Agnostic Framework for the Classification/Identification of Organisms Based on RNA Post-Transcriptional Modifications.

We propose a novel approach for building a classification/identification framework based on the full complement of RNA post-transcriptional modifications (rPTMs) expressed by an organism at basal conditions. The approach relies on advanced mass spectrometry techniques to characterize the products of exonuclease digestion of total RNA extracts. Sample profiles comprising identities and relative abundances of all detected rPTM were used to train and test the capabilities of different machine learning (ML) algorithms. Each algorithm proved capable of identifying rigorous decision rules for differentiating closely related classes and correctly assigning unlabeled samples. The ML classifiers resolved different members of the Enterobacteriaceae family, alternative Escherichia coli serotypes, a series of Saccharomyces cerevisiae knockout mutants, and primary cells of the Homo sapiens central nervous system, which shared very similar genetic backgrounds. The excellent levels of accuracy and resolving power achieved by training on a limited number of classes were successfully replicated when the number of classes was significantly increased to escalate complexity. A dendrogram generated from ML-curated data exhibited a hierarchical organization that closely resembled those afforded by established taxonomic systems. Finer clustering patterns revealed the extensive effects induced by the deletion of a single pivotal gene. This information provided a putative roadmap for exploring the roles of rPTMs in their respective regulatory networks, which will be essential to decipher the epitranscriptomics code. The ubiquitous presence of RNA in virtually all living organisms promises to enable the broadest possible range of applications, with significant implications in the diagnosis of RNA-related diseases.

2'-O-ribose methylation of transfer RNA promotes recovery from oxidative stress in Saccharomyces cerevisiae.

Chemical modifications that regulate protein expression at the translational level are emerging as vital components of the cellular stress response. Transfer RNAs (tRNAs) are significant targets for methyl-based modifications, which are catalyzed by tRNA methyltransferases (Trms). Here, Saccharomyces cerevisiae served as a model eukaryote system to investigate the role of 2'-O-ribose tRNA methylation in the cell's response to oxidative stress. Using 2'-O-ribose deletion mutants for trms 3, 7, 13, and 44, in acute and chronic exposure settings, we demonstrate a broad cell sensitivity to oxidative stress-inducing toxicants (i.e., hydrogen peroxide, rotenone, and acetic acid). A global analysis of hydrogen peroxide-induced tRNA modifications shows a complex profile of decreased, or undetectable, 2'-O-ribose modification events in 2'-O-ribose trm mutant strains, providing a critical link between this type of modification event and Trm status post-exposure. Based on the pronounced oxidative stress sensitivity observed for trm7 mutants, we used a bioinformatic tool to identify transcripts as candidates for regulation by Trm7-catalyzed modifications (i.e., enriched in UUC codons decoded by tRNAPheGmAA). This screen identified transcripts linked to diverse biological processes that promote cellular recovery after oxidative stress exposure, including DNA repair, chromatin remodeling, and nutrient acquisition (i.e., CRT10, HIR3, HXT2, and GNP1); moreover, these mutants were also oxidative stress-sensitive. Together, these results solidify a role for TRM3, 7, 13, and 44, in the cellular response to oxidative stress, and implicate 2'-O-ribose tRNA modification as an epitranscriptomic strategy for oxidative stress recovery.