aBIOTECH最新文献

The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.

Small RNA (sRNA)-mediated RNA silencing (also known as RNA interference, or RNAi) is a conserved mechanism in eukaryotes that includes RNA degradation, DNA methylation, heterochromatin formation and protein translation repression. In plants, sRNAs can move either cell-to-cell or systemically, thereby acting as mobile silencing signals to trigger noncell autonomous silencing. However, whether and what proteins are also involved in noncell autonomous silencing have not been elucidated. In this study, we utilized a previously reported inducible RNAi plant, PDSi, which can induce systemic silencing of the endogenous PDS gene, and we demonstrated that DCL3 is involved in systemic PDS silencing through its RNA binding activity. We confirmed that the C-terminus of DCL3, including the predicted RNA-binding domain, is capable of binding short RNAs. Mutations affecting RNA binding, but not processing activity, reduced systemic PDS silencing, indicating that DCL3 binding to RNAs is required for the induction of systemic silencing. Cucumber mosaic virus infection assays showed that the RNA-binding activity of DCL3 is required for antiviral RNAi in systemically noninoculated leaves. Our findings demonstrate that DCL3 acts as a signaling agent involved in noncell autonomous silencing and an antiviral effect in addition to its previously known function in the generation of 24-nucleotide sRNAs.

Supplementary information: The online version contains supplementary material available at 10.1007/s42994-023-00124-6.

Soybean (Glycine max) is an essential economic crop that provides vegetative oil and protein for humans, worldwide. Increasing soybean yield as well as improving seed quality is of great importance. Seed weight/size, oil and protein content are the three major traits determining seed quality, and seed weight also influences soybean yield. In recent years, the availability of soybean omics data and the development of related techniques have paved the way for better research on soybean functional genomics, providing a comprehensive understanding of gene functions. This review summarizes the regulatory genes that influence seed size/weight, oil content and protein content in soybean. We also provided a general overview of the pleiotropic effect for the genes in controlling seed traits and environmental stresses. Ultimately, it is expected that this review will be beneficial in breeding improved traits in soybean.

We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. Searching for TF-binding sites in the promoters of the co-expressed and co-regulated (CCGs) genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each subnetwork. Finally, six potential candidate TFs-WOX9A, LEC2, PGA37, WIP5, PEI1, and AIL1 from subnetwork 1-were identified, and their roles in somatic embryogenesis (GO:0010262) and regeneration (GO:0031099) were discussed, so were the TFs in Subnetwork 12 and 17 associated with regeneration. The TFs identified were also assessed using the CIS-BP database and Expression Atlas. Our analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to regeneration. The tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.

Supplementary information: The online version contains supplementary material available at 10.1007/s42994-023-00121-9.