{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T14:24:55Z","timestamp":1791555895682,"version":"4.3.4"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001700","name":"MEXT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>\n                    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\n                    <jats:italic>Escherichia coli<\/jats:italic>\n                    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\n                    <jats:italic>E<\/jats:italic>\n                    .\n                    <jats:italic>coli<\/jats:italic>\n                    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\u2019s correlation coefficients suggested that four genes, including\n                    <jats:italic>lpp<\/jats:italic>\n                    , the gene encoding the major outer membrane lipoprotein, were strongly associated with the image features of enoxacin-resistant cells.\n                  <\/jats:p>","DOI":"10.3389\/fmicb.2022.839718","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T05:19:31Z","timestamp":1647321571000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Identification of Bacterial Drug-Resistant Cells by the Convolutional Neural Network in Transmission Electron Microscope Images"],"prefix":"10.3389","volume":"13","author":[{"given":"Mitsuko","family":"Hayashi-Nishino","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kota","family":"Aoki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akihiro","family":"Kishimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuna","family":"Takeuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiko","family":"Fukushima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazushi","family":"Uchida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomio","family":"Echigo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasushi","family":"Yagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mika","family":"Hirose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Iwasaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eitaro","family":"Shin\u2019ya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Washio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chikara","family":"Furusawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunihiko","family":"Nishino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1021\/bi5000564","article-title":"Mechanism of quinolone action and resistance","volume":"53","author":"Aldred","year":"2014","journal-title":"Biochemistry"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1016\/j.cell.2007.03.004","article-title":"Molecular mechanisms of antibacterial multidrug resistance","volume":"128","author":"Alekshun","year":"2007","journal-title":"Cell"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"fny199","DOI":"10.1093\/femsle\/fny199","article-title":"Lpp, the Braun lipoprotein, turns 50-major achievements and remaining issues","volume":"365","author":"Asmar","year":"2018","journal-title":"FEMS Microbiol. Lett."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"e2004303","DOI":"10.1371\/journal.pbio.2004303","article-title":"Communication across the bacterial cell envelope depends on the size of the periplasm","volume":"15","author":"Asmar","year":"2017","journal-title":"PLoS Biol."},{"key":"ref5","article-title":"\u201cDeep neural networks segment neuronal membranes in electron microscopy images,\u201d","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems, Vol. 2.","author":"Cire\u015fan","year":"2012"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1128\/JCM.41.1.5-14.2003","article-title":"Cell wall thickening is a common feature of vancomycin resistance in Staphylococcus aureus","volume":"41","author":"Cui","year":"2003","journal-title":"J. Clin. Microbiol."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.mib.2013.12.005","article-title":"Salmonellae PhoPQ regulation of the outer membrane to resist innate immunity","volume":"17","author":"Dalebroux","year":"2014","journal-title":"Curr. Opin. Microbiol."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1099\/00222615-23-1-83","article-title":"The response of Escherichia coli to ciprofloxacin and norfloxacin","volume":"23","author":"Elliott","year":"1987","journal-title":"J. Med. Microbiol."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1109\/TMI.2016.2613019","article-title":"Residual deconvolutional networks for brain electron microscopy image segmentation","volume":"36","author":"Fakhry","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.copbio.2018.01.026","article-title":"Toward prediction and control of antibiotic-resistance evolution","volume":"54","author":"Furusawa","year":"2018","journal-title":"Curr. Opin. Biotechnol."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1038\/s42003-018-0093-8","article-title":"Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli","volume":"1","author":"Germond","year":"2018","journal-title":"Commun. Biol."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1128\/MMBR.62.4.1371-1414.1998","article-title":"Staphylococcal cell wall: morphogenesis and fatal variations in the presence of penicillin","volume":"62","author":"Giesbrecht","year":"1998","journal-title":"Microbiol. Mol. Biol. Rev."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1146\/annurev.micro.57.030502.090913","article-title":"Multiple sigma subunits and the partitioning of bacterial transcription space","volume":"57","author":"Gruber","year":"2003","journal-title":"Annu. Rev. Microbiol."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1610","DOI":"10.1038\/ncomms2607","article-title":"\u03b2-Lactam antibiotics promote bacterial mutagenesis via an RpoS-mediated reduction in replication fidelity","volume":"4","author":"Gutierrez","year":"2013","journal-title":"Nat. Commun."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1038\/s41592-018-0106-z","article-title":"CDeep3M\u2014plug-and-play cloud-based deep learning for image segmentation","volume":"15","author":"Haberl","year":"2018","journal-title":"Nat. Methods"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1128\/MMBR.66.3.373-395.2002","article-title":"Signal transduction and regulatory mechanisms involved in control of the sigma(S) (RpoS) subunit of RNA polymerase","volume":"66","author":"Hengge-Aronis","year":"2002","journal-title":"Microbiol. Mol. Biol. Rev."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"313","DOI":"10.3389\/fcimb.2019.00313","article-title":"Maintaining integrity under stress: envelope stress response regulation of pathogenesis in gram-negative bacteria","volume":"9","author":"Hews","year":"2019","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1101\/SQB.1968.033.01.077","article-title":"Thermosensitive mutants of E. coli affected in the processes of DNA synthesis and cellular division","volume":"33","author":"Hirota","year":"1968","journal-title":"Cold Spring Harb. Symp. Quant. Biol."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"7619","DOI":"10.1128\/JB.187.22.7619-7630.2005","article-title":"High-resolution visualization of Pseudomonas aeruginosa PAO1 biofilms by freeze-substitution transmission electron microscopy","volume":"187","author":"Hunter","year":"2005","journal-title":"J. Bacteriol."},{"key":"ref20","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015","journal-title":"Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1111\/j.1365-2958.1990.tb00640.x","article-title":"The \u2018Bayer bridges\u2019 confronted with results from improved electron microscopy methods","volume":"4","author":"Kellenberger","year":"1990","journal-title":"Mol. Microbiol."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"D543","DOI":"10.1093\/nar\/gkw1003","article-title":"The EcoCyc database: reflecting new knowledge about Escherichia coli K-12","volume":"45","author":"Keseler","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref23","article-title":"Adam: a method for stochastic optimization","volume-title":"Proceedings 3rd International Conference for Learning Representations.","author":"Kingma","year":"2015"},{"key":"ref24","article-title":"\u201cImageNet classification with deep convolutional neural networks.\u201d","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing System, Vol. 1.","author":"Krizhevsky","year":"2012"},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-62703-776-1","volume-title":"Electron Microscopy: Methods and Protocols.","author":"Kuo","year":"2014"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1016\/j.cell.2014.02.033","article-title":"Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources","volume":"157","author":"Li","year":"2014","journal-title":"Cell"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"5970","DOI":"10.1038\/s41467-020-19713-w","article-title":"High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli","volume":"11","author":"Maeda","year":"2020","journal-title":"Nat. Commun."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"13282","DOI":"10.1038\/s41598-017-13565-z","article-title":"Neural network for nanoscience scanning electron microscope image recognition","volume":"7","author":"Modarres","year":"2017","journal-title":"Sci. Rep."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1002\/biot.201000446","article-title":"Evaluating 13C enrichment data of free amino acids for precise metabolic flux analysis","volume":"6","author":"Mori","year":"2011","journal-title":"Biotechnol. J."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1128\/MMBR.67.4.593-656.2003","article-title":"Molecular basis of bacterial outer membrane permeability revisited","volume":"67","author":"Nikaido","year":"2003","journal-title":"Microbiol. Mol. Biol. Rev."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"8024","DOI":"10.48550\/arXiv.1912.01703","article-title":"PyTorch: an imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"5641","DOI":"10.1128\/JB.00448-09","article-title":"An alternative route for recycling of N-acetylglucosamine from peptidoglycan involves the N-acetylglucosamine phosphotransferase system in Escherichia coli","volume":"191","author":"Plumbridge","year":"2009","journal-title":"J. Bacteriol."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref34","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/ICCV.2017.74","article-title":"\u201cGrad-CAM: visual explanations from deep networks via gradient-based localization,\u201d","volume-title":"2017 IEEE International Conference on Computer Vision (ICCV).","author":"Selvaraju","year":"2017"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"18464","DOI":"10.1074\/jbc.M110.118448","article-title":"Compensations for diminished terminal oxidase activity in Escherichia coli: cytochrome bd-II-mediated respiration and glutamate metabolism","volume":"285","author":"Shepherd","year":"2010","journal-title":"J. Biol. Chem."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"5792","DOI":"10.1038\/ncomms6792","article-title":"Prediction of antibiotic resistance by gene expression profiles","volume":"5","author":"Suzuki","year":"2014","journal-title":"Nat. Commun."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/S0091-679X(08)00409-3","article-title":"Close-to-native ultrastructural preservation by high pressure freezing","volume":"88","author":"Vanhecke","year":"2008","journal-title":"Methods Cell Biol."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"4935","DOI":"10.1128\/jb.169.11.4935-4940.1987","article-title":"Mutant isolation and molecular cloning of mre genes, which determine cell shape, sensitivity to mecillinam, and amount of penicillin-binding proteins in Escherichia coli","volume":"169","author":"Wachi","year":"1987","journal-title":"J. Bacteriol."},{"key":"ref39","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MLHPC.2018.8638633","article-title":"\u201cAutomated labeling of electron microscopy images using deep learning.\u201d","volume-title":"2018 IEEE\/ACM Machine Learning in HPC Environments (MLHPC)","author":"Weber","year":"2018"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1128\/jb.133.3.1419-1426.1978","article-title":"Physiological characterization of an Escherichia coli mutant altered in the structure of murein lipoprotein","volume":"133","author":"Yem","year":"1978","journal-title":"J. Bacteriol."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"2555","DOI":"10.1093\/bioinformatics\/btx188","article-title":"DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation","volume":"33","author":"Zeng","year":"2017","journal-title":"Bioinformatics"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1186\/s12859-017-1757-y","article-title":"A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy","volume":"18","author":"Zhu","year":"2017","journal-title":"BMC Bioinf."}],"container-title":["Frontiers in Microbiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2022.839718\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T05:19:49Z","timestamp":1647321589000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2022.839718\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,15]]},"references-count":42,"alternative-id":["10.3389\/fmicb.2022.839718"],"URL":"https:\/\/doi.org\/10.3389\/fmicb.2022.839718","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.10.19.464925","asserted-by":"object"}]},"ISSN":["1664-302X"],"issn-type":[{"value":"1664-302X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,15]]},"article-number":"839718"}}