In vitro and in silico prediction of antibacterial interaction between essential oils via graph embedding approach.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 11 2023
02 11 2023
Historique:
received:
21
06
2023
accepted:
31
10
2023
medline:
6
11
2023
pubmed:
3
11
2023
entrez:
3
11
2023
Statut:
epublish
Résumé
Essential oils contain a variety of volatile metabolites, and are expected to be utilized in wide fields such as antimicrobials, insect repellents and herbicides. However, it is difficult to foresee the effect of oil combinations because hundreds of compounds can be involved in synergistic and antagonistic interactions. In this research, it was developed and evaluated a machine learning method to classify types of (synergistic/antagonistic/no) antibacterial interaction between essential oils. Graph embedding was employed to capture structural features of the interaction network from literature data, and was found to improve in silico predicting performances to classify synergistic interactions. Furthermore, in vitro antibacterial assay against a standard strain of Staphylococcus aureus revealed that four essential oil pairs (Origanum compactum-Trachyspermum ammi, Cymbopogon citratus-Thujopsis dolabrata, Cinnamomum verum-Cymbopogon citratus and Trachyspermum ammi-Zingiber officinale) exhibited synergistic interaction as predicted. These results indicate that graph embedding approach can efficiently find synergistic interactions between antibacterial essential oils.
Identifiants
pubmed: 37919469
doi: 10.1038/s41598-023-46377-5
pii: 10.1038/s41598-023-46377-5
pmc: PMC10622510
doi:
Substances chimiques
Oils, Volatile
0
Anti-Bacterial Agents
0
Insect Repellents
0
Plant Oils
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
18947Informations de copyright
© 2023. The Author(s).
Références
Front Pharmacol. 2022 Jan 28;12:814858
pubmed: 35153767
New Phytol. 2013 Apr;198(1):16-32
pubmed: 23383981
Sci Rep. 2020 Dec 7;10(1):21349
pubmed: 33288845
Curr Med Chem. 2003 May;10(10):813-29
pubmed: 12678685
Mol Syst Biol. 2011 Mar 1;7:472
pubmed: 21364574
Genome Res. 2003 Nov;13(11):2498-504
pubmed: 14597658
Front Genet. 2019 May 01;10:381
pubmed: 31118945
Chem Biodivers. 2018 Dec;15(12):e1800405
pubmed: 30362637
Food Chem Toxicol. 2008 Feb;46(2):446-75
pubmed: 17996351
Bioinformatics. 2020 Feb 15;36(4):1241-1251
pubmed: 31584634
Cancer. 1950 Jan;3(1):32-5
pubmed: 15405679
Chem Biodivers. 2021 Nov;18(11):e2100345
pubmed: 34533273
Comput Struct Biotechnol J. 2022 Jun 15;20:3223-3233
pubmed: 35832624
Molecules. 2012 Apr 02;17(4):3989-4006
pubmed: 22469594
Evid Based Complement Alternat Med. 2015;2015:561024
pubmed: 26457111
PLoS One. 2023 May 15;18(5):e0285716
pubmed: 37186641
Front Microbiol. 2012 Jan 25;3:12
pubmed: 22291693
Nucleic Acids Res. 2023 Jan 6;51(D1):D29-D38
pubmed: 36370100
Crit Rev Microbiol. 2014 Feb;40(1):76-94
pubmed: 23445470
Front Genet. 2021 Jun 21;12:680117
pubmed: 34234813
KDD. 2016 Aug;2016:855-864
pubmed: 27853626
Front Pharmacol. 2022 Aug 24;13:956541
pubmed: 36091825
Planta Med. 2011 Jul;77(11):1168-82
pubmed: 21283954
Front Pharmacol. 2021 Jan 08;11:586548
pubmed: 33488385
Phytother Res. 2014 Oct;28(10):1423-46
pubmed: 24831562
Appl Environ Microbiol. 2002 Apr;68(4):1561-8
pubmed: 11916669