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With accumulating evidence of pulmonary infection via aerosolized nontuberculous mycobacteria (NTM), it is important to characterize their persistence in wastewater treatment, especially in arid regions where treated municipal wastewater is extensively reused. To achieve this goal, microbial diversity of the genus Mycobacterium was screened for clinically and environmentally relevant species using pyrosequencing. Analysis of the postdisinfected treated wastewater showed the presence of clinically relevant slow growers like M. kansasii, M. szulgai, M. gordonae, and M. asiaticum; however, in these samples, rapid growers like M. mageritense occurred at much higher relative abundance. M. asiaticum and M. mageritense have been isolated in pulmonary samples from NTM-infected patients in the region. Diversity analysis along the treatment train found environmentally relevant organisms like M. poriferae and M. insubricum to increase in relative abundance across the chlorine disinfection step. A comparison to qPCR results across the chlorine disinfection step saw no significant change in slow grower counts at CT disinfection values ≤90 mg·min/L; only an increase to 180 mg·min/L in late May brought slow growers to below detection levels. The study confirms the occurrence of clinically and environmentally relevant mycobacteria in treated municipal wastewater, suggesting the need for vigilant monitoring of treated wastewater quality and disinfection effectiveness prior to reuse.

Effective and sensitive monitoring of human pathogenic bacteria in municipal wastewater treatment is important not only for managing public health risk related to treated wastewater reuse, but also for ensuring proper functioning of the treatment plant. In this study, three different 16S rRNA gene molecular analysis methodologies were employed to screen bacterial pathogens in samples collected at three different stages of an activated sludge plant. Overall bacterial diversity was analyzed using next generation sequencing (NGS) on the Illumina MiSeq platform, as well as PCR-DGGE followed by band sequencing. In addition, a microdiversity analysis was conducted using PCR-DGGE, targeting Escherichia coli. Bioinformatics analysis was performed using QIIME protocol by clustering sequences against the Human Pathogenic Bacteria Database. NGS data were also clustered against the Greengenes database for a genera-level diversity analysis. NGS proved to be the most effective approach screening the sequences of 21 potential human bacterial pathogens, while the E. coli microdiversity analysis yielded one (O157:H7 str. EDL933) out of the two E. coli strains picked up by NGS. Overall diversity using PCR-DGGE did not yield any pathogenic sequence matches even though a number of sequences matched the NGS results. Overall, sequences of Gram-negative pathogens decreased in relative abundance along the treatment train while those of Gram-positive pathogens increased.