AMR-Diag: Neural network based genotype-to-phenotype prediction of resistance towards β-lactams in Escherichia coli and Klebsiella pneumoniae .
| Autor: | Avershina E; Department of Biotechnology, Inland Norway University of Applied Sciences, Holsetgata 22, 2317 Hamar, Norway., Sharma P; Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India., Taxt AM; Department of Biotechnology, Inland Norway University of Applied Sciences, Holsetgata 22, 2317 Hamar, Norway.; Department of Microbiology, Division of Laboratory Medicine, Oslo University Hospital, PB 4956, Nydalen, 0424 Oslo, Norway., Singh H; Informatics, System and Research Management, Indian Council of Medical Research, New Delhi, India., Frye SA; Department of Microbiology, Division of Laboratory Medicine, Oslo University Hospital, PB 4956, Nydalen, 0424 Oslo, Norway., Paul K; Department of Computer Science & Engineering, IIT Delhi, New Delhi, India., Kapil A; Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India., Naseer U; Department of Zoonotic, Food- and Waterborne Infections, 0213 Oslo, Norwegian Institute of Public Health, Oslo, Norway., Kaur P; Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India., Ahmad R; Department of Biotechnology, Inland Norway University of Applied Sciences, Holsetgata 22, 2317 Hamar, Norway.; Institute of Clinical Medicine, Faculty of Health Sciences, UiT - The Arctic University of Norway, Hansine Hansens veg 18, 9019 Tromsø, Norway. |
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| Jazyk: | angličtina |
| Zdroj: | Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2021 Mar 29; Vol. 19, pp. 1896-1906. Date of Electronic Publication: 2021 Mar 29 (Print Publication: 2021). |
| DOI: | 10.1016/j.csbj.2021.03.027 |
| Abstrakt: | Antibiotic resistance poses a major threat to public health. More effective ways of the antibiotic prescription are needed to delay the spread of antibiotic resistance. Employment of sequencing technologies coupled with the use of trained neural network algorithms for genotype-to-phenotype prediction will reduce the time needed for antibiotic susceptibility profile identification from days to hours. In this work, we have sequenced and phenotypically characterized 171 clinical isolates of Escherichia coli and Klebsiella pneumoniae from Norway and India. Based on the data, we have created neural networks to predict susceptibility for ampicillin, 3rd generation cephalosporins and carbapenems. All networks were trained on unassembled data, enabling prediction within minutes after the sequencing information becomes available. Moreover, they can be used both on Illumina and MinION generated data and do not require high genome coverage for phenotype prediction. We cross-checked our networks with previously published algorithms for genotype-to-phenotype prediction and their corresponding datasets. Besides, we also created an ensemble of networks trained on different datasets, which improved the cross-dataset prediction compared to a single network. Additionally, we have used data from direct sequencing of spiked blood cultures and found that AMR-Diag networks, coupled with MinION sequencing, can predict bacterial species, resistome, and phenotype as fast as 1-8 h from the sequencing start. To our knowledge, this is the first study for genotype-to-phenotype prediction: (1) employing a neural network method; (2) using data from more than one sequencing platform; and (3) utilizing sequence data from spiked blood cultures. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2021 The Author(s).) |
| Databáze: | MEDLINE |
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