임한혁 (서울대학교) | 한빛사논문 > 한빛사

한빛사논문

조회1,487

서울대학교

Proc. Natl. Acad. Sci. USA, May 18, 2021, 118 (20) : e2018877118 | https://doi.org/10.1073/pnas.2018877118 Pathogenic potential assessment of the Shiga toxin–producing Escherichia coli by a source attribution–considered machine learning model

Hanhyeok Im^a,b, Seung-Ho Hwang^a,b, Byoung Sik Kim^c, and Sang Ho Choi^a,b,d,1

^aNational Research Laboratory of Molecular Microbiology and Toxicology, Seoul National University, 08826 Seoul, Republic of Korea; ^bDepartment of Agricultural Biotechnology and Center for Food Safety and Toxicology, Seoul National University, 08826 Seoul, Republic of Korea; ^cDepartment of Food Science and Engineering, Ewha Womans University, 03760 Seoul, Republic of Korea; and ^dCenter for Food and Bioconvergence, Seoul National University, 08826 Seoul, Republic of Korea

¹To whom correspondence may be addressed. Email: choish@snu.ac.kr

Abstract

Instead of conventional serotyping and virulence gene combination methods, methods have been developed to evaluate the pathogenic potential of newly emerging pathogens. Among them, the machine learning (ML)–based method using whole-genome sequencing (WGS) data are getting attention because of the recent advances in ML algorithms and sequencing technologies. Here, we developed various ML models to predict the pathogenicity of Shiga toxin–producing Escherichia coli (STEC) isolates using their WGS data. The input dataset for the ML models was generated using distinct gene repertoires from positive (pathogenic) and negative (nonpathogenic) control groups in which each STEC isolate was designated based on the source attribution, the relative risk potential of the isolation sources. Among the various ML models examined, a model using the support vector machine (SVM) algorithm, the SVM model, discriminated between the two control groups most accurately. The SVM model successfully predicted the pathogenicity of the isolates from the major sources of STEC outbreaks, the isolates with the history of outbreaks, and the isolates that cannot be assessed by conventional methods. Furthermore, the SVM model effectively differentiated the pathogenic potentials of the isolates at a finer resolution. Permutation importance analyses of the input dataset further revealed the genes important for the estimation, proposing the genes potentially essential for the pathogenicity of STEC. Altogether, these results suggest that the SVM model is a more reliable and broadly applicable method to evaluate the pathogenic potential of STEC isolates compared with conventional methods.

STEC, machine learning, risk assessment, pathogenic potential

논문정보

형식Research article
게재일2021년 05월 (BRIC 등록일 2021-05-24)
연구진국내 연구진
분야 생명과학 > 미생물학

댓글 작성 로그인

CV 열람하기

연락처 보기