Mining candidate genes of grape berry cracking based on high density genetic map

Fruit cracking is a phenomenon in which the peel cracks during grape berry development,which seriously affects the yield and quality of the fruit.However,there are few studies on the mining of candidate genes related to berry cracking.In order to better understand the genetic basis of berry cracking,we used the results of previous quantitative trait locus(QTL)mapping,combined with field surveys of berry-cracking types and the berry-cracking rate,to mine candidate berry-cracking genes.The results showed that three identical QTL loci were detected in two years(2019 and 2020);and three candidate genes were annotated in the QTL interval.In mature berries,the expressions of the candidate genes were more abundant in the cracking-susceptible parent(‘Crimson Seedless’)than in the cracking-resistant parent(‘Muscat Hamburg’).Grape berry cracking is a complex trait controlled by multiple genes,mainly including genes encoding cellulose synthase–like protein H1,glucan endo-1,3-beta-glucosidase 12,and brassinosteroid insensitive 1-associated receptor kinase 1.The high expression of the candidate berry-cracking genes may promote the occurrence of berry cracking.This study helps elucidate the genetic mechanism of grape berry cracking.

Intercellular Symplastic Connection and Isolation of the Unloading Zone in Flesh of the Developing Grape Berry

The ultrastructure and intercellular connection of the sugar unloading zone (i.e. the phloem in the dorsal vascular bundle and the phloem_surrounding the assimilate sink_cells) of grape ( Vitis vinifera× V. labrusca cv. Jingchao) berry was observed via transmission electron microscopy. The results showed that during the early developmental stages of grape berry, numerous plasmodesmata were found in the phloem between sieve element (SE) and companion cell (CC), between SE/CC complexes, between SE/CC complex and phloem parenchyma cell and in between phloem parenchyma cells, which made the phloem a symplastic integration, facilitating sugar unloading from sieve elements into both companion cells and phloem parenchyma cells via a symplastic pathway. On the contrary, there was almost no plasmodesma between phloem and its surrounding flesh photoassimilate sink_cells, neither in between the flesh photoassimilate sink_cells giving rise to a symplastic isolation both between phloem and its surrounding flesh photoassimilate sink_cells, as well as among the flesh photoassimilate sink_cells. This indicated that both the sugar unloading from phloem and postphloem transport of sugars should be mainly via an apoplastic pathway. During the ripening stage, most of the plasmodesmata between SE/CC complex and the surrounding phloem parenchyma cells were shown to be blocked by the electron_opaque globules, and a phenomenon of plasmolysis was found in a number of companion cells, indicating a symplastic isolation between SE/CC complex and its surrounding parenchyma cells during this phase. The symplastic isolation between the whole phloem and its surrounding photoassimilate sink_cells during the early developmental stages shifted to a symplastic isolation within the phloem during the ripening phase, and thus the symplastic pathway of sugar unloading from SE/CC complex during the early development stages should be replaced by a dominant apoplastic unloading pathway from SE/CC complex in concordance.

The Effect of Vineyard Cover Crop on Main Monomeric Phenols of Grape Berry and Wine in Vitis viniferal L. cv. Cabernet Sauvignon

This study was conducted to determine the effect of cover crop inter-row in vineyard on main mono-phenol content of grape berry and wine. Three such cover crops, two perennial legumes （white clover and alfalfa） and a perennial gramineous grass （tall fescue） were sown in vineyard. The main phenolic compounds of mature grape berry and wines vinified under the same conditions were extracted with ethyl acetate and diethyl ether and analyzed by high- performance liquid chromatography （HPLC） by comparing to soil tillage. A total of ten phenolic compounds were identified and quantified in the different grape berry and wines, including nonflavonoids （hydroxybenzoic and hydroxycinnamic acids） and flavonoids （flavanols and flavonols）. The concentration of flavonoid compounds （409.43 to 538.63 mg kg^-1 and 56.16 to 81.30 mg L^-1） was higher than nonflavonoids （76.91 to 98.85 mg kg^-1 and 30.65 to 41.22 mg L^-1） for Cabernet Sauvignon grape and wine under different treatments, respectively. In the flavonoid phenolics, Catechin was the most abundant in the different grapes and wines, accounting for 74.94 to 79.70% and 48.60 to 50.62% of total nonanthocyanin phenolics quantified, respectively. Compared to soil tillage, the sward treatments showed a higher content of main mono-phenol and total nonanthocyanin phenolics in grapes and wines. There were significant differences between two cover crop treatments （tall fescue and white clover） and soil tillage for the content of benzoic acid, salicylic acid, caffeic acid, catechin, and total phenolics in the grape berry （P 〈 0.05 or P〈0.01）. The wine from tall fescue cover crop had the highest gallic acid, caffeic acid and catechin. Cover crop system increased the total nonanthocyanin phenolics of grapes and wines in order of the four treatments： tall fescue, white clover, alfalfa, and soil tillage （control）. Cover crop in vineyard increased total phenols of grape berry and wine, and thus improved the quality of wine evidently.

Transcriptional Analysis of 9-Cis-Epoxycarotenoid Dioxygenase, Glucosyltransferase, 8＇-Hydroxylase and B-Glucosidase Genes that Regulate Abscisic Acid Homeostasis around the Onset of Grape Berry Ripening

The mechanisms for fine-tuning ABA level related to grape berry ripening remain elusive. Here, transcription analysis showed that the mRNA expression level of 9-cis-epoxycarotenoid dioxygenase gene （VvNCED1） increases first, rapidly in mesocarp before the onset of grape berry ripening. After VvNCED1 peaks its expression level, ABA content increases rapidly in mesocarp coupled with an increase in both soluble sugar content and pH value. On the onset of berry ripening, VvNCED1 transcripts decline rapidly to its lowest point, then increases slightly. Whereas, the mRNA expression level of B-glucosidase gene VvBGI, on the whole, increases constantly during grape berry ripening. During berry de-greening, ABA glucosyltransferase （VvGT） and ABA 8＇-hydroxylase （VvCYPI） equilibrate ABA level; during berry coloring-up, VvGT predominantly equilibrates ABA level, namely, the up-regulation of ABA level mainly leads from VvNCED1 and VvBG1 gene high expression; the down-regulation of ABA level leads mainly from VvCYP! transcript level both in ABA content- and developmental phase-dependence manner. In conclusion, our main results show that VvNCED1 and VvBG1 genes are closely related to grape berry ripening.

t-SNE:A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters

In recent years there is a growing importance in using machine learning techniques to improve procedures in precision agriculture:in this work we perform a study on models capable of predicting oenological parameters from hyperspectral images of wine grape berries,a specially relevant topic to boost production tasks for winemakers.Specifically,we explore the capabilities of a novel technique mostly used for visualization,t-Distributed Stochastic Neighbor Embedding(t-SNE),for reducing the dimensionality of the highly complex hyperspectral data and compare its performance with Principal Component Analysis(PCA)method,which despite the introduction of many nonlinear dimensionality reduction techniques over the years,had achieved the best results for real-world data across several studies in literature.Additionally we explore the potential of Kernel t-SNE,an extension to the t-SNE method that allows for the usage of the technique in streaming data or online scenarios.Our results show that,in a direct comparison,t-SNE achieves better metrics than PCA for most of the data sets in this work and that the regressor(Support Vector Regression,SVR)performs better with the t-SNE reduced features as inputs,accomplishing better predictions with lower error rates.Comparing the results with current literature,our shallow learning model paired with t-SNE achieves either better or on par results than those reported,even competing with more advanced models that use deep learning techniques,which should propel the introduction of t-SNE in more studies that require dimensionality reduction.