Multi-level gene regulatory network models to understand complex mechanisms underlying plant development

Role of long non-coding RNAs in rice reproductive development

Rice is a staple crop, feeding over half of the global population. The future demand of population growth and climate change requires substantial rice improvement. Recent advances in rice genomics have highlighted the vital role of the non-coding part of the genome. The protein-coding regions account for only a tiny portion of the eukaryotic genome, and most of the genomic regions transcribe copious amounts of non-coding RNAs. Of these, the long non-coding RNAs, including linear non-coding RNAs (lncRNAs) and circular non-coding RNAs (circRNAs), have been shown to play critical roles in various developmental processes by regulating the expression of genes and functions of proteins at transcriptional, post-transcriptional and post-translational levels. With the advances in next-generation sequencing technologies, a substantial number of long non-coding RNAs have been found to be expressed in plant reproductive organs in a cell- and tissue-specific manner suggesting their reproductive development-related functions. Accumulating evidence points towards the critical role of these non-coding RNAs in flowering, anther, and pollen development, ovule and seed development and photoperiod and temperature regulation of male fertility. In this mini review, we provide a brief overview of the role of the linear and circular long non-coding RNAs in rice reproductive development and control of fertility and crop yield.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

The unappreciated generative role of cell movements in pattern formation

The mechanisms underpinning the formation of patterned cellular landscapes has been the subject of extensive study as a fundamental problem of developmental biology. In most cases, attention has been given to situations in which cell movements are negligible, allowing researchers to focus on the cell-extrinsic signalling mechanisms, and intrinsic gene regulatory interactions that lead to pattern emergence at the tissue level. However, in many scenarios during development, cells rapidly change their neighbour relationships in order to drive tissue morphogenesis, while also undergoing patterning. To draw attention to the ubiquity of this problem and propose methodologies that will accommodate morphogenesis into the study of pattern formation, we review the current approaches to studying pattern formation in both static and motile cellular environments. We then consider how the cell movements themselves may contribute to the generation of pattern, rather than hinder it, with both a species specific and evolutionary viewpoint.

The quiescent centre and root apical meristem: organization and function

This special issue is dedicated to the 100th anniversary of the birth of Frederick Albert Lionel Clowes, who discovered the quiescent centre (QC) of the root apical meristem (RAM). His discovery was a foundation for contemporary studies of the QC and RAM function, maintenance, and organization. RAM function is fundamental for cell production and root growth. This special issue bundles reviews on the main tendencies, hypotheses, and future directions, and identifies unknowns in the field.

Transcriptional regulation in plants: Using omics data to crack the cis-regulatory code

Innovative omics technologies, advanced bioinformatics, and machine learning methods are rapidly becoming integral tools for plant functional genomics, with tremendous recent advances made in this field. In transcriptional regulation, an initial lag in the accumulation of plant omics data relative to that of animals stimulated the development of computational methods capable of extracting maximum information from the available data sets. Recent comprehensive studies of transcription factor-binding profiles in Arabidopsis and maize and the accumulation of uniformly processed omics data in public databases have brought plant biologists into the big leagues, with many cutting-edge methods available. Here, we summarize the state-of-the-art bioinformatics approaches used to predict or infer the cis-regulatory code behind transcriptional gene regulation, focusing on their plant research applications.

QTL Mapping for Leaf Area of Tea Plants (Camellia sinensis) Based on a High-Quality Genetic Map Constructed by Whole Genome Resequencing

High-quality genetic maps play important roles in QTL mapping and molecular marker-assisted breeding. Tea leaves are not only important vegetative organs but are also the organ for harvest with important economic value. However, the key genes and genetic mechanism of regulating leaf area have not been clarified. In this study, we performed whole-genome resequencing on "Jinxuan," "Yuncha 1" and their 96 F1 hybrid offspring. From the 1.84 Tb of original sequencing data, abundant genetic variation loci were identified, including 28,144,625 SNPs and 2,780,380 indels. By integrating the markers of a previously reported genetic map, a high-density genetic map consisting of 15 linkage groups including 8,956 high-quality SNPs was constructed. The total length of the genetic map is 1,490.81 cM, which shows good collinearity with the genome. A total of 25 representative markers (potential QTLs) related to leaf area were identified, and there were genes differentially expressed in large and small leaf samples near these markers. GWAS analysis further verified the reliability of QTL mapping. Thirty-one pairs of newly developed indel markers located near these potential QTLs showed high polymorphism and had good discrimination between large and small leaf tea plant samples. Our research will provide necessary support and new insights for tea plant genetic breeding, quantitative trait mapping and yield improvement.