Untargeted Characterization of Chestnut (Castanea sativa Mill.) Shell Polyphenol Extract: A Valued Bioresource for Prostate Cancer Cell Growth Inhibition.
Nunzio Antonio CacciolaAndrea CerratoAnna Laura CapriottiChiara CavaliereMaria D'ApolitoCarmela Maria MontoneSusy PiovesanaGiuseppe SquillaciGianfranco PelusoAldo LaganàPublished in: Molecules (Basel, Switzerland) (2020)
Chestnut seeds are used for fresh consumption and for the industrial preparation of derivatives, such as chestnut flour. During industrial processing, large amounts of by-products are generally produced, such as leaves, flowers, shells and burs. In the present study, chestnut shells were extracted by boiling water in order to obtain polyphenol-rich extracts. Moreover, for the removal or non-phenolic compounds, a separation by preparative reverse phase chromatography in ten fractions was carried out. The richest fractions in terms of phenolic content were characterized by means of untargeted high-resolution mass spectrometric analysis together with a dedicated and customized data processing workflow. A total of 243 flavonoids, phenolic acids, proanthocyanidins and ellagitannins were tentatively identified in the five richest fractions. Due its high phenolic content (450.03 µg GAE per mg of fraction), one tumor cell line (DU 145) and one normal prostate epithelial cell line (PNT2) were exposed to increasing concentration of fraction 3 dry extract for 24, 48 and 72 h. Moreover, for DU 145 cell lines, increase of apoptotic cells and perturbation of cell cycle was demonstrated for the same extract. Those outcomes suggest that chestnut industrial by-products could be potentially employed as a source of bioresources.
Keyphrases
- prostate cancer
- cell cycle
- mass spectrometry
- liquid chromatography
- heavy metals
- wastewater treatment
- high resolution
- oxidative stress
- anti inflammatory
- cell proliferation
- tandem mass spectrometry
- radical prostatectomy
- electronic health record
- high resolution mass spectrometry
- cell death
- cell cycle arrest
- high speed
- machine learning
- metabolic syndrome
- type diabetes
- big data
- simultaneous determination
- high performance liquid chromatography
- artificial intelligence
- deep learning
- glycemic control
- molecularly imprinted