tRNA base methylation identification and quantification via high-throughput sequencing

Wesley C. Clark, Molly E. Evans, Dan Dominissini, Guanqun Zheng, Tao Pan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

157 Scopus citations

Abstract

Eukaryotic transfer RNAs contain on average 14 modifications. Investigations of their biological functions require the determination of the modification sites and the dynamic variations of the modification fraction. Base methylation represents a major class of tRNA modification. Although many approaches have been used to identify tRNA base methylations, including sequencing, they are generally qualitative and do not report the information on the modification fraction. Dynamic mRNA modifications have been shown to play important biological roles; yet, the extent of tRNA modification fractions has not been reported systemically. Here we take advantage of a recently developed high-throughput sequencing method (DM-tRNA-seq) to identify and quantify tRNA base methylations located at the Watson-Crick face in HEK293T cells at single base resolution. We apply information derived from both base mutations and positional stops from sequencing using a combination of demethylase treatment and cDNA synthesis by a thermophilic reverse transcriptase to compile a quantitative "Modification Index" (MI) for six base methylations in human tRNA and rRNA. MI combines the metrics for mutational and stop components from alignment of sequencing data without demethylase treatment, and the modifications are validated in the sequencing data upon demethylase treatment. We identify many new methylation sites in both human nuclear and mitochondrial-encoded tRNAs not present in the RNA modification databases. The potentially quantitative nature of the MI values obtained from sequencing is validated by primer extension of several tRNAs. Our approach should be widely applicable to identify tRNA methylation sites, analyze comparative fractional modifications, and evaluate the modification dynamics between different samples.

Original languageEnglish
Pages (from-to)1771-1784
Number of pages14
JournalRNA
Volume22
Issue number11
DOIs
StatePublished - Nov 2016
Externally publishedYes

Funding

FundersFunder number
National Institutes of Health
National Institute of General Medical SciencesT32GM008720, DP1GM105386
National Science Foundation1144082

    Keywords

    • High-throughput sequencing
    • Methylation
    • Modification
    • Quantification
    • tRNA

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