Landscape and Regulation of m6A and m6Am Methylome across Human and Mouse Tissues

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The detection and functions of RNA modification m6A based on m6A writers and erasers

N⁶-methyladenosine (m⁶A) is the most frequent chemical modification in eukaryotic mRNA and is known to participate in a variety of physiological processes, including cancer progression and viral infection. The reversible and dynamic m⁶A modification is installed by m⁶A methyltransferase (writer) enzymes and erased by m⁶A demethylase (eraser) enzymes. m⁶A modification recognized by m⁶A binding proteins (readers) regulates RNA processing and metabolism, leading to downstream biological effects such as promotion of stability and translation or increased degradation. The m⁶A writers and erasers determine the abundance of m⁶A modifications and play decisive roles in its distribution and function. In this review, we focused on m⁶A writers and erasers and present an overview on their known functions and enzymatic molecular mechanisms, showing how they recognize substrates and install or remove m⁶A modifications. We also summarize the current applications of m⁶A writers and erasers for m⁶A detection and highlight the merits and drawbacks of these available methods. Lastly, we describe the biological functions of m⁶A in cancers and viral infection based on research of m⁶A writers and erasers and introduce new assays for m⁶A functionality via programmable m⁶A editing tools.     

Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning

N6-methyladenosine (m⁶A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m⁶A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m⁶A sites with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m⁶A sites from miCLIP data remains challenging. Here, we present miCLIP2 in combination with machine learning to significantly improve m⁶A detection. The optimized miCLIP2 results in high-complexity libraries from less input material. Importantly, we established a robust computational pipeline to tackle the inherent issue of false positives in antibody-based m⁶A detection. The analyses were calibrated with Mettl3 knockout cells to learn the characteristics of m⁶A deposition, including m⁶A sites outside of DRACH motifs. To make our results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m⁶A sites in miCLIP2 data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m⁶A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m⁶A identification.     

RNA m6A methylation across the transcriptome

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Stability of RNA hairpin loops: A 6 -C m -U 6

The thermodynamics and circular dichroism of a series of A₆-C_m-U₆ (m = 4, 5, 6 or 8) oligoribonucleotides have been studied. These molecules form intramolecular hairpin loops at low temperatures and therefore are useful models for similar structures which occur in larger, natural RNA molecules. The stability of the helix forming the stem of these loops was found to be considerably greater than an intermolecular helix with the same length and composition. The most stable loop is m = 6. The enthalpy for initiation of the loop is unfavorable; it ranges from + 24 kcal, for m = 4 to + 21 kcal, for m = 6. The maximum in stability for the C₆ loop and the large positive enthalpy for loop initiation are in disagreement with expectations from simple theories assuming a Gaussian distribution of end-to-end distances. Loop strain for m = 4 and m = 5 and the unstacking of the cytosines on loop formation are likely physical explanations for these thermodynamic data. The circular dichroism spectrum of cytosine residues in the C₆ and C₈ loops is very similar to the spectrum of single-stranded oligoribocytidylate. However, the cytosine residues in the C₅ loop have a very different circular dichroism spectrum from the corresponding oligo(C₅) spectrum. In accordance with the thermodynamic data, we conclude from the circular dichroism data that the C₅ loop has an altered conformation from the C₅ and C₈ loops.     

Exclusion of m6A from splice-site proximal regions by the exon junction complex dictates m6A topologies and mRNA stability

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RNA m6A meets transposable elements and chromatin

N6-methyladenosine (m6A) is the most abundant internal chemical mark in eukaryotic messenger RNAs (mRNAs),regulating various processes in the life cycle of mRNA...     

Prognostic signature and immune efficacy of m1A-, m5C- and m6A-related regulators in cutaneous melanoma

Cutaneous melanoma (CM) is an aggressive cancer; given that initial and specific signs are lacking, diagnosis is often late and the prognosis is poor. RNA modification has been widely studied in tumour progression. Nevertheless, little progress has been made in the signature of N¹-methyladenosine (m¹A), 5-methylcytosine (m⁵C), N⁶-methyladenosine (m⁶A)-related regulators and the tumour microenvironment (TME) cell infiltration in CM. Our study identified the characteristics of m¹A-, m⁵C- and m⁶A-related regulators based on 468 CM samples from the public database. Using univariate, multivariate and LASSO Cox regression analysis, a risk model of regulators was established and validated by a nomogram on independent prognostic factors. The gene set variation analysis (GSVA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) clarified the involved functional pathways. A combined single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT approach revealed TME of regulator-related prognostic signature. The nine-gene signature stratified the patients into distinct risk subgroups for personalized prognostic assessment. Additionally, functional enrichment, immune infiltration and immunotherapy response analysis indicated that the high-risk group was correlated with T-cell suppression, while the low-risk group was more sensitive to immunotherapy. The findings presented here contribute to our understanding of the TME molecular heterogeneity in CM. Nine m¹A-, m⁵C- and m⁶A-related regulators may also be promising biomarkers for future research.