Identification of Transcription Factors of Santalene Synthase Gene Promoters and SaSSY Cis-Elements through Yeast One-Hybrid Screening in Santalum album L.

The main components of sandalwood heartwood essential oil are terpenoids, approximately 80% of which are α-santalol and ß-santalol. In the synthesis of the main secondary metabolites of sandalwood heartwood, the key gene, santalene synthase (SaSSY), can produce α-santalene and ß-santalene by catalyzed (E, E)-FPP. Furthermore, santalene is catalyzed by the cytochrome monooxygenase SaCYP736A167 to form sandalwood essential oil, which then produces a fragrance. However, the upstream regulatory mechanism of the key gene santalene synthase remains unclear. In this study, SaSSY (Sal3G10690) promoter transcription factors and SaSSY cis-elements were screened. The results showed that the titer of the sandalwood cDNA library was 1.75 × 107 CFU/mL, 80% of the inserted fragments identified by PCR were over 750 bp in length, and the positivity rate of the library was greater than 90%. The promoter region of the SaSSY gene was shown to have the structural basis for potential regulatory factor binding. After sequencing and bioinformatics analysis, we successfully obtained 51 positive clones and identified four potential SaSSY transcriptional regulators. Sal6G03620 was annotated as the transcription factor MYB36-like, and Sal8G07920 was annotated as the small heat shock protein HSP20 in sandalwood. Sal1G00910 was annotated as a hypothetical protein of sandalwood. Sal4G10880 was annotated as a homeobox-leucine zipper protein (ATHB-15) in sandalwood. In this study, a cDNA library of sandalwood was successfully constructed using a yeast one-hybrid technique, and the transcription factors that might interact with SaSSY gene promoters were screened. This study provides a foundation for exploring the molecular regulatory mechanism involved in the formation of sandalwood heartwood.

Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system.

BACKGROUND: The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed in the area of digestive endoscopy. To improve the efficiency and accuracy of the endoscopist's Hill classification and assist in incorporating it into routine endoscopy reports and GERD assessment examinations, this study first employed DL to establish a four-category model based on the Hill classification. MATERIALS AND METHODS: A dataset consisting of 3256 GEFV endoscopic images has been constructed for training and evaluation. Furthermore, a new attention mechanism module has been provided to improve the performance of the DL model. Combined with the attention mechanism module, numerous experiments were conducted on the GEFV endoscopic image dataset, and 12 mainstream DL models were tested and evaluated. The classification accuracy of the DL model and endoscopists with different experience levels was compared. RESULTS: 12 mainstream backbone networks were trained and tested, and four outstanding feature extraction backbone networks (ResNet-50, VGG-16, VGG-19, and Xception) were selected for further DL model development. The ResNet-50 showed the best Hill classification performance; its area under the curve (AUC) reached 0.989, and the classification accuracy (93.39%) was significantly higher than that of junior (74.83%) and senior (78.00%) endoscopists. CONCLUSIONS: The DL model combined with the attention mechanism module in this paper demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.

A new attention mechanism module has been proposed and integrated into the DL model.According to our knowledge, this is the first study to establish a four-category DL model based on the Hill grading.The DL model demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.

Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System.

OBJECTIVES: Gastroesophageal reflux disease (GERD) is a complex disease with a high worldwide prevalence. The Los Angeles classification (LA-grade) system is meaningful for assessing the endoscopic severity of GERD. Deep learning (DL) methods have been widely used in the field of endoscopy. However, few DL-assisted researches have concentrated on the diagnosis of GERD. This study is the first to develop a five-category classification DL model based on the LA-grade using explainable artificial intelligence (XAI). MATERIALS AND METHODS: A total of 2081 endoscopic images were used for the development of a DL model, and the classification accuracy of the models and endoscopists with different levels of experience was compared. RESULTS: Some mainstream DL models were utilized, of which DenseNet-121 outperformed. The area under the curve (AUC) of the DenseNet-121 was 0.968, and its classification accuracy (86.7%) was significantly higher than that of junior (71.5%) and experienced (77.4%) endoscopists. An XAI evaluation was also performed to explore the perception consistency between the DL model and endoscopists, which showed meaningful results for real-world applications. CONCLUSIONS: The DL model showed a potential in improving the accuracy of endoscopists in LA-grading of GERD, and it has noticeable clinical application prospects and is worthy of further promotion.

The yellowhorn AGL transcription factor gene XsAGL22 contributes to ABA biosynthesis and drought tolerance in poplar.

Regulation of abscisic acid (ABA) biosynthesis helps plants adapt to drought stress, but the underlying molecular mechanisms are largely unclear. Here, a drought-induced transcription factor XsAGL22 was isolated from yellowhorn (Xanthoceras sorbifolium Bunge). Yeast one-hybrid and electrophoretic mobility shift assays indicated that XsAGL22 can physically bind to the promoters of the ABA biosynthesis-related genes XsNCED6 and XsBG1, and a dual-luciferase assay showed that XsAGL22 activates the promoters of the later two genes. Transient overexpression of XsAGL22 in yellowhorn leaves also increased the expression of XsNCED6 and XsBG1 and increased cellular ABA levels. Finally, heterologous overexpression of XsAGL22 in poplar increased ABA content, reduced stomatal aperture and increased drought resistance. Our results suggest that XsAGL22 is a powerful regulator of ABA biosynthesis and plays a critical role in drought resistance in plants.

[Effect of afforestation modes on soil microbial community and nitrogen functional genes in Hippophae rhamnoides plantation].

The study aimed to assess the effect of different afforestation modes on microbial composition and nitrogen functional genes in soil. Soil samples from a pure Hippophae rhamnoides stand (SS) and three mixed stands, namely, H. rhamnoides and Pinus tabuliformis (SY), H. rhamnoides and Platycladus orientalis (SB), H. rhamnoides and Robinia pseucdoacacia (SC) were selected. The results showed that the total PLFA (TPLFA), bacterial PLFA, gram positive bacterial PLFA (G⁺PLFA) were significantly higher in soil samples from other three stands than those of the pure one. However, no significant difference was found for fungal PLFA among them. The abundance of nifH, amoA, nirK and narG genes were higher in SY and SC than in SS. The TPLFA, G⁺PLFA, gram negative bacterial PLFA (G⁻PLFA), and all of the detected gene abundance were significantly and positively correlated with soil pH, total organic carbon, total nitrogen, ammonium nitrogen and available potassium. Afforestation modes affected indirectly soil microbial composition and functional genes through soil properties. Mixing P. tabuliformis or P. orientalis with H. rhamnoides might be suitable afforestation modes, which might improve soil quality.