Bioactive polyketides from the pathogenic fungus of Epicoccum sorghinum.

MAIN CONCLUSION: We discovered and identified a series of characteristic substances, including one new polyketide, epicorepoxydon B, of the important pathogenic fungus, Epicoccum sorghinum, of sorghum. The fungal extract and some isolated polyketides are sensitive to a malignant triple-negative breast cancer cell line, MDA-MB-231. Sorghum (Kaoliang) grain is an important crop with high economic value and several applications. In Taiwan, sorghum has been used in the wine industry, and "Kinmen Kaoliang Liquor" is a well-known Asian brand. Fungal contamination is one of the major threats affecting the production of sorghum grain resulting in economic losses as well as human and animal health problems. Several fungal species can infect sorghum grain and generate some toxic secondary metabolites. Epicoccum sorghinum is one of the major fungal contaminants of sorghum grains and a potent producer of mycotoxins such as tenuazonic acid (TeA). However, except for TeA, few studies focused on chemical compounds produced by this fungus. To explore the potential biological and toxic effects of E. sorghinum, a chemical investigation was carried out on the ethyl acetate extract of the fungus because it showed cytotoxic activity against a triple-negative breast cancer cell line, MDA-MB-231 (54.82% inhibition at 20 µg/mL). One new polyketide, epicorepoxydon B (1), along with six known compounds including 4,5-dihydroxy-6-(6'-methylsalicyloxy)-2-hydroxymethyl-2-cyclohexenl-one (2), epicorepoxydon A (3), 3-hydroxybenzyl alcohol (4), 6-methylsalicylic acid (5), gentisyl alcohol (6), and 6-(hydroxymethyl)benzene-1,2,4-triol (7) were obtained, and their structures were established by the interpretation of their MS and NMR spectroscopic data. The cytotoxic activity of all isolated polyketides 1-7 was evaluated, and compounds 2, 6, and 7 exhibited potent activities against A549, HepG2, and MDA-MB-231 human cancer cell lines with IC50 value ranging from 1.86 to 18.31 µM. The structure-activity relationship of the isolated compounds was proposed.

Anti-inflammatory and Antiphytopathogenic Fungal Activity of 2,3-seco-Tirucallane Triterpenoids Meliadubins A and B from Melia dubia Cav. Barks with ChemGPS-NP and In Silico Prediction.

Two new rearranged 2,3-seco-tirucallane triterpenoids, meliadubins A (1) and B (2), along with four known compounds, 3-6, were isolated from the barks of Melia dubia Cav. Compound 2 exhibited a significant inflammatory inhibition effect toward superoxide anion generation in human neutrophils (EC50 at 5.54 ± 0.36 µM). It bound to active sites of a human inducible nitric oxide synthase (3E7G) through interactions with the residues of GLU377 and PRO350, which may benefit in reducing the neutrophilic inflammation effect. The ChemGPS-NP interpretation combined with bioactivity assay and in silico prediction results suggested 2 to be an agent for targeting iNOS with different mechanisms as compared to a selected set of current approved drugs. Moreover, compounds 1 and 2 showed remarkable inhibition against the rice pathogenic fungus Magnaporthe oryzae in a dose-dependent manner with IC50 values of 137.20 ± 9.55 and 182.50 ± 18.27 µM, respectively. Both 1 and 2 displayed interactions with the residue of TYR223, a key active site of trihydroxynaphthalene reductase (1YBV). The interpretation of 1 and 2 in the ChemGPS-NP physical-chemical property space indicated that both compounds are quite different compared to all members of a selected set of reference compounds. In light of demonstrated biological activity and in silico prediction experiments, both compounds possibly exhibited activity against phytopathogenic fungi via a novel mode of action.

Anti-inflammatory, Antiplatelet Aggregation, and Antiangiogenesis Polyketides from Epicoccum sorghinum: Toward an Understating of Its Biological Activities and Potential Applications.

The ethyl acetate extract of an endophyte Epicoccum sorghinum exhibited anti-inflammatory activity at a concentration of <10 µg/mL. By bioassay-guided fractionation, one new compound, named epicorepoxydon A (1), and one unusual bioactive compound, 6-(hydroxymethyl)benzene-1,2,4-triol (6), together with six known compounds, were isolated from E. sorghinum. The structures of all isolates were established by spectroscopic analyses. The relative configuration of 1 was deduced by the NOESY spectrum and its absolute configuration was determined by X-ray single-crystal analysis. The biological activities of all isolates were evaluated using four types of bioassays including cytotoxicity, anti-inflammatory, antiplatelet aggregation, and antiangiogenesis activities. Compounds 4 and 6 showed potent anti-inflammatory activity, compound 2 possessed potent antiplatelet aggregation and antiangiogenesis activities, and compound 6 demonstrated antiangiogenesis activity. This fungal species can cause a human hemorrhagic disorder known as onyalai. In this study, we identified the active components with antiplatelet aggregation and antiangiogenesis activities, which may be related to the hemorrhagic disorder caused by this fungus. Moreover, we proposed a biosynthetic pathway of the isolated polyketide secondary metabolites and investigated their structure-activity relationship (SAR). Our results suggested that E. sorghinum is a potent source of biologically active compounds that can be developed as antiplatelet aggregation and anti-inflammatory agents.

Prediction of protein essentiality by the support vector machine with statistical tests.

Essential proteins include the minimum required set of proteins to support cell life. Identifying essential proteins is important for understanding the cellular processes of an organism. However, identifying essential proteins experimentally is extremely time-consuming and labor-intensive. Alternative methods must be developed to examine essential proteins. There were two goals in this study: identifying the important features and building learning machines for discriminating essential proteins. Data for Saccharomyces cerevisiae and Escherichia coli were used. We first collected information from a variety of sources. We next proposed a modified backward feature selection method and build support vector machines (SVM) predictors based on the selected features. To evaluate the performance, we conducted cross-validations for the originally imbalanced data set and the down-sampling balanced data set. The statistical tests were applied on the performance associated with obtained feature subsets to confirm their significance. In the first data set, our best values of F-measure and Matthews correlation coefficient (MCC) were 0.549 and 0.495 in the imbalanced experiments. For the balanced experiment, the best values of F-measure and MCC were 0.770 and 0.545, respectively. In the second data set, our best values of F-measure and MCC were 0.421 and 0.407 in the imbalanced experiments. For the balanced experiment, the best values of F-measure and MCC were 0.718 and 0.448, respectively. The experimental results show that our selected features are compact and the performance improved. Prediction can also be conducted by users at the following internet address: http://bio2.cse.nsysu.edu.tw/esspredict.aspx.