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  • Transcriptome-wide profiling of multiple RNA modifications simultaneously at single-base resolution.

Transcriptome-wide profiling of multiple RNA modifications simultaneously at single-base resolution.

Proceedings of the National Academy of Sciences of the United States of America (2019-03-16)
Vahid Khoddami, Archana Yerra, Timothy L Mosbruger, Aaron M Fleming, Cynthia J Burrows, Bradley R Cairns
ABSTRACT

The breadth and importance of RNA modifications are growing rapidly as modified ribonucleotides can impact the sequence, structure, function, stability, and fate of RNAs and their interactions with other molecules. Therefore, knowing cellular RNA modifications at single-base resolution could provide important information regarding cell status and fate. A current major limitation is the lack of methods that allow the reproducible profiling of multiple modifications simultaneously, transcriptome-wide and at single-base resolution. Here we developed RBS-Seq, a modification of RNA bisulfite sequencing that enables the sensitive and simultaneous detection of m5C, Ψ, and m1A at single-base resolution transcriptome-wide. With RBS-Seq, m5C and m1A are accurately detected based on known signature base mismatches and are detected here simultaneously along with Ψ sites that show a 1-2 base deletion. Structural analyses revealed the mechanism underlying the deletion signature, which involves Ψ-monobisulfite adduction, heat-induced ribose ring opening, and Mg2+-assisted reorientation, causing base-skipping during cDNA synthesis. Detection of each of these modifications through a unique chemistry allows high-precision mapping of all three modifications within the same RNA molecule, enabling covariation studies. Application of RBS-Seq on HeLa RNA revealed almost all known m5C, m1A, and ψ sites in tRNAs and rRNAs and provided hundreds of new m5C and Ψ sites in noncoding RNAs and mRNAs. However, our results diverge greatly from earlier work, suggesting ∼10-fold fewer m5C sites in noncoding and coding RNAs and the absence of substantial m1A in mRNAs. Taken together, the approaches and refined datasets in this work will greatly enable future epitranscriptome studies.