Haloxylon ammodendron, a perennial xero-halophyte, is an essential species for investigating the effects of drought on desert tree. To gain a comprehensive knowledge on the responses of H. ammodendron to drought stress, we specially performed the molecular and physiological analysis of H. ammodendron in response to −0.75 MPa osmotic stress for six and 24 h in lab condition via RNA-seq and digital gene expression (DGE). In total, 87,109 unigenes with a mean length of 680 bp and 13,486 potential simple sequence repeats (SSRs) were generated, and 3353 differentially expressed genes (DEGs) in shoots and 4564 in roots were identified under stress. These DEGs were mainly related to ion transporters, signal transduction, ROS-scavenging, photosynthesis, cell wall organization, membrane stabilization and hormones. Moreover, the physiological changes of inorganic ions and organic solute content, peroxidase (POD) activity and osmotic potential were in accordance with dynamic transcript profiles of the relevant genes. In this study, a detailed investigation of the pathways and candidate genes identified promote the research on the molecular mechanisms of abiotic stress tolerance in the xero-halophytic species. Our data provides valuable genetic resources for future improvement of forage and crop species for better adaptation to abiotic stresses.

Abnormal microRNA (miRNA) functions play significant roles in various pathological processes. Thus, predicting drug–miRNA associations (DMA) may hold great promise for identifying the potential targets of drugs. However, discovering the associations between drugs and miRNAs through wet experiments is time-consuming and laborious. Therefore, it is significant to develop computational prediction methods to improve the efficiency of identifying DMA on a large scale. In this paper, a multiple features integration model (MFIDMA) is proposed to predict drug–miRNA association. Specifically, we first formulated known DMA as a bipartite graph and utilized structural deep network embedding (SDNE) to learn the topological features from the graph. Second, the Word2vec algorithm was utilized to construct the attribute features of the miRNAs and drugs. Third, two kinds of features were entered into the convolution neural network (CNN) and deep neural network (DNN) to integrate features and predict potential target miRNAs for the drugs. To evaluate the MFIDMA model, it was implemented on three different datasets under a five-fold cross-validation and achieved average AUCs of 0.9407, 0.9444 and 0.8919. In addition, the MFIDMA model showed reliable results in the case studies of Verapamil and hsa-let-7c-5p, confirming that the proposed model can also predict DMA in real-world situations. The model was effective in analyzing the neighbors and topological features of the drug–miRNA network by SDNE. The experimental results indicated that the MFIDMA is an accurate and robust model for predicting potential DMA, which is significant for miRNA therapeutics research and drug discovery.

Tillage practices can regulate soil environmental factors and, thus, affect crop yield. Farmers’ acceptance of this is not high because of a lack of awareness, and, in the dryland farming region of the Longdong Loess Plateau in China, the lack of acceptance is due to the established use of the no-till operation. It is urgent to explore suitable tillage practices for maize (Zea mays L., Longdan No.8) planting in this area. The impact of tillage practices on the soil water content, soil temperature, field water consumption structure, yield, and water use efficiency (WUE) of maize was determined. Six tillage practices were implemented in 2021 and their effects were determined in 2021 and 2022, including conventional tillage with no straw (T), conventional tillage with straw incorporated (TS), subsoiling tillage with no straw (SST), subsoiling tillage with straw incorporated (SSTS), no-tillage with no straw (NT) and no-tillage with straw mulching (NTS). Over two years, compared to T, the soil volumetric water content (SW_v) with SSTS was significantly increased in the 5–10 cm soil layer at the V12 (big flare stage of maize) stage in 2022. SSTS significantly reduced soil temperature (ST) in the 20 and 25 cm soil depths at the V12 stage, and in every soil layer of the R2 (grain-filling stage of maize) stage. SSTS significantly reduced soil evaporation during the growing season (Ec), and significantly increased crop transpiration (Tc) when compared to T. Compared with T, SST and SSTS significantly increased biomass yield (BY), by 29.7–32.1 and 41.2–53.5%, respectively, increased grain number per ear by 6.3–16.5 and 10.4–38.8%, respectively, improved grain yield (GY) by 4.9–6.9 and 6.2–13.7%, respectively; SSTS significantly increased WUE by 5.5–15.4%. The correlation between soil volumetric water content at the V12 stage and grain yield was highly significant; the ST at the R2 stage had a significant positive correlation with grain number per ear, GY, and BY. Therefore, subsoiling tillage with straw incorporated increased the soil moisture content and reduced the soil temperature, optimized the water consumption structure, and improved the effective utilization of soil water, resulting in the accumulation of a higher biomass yield, and increased the number of ears, obtaining a higher yield, and improved water use efficiency. Therefore, subsoiling tillage with straw incorporated is a suitable tillage practice in the dry farming area of Longdong Loess Plateau, China.

The accumulation of chlorophyll and antinutritional glycoalkaloids in potato tubers resulting from exposure to light has been widely recognized as a cause of unpredictable quality loss of potato tuber. While transcriptional regulation of light-induced chlorophyll and glycoalkaloids accumulation has been extensively investigated, the mechanisms of post-transcriptional regulation through miRNA remain largely unexplored. An experimental model, the tubers of Solanum chacoense, were used to identify light-responsive miRNA–target interactions (MTIs) related to tuber greening and glycoalkaloid biosynthesis by employing multi-omics approaches (miRNA-seq and degradome-seq). A total of 732 unique mature miRNAs have been identified in S. chacoense. In total, 6335 unique target transcripts were cleaved by 489 known miRNAs and 153 novel miRNAs. The results revealed that light-responsive miRNAs can be grouped into eight temporally related clusters and play important roles in various physiological processes such as plant growth, stress responses, and primary and secondary metabolism. Multi-omics analyses have revealed that the modulation of transcript abundance of MYB59, HSPs, and EBF1/EBF2 by light-responsive miRNAs is pivotal for their function in cross-tolerance responses to both abiotic and biotic stresses. Furthermore, our findings suggest that many light-responsive miRNAs are crucial regulators in various biosynthetic pathways, including tetrapyrrole biosynthesis, suberin biosynthesis, and steroid biosynthesis. These findings highlight the significant role of light-responsive miRNAs in secondary metabolic pathways, particularly in isoprenoid, terpenoid, and glycoalkaloid biosynthesis, and have implications for the precise manipulation of metabolic pathways to produce new potato varieties with improved resistance to greening and lower glycoalkaloid levels.