Genome-Wide Identification and Expression Analysis of the CAD Gene Family in Walnut (Juglans regia L.).

Lignin deficiency in the endocarp of walnuts causes kernel bare, leads to inconvenient processing and transportation of walnuts, and easily produces insect damage and mildew, thereby affecting the quality of walnuts. Cinnamyl alcohol dehydrogenase (CAD) is one of the key rate-limiting enzymes in lignin synthesis and plays an important role in the synthesis of lignin in the endocarp of walnut. However, knowledge about CAD gene family members and their evolutionary and functional characteristics in walnuts is limited. In this study, all 18 JrCADs were identified, and phylogenetic relationships, gene structure, protein motifs, collinearity analysis, and expression patterns of the JrCADs were also analyzed. All JrCADs could be divided into three groups based on the phylogenetic tree, gene structure, and motif analysis also support this grouping. Transcriptome data demonstrated that JrCADs have different expression patterns in walnut endocarps at different developmental stages. Combined with qRT-PCR data, we finally identified several candidate JrCADs involved in the process of endocarp sclerosis. This study showed that the JrCAD family members are highly conservative in evolutionary characteristics and they might participate in a variety of hormone responses. JrCAD17 and JrCAD18 are highly expressed in all periods of walnut endocarp harding, they are closely related to lignin accumulation.

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things.

The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms in screening differentially expressed genes and function prediction during the walnut endocarp hardening stage. The paper walnut is used as the research object to analyze the biological information of paper walnut. The changes of lignin deposition during endocarp hardening from 50 days to 90 days are observed by microscope. Then, the Convolutional Neural Network (CNN) and Long and Short-term Memory (LSTM) network model are adopted to construct an expression gene screening and function prediction model. Then, the transcriptome and proteome sequencing and biological information of walnut endocarp samples at 50, 57, 78, and 90 days after flowering are analyzed and taken as the training data set of the CNN + LSTM model. The experimental results demonstrate that the endocarp of paper walnut began to harden at 57 days, and the endocarp tissue on the hardened inner side also began to stain. This indicates that the endocarp hardened laterally from outside to inside. The screening and prediction results show that the CNN + LSTM model's highest accuracy can reach 0.9264. The Accuracy, Precision, Recall, and F1-score of the CNN + LSTM model are better than the traditional machine learning algorithm. Moreover, the Receiver Operating Curve (ROC) area enclosed by the CNN + LSTM model and coordinate axis is the largest, and the Area Under Curve (AUC) value is 0.9796. The comparison of ROC and AUC proves that the CNN + LSTM model is better than the traditional algorithm for screening differentially expressed genes and function prediction in the walnut endocarp hardening stage. Using deep learning to predict expressed genes' function accurately can reduce the breeding cost and significantly improve the yield and quality of crops. This research provides scientific guidance for the scientific breeding of paper walnut.