., 2023). In particular, blaTEM was the most prevalent gene family among the ESBLs examined (blaCTX-M, blaTEM, and blaSHV), detected in 47.66% of the 214 E. coli isolates from Laguna Lake (Salvador-Membreve and Rivera, 2021).
The abundance of β-lactam resistance genes in the environment may be attributed to the fact that β-lactams are the most widely used class of antibiotics. Resistance to this class is commonly due to the production of β-lactamases, enzymes that hydrolyze and inactivate the β-lactam ring (Bush and Bradford, 2016). A major cause for concern is the presence of ESBL genes (e.g., blaTEM), generally found in Enterobacteriaceae, as these encode enzymes that can inactivate a broad range of β-lactams, including later generations (Castanheira et al., 2021).
4.1.2 Bacitracin resistance genes
Another prevalent ARG-type detected in the present study is bacitracin resistance. This is a novel finding, considering that bacitracin resistance has never been reported in any genotypic or phenotypic studies within Laguna Lake. Bacitracin is primarily used against Gram-positive bacteria; however, since prior studies focused on Gram-negative isolates, bacitracin resistance was neither targeted in PCR detection nor assessed through antibiotic susceptibility testing. Nevertheless, bacitracin resistance genes have been reported in a crab pond wastewater purification system (Chen et al., 2022) and in a non-intensive aquaculture farm (Tian et al., 2024) in China. Moreover, a study examining publicly available metagenomes of freshwater lakes from various countries identified bacitracin as among the most widely detected ARGs globally (Chen et al., 2019). Therefore, while the findings from Laguna Lake are novel at the local scale, they reflect global trends in the spread of environmental antibiotic resistance. This result underscores the importance of including bacitracin resistance in future research in the Philippines to better assess its prevalence and identify contributing factors.
Moreover, the dominant subtype of bacitracin resistance identified in this study was the bacA gene. In E. coli, bacA encodes the undecaprenyl pyrophosphate phosphatase (UppP), which rapidly dephosphorylates undecaprenyl pyrophosphate (C55-PP) to undecaprenyl phosphate (C55-P) (Ghachi et al., 2004). C55-P is a crucial lipid carrier involved in transporting hydrophilic precursors across the cell membrane for the biosynthesis of peptidoglycan, lipopolysaccharide, and teichoic acids. Since bacitracin targets C55-PP, the rapid conversion to C55-P by UppP confers resistance (Ghachi et al., 2004). Additionally, bacA homologs in clinically relevant Gram-positive bacteria, such as Staphylococcus aureus and Streptococcus pneumoniae, have been implicated not only in bacitracin resistance but also in virulence. A study has shown that bacA mutants exhibit increased sensitivity to bacitracin and reduced virulence in mouse models (Chalker et al., 2000).
4.1.3 Polymyxin resistance genes
Another novel finding is the detection of polymyxin resistance genes. Although polymyxin is an important drug for treating Gram-negative infections, its resistance has not been investigated in Laguna Lake. Meanwhile, numerous reports have documented the presence of polymyxin resistance in other countries (Chen et al., 2019, 2022; Tian et al., 2024), indicating that such resistance is already a global concern. Its detection in Laguna Lake potentially raises public health concerns, given that polymyxin is considered a last-resort antibiotic for treating multidrug-resistant Gram-negative infections (Mohapatra et al., 2021). While this suggests that untreatable bacterial strains may already be emerging within the lake ecosystem, more definitive proof through the isolation and resistance characterization of pathogenic bacteria is necessary.
In this study, the dominant gene associated with polymyxin resistance was ugd, also known as pmrE, which encodes UDP-glucose dehydrogenase, an enzyme involved in the biosynthesis of 4-amino-4-deoxy-L-arabinopyranose (L-Ara4N). Gram-negative bacteria utilize cationic molecules such as L-Ara4N to modify the phosphate groups of lipid A, thereby reducing the overall negative charge of the lipopolysaccharide layer. This modification diminishes the binding affinity of cationic antimicrobial peptides like polymyxin, which depend on electrostatic interactions with negatively charged lipid A to exert their antibacterial effects (Chen et al., 2011; Joo et al., 2023). A previous study has shown that ugd expression is correlated with polymyxin B resistance in Klebsiella pneumoniae (Chen et al., 2011), while pmrE genes identified in environmental samples have also been implicated in contributing to polymyxin resistance (Joo et al., 2023).
4.1.4 Other ARGs
Aside from β-lactam resistance, multidrug and chloramphenicol resistance have also been documented in Laguna Lake and its surrounding waters (Vital et al., 2018; Ntabugi et al., 2021). Previous studies have shown the resistance of E. coli isolates through antibacterial susceptibility testing. The present study provides genetic confirmation of these previous findings by reporting the presence of multidrug and chloramphenicol resistance genes in the lake. Quantification of ARGs further showed that these two classes were relatively prevalent (>0.01 cpc) in the West Bay of Laguna Lake. In contrast, sulfonamide, aminoglycoside, and tetracycline resistance genes, which have been previously monitored through PCR-based techniques in Laguna Lake (Suzuki et al., 2013; Vital et al., 2018; Salvador-Membreve and Rivera, 2021), were comparatively less abundant (less than 0.01 cpc) in the current study. Moreover, while prior monitoring efforts in the lake focused solely on the tet(A) gene for tetracycline resistance (Salvador-Membreve and Rivera, 2021), we identified tet(C) as the dominant tetracycline resistance gene. Together, these findings underscore the value of metagenomic approaches in ARG monitoring, as they enable the identification of dominant resistance gene types prior to the implementation of more extensive surveillance strategies.
The dominant genes associated with multidrug resistance in this study were identified as members of three major efflux pump families: RND, MFS, and ABC transporters. These drug efflux pumps contribute to antibiotic resistance by actively transporting antimicrobial agents out of the cell, thereby lowering intracellular drug concentrations (Yamasaki et al., 2023). In addition to their role in resistance, they are also implicated in various virulence-related processes, including biofilm formation, quorum sensing, and the excretion of potentially harmful host-derived metabolites (Yamasaki et al., 2023). In particular, RND transporters are primarily found in Gram-negative bacteria and typically form a tripartite complex that spans both the inner and outer membranes, facilitating the direct export of substances out of the cell. MFS pumps, on the other hand, are widespread across all domains of life and typically function as single-membrane transporters. Although highly diverse in structure and substrate range, individual MFS pumps are often substrate-specific, including specificity for certain antibiotics. Finally, ABC transporters function as ATP-hydrolyzing, unidirectional pumps that mediate the outward transport of substrates (Zack et al., 2024).
For chloramphenicol resistance, the dominant gene identified was catI, which encodes the enzyme chloramphenicol acetyltransferase. This enzyme inactivates chloramphenicol by transferring an acetyl group from acetyl-CoA to the antibiotic molecule. The resulting acetylated form of chloramphenicol can no longer bind effectively to the 50S subunit of the bacterial ribosome, thereby preventing it from inhibiting protein synthesis (Mosa et al., 2015).
4.2 Mobile ARGs in Laguna Lake
Plasmids, as mobile genetic elements, enable the rapid dissemination of resistance genes between bacteria (Wang et al., 2023). Notably, conjugative plasmids exhibit significantly higher transfer rates than chromosomal elements. Furthermore, many bacterial species rely on interspecies plasmid transfer as their main mechanism for acquiring ARGs (Lehtinen et al., 2021). In addition to plasmids, other MGEs include transposons, integrons, and bacteriophages, all of which contribute to the dissemination of ARGs via horizontal gene transfer (Brown et al., 2022). In this study, we assessed the potential mobility of ARCs by determining whether the contigs were plasmid-associated and carried MGE genes. Assessing the mobility of ARGs improves risk prediction because if genes are mobile, they can more easily spread across bacterial populations especially to pathogenic species (Lehtinen et al., 2021; Czatzkowska et al., 2022; Wang et al., 2023).
β-lactamase genes, particularly blaTEM, have been predominantly found on plasmids in clinical E. coli isolates from Luzon (Cruz and Hedreyda, 2017). This aligns with the present findings, in which blaTEM was the most prevalent β-lactam ARG and all ARCs carrying β-lactam resistance genes were predicted to be plasmid-associated. Moreover, blaTEM—along with blaSHV—were among the first plasmid-borne enzymes from which many ESBLs were later derived (Cruz and Hedreyda, 2017; Castanheira et al., 2021). Plasmid-borne blaTEM genes have also been frequently associated with transposons and insertion sequences, which are small transposable elements (Castanheira et al., 2021). This supports the present finding that β-lactam ARCs also contain RRR- and IE-type MGE genes, which, according to the mobileOG database classification, are involved in the mobilization processes of plasmids, transposons, and insertion sequences (Brown et al., 2022).
The plasmid association of chloramphenicol ARGs is also supported by previous studies. Chloramphenicol resistance, often conferred by chloramphenicol acetyltransferase (CAT) genes, can be either chromosomal or plasmid-borne (Biswas et al., 2012). CAT genes have also been reported to associate frequently with other MGEs, such as transposons and gene cassettes (Roberts and Schwarz, 2009). In this study, catI—the most prevalent chloramphenicol ARG detected—is known to be widespread among Gram-negative bacteria and has been reported to be transposon-borne and commonly located on large plasmids alongside other ARGs (Roberts and Schwarz, 2009). Additionally, catI was identified as a plasmid-encoded ARG in multidrug-resistant Klebsiella pneumoniae isolates in a study conducted in China (Chen et al., 2023). The present finding that catI-carrying ARCs frequently contain IE-type MGEs thus supports its described mobility, consistent with the cited literature.
Tetracycline resistance is commonly acquired through the uptake of genes carried by mobile plasmids, particularly those encoding tetracycline efflux pumps (Roberts and Schwarz, 2009). In this study, tet(C) was the primary tetracycline ARG identified. This gene is typically associated with Gram-negative genera and is frequently linked to mobile genetic elements, including plasmids and transposons (Roberts and Schwarz, 2009; Shi et al., 2021). Moreover, tet(C) is considered one of the most widely distributed tetracycline resistance genes in environmental settings such as municipal wastewater treatment plants, fishponds, rivers, and soils (Shi et al., 2021). Similar to previously discussed mobile ARCs, the tet(C)-carrying ARCs in this study were also found to contain RRR- and IE-type MGE genes, supporting the described mobility of the tet(C) gene as reported in the cited literature.
Lastly, sulfonamide resistance is frequently mediated by the acquisition of sul genes. In this study, sul1 and sul2 were the predominant sulfonamide ARGs associated with ARCs. These genes are commonly plasmid-borne and have been widely reported in clinical isolates of Gram-negative bacteria such as E. coli, K. pneumoniae, and Acinetobacter baumannii (Lin et al., 2021; Venkatesan et al., 2023).
4.2.1 Implications of ARG mobility for antibiotic contamination in Laguna Lake
The prevalence of mobile ARGs in Laguna Lake may imply the presence of antibiotic contamination. In previous literature, the increase in ARGs on conjugative plasmids has been correlated with the growing use of antibiotics worldwide (Wang et al., 2023). Moreover, it has been noted that mobile genes, particularly those that are only moderately beneficial under normal conditions, are especially sensitive to selection pressures. For instance, essential genes are typically located on the chromosome rather than on plasmids due to the risk of plasmid loss during cell division. In the absence of strong selection, plasmid-borne genes offer no advantage and are often lost (Lehtinen et al., 2021). However, under intense selective pressure—such as the presence of antibiotics—bacteria harboring resistance genes gain a fitness advantage. If these genes are mobile, they can be horizontally transferred, facilitating their spread and enrichment in the environment (Lehtinen et al., 2021; Czatzkowska et al., 2022; Wang et al., 2023).
Although there are no published reports confirming the presence of antimicrobial residues in the waters of Laguna Lake, their presence cannot be ruled out, as indirect evidence for antibiotics in the lake suggests their likely presence. For example, β-lactams are among the most commonly misused drugs, according to a 2015 cross-sectional survey conducted in Central Visayas, Philippines (Barber et al., 2017). Meanwhile, tetracycline and chloramphenicol are commonly associated with aquaculture (Tendencia and de la Peña, 2001; Revilleza et al., 2021). Lastly, sulfonamides, which are among the oldest classes of antibacterial drugs, remain widely used in both human and veterinary medicine due to their broad-spectrum activity (Venkatesan et al., 2023). For example, sulfamethoxazole, a sulfonamide, is commonly used in pig farming in the Philippines (Calayag et al., 2021). These suggest potential sources of antibiotic contamination for the lake.
Overall, the prevalence and the mobility of the aforementioned ARGs indicate potential antibiotic contamination in Laguna Lake. The findings underscore the need to expand ARG surveillance in Laguna Lake, to improve AMR risk prediction and to inform evidence-based interventions. Laguna Lake, being situated at the interface of human settlements, agricultural activities, and aquatic ecosystems, represents a critical One Health convergence point where AMR determinants can emerge and disseminate across environmental, human, and animal reservoirs. The present study can thus be viewed as a baseline assessment that establishes the initial reference data on ARG presence in the lake system. Building on this foundation, future studies can integrate longitudinal sampling, clinical and veterinary AMR data, and environmental drivers to integrate surveillance data into actionable management policies.
Data availability statement
Sequencing was conducted on the Illumina NovaSeq platform to generate 2 × 150 bp paired-end reads, which were deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1381765, with run accessions SRR36471021–SRR36471026.
Author contributions
LF: Software, Formal analysis, Data curation, Writing – original draft, Methodology, Investigation, Conceptualization, Visualization, Writing – review & editing. LP: Visualization, Formal analysis, Investigation, Writing – review & editing, Validation, Methodology, Writing – original draft, Conceptualization. WR: Resources, Validation, Funding acquisition, Project administration, Writing – original draft, Supervision, Writing – review & editing, Conceptualization.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The study was supported by the International Development Research Centre (IDRC) through the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) project ARPHILAKE: “Combating Antibiotic Resistance in Philippine Lakes: One Health upstream interventions to reduce the burden.” The funders had no role in the study design, data collection, analysis, the decision to publish, or the preparation of the manuscript.
Acknowledgments
The authors acknowledge the technical support given by Laguna Lake Development Authority during the sample collection. The authors would like to thank the research associates of the Pathogen-Host-Environment Interactions Research Laboratory (PHEIRL), Institute of Biology, University of the Philippines Diliman for their technical assistance, especially to Kherson Gandola, Patrick Salvador, Aryana Bertuso, and Aira De Guzman of the ARPHILAKE Project Team.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2026.1742578/full#supplementary-material
Supplementary Data S1 | Abundance data of detected antibiotic resistance gene (ARG) types and subtypes in ARGs-OAP. Sheet 1 (ARG_Types): Abundance of ARG types, normalized to copies per cell (cpc), across samples. Sheet 2 (ARG_Subtypes): Abundance of ARG subtypes, normalized to cpc, across samples. “OVERALL” represents the cumulative abundance of ARG types from all samples, normalized to the total cell copy number.
Supplementary Data S2 | Detailed information on antibiotic resistance contigs (ARCs). Sheet 1 (ARC_Coverage_Stats): Coverage statistics of ARCs. Sheet 2 (ARC_Profile_Summary): Abundance (copies per cell, cpc), detected antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and PlasFlow classification of ARCs. Sheet 3 (ARG&MGE_hits): Annotation results from reference databases, including ARG types and subtypes and MGE categories. Sheet 4 (plasmid_ARCs): Relative abundance of ARCs classified as plasmids. Sheet 5 (MGE_ARCs): Relative abundance of ARCs carrying MGEs.
Footnotes
References
Alcock, B. P., Huynh, W., Chalil, R., Smith, K. W., Raphenya, A. R., Wlodarski, M. A., et al. (2023). CARD 2023: expanded curation, support for machine learning, and resistome prediction at the comprehensive antibiotic resistance database. Nucleic Acids Res. 51, D690–D699. doi: 10.1093/nar/gkac920,
Bai, Y., Ruan, X., Li, R., Zhang, Y., and Wang, Z. (2022). Metagenomics-based antibiotic resistance genes diversity and prevalence risk revealed by pathogenic bacterial host in Taihu Lake, China. Environ. Geochem. Health 44, 2531–2543. doi: 10.1007/s10653-021-01021-x,
Barber, D. A., Casquejo, E., Ybañez, P. L., Pinote, M. T., Casquejo, L., Pinote, L. S., et al. (2017). Prevalence and correlates of antibiotic sharing in the Philippines: antibiotic misconceptions and community-level access to non-medical sources of antibiotics. Trop. Med. Int. Health 22, 567–575. doi: 10.1111/tmi.12854,
Biswas, T., Houghton, J. L., Garneau-Tsodikova, S., and Tsodikov, O. V. (2012). The structural basis for substrate versatility of chloramphenicol acetyltransferase CATI. Protein Sci. 21, 520–530. doi: 10.1002/pro.2036,
Brown, C. L., Mullet, J., Hindi, F., Stoll, J. E., Gupta, S., Choi, M., et al. (2022). MobileOG-db: a manually curated database of protein families mediating the life cycle of bacterial mobile genetic elements. Appl. Environ. Microbiol. 88, e00991–e00922. doi: 10.1128/aem.00991-22,
Buchfink, B., Reuter, K., and Drost, H.-G. (2021). Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368. doi: 10.1038/s41592-021-01101-x,
Burch, T. R., Newton, R. J., Kimbell, L. K., LaMartina, E. L., O'Malley, K., Thomson, S. M., et al. (2022). Targeting current and future threats: recent methodological trends in environmental antimicrobial resistance research and their relationships to risk assessment. Environ. Sci. Water Res. Technol. 8, 1787–1802. doi: 10.1039/D2EW00087C
Bush, K., and Bradford, P. A. (2016). Β-Lactams and β-lactamase inhibitors: an overview. Cold Spring Harb. Perspect. Med. 6:a025247. doi: 10.1101/cshperspect.a025247,
Calayag, A. M. B., Widmer, K. W., and Rivera, W. L. (2021). Antimicrobial susceptibility and frequency of bla and qnr genes in Salmonella enterica isolated from slaughtered pigs. Antibiotics 10:1442. doi: 10.3390/antibiotics10121442,
Castanheira, M., Simner, P. J., and Bradford, P. A. (2021). Extended-spectrum β-lactamases: an update on their characteristics, epidemiology and detection. JAC-Antimicrob. Resist. 3:dlab092. doi: 10.1093/jacamr/dlab092,
Castro, A. E., Montecillo, A. D., Villanueva, R. M. D., and Obusan, M. C. M. (2024). Bacterial community profiles of select tributaries of Laguna Lake in the Philippines. Microbiol. Resour. Announc. 13, e01161–e01123. doi: 10.1128/mra.01161-23,
Chalker, A. F., Ingraham, K. A., Lunsford, R. D., Bryant, A. P., Bryant, J., Wallis, N. G., et al. (2000). The bacA gene, which determines bacitracin susceptibility in Streptococcus pneumoniae and Staphylococcus aureus, is also required for virulenceThe GenBank accession number for the sequence reported in this paper is AF228662. Microbiology 146, 1547–1553. doi: 10.1099/00221287-146-7-1547,
Chen, S. (2023). Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2:e107. doi: 10.1002/imt2.107,
Chen, H., Jing, L., Yao, Z., Meng, F., and Teng, Y. (2019). Prevalence, source and risk of antibiotic resistance genes in the sediments of Lake Tai (China) deciphered by metagenomic assembly: a comparison with other global lakes. Environ. Int. 127, 267–275. doi: 10.1016/j.envint.2019.03.048,
Chen, Y. Y., Ko, T. P., Lin, C. H., Chen, W. H., and Wang, A. H. (2011). Conformational change upon product binding to Klebsiella pneumoniae UDP-glucose dehydrogenase: a possible inhibition mechanism for the key enzyme in polymyxin resistance. J. Struct. Biol. 175, 300–310. doi: 10.1016/j.jsb.2011.04.010,
Chen, J., Yang, Y., Jiang, X., Ke, Y., He, T., and Xie, S. (2022). Metagenomic insights into the profile of antibiotic resistomes in sediments of aquaculture wastewater treatment system. J. Environ. Sci. 113, 345–355. doi: 10.1016/j.jes.2021.06.026,
Chen, T., Xu, H., Chen, Y., Ji, J., Ying, C., Liu, Z., et al. (2023). Identification and characterization of OXA-232-producing sequence type 231 multidrug resistant Klebsiella pneumoniae strains causing bloodstream infections in China. Microbiol. Spectr. 11, e02607–e02622. doi: 10.1128/spectrum.02607-22
Cruz, M. C., and Hedreyda, C. T. (2017). Detection of plasmid-borne β-lactamase genes in extendedspectrum β-lactamase (ESBL) and non-ESBL-producing Escherichia coli clinical isolates. Philipp. J. Sci. 146, 167–175. doi: 10.1016/j.vetmic.2016.08.010
Czatzkowska, M., Wolak, I., Harnisz, M., and Korzeniewska, E. (2022). Impact of anthropogenic activities on the dissemination of ARGs in the environment—a review. Int. J. Environ. Res. Public Health 19:12853. doi: 10.3390/ijerph191912853
Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., et al. (2021). Twelve years of SAMtools and BCFtools. Gigascience 10:giab008. doi: 10.1093/gigascience/giab008
dela Peña, L. B. R. O., Nacario, M. A. G., Bolo, N. R., and Rivera, W. L. (2022). Multiple antibiotic resistance in Escherichia coli isolates from fecal and water sources in Laguna Lake, Philippines. Water 14:1517. doi: 10.3390/w14091517
DENR (n.d.). Water bodies in the Philippines: classification according to beneficial use. Available online at: https://water.emb.gov.ph/?page_id=849 (Accessed August 8, 2024).
Díaz-Torres, O., Valencia-de Los Cobos, E. O., Kreft, J. U., Loge, F. J., Díaz-Vázquez, D., Mahlknecht, J., et al. (2024). A metagenomic study of antibiotic resistance genes in a hypereutrophic subtropical lake contaminated by anthropogenic sources. Sci. Total Environ. 927:172216. doi: 10.1016/j.scitotenv.2024.172216
Edwards, F., MacGowan, A., and Macnaughton, E. (2021). Antibiotic resistance. Medicine 49, 632–637. doi: 10.1016/j.mpmed.2021.07.006
Ghachi, M. E., El Ghachi, M., Bouhss, A., Blanot, D., and Mengin-Lecreulx, D. (2004). The bacA gene of Escherichia coli encodes an undecaprenyl pyrophosphate phosphatase activity. J. Biol. Chem. 279, 30106–30113. doi: 10.1074/jbc.M401701200
Hyatt, D., Chen, G. L., LoCascio, P. F., Land, M. L., Larimer, F. W, and Hauser, L. J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119. doi: 10.1186/1471-2105-11-119
Joo, H., Eom, H., Cho, Y., Rho, M., and Song, W. J. (2023). Discovery and characterization of polymyxin-resistance genes pmrE and pmrF from sediment and seawater microbiome. Microbiol. Spectr. 11, e02736–e02722. doi: 10.1128/spectrum.02736-22
Karkman, A., Pärnänen, K., and Larsson, D. G. J. (2019). Fecal pollution can explain antibiotic resistance gene abundances in anthropogenically impacted environments. Nat. Commun. 10:80. doi: 10.1038/s41467-018-07992-3,
Kolde, R. (2019). pheatmap: Pretty Heatmaps. Available online at: https://CRAN.R-project.org/package=pheatmap (Accessed August 22, 2025).
Krawczyk, P. S., Lipinski, L., and Dziembowski, A. (2018). PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res. 46:e35. doi: 10.1093/nar/gkx1321,
Larsson, D. G. J., and Flach, C.-F. (2022). Antibiotic resistance in the environment. Nat. Rev. Microbiol. 20, 257–269. doi: 10.1038/s41579-021-00649-x,
Lehtinen, S., Huisman, J. S., and Bonhoeffer, S. (2021). Evolutionary mechanisms that determine which bacterial genes are carried on plasmids. Evol. Lett. 5, 290–301. doi: 10.1002/evl3.226,
Li, H., and Durbin, R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. bioinformatics, 25, 1754–1760. doi: 10.1093/bioinformatics/btp324
Li, X., Gu, A. Z., Zhang, Y., Xie, B., Li, D., and Chen, J. (2019). Sub-lethal concentrations of heavy metals induce antibiotic resistance via mutagenesis. J. Hazard. Mater. 369, 9–16. doi: 10.1016/j.jhazmat.2019.02.006,
Lin, H., Sun, W., Jin, D., Yu, Q., Yang, Y., Zhang, Z., et al. (2021). Effect of composting on the conjugative transmission of sulfonamide resistance and sulfonamide-resistant bacterial population. J. Clean. Prod. 285:125483. doi: 10.1016/j.jclepro.2020.125483
LLDA (2024). LLDA quarterly monitoring report: Laguna lake and tributary rivers (January to March 2024). Available online at: https://llda.gov.ph/wp-content/uploads/dox/waterqualityrpt/2024/q12024.pdf (Accessed May 24, 2025).
Mamawal, D. R. D., Calayo, J. D. V., Gandola, K. P., Nacario, M. A. G., Vejano, M. R. A., dela Peña, L., et al. (2023). Genotypic detection of β-lactamase-producing Escherichia coli isolates obtained from Seven Crater Lakes of San Pablo, Laguna, Philippines. J. Water Health 21, 1518–1529. doi: 10.2166/wh.2023.157,
Mohapatra, S. S., Dwibedy, S. K., and Padhy, I. (2021). Polymyxins, the last-resort antibiotics: mode of action, resistance emergence, and potential solutions. J. Biosci. 46:85. doi: 10.1007/s12038-021-00209-8,
Mosa, A., Hutter, M. C., Zapp, J., Bernhardt, R., and Hannemann, F. (2015). Regioselective acetylation of C21 hydroxysteroids by the bacterial chloramphenicol acetyltransferase I. Chembiochem 16, 1670–1679. doi: 10.1002/cbic.201500125,
Nnadozie, C. F., and Odume, O. N. (2019). Freshwater environments as reservoirs of antibiotic resistant bacteria and their role in the dissemination of antibiotic resistance genes. Environ. Pollut. 254:113067. doi: 10.1016/j.envpol.2019.113067,
Ntabugi, M.-M. K., Kikongo Ntabugi, M.-M., Manegabe, B. J., Dewar, J. B., and Sekomo Birame, C. (2023). Class 1 and 2 integrons and antibiotic resistance profile in Salmonella spp. from San Cristobal River, Laguna, Philippines. Int. J. Environ. Health Res. 34, 3056–3067. doi: 10.1080/09603123.2023.2289047
Ntabugi, M.-M. K., Manegabe, B. J., Dewar, J. B., Simbahan, J. F., Flavier, M. E., and Sekomo, C. B. (2021). Synergistic increase in antibiotic resistance with tolerance to cadmium and lead in environmental bacteria isolated from the San Cristobal River, Laguna De Bay, Philippines. Int. J. Environ. Stud. 78, 165–183. doi: 10.1080/00207233.2020.1834307
Nurk, S., Meleshko, D., Korobeynikov, A., and Pevzner, P. A. (2017). metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834. doi: 10.1101/gr.213959.116,
Parmanik, A., Das, S., Kar, B., Bose, A., Dwivedi, G. R., and Pandey, M. M. (2022). Current treatment strategies against multidrug-resistant bacteria: a review. Curr. Microbiol. 79:388. doi: 10.1007/s00284-022-03061-7,
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., and Segata, N. (2017). Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844. doi: 10.1038/nbt.3935,
Revilleza, M. E. P., Salamat, S. E. A., and Paraso, M. G. V. (2021). Antibacterial residues in cultured Nile tilapia (Oreochromis niloticus) in the lakeshore barangays of Los Baños, Laguna, Philippines. Philipp. J. Vet. Med. 58, 231–238.
Roberts, M. C., and Schwarz, S. (2009). “Tetracycline and chloramphenicol resistance mechanisms” in Antimicrobial drug resistance: mechanisms of drug resistance. ed. D. L. Mayers (Totowa, NJ: Humana Press), 183–193.
Salvador-Membreve, D. M., and Rivera, W. L. (2021). Predominance of blaTEM and tetA genes in antibiotic-resistant Escherichia coli isolates from Laguna Lake, Philippines. J. Water Sanit. Hyg. Dev. 11, 814–823. doi: 10.2166/washdev.2021.067
Shen, W., Sipos, B., and Zhao, L. (2024). SeqKit2: a Swiss army knife for sequence and alignment processing. iMeta 3:e191. doi: 10.1002/imt2.191,
Shi, Y., Zhang, Y., Wu, X., Zhang, H., Yang, M., and Tian, Z. (2021). Potential dissemination mechanism of the tetC gene in Aeromonas media from the aerobic biofilm reactor under oxytetracycline stresses. J. Environ. Sci. 105, 90–99. doi: 10.1016/j.jes.2020.12.038,
Suzuki, S., Ogo, M., Miller, T. W., Shimizu, A., Takada, H., and Siringan, M. A. T. (2013). Who possesses drug resistance genes in the aquatic environment? Sulfamethoxazole (SMX) resistance genes among the bacterial community in water environment of Metro-Manila, Philippines. Front. Microbiol. 4:102. doi: 10.3389/fmicb.2013.00102
Tendencia, E. A., and de la Peña, L. D. (2001). Antibiotic resistance of bacteria from shrimp ponds. Aquaculture 195, 193–204. doi: 10.1016/S0044-8486(00)00570-6
Tian, L., Fang, G., Li, G., Li, L., Zhang, T., and Mao, Y. (2024). Metagenomic approach revealed the mobility and co-occurrence of antibiotic resistomes between non-intensive aquaculture environment and human. Microbiome 12:107. doi: 10.1186/s40168-024-01824-x,
Venkatesan, M., Fruci, M., Verellen, L. A., Skarina, T., Mesa, N., Flick, R., et al. (2023). Molecular mechanism of plasmid-borne resistance to sulfonamide antibiotics. Nat. Commun. 14:4031. doi: 10.1038/s41467-023-39778-7
Vital, P. G., Zara, E. S., Paraoan, C. E. M., Dimasupil, M. A. Z., Abello, J. J. M., Santos, I. T. G., et al. (2018). Antibiotic resistance and extended-spectrum beta-lactamase production of Escherichia coli isolated from irrigation waters in selected urban farms in Metro Manila, Philippines. Water 10:548. doi: 10.3390/w10050548
Wang, Y., Zhang, H., Long, X., Xu, X., Ren, H., Mao, D., et al. (2020). Non-antibiotic pharmaceuticals enhance the transmission of exogenous antibiotic resistance genes through bacterial transformation. ISME J. 14, 2179–2196. doi: 10.1038/s41396-020-0679-2
Wang, X., Lu, J., Engelstädter, J., Zhang, S., Ding, P., Mao, L., et al. (2023). Global increase of antibiotic resistance genes in conjugative plasmids. Microbiol. Spectr. 11, e04478–e04422. doi: 10.1128/spectrum.04478-22
Yamasaki, S., Zwama, M., Yoneda, T., Hayashi-Nishino, M., and Nishino, K. (2023). Drug resistance and physiological roles of RND multidrug efflux pumps in Salmonella enterica, Escherichia coli and Pseudomonas aeruginosa. Microbiology 169:001322. doi: 10.1099/mic.0.001322
Yin, X., Zheng, X., Li, L., Zhang, A. N., Jiang, X. T., and Zhang, T.. (2023). ARGs-OAP v3.0: antibiotic-resistance gene database curation and analysis pipeline optimization. Engineering 27, 234–241. doi: 10.1016/j.eng.2022.10.011
Zack, K. M., Sorenson, T., and Joshi, S. G. (2024). Types and mechanisms of efflux pump systems and the potential of efflux pump inhibitors in the restoration of antimicrobial susceptibility, with a special reference to Acinetobacter baumannii. Pathogens 13:197. doi: 10.3390/pathogens13030197
Zhang, Y., Shen, G., Hu, S., He, Y., Li, P., and Zhang, B. (2021). Deciphering of antibiotic resistance genes (ARGs) and potential abiotic indicators for the emergence of ARGs in an interconnected lake-river-reservoir system. J. Hazard. Mater. 410:124552. doi: 10.1016/j.jhazmat.2020.124552,
Zhang, H., Chang, F., Shi, P., Ye, L., Zhou, Q., and Pan, Y. (2019). Antibiotic resistome alteration by different disinfection strategies in a full-scale drinking water treatment plant deciphered by metagenomic assembly. Environ. Sci. Technol. 53, 2141–2150. doi: 10.1021/acs.est.8b05907
Keywords: antibiotic resistance genes, antimicrobial resistance, Laguna Lake, mobile genetic elements, One Health, shotgun metagenomics
Citation: Farinas LMF, dela Peña LBRO and Rivera WL (2026) Shotgun metagenomics reveals the prevalence and mobility of antibiotic resistance genes in the West Bay of the human-impacted Laguna Lake. Front. Microbiol. 17:1742578. doi: 10.3389/fmicb.2026.1742578
Edited by:
Jon L. Hobman, University of Nottingham, United KingdomReviewed by:
Vandan Patel, Charotar University of Science and Technology, IndiaJavier Campanini-Salinas, Universidad San Sebastian - Campus Pichi Pelluco, Chile
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*Correspondence: Windell L. Rivera, d2xyaXZlcmFAdXAuZWR1LnBo