Novel insights into the pervasive role of RNA structure in post-transcriptional regulation of gene expression in plants

RNA Structure: Function and Application in Plant Biology

RNA orchestrates intricate structures that influence gene expression and protein production in all living organisms, with implications for fundamental biology, medicine, and agriculture. Although extensive research has been conducted on RNA biology, many regulatory mechanisms remain elusive due to the complex and dynamic nature of RNA structures and past technological limitations. Recent advancements in RNA structure technology have revolutionized plant RNA biology research. Here, we review cutting-edge technologies for studying RNA structures in plants and their functional significance in diverse biological processes. Additionally, we highlight the pivotal role of RNA structure in influencing plant growth, development, and responses to environmental stresses. We also discuss the potential evolutionary significance of RNA structure in natural adaptation and crop domestication. Finally, we propose leveraging RNA structure-mediated gene regulation as an innovative strategy to bolster plant resilience against climate change.

Decoding plant specialized metabolism: new mechanistic insights

Secondary metabolite (SM) production provides biotic and abiotic stress resistance and enables plants to adapt to the environment. Biosynthesis of these metabolites involves a complex interplay between transcription factors (TFs) and regulatory elements, with emerging evidence suggesting an integral role for chromatin dynamics. Here we review key TFs and epigenetic regulators that govern SM biosynthesis in different contexts. We summarize relevant emerging technologies and results from the model species arabidopsis (Arabidopsis thaliana) and outline aspects of regulation that may also function in food, feed, fiber, oil, or industrial crop plants. Finally, we highlight how effective translation of fundamental knowledge from model to non-model species can benefit understanding of SM production in a variety of ecological, agricultural, and pharmaceutical contexts.

Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response

BACKGROUND

Plants respond to stress through highly tuned regulatory networks. While prior works identified master regulators of iron deficiency responses in A. thaliana from whole-root data, identifying regulators that act at the cellular level is critical to a more comprehensive understanding of iron homeostasis. Within the root epidermis complex molecular mechanisms that facilitate iron reduction and uptake from the rhizosphere are known to be regulated by bHLH transcriptional regulators. However, many questions remain about the regulatory mechanisms that control these responses, and how they may integrate with developmental processes within the epidermis. Here, we use transcriptional profiling to gain insight into root epidermis-specific regulatory processes.

RESULTS

Set comparisons of differentially expressed genes (DEGs) between whole root and epidermis transcript measurements identified differences in magnitude and timing of organ-level vs. epidermis-specific responses. Utilizing a unique sampling method combined with a mutual information metric across time-lagged and non-time-lagged windows, we identified relationships between clusters of functionally relevant differentially expressed genes suggesting that developmental regulatory processes may act upstream of well-known Fe-specific responses. By integrating static data (DNA motif information) with time-series transcriptomic data and employing machine learning approaches, specifically logistic regression models with LASSO, we also identified putative motifs that served as crucial features for predicting differentially expressed genes. Twenty-eight transcription factors (TFs) known to bind to these motifs were not differentially expressed, indicating that these TFs may be regulated post-transcriptionally or post-translationally. Notably, many of these TFs also play a role in root development and general stress response.

CONCLUSIONS

This work uncovered key differences in -Fe response identified using whole root data vs. cell-specific root epidermal data. Machine learning approaches combined with additional static data identified putative regulators of -Fe response that would not have been identified solely through transcriptomic profiles and reveal how developmental and general stress responses within the epidermis may act upstream of more specialized -Fe responses for Fe uptake.

Beyond transcription: compelling open questions in plant RNA biology

The study of RNAs has become one of the most influential research fields in contemporary biology and biomedicine. In the last few years, new sequencing technologies have produced an explosion of new and exciting discoveries in the field but have also given rise to many open questions. Defining these questions, together with old, long-standing gaps in our knowledge, is the spirit of this article. The breadth of topics within RNA biology research is vast, and every aspect of the biology of these molecules contains countless exciting open questions. Here, we asked 12 groups to discuss their most compelling question among some plant RNA biology topics. The following vignettes cover RNA alternative splicing; RNA dynamics; RNA translation; RNA structures; R-loops; epitranscriptomics; long non-coding RNAs; small RNA production and their functions in crops; small RNAs during gametogenesis and in cross-kingdom RNA interference; and RNA-directed DNA methylation. In each section, we will present the current state-of-the-art in plant RNA biology research before asking the questions that will surely motivate future discoveries in the field. We hope this article will spark a debate about the future perspective on RNA biology and provoke novel reflections in the reader.

G4Atlas: a comprehensive transcriptome-wide G-quadruplex database

RNA G-quadruplex (rG4) is a vital RNA tertiary structure motif that involves the base pairs on both Hoogsteen and Watson-Crick faces of guanines. rG4 is of great importance in the post-transcriptional regulation of gene expression. Experimental technologies have advanced to identify in vitro and in vivo rG4s across diverse transcriptomes. Building on these recent advances, here we present G4Atlas, the first transcriptome-wide G-quadruplex database, in which we have collated, classified, and visualized transcriptome rG4 experimental data, generated from rG4-seq, chemical profiling and ligand-binding methods. Our comprehensive database includes transcriptome-wide rG4s generated from 82 experimental treatments and 238 samples across ten species. In addition, we have also included RNA secondary structure prediction information across both experimentally identified and unidentified rG4s to enable users to display any potential competitive folding between rG4 and RNA secondary structures. As such, G4Atlas will enable users to explore the general functions of rG4s in diverse biological processes. In addition, G4Atlas lays the foundation for further data-driven deep learning algorithms to examine rG4 structural features.

Deep Learning in RNA Structure Studies

Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.

The Potential Role of RNA Structure in Crop Molecular Breeding

The continually growing human population creates a concomitantly increasing demand for nutritious crops with high yields. Advances in high throughput sequencing technologies have revealed the genetic architecture of major crops. This includes extensive information enabling comprehensive genetic markers for breeding selection, new gene discoveries, and novel gene regulatory strategies for crop editing. RNA structure is an important type of genetic feature, essential for post-transcriptional regulation of gene expression. Here, we summarize recent advances in genome-wide RNA structure studies in crops and review the associated RNA structure-mediated regulation of gene expression. We also discuss the functional importance of those single nucleotide variations that induce large RNA structure disparities. Lastly, we discuss the potential role of RNA structure in crop molecular breeding.