Human cancer cells compensate the genes unfavorable for translation by <i>N</i> <sup>6</sup>-methyladenosine modification and enhance their translation efficiency

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Abstract

Background

N ⁶-methyladenosine (m ⁶A) is the methylation of RNA adenosines that participates in multiple biological processes, such as facilitating translation of host genes via the reader protein YTHDF1. The core writer protein of m ⁶A in humans is METTL3.

Methods

We utilized YTHDF1 target genes and normal or si- METTL3 NGS (next-generation sequencing) data from HeLa cells generated by a previous work and collected known human oncogenes from a website. We evaluated the translation capability of these m ⁶A genes or oncogenes by comparing their mRNA lengths and codon usage bias. Additionally, we calculated the translation efficiency of all genes expressed in the normal or si- METTL3 HeLa cells using NGS data.

Results

The m ⁶A genes are enriched in oncogenes compared to the non-m ⁶A genes. We observed significantly longer mRNA lengths for the m ⁶A genes, especially for the oncogenes. We also observed stronger codon usage bias for the m ⁶A genes than for the non-m ⁶A genes. We provided evidence that the longer mRNA lengths and stronger codon bias were unfavorable for translation. However, this disadvantage was compensated by m ⁶A modification because the m ⁶A genes but not the non-m ⁶A genes showed higher translation efficiencies in normal cells than in si- METTL3 cells.

Conclusions

HeLa cells compensate for genes unfavorable for translation by m ⁶A modification and enhance their translation efficiency. This compensation could originally have been designed for oncogenes, since we observed enrichment of m ⁶A genes in the oncogenes. If oncogenes modified by m ⁶A obtain higher translation efficiencies and eventually facilitate cancer cell proliferation, then this strategy may be used by cancers for rapid cell growth.

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Most cited references 31

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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HTSeq—a Python framework to work with high-throughput sequencing data

Simon Anders, Paul Pyl, Wolfgang Huber (2014)

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

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A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.

Pablo E. Cingolani, Adrian E. Platts, Le Lily Wang … (2012)

We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w(1118); iso-2; iso-3 strain and the reference y(1); cn(1) bw(1) sp(1) strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in the 5'UTR. We found, as expected, that the SNP frequency is proportional to the recombination frequency (i.e., highest in the middle of chromosome arms). We also found that start-gain or stop-lost SNPs in Drosophila melanogaster often result in additions of N-terminal or C-terminal amino acids that are conserved in other Drosophila species. It appears that the 5' and 3' UTRs are reservoirs for genetic variations that changes the termini of proteins during evolution of the Drosophila genus. As genome sequencing is becoming inexpensive and routine, SnpEff enables rapid analyses of whole-genome sequencing data to be performed by an individual laboratory.

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Author and article information

Journal

Journal ID (nlm-ta): Transl Cancer Res

Journal ID (iso-abbrev): Transl Cancer Res

Journal ID (publisher-id): TCR

Title: Translational Cancer Research

Publisher: AME Publishing Company

ISSN (Print): 2218-676X

ISSN (Electronic): 2219-6803

Publication date (Print and electronic): April 2019

Publication date (Print): April 2019

Volume: 8

Issue: 2

Pages: 499-508

Affiliations

[1]deptCollege of Life Sciences , Beijing Normal University , Beijing 100000, China

Author notes

Contributions: (I) Conception and design: L Wei; (II) Administrative support: None; (III) Provision of study materials or patients: None; (IV) Collection and assembly of data: All authors; (V) Data analysis and interpretation: None; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Lai Wei, PhD. College of Life Sciences, Beijing Normal University, No. 19 Xinjiekouwai Street, Haidian District, Beijing 100000, China. Email: weilai_bnu@ 123456163.com .

Article

Publisher ID: tcr-08-02-499

DOI: 10.21037/tcr.2019.03.04

PMC ID: 8797713

PubMed ID: 35116782

SO-VID: 5c30ab8f-370c-486f-a41f-ca9581e6922e

License:

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

History

Date received : 23 October 2018

Date accepted : 05 March 2019

Funding

Funded by: the National Natural Science Foundation of China

Award ID: Grant no. 31770213

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