Abstract
The low rate of discovery and rapid spread of resistant pathogens have made antibiotic discovery a worldwide priority. In cell-based screening, the mechanism of action (MOA) is identified after antimicrobial activity. This increases rediscovery, impairs low potency candidate detection, and does not guide lead optimization. In this study, high-throughput Fourier-transform infrared (FTIR) spectroscopy was used to discriminate the MOA of 14 antibiotics at pathway, class, and individual antibiotic level. For that, the optimal combinations and parametrizations of spectral preprocessing were selected with cross-validated partial least squares discriminant analysis, to which various machine learning algorithms were applied. This coherently resulted in very good accuracies, independently of the algorithms, and at all levels of MOA. Particularly, an ensemble of subspace discriminants predicted the known pathway (98.6%), antibiotic classes (100%), and individual antibiotics (97.8%) with exceptional accuracy, and similar results were obtained for simulated novel MOA. Even at very low concentrations (1 μg/mL) and growth inhibition (15%), over 70% pathway and class accuracy was achieved, suggesting FTIR spectroscopy can probe the grey chemical matter. Prediction of inhibitory effect was also examined, for which a squared exponential Gaussian process regression yielded a root mean square error of 0.33 and a R2 of 0.92, indicating that metabolic alterations leading to growth inhibition are intrinsically reflected on FTIR spectra beyond cell density.
Key points
• Antibiotic MOA and potency estimated with high-throughput FTIR spectroscopy
• Sub-inhibitory MOA identification suggests ability to explore grey chemical matter
• Data analysis optimization improved MOA identification at antibiotic level by 38%
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgments
The present work was partially conducted in the Engineering & Health Laboratory, which results from a collaboration protocol established between Universidade Católica Portuguesa and Instituto Politécnico de Lisboa.
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This study was funded by Fundação para a Ciência e Tecnologia (grant number PTDC/BIO/69242/2006), Agência de Inovação (grant CLARO), and Instituto Politécnico de Lisboa (grants IDI&CA/IPL/2017/DrugsPlatf/ISEL and IDI&CA/IPL/2018/RenalProg/ISEL).
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Conceptualization, B.R.d.C; methodology, B.R.d.C.; software, B.R.d.C.; formal analysis, B.R.d.C.; investigation, B.R.d.C.; writing—original draft preparation, B.R.d.C; writing—review and editing, B.R.d.C, L.P.F., and C.R.C.C.; supervision, L.P.F. and C.R.C.C. All authors have read and agreed to the published version of the manuscript.
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Ribeiro da Cunha, B., Fonseca, L.P. & Calado, C.R.C. Simultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learning. Appl Microbiol Biotechnol 105, 1269–1286 (2021). https://doi.org/10.1007/s00253-021-11102-7
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DOI: https://doi.org/10.1007/s00253-021-11102-7