Silent Waterborne Carriers of Carbapenem-Resistant Gram-Negative Bacilli and Antimicrobial Resistance Genes in Rio de Janeiro’s Aquatic Ecosystems

TL;DR

This study found antibiotic-resistant bacteria and resistance genes in polluted water bodies in Rio de Janeiro, linking water contamination to public health risks.

Contribution

The study identifies carbapenem-resistant Gram-negative bacteria and resistance genes in recreational waters exposed to human effluents.

Findings

Carbapenem-resistant isolates accounted for 49.4% of total isolates, with blaKPC, blaTEM, blaVIM, and blaSPM genes detected.

A Citrobacter braakii isolate showed resistance to all tested antimicrobials, indicating untreatable infection risks.

The intl1 gene was found in 10% of isolates, suggesting potential horizontal gene transfer.

Abstract

Background/Objectives: Water pollution caused by human activities disrupts ecosystems and promotes the spread of antimicrobial resistance genes (ARGs), posing a public health threat. This study investigated the presence of resistant Gram-negative bacteria and resistance genes in water from two sites occasionally exposed to domestic and hospital effluents, the Carioca River (CR) and Rodrigo de Freitas Lagoon (RFL), both used for recreation. Methods: Physicochemical parameters and coliform levels were measured. Bacterial isolates were identified by Matrix-Assisted Laser Desorption Ionization–Time-of-Flight Mass Spectrometry (MALDI-TOF MS) and tested for antimicrobial susceptibility using disk diffusion. The Minimum Inhibitory Concentration (MIC) was determined using the E-test® and broth microdilution methods. PCR was used to detect carbapenem resistance and other ARGs from the DNA of bacterial isolates obtained from water samples. Results: CR presented signs of environmental degradation, with low dissolved oxygen and high coliform counts. One Citrobacter braakii isolate showed resistance to all tested antimicrobials, raising concern for untreatable infections. Carbapenem-resistant isolates accounted for 49.4% of the total, harboring blaKPC (20%), blaTEM (5%), blaVIM (5%), and blaSPM (5%). The intl1 gene was found in 10% of isolates, indicating potential horizontal gene transfer. Conclusions: The findings from a one-day sampling reveal the presence of multidrug-resistant bacteria that carry antimicrobial resistance genes in polluted aquatic systems. These highlight the connection between water contamination and antimicrobial resistance. The evidence underscores the urgent need for environmental monitoring and effective management strategies to reduce public health risks.

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1. Introduction

Antimicrobial resistance (AMR) represents a growing threat to global health in the 21st century, highlighted by the World Health Organization (WHO) as one of the top ten threats to public health. Its economic impacts can be devastating, with estimates that the cost to the global economy could reach USD 100 trillion by 2050, directly affecting global Gross Domestic Product (GDP) [1]. Some researchers classify AMR as a “silent pandemic”, requiring immediate action [2]. Currently, this resistance constitutes a serious public health crisis, compromising food safety and economic stability, in addition to reducing the effectiveness of treatments for infections previously controlled by antibiotics [3].

Studies indicate that animals, soils, and water play a crucial role in the spread of AMR, positioning it as a “One Health” issue that encompasses agricultural and human ecosystems [4]. The evolution of microbial species is influenced by the interaction of their genomes with the environment, resulting in significant microbial diversity. Abiotic factors such as temperature, pH, nutrients, hydrostatic pressure, and radiation affect the distribution and functioning of these populations, controlling microbial growth in certain ecosystems [5]. In contrast, many plasmids carry genes that confer resistance to antibiotics and heavy metals, promoting bacterial survival in environments where toxic compounds are present [6]. Excessive use of antimicrobials in humans and animals has been one of the primary factors that contribute to the origin and/or spread of resistance [7,8]. In aquatic environments, the spread of pathogens and resistance genes can be facilitated, such as in ballast water, which contributes to the long-distance migration of these genes [9,10].

Water contaminated by domestic sewage, sewage sludge, and groundwater serve as reservoirs and dissemination pathways for resistance due to industrial pollution and sewage [11,12,13,14,15]. In addition to serving as reservoirs for resistant bacteria, these environments act as sites for the exchange and spread of mobile genetic elements, which can be spread to other locations or to human and animal hosts [16]. In this context, aquatic ecosystems play a central role in the evolution of resistance, acting both as reservoirs and vectors for resistance genes [17]. Surface waters, being critical points of contamination, receive discharge from various sources, such as industrial, agricultural, and domestic pollutants, acting as reservoirs and vectors for new resistant isolates, facilitated by horizontal gene transfer, often mediated by bacteriophages or integrons [15].

Although both Gram-negative and Gram-positive bacteria are naturally present in aquatic environments, Gram-negative bacteria, especially those resistant to carbapenems, antimicrobials that, in many cases, represent the last therapeutic option in clinical practice, rank among the most significant pathogens that contribute to public health issues [9,10,11,12,13,14,15].

Bacterial resistance to β-lactams is predominantly caused by the production of β-lactamases, which inactivate this antibiotic class by hydrolyzing the β-lactam ring [18]. These enzymes were present in bacteria even before the introduction of antibiotics and modify peptidoglycan during cell division [19]. Carbapenems, such as meropenem, imipenem, and ertapenem, are used as reserve therapies when other options fail [20]. Among β-lactamases, KPC and NDM are the most prevalent, with the blaKPC gene mostly located in the transposon Tn4401 [21]. Polymyxins, cyclic lipopeptides discovered in the 1940s, have been reintroduced as a last line of defense against resistant Gram-negative infections [22]. Resistance to colistin (polimixin E), initially associated with the mcr-1 gene, now includes new alleles, such as mcr-2 to mcr-9 [23,24].

Until the 19th century, the Carioca River (CR) was the main source of water supply for Rio de Janeiro, with its course protected by legislation in the 17th and 18th centuries [25]. During the colonial period, the river underwent significant alterations in its course and water quality [26]. The CR originates in the Tijuca Forest, crosses several neighborhoods in Rio de Janeiro, and flows to the River Treatment Unit (RTU) before flowing into Flamengo Beach. Rodrigo de Freitas Lagoon (RFL) connects to the ocean through a narrow 835 m canal, the Jardim de Alah, which regulates water exchange between the lagoon and the sea [27]. This connection is crucial for the lagoon’s balance, although water mass renewal only occurs during syzygy tides, limiting its effectiveness [28]. Surrounded by buildings, the lagoon also serves as an important leisure area [29]. The RTU of the CR was deactivated in May 2022, and its mouth was connected to the oceanic interceptor, responsible for directing sewage to the Ipanema Submarine Outfall [30]. In the process of revitalizing the lagoon, RFL’s effluents were also directed to the Ipanema Submarine Outfall [31].

Given the importance of the CR and RFL to biology, tourism, leisure, and sports, this study aimed to investigate the presence of Gram-negative resistant bacteria and their resistance genes in these waters, as this bacterial group encompasses a major problem in bacterial infections related to health assistance [1,2,3]. This research was based on the principle that antimicrobial resistance takes place in a complex interaction between different microbial populations, with potential implications for human, animal, and environmental health.

2. Materials and Methods

2.1. Study Locations and Sample Collection

Surface water samples were obtained from the Carioca River and Rodrigo de Freitas Lagoon, selected from locations specifically vulnerable to anthropogenic impacts, including areas subject to runoff from rainwater and/or sewage. The collection took place on 12 July 2023 (Figure S1) at the following points: CR 1—before the CR treatment station (22°56′0.413″ S 43°10′20.446″ W); CR 2—after the CR treatment station—inoperative (22°56′4.495″ S 43°10′19.553″ W); CR 3—point where the CR discharges into Flamengo Beach (22°56′8.933″ S 43°10′11.615″ W); RFL 1—near the pedal boats, in leisure areas along the lagoon (22°58′27.278″ S, 43°12′9.212″ W); RFL 2—near the Ilha dos Caiçaras (22°58′48.7″ S, 43°12′43.5″ W); RFL 3—near the Jardim de Alah Canal (22°58′48.324″ S, 43°12′49.622″ W); RFL 4—the outfall of the General Garzon Street Canal into the lagoon, extending into the watercourse of the Rio dos Macacos (22°58′3.810″ S, 43°13′2.082″ W); and RFL 5—near a parish (22°57′51.381″ S, 43°12′51.492″ W).

In total, 500 mL of water per sample was collected in the morning and stored in sterile glass bottles. After collection, samples were immediately cooled with ice and transported to the laboratory following the guidelines established by the Standard Methods for the Examination of Water and Wastewater [32].

2.2. Physicochemical and Colorimetric Analysis of Water

Physicochemical analyses of the water were performed at the sampling points using the multiparameter meter model HI9829 from HANNA Instruments (Barueri, SP, Brazil) to assess water quality. The analyzed physicochemical parameters included the following: oxidation–reduction potential, dissolved oxygen, conductivity, salinity, turbidity, temperature, total dissolved solids, resistivity, and pH.

Quantitative colorimetric analysis for the determination of total coliforms and Escherichia coli in the water samples was performed using the Colilert system (IDEXX Laboratories, Westbrook, ME, USA), following the manufacturer’s instructions. The results were expressed as the Most Probable Number (MPN) of cells per 100 mL.

2.3. Isolation and Identification of Bacterial Isolates

For bacterial isolation, water samples were diluted in saline solution (NaCl) at concentrations of 10^−1^, 10^−2^, and 10^−3^. Afterward, 200 μL each of the non-diluted and diluted water was spread onto a chromogenic and selective culture medium for carbapenem-resistant samples: CHROMagar™ KPC (Plastlabor, Rio de Janeiro, RJ, Brazil). Furthermore, 1 mL of each sample was transferred to a sterile 50 mL Falcon™ tube containing MacConkey broth (Laborclin—Pinhais, PR, Brazil), selective for Gram-negative bacteria, and incubated at 35 °C ± 2 °C for 24 h, without aeration. After incubation, 200 μL of bacterial growth was spread onto CHROMagar™ KPC and incubated at 35 °C ± 2 °C for 24 h.

Subsequently, the Colony Forming Units (CFUs) were counted, and distinct colonies were subcultured on Nutrient Agar (NA) plates (KASVI—Pinhais, PR, Brazil) for growth and purity analysis. The isolates were stored in BHI broth (Brain Heart Infusion—Merck, Darmstadt, Germany) supplemented with 20% glycerol (Sigma-Aldrich, St. Louis, MO, USA) at −20 °C. The purity of bacterial isolates was verified. The Gram-staining method was applied, and biochemical tests were conducted using Vitek 2—Systems version 9.02.3 (Biomerieux, Nurtingen, Germany) identification cards for Gram-negative bacteria. Additional biochemical tests were performed, including amylase production, the O/F test, and the oxidase test. The isolates were identified by mass spectrometry using the Matrix-Assisted Laser Desorption–Ionization Time-of-Flight (MALDI-TOF MS) technology on the MALDI-TOF MS Microflex LT instrument from Bruker Daltonics (Bremen, Germany). The results were interpreted based on scores, with values between 2.00 and 3.00 indicating identification with high confidence and values between 1.70 and 1.99 indicating low confidence.

Furthermore, the level of consistency was considered (category A indicates high consistency and high-confidence identification; category B, low consistency; and category C, absence of consistency).

2.4. Phenotypic Assays for Detection of Antimicrobial Resistance

Susceptibility was evaluated by the disk diffusion method on agar, according to CLSI guidelines [33,34]. A variety of antibiotics were used according to the CLSI instructions for each bacterial group: Piperacillin–Tazobactam (PPT), 100/10 μg; Cefepime (CPM), 30 μg; Cefotaxime (CTX), 30 μg; Ceftriaxone (CRO), 30 μg; Cefoxitin (CFO), 30 μg; Ceftazidime (CAZ), 30 μg; Aztreonam (ATM), 30 μg; Ertapenem (ETP), 10 μg; Imipenem (IMP), 10 μg; Meropenem (MER), 10 μg; Gentamicin (GEN), 10 μg; Tobramycin (TOB), 10 μg; Amikacin (AMI), 30 μg; Tetracycline (TET), 30 μg; Ciprofloxacin (CIP), 5 μg; Norfloxacin (NOR), 10 μg; and Sulfamethoxazole–Trimethoprim (SUT), 1.25/23.75 μg.

Due to the absence of established CLSI breakpoints in the disk diffusion method for some bacterial species, the Minimum Inhibitory Concentration (MIC) assay was performed using Etest^®^ reagent strips (Liofilchem—Roseto degli Abruzzi, TE, Italy). Antibiotics tested were ceftriaxone, imipenem, meropenem, gentamicin, and levofloxacin. The results were interpreted based on MIC breakpoints for other non-Enterobacterales microorganisms, according to the 2025 CLSI guidelines.

Among isolates that showed resistance or intermediate results to at least one of the tested carbapenems, conventional broth microdilution was used to determine the Minimum Inhibitory Concentration (MIC) of polymyxin B (PMB) [35], and the drop test was performed [36].

2.5. Determination of the Multiple Antibiotic Resistance Index (MARI) and Phenotypic Pattern of Multidrug Resistance (MARP)

Isolates resistant to three or more antimicrobials were evaluated for the MARP pattern. The MARI was calculated using the mathematical equation proposed by Krumperman [37,38,39]:

$$[eqn]$$

where “a” represents the number of antibiotics to which the isolate was resistant, and “b” is the total number of antibiotics the isolate was exposed to. An MARI greater than 0.20 indicates intensive antibiotic use, characterizing a “high-risk” contamination source [38].

2.6. Genotypic Assays for Detection of Antimicrobial Resistance Determinants

DNA extraction was performed using the Wizard^®^ Genomic DNA Purification Kit (Promega—Madison, WI, USA). DNA quantification was then performed on the NanoDrop One spectrophotometer (Thermo Scientific—Waltham, MA, USA).

Conventional Polymerase Chain Reaction (PCR) (Table S1) was used to detect genes encoding β-lactamases in a singleplex manner (blaSPM, blaSHV, blaTEM, blaCTX-M, blaKPC, blaNDM, blaOXA-48-like, blaGES, blaIMP, and blaVIM) [40,41,42,43]. In addition, the detection of genes conferring mobile colistin resistance (mcr-1 to mcr-9) was performed [23,24]. PCR was conducted on isolates showing resistance or intermediate results to at least one type of carbapenem tested, including amplification of the intl1 gene [44]. For Acinetobacter species, the presence of oxacillinase-encoding genes was also investigated by multiplex PCR (blaOXA-23, blaOXA-24, blaOXA-51, blaOXA-58, blaOXA-143, and blaOXA-235) [45,46,47].

The detection of transposon Tn4401 and associated regions (ISKpn6, ISKpn7, tnpA, and IR) was performed [48,49].

2.7. Statistical Analysis

The variable values were initially normalized on a score scale of 0 to 1.0 to facilitate statistical analysis and minimize the influence of different ranges of each variable on the results. For each variable analyzed, the highest observed value was assigned a score of 1.0, and the subsequent values received proportional scores. Spreadsheets and figures were generated using Past 4.0 and Microsoft Excel.

The variables of the bacterial taxonomic groups, along with ppmDO (dissolved oxygen concentration in parts per million) data and quantitative colorimetry results, were subjected to cluster analysis using the UPGMA method (Unweighted Pair Group Method with Arithmetic Mean) and the Spearman Rank Correlation Coefficient (ρ), termed “Rho.”

Spearman correlation graphs were used to analyze correlations between variables, as these correlations were not linear. The comparative analysis included Colony Forming Unit (CFU) count data, physicochemical parameters, and quantitative colorimetry results. The graphs were generated from normalized data on a 0 to 1.0 scale.

3. Results

3.1. Physicochemical and Colorimetric Analysis of Water

Figure 1 shows the results obtained by statistical analysis of the biological and physicochemical variables. Two clusters can be seen: CR and RFL. Group 2 (CR 1, CR 2, and CR 3) includes the points with the highest number of isolates from the Enterobacterales order (CR 1 and CR 2), higher counts of fecal and thermotolerant coliforms (Table 1), and lower concentrations of dissolved oxygen (Table 1) compared to Group 1. The correlation between the two groups was above 0.825 (Rho), indicating a high level of intra-group similarity. Figure 2 presents Pearson and Spearman analyses: Pearson correlation showed a significant positive correlation between purple, blue, and pink bacterial colonies, which grew on the selective medium and were presumed to be enterobacteria, and the counts of total and thermotolerant coliforms, as expected (Figure 2A; Tables S2 and S3). Additionally, a negative correlation was observed between the number of total coliforms and the percentage and concentration of dissolved oxygen in the analyzed water matrices. Spearman’s correlation revealed that white and cream bacterial colonies, which grew on the selective medium and were presumed to be non-fermenting Gram-negative bacilli (NFGNB), showed a significantly negative correlation with pH, oxidation–reduction potential (ORP), as well as with the percentage and concentration of dissolved oxygen (Figure 2B). The total number of bacterial isolates grown on the selective medium showed a negative correlation with pH. Furthermore, total coliforms showed a negative correlation with the percentage and concentration of dissolved oxygen, as well as with ORP.

3.2. Identification of Bacterial Isolates

A total of 81 bacterial isolates were recovered: 60 from the CR and 21 from RFL. Among these isolates, 56.8% (46/81) were identified as NFGNB, with the majority corresponding to Acinetobacter species (65.2%, 30/46), followed by Pseudomonas species (34.8%, 16/46). Additionally, 19.8% (16/81) of the isolates were Enterobacterales species, with most belonging to the Enterobacter genus (75%, 12/16). Aeromonas represented 18.5% (15/81) of the isolates, with the majority identified as A. caviae (53.3%, 8/15). Two isolates identified as Comamonas aquatica (2.5%), one Acidovorax caeni, and one Vibrio fluvialis (1.2% each) were recovered.

3.3. Phenotypic Assays for Detection of Antimicrobial Resistance

Out of 81 isolates, 49.4% (40/81) presented resistance to one or more antimicrobials tested (Figure 3, Figure 4 and Figure 5), while 50.6% (41/81) were susceptible to at least one carbapenem. For the C. aquatica isolates (points CR 1 and CR 2) and the A. caeni isolate (CR 2 point), the MIC was determined using Etest^®^ strips. The C. aquatica isolate from point CR 1 showed an intermediate result for levofloxacin (MIC of 4 mg/L) and was susceptible to carbapenems, with MIC values of 0.125 mg/L for meropenem and 4 mg/L for imipenem. The C. aquatica isolate from point CR 2 was sensitive to all tested antimicrobials, with MIC values of 0.120 mg/L for meropenem and 4 mg/L for imipenem. The A. caeni isolate showed resistance to ceftriaxone (MIC > 256 mg/L) and imipenem (MIC of 24 mg/L) while maintaining susceptibility to meropenem (MIC of 0.75 mg/L). Among the 40 non-susceptible carbapenem isolates, 22.5% (9/40) exhibited resistance to polymyxin, with most of these isolates originating from point CR 2 (Table 2 and Table S4).

3.4. Determination of the Multiple Antibiotic Resistance Index (MARI) and the Phenotypic Pattern of Multiple Antibiotic Resistance (MARP)

Among the isolates, 20% (16/81) exhibited an MARI greater than 0.20 (Figure 3, Figure 4 and Figure 5). Of these, 37.5% (6/16) were Enterobacterales species, 37.5% (6/16) were A. caviae, and 18.8% (3/16) were Pseudomonas species. Most Acinetobacter species were susceptible to the tested antimicrobials, with one A. venetianus isolate showing a high MARI of 0.50. The isolates with the highest MARI values included a sample of E. bugandensis resistant to all tested antibiotics (MARI = 1.00) from point one of the CR; a sample of C. braakii (MARI = 0.93), K. pneumoniae (MARI = 0.93), and A. caviae (MARI = 0.73) from point two of the CR; and a sample of A. caviae MARI = 0.67) from point one of the CR.

3.5. Genotypic Assays for the Detection of Antibiotic Resistance Determinants

Among the 40 carbapenem non-susceptible isolates, 20% (8/40) showed amplification of the blaKPC gene by PCR, 5% (2/40) exhibited the blaTEM gene, 5% (2/40) harbored the blaVIM gene, 5% (2/40) carried the blaSPM gene, and 10% (4/40) showed amplification of intl1 (Table 3). Among the seven carbapenem non-susceptible isolates that carried the blaKPC gene, the following characteristics were observed: two isolates amplified the surrounding insertion sequences ISKpn6, ISKpn7, and tnpA by PCR; one isolate amplified only ISKpn6; and four isolates did not amplify any regions of Tn4401. Points 1 and 2 of the CR had the highest number of bacterial isolates carrying resistance genes, in addition to the detection of intl1 at these points. Some RFL points, such as points 1 and 5, showed no growth in the selective medium used. Among the points with bacterial growth (RFL 2, 3, and 4), points RFL 2 and RFL 4 contained bacterial isolates with resistance genes such as blaSPM and blaTEM.

4. Discussion

Water quality in aquatic ecosystems is influenced by a complex interaction between physical, chemical, and biological factors that vary both spatially and temporally. Two water collections with different characteristics from a biological, physical–chemical, and preservation point of view were studied: the Carioca River (CR) and Lagoa Rodrigo de Freitas (RFL). The CR and RFL reveal a series of environmental conditions and anthropogenic impacts that directly affect the aquatic microbiota and, consequently, public health.

During sampling, weather conditions were characterized by sunny skies, no precipitation on the previous day, and high tide, which promoted the entry of saline water into the river’s mouth. This phenomenon may have contributed to the dilution of the water samples due to the influx of saline water, which can be harmful to non-halotolerant microorganisms. It is noteworthy that high salt concentrations can dehydrate cells and denature proteins, affecting enzymatic activity and microbial survival. Solar radiation also plays a crucial role in microbial dynamics. According to data from the National Institute of Meteorology (INMET), the global radiation average (kJ/m^2^) in Rio de Janeiro was 934.83 kJ/m^2^ on the day before sampling (11 July 2023) and 1115.03 kJ/m^2^ on the sampling day (12 July 2023), which are considered high levels of radiation [50]. Pathogenic bacteria, such as those originating from sewage (and isolated in the present study), may persist in the environment for shorter periods than autochthonous bacteria, which are better adapted to environmental stresses [51,52].

The analysis of sampling points CR 1, CR 2, CR 3, and RFL 4 revealed dissolved oxygen concentrations (Table 1) below the limits set by the National Environmental Council (CONAMA) for the preservation of aquatic life [53]. Microbial decomposition of organic matter consumes oxygen, creating hypoxic conditions that can lead to the death of aquatic organisms, such as fish [5]. Colorimetric analysis revealed high total and thermotolerant coliform counts (Table 1), exceeding CONAMA limits, with a negative correlation between coliform counts and dissolved oxygen concentration (Figure 2). Although water samples contained fecal and thermotolerant coliforms, the culture method used did not isolate E. coli.

Oxygen depletion is associated with organic matter decomposition through heterotrophic microorganisms, which maintain lower redox potentials in polluted environments [5]. The pH remained within the acceptable range but at levels optimal for the enzymatic activity of microorganisms such as Escherichia coli, Pseudomonas aeruginosa, and Enterobacter aerogenes [5,54].

Figure 3, Figure 4 and Figure 5 and Table 3 show the presence of pathogens classified by the WHO as a critical priority; there was greater species diversity and more antimicrobial resistance genes at CR 1 and CR 2, which receive different types of waste. There was also resistance to β-lactams, cephalosporins, and carbapenems, such as imipenem [55]. Enterobacterales, such as Enterobacter and Citrobacter, predominated, exhibiting phenotypic and genotypic resistance profiles that suggest contamination or previous exposure to antibiotics, hospital waste, and human excreta [37].

The E. cloacae complex comprises closely related isolates and represents a group of common hospital-associated pathogens capable of causing a wide range of infections, including pneumonia and septicemia [56]. Recent studies have reported the emergence of carbapenem resistance within this group, as well as the occurrence of resistance to polymyxins, possibly associated with the selective pressure exerted by the indiscriminate use of these antimicrobials in hospital environments [57,58,59].

The presence of resistance genes such as blaKPC and blaTEM, along with the high MARI at CR 1 and CR 2 (Figure 3, Figure 4 and Figure 5; Table 3), indicates high-risk environments for antimicrobial contamination. Among the blaKPC-producing isolates, four did not amplify the Tn4401 transposon, corroborating studies which demonstrate that, in Brazil, there is an adaptation where the blaKPC-2 gene may be present in an element unrelated to Tn4401, in the so-called NTEKPC, carried by plasmids of the IncQ1 type [60].

The intI1 gene, a marker of environmental pollution, was amplified in isolates with high MARI, indicating bacterial adaptation to antibiotics, hydrocarbons, and metals [61,62]. It has been suggested that the COVID-19 pandemic exacerbated this scenario, with increased use of disinfectants and antimicrobials, resulting in the release of chemical by-products into the environment [63].

Resistance to PMB and carbapenems (Table 2), critical drugs for treating infections caused by multidrug-resistant Gram-negative bacteria, was identified in isolates of Enterobacter, Aeromonas, and Acidovorax caeni, highlighting the severity of the current scenario (Table 2). Recent studies have investigated the rise in resistance among E. cloacae complex species, demonstrating that the KexD efflux pump subunit of E. bugandensis and the small transmembrane protein CrrC, whose genes are highly overexpressed in response to PMB, play a significant role in resistance and heteroresistance to this antimicrobial [64]. Additionally, in the present study, we observed A. hydrophila isolates resistant to PMB (Table 2). Aeromonas species have demonstrated high levels of resistance to polymyxin, with A. hydrophila exhibiting elevated MIC values [65].

Points CR 1 and CR 2, which receive residential and hospital effluents, stand out as critical contamination hotspots. The detection of Pseudomonas species carrying the blaSPM gene (Table 3) reinforces the spread of antimicrobial resistance in these environments. This scenario is corroborated by recent studies, such as one conducted in Rio Grande do Sul (Brazil), which identified Pseudomonas aeruginosa isolates carrying the blaSPM gene in municipal wastewater treatment plants [66]. Furthermore, reduced outer membrane permeability and the presence of subinhibitory antibiotic concentrations may favor mutations and horizontal gene transfer, increasing bacterial resistance [67,68].

Point CR 1 corresponds to the entry of these effluents into a non-operational sewage treatment plant, while point CR 2 marks the diversion of these effluents to the Ipanema Submarine Sewage Outfall (ESEI). The discharge of multidrug-resistant isolates carrying resistance genes, without proper treatment, could reach areas near Ilha de Palmas, which is part of the Cagarras Islands Natural Monument (MONA Cagarras). This archipelago, an important conservation unit, hosts rich marine biodiversity and is recognized as a “Hope Spot,” a critical point for ocean health [69]. Ilha de Palmas is located closer to the ESEI [70]. A recent Brazilian study highlighted pollution by plastics (macro and micro) and sewage-derived waste in MONA Cagarras and surrounding waters, along with personal hygiene items and hair strands, suggesting contamination from sewage reaching the area via the ESEI [71]. It is worth noting that microplastics can act as vectors for antibiotic resistance genes and potentially pathogenic bacteria [72]. However, further studies are needed to assess the transport of multidrug-resistant isolates from the ESEI to the vicinity of the islands in MONA Cagarras.

In Rio de Janeiro, Guanabara Bay, the second-largest bay on the Brazilian coast, covering approximately 380 km^2^, continuously receives large volumes of treated and untreated sewage. This heavily polluted estuarine system, located along the city’s northeastern coast, is directly connected to recreational waters of tourist beaches [13,73]. The CR and RFL, the focus of this study, maintain connections with Guanabara Bay. Despite the Carioca River’s channeling into an oceanic interceptor, it still discharges into a beach adjacent to the bay. Similarly, RFL receives water from a beach near Guanabara Bay, reinforcing the interaction between these water bodies.

Recent studies highlight that Guanabara Bay and its adjacent water bodies serve as reservoirs for antimicrobial-resistant bacteria and resistance genes, posing significant risks to human health. Exposure to these waters and the consumption of seafood from the region may facilitate the spread of resistant pathogens. Investigations conducted in the region have consistently reported the detection of resistant organisms, which indicates that multi-point field sampling provides results that are in agreement with the broader microbial patterns previously described for this area. Enterobacter and Acinetobacter species are frequently identified in these environments, with resistance genes such as blaKPC-2, blaGES-5, and blaGES-16 being the most prevalent [11,13,74,75,76,77]. The presence of the blaKPC gene, which encodes the KPC carbapenemase enzyme, endemic in Brazil and other Latin American countries since 2015, underscores the severity of the scenario [78].

Inadequate sewage treatment, with more than 30% improperly treated in Rio de Janeiro [13,79], is one of the main challenges for the recovery of Guanabara Bay and its adjacent water bodies, including the CR and RFL. The implementation of efficient basic sanitation systems is essential to mitigate environmental degradation and public health risks. Antimicrobial resistance in these aquatic ecosystems not only threatens biodiversity but also facilitates the spread of resistant pathogens, requiring continuous and adaptive monitoring strategies, especially considering the influence of seasonal factors such as tides and rainfall.

The present study has the limitation that sampling was conducted on a single day in July 2023.

5. Conclusions

The data from this study, obtained through single-day sampling, suggest that the Carioca River, particularly at sites CR1 and CR2, may serve as a potential reservoir for multidrug-resistant bacteria and resistance genes. The degraded environmental conditions, as evidenced by low dissolved oxygen concentrations and high counts of total and thermotolerant coliforms, underscore the urgency of intervention. Despite the temporary influence of tides on water quality, the presence of resistant organisms was observed. The detection of genes such as blaKPC and blaSPM, associated with environmental changes and human exposure, reinforces the need for continuous monitoring. However, additional studies involving sequential sampling and collections conducted in different seasons are essential to robustly confirm the role of this environment as a reservoir of multidrug-resistant bacteria and resistance genes. The recovery of these ecosystems depends on the adoption of integrated measures, including improvements in sanitation and regulation of effluent disposal.