The role of enoyl reductase genes in phloridzin biosynthesis in apple

Phloridzin is the predominant polyphenol in apple (Malus× domestica Borkh.) where it accumulates to high concentrations in many tissues including the leaves, bark, roots and fruit. Despite its relative abundance in apple the biosynthesis of phloridzin and other related dihydrochalcones remains only partially understood. The key unidentified enzyme in phloridzin biosynthesis is a putative carbon double bond reductase which is thought to act on p-coumaroyl-CoA to produce the dihydro p-coumaroyl-CoA precursor. A functional screen of six apple enoyl reductase-like (ENRL) genes was carried out using transient infiltration into tobacco and gene silencing by RNA interference (RNAi) in order to determine carbon double bond reductase activity and contribution to foliar phloridzin concentrations. The ENRL-3 gene caused a significant increase in phloridzin concentration when infiltrated into tobacco leaves whilst a second protein ENRL-5, with over 98% amino acid sequence similarity to ENRL-3, showed p-coumaroyl-CoA reductase activity in enzyme assays. Finally, an RNAi study showed that reducing the transcript levels of ENRL-3 in transgenic 'Royal Gala' led to a 66% decrease in the concentration of dihydrochalcones in the leaves in the one available silenced line. Overall these results suggest that ENRL-3, and its close homolog ENRL-5, may contribute to the biosynthesis of phloridzin in apple.

Low-dose curcumin stimulates proliferation, migration and phagocytic activity of olfactory ensheathing cells

One of the promising strategies for neural repair therapies is the transplantation of olfactory ensheathing cells (OECs) which are the glial cells of the olfactory system. We evaluated the effects of curcumin on the behaviour of mouse OECs to determine if it could be of use to further enhance the therapeutic potential of OECs. Curcumin, a natural polyphenol compound found in the spice turmeric, is known for its anti-cancer properties at doses over 10 µM, and often at 50 µM, and it exerts its effects on cancer cells in part by activation of MAP kinases. In contrast, we found that low-dose curcumin (0.5 µM) applied to OECs strikingly modulated the dynamic morphology, increased the rate of migration by up to 4-fold, and promoted significant proliferation of the OECs. Most dramatically, low-dose curcumin stimulated a 10-fold increase in the phagocytic activity of OECs. All of these potently stimulated behavioural characteristics of OECs are favourable for neural repair therapies. Importantly, low-dose curcumin gave a transient activation of p38 kinases, which is in contrast to the high dose curcumin effects on cancer cells in which these MAP kinases tend to undergo prolonged activation. Low-dose curcumin mediated effects on OECs demonstrate cell-type specific stimulation of p38 and ERK kinases. These results constitute the first evidence that low-dose curcumin can modulate the behaviour of olfactory glia into a phenotype potentially more favourable for neural repair and thereby improve the therapeutic use of OECs for neural repair therapies

Efficient regression analysis with ranked-set sampling

This article is motivated by a lung cancer study where a regression model is involved and the response variable is too expensive to measure but the predictor variable can be measured easily with relatively negligible cost. This situation occurs quite often in medical studies, quantitative genetics, and ecological and environmental studies. In this article, by using the idea of ranked-set sampling (RSS), we develop sampling strategies that can reduce cost and increase efficiency of the regression analysis for the above-mentioned situation. The developed method is applied retrospectively to a lung cancer study. In the lung cancer study, the interest is to investigate the association between smoking status and three biomarkers: polyphenol DNA adducts, micronuclei, and sister chromatic exchanges. Optimal sampling schemes with different optimality criteria such as A-, D-, and integrated mean square error (IMSE)-optimality are considered in the application. With set size 10 in RSS, the improvement of the optimal schemes over simple random sampling (SRS) is great. For instance, by using the optimal scheme with IMSE-optimality, the IMSEs of the estimated regression functions for the three biomarkers are reduced to about half of those incurred by using SRS.