One mg of each crude extract (of the ten samples under investigation) was weighted using sensitive electric balance (Sartorius, type 1712, Germany) and dissolved in 1 mL HPLC grade methanol then it was analyzed according to Abdelmohsen et al.36 on an Acquity Ultra Performance Liquid Chromatography system coupled to a Synapt G2 HDMS quadrupole time-of-flight hybrid mass spectrometer (Waters, Milford, MA, USA). Chromatographic separation was performed on a BEH C18 column (2.1 × 100 mm, 1.7 µm particle size; Waters, Milford, MA, USA) with a guard column (2.1 × 5 mm, 1.7 µm particle size) and a linear binary solvent gradient of 0–100% eluent B over 6 min at a flow rate of 0.3 mL min–1 using 0.1% formic acid in water (v/v) as solvent A and acetonitrile as solvent B. All reagents were of analytical grade and were purchased (Fisher Scientific, Hemel Hempstead, UK). The injection volume was 2 µL and the column temperature was 40 °C. After chromatographic separation, the metabolites were detected by mass spectrometry using electrospray ionization in the positive mode; the source operated at 120 °C. The electrospray ionization capillary voltage was set to 0.8 kV, the sampling cone voltage was set to 25 V, and nitrogen was used as the desolvation gas (at 350 °C and a flow rate of 800 L h–1) and the cone gas (at a flow rate of 30 L h–1). The mass range for time-of-flight mass spectrometry was set to m/z (mass-to-charge ratio) 50–1200. Ms converter software was used in order to convert the raw data into divided positive and negative ionization files. Obtained files were then subjected to the data mining software MZmine 2.12 (https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-395) for deconvolution, peak picking, alignment, deisotoping, and formula prediction. For the combination of negative and positive ionization mode data files that were generated by MZmine, Excel macros were used. Both negative and positive ionization switch modes were used to include the highest number of metabolites from the investigated methanol extracts subjected to LC–HR-ESIMS analysis. The dereplication was achieved for each m/z ion peak with metabolites recorded in the customized databases based on established parameters (m/z threshold of ± 3 ppm and retention time), consequently, the number of the remaining unknown metabolites in each species was refine. The raw data was processed, aligned and merged into one dataset according to the method previously developed in our lab20,22,37.

The molecular correlation analysis was created via specific application of the cytoscape software (version 3.4.0) as reported in our previous work25. The Expression Correlation app, implemented by Sander Group (Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York City), was used to compute a similarity network from either observation (active fractions) or their corresponding features (m/z) in data matrix. Similarity network is using the Pearson correlation coefficient to link the active fractions (observations correlation network) or their corresponding metabolites (features correlation network). A feature correlation network was created to explore which of the metabolites will be highly correlated with the bioactivity (represented by percentage of viability). The negative correlation threshold was set to 0.7 whereas the positive one was neglected. The network was mapped via organic y files layout, a kind of spring-embedded algorithm.

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