Comparative Analysis of the Evolution of Green Leaf Volatiles and Aroma in Six Vitis vinifera L. Cultivars during Berry Maturation in the Chinese Loess Plateau Region.

Green leaf volatiles (GLVs) are important in giving grape a fresh and green aroma. But the changes in GLVs during the phenological development of grapevines are not well known. This study analyzed the GLVs and transcription levels of associated biosynthetic genes in six grape species from the Loess Plateau region at five stages of maturation. Thirteen GLVs were detected, showing unique patterns for each grape type at various growth phases. The primary components in six grapes were (E)-2-hexenal, (E)-2-hexen-1-ol, and hexanal. With the exception of Cabernet Franc in 2019, the overall GLV contents of the six types generally increased during growth and development, peaking or stabilizing at harvest. And Sauvignon Blanc, Cabernet Gernischt, and Cabernet Sauvignon exhibited higher total contents among the varieties. PLS-DA analysis revealed 3-hexenal's high VIP scores across two years, underscoring its critical role in grape variety classification. Correlation analysis revealed a strong positive correlation between the levels of hexanal, 1-hexanol, (E)-2-hexen-1-ol, (Z)-3-hexenyl acetate, nonanal, and (E, E)-2,6-nonadienal and the expression of VvHPL and VvAAT genes in the LOX-HPL pathway. Specifically, VvHPL emerges as a potential candidate gene responsible for species-specific differences in GLV compounds. Comprehending the changing patterns in the biosynthesis and accumulation of GLVs offers viticulturists and enologists the opportunity to devise targeted strategies for improving the aromatic profile of grapes and wines.

A General Method to Access Sterically Encumbered Geminal Bis(boronates) via Formal Umpolung Transformation of Terminal Diboron Compounds.

General methods for the preparation of geminal bis(boronates) are of great interest due to their widespread applications in organic synthesis. While the terminal gem-diboron compounds are readily accessible, the construction of the sterically encumbered, internal analogues has remained a prominent challenge. Herein, we report a formal umpolung strategy to access these valuable building blocks. The readily available 1,1-diborylalkanes were first converted into the corresponding α-halogenated derivatives, which then serve as electrophilic components, undergoing a formal substitution with a diverse array of nucleophiles to form a series of C-C, C-O, C-S, and C-N bonds. This protocol features good tolerance to steric hindrance and a wide variety of functional groups and heterocycles. Notably, this strategy can also be extended to the synthesis of diaryl and terminal gem-diboron compounds, therefore providing a general approach to various types of geminal bis(boronates).

Facile Access to Cyclopropylboronates via Stereospecific Deborylative Cyclization: A Leaving Group-Assisted Activation of Geminal Diborons.

Herein we reported a transition metal-free deborylative cyclization strategy, based on which two routes have been developed, generating racemic and enantioenriched cyclopropylboronates. The cyclization of geminal-bis(boronates) bearing a leaving group was highly diastereoselective, tolerating a few functional groups and applicable to heterocycles. When optically active epoxides were used as the starting materials, enantioenriched cyclopropylboronates could be efficiently prepared with >99 % stereospecificity. Mechanistic studies showed that the leaving group at the γ-position played a crucial role and significantly promoted the activation of the gem-diboron moiety.

A novel approach to analyze the association characteristics between post-spliced introns and their corresponding mRNA.

Studies have shown that post-spliced introns promote cell survival when nutrients are scarce, and intron loss/gain can influence many stages of mRNA metabolism. However, few approaches are currently available to study the correlation between intron sequences and their corresponding mature mRNA sequences. Here, based on the results of the improved Smith-Waterman local alignment-based algorithm method (SW method) and binding free energy weighted local alignment algorithm method (BFE method), the optimal matched segments between introns and their corresponding mature mRNAs in Caenorhabditis elegans (C.elegans) and their relative matching frequency (RF) distributions were obtained. The results showed that although the distributions of relative matching frequencies on mRNAs obtained by the BFE method were similar to the SW method, the interaction intensity in 5'and 3'untranslated regions (UTRs) regions was weaker than the SW method. The RF distributions in the exon-exon junction regions were comparable, the effects of long and short introns on mRNA and on the five functional sites with BFE method were similar to the SW method. However, the interaction intensity in 5'and 3'UTR regions with BFE method was weaker than with SW method. Although the matching rate and length distribution shape of the optimal matched fragment were consistent with the SW method, an increase in length was observed. The matching rates and the length of the optimal matched fragments were mainly in the range of 60%-80% and 20-30bp, respectively. Although we found that there were still matching preferences in the 5'and 3'UTR regions of the mRNAs with BFE, the matching intensities were significantly lower than the matching intensities between introns and their corresponding mRNAs with SW method. Overall, our findings suggest that the interaction between introns and mRNAs results from synergism among different types of sequences during the evolutionary process.

Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits.

Kidney renal clear cell carcinoma (KIRC) is one of the common malignant tumors of the urinary system. Patients with different risk levels are other in terms of disease progression patterns and disease regression. The poorer prognosis for high-risk patients compared to low-risk patients. Therefore, it is essential to accurately high-risk screen patients and gives accurate and timely treatment. Differential gene analysis, weighted correlation network analysis, Protein-protein interaction network, and univariate Cox analysis were performed sequentially on the train set. Next, the KIRC prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO), and the Cancer Genome Atlas (TCGA) test set and the Gene Expression Omnibus dataset verified the model's validity. Finally, the constructed models were analyzed; including gene set enrichment analysis (GSEA) and immune analysis. The differences in pathways and immune functions between the high-risk and low-risk groups were observed to provide a reference for clinical treatment and diagnosis. A four-step key gene screen resulted in 17 key factors associated with disease prognosis, including 14 genes and 3 clinical features. The LASSO regression algorithm selected the seven most critical key factors to construct the model: age, grade, stage, GDF3, CASR, CLDN10, and COL9A2. In the training set, the accuracy of the model in predicting 1-, 2- and 3-year survival rates was 0.883, 0.819, and 0.830, respectively. The accuracy of the TCGA dataset was 0.831, 0.801, and 0.791, and the accuracy of the GSE29609 dataset was 0.812, 0.809, and 0.851 in the test set. Model scoring divided the sample into a high-risk group and a low-risk group. There were significant differences in disease progression and risk scores between the two groups. GSEA analysis revealed that the enriched pathways in the high-risk group mainly included proteasome and primary immunodeficiency. Immunological analysis showed that CD8 (+) T cells, M1 macrophages, PDCD1, and CTLA4 were upregulated in the high-risk group. In contrast, antigen-presenting cell stimulation and T-cell co-suppression were more active in the high-risk group. This study added clinical characteristics to constructing the KIRC prognostic model to improve prediction accuracy. It provides help to assess the risk of patients more accurately. The differences in pathways and immunity between high and low-risk groups were also analyzed to provide ideas for treating KIRC patients.

Bioinformatics Analysis Identifies IL6ST as a Potential Tumor Suppressor Gene for Triple-Negative Breast Cancer.

Improved insight into the molecular mechanisms of triple-negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study was to identify genes significantly associated with TNBC and further analyze their prognostic significance. The Cancer Genome Atlas (TCGA) TNBC database and gene expression profiles of GSE76275 from Gene Expression Omnibus (GEO) were used to explore differentially co-expressed genes in TNBC compared with those in normal tissues and non-TNBC breast cancer tissues. Differential gene expression and weighted gene co-expression network analyses identified 24 differentially co-expressed genes. Functional annotation suggested that these genes were primarily enriched in processes such as metabolism, membrane, and protein binding. The protein-protein interaction (PPI) network further identified ten hub genes, five of which (MAPT, CBS, SOX11, IL6ST, and MEX3A) were confirmed to be differentially expressed in an independent dataset (GSE38959). Moreover, CBS and MEX3A expression was upregulated, whereas IL6ST expression was downregulated in TNBC tissues compared to that in other breast cancer subtypes. Furthermore, lower expression of IL6ST was associated with worse overall survival in patients with TNBC. Thus, IL6ST might play an important role in TNBC progression and could serve as a tumor suppressor gene for diagnosis and treatment.

Key Genes and Prognostic Analysis in HER2+ Breast Cancer.

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes (CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis.

BACKGROUND: Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. METHODS: We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein-protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. RESULTS: We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein-protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. CONCLUSIONS: NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China.

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord [Formula: see text] method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method's ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.

Effect of rain-shelter cultivation of Vitis vinifera cv. Cabernet Gernischet on the phenolic profile of berry skins and the incidence of grape diseases.

Rain-shelter cultivation is an effective cultural method to prevent rainfall damage during grape harvest and widely applied in the Chinese rainy regions. In this study we investigated the effect of rain-shelter cultivation on grape diseases and phenolic composition in the skins of Vitis vinifera cv. Cabernet Gernischet grape berries through the comparison with open-field cultivation at two vintages (2010 and 2011). The results showed that rain-shelter cultivation reduced the incidence of grape diseases significantly and delayed the maturation of Cabernet Gernischet fruits. With regards to most of the phenolic compounds identified in this study, their content in grape samples under rain-shelter cultivation was decreased compared to those under open-field cultivation. However, rain-shelter cultivation stimulated the accumulation of dihydroquercetin-3-O-rhamnoside in grape skins during grape maturation. These were related with micrometeorological alterations in vineyards by using plastic covering under rain-shelter cultivation. It suggests the rain-shelter cultivation makes possible the cultivation of "Cabernet Gernischet" grapes in an organic production system, for providing a decrease in the incidence of diseases and the dependence on chemical pesticides in the grape and wine industry.