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An Improved Approach to Identify Bacterial Pathogens to Human in Environmental Metagenome

Journal of Microbiology and Biotechnology 2020년 30권 9호 p.1335 ~ 1342
양지훈, Howe Adina, 이재진, Yoo Keun-Je, 박준홍,
소속 상세정보
양지훈 ( Yang Ji-Hoon ) - Yonsei University Department of Civil and Environmental Engineering
 ( Howe Adina ) - Iowa State University Department of Agricultural and Biosystems Engineering
이재진 ( Lee Jae-Jin ) - Iowa State University Department of Agricultural and Biosystems Engineering
 ( Yoo Keun-Je ) - Korea Maritime and Ocean University Department of Environmental Engineering
박준홍 ( Park Joon-Hong ) - Yonsei University Department of Civil and Environmental Engineering

Abstract


The identification of bacterial pathogens to humans is critical for environmental microbial risk assessment. However, current methods for identifying pathogens in environmental samples are limited in their ability to detect highly diverse bacterial communities and accurately differentiate pathogens from commensal bacteria. In the present study, we suggest an improved approach using a combination of identification results obtained from multiple databases, including the multilocus sequence typing (MLST) database, virulence factor database (VFDB), and pathosystems resource integration center (PATRIC) databases to resolve current challenges. By integrating the identification results from multiple databases, potential bacterial pathogens in metagenomes were identified and classified into eight different groups. Based on the distribution of genes in each group, we proposed an equation to calculate the metagenomic pathogen identification index (MPII) of each metagenome based on the weighted abundance of identified sequences in each database. We found that the accuracy of pathogen identification was improved by using combinations of multiple databases compared to that of individual databases. When the approach was applied to environmental metagenomes, metagenomes associated with activated sludge were estimated with higher MPII than other environments (i.e., drinking water, ocean water, ocean sediment, and freshwater sediment). The calculated MPII values were statistically distinguishable among different environments (p<0.05). These results demonstrate that the suggested approach allows more for more accurate identification of the pathogens associated with metagenomes.

키워드

Environmental metagenome; bacterial pathogens; metagenomic pathogen identification; microbial risk assessment

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