A novel method for the identification and quantification of N6-methyladenosine motifs in RNA transcripts

Background N6-methyladenosine profiles of mRNA transcripts regulate their translocation from the nucleus to the cytosol, stability, and translational efficiency; hence, they have been implicated in gene expression and disease progression. The m 6 A-methylation is widely associated with various cancers and neurological, cardiovascular, and developmental disorders, which demand early diagnosis. A robust m 6 A-motif prediction is necessary to enable us to identify the regulatory nucleic acid sequences that determine mRNA fate in normal and diseased conditions. Methods and Results We have developed a transcript-aware computational pipeline, termed m 6 A Functional Index in Transcription (m 6 A-FINDiT), that can identify potential m 6 A sites on mRNA transcripts, considering molecular intricacies associated with their secondary structure. This tool can separately identify m 6 A motifs within the coding sequences as well as in non-translatable regions, i.e., 5’UTR and 3’UTR, of mRNA transcripts. Parallelly, another technique was developed that quantifies specific m 6 A methylation motifs through a probe-based ELISA process, MAQ-G. This second method successfully validated the N⁶-methyladenosine motifs predicted by the initially developed motif-finder program. Conclusion This integrated m 6 A-FINDiT and MAQ-G, coupled with a real-time qPCR assay, could correlate the methylation profiles of N6-methyladenosine motifs with the expression and stability contours of a gene. To establish the physiological implications of these techniques, we chose three tumour-suppressor genes, viz., IRF8 , RB1 , and TP53 mRNA transcripts, which may undergo m 6 A methylation at certain DRACH motifs. The m 6 A-FINDiT pipeline could successfully predict the specific m 6 A motifs, and the MAQ-G confirmed the methylation profile of the latter. These duo techniques hold potential for use in clinical settings for early cancer detection.