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      m 6A independent genome-wide METTL3 and METTL14 redistribution drives senescence-associated secretory phenotype

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          Abstract

          Methyltransferase-like 3 (METTL3) and 14 (METTL14) are core subunits of the methyltransferase complex (MTC) that catalyzes mRNA N 6-methyladenosine (m 6A) modification. Despite the expanding list of m 6A-dependent function of the MTC, m 6A independent function of the METTL3 and METTL14 complex remains poorly understood. Here we show that genome-wide redistribution of METTL3 and METTL14 transcriptionally drives senescence-associated secretory phenotype (SASP) in a m 6A-independent manner. METTL14 is redistributed to the enhancers, while METTL3 is localized to the pre-existing NF-κB sites within the promoters of SASP genes during senescence. METTL3 and METTL14 are necessary for SASP. However, SASP is not regulated by m 6A mRNA modification. METTL3 and METTL14 are required for both the tumor-promoting and immune surveillance functions of senescent cells mediated by SASP in vivo in mouse models. In summary, our results report a m 6A independent function of the METTL3 and METTL14 complex in transcriptionally promoting SASP during senescence.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          Michael Love, Wolfgang Huber, Simon Anders (2014)
          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Fast gapped-read alignment with Bowtie 2.

            Ben Langmead, Steven L Salzberg (2022)
            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

              Bo Li, Colin Dewey (2011)
              Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 100890575
                Journal ID (pubmed-jr-id): 21417
                Journal ID (nlm-ta): Nat Cell Biol
                Journal ID (iso-abbrev): Nat Cell Biol
                Title: Nature cell biology
                ISSN (Print): 1465-7392
                ISSN (Electronic): 1476-4679
                Publication date Nihms-submitted: 4 March 2021
                Publication date (Electronic): 01 April 2021
                Publication date (Print): April 2021
                Publication date PMC-release: 01 October 2021
                Volume: 23
                Issue: 4
                Pages: 355-365
                Affiliations
                [1 ]Immunology, Microenvironment and Metastasis Program, The Wistar Institute, Philadelphia, PA 19104, USA
                [2 ]Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
                [3 ]Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
                Author notes

                Author Contributions

                P.L., F.L., J.L., T.F., T.N., and X.H. performed the experiments and analysed data. A.V.K. performed the bioinformatic analysis. P.L. and R.Z. designed the experiments. F.L., J.L., T.F. and T.N. contributed to study design. P.L., A.V.K and R.Z. wrote the manuscript. C.S. and R.Z. supervised studies. R.Z. conceived the study.

                [* ]Correspondence should be addressed to: Rugang Zhang, Ph.D., rzhang@ 123456wistar.org
                Article
                Manuscript ID: NIHMS1678412
                DOI: 10.1038/s41556-021-00656-3
                PMC ID: 8035315
                PubMed ID: 33795874
                SO-VID: 999bc3ab-a044-4051-9244-d4cd77ad1ca1
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                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                ScienceOpen disciplines: Cell biology
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                ScienceOpen disciplines: Cell biology

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