A High-Throughput Microfluidic Quantitative PCR Platform for the Simultaneous Quantification of Pathogens, Fecal Indicator Bacteria, and Microbial Source Tracking Markers

Abstract

Contamination of water with bacterial, viral, and protozoan pathogens can cause human diseases. Both humans and non-humans can release these pathogens through their feces. To identify the sources of fecal contamination in the water environment, microbial source tracking (MST) approaches have been developed; however, the relationship between MST markers and pathogens is still not well understood most likely due to the lack of comprehensive datasets of pathogens and MST marker concentrations. In this study, we developed a novel microfluidic quantitative PCR (MFQPCR) platform for the simultaneous quantification of MST markers, fecal indicator bacteria (FIB), and bacterial, viral, and protozoan pathogens in many samples. A total of 80 previously validated TaqMan probe assays were applied on the MFQPCR chips, including those for two FIB, 22 bacterial pathogens, 11 viral pathogens, five protozoan pathogens, 37 MST markers for various host species, and three process controls. Specific and sensitive detection was verified for most assays on the MFQPCR platform. The MFQPCR chip was applied to analyze pathogen removal rates during the wastewater treatment processes. In addition, multiple host-specific MST markers, FIB, and pathogens were successfully quantified in human and avian-impacted surface waters. While the genes for pathogens were relatively infrequently detected, positive correlations were observed between some potential pathogens such as Clostridium perfringens and Mycobacterium spp., and human MST markers. The MFQPCR chips developed in this study, therefore, can provide useful information to monitor and improve water quality.

High-Throughput Microfluidic Quantitative PCR Platform for the Simultaneous Quantification of Pathogens, Fecal Indicator Bacteria, and Microbial Source Tracking Markers

“…Correlations were also seen between human MST markers and water quality parameters (e.g., water temperature, turbidity, and conductivity). This suggests that multivariate regression or similar modeling approach can be used to predict the occurrence of pathogens in a given water sample based on the concentrations of MST markers, FIB, and/or additional physicochemical parameters . Such a modeling approach could expand the toolkit available for water quality monitoring and QMRA, and therefore, should be tested in the near future.…”

“…Comprehensive data on various pathogens, FIB, and MST markers obtained by MFQPCR could also provide an opportunity to develop predictive models to estimate the presence of pathogens in a given water sample based on the concentrations of MST markers, FIB, and/or additional physicochemical parameters. This remains as a future task …”