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. 2025 May 31;88(1):57. doi: 10.1007/s00248-025-02558-5

Lima Megacity’s Influence on Aquatic Microbial Communities in the Rímac River: Dominance Over Spatial and Seasonal Variations

Tatyana Samaniego 1, Renato La Torre 1, Gisella Orjeda 1, Jorge L Ramirez 1,✉
PMCID: PMC12126359  PMID: 40448709

Abstract

The Rímac River, a vital watershed on the Peruvian coast, is confronted with substantial environmental challenges stemming from intensive exploitation and widespread contamination. As the primary source of water for Lima, supplying approximately 80% of the city’s needs, the river is heavily impacted by pollutants from domestic, hospital, industrial, and mining effluents. These contaminants introduce microbiota that pose significant public health risks. This study utilizes 16S rRNA gene metabarcoding to characterize the bacterial communities along the Rímac River, examining both spatial (upper, middle, and lower basins) and temporal (dry and rainy seasons) variations. Over a year-long sampling period, DNA sequencing revealed pronounced microbiological differences between the Metropolitan and Regional zones, primarily driven by anthropogenic activities. Key findings include a significant reduction in microbial diversity and an increase in pathogenic bacteria within the Metropolitan zone, while the influence of seasonal variations and altitudinal gradients was comparatively minor. Betaproteobacteria emerged as the most abundant class across most samples. Notably, Aliarcobacter cryaerophilus, an indicator of fecal contamination and a potential public health hazard, was predominantly detected in the Metropolitan zone. These results underscore the necessity for comprehensive monitoring of the Rímac River’s microbiota, incorporating advanced molecular techniques to effectively track and mitigate pollution. The study emphasizes the urgent need for robust water quality management strategies to protect this critical resource, ensuring the health and sustainability of Lima and its surrounding regions.

Keywords: Metabarcoding, 16S, Nanopore, Contamination, Anthropic impact

Introduction

The Rímac River represents one of the most critical watersheds along the Peruvian coast, serving as a primary source of water for the populations of Lima and Callao [1]. However, escalating anthropogenic activities have led to intensified exploitation and contamination, resulting in severe environmental degradation of the river’s aquatic ecosystem. The Rímac River supplies approximately 80% of the water consumed by the Metropolitan area of Lima, yet it concurrently receives discharges of domestic and hospital wastewater, industrial effluents, and mining tailings. These pollutants introduce a distinct microbiota, posing potential public health risks to the urban population. Investigations conducted by the National Water Authority [1] identified 1185 contamination points across the river basin, with 260 (22%) located in the upper basin, 336 (28%) in the middle basin, and 589 (50%) in the lower basin, where Lima is situated. This extensive pollution has precipitated detrimental ecological imbalances and a decline in water flow, exacerbating the vulnerability of the river system [2].

Microbiological contamination in the Rímac River has been the focus of extensive monitoring over the years, with contributions from various institutions, including ANA, SEDAPAL, and OEFA [1]. These entities are committed to preserving this critical water resource by investigating environmental stressors across the entire basin [1] and across different seasonal conditions. Historically, many of these studies have relied on conventional culture-based microbiological methods, which are limited by their slow processing times and inability to assess unculturable microbes, thus failing to comprehensively address the river’s complex contamination issues [3]. In recent years, however, the advent of advanced, rapid, and less labor-intensive molecular techniques has transformed the field of aquatic microbiology, enabling more reliable and versatile analyses through portable and high-throughput technologies [3].

High throughput sequencing has proven to be an invaluable tool for understanding the effects of urban pollution on aquatic environments [4–7]. Numerous studies have shown that rivers are contaminated by various sources, including agricultural runoff [8, 9], direct discharges from industrial waters [10, 11], and domestic wastewater [5, 7, 12]. Domestic wastewater, in particular, has been identified as a significant source of pathogens, which can alter the composition of bacterial communities and degrade water quality downstream. In Peru, the first comprehensive effort to characterize the microbiota of the Rímac River was conducted by Romero et al. [5], who utilized 16S rRNA gene sequencing and Illumina technology during the summer of the year 2020. Their findings revealed reduced microbial diversity in the Metropolitan zone (Lower Rímac), dominated by Arcobacter cryaerophilus, an emerging pathogen of public health concern.

In this study, we employ 16S rRNA gene metabarcoding to characterize the microbial communities along the Rímac River, incorporating both spatial and temporal dimensions by comparing dry and rainy seasons. We hypothesize that the microbial disparities observed by Romero et al. [5] in the Metropolitan zone will persist over time due to sustained anthropogenic pressures, while seasonal variations will naturally influence the river’s microbiota. This research highlights the critical need for advanced molecular techniques in monitoring pollution in the Rímac River, offering essential data to inform strategies for the protection and sustainable management of this vital natural resource for Peru.

Material and Methods

Sampling Methodology [13, 14]

The Rímac River exhibits a bimodal seasonal pattern driven by precipitation. The rainy season extends from November to April, with peak precipitation occurring between December and March [13]. Precipitation during this season is spatially variable: the upper Rímac experiences consistent rainfall, the middle Rímac receives irregular precipitation, and the lower Rímac is characterized by low rainfall throughout the year [14]. The Department of Lima is administratively divided into provinces, with Metropolitan Lima being the most densely populated area within the Province of Lima. The Rímac River flows from east to west, traversing two provinces: Huarochirí, which is predominantly rural, and Lima, which transitions from rural landscapes in the east to the highly urbanized Metropolitan area in the west. This work uses the terms Regional and Metropolitan to refer to these areas, respectively. Sampling was conducted across four campaigns to capture seasonal variations: August 2020 (dry season), November 2020 (rainy season), April 2021 (rainy season), and June 2021 (dry season). These campaigns spanned a full year and encompassed the entire altitudinal gradient of the Rímac River, from 4500 m above sea level (msl) at the east, to its lower reaches. A total of 15 georeferenced sampling points were selected along the river, including Chicla, San Mateo, Tamboraque, Hidroeléctrica, Chacahuaro, Matucana, Surco, Santa Eulalia, Huachipa, Chaclacayo, Libertadores, Puente Nuevo, Universitaria, Faucett, and Gambetta, with the latter five located in the Metropolitan zone (Fig. 1). Surface water samples (~ 1.5 l) were collected using sterile plastic bottles. In the Metropolitan zone, samples were obtained from the midpoints of bridges to ensure representative sampling. Water filtration was performed on-site using 0.2 µm Sterivex filters and a portable Masterflex pump to preserve microbial integrity for downstream analysis.

Fig. 1.

Fig. 1

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Location of sampling points along the Rímac River. Upper Rimac: 1. Chicla, 2. San Mateo, 3. Tamboraque, 4. Hidroeléctrica, 5. Chacahuaro, 6. Matucana, 7. Surco. Middle Rimac: 8. Santa Eulalia, 9. Chaclacayo, 10. Huachipa. Lower Rimac (Metropolitan area): 11. Libertadores, 12. Nuevo, 13. Universitaria, 14. Faucett, 15. Gambetta

DNA Extraction and Sequencing

Environmental DNA was isolated from the Sterivex filters using the NucleoMag© DNA/RNA Water Kit (Macherey–Nagel), following the manufacturer’s instructions. DNA quality and yield were quantified using a Nanodrop spectrophotometer (Thermo Fisher Scientific, USA). Metagenomic DNA samples were diluted to a concentration of 5 ng/µl for sequencing. The multiplex 16S Barcoding Kit 1–24 SQK-16S024 (Oxford Nanopore, USA) was used following the manufacturer protocol for Flongle flow cells but adding double the suggested input DNA. This protocol involves the amplification of the full-length 16S rRNA gene with barcoded primers. PCR-amplified libraries for up to 24 samples were bead-purified and pooled in approximately equimolar amounts, followed by the addition of the rapid adaptor (RAP). Four sequencing pools were prepared and sequenced for 8 h on different Flongle flow cells using a MinION Mk1 C. Reads were basecalled using Guppy with its high accuracy model (HAC) and only those with an average quality score (Q) greater than 9 were retained for the analysis.

Bioinformatics and Data Analyses

Sequencing reads were demultiplexed and classified to a lowest common ancestor (LCA) using the FASTQ16S workflow incorporated in the EPI2ME Desktop Agent software. For the classification, reads were queried against the 16S + 18S rRNA database from NCBI (National Center for Biotechnology Information) with a minimum target coverage of 30%, minimum identity of 70%, and a maximum expectation value (E) of 0.01.Alpha diversity indices (Chao1, Shannon, and Simpson) were estimated from the rarefied reads obtained using the Phyloseq package [15], and clustering dendrograms were generated. To test if alpha diversity indices differ significantly, we checked normality with the Shapiro–Wilk in the agricolae package [16]. For normal indices, we used Tukey’s post hoc tests; for non-normal indices, we used a Kruskal test. To estimate beta diversity, the OTU matrix was normalized using the variance stabilizing transformation (VST) method available in the DESeq2 package [17]. Subsequently, a multidimensional scaling (MDS) method utilizing a Euclidean similarity matrix was employed to compare beta diversity. Due to the observed differences in beta dispersion between groups, an Analysis of Similarity (ANOSIM) was employed, as it does not assume equal variance among groups [18]. The ampvis2 package [19] was employed to search for the most predominant ASV, generating heat maps of occurrences, and stack plots to view abundances. Differential abundance analysis was conducted utilizing Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2) [20], focusing on absolute abundance at the genus level across Metropolitan and Regional areas. p-values were adjusted using the Holm-Bonferroni method, and sensitivity analyses were incorporated to evaluate taxon significance.

Results

A total of 415,970 reads were sequenced across all samples, with an average of 7300 ± 4300 reads per sample. The read lengths averaged the expected full-length 16S rRNA gene length (~ 1.5 kb), and both Q scores and DNA throughput remained consistent and within the expected throughout the sequencing runs. Taxonomic analysis revealed that the most predominant class and phylum were Betaproteobacteria and Pseudomonadota, respectively. Additionally, 162 orders were identified, with Burkholderiales being the most abundant. At a finer taxonomic resolution, 3829 operational taxonomic units (OTUs) were detected.

Figure 2 illustrates the relative abundance of bacterial classes across each sampling point. Betaproteobacteria was the predominant class in most samples; however, shifts in microbial composition were observed in the Metropolitan zone, where Gammaproteobacteria and Epsilonproteobacteria were more prevalent. Figure 3 presents a heatmap of genus-level abundances, revealing that Comamonadaceae were the most abundant taxa in the upper and middle Rímac River throughout the year, while Aliarcobacter cryaerophilus and Acinetobacter johnsonii dominated the Metropolitan area.

Fig. 2.

Fig. 2

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Stacked bar diagram of the frequencies of the most common bacterial classes in the Rímac River, along its course. The sites are numbered from 1 to 15 according to Fig. 1. The histogram above shows the annual precipitation (1981–2010) in the upper Rímac (Chicla, site 1, and Matucana, site 6) and the middle Rímac (Chosica, site 9). The lower part of the Rímac is a desert with scarce precipitation

Fig. 3.

Fig. 3

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A Heatmap of the abundance of the ten most frequent bacterial taxa in the Rímac River. In the x-axis, the sites are numbered from 1 to 15 according to Fig. 1. B Significantly different abundant bacterial genus taxa according to ANCOM-BC2 (p < 0.05). Bars indicate log2-fold differences in abundance between Regional (negative) and Metropolitan (positive) areas

Hierarchical clustering analysis of microbial communities by sampling event is depicted in Fig. 4. Two distinct clusters were identified, with one primarily composed of samples from the Metropolitan area, indicating high similarity in microbial composition within this region. Similarly, multidimensional scaling (MDS) analysis (Fig. 4) demonstrated a clear separation between Metropolitan samples and those from the rest of the basin (Regional), both when analyzed by season and as a whole. ANOSIM indicated a significant separation between the Regional and Metropolitan areas (R = 0.48, p = 0.001). Additionally, ANOSIM showed no difference between the upper basin (points 1–7) and the middle basin (points 8–10) (p < 0.21) but did indicate differences between the lower part of the basin (Metropolitan area, points 11–15) and the rest of the basin (p < 0.001). Conversely, there was no significant difference between the dry and rainy seasons (R = 0.05, p = 0.025). When considering only samples from the Regional part by month of collection, all comparisons were significant (p < 0.05). On the other hand, samples from the Metropolitan part do not differ statistically from each other (p > 0.01).

Fig. 4.

Fig. 4

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Multidimensional scaling in all samples with respect to regions and time of year. Dendrograms show the clustering of microbial composition in the 4 samplings carried out along the Rímac River. The sites are numbered from 1 to 15 according to Fig. 1

Alpha diversity indices, as shown in Fig. 5, revealed notable spatial and temporal variations. The indices indicated significant differences (p < 0.05) during the first three sampling events, with lower richness, diversity, and evenness in the Metropolitan region compared to the Regional area. This trend was reversed in the fourth sampling, suggesting dynamic shifts in microbial community structure over time, although this difference was not statistically significant. These findings underscore the influence of anthropogenic activities on microbial diversity in the Metropolitan zone and highlight the importance of temporal considerations in ecological assessments.

Fig. 5.

Fig. 5

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Alpha, Chao1 (richness), Shannon (diversity), and Simpson (evenness) diversity indices of the four samples campaigns. Letters above boxplots represent significant differences (p < 0.05) between average values of each sub-basin

Discussion

In contemporary environmental microbiology, it is imperative not only to identify bacterial communities within aquatic ecosystems but also to elucidate the spatial and temporal dynamics governing their composition [21]. This study, encompassing a year-long sampling effort, provides the most comprehensive characterization to date of the bacterial community diversity along the Rímac River, utilizing a 16S rRNA gene metabarcoding approach. We systematically compared microbial assemblages across seasonal variations (dry and rainy seasons) and spatial gradients (Regional vs. Metropolitan zones). Sampling locations included those previously examined by Romero et al. [5], supplemented by two additional sites in the mid-basin region.

Both hierarchical clustering and multidimensional scaling (MDS) analyses revealed distinct subdivisions within the microbial communities of the Rímac River (Fig. 4). Hierarchical clustering grouped Metropolitan samples into a single cluster, indicative of compositional homogeneity, whereas Regional samples formed separate clusters, reflecting greater variability (Fig. 4). Similarly, MDS and ANOSIM analyses demonstrated a significant separation between Metropolitan and Regional samples across all sampling events (Fig. 4). Notably, Regional samples exhibited higher dispersion and diversity indices (Fig. 5), while Metropolitan samples were significantly more uniform and less diverse, with the exception of June 2021. The dispersion observed in Regional samples may be attributable to altitudinal gradients (0–4500 m above sea level), as evidenced by MDS. Specifically, upper Rímac sites (localities 1–5) clustered more closely together than mid-Rímac sites (localities 6–9), particularly during the dry season (Fig. 4), although no statistical difference in bacterial communities between the upper and the middle basin was observed. Additionally, no significant differences were found according to seasonal variations between rainy and dry periods. However, when samples from the Metropolitan area are excluded from the analysis, significant monthly differences in the Regional area are detected. It could be observed in the samples collected in April 2021 within the Regional zone that reduced dispersion and limited overlap with those from November 2020 likely reflect the influence of peak rainfall occurring in February and March. Our results indicate that Lima exerts a significant impact over the microbial communities in the Rimac River, overshadowing other factors, such as seasonal variations. Metropolitan areas significantly alter river systems, leading to notable changes in microbial communities. This phenomenon has been observed in various countries, including Brazil [6] and Nepal [4]. This difference has been linked to bacterial genera that are associated with human pathogens originating from fecal matter [4–7, 22].

Betaproteobacteria, a class within the phylum Pseudomonadota, emerged as the most abundant taxon across most samples. This group [21] is commonly found in aquatic environments, including rivers [23, 24] and lakes [25, 26]. Interestingly, Romero et al. [5] reported Bacteroidia, particularly Flavobacterium, as the dominant class, whereas we observed Flavobacteriia at lower abundances. This discrepancy likely stems from the use of different sequencing platforms, as biases inherent to each technology have been reported [27, 28]. Despite these variations, both studies consistently identified distinct microbial patterns between Regional and Metropolitan zones.

The shift in bacterial composition within the Metropolitan zone is characterized by increased abundances of Gammaproteobacteria and Epsilonproteobacteria (Fig. 2). Nine genera, including Aliarcobacter and Acinetobacter, were found to be differentially abundant in the Metropolitan area (Fig. 3B). Most of these genera have previously been identified in rivers and are often linked to water pollution and sewage impact [29–33], supporting the evidence of human impact in the lower part of the Rimac River. Notably, Aliarcobacter cryaerophilus (Epsilonproteobacteria), a genus indicative of fecal contamination and a potential public health risk [6], was the most prevalent species in three of the four sampling campaigns (Fig. 3A). This finding aligns with Romero et al. [5], who identified Arcobacter cryaerophilus (synonymous with Aliarcobacter cryaerophilus) as the dominant species. Classified as a serious hazard by the International Commission on Microbiological Specifications for Food (ICMSF, 2002), A. cryaerophilus is associated with intestinal and extraintestinal infections, including bacteremia, peritonitis, and severe diarrhea [34]. Its resilience in water and wastewater systems [35], coupled with antibiotic resistance and potential for horizontal gene transfer [36], underscores its public health significance. Despite its prevalence in the Rímac River, reports of Aliarcobacter-related infections in Peru remain limited, with only one study documenting its presence in children with diarrhea [37]. The second most abundant species in the Metropolitan zone was Acinetobacter johnsonii (Gammaproteobacteria), except in November 2020, when it was the most dominant. Acinetobacter spp. are ubiquitous in natural environments, including soil, water, and sewage [38], with A. johnsonii frequently isolated from human skin and feces [38].

This study highlights the pronounced and consistent differences in microbial communities between the Metropolitan and Regional zones, driven by reduced diversity and the prevalence of pathogenic bacteria (i.e., Aliarcobacter cryaerophilus) in the Metropolitan area. These findings underscore the profound anthropogenic impact of Lima on the Rímac River. Municipal wastewater is the main source of pollution, accounting for 62% of the total [39]. This includes 450 drainage tubes that are discharged directly into the river without any treatment [39]. Most of them are in Lurigancho (Chosica), near point 11 and 12, the point with more presence of Aliarcobacter cryaerophilus. Our findings underscore the necessity of establishing a national plan to enhance the water quality of the Rimac River, which includes constructing new wastewater treatment plants (WWTP) and augmenting the capacity of existing facilities. Also, a robust monitoring program is essential, incorporating expanded sampling efforts, particularly during the rainy season, which was herein underrepresented due to COVID-19-related restrictions. Such initiatives are critical for informing conservation strategies and mitigating public health risks associated with microbial contamination in this vital water resource.

Author Contribution

All authors contributed to the study conception and design. TS, GO, and RLT contributed to material preparation and data collection. TS, RLT, and JLR were responsible for analysis. The first draft of the manuscript was written by TS. JLR and GO contributed to the revision and editing of the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This research was supported by Universidad Nacional Mayor de San Marcos – R. R. No 009412–2021-UNMSM and Project number B2110006i – PINTERDIS – 2021.

Data Availability

Sequence data that support the findings of this study have been deposited in NCBI BioProject database (PRJNA1223955).

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.ANA (2012) Resultado del monitoreo de la calidad del agua en la cuenca del río Rímac. Dirección de Gestión de Calidad de los Recursos Hídricos. Informe técnico N° 006–2012-ANA-DGCRH/JJOS. Lima, Perú
  • 2.De Oliveira LFV, Margis R (2015) The source of the river as a nursery for microbial diversity. PLoS ONE 10:e0120608. 10.1371/JOURNAL.PONE.0120608 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Garner E, Davis BC, Milligan E et al (2021) Next generation sequencing approaches to evaluate water and wastewater quality. Water Res 194:116907. 10.1016/J.WATRES.2021.116907 [DOI] [PubMed] [Google Scholar]
  • 4.Pantha K, Acharya K, Mohapatra S et al (2021) (2021) Faecal pollution source tracking in the holy Bagmati River by portable 16S rRNA gene sequencing. npj Clean Water 4:1–10. 10.1038/s41545-021-00099-1 [Google Scholar]
  • 5.Romero PE, Calla-Quispe E, Castillo-Vilcahuaman C et al (2021) From the Andes to the desert: 16S rRNA metabarcoding characterization of aquatic bacterial communities in the Rimac river, the main source of water for Lima. Peru PLoS One 16:e0250401. 10.1371/journal.pone.0250401 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Godoy RG, Marcondes MA, Pessôa R et al (2020) (2020) Bacterial community composition and potential pathogens along the Pinheiros River in the southeast of Brazil. Sci Rep 10:1–9. 10.1038/s41598-020-66386-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Lou LE, Mohaimani AA, Newton RJ (2021) Urban wastewater bacterial communities assemble into seasonal steady states. Microbiome 9:116. 10.1186/s40168-021-01038-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Chen W, Wilkes G, Khan IUH et al (2018) Aquatic bacterial communities associated with land use and environmental factors in agricultural landscapes using a metabarcoding approach. Front Microbiol 9:373177. 10.3389/FMICB.2018.02301/XML/NLM [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Zehr JP, Beman JM, Hewson I et al (2020) Freshwater sediment microbial communities are not resilient to disturbance from agricultural land runoff. Front Microbiol 11:539921. 10.3389/fmicb.2020.539921 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Zhang L, Zhou Y, Cheng Y et al (2021) Effect of different types of industrial wastewater on the bacterial community of urban rivers. J Freshw Ecol 36:31–48. 10.1080/02705060.2021.1871978 [Google Scholar]
  • 11.Wei Y, Li Y, Wang Y et al (2022) The microbial diversity in industrial effluents makes high-throughput sequencing-based source tracking of the effluents possible. Environ Res 212:113640. 10.1016/J.ENVRES.2022.113640 [DOI] [PubMed] [Google Scholar]
  • 12.Relucio-San Diego MA, Gloria PCT, Obusan MCM (2024) 16S metabarcoding of the bacterial community of a poultry wastewater treatment plant in the Philippines. Front Environ Sci 12:1390323. 10.3389/fenvs.2024.1390323 [Google Scholar]
  • 13.Servicio Nacional de Meteorología Hidrología (2013) Caracterización de periodos secos y humedos cuenca del río Rimac. Lima, Perú
  • 14.Zimmermann DR (2023) Evaluación Climática De La Precipitación En La Cuenca Del Río Rímac Y Establecimiento De Umbrales. Servicio Nacional de Meteorología e Hidrología del Perú. Lima, Perú
  • 15.McMurdie PJ, Holmes S (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8:e61217 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.De Mendiburu F (2023) Agricolae: statistical procedures for agricultural research. R Package Version 1.3-7. https://cran.r-project.org/web/packages/agricolae/index.html
  • 17.Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. 10.1186/s13059-014-0550-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Somerfield PJ, Clarke KR, Gorley RN (2021) A generalised analysis of similarities (ANOSIM) statistic for designs with ordered factors. Austral Ecol 46:901–910. 10.1111/AEC.13043 [Google Scholar]
  • 19.Andersen KS, Kirkegaard RH, Karst SM, Albertsen M (2018) ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. bioRxiv 299537. 10.1101/299537
  • 20.Lin H (2023) Peddada S Das (2023) Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nature Methods 21:83–91. 10.1038/s41592-023-02092-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Yu S, He R, Song A et al (2019) Spatial and temporal dynamics of bacterioplankton community composition in a subtropical dammed karst river of southwestern China. Microbiologyopen 8:e00849. 10.1002/MBO3.849 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Donovan E, Unice K, Roberts JD et al (2008) Risk of gastrointestinal disease associated with exposure to pathogens in the water of the Lower Passaic River. Appl Environ Microbiol 74:994–1003. 10.1128/AEM.00601-07/ASSET/087EE141-2D89-4ED1-A0E4-8A4FD443A817/ASSETS/GRAPHIC/ZAM0040886180003.JPEG [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Balmonte JP, Arnosti C, Underwood S et al (2016) Riverine bacterial communities reveal environmental disturbance signatures within the Betaproteobacteria and Verrucomicrobia. Front Microbiol 7:207222. 10.3389/FMICB.2016.01441/BIBTEX [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Thoetkiattikul H, Mhuantong W, Pinyakong O et al (2017) Culture-independent study of bacterial communities in tropical river sediment. Biosci Biotechnol Biochem 81:200–209. 10.1080/09168451.2016.1234927 [DOI] [PubMed] [Google Scholar]
  • 25.Schweitzer B, Huber I, Amann R et al (2001) α- and β-proteobacteria control the consumption and release of amino acids on lake snow aggregates. Appl Environ Microbiol 67:632. 10.1128/AEM.67.2.632-645.2001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Huang W, Chen X, Jiang X, Zheng B (2017) Characterization of sediment bacterial communities in plain lakes with different trophic statuses. Microbiologyopen 6:e00503. 10.1002/MBO3.503 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Stevens BM, Creed TB, Reardon CL (2023) Manter DK (2023) Comparison of Oxford Nanopore Technologies and Illumina MiSeq sequencing with mock communities and agricultural soil. Sci Rep 13:1–11. 10.1038/s41598-023-36101-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Nygaard AB, Tunsjø HS, Meisal R (2020) Charnock C (2020) A preliminary study on the potential of Nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Scientific Reports 10:1–10. 10.1038/s41598-020-59771-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Some S, Mondal R, Mitra D et al (2021) Microbial pollution of water with special reference to coliform bacteria and their nexus with environment. Energy Nexus 1:100008. 10.1016/J.NEXUS.2021.100008 [Google Scholar]
  • 30.Ho JY, Jong MC, Acharya K et al (2021) Multidrug-resistant bacteria and microbial communities in a river estuary with fragmented suburban waste management. J Hazard Mater 405:124687. 10.1016/J.JHAZMAT.2020.124687 [DOI] [PubMed] [Google Scholar]
  • 31.Narain Singh D, Pandey P, Shankar Singh V, Kumar Tripathi A (2025) Evidence for high-risk pollutants and emerging microbial contaminants at two major bathing ghats of the river Ganga using high-resolution mass spectrometry and metagenomics. Gene 933:148991. 10.1016/J.GENE.2024.148991 [DOI] [PubMed] [Google Scholar]
  • 32.Xie Y, Liu X, Wei H et al (2022) Insight into impact of sewage discharge on microbial dynamics and pathogenicity in river ecosystem. Sci Rep 12:6894. 10.1038/S41598-022-09579-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Wilkes RA, Zhou N, Carroll AL et al (2024) Mechanisms of polyethylene terephthalate pellet fragmentation into nanoplastics and assimilable carbons by wastewater Comamonas. Environ Sci Technol 58:19338. 10.1021/ACS.EST.4C06645/ASSET/IMAGES/LARGE/ES4C06645_0007.JPEG [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Figueras MJ, Levican A, Pujol I et al (2014) A severe case of persistent diarrhoea associated with Arcobacter cryaerophilus but attributed to Campylobacter sp. and a review of the clinical incidence of Arcobacter spp. New Microbes New Infect 2:31–37. 10.1002/2052-2975.35 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kristensen JM, Nierychlo M, Albertsen M, Nielsen PH (2020) Bacteria from the genus arcobacter are abundant in effluent from wastewater treatment plants. Appl Environ Microbiol 86:e03044. 10.1128/AEM.03044-19/SUPPL_FILE/AEM.03044-19-SD002.XLSX [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Müller E, Hotzel H, Ahlers C et al (2020) Genomic analysis and antimicrobial resistance of Aliarcobacter cryaerophilus strains from German water poultry. Front Microbiol 11:1549. 10.3389/FMICB.2020.01549/FULL [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Zerpa Larrauri R, Alarcón Villaverde JO, Lezama Vigo PE, et al. (2014) Identificación de Arcobacter en heces de niños y adultos con/sin diarrea y en reservorios animales. Anales de la Facultad de Medicina 75:. 10.15381/ANALES.V75I2.8389
  • 38.Kämpfer P (2014) Acinetobacter. Encyclopedia of Food Microbiology: Second Edition 11–17. 10.1016/B978-0-12-384730-0.00002-1
  • 39.Fondo de Agua para Lima y Callao - Aquafondo (2023) Nota conceptual. Programa de Inversión Pública. Mejoramiento de la calidad del agua del Río Rímac. Lima, Perú

Associated Data

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

Data Availability Statement

Sequence data that support the findings of this study have been deposited in NCBI BioProject database (PRJNA1223955).

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