Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
14 02 2020
14 02 2020
Historique:
received:
30
07
2019
accepted:
30
01
2020
entrez:
16
2
2020
pubmed:
16
2
2020
medline:
13
11
2020
Statut:
epublish
Résumé
Recurrent and chronic respiratory tract infections in cystic fibrosis (CF) patients result in progressive lung damage and represent the primary cause of morbidity and mortality. Staphylococcus aureus (S. aureus) is one of the earliest bacteria in CF infants and children. Starting from early adolescence, patients become chronically infected with Gram-negative non-fermenting bacteria, and Pseudomonas aeruginosa (P. aeruginosa) is the most relevant and recurring. Intensive use of antimicrobial drugs to fight lung infections inevitably leads to the onset of antibiotic resistant bacterial strains. New antimicrobial compounds should be identified to overcome antibiotic resistance in these patients. Recently interesting data were reported in literature on the use of natural derived compounds that inhibited in vitro S. aureus and P. aeruginosa bacterial growth. Essential oils, among these, seemed to be the most promising. In this work is reported an extensive study on 61 essential oils (EOs) against a panel of 40 clinical strains isolated from CF patients. To reduce the in vitro procedure and render the investigation as convergent as possible, machine learning clusterization algorithms were firstly applied to pick-up a fewer number of representative strains among the panel of 40. This approach allowed us to easily identify three EOs able to strongly inhibit bacterial growth of all bacterial strains. Interestingly, the EOs antibacterial activity is completely unrelated to the antibiotic resistance profile of each strain. Taking into account the results obtained, a clinical use of EOs could be suggested.
Identifiants
pubmed: 32060344
doi: 10.1038/s41598-020-59553-8
pii: 10.1038/s41598-020-59553-8
pmc: PMC7021809
doi:
Substances chimiques
Anti-Infective Agents
0
CFTR protein, human
0
Oils, Volatile
0
Cystic Fibrosis Transmembrane Conductance Regulator
126880-72-6
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2653Références
Harris, A. & Argent, B. E. The cystic fibrosis gene and its product CFTR. Semin. Cell. Biol. 4, 37–44 (1993).
doi: 10.1006/scel.1993.1005
Anderson, G. G. Pseudomonas aeruginosa Biofilm Formation in the CF Lung and Its Implications for Therapy. In Sriramulu, D. (ed), Cystic Fibrosis IntechOpen, Rijeka (2012).
Gibson, R. L., Burns, J. L. & Ramsey, B. W. Pathophysiology and management of pulmonary infections in cystic fibrosis. Am. J. Respir. Crit. Care Med. 168, 918–951 (2003).
doi: 10.1164/rccm.200304-505SO
Hauser, A. R., Jain, M., Bar-Meir, M. & McColley, S. A. Clinical significance of microbial infection and adaptation in cystic fibrosis. Clin. Microbiol. Rev. 24, 29–70 (2011).
doi: 10.1128/CMR.00036-10
Malhotra, S., Limoli, D. H., English, A. E., Parsek, M. R. & Wozniak, D. J. Mixed Communities of Mucoid and Nonmucoid Pseudomonas aeruginosa Exhibit Enhanced Resistance to Host Antimicrobials. MBio 9, e00275–18 (2018).
doi: 10.1128/mBio.00275-18
MacKenzie, T. et al. Longevity of patients with cystic fibrosis in 2000 to 2010 and beyond: survival analysis of the Cystic Fibrosis Foundation patient registry. Ann. Intern. Med. 161, 233–241 (2014).
doi: 10.7326/M13-0636
Koo, H., Allan, R. N., Howlin, R. P., Stoodley, P. & Hall-Stoodley, L. Targeting microbial biofilms: current and prospective therapeutic strategies. Nat. Rev. Microbiol. 15, 740–755 (2017).
doi: 10.1038/nrmicro.2017.99
Molchanova, N., Hansen, P. R. & Franzyk, H. Advances in Development of Antimicrobial Peptidomimetics as Potential. Drugs. Molecules 22, E1430 (2017).
doi: 10.3390/molecules22091430
Smith, W. D. et al. Current and future therapies for Pseudomonas aeruginosa infection in patients with cystic fibrosis. FEMS Microbiol Lett 364 (2017).
Patsilinakos, A. et al. Machine Learning Analyses on Data including Essential Oil Chemical Composition and In Vitro Experimental Antibiofilm Activities against Staphylococcus Species. Molecules 24, E890 (2019).
doi: 10.3390/molecules24050890
Artini, M. et al. Antimicrobial and Antibiofilm Activity and Machine Learning Classification Analysis of Essential Oils from Different Mediterranean Plants against Pseudomonas aeruginosa. Molecules 23, E482 (2018).
doi: 10.3390/molecules23020482
Papa, R. et al. Anti-Biofilm Activities from Marine Cold Adapted Bacteria Against Staphylococci and Pseudomonas aeruginosa. Front. Microbiol. 6, 1333 (2015).
doi: 10.3389/fmicb.2015.01333
Perkel, J. M. Pick up Python. Nature 518, 125–126 (2015).
doi: 10.1038/518125a
Cafiso, V. et al. Agr-Genotyping and transcriptional analysis of biofilm-producing Staphylococcus aureus. FEMS Immunol. Med. Microbiol. 51, 220–227 (2007).
doi: 10.1111/j.1574-695X.2007.00298.x
Perez, L. R. R. & Barth, A. L. Biofilm production using distinct media and antimicrobial susceptibility profile of Pseudomonas aeruginosa. Braz. J. Infect. Dis. 15, 301–304 (2011).
pubmed: 21860998
Rousseeuw, P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
doi: 10.1016/0377-0427(87)90125-7
Celebi, M. E., Kingravi, H. A. & Vela, P. A. A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert. Syst. Appl. 40, 200–210 (2013).
doi: 10.1016/j.eswa.2012.07.021
Lopez-Causape, C., Rojo-Molinero, E., Macia, M. D. & Oliver, A. The problems of antibiotic resistance in cystic fibrosis and solutions. Expert. Rev. Respir. Med. 9, 73–88 (2015).
doi: 10.1586/17476348.2015.995640
Dodemont, M. et al. Emergence of livestock-associated MRSA isolated from cystic fibrosis patients: Result of a Belgian national survey. J. Cyst. Fibros. 18, 86–93 (2019).
doi: 10.1016/j.jcf.2018.04.008
Murray, J. L., Kwon, T., Marcotte, E. M. & Whiteley, M. Intrinsic Antimicrobial Resistance Determinants in the Superbug Pseudomonas aeruginosa. MBio 6, e01603–01615 (2015).
doi: 10.1128/mBio.01603-15
Cegelski, L., Marshall, G. R., Eldridge, G. R. & Hultgren, S. J. The biology and future prospects of antivirulence therapies. Nat Rev. Microbiol. 6, 17–27 (2008).
doi: 10.1038/nrmicro1818
Topa, S. H. et al. Cinnamaldehyde disrupts biofilm formation and swarming motility of Pseudomonas aeruginosa. Microbiology. 164, 1087–1097 (2018).
doi: 10.1099/mic.0.000692
Vasireddy, L., Bingle, L. E. H. & Davies, M. S. Antimicrobial activity of essential oils against multidrug-resistant clinical isolates of the Burkholderia cepacia complex. PLoS One. 13, e0201835 (2018).
doi: 10.1371/journal.pone.0201835
Poma, P. et al. Essential Oil Composition of Alluaudia procera and in Vitro Biological Activity on Two Drug-Resistant Models. Molecules. 24, E2871 (2019).
doi: 10.3390/molecules24162871
Karumathil, D. P., Nair, M. S., Gaffney, J., Kollanoor-Johny, A. & Venkitanarayanan, K. Trans-Cinnamaldehyde and Eugenol Increase Acinetobacter baumannii Sensitivity to Beta-Lactam Antibiotics. Front. Microbiol. 23, 1011 (2018).
doi: 10.3389/fmicb.2018.01011
Rosato, A. et al. Elucidation of the synergistic action of Mentha Piperita essential oil with common antimicrobials. PLoS One. 13, e0200902 (2018).
doi: 10.1371/journal.pone.0200902
Tetard, A., Zedet, A., Girard, C., Plésiat, P. & Llanes, C. Cinnamaldehyde Induces Expression of Efflux Pumps and Multidrug Resistance in Pseudomonas aeruginosa. Antimicrob Agents Chemother. 63, e01081–19 (2019).
doi: 10.1128/AAC.01081-19
Mikkelsen, H., McMullan, R. & Filloux, A. The Pseudomonas aeruginosa reference strain PA14 displays increased virulence due to a mutation in ladS. PLoS One 6, e29113 (2011).
doi: 10.1371/journal.pone.0029113
Kerem, E., Conway, S., Elborn, S., Heijerman, H. & Consensus, C. Standards of care for patients with cystic fibrosis: a European consensus. J. Cyst. Fibros. 4, 7–26 (2005).
doi: 10.1016/j.jcf.2004.12.002
Levin, T. P., Suh, B., Axelrod, P., Truant, A. L. & Fekete, T. Potential clindamycin resistance in clindamycin-susceptible, erythromycin-resistant Staphylococcus aureus: report of a clinical failure. Antimicrob. Agents. Chemother. 49, 1222–1224 (2005).
doi: 10.1128/AAC.49.3.1222-1224.2005
Palzkill, T. Metallo-beta-lactamase structure and function. Ann N Y Acad Sci 1277, 91–104 (2013).
doi: 10.1111/j.1749-6632.2012.06796.x
Meletis, G. & Bagkeri, M. Pseudomonas aeruginosa: Multi-Drug-Resistance Development and Treatment Options. In Basak, S. (ed), Cystic Fibrosis IntechOpen, Rijeka (2013).
Humphries, R. M. et al. CLSI Methods Development and Standardization Working Group Best Practices for Evaluation of Antimicrobial Susceptibility Tests. J. Clin. Microbiol. 56, 10 (2018).
Kluyver, T. et al. Jupyter development t. Jupyter Notebooks? a publishing format for reproducible computational workflows, p 87–90. In Loizides, F., Scmidt, B. (ed), IOS Press (2016).
McKinney, W. Data Structures for Statistical Computing in Python, p 51–56. In Millman SvdWaJ (ed) (2010).
Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. J.Mach. Learn.Res. 12, 2825–2830 (2011).
Hunter, J. D. Matplotlib: A 2D graphics environment. Computing in Science & Engineering 9, 90–95 (2007).
doi: 10.1109/MCSE.2007.55