Dissecting the effects of METTL3 on alternative splicing in prostate cancer

Abstract

Although the role of METTL3 has been extensively studied in many cancers, its role in isoform switching in prostate cancer (PCa) has been poorly explored. To investigate its role, we applied standard RNA-sequencing and long-read direct RNA-sequencing from Oxford Nanopore to examine how METTL3 affects alternative splicing (AS) in two PCa cell lines. By dissecting genome-wide METTL3-regulated AS events, we noted that two PCa cell lines (representing two different PCa subtypes, androgen-sensitive or resistant) behave differently in exon skipping and intron retention events following METTL3 depletion, suggesting AS heterogeneity in PCa. Moreover, we revealed that METTL3-regulated AS is dependent on N6-methyladenosine (m6A) and distinct splicing factors. Analysis of the AS landscape also revealed cell type specific AS signatures for some genes (e.g., MKNK2) involved in key functions in PCa tumorigenesis. Finally, we also validated the clinical relevance of MKNK2 AS events in PCa patients and pointed to the possible regulatory mechanism related to m6A in the exon14a/b region and SRSF1. Overall, we characterize the role of METTL3 in regulating PCa-associated AS programs, expand the role of METTL3 in tumorigenesis, and suggest that MKNK2 AS events may serve as a new potential prognostic biomarker.

Principal Component Analysis of Alternative Splicing Profiles Revealed by Long-Read ONT Sequencing in Human Liver Tissue and Hepatocyte-Derived HepG2 and Huh7 Cell Lines

“…In fact, they are a substitute of truly AS profiling in terms of splice variants (transcript isoforms) per gene. These metrics continued to be used in the ONT data analysis (e.g., [23][24][25]) despite the fact that, in long-read ONT sequencing, the standard treatment of raw sequencing data directly provides the abundance of each and every transcript isoform and, consequently, allows us to easily calculate the number of splice variant (transcript isoforms) expressed by each gene. That makes the number of transcript isoforms per gene an intrinsically suitable metric for alternative splicing (AS) profiling in the application to a particular type of RNA sequencing such as ONT sequencing.…”

Comparison of Alternative Splicing Landscapes Revealed by Long-Read Sequencing in Hepatocyte-Derived HepG2 and Huh7 Cultured Cells and Human Liver Tissue

“…Yet, they can rather be considered as substitutes for true AS profiling, and their wide use in the short-read RNA-seq is stipulated by ambiguities in the identification and quantification of different transcript isoforms that are originated from the same gene. Though these metrics continue to be used in the analysis of long-read ONT sequencing data (e.g., [10][11][12]), AS profiles are also described in terms of the expression of single transcript isoforms (as opposed to the gene expression, which is an integral expression of all the detected transcript isoforms assigned to the gene) [13][14][15], since, in this case, the standard treatment of raw sequencing data directly provides the splice variant abundances for each and every gene. Here, we utilized both approaches-by presenting AS profiles either as an array of all the transcript isoforms with quantified abundances or as an array of the genes with the corresponding DAS values-and applied them to differentiate the AS in three types of biosamples: from normal human liver tissue and from two hepatocyte-derived malignant cell lines.…”

“…This makes the normalized abundance of single (individual) transcript isoforms (rather than the gene expression measured as an integral normalized abundance of transcript isoforms ascribed to the gene) the more appropriate metric for the analysis of AS profiles in the case of long-read ONT sequencing than the 'exon usage' or PSI index. Indeed, though the 'exon usage' or PSI index continue to be used for AS profiling based on long-read sequencing data (e.g., [10][11][12]), the description of AS profiles in terms of the abundance of single isoforms has also been utilized in ONT-based transcriptome-wide studies (e.g., [13][14][15]). On the other hand, as we recently suggested [16], AS profiles can be described regardless of a particular expression of a given transcript isoform as arrays of genes, where each gene is characterized by the number of detected splice variants ascribed to that gene (here referred to as the 'degree of alternative splicing', or the DAS).…”