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Biology subjects

Barry, M. L.

Publications and source records attributed to Barry, M. L..

4 recordsLinked to original sources

Pseudouridylation landscape across 42 S. cerevisiae cytosolic tRNA isoacceptors via Nanopore direct RNA sequencing

Pseudouridine is the most abundant RNA base modification due to its prevalence in tRNA and rRNA, where it serves as a key modulator of structure and function. Yet, in the widely used model organism, the budding yeast Saccharomyces cerevisiae, tRNA pseudouridine sites have not been comprehensively annotated. Here, we performed two high-throughput methods established for detecting tRNA pseudouridylation positions: Nanopore direct RNA sequencing (DRS) and 2-bromoacrylamide-assisted cyclization sequencing (BACS). Using DRS, we sequenced cytosolic tRNA from seven pseudouridine synthase (PUS) knockout S. cerevisiae strains, including deletion strains of Pus1, Pus3, and Pus7. Analysis of these data verified thirty-two existing pseudouridine annotations and predicted an additional ten previously unannotated pseudouridine sites. Our analysis also revealed DRS signal changes at several non-uridine sites with the loss of a PUS, including apparent changes in modification abundances at position 37 upon deletion of Pus3. Liquid chromatography tandem mass spectrometry (LC-MS/MS) and primer extension assays, however, indicated no change in the abundance of these modifications with the loss of Pus3, underscoring the need for caution in interpreting DRS-based signal changes. Using BACS, we confirmed the ten DRS-predicted novel sites, while further detecting eleven additional previously unannotated pseudouridine sites. Combining existing modification annotations from the Modomics database with our DRS and BACS datasets, we created a map of all detected pseudouridines --totaling 126 sites, including 21 novel sites--and the enzymes responsible for their catalysis, across the forty-two nuclear-encoded S. cerevisiae tRNA isoacceptors.

molecular biology↗

tRNA isodecoder analysis using Nanopore ionic current signals and deep learning

tRNA are short non-coding RNA characterized by their distinct tertiary structure and abundant chemical modifications. Conventional analysis strategies do not fully characterize tRNA isodecoders. We demonstrate that this limitation can be resolved for tRNA using nanopore ionic current data. We developed tRNAZAP, a deep learning strategy that uses nanopore ionic current signal information to classify native tRNA strands at isodecoder-level resolution without relying on sequence information. Additionally, the ionic current level classification allows for pairwise alignment of read sequences to reference sequences, producing optimal tRNA alignments. We applied tRNAZAP to direct tRNA sequencing data from Escherichia coli and Saccharomyces cerevisiae, and recovered 2.6% and 13.1% more aligned reads than BWA-MEM, respectively. tRNAZAP resolved these reads at an isodecoder-level and with consistently higher alignment identity. tRNAZAP is a powerful complement to sequence-based profiling and can contribute towards resolving the isodecoder landscape in more complex organisms including humans.

genomics↗

A pseudouridine synthase shapes tRNA structural dynamics through both catalysis and remodeling

Transfer RNA has long served as an exemplar of a thermodynamically stable, structured RNA. Yet it undergoes significant structural changes upon binding and catalysis by diverse modification enzymes. We leveraged optical binding assays and single-molecule FRET to observe the structural dynamics of two yeast tRNAs, in isolation, and upon interaction with the conserved pseudouridine synthase Pus4/TruB. We show that unmodified and pseudouridylated tRNAeMet(CAU) and tRNAThr(AGU) all sample open, compact and intermediate conformations, though tRNAThr(AGU) exhibits faster dynamics. Consistent with its role in modifying RNAs with different structural properties, Pus4 binds robustly to unmodified tRNA, tRNA that was pseudouridylated prior to engaging Pus4 ("pre-modified"), and even an unrelated riboswitch RNA. Pus4 binding to tRNAeMet(CAU) leads to additional tRNA conformational states. The ensemble of conformations explored by Pus4-bound pre-modified tRNA resolved within minutes back into open, compact and intermediate states. tRNAeMet(CAU) that is initially unmodified more gradually approached the ensemble of structures that were attained rapidly by pre-modified tRNA. Thus, Pus4 both catalyzes a lasting chemical change on tRNA and remodels it over time after catalysis, perhaps to promote subsequent steps of tRNA maturation.

biophysics↗

Natural variation in codon bias and mRNA folding strength interact synergistically to modify protein expression in Saccharomyces cerevisiae

Codon bias and mRNA folding strength (mF) are hypothesized molecular mechanisms by which polymorphisms in genes modify protein expression. Natural patterns of codon bias and mF across genes as well as effects of altering codon bias and mF suggest the influence of these two mechanisms may vary depending on the specific location of polymorphisms within a transcript. Despite the central role codon bias and mF may play in natural trait variation within populations, systematic studies of how polymorphic codon bias and mF relate to protein expression variation are lacking. To address this need, we analyzed genomic, transcriptomic, and proteomic data for 22 Saccharomyces cerevisiae isolates, estimated protein accumulation for each allele of 1620 genes as the log of protein molecules per RNA molecule (logPPR), and built linear mixed effects models associating allelic variation in codon bias and mF with allelic variation in logPPR. We found codon bias and mF interact synergistically in a positive association with logPPR and this interaction explains almost all the effect of codon bias and mF. We examined how the locations of polymorphisms within transcripts influence their effects and found that codon bias primarily acts through polymorphisms in domain encoding and 3 coding sequences while mF acts most significantly through coding sequences with weaker effects from UTRs. Our results present the most comprehensive characterization to date of how polymorphisms in transcripts influence protein expression.

genetics↗