TRUB1 is the predominant pseudouridine synthase acting on mammalian mRNA via a predictable and conserved code

Abstract

Following synthesis, RNA can be modified with over 100 chemically distinct modifications, which can potentially regulate RNA expression post-transcriptionally. Pseudouridine (Ψ) was recently established to be widespread and dynamically regulated on yeast mRNA, but less is known about Ψ presence, regulation, and biogenesis in mammalian mRNA. Here, we sought to characterize the Ψ landscape on mammalian mRNA, to identify the main Ψ-synthases (PUSs) catalyzing Ψ formation, and to understand the factors governing their specificity toward selected targets. We first developed a framework allowing analysis, evaluation, and integration of Ψ mappings, which we applied to >2.5 billion reads from 30 human samples. These maps, complemented with genetic perturbations, allowed us to uncover TRUB1 and PUS7 as the two key PUSs acting on mammalian mRNA and to computationally model the sequence and structural elements governing the specificity of TRUB1, achieving near-perfect prediction of its substrates (AUC = 0.974). We then validated and extended these maps and the inferred specificity of TRUB1 using massively parallel reporter assays in which we monitored Ψ levels at thousands of synthetically designed sequence variants comprising either the sequences surrounding pseudouridylation targets or systematically designed mutants perturbing RNA sequence and structure. Our findings provide an extensive and high-quality characterization of the transcriptome-wide distribution of pseudouridine in human and the factors governing it and provide an important resource for the community, paving the path toward functional and mechanistic dissection of this emerging layer of post-transcriptional regulation.

Base-Resolution Mapping Reveals Distinct m1A Methylome in Nuclear- and Mitochondrial-Encoded Transcripts

“…In addition, the fact that both TRMT6/61A and TRMT61B are known tRNA modification enzymes also reminds us of the modification machinery for other mRNA modifications. For instance, eukaryotic ψ synthases PUS1, PUS7 and TRUB1 can work on both tRNA and mRNA (Carlile et al, 2014; Li et al, 2015; Lovejoy et al, 2014; Safra et al, 2017; Schwartz et al, 2014a); yet the modification complex for m 6 A, consisting at least METTL3, METTL14, WTAP and KIAA1429, appears to be dedicated to mRNA (Bokar et al, 1997; Liu et al, 2014; Ping et al, 2014; Schwartz et al, 2014b). In the case of m 1 A, the enzyme(s) responsible for the majority of the modification sites in mRNA remains to be identified.…”

mRNA pseudouridylation affects RNA metabolism in the parasite Toxoplasma gondii

“…These enzymes can be divided into RNA-independent PUSs, which recognize their targets based on a combination of sequence and secondary structure, and RNA-dependent PUSs, which recognize their targets with the help of a protein complex containing an H/ACA guide RNA that is complementary to the target sequence (Ofengand 2002;Hamma and Ferré-D'Amaré 2006). While RNA-independent PUSs target tRNAs, snRNAs, mRNAs, and prokaryotic rRNA, RNA-dependent PUSs target snRNAs, mRNAs, and eukaryotic rRNA (Spenkuch et al 2014;Safra et al 2017).…”

Linear models enable powerful differential activity analysis in massively parallel reporter assays

“…Characterization studies examine thousands of different putative regulatory elements that have a wide variety of sequence features and try to correlate these sequence features with measured activity levels (Grossman et al, 2017;Guo et al, 2017;Safra et al, 2017;Levo et al, 2017;Maricque, Dougherty, and Cohen, 2017;Groff et al, 2016;Ernst et al, 2016;White, Kwasnieski, et al, 2016;Ferreira et al, 2016;Fiore and Cohen, 2016;Farley et al, 2015;Kamps-Hughes et al, 2015;Dickel et al, 2014;Kwasnieski, Fiore, et al, 2014;Mogno, Kwasnieski, and Cohen, 2013;Gisselbrecht et al, 2013;White, Myers, et al, 2013;Smith et al, 2013). Typical statistical analyses use regression to study the impact of multiple features simultaneously.…”