Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes.

Andreas Hildebrandt,Christoph Falschlunger,Claudia Höbartner,Eric Ennifar,Guillaume Bec,Lukas Schmidt,Maksim V Sednev,Mark Helm,Ronald Micura,Stephan Werner,Thomas Kemmer,Virginie Marchand,Yuri Motorin

doi:10.1093/nar/gkaa113

Abstract

Reverse transcription (RT) of RNA templates containing RNA modifications leads to synthesis of cDNA containing information on the modification in the form of misincorporation, arrest, or nucleotide skipping events. A compilation of such events from multiple cDNAs represents an RT-signature that is typical for a given modification, but, as we show here, depends also on the reverse transcriptase enzyme. A comparison of 13 different enzymes revealed a range of RT-signatures, with individual enzymes exhibiting average arrest rates between 20 and 75%, as well as average misincorporation rates between 30 and 75% in the read-through cDNA. Using RT-signatures from individual enzymes to train a random forest model as a machine learning regimen for prediction of modifications, we found strongly variegated success rates for the prediction of methylated purines, as exemplified with N1-methyladenosine (m1A). Among the 13 enzymes, a correlation was found between read length, misincorporation, and prediction success. Inversely, low average read length was correlated to high arrest rate and lower prediction success. The three most successful polymerases were then applied to the characterization of RT-signatures of other methylated purines. Guanosines featuring methyl groups on the Watson-Crick face were identified with high confidence, but discrimination between m1G and m22G was only partially successful. In summary, the results suggest that, given sufficient coverage and a set of specifically optimized reaction conditions for reverse transcription, all RNA modifications that impede Watson-Crick bonds can be distinguished by their RT-signature.

Highlights

With the discovery of retroviral reverse transcriptases (RT) in 1970 by Howard Temin [1] and David Baltimore [2], the possibility of synthesizing cDNA copies of RNA substrates revolutionized the field of molecular biology and found application in various analytical and biotechnological methodologies, including transcriptome profiling, RNAC The Author(s) 2020
Yeast total tRNA was submitted to an RNA-Seq based on a library preparation protocol designed to capture misincorporation events as well as abortive cDNA [43]
From all m1A sites listed in Modomics and the tRNA database, those were excluded from further analysis, for which the coverage was insufficient to extract a statistically relevant Reverse transcription (RT)-signature

Summary

Introduction

With the discovery of retroviral reverse transcriptases (RT) in 1970 by Howard Temin [1] and David Baltimore [2], the possibility of synthesizing cDNA copies of RNA substrates revolutionized the field of molecular biology and found application in various analytical and biotechnological methodologies, including transcriptome profiling, RNAC The Author(s) 2020. Known reverse transcriptases exhibit two catalytic activities: a DNA polymerase activity and an associated RNase activity. Thereby, the DNA polymerase activity is used to copy both, RNA and DNA templates and the RNase activity, termed RNase H, degrades RNA in RNA–DNAhybrid duplexes. AMV RT is a heterodimer in which both subunits have DNA polymerase and RNase H activity, while MMLV RT is a monomer, that exhibits both activities [6]

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Conclusion