Metabolically Active Prokaryotes and Actively Transcribed Antibiotic Resistance Genes in Sewer Systems: Implications for Public Health and Microbially Induced Corrosion

Abstract

Sewer systems are reservoirs of pathogens and bacteria carrying antibiotic resistance genes (ARGs). However, most recent high-throughput studies rely on DNA-based techniques that cannot provide information on the physiological state of the cells nor expression of ARGs. In this study, wastewater and sewer sediment samples were collected from combined and separate sanitary sewer systems. The metabolically active prokaryote community was evaluated using 16S rRNA amplicon sequencing and actively transcribed ARG abundance was measured using mRNA RT-qPCR. Three (sul1, bla_TEM, tet(G)) of the eight tested ARGs were quantifiable in select samples. Sewer sediment samples had greater abundance of actively transcribed ARGs compared to wastewater. Microbiome analysis showed the presence of metabolically active family taxa that contain clinically relevant pathogens (Pseudomonadaceae, Enterobacteraceae, Streptococcaceae, Arcobacteraceae, and Clostridiaceae) and corrosion-causing prokaryotes (Desulfobulbaceae and Desulfovibrionaceae) in both matrices. Spirochaetaceae and methanogens were more common in the sediment matrix while Mycobacteraceae were more common in wastewater. The microbiome obtained from 16S rRNA sequencing had a significantly different structure from the 16S rRNA gene microbiome. Overall, this study demonstrates active transcription of ARGs in sewer systems and provides insight into the abundance and physiological state of taxa of interest in the different sewer matrices and sewer types relevant for wastewater-based epidemiology, corrosion, and understanding the hazard posed by different matrices during sewer overflows.

Keywords: 16S rRNA sequencing; Antibiotic resistance gene transcripts; Sewer microbiome; Sewer resistome; Wastewater-based epidemiology.

Fig. 1

Abundance of actively transcribed ARGs based on mRNA RT-qPCR in combined (C) and separate (S) sanitary sewer system from sediment and wastewater matrices. Two identical symbols in sewer sediment samples S1, S2, and C1 represent the replicate samples collected on different dates (n = 2) Error bars represent the standard deviation of technical replicates (n = 3). The range of the LOQ was established based on the qPCR lowest value of the standard curve calculated with the sample that had the highest and lowest cDNA concentration (Log₁₀ (8.84) to Log₁₀ (8.99) gene copies/g of DNA). Points above the dotted lines represent samples above the LOQ. Points below the red dotted line represent the samples that were below the LOQ but detected by traditional PCR. Points below the LOQ were assigned with a random value between Log₁₀ 7.0 and Log₁₀ 8.0

Fig. 2

Non-metric multidimensional scaling (nMDS) showing the A prokaryote community structure based on 16S rRNA analysis (stress = 0.073) as a factor of sewer system (shapes) and matrix-sewer type (color), and B a comparison between the prokaryote community structure obtained from 16S rRNA vs. 16S rRNA gene sequencing (stress = 0.065) as a factor of matrix-starting nucleic acid used for analysis (colors) and sewer system (shapes)

Fig. 3

Beta-diversity analysis of the microbiomes of combined (C) and separate (S) sanitary sewer wastewater (WW) and sediment (Sed) matrices. A A cluster analysis illustrating the percent similarity between each sample. B a heatmap showing the microbiome at the family level of the samples. Red corresponds to a higher relative abundance of sequences corresponding to the taxon while yellow corresponds to a lower number of sequences

Fig. 4

Bar plots comparing the relative abundance of clinically relevant family taxa between the 16S rRNA and 16S rRNA gene microbiome analysis. Error bars represent the standard deviation (n = 2)