Structure-based chemical ontology improves chemometric prediction of antibacterial essential oils.

Plants are valuable resources for drug discovery as they produce diverse bioactive compounds. However, the chemical diversity makes it difficult to predict the biological activity of plant extracts via conventional chemometric methods. In this research, we propose a new computational model that integrates chemical composition data with structure-based chemical ontology. For a model validation, two training datasets were prepared from literature on antibacterial essential oils to classify active/inactive oils. Random forest classifiers constructed from the data showed improved prediction performance in both test datasets. Prior feature selection using hierarchical information criterion further improved the performance. Furthermore, an antibacterial assay using a standard strain of Staphylococcus aureus revealed that the classifier correctly predicted the activity of commercially available oils with an accuracy of 83% (= 10/12). The results of this study indicate that machine learning of chemical composition data integrated with chemical ontology can be a highly efficient approach for exploring bioactive plant extracts.

In vitro and in silico prediction of antibacterial interaction between essential oils via graph embedding approach.

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.

Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space.

Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screening strategy that classifies bioactive compounds and plants in semantic space generated by word embedding algorithm. The classifier showed good performance in binary (presence/absence of bioactivity) classification for both compounds and plant genera. Furthermore, the strategy led to the discovery of antimicrobial activity of essential oils from Lindera triloba and Cinnamomum sieboldii against Staphylococcus aureus. The results of this study indicate that machine-learning classification in semantic space can be a highly efficient approach for exploring bioactive plant extracts.

Prophylactic Oral Administration of Magnesium Ameliorates Dextran Sulfate Sodium-Induced Colitis in Mice through a Decrease of Colonic Accumulation of P2X7 Receptor-Expressing Mast Cells.

The number of patients with colitis has been increasing year by year. Recently, intestinal inflammation, as one of the factors for its onset, has been demonstrated to be induced by P2X7 receptor-mediated activation of colonic immune cells such as mast cells. Activation of P2X7 receptor (P2X7R) is known to be inhibited by divalent metal cations such as magnesium, but whether or not magnesium administration prevents/relieves colitis is unknown so far. Here, we report that oral (per os (p.o.)) administration of MgCl2 and ingestion of commercially available magnesium-rich mineral hard water relieves dextran sulfate sodium (DSS)-induced colitis in mice. Colitis was induced through ingestion of a 3% (w/v) DSS solution ad libitum for 10 d. Brilliant blue G (BBG, a P2X7R antagonist), MgCl2 or magnesium-rich mineral hard water was administered p.o. to mice via gastric intubation once a day or ad libitum from a day before DSS administration for 11 times or 11 d, respectively. DSS-treated mice exhibited a low disease activity index, a short colon and a high histological score compared to in control mice. As BBG (250 mg/kg, p.o.), administration of a MgCl2 solution (100 or 500 mg/kg, p.o.) and ad libitum ingestion of the magnesium-rich mineral hard water (212 ppm as magnesium) partially, but significantly, attenuated the severity of colitis by decreasing the accumulation of P2X7R-immunopositive mast cells in the colon. Therefore, prophylactic p.o. administration/ingestion of magnesium is considered to be partially effective to protect mice against DSS-induced colitis by inhibiting P2X7R-mediated activation/accumulation of colonic mast cells.

Species Difference in Sensitivity of Human and Mouse P2X7 Receptors to Inhibitory Effects of Divalent Metal Cations.

P2X7 receptor (P2X7R), a purinergic receptor, is involved in pathophysiological events such as inflammation and cell death, and thus is an attractive target for therapeutic approaches. It is reported that divalent metal cations (DMCs) inhibit P2X7R activation and that there are species differences in their inhibitory effects. To extrapolate the findings in experimental animals to humans, these species differences have to be clarified, but species differences in the sensitivity of P2X7R to DMCs between man and mouse have not been demonstrated. Here we performed direct comparison of the inhibitory effects of DMCs on human and mouse P2X7R activation. Cell lines constitutively expressing human and mouse P2X7R were used, and their P2X7R activation was evaluated as means of YO-PRO-1 dye uptake. MgCl2, NiCl2, ZnCl2, CuCl2 and CaCl2 dose-dependently decreased agonist-induced YO-PRO-1 uptake via both human and mouse P2X7Rs. Apparent differences in the inhibitory profiles for NiCl2 and CaCl2 between them were found, and the IC50 values of DMCs were in the order of CaCl2>MgCl2>NiCl2≈ZnCl2>CuCl2 for both human and mouse P2X7Rs. In this study, we demonstrate that human P2X7R exhibits different sensitivity to nickel and calcium compared with the case of the mouse one, while there is no species difference in the sensitivity of their P2X7Rs to magnesium, zinc and copper, suggesting that the effects of magnesium, zinc and copper on P2X7R-associated pathophysiological events in humans might be predicted from those in mice.