Germline Elongator mutations in Sonic Hedgehog medulloblastoma

Nature



  • 1.

    Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).




  • 2.

    Zhang, J. et al. Germline Mutations in Predisposition Genes in Pediatric Cancer. N. Engl. J. Med. 373, 2336–2346 (2015).




  • 3.

    Waszak, S. M. et al. Spectrum and prevalence of genetic predisposition in medulloblastoma: a retrospective genetic study and prospective validation in a clinical trial cohort. Lancet Oncol. 19, 785–798 (2018).




  • 4.

    Cavalli, F. M. G. et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 31, 737–754 (2017).




  • 5.

    Hawer, H. et al. Roles of elongator dependent tRNA Modification pathways in neurodegeneration and Cancer. Genes 10, E19 (2018).




  • 6.

    Johansson, M. J. O., Xu, F. & Byström, A. S. Elongator-a tRNA modifying complex that promotes efficient translational decoding. Biochim. Biophys. Acta. Gene Regul. Mech. 1861, 401–408 (2018).




  • 7.

    Goffena, J. et al. Elongator and codon bias regulate protein levels in mammalian peripheral neurons. Nat. Commun. 9, 889 (2018).




  • 8.

    Laguesse, S. et al. A dynamic unfolded protein response contributes to the control of cortical neurogenesis. Dev. Cell 35, 553–567 (2015).




  • 9.

    Nedialkova, D. D. & Leidel, S. A. Optimization of codon translation rates via tRNA modifications maintains proteome integrity. Cell 161, 1606–1618 (2015).




  • 10.

    Rahman, N. Realizing the promise of cancer predisposition genes. Nature 505, 302–308 (2014).




  • 11.

    Aydin, D. et al. Mobile phone use and brain tumors in children and adolescents: a multicenter case-control study. J. Natl. Cancer Inst. 103, 1264–1276 (2011).


  • 12.

    Karczewski, K. J. et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. Preprint at https://www.bioRxiv.org/content/10.1101/531210v3 (2019).


  • 13.

    The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).




  • 14.

    Kool, M. et al. Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition. Cancer Cell 25, 393–405 (2014).




  • 15.

    Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).




  • 16.

    Schwalbe, E. C. et al. Novel molecular subgroups for clinical classification and outcome prediction in childhood medulloblastoma: a cohort study. Lancet Oncol. 18, 958–971 (2017).




  • 17.

    Robinson, G. W. et al. Risk-adapted therapy for young children with medulloblastoma (SJYC07): therapeutic and molecular outcomes from a multicentre, phase 2 trial. Lancet Oncol. 19, 768–784 (2018).




  • 18.

    Dauden, M. I. et al. Architecture of the yeast Elongator complex. EMBO Rep. 18, 264–279 (2017).




  • 19.

    Setiaputra, D. T. et al. Molecular architecture of the yeast Elongator complex reveals an unexpected asymmetric subunit arrangement. EMBO Rep. 18, 280–291 (2017).




  • 20.

    Rubin, B. Y. & Anderson, S. L. IKBKAP/ELP1 gene mutations: mechanisms of familial dysautonomia and gene-targeting therapies. Appl. Clin. Genet. 10, 95–103 (2017).




  • 21.

    Yoshida, M. et al. Rectifier of aberrant mRNA splicing recovers tRNA modification in familial dysautonomia. Proc. Natl Acad. Sci. USA 112, 2764–2769 (2015).




  • 22.

    Gold-von Simson, G., Romanos-Sirakis, E., Maayan, C. & Axelrod, F. B. Neoplasia in familial dysautonomia: a 20-year review in a young patient population. J. Pediatr. 155, 934–936 (2009).




  • 23.

    Shvartsbeyn, M., Rapkiewicz, A., Axelrod, F. & Kaufmann, H. Increased incidence of tumors with the IKBKAP gene mutation? A case report and review of the literature. World J. Oncol. 2, 41–44 (2011).




  • 24.

    Hetz, C. & Saxena, S. ER stress and the unfolded protein response in neurodegeneration. Nat. Rev. Neurol. 13, 477–491 (2017).




  • 25.

    Forget, A. et al. Aberrant ERBB4–SRC signaling as a hallmark of group 4 medulloblastoma revealed by integrative phosphoproteomic profiling. Cancer Cell 34, 379–395 (2018).




  • 26.

    Argelaguet, R. et al. Multi-omics factor analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).




  • 27.

    Creppe, C. et al. Elongator controls the migration and differentiation of cortical neurons through acetylation of α-tubulin. Cell 136, 551–564 (2009).




  • 28.

    Huang, B., Johansson, M. J. & Byström, A. S. An early step in wobble uridine tRNA modification requires the Elongator complex. RNA 11, 424–436 (2005).




  • 29.

    Murphy, F. V. IV, Ramakrishnan, V., Malkiewicz, A. & Agris, P. F. The role of modifications in codon discrimination by tRNALysUUU. Nat. Struct. Mol. Biol. 11, 1186–1191 (2004).



  • 30.

    The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).




  • 31.

    Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).




  • 32.

    Carter, R. A. et al. A single-cell transcriptional atlas of the developing murine cerebellum. Curr. Biol. 28, 2910–2920 (2018).




  • 33.

    Begemann, M. et al. Germline GPR161 mutations predispose to pediatric medulloblastoma. J. Clin. Oncol. 38, 43–50 (2019).




  • 34.

    Tan, A., Abecasis, G. R. & Kang, H. M. Unified representation of genetic variants. Bioinformatics 31, 2202–2204 (2015).




  • 35.

    McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).




  • 36.

    Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).




  • 37.

    Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).




  • 38.

    Mallick, S. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016).




  • 39.

    Waszak, S. M. et al. Population variation and genetic control of modular chromatin architecture in humans. Cell 162, 1039–1050 (2015).




  • 40.

    Ainsworth, H. F., Shin, S. Y. & Cordell, H. J. A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements. Genet. Epidemiol. 41, 577–586 (2017).




  • 41.

    Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).




  • 42.

    Schubert, O. T. et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat. Protoc. 10, 426–441 (2015).




  • 43.

    Poullet, P., Carpentier, S. & Barillot, E. myProMS, a web server for management and validation of mass spectrometry-based proteomic data. Proteomics 7, 2553–2556 (2007).




  • 44.

    Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).




  • 45.

    Shao, W. et al. Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins. Nat. Commun. 10, 2524 (2019).




  • 46.

    Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014).




  • 47.

    Aken, B. L. et al. The Ensembl gene annotation system. Database (Oxford) 2016, baw093 (2016).




  • 48.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).




  • 49.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).


  • 50.

    Sergushichev, A. An algorithm for fast preranked gene set enrichment. Preprint at https://www.bioRxiv.org/content/10.1101/060012v1 (2016).




  • 51.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).




  • 52.

    Doerks, T., Copley, R. R., Schultz, J., Ponting, C. P. & Bork, P. Systematic identification of novel protein domain families associated with nuclear functions. Genome Res. 12, 47–56 (2002).




  • 53.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).




  • 54.

    Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.11–11.10.33 (2013).




  • 55.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).




  • 56.

    Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).




  • 57.

    Su, D. et al. Quantitative analysis of ribonucleoside modifications in tRNA by HPLC-coupled mass spectrometry. Nat. Protoc. 9, 828–841 (2014).




  • 58.

    Machnicka, M. A. et al. MODOMICS: a database of RNA modification pathways—2013 update. Nucleic Acids Res. 41, D262–D267 (2013).

  • Articles You May Like

    Demo-2 launch wins political praise, but future funding uncertain
    Rise of remote working is ‘biggest threat to oil demand,’ says analyst
    The mystery of missing marine plastic – Physics World
    Jeremy Conrad left his own VC firm to start a company, and investors like what he’s building
    Russia applauds SpaceX launch but calls Trump’s reaction ‘hysteria’

    Leave a Reply

    Your email address will not be published. Required fields are marked *