HDAC inhibitor treatment of hepatoma cells induces both TRAIL-independent apoptosis and restoration of sensitivity to TRAIL

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The role of biofactors in the prevention and treatment of age‐related diseases

The present demographic changes toward an aging society caused a rise in the number of senior citizens and the incidence and burden of age‐related diseases (such as cardiovascular diseases [CVD], cancer, nonalcoholic fatty liver disease [NAFLD], diabetes mellitus, and dementia), of which nearly half is attributable to the population ≥60 years of age. Deficiencies in individual nutrients have been associated with increased risks for age‐related diseases and high intakes and/or blood concentrations with risk reduction. Nutrition in general and the dietary intake of essential and nonessential biofactors is a major determinant of human health, the risk to develop age‐related diseases, and ultimately of mortality in the older population. These biofactors can be a cost‐effective strategy to prevent or, in some cases, even treat age‐related diseases. Examples reviewed herein include omega‐3 fatty acids and dietary fiber for the prevention of CVD, α‐tocopherol (vitamin E) for the treatment of biopsy‐proven nonalcoholic steatohepatitis, vitamin D for the prevention of neurodegenerative diseases, thiamine and α‐lipoic acid for the treatment of diabetic neuropathy, and the role of folate in cancer epigenetics. This list of potentially helpful biofactors in the prevention and treatment of age‐related diseases, however, is not exhaustive and many more examples exist. Furthermore, since there is currently no generally accepted definition of the term biofactors , we here propose a definition that, when adopted by scientists, will enable a harmonization and consistent use of the term in the scientific literature

6- and 8-Prenylnaringenin, Novel Natural Histone Deacetylase Inhibitors Found in Hops, Exert Antitumor Activity on Melanoma Cells

Background/Aims: Prenylnaringenins are natural prenylflavonoids with anticancer properties. However, the underlying mechanisms have not been elucidated yet. Here we report a novel mode of action of 6- and 8-prenylnaringenin (PN) on human melanoma cells: Inhibition of cellular histone deacetylases (HDACs). Methods: We performed in silico and in vitro analyses using 6-PN or 8-PN to study a possible interaction of 6-PN or 8-PN with HDAC as well as Western blot and FACS analyses, real-time cell proliferation and cell viability assays to assess the impact of 6-PN and 8-PN on human metastatic melanoma cells. Results: In silico, 6-PN and 8-PN fit into the binding pocket of HDAC2, 4, 7 and 8, binding to the zinc ion of their catalytic center that is essential for enzymatic activity. In vitro, 100 µmol/L of 6-PN or 8-PN inhibited all 11 conserved human HDAC of class I, II and IV. In clinical oncology HDAC inhibitors are currently investigated as new anticancer compounds. In line, treatment of SK-MEL-28 cells with 6-PN or 8-PN induced a hyperacetylation of histone complex H3 within 2 h. Further, 6-PN or 8-PN mediated a prominent, dose-dependent reduction of cellular proliferation and viability of SK-MEL-28 and BLM melanoma cells. This effect was apoptosis-independent and accompanied by down-regulation of mTOR-specific pS6 protein via pERK/pP90 in SK-MEL-28 cells. Conclusion: The identification of a broad inhibitory capacity of 6-PN and 8-PN for HDAC enzymes with antiproliferative effects on melanoma cells opens the perspective for clinical application as novel anti-melanoma drugs and the usage as innovative lead structures for chemical modification to enhance pharmacology or inhibitory activities

Functional and informatics analysis enables glycosyltransferase activity prediction

The elucidation and prediction of how changes in a protein result in altered activities and selectivities remain a major challenge in chemistry. Two hurdles have prevented accurate family-wide models: obtaining (i) diverse datasets and (ii) suitable parameter frameworks that encapsulate activities in large sets. Here, we show that a relatively small but broad activity dataset is sufficient to train algorithms for functional prediction over the entire glycosyltransferase superfamily 1 (GT1) of the plant Arabidopsis thaliana. Whereas sequence analysis alone failed for GT1 substrate utilization patterns, our chemical–bioinformatic model, GT-Predict, succeeded by coupling physicochemical features with isozyme-recognition patterns over the family. GT-Predict identified GT1 biocatalysts for novel substrates and enabled functional annotation of uncharacterized GT1s. Finally, analyses of GT-Predict decision pathways revealed structural modulators of substrate recognition, thus providing information on mechanisms. This multifaceted approach to enzyme prediction may guide the streamlined utilization (and design) of biocatalysts and the discovery of other family-wide protein functions