Deciphering the rules of mRNA structure differentiation in Saccharomyces cerevisiae in vivo and in vitro with deep neural networks

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.

IRC3 regulates mitochondrial translation in response to metabolic cues in Saccharomyces cerevisiae

Mitochondrial oxidative phosphorylation (OXPHOS) enzymes are made up of dual genetic origin. Mechanisms regulating the expression of nuclear-encoded OXPHOS subunits in response to metabolic cues (glucose vs. glycerol), is significantly understood while regulation of mitochondrially encoded OXPHOS subunits is poorly defined. Here, we show that IRC3 a DEAD/H box helicase, previously implicated in mitochondrial DNA maintenance, is central to integrating metabolic cues with mitochondrial translation. Irc3 associates with mitochondrial small ribosomal subunit in cells consistent with its role in regulating translation elongation based on Arg8^m reporter system. IRC3 deleted cells retained mitochondrial DNA despite growth defect on glycerol plates. Glucose grown Δirc3ρ⁺ and irc3 temperature-sensitive cells at 37⁰C have reduced translation rates from majority of mRNAs. In contrast, when galactose was the carbon source, reduction in mitochondrial translation was observed predominantly from Cox1 mRNA in Δirc3ρ⁺ but no defect was observed in irc3 temperature-sensitive cells, at 37⁰C. In support, of a model whereby IRC3 responds to metabolic cues to regulate mitochondrial translation, suppressors of Δirc3 isolated for restoration of growth on glycerol media restore mitochondrial protein synthesis differentially in presence of glucose vs. glycerol.

RSVdb: a comprehensive database of transcriptome RNA structure

RNA fulfills a crucial regulatory role in cells by folding into a complex RNA structure. To date, a chemical compound, dimethyl sulfate (DMS), has been developed to probe the RNA structure at the transcriptome level effectively. We proposed a database, RSVdb (https://taolab.nwafu.edu.cn/rsvdb/), for the browsing and visualization of transcriptome RNA structures. RSVdb, including 626 225 RNAs with validated DMS reactivity from 178 samples in eight species, supports four main functions: information retrieval, research overview, structure prediction and resource download. Users can search for species, studies, transcripts and genes of interest; browse the quality control of sequencing data and statistical charts of RNA structure information; preview and perform online prediction of RNA structures in silico and under DMS restraint of different experimental treatments and download RNA structure data for species and studies. Together, RSVdb provides a reference for RNA structure and will support future research on the function of RNA structure at the transcriptome level.

The landscape of eukaryotic mRNPs

The proper regulation of mRNA processing, localization, translation, and degradation occurs on mRNPs. However, the global principles of mRNP organization are poorly understood. We utilize the limited, but existing, information available to present a speculative synthesis of mRNP organization with the following key points. First, mRNPs form a compacted structure due to the inherent folding of RNA. Second, the ribosome is the principal mechanism by which mRNA regions are partially decompacted. Third, mRNPs are 50%-80% protein by weight, consistent with proteins modulating mRNP organization, but also suggesting the majority of mRNA sequences are not directly interacting with RNA-binding proteins. Finally, the ratio of mRNA-binding proteins to mRNAs is higher in the nucleus to allow effective RNA processing and limit the potential for nuclear RNA based aggregation. This synthesis of mRNP understanding provides a model for mRNP biogenesis, structure, and regulation with multiple implications.

5' untranslated regions: the next regulatory sequence in yeast synthetic biology

When developing industrial biotechnology processes, Saccharomyces cerevisiae (baker's yeast or brewer's yeast) is a popular choice as a microbial host. Many tools have been developed in the fields of synthetic biology and metabolic engineering to introduce heterologous pathways and tune their expression in yeast. Such tools mainly focus on controlling transcription, whereas post-transcriptional regulation is often overlooked. Herein we discuss regulatory elements found in the 5' untranslated region (UTR) and their influence on protein synthesis. We provide not only an overall picture, but also a set of design rules on how to engineer a 5' UTR. The reader is also referred to currently available models that allow gene expression to be tuned predictably using different 5' UTRs.