Metagenomics insights into bacterial diversity and antibiotic resistome of the sewage in the city of Belém, Pará, Brazil

Abstract

Introduction: The advancement of antimicrobial resistance is a significant public health issue today. With the spread of resistant bacterial strains in water resources, especially in urban sewage, metagenomic studies enable the investigation of the microbial composition and resistance genes present in these locations. This study characterized the bacterial community and antibiotic resistance genes in a sewage system that receives effluents from various sources through metagenomics.

Methods: One liter of surface water was collected at four points of a sewage channel, and after filtration, the total DNA was extracted and then sequenced on an NGS platform (Illumina® NextSeq). The sequenced data were trimmed, and the microbiome was predicted using the Kraken software, while the resistome was analyzed on the CARD webserver. All ecological and statistical analyses were performed using the. RStudio tool.

Results and discussion: The complete metagenome results showed a community with high diversity at the beginning and more restricted diversity at the end of the sampling, with a predominance of the phyla Bacteroidetes, Actinobacteria, Firmicutes, and Proteobacteria. Most species were considered pathogenic, with an emphasis on those belonging to the Enterobacteriaceae family. It was possible to identify bacterial groups of different threat levels to human health according to a report by the U.S. Centers for Disease Control and Prevention. The resistome analysis predominantly revealed genes that confer resistance to multiple drugs, followed by aminoglycosides and macrolides, with efflux pumps and drug inactivation being the most prevalent resistance mechanisms. This work was pioneering in characterizing resistance in a sanitary environment in the Amazon region and reinforces that sanitation measures for urban sewage are necessary to prevent the advancement of antibiotic resistance and the contamination of water resources, as evidenced by the process of eutrophication.

Keywords: antibiotic resistant bacteria; metagenome; microbial diversity; resistome; sewerage.

Figure 1

Methodological steps of sample processing for microbial profiling (left) and resistome (right), including analysis of MAGs of pathogens reported by CDC. DAPC, discriminant analysis of principal components; CARD, the Comprehensive Antibiotic Resistance Database; CDC, Centers for Disease Control and Prevention; MAG, metagenome-assembled genome; AMR, antimicrobial resistance; ARG, antimicrobial resistance gene; ATM, antimicrobial.

Figure 2

Discriminant analysis of principal components (DAPC) (A), Shannon alpha diversity (B), relative abundance of the phyla (C), families (D) and genera (E) most representative of the microbiome.

Figure 3

Heatmap with the abundance (in log scale) of bacterial genera of medical importance and the respective families that compose them.

Figure 4

Heatmap of the 80 most predominant ARGs in the resistome and their respective antimicrobials to which they confer resistance. AMG, Aminoglycoside; AMC, Aminocoumarin; DMP, Diaminopyrimidine; Agents and Dyes, Disinfecting agents and intercalating dyes; FQN, Fluoroquinolone; FOS, Fosfomycin; GLC, Glycylcycline; TET, Tetracycline; MAC, Macrolide; MDR, Multidrug resistant; NIT, Nitroimidazole; NUC, Nucleoside; PEP, Peptide; PHE, Phenicol; RIF, Rifamycin; SUL, Sulfonamide; TET, Tetracycline.

Figure 5

Frequency of ARGs (number of contigs) by antimicrobial class (A) and by type of resistance mechanism (B). Venn plot for drug classes (C) and mechanisms (D) at each collection site. AMC, Aminocoumarin; AMG, Aminoglycoside; DMP, Diaminopyrimidine; Agents and Dyes, Disinfecting agents and intercalating dyes; FQN, Fluoroquinolone; RIF, Rifamycin; PEP, Peptide; FOS, Fosfomycin; GPD, Glycopeptide; TET, Tetracycline; GLC, Glycylcycline; LIN, Lincosamide; NUC, Nucleoside; MON, Monobactam; NIT, Nitroimidazole; MAC, Macrolide; PHE, Phenicol; SUL, Sulfonamide.

Figure 6

Number of ARGs (A) in each pathogen selected from the CDC report and antimicrobial classes related to them (B). AMG, Aminoglycoside; DMP, Diaminopyrimidine; RIF, Rifamycin; PNA, Penam; FOS, Fosfomycin; TET, Tetracycline; PHE, Phenicol.