It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.
RNA’s role in cellular machinery: insights from transcriptome studies
Regulatory RNAs, such as long noncoding RNAs (lncRNAs, RNAs that do not code for proteins), microRNAs (miRNAs, small RNAs that regulate gene expression), and circular RNAs (circRNAs, RNAs that form a covalently closed continuous loop), are important in controlling gene expression. The exact ways and roles of these RNAs are not completely known. This study by Joo et al. reviews current knowledge on regulatory RNAs, focusing on their structure and function in cell parts. The authors talk about the different methods used to study these RNAs, including RNA-Chromatin, RNA-Protein, and RNA structure sequencing. They also emphasize the role of RNA modifications in controlling gene expression and the potential of deep learning (a type of machine learning) in predicting RNA functions. The study concludes that understanding regulatory RNAs better could lead to new treatment strategies for various diseases.
This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
