Exclusion of m6A from splice-site-proximal regions by the exon-junction complex dictates m6A topologies and mRNA stability
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ABSTRACT:
PROVIDER: PRJNA842956 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR19429600_1.fastq.gz | Fastqsanger.gz | |||
| SRR19429600_2.fastq.gz | Fastqsanger.gz | |||
| SRR19429700_1.fastq.gz | Fastqsanger.gz | |||
| SRR19429700_2.fastq.gz | Fastqsanger.gz | |||
| SRR19429601_1.fastq.gz | Fastqsanger.gz |
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Project description:N6-methyladenosine (m6A), a widespread destabilizing mark on mRNA, is non-uniformly distributed across the transcriptome, yet the basis for its selective deposition is unknown. Here, we uncover that m6A deposition is not selective. Instead, m6A distribution is exclusion-based: m6A-consensus harboring sites are methylated by default, unless they are within a window of up to ~200 nt from an exon-intron junction. A simple model which we extensively validate, relying exclusively on presence of m6A motifs and exon-intron architecture allows high accuracy recapitulation of experimentally-measured m6A profiles and of the vast majority of m6A hallmarks. Finally, we provide strong evidence that this exclusion is mediated by the exon-junction complex. Our findings establish a mechanism by which the memory of nuclear RNA splicing is covalently etched on an mRNA in the form of m6A, and determines its cytoplasmic stability, with broad implications on the regulation, function, and evolution of the exon-junction complex and m6A.
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Project description:N6-methyladenosine (m6A) is the most abundant post-transcriptional methylation of mature mRNA and plays a crucial regulatory role in various biological functions, involving development and cancer progression. m6A is deposited by methyltransferase complex composed of METTL3 and METTL14 cotranscriptionally, and removed by demethylase FTO or ALKBH5. m6A is highly enriched within the 3’ UTRs and in the vicinity of the stop codon of mature mRNA. However, the mechanism that causes this distribution pattern is still enigmatic. Here, we show that EJC packages mRNAs to shape mRNA m6A landscape. We first tested the possibility that demethylase removes m6A during spicing and found that ALKBH5 depletion had a minor effect on m6A levels. Thus, we ruled out the demethylation model. We then hypothesized that splicing factors inhibit the methylation process of internal exons, but not the 3’ UTR. Knockdown of EIF4A3, a core component of the exon junction complex, significantly increased m6A levels in mature mRNAs. The hypermethylated sites are enriched in short internal exons. Furthermore, EIF4A3 depletion upregulates METTL3 binding affinity to mRNA to deposit m6As. In conclusion, Our results demonstrate that EIF4A3 blocks METTL3 binding and methylating internal short exons of mRNA. These findings shed light on the fact that RNA packaging formed during splicing acts as a regulator that shapes mRNA modifications.
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Project description:MudPIT datasets for NaOH-extracted and Salt-and-Detergent (SD)-extracted rat nuclear membranes were obtained from skeletal muscle and liver tissue as described in the following references:
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The trypsin-digested MS datasets from these studies were searched again using ProLuCID against a database containing tissue-specific junction sequences as such:
We analyzed RNA-Seq data from five rat tissues (heart, skeletal muscle, liver, brain, and testes) produced by two separate research groups, hereby referred to as GSE4 (Yu, Y., J.C. Fuscoe, C. Zhao, C. Guo, M. Jia, T. Qing, et al., A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages. Nat Commun, 2014; 5: 3230.) and GSE5 (Merkin, J., C. Russell, P. ChenC.B. Burge, Evolutionary dynamics of gene and isoform regulation in Mammalian tissues. Science, 2012; 338(6114): 1593-9.)
GSE4 has higher read coverage over junctions, but only one suitable sample per tissue while GSE5 has lower coverage but many replicates. Novel junctions identified in both datasets were considered to be high confidence. Differentially spliced transcripts were determined and quantified using Modeling Alternative Junction Inclusion Quantification (MAJIQ) software. Splice junctions rather than whole isoforms were quantified due to the known limitations in reconstructing whole transcripts from short read data. The software builds splice graphs from RNA-Seq reads spanning spliced junctions, incorporates un-annotated junctions, and uses information from replicate samples to build a Bayesian posterior distribution, outputting the expected percentage spliced in values (E|PSI|) corresponding to the percentage of transcripts that are expected to contain the given splice junction or in the case of a comparison, change in PSI (E|dPSI). In accordance with previous literature, we considered an alternative splicing event between tissues when E|dPSI greater than 0.2.
Tissue-specific junction sequence databases were generated for rat muscle and liver tissues. In each case the union of all splice junctions detected by STAR aligner for each replicate from GSE4 and GSE5 was taken. Junctions were filtered to remove junctions supported by less than 6 reads and junctions predicting an intron length of less than 60 nucleotides (a standard cut-off as there are very few smaller annotated junctions). Junction coordinates were extended by 66 nucleotides in both directions, and then translated in three frames according to the directionality of the gene. This produced peptide sequences of about 44 amino acids in length. Sequences were removed if the translation produced a stop codon before the junction based on standard practice. Novel exons and intron retentions predicted by MAJIQ were similarly translated in three frames and added to the database, but were removed if the translated sequence was less than 7 amino acids long. All novel and annotated junctions were combined with novel exons, intron retentions, and Ensembl rat protein database sequences to produce a final protein sequence database. Finally, coordinates of junction peptides were checked against the original fasta sequence to be sure that the peptide crossed the junction with a start less or equal to 22 amino acids away and an end greter or equal to 22 amino acids away.
2018-01-18 | MSV000081951 | MassIVE
Project description:N6–methyladenosine (m6A) is the most abundant mRNA modification and plays crucial roles in diverse physiological processes. Utilizing a Massively Parallel Assay for m6A (MPm6A), we discover that m6A specificity is globally regulated by “suppressors” that prevent m6A deposition in unmethylated transcriptome regions. We identify Exon Junction Complexes (EJCs) as m6A suppressors that protect exon junction-proximal RNA within coding sequences from methylation and regulate mRNA stability through m6A suppression. EJC suppression of m6A underlies multiple global characteristics of mRNA m6A specificity, with the local range of EJC protection sufficient to suppress m6A deposition in average-length internal exons, but not in long internal and terminal exons. EJC-suppressed methylation sites co-localize with EJC-suppressed splice sites, suggesting that exon architecture broadly determines local mRNA accessibility to regulatory complexes.
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Project description:HITI junction characterization
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Project description:N6-methyladenosine (m6A) is one of the most abundant modifications in eukaryotic RNA. Recent mapping of m6A methylomes in mammals, yeast, and plants as well as characterization of m6A methyltransferases, demethylases, and binding proteins have revealed regulatory functions of this dynamic RNA modification. In bacteria, although m6A is present in ribosomal RNA (rRNA), its occurrence in messenger RNA (mRNA) still remains elusive. Here, we used liquid chromatography-mass spectrometry (LC-MS) to calculate the m6A/A ratio in mRNA from a wide range of bacterial species, which demonstrates that m6A is an abundant mRNA modification in tested bacteria. Subsequent transcriptome-wide m6A profiling in Escherichia coli and Pseudomonas aeruginosa revealed a conserved distinct m6A pattern that is significantly different from that in eukaryotes. Most m6A peaks are located inside open reading frames (ORF), and carry a unique consensus motif (GCCAU). Functional enrichment analysis of bacterial m6A peaks indicates that the majority of m6A-modified transcripts are associated with respiration, amino acids metabolism, stress response, and small RNAs genes, suggesting potential regulatory roles of m6A in these pathways. m6A profiling in E.coli and P.aeruginosa mRNA
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