期刊
FRONTIERS IN MICROBIOLOGY
卷 13, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2022.839718
关键词
drug resistance; convolutional neural network; transmission electron microscope; identification; Escherichia coli; quinolone; enoxacin; LPP
类别
资金
- MEXT (Ministry of Education, Culture, Sports, Science and Technology of Japan)/JSPS (Japan Society for the Promotion of Science) KAKENHI [17H06422, 17H03983, 18K19451, 21H03542]
- Naito Foundation
- Takeda Science Foundation
- Network Joint Research Center for Materials and Devices
- Dynamic Alliance for Open Innovation Bridging Human
- Center of Innovation Program and Core Research for Evolutional Science and Technology [JPMJCR20H9]
- Japan Science and Technology Agency, JST
- [CR-19-05]
- [17K08827]
- Grants-in-Aid for Scientific Research [17H06422, 17H03983, 18K19451, 21H03542] Funding Source: KAKEN
This study utilizes a computational method to identify antibiotic-resistant bacteria cells without the use of antibiotics. By classifying TEM images of enoxacin-sensitive and enoxacin-resistant Escherichia coli cells using a convolutional neural network, the method accurately identifies resistant cells and distinguishes them based on differences in the envelope. The study also identifies four genes strongly associated with the image features of enoxacin-resistant cells.
The emergence of bacteria that are resistant to antibiotics is common in areas where antibiotics are used widely. The current standard procedure for detecting bacterial drug resistance is based on bacterial growth under antibiotic treatments. Here we describe the morphological changes in enoxacin-resistant Escherichia coli cells and the computational method used to identify these resistant cells in transmission electron microscopy (TEM) images without using antibiotics. Our approach was to create patches from TEM images of enoxacin-sensitive and enoxacin-resistant E. coli strains, use a convolutional neural network for patch classification, and identify the strains on the basis of the classification results. The proposed method was highly accurate in classifying cells, achieving an accuracy rate of 0.94. Using a gradient-weighted class activation mapping to visualize the region of interest, enoxacin-resistant and enoxacin-sensitive cells were characterized by comparing differences in the envelope. Moreover, Pearson's correlation coefficients suggested that four genes, including lpp, the gene encoding the major outer membrane lipoprotein, were strongly associated with the image features of enoxacin-resistant cells.
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