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

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 m⁶A-methylation is widely associated with various cancers and neurological, cardiovascular, and developmental disorders, which demand early diagnosis. A robust m⁶A-motif prediction is necessary to enable us to identify the regulatory nucleic acid sequences that determine mRNA fate in normal and diseased conditions.

We have developed a transcript-aware computational pipeline, termed m⁶A Functional Index in Transcription (m⁶A-FINDiT), that can identify potential m⁶A sites on mRNA transcripts, considering molecular intricacies associated with their secondary structure. This tool can separately identify m⁶A 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 m⁶A 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.

This integrated m⁶A-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 m⁶A methylation at certain DRACH motifs. The m⁶A-FINDiT pipeline could successfully predict the specific m⁶A 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.