The desert plant Populus euphratica Oliv. has typical heterophylly; linear (Li), lanceolate (La), ovate (Ov) and broad-ovate (Bo) leaves grow in turn as trees develop to maturity. P. euphratica is therefore a potential model organism for leaf development. To investigate the roles of RNAs (including mRNAs, miRNAs, lncRNAs and circRNAs) in the morphogenesis of P. euphratica heterophylls, juvenile heterophylls were sampled individually, and then, the expression patterns of miRNAs, mRNAs, lncRNAs and circRNAs were analysed by small RNA sequencing and strand-specific RNA sequencing. We found that 1374 mRNAs, 19 miRNAs, 71 lncRNAs and 2 circRNAs were P. euphratica heterophyll morphogenesis–associated (PHMA) RNAs; among them, 17 PHMA miRNAs could alter the expression of 46 PHMA mRNAs. Furthermore, 11 lncRNAs and 2 circRNAs interacted with 27 PHMA mRNAs according to the ceRNA hypothesis. According to GO and KEGG pathway analysis, PHMA RNAs were mainly involved in metabolism, response to stimulus and developmental processes. Our results indicated that external environmental factors and genetic factors in P. euphratica co-regulated the expression of PHMA RNAs, repressed cell division, reinforced cell growth, and ultimately resulted in the morphogenesis of P. euphratica heterophylls.

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Circular RNAs (circRNAs) are a novel class of non-coding RNAs that are characterized by a covalently closed circular structure. They have been widely found in Populus euphratica Oliv. heteromorphic leaves (P. hl). To study the role of circRNAs related to transcription factors (TFs) in the morphogenesis of P. hl, the expression profiles of circRNAs in linear, lanceolate, ovate, and broad-ovate leaves of P. euphratica were elucidated by strand-specific sequencing. We identified and characterized 22 circRNAs related to TFs in P. hl at the four developmental stages. Using the competing endogenous RNAs hypothesis as a guide, we constructed circRNA–miRNA–TF mRNA regulatory networks, which indicated that circRNAs antagonized microRNAs (miRNAs), thereby influencing the expression of the miRNA target genes and playing a significant role in transcriptional regulation. Gene ontology annotation of the target TF genes predicted that these circRNAs were associated mainly with the regulation of leaf development, leaf morphogenesis, signal transduction, and response to abiotic stress. These findings implied that the circRNAs affected the size and number of cells in P. hl by regulating the expression of TF mRNAs. Our results provide a basis for further studies of leaf development in poplar trees.

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.