The MdBBX22-miR858-MdMYB9/11/12 module regulates proanthocyanidin biosynthesis in apple peel.

Proanthocyanidins (PAs) have antioxidant properties and are beneficial to human health. The fruit of apple (Malus × domestica Borkh.), especially the peel, is rich in various flavonoids, such as PAs, and thus is an important source of dietary antioxidants. Previous research on the regulation of PAs in apple has mainly focussed on the transcription level, whereas studies conducted at the post-transcriptional level are relatively rare. In this study, we investigated the function of mdm-miR858, a miRNA with multiple functions in plant development, in the peel of apple fruit. We showed that mdm-miR858 negatively regulated PA accumulation by targeting MdMYB9/11/12 in the peel. During fruit development, mdm-miR858 expression was negatively correlated with MdMYB9/11/12 expression and PA accumulation. A 5'-RACE experiment, GUS staining assays and transient luminescent assays indicated that mdm-miR858 cleaved and inhibited the expression of MdMYB9/11/12. Overexpression of mdm-miR858 in apple calli, tobacco and Arabidopsis reduced the accumulation of PAs induced by overexpression of MdMYB9/11/12. Furthermore, we found that MdBBX22 bound to the mdm-miR858 promoter and induced its expression. Overexpression of MdBBX22 induced the expression of mdm-miR858 to inhibit the accumulation of PAs in apple calli overexpressing MdMYB9/11/12. Under light stress, MdBBX22 induced mdm-miR858 expression to inhibit PA accumulation and thereby indirectly enhanced anthocyanin synthesis in the peel. The present results revealed that the MdBBX22-miR858-MdMYB9/11/12 module regulates PA accumulation in apple. The findings provide a reference for further studies of the regulatory mechanism of PA accumulation and the relationship between PAs and anthocyanins.

Metabolomic insights into the browning of the peel of bagging 'Rui Xue' apple fruit.

BACKGROUND: Bagging is one of the most important techniques for producting high-quality fruits. In the actual of cultivating, we found a new kind of browning in peel of apple fruit that occurs before harvest and worsen during storage period. There are many studies on metabonomic analysis of browning about storage fruits, but few studies on the mechanism of browning before harvest. RESULTS: In this study, five-year-old trees of 'Rui Xue' (CNA20151469.1) were used as materials. Bagging fruits without browning (BFW) and bagging fruits with browning (BFB) were set as the experimental groups, non-bagging fruits (NBF) were set as control. After partial least squares discriminant analysis (PLS-DA), 50 kinds of metabolites were important with predictive VIP > 1 and p-value < 0.05. The most important differential metabolites include flavonoids and lipids molecules, 11 flavonoids and 6 lipids molecules were significantly decreased in the BFW compared with NBF. After browning, 11 flavonoids and 7 lipids were further decreased in BFB compared with BFW. Meanwhile, the significantly enriched metabolic pathways include galactose metabolism, ABC membrane transporter protein, flavonoid biosynthesis and linoleic acid metabolism pathways et al. Physiological indicators show that, compared with NBF, the content of malondialdehyde (MDA), hydrogen peroxide (H2O2), superoxide anion (O2-) in peel of BFW and BFB were significantly increased, and the difference of BFB was more significant. Meanwhile, the antioxidant enzyme activities of BFW and BFB were inhibited, which accelerated the destruction of cell structure. In addition, the metabolome and physiological data showed that the significantly decrease of flavonoid was positively correlated with peel browning. So, we analyzed the expression of flavonoid related genes and found that, compared with NBF, the flavonoid synthesis genes MdLAR and MdANR were significantly up-regulated in BFW and BFB, but, the downstream flavonoids-related polymeric genes MdLAC7 and MdLAC14 were also significantly expressed. CONCLUSIONS: Our findings demonstrated that the microenvironment of fruit was changed by bagging, the destruction of cell structure, the decrease of flavonoids and the increase of triterpenoids were the main reasons for the browning of peel.

Plasma Membrane-Localized Transporter MdSWEET12 Is Involved in Sucrose Unloading in Apple Fruit.

Sugar content is an important factor determining the flavor in apple fruit. Sugar unloading is a prerequisite step for sugar accumulation. However, little is known about sugar unloading mechanisms in apple. Transcriptomic sequencing of two apple varieties, "Envy" and "Pacific Rose," with significantly different sugar content was performed. MdSWEET12a from the SWEET transporter family was differentially expressed. Further study of the MdSWEET12a showed that this plasma membrane-localized transporter protein-encoding gene was mainly expressed in sieve element-companion cells (SE-CC) in the fruit, which was positively correlated with the sucrose accumulation during the development of "Envy" apple. Consistently manipulating the gene expression through either transient overexpression or silencing significantly increased or decreased the sugar content in apple fruit, respectively. Complementary growth experiments in mutant yeast cells indicated that MdSWEET12a transported sucrose. Heterologous expression of MdSWEET12a in tomato increased the expression of genes related to sugar metabolism and transport, leading to increased sugar content. These findings underpin the involvement of MdSWEET12a in sugar unloading in apple fruit.

DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.

The prediction of protein-protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning-based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.

MdBBX21, a B-Box Protein, Positively Regulates Light-Induced Anthocyanin Accumulation in Apple Peel.

The red coloration of apple (Malus × domestica Borkh.) is due to the accumulation of anthocyanins in the fruit peel. Light is essential for anthocyanin biosynthesis in apple. In this study, we performed a transcriptome sequencing (RNA-seq) analysis of apple fruit exposed to light after unbagging. The identified differentially expressed genes included MdBBX21, which is homologous to Arabidopsis BBX21, suggesting it may be involved in light-induced anthocyanin biosynthesis. Additionally, MdBBX21 was localized in the nucleus and its gene was expressed earlier than MdMYB1 in apple peel treated with light. Overexpressing MdBBX21 in Arabidopsis and apple calli under light increased anthocyanin accumulation. Dual-luciferase and yeast one-hybrid assays confirmed that MdBBX21 binds to the MdHY5, MdBBX20, and MdBBX22-1/2 promoters and induces expression. At the same time, MdHY5 can also activate the expression of MdBBX21. Furthermore, bimolecular fluorescence complementation and yeast two-hybrid assays demonstrated that MdBBX21 can interact with MdHY5. This interaction can significantly enhance MdMYB1 promoter activity. These findings clarify the molecular mechanism by which MdBBX21 positively regulates light-induced anthocyanin accumulation in apple.

Improved prediction of drug-target interactions based on ensemble learning with fuzzy local ternary pattern.

Introduction: The prediction of interacting drug-target pairs plays an essential role in the field of drug repurposing, and drug discovery. Although biotechnology and chemical technology have made extraordinary progress, the process of dose-response experiments and clinical trials is still extremely complex, laborious, and costly. As a result, a robust computer-aided model is of an urgent need to predict drug-target interactions (DTIs). Methods: In this paper, we report a novel computational approach combining fuzzy local ternary pattern (FLTP), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) to identify DTIs. More specially, the target primary sequence is first numerically characterized into PSSM which records the biological evolution information. Afterward, the FLTP method is applied in extracting the highly representative descriptors of PSSM, and the combinations of FLTP descriptors and drug molecular fingerprints are regarded as the complete features of drug-target pairs. Results: Finally, the entire features are fed into rotation forests for inferring potential DTIs. The experiments of 5-fold cross-validation (CV) achieve mean accuracies of 89.08%, 86.14%, 82.41%, and 78.40% on Enzyme, Ion Channel, GPCRs, and Nuclear Receptor datasets. Discussion: For further validating the model performance, we performed experiments with the state-of-art support vector machine (SVM) and light gradient boosting machine (LGBM). The experimental results indicate the superiorities of the proposed model in effectively and reliably detect potential DTIs. There is an anticipation that the proposed model can establish a feasible and convenient tool to identify high-throughput identification of DTIs.

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

mdm-miR828 Participates in the Feedback Loop to Regulate Anthocyanin Accumulation in Apple Peel.

Anthocyanins are responsible for the red pigmentation in the peel of apple (Malus × domestica Borkh.) fruit. Relatively few studies have investigated anthocyanins at the posttranscriptional level. MicroRNAs play an important role in plant growth and development by regulating gene expression at the posttranscriptional level. In this study, mdm-miR828 showed a relatively low expression level during the rapid fruit coloration period. However, the mdm-miR828 expression level increased in the late fruit coloration stage. Overexpression of mdm-miR828 inhibited anthocyanin synthesis in apple and Arabidopsis. Dual-luciferase and yeast one-hybrid assays showed that MdMYB1 is capable of binding to the promoter of mdm-MIR828b to promote its expression. The results indicate that mdm-miR828 is involved in a feedback regulatory mechanism associated with anthocyanin accumulation in apple. In addition, mdm-miR828 is involved in the inhibition of anthocyanin accumulation in response to high temperature.

[Surveillance and forecast system of schistosomiasis in Jiangsu Province. VI. Detection technology of water infectivity based on enrichment of Schistosoma japonicum cercariae on water surface].

OBJECTIVE: To explore the enrichment technique of Schistosoma japonicum cercariae on the water surface, so as to establish a new method combined with the existing technology to detect the cercarial infested water body quickly and sensitively. METHODS: Soybean oil, gasoline, kerosene and isophorone were screened as expanding agents. The cercariae were enriched by the thrust of the expanding agents when diffusing on the water surface, and PE adsorption film and C-6 film were applied to seize them so as to determine the infectivity of the water quickly. The relationship between the dose of expanding agents and diffusion radius were explored. RESULTS: Gasoline, kerosene and isophorone were suitable expanding agents, and the diffusion effect of isophorone was the best. After the enrichment by the expanding agents, the detection rate of cercariae of the method seizing cercariae with the film significantly improved in the water. CONCLUSION: This new method could effectively improve the detection rate of the cercarial infested water and is suitable for the low-degree infested water.