In biological systems, the synthesis of nucleic acids, such as DNA and RNA, is catalyzed by enzymes in various aqueous solutions. Our engine can be used to profile Ψ-occupancy across cell types and cell states, thus providing critical insights about physiological relevance of Ψ modification to mRNAs. We show that applying our model is critical for quantification, especially in low-abundance mRNAs. Our supervised machine learning models reveal that for every site studied, different signal parameters are required to maximize Ψ classification accuracy. In this work, we apply supervised machine learning models that are trained on sequence-specific, synthetic controls to endogenous transcriptome data and achieve the first quantitative Ψ occupancy measurement in human mRNAs. Nanopores may be used to directly identify Ψ sites in RNAs using a systematically miscalled base, however, this approach is not quantitative and highly sequence dependent. The pseudouridine (Ψ) mRNA modification is also highly abundant but difficult to detect and quantify because there is no available antibody, it is mass silent, and maintains canonical basepairing with adenine. Existing studies have typically focused on the identification and impact of adenine modifications on mRNA (m ⁶ A and inosine) due to the availability of analytical methods. Our proof-of-concept experiments for higher-fidelity incorporation of uridine analogs during IVT provide guidelines when choosing RNAPs for the generation of modified uridine-containing mRNAs in vitro.Įnzyme-mediated chemical modifications to mRNA are important for fine-tuning gene expression, but they are challenging to quantify due to low copy number and limited tools for accurate detection. Based on our findings, we introduce a novel method to improve uridine analog incorporation fidelity during IVT. We also show that the array of nucleotide misincorporation is not dependent on the template DNA sequence context and that the distribution of these misincorporated nucleotides is not localized to any specific region along the length of the RNA. The fidelity of nucleotide incorporation differs between RNA polymerases however, the spectrum of mutations observed between the RNAPs is similar. We demonstrate that m1Ψ is incorporated with higher fidelity than Ψ. To decipher the fidelity with which these modifications are incorporated during the in vitro transcription (IVT) process, we compared the incorporation fidelity of uridine analogs with different RNA polymerases. To overcome the inherent immunogenicity, as well as to increase the therapeutic efficacy of the molecules, uridine analogs-such as pseudouridine (Ψ) and N1-methyl-pseudouridine (m1Ψ), are incorporated in the synthetic mRNA. In vitro transcribed synthetic messenger RNAs (mRNAs) represent a novel therapeutic modality. These results may aid in future efforts that employ RNA polymerases to make therapeutic mRNAs with sub-stoichiometric amounts of m1Ψ. The SP6 polymerase introduced m1ΨTP when competed with UTP with a smaller window of yields (15-30%) across all sequence contexts studied. Experiments with SP6 RNA polymerase, as well as chemically-modified triphosphates and DNA templates provide insight to explain the observations. A significant sequence context dependency was observed for T7 RNA polymerase with insertion yields for ΨTP versus UTP spanning a range of 20-65%, and m1ΨTP versus UTP producing variable yields that differ by 15-70%. Understanding the nanopore signatures for Ψ and m1Ψ enabled a running start T7 RNA polymerase assay to study the selection of UTP versus ΨTP or m1ΨTP competing mixtures in all possible adjacent sequence contexts. The base calling data for Ψ or m1Ψ were similar but different from U allowing their detection. Direct RNA sequencing with a commercial nanopore platform was used to sequence RNA containing uridine (U), pseudouridine (Ψ) or N1-methylpseudouridine (m1Ψ) in >100 different 5-nucleotide contexts.
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