Pleurotus ferulae is an edible and medicinal mushroom with various bioactivities. Here, the ethanol extracts of wild and cultivated P. ferulae (PFEE-W and PFEE-C) and their subfractions including petroleum ether (Pe-W/Pe-C), ethyl acetate (Ea-W/Ea-C) and n-butanol (Ba-W/Ba-C) were prepared to evaluate their antioxidant and antitumor activities. Both PFEE-W and PFEE-C show the antioxidant activity and PFEE-W is stronger than PFEE-C. The antioxidant activities of their subfractions are in the following order: Ea > Ba > Pe. Moreover, PFEE-W and PFEE-C significantly inhibit the proliferation of murine melanoma B16 cells, human esophageal cancer Eca-109 cells, human gastric cancer BGC823 cells and human cervical cancer HeLa cells through induction of apoptosis, which partially mediated by reactive oxygen species. The antitumor activities of their subfractions are in the following order: Ea ≥ Pe > Ba. Pe-W shows higher antitumor activity compared with Pe-C, which might be correlated with the difference of their components identified by gas chromatography-mass spectrometry. These results suggest that both wild and cultivated P. ferulae have antioxidant and antitumor activities, and cultivated P. ferulae could be used to replace wild one in some functions.
Background Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications. Methods We investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane. Results We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida. Conclusions We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.