Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis

Introduction

Raspberries are becoming increasingly popular due to their reported health beneficial properties. Despite the presence of only trace amounts of anthocyanins, yellow varieties seems to show similar or better effects in comparison to conventional raspberries.

Objectives

The aim of this work is to characterize the metabolic differences between red and yellow berries, focussing on the compounds showing a higher concentration in yellow varieties.

Methods

The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC–TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC–MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333).

Results

Among the metabolites showing higher concentration in yellow raspberries it was possible to identify a series of compounds showing a pseudospectrum similar to that of A-type procyanidin polymers. The annotation of this group of compounds was confirmed by specific MS/MS experiments and performing standard injections.

Conclusions

In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers.

     

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.     

Regional Activation of Myosin II in Cancer Cells Drives Tumor Progression via a Secretory Cross-Talk with the Immune Microenvironment

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Phenolic and anthocyanin fractions from wild blueberries (V. angustifolium) differentially modulate endothelial cell migration partially through RHOA and RAC1

The present study investigates the effect of anthocyanin (ACN), phenolic acid (PA) fractions, and their combination (ACNs:PAs) from wild blueberry powder (Vaccinum angustifolium) on the speed of endothelial cell migration, gene expression, and protein levels of RAC1 and RHOA associated with acute exposure to different concentrations of ACNs and PAs. Time-lapse videos were analyzed and endothelial cell speed was calculated. Treatment with ACNs at 60 μg/mL inhibited endothelial cell migration rate ( P ≤ 0.05) while treatment with PAs at 0.002 μg/mL ( P ≤ 0.0001), 60 μg/mL ( P ≤ 0.0001), and 120 μg/mL ( P ≤ 0.01) significantly increased endothelial cell migration rate compared with control. Moreover, exposure of HUVECs to ACNs:PAs at 8:8 μg/mL ( P ≤ 0.05) and 60:60 μg/mL increased ( P ≤ 0.001) endothelial cell migration. Gene expression of RAC1 and RHOA significantly increased 2 hours after exposure with all treatments. No effect of the above fractions was observed on the protein levels of RAC1 and RHOA. Findings suggest that endothelial cell migration is differentially modulated based on the type of blueberry extract (ACN or PA fraction) and is concentration-dependent. Future studies should determine the mechanism of the differential action of the above fractions on endothelial cell migration.