Enhanced ac4C detection in RNA via chemical reduction and cDNA synthesis with modified dNTPs

The functional analysis of epitranscriptomic modifications in RNA is constrained by a lack of methods that accurately capture their locations and levels. We previously demonstrated that the RNA modification N4-acetylcytidine (ac4C) can be mapped at base-resolution through sodium borohydride reduction to tetrahydroacetylcytidine (tetrahydro-ac4C), followed by cDNA synthesis to misincorporate adenosine opposite reduced ac4C sites, culminating in C:T mismatches at acetylated cytidines (RedaC:T). However, this process is relatively inefficient, resulting in less than 20% C:T mismatches at a fully modified ac4C site in 18S rRNA. Considering that ac4C locations in other substrates including mRNA are unlikely to reach full penetrance, this method is not ideal for comprehensive mapping. Here, we introduce "RetraC:T" (reduction to tetrahydro-ac4C and reverse transcription with amino-dATP to induce C:T mismatches) as a method with enhanced ability to detect ac4C in cellular RNA. In brief, RNA is reduced through NaBH4 or the closely related reagent sodium cyanoborohydride (NaCNBH3) followed by cDNA synthesis in the presence of a modified DNA nucleotide, 2-amino-dATP, that preferentially binds to tetrahydro-ac4C. Incorporation of the modified dNTP substantially improved C:T mismatch rates, reaching stoichiometric detection of ac4C in 18S rRNA. Importantly, 2-amino-dATP did not result in truncated cDNA products nor increase mismatches at other locations. Thus, modified dNTPs are introduced as a new addition to the toolbox for detecting ac4C at base resolution.

Detection of ac4C in human mRNA is preserved upon data reassessment

We recently reported the distribution of N4-acetylcytidine (ac4C) in HeLa mRNA at base resolution through chemical reduction and the induction of C:T mismatches in sequencing (RedaC:T-seq). Our results contradicted an earlier report from Schwartz and colleagues utilizing a similar method termed ac4C-seq. Here, we revisit both datasets and reaffirm our findings. Through RedaC:T-seq reanalysis, we establish a low basal error rate at unmodified nucleotides that is not skewed to any specific mismatch type and a prominent increase in C:T substitutions as the dominant mismatch type in both treated wild-type replicates, with a high degree of reproducibility across replicates. In contrast, through ac4C-seq reanalysis, we uncover significant data quality issues including insufficient depth, with one wild-type replicate yielding 2.7 million reads, inconsistencies in reduction efficiencies between replicates, and an overall increase in mismatches involving thymine that could obscure ac4C detection. These analyses bolster the detection of ac4C in HeLa mRNA through RedaC:T-seq.

Multivariate Analysis of RNA Chemistry Marks Uncovers Epitranscriptomics-Based Biomarker Signature for Adult Diffuse Glioma Diagnostics

One of the main challenges in cancer management relates to the discovery of reliable biomarkers, which could guide decision-making and predict treatment outcome. In particular, the rise and democratization of high-throughput molecular profiling technologies bolstered the discovery of “biomarker signatures” that could maximize the prediction performance. Such an approach was largely employed from diverse OMICs data (i.e., genomics, transcriptomics, proteomics, metabolomics) but not from epitranscriptomics, which encompasses more than 100 biochemical modifications driving the post-transcriptional fate of RNA: stability, splicing, storage, and translation. We and others have studied chemical marks in isolation and associated them with cancer evolution, adaptation, as well as the response to conventional therapy. In this study, we have designed a unique pipeline combining multiplex analysis of the epitranscriptomic landscape by high-performance liquid chromatography coupled to tandem mass spectrometry with statistical multivariate analysis and machine learning approaches in order to identify biomarker signatures that could guide precision medicine and improve disease diagnosis. We applied this approach to analyze a cohort of adult diffuse glioma patients and demonstrate the existence of an “epitranscriptomics-based signature” that permits glioma grades to be discriminated and predicted with unmet accuracy. This study demonstrates that epitranscriptomics (co)evolves along cancer progression and opens new prospects in the field of omics molecular profiling and personalized medicine.