Stress-induced nuclear speckle reorganization is linked to activation of immediate early gene splicing.

Current models posit that nuclear speckles (NSs) serve as reservoirs of splicing factors and facilitate posttranscriptional mRNA processing. Here, we discovered that ribotoxic stress induces a profound reorganization of NSs with enhanced recruitment of factors required for splice-site recognition, including the RNA-binding protein TIAR, U1 snRNP proteins and U2-associated factor 65, as well as serine 2 phosphorylated RNA polymerase II. NS reorganization relies on the stress-activated p38 mitogen-activated protein kinase (MAPK) pathway and coincides with splicing activation of both pre-existing and newly synthesized pre-mRNAs.

Insights into the Uptake, Distribution, and Metabolism of 3-Isobutyl-2-hydroxypyrazine in Grapevine Using a Stable Isotope Tracer.

Methoxypyrazines (MPs) are potent aroma compounds that have been predominately studied in grape berries but can also be detected in other vine tissues. The synthesis of MPs in berries from hydroxypyrazines by VvOMT3 is well established, but the origin of MPs in vine tissues that have negligible gene expression is unknown. This research gap was addressed through the application of stable isotope tracer 3-isobutyl-2-hydroxy-[H]-pyrazine (-IBHP) to the roots of Pinot Meunier L1 microvines and high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) quantification of HPs from grapevine tissues following a novel solid-phase extraction method.

Grape Heterogeneity Index: Assessment of Overall Grape Heterogeneity Using an Aggregation of Multiple Indicators.

Uniform grape maturity can be sought by producers to minimise underripe and/or overripe proportions of fruit and limit any undesirable effects on wine quality. Considering that grape heterogeneity is a multifaceted phenomenon, a composite index summarising overall grape heterogeneity was developed to benefit vineyard management and harvest date decisions. A grape heterogeneity index (GHI) was constructed by aggregating the sum of absolute residuals multiplied by the range of values from measurements of total soluble solids, pH, fresh weight, total tannins, absorbance at 520 nm (red colour), 3-isobutyl-2-methoxypyrazine, and malic acid.

Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine.

Generations of sensors have been developed for predicting food sensory profiles to circumvent the use of a human sensory panel, but a technology that can rapidly predict a suite of sensory attributes from one spectral measurement remains unavailable. Using spectra from grape extracts, this novel study aimed to address this challenge by exploring the use of a machine learning algorithm, extreme gradient boosting (XGBoost), to predict twenty-two wine sensory attribute scores from five sensory stimuli: aroma, colour, taste, flavour, and mouthfeel. Two datasets were obtained from absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy with different fusion methods: variable-level data fusion of absorbance and fluorescence spectral fingerprints, and feature-level data fusion of A-TEEM and CIELAB datasets.

Methoxypyrazine concentrations in the grape bunch rachis of Vitis vinifera L. Cv Shiraz: Influence of rootstock, region and light.

Vitis vinifera L. cv Shiraz appears unable to synthesise 3-alkyl-2-methoxypyrazines (MPs) in the berry, but can still produce significant concentrations in rachis. MPs are readily extracted from rachis during fermentation, producing Shiraz wines with uncharacteristic "green" flavours.