Thanks to visit codestin.com
Credit goes to link.springer.com

Skip to main content
Springer Nature Link
Log in

“Matreshka” genes with alternative reading frames

  • Reviews and Theoretical Articles
  • Published:
Russian Journal of Genetics Aims and scope Submit manuscript

Abstract

Although a relatively small part of the human genome contains protein encoding genes, the latest data on the discovery of alternative open reading frames (ORFs) in conventional mRNAs has highlighted the expanded coding potential of these genes. Until recently, it was believed that each mRNA transcript encodes a single protein. Recent proteogenomics data indicate the existence of exceptions to this rule, which greatly changes the usual meaning of the term “gene.” The topology of a gene with overlapping ORFs resembles a Russian “matreshka” toy. There are two levels of “matreshka” genetic systems. First, the chromosomal level, when the “nested” gene is located within introns and exons of the main chromosomal gene, both in the sense and antisense orientation relative to the external gene. The second level is a mature mRNA molecule containing overlapping ORFs or an ORF with an alternative start codon. In this review, we will focus on the properties of “matreshka” genes of the second type and methods for their detection and verification. Particular attention is paid to the biological properties of the polypeptides encoded by these genes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from £29.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  1. Griffiths, P.E. and Stotz, K., Genes in the postgenomic era, Theor. Med. Bioeth., 2006, vol. 27, no. 6, pp. 499–521. doi 10.1007/s11017-006-9020-y

    Article  PubMed  Google Scholar 

  2. Gerstein, M.B., Bruce, C., Rozowsky, J.S., et al., What is a gene, post-ENCODE? History and updated definition, Genome Res., 2007, vol. 17, no. 6, pp. 669–681. doi 10.1101/gr.6339607

    CAS  PubMed  Google Scholar 

  3. Rogic, S., Mackworth, A.K., and Ouellette, F.B., Evaluation of gene-finding programs on mammalian sequences, Genome Res., 2001, vol. 11, no. 5, pp. 817–832. doi 10.1101/gr.147901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chow, L.T., Gelinas, R.E., Broker, T.R., and Roberts, R.J., An amazing sequence arrangement at the 5’ ends of adenovirus 2 messenger RNA, Cell, 1977, vol. 12, no. 1, pp. 1–8.

    Article  CAS  PubMed  Google Scholar 

  5. Brosius, J., The fragmented gene, Ann. New York Acad. Sci., 2009, vol. 1178, pp. 186–193. doi 10.1111/j.1749-6632.2009.05004x

    Article  CAS  Google Scholar 

  6. Yazaki, J., Gregory, B.D., and Ecker, J.R., Mapping the genome landscape using tiling array technology, Curr. Opin. Plant Biol., 2007, vol. 10, no. 5, pp. 534–542. doi 10.1016/jpbi.2007.07.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mortazavi, A., Williams, B.A., McCue, K., et al., Mapping and quantifying mammalian transcriptomes by RNA-Seq, Nat. Methods, 2008, vol. 5, no. 7, pp. 621–628. doi 10.1038/nmeth.1226

    Article  CAS  PubMed  Google Scholar 

  8. The ENCODE Project Consortium, Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project, Nature, 2007, vol. 447, no. 7146, pp. 799–816. doi: 10.1038/nature05874

    Article  CAS  Google Scholar 

  9. Mercer, T.R. and Mattick, J.S., Understanding the regulatory and transcriptional complexity of the genome through structure, Genome Res., 2013, vol. 23, no. 7, pp. 1081–1088. doi 10.1101/gr.156612.113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mercer, T.R., Clark, M.B., Andersen, S.B., et al., Genome-wide discovery of human splicing branchpoints, Genome Res., 2015, vol. 25, no. 2, pp. 290–303. doi 10.1101/gr.182899.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Baboo, S. and Cook, P.R., “Dark matter” worlds of unstable RNA and protein, Nucl. Austin Tex., 2014, vol. 5, no. 4, pp. 281–286. doi 10.4161/nucl.29577

    Google Scholar 

  12. Goldman, S.R., Ebright, R.H., and Nickels, B.E., Direct detection of abortive RNA transcripts in vivo, Science, 2009, vol. 324, no. 5929, pp. 927–928. doi 10.1126/science.1169237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kapranov, P. and St Laurent, G., Dark matter RNA: existence, function, and controversy, Front. Genet., 2012, vol. 3, p. 60. doi 10.3389/fgene.2012.00060

    CAS  PubMed  Google Scholar 

  14. Pearson, H., Genetics: what is a gene?, Nature, 2006, vol. 441, no. 7092, pp. 398–401. doi 10.1038/441398a

    Article  CAS  PubMed  Google Scholar 

  15. Raabe, C.A. and Brosius, J., Does every transcript originate from a gene?, Ann. New York Acad. Sci., 2015, vol. 1341, pp. 136–148. doi 10.1111/nyas.12741

    Article  CAS  Google Scholar 

  16. International Human Genome Sequencing Consortium, Initial sequencing and analysis of the human genome, Nature, 2001, vol. 409, no. 6822, pp. 860–921. doi 10.1038/35057062

    Article  Google Scholar 

  17. Liang, F., Holt, I., Pertea, G., et al., Gene index analysis of the human genome estimates approximately 120,000 genes, Nat. Genet., 2000, vol. 25, no. 2, pp. 239–240. doi 10.1038/76126

    Article  CAS  PubMed  Google Scholar 

  18. Claverie, J.M., Gene number: what if there are only 30,000 human genes?, Science, 2001, vol. 291, no. 5507, pp. 1255–1257.

    Article  CAS  PubMed  Google Scholar 

  19. Ezkurdia, I., Juan, D., Rodriguez, J.M., et al., Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes, Hum. Mol. Genet., 2014, vol. 23, no. 22, pp. 5866–5878. doi 10.1093/hmg/ddu309

    Article  PubMed  PubMed Central  Google Scholar 

  20. Flicek, P., Amode, M.R., Barrell, D., et al., Ensembl 2014, Nucleic Acids Res., 2014, vol. 42, pp. 749–755. doi 10.1093/nar/gkt1196

    Article  CAS  Google Scholar 

  21. Bamshad, M.J., Chong, J.X., Buckingham, K.J., et al., The genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities, Am. J. Hum. Genet., 2015, vol. 97, no. 2, pp. 199–215. doi 10.1016/jajhg.2015.06.009

    PubMed  Google Scholar 

  22. Tattini, L., D’Aurizio, R., and Magi, A., Detection of genomic structural variants from next-generation sequencing data, Front. Bioeng. Biotechnol., 2015, vol. 3, p. 92. doi 10.3389/fbioe.2015.00092

    Article  PubMed  PubMed Central  Google Scholar 

  23. Harrison, P.M., Kumar, A., Lang, N., et al., A question of size: the eukaryotic proteome and the problems in defining it, Nucleic Acids Res., 2002, vol. 30, no. 5, pp. 1083–1090.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sudmant, P.H., Mallick, S., Nelson, B.J., et al., Global diversity, population stratification, and selection of human copy number variation, Science, 2015, p. aab3761. doi 10.1126/scienceaab3761

    Google Scholar 

  25. Zhuravleva, G.A., The birth and death of genes, Russ. J. Genet., 2015, vol. 51, no. 1, pp. 9–21. doi 10.1134/S1022795415010159

    Article  CAS  Google Scholar 

  26. Jeffery, C.J., Moonlighting proteins: old proteins learning new tricks, Trends Genet., 2003, vol. 19, no. 8, pp. 415–417. doi 10.1016/S0168-9525(03)00167-7

    Article  CAS  PubMed  Google Scholar 

  27. Jung, D.-W., Kim, W.-H., and Williams, D.R., Chemical genetics and its application to moonlighting in glycolytic enzymes, Biochem. Soc. Trans., 2014, vol. 42, no. 6, pp. 1756–1761. doi 10.1042/BST20140201

    Article  CAS  PubMed  Google Scholar 

  28. Dorokhov, Y.L., Komarova, T.V., Petrunia, I.V., et al., Methanol may function as a cross-kingdom signal, PLoS One, 2012, vol. 7, no. 4. e36122. doi 10.1371/journalpone.0036122

    Article  CAS  Google Scholar 

  29. Shindyapina, A.V., Petrunia, I.V., Komarova, T.V., et al., Dietary methanol regulates human gene activity, PLoS One, 2014, vol. 9, no. 7. e102837. doi 10.1371/journalpone.0102837

    Article  Google Scholar 

  30. Xu, H., Wang, P., Fu, Y., et al., Length of the ORF, position of the first AUG and the Kozak motif are important factors in potential dual-coding transcripts, Cell Res., 2010, vol. 20, no. 4, pp. 445–457. doi 10.1038/cr.2010.25

    CAS  PubMed  Google Scholar 

  31. Gibson, C.W., Thomson, N.H., Abrams, W.R., and Kirkham, J., Nested genes: biological implications and use of AFM for analysis, Gene, 2005, vol. 350, no. 1, pp. 15–23. doi 10.1016/jgene.2004.12.045

    Article  CAS  PubMed  Google Scholar 

  32. Ho, M.-R., Tsai, K.-W., and Lin, W., A unified framework of overlapping genes: towards the origination and endogenic regulation, Genomics, 2012, vol. 100, no. 4, pp. 231–239. doi 10.1016/jygeno.2012.06.011

    Article  CAS  PubMed  Google Scholar 

  33. Ribrioux, S., Brüngger, A., Baumgarten, B., et al., Bioinformatics prediction of overlapping frameshifted translation products in mammalian transcripts, BMC Genomics, 2008, vol. 9, p. 122. doi 10.1186/14712164-9-122

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Andrews, S.J. and Rothnagel, J.A., Emerging evidence for functional peptides encoded by short open reading frames, Nat. Rev. Genet., 2014, vol. 15, no. 3, pp. 193–204. doi 10.1038/nrg3520

    Article  CAS  PubMed  Google Scholar 

  35. Hayden, C.A. and Bosco, G., Comparative genomic analysis of novel conserved peptide upstream open reading frames in Drosophila melanogaster and other dipteran species, BMC Genomics, 2008, vol. 9, p. 61. doi 10.1186/1471-2164-9-61

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Yang, X., Tschaplinski, T.J., Hurst, G.B., et al., Discovery and annotation of small proteins using genomics, proteomics, and computational approaches, Genome Res., 2011, vol. 21, no. 4, pp. 634–641. doi 10.1101/gr.109280.110

    CAS  PubMed  Google Scholar 

  37. Law, G.L., Raney, A., Heusner, C., and Morris, D.R., Polyamine regulation of ribosome pausing at the upstream open reading frame of S-adenosylmethionine decarboxylase, J. Biol. Chem., 2001, vol. 276, no. 41, pp. 38036–38043. doi 10.1074/jbc.M105944200

    CAS  PubMed  Google Scholar 

  38. Iacono, M., Mignone, F., and Pesole, G., uAUG and uORFs in human and rodent 5’ untranslated mRNAs, Gene, 2005, vol. 349, pp. 97–105. doi 10.1016/jgene.2004.11.041

    Article  CAS  PubMed  Google Scholar 

  39. Crowe, M.L., Wang, X.-Q., and Rothnagel, J.A., Evidence for conservation and selection of upstream open reading frames suggests probable encoding of bioactive peptides, BMC Genomics, 2006, vol. 7, p. 16. doi 10.1186/1471-2164-7-16

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Mercer, T.R., Wilhelm, D., Dinger, M.E., et al., Expression of distinct RNAs from 3’ untranslated regions, Nucleic Acids Res., 2011, vol. 39, no. 6, pp. 2393–2403. doi 10.1093/nar/gkq1158

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chew, G.-L., Pauli, A., Rinn, J.L., et al., Ribosome profiling reveals resemblance between long non-coding RNAs and 5’ leaders of coding RNAs, Dev. Camb. Engl., 2013, vol. 140, no. 13, pp. 2828–2834. doi 10.1242/dev.098343

    CAS  Google Scholar 

  42. Ladoukakis, E., Pereira, V., Magny, E.G., et al., Hundreds of putatively functional small open reading frames in Drosophila, Genome Biol., 2011, vol. 12, no. 11, p. R118. doi 10.1186/gb-2011-12-11-r118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Slavoff, S.A., Mitchell, A.J., Schwaid, A.G., et al., Peptidomic discovery of short open reading frameencoded peptides in human cells, Nat. Chem. Biol., 2013, vol. 9, no. 1, pp. 59–64. doi 10.1038/nchembio.1120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Lauressergues, D., Couzigou, J.-M., Clemente, H.S., et al., Primary transcripts of microRNAs encode regulatory peptides, Nature, 2015, vol. 520, no. 7545, pp. 90–93. doi 10.1038/nature14346

    Article  CAS  PubMed  Google Scholar 

  45. Waterhouse, P.M. and Hellens, R.P., Plant biology: coding in non-coding RNAs, Nature, 2015, vol. 520, no. 7545, pp. 41–42. doi 10.1038/nature14378

    Article  CAS  PubMed  Google Scholar 

  46. Brent, M.R. and Guigó, R., Recent advances in gene structure prediction, Curr. Opin. Struct. Biol., 2004, vol. 14, no. 3, pp. 264–272. doi 10.1016/jsbi.2004.05.007

    Article  CAS  PubMed  Google Scholar 

  47. Wang, J., Li, S., Zhang, Y., et al., Vertebrate gene predictions and the problem of large genes, Nat. Rev. Genet., 2003, vol. 4, no. 9, pp. 741–749. doi 10.1038/nrg1160

    Article  CAS  PubMed  Google Scholar 

  48. Sleator, R.D., An overview of the current status of eukaryote gene prediction strategies, Gene, 2010, vol. 461, nos. 1–2, pp. 1–4. doi 10.1016/jgene.2010.04.008

    Article  CAS  PubMed  Google Scholar 

  49. Frith, M.C., Forrest, A.R., Nourbakhsh, E., et al., The abundance of short proteins in the mammalian proteome, PLoS Genet., 2006, vol. 2, no. 4. e52. doi 10.1371/journalpgen.0020052

    Article  CAS  Google Scholar 

  50. Hanada, K., Zhang, X., Borevitz, J.O., et al., A large number of novel coding small open reading frames in the intergenic regions of the Arabidopsis thaliana genome are transcribed and/or under purifying selection, Genome Res., 2007, vol. 17, no. 5, pp. 632–640. doi 10.1101/gr.5836207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cheng, H., Chan, W.S., Li, Z., et al., Small open reading frames: current prediction techniques and future prospect, Curr. Protein Pept. Sci., 2011, vol. 12, no. 6, pp. 503–507.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Hanada, K., Akiyama, K., Sakurai, T., et al., sORF finder: a program package to identify small open reading frames with high coding potential, Bioinform. Oxf. Engl., 2010, vol. 26, no. 3, pp. 399–400. doi 10.1093/bioinformatics/btp688

    Article  CAS  Google Scholar 

  53. Vanderperre, B., Lucier, J.-F., and Roucou, X., HAltORF: a database of predicted out-of-frame alternative open reading frames in human, Database J. Biol. Databases Curation, 2012, vol. 2012. bas025. doi 10.1093/database/bas025

    Google Scholar 

  54. Skarshewski, A., Stanton-Cook, M., Huber, T., et al., uPEPperoni: an online tool for upstream open reading frame location and analysis of transcript conservation, BMC Bioinform., 2014, vol. 15, p. 36. doi 10.1186/1471-2105-15-36

    Article  CAS  Google Scholar 

  55. Zhang, Z. and Dietrich, F.S., Identification and characterization of upstream open reading frames (uORF) in the 5’ untranslated regions (UTR) of genes in Saccharomyces cerevisiae, Curr. Genet., 2005, vol. 48, no. 2, pp. 77–87. doi 10.1007/s00294-005-0001-x

    Article  CAS  PubMed  Google Scholar 

  56. Clamp, M., Fry, B., Kamal, M., et al., Distinguishing protein-coding and noncoding genes in the human genome, Proc. Natl. Acad. Sci. U.S.A., 2007, vol. 104, no. 49, pp. 19428–19433. doi 10.1073/pnas. 0709013104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kozak, M., An analysis of 5’-noncoding sequences from 699 vertebrate messenger RNAs, Nucleic Acids Res., 1987, vol. 15, no. 20, pp. 8125–8148.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Noderer, W.L., Flockhart, R.J., Bhaduri, A., et al., Quantitative analysis of mammalian translation initiation sites by FACS-seq, Mol. Syst. Biol., 2014, vol. 10, p. 748.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Karlin, S., Campbell, A.M., and Mrázek, J., Comparative DNA analysis across diverse genomes, Annu. Rev. Genet., 1998, vol. 32, pp. 185–225. doi 10.1146/annurevgenet.32.1.185

    Article  CAS  PubMed  Google Scholar 

  60. Bateman, A., Coin, L., Durbin, R., et al., The Pfam protein families database, Nucleic Acids Res., 2004, vol. 32, database issue, pp. 138–141. doi 10.1093/nar/gkh121

    Article  CAS  Google Scholar 

  61. Castrignanò, T., Canali, A. Grillo, G., et al., CSTminer: a web tool for the identification of coding and noncoding conserved sequence tags through crossspecies genome comparison, Nucleic Acids Res., 2004, vol. 32, web server issue, pp. 624–627. doi 10.1093/nar/gkh486

    Article  CAS  Google Scholar 

  62. Badger, J.H. and Olsen, G.J., CRITICA: coding region identification tool invoking comparative analysis, Mol. Biol. Evol., 1999, vol. 16, no. 4, pp. 512–524.

    Article  CAS  PubMed  Google Scholar 

  63. Kong, L., Zhang, Y., Ye, Z.-Q., et al., CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine, Nucleic Acids Res., 2007, vol. 35, web server issue, pp. 345–349. doi 10.1093/nar/gkm391

    Article  Google Scholar 

  64. Ingolia, N.T., Lareau, L.F., and Weissman, J.S., Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes, Cell, 2011, vol. 147, no. 4, pp. 789–802. doi 10.1016/jcell.2011.10.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Studtmann, K., Olschläger-Schütt, J., Buck, F., et al., A non-canonical initiation site is required for efficient translation of the dendritically localized Shank1 mRNA, PLoS One, 2014, vol. 9, no. 2. e88518. doi 10.1371/journalpone.0088518

    Article  CAS  Google Scholar 

  66. Ingolia, N.T., Ghaemmaghami, S., Newman, J.R.S., and Weissman, J.S., Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling, Science, 2009, vol. 324, no. 5924, pp. 218–223. doi 10.1126/science.1168978

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Ivanov, I.P., Firth, A.E., Michel, A.M., et al., Identification of evolutionarily conserved non-AUG-initiated N-terminal extensions in human coding sequences, Nucleic Acids Res., 2011, vol. 39, no. 10, pp. 4220–4234. doi 10.1093/nar/gkr007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Carninci, P., Sandelin, A., Lenhard, B., et al., Genome-wide analysis of mammalian promoter architecture and evolution, Nat. Genet., 2006, vol. 38, no. 6, pp. 626–635. doi 10.1038/ng1789

    Article  CAS  PubMed  Google Scholar 

  69. The FANTOM Consortium and Riken Omics Science Center, The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line, Nat. Genet., 2009, vol. 41, no. 5, pp. 553–562. doi 10.1038/ng.375

    Article  CAS  Google Scholar 

  70. Kodzius, R., Kojima, M., Nishiyori, H., et al., CAGE: cap analysis of gene expression, Nat. Methods, 2006, vol. 3, no. 3, pp. 211–222. doi 10.1038/nmeth0306211

    Article  CAS  PubMed  Google Scholar 

  71. Ni, T., Corcoran, D.L., Rach, E.A., et al., A pairedend sequencing strategy to map the complex landscape of transcription initiation, Nat. Methods, 2010, vol. 7, no. 7, pp. 521–527. doi 10.1038/nmeth.1464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Plessy, C., Bertin, N., Takahashi, H., et al., Linking promoters to functional transcripts in small samples with nanoCAGE and CAGEscan, Nat. Methods, 2010, vol. 7, no. 7, pp. 528–534. doi 10.1038/nmeth.1470

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Batut, P., Dobin, A., Plessy, C., et al., High-fidelity promoter profiling reveals widespread alternative promoter usage and transposon-driven developmental gene expression, Genome Res., 2013, vol. 23, no. 1, pp. 169–180. doi 10.1101/gr.139618.112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Andreev, D.E., O’Connor, P.B.F., Fahey, C., et al., Translation of 5’ leaders is pervasive in genes resistant to eIF2 repression, eLife, 2015, vol. 4. e03971. doi 10.7554/eLife.03971

    Article  PubMed  Google Scholar 

  75. Andreev, D.E., O’Connor, P.B.F., Zhdanov, A.V., et al., Oxygen and glucose deprivation induces widespread alterations in mRNA translation within 20 minutes, Genome Biol., 2015, vol. 16, p. 90. doi 10.1186/s13059-015-0651-z

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Huang, M.T., Harringtonine, an inhibitor of initiation of protein biosynthesis, Mol. Pharmacol., 1975, vol. 11, no. 5, pp. 511–519.

    CAS  PubMed  Google Scholar 

  77. Menschaert, G., Van Criekinge, W., Notelaers, T., et al., Deep proteome coverage based on ribosome profiling aids mass spectrometry-based protein and peptide discovery and provides evidence of alternative translation products and near-cognate translation initiation events, Mol. Cell. Proteomics, 2013, vol. 12, no. 7, pp. 1780–1790. doi 10.1074/mcp.M113.027540

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Guttman, M., Russell, P., Ingolia, N.T., et al., Ribosome profiling provides evidence that large noncoding RNAs do not encode proteins, Cell, 2013, vol. 154, no. 1, pp. 240–251. doi 10.1016/jcell.2013.06.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Lykke-Andersen, J. and Bennett, E.J., Protecting the proteome: eukaryotic cotranslational quality control pathways, J. Cell Biol., 2014, vol. 204, no. 4, pp. 467–476. doi 10.1083/jcb.201311103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Boisvert, F.-M., Ahmad, Y., Gierlinski, M., et al., A quantitative spatial proteomics analysis of proteome turnover in human cells, Mol. Cell. Proteomics, 2012, vol. 11, no. 3, M111.011429. doi 10.1074/mcp. M111.011429

  81. Fritsch, C., Herrmann, A., Nothnagel, M., et al., Genome-wide search for novel human uORFs and Nterminal protein extensions using ribosomal footprinting, Genome Res., 2012, vol. 22, no. 11, pp. 2208–2218. doi 10.1101/gr.139568.112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Krug, K., Nahnsen, S., and Macek, B., Mass spectrometry at the interface of proteomics and genomics, Mol. Biosyst., 2011, vol. 7, no. 2, pp. 284–291. doi 10.1039/c0mb00168f

    Article  CAS  PubMed  Google Scholar 

  83. Chu, Q., Ma, J., and Saghatelian, A., Identification and characterization of sORF-encoded polypeptides, Crit. Rev. Biochem. Mol. Biol., 2015, vol. 50, no. 2, pp. 134–141. doi 10.3109/10409238.2015.1016215

    Article  CAS  PubMed  Google Scholar 

  84. Vanderperre, B., Lucier, J.-F., Bissonnette, C., et al., Direct detection of alternative open reading frames translation products in human significantly expands the proteome, PLoS One, 2013, vol. 8, no. 8. e70698. doi 10.1371/journalpone.0070698

    Article  CAS  Google Scholar 

  85. Oyama, M., Itagaki, C., Hata, H., et al., Analysis of small human proteins reveals the translation of upstream open reading frames of mRNAs, Genome Res., 2004, vol. 14, no. 10B, pp. 2048–2052. doi 10.1101/gr.2384604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Oyama, M., Kozuka-Hata, H., Suzuki, Y., et al., Diversity of translation start sites may define increased complexity of the human short ORFeome, Mol. Cell. Proteomics, 2007, vol. 6, no. 6, pp. 1000–1006. doi 10.1074/mcp.M600297-MCP200

    Article  CAS  PubMed  Google Scholar 

  87. Michel, A.M., Choudhury, K.R., Firth, A.E., et al., Observation of dually decoded regions of the human genome using ribosome profiling data, Genome Res., 2012, vol. 22, no. 11, pp. 2219–2229. doi 10.1101/gr.133249.111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Sanna, C.R., Li, W.-H., and Zhang, L., Overlapping genes in the human and mouse genomes, BMC Genomics, 2008, vol. 9, p. 169. doi 10.1186/1471-2164-9-169

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Kim, D.-S., Cho, C.-Y., Huh, J.-W., et al., EVOG: a database for evolutionary analysis of overlapping genes, Nucleic Acids Res., 2009, vol. 37, database issue, pp. 698–702. doi 10.1093/nar/gkn813

    Article  CAS  Google Scholar 

  90. Ho, M.-R., Tsai, K.-W., and Lin, W., A unified framework of overlapping genes: towards the origination and endogenic regulation, Genomics, 2012, vol. 100, no. 4, pp. 231–239. doi 10.1016/jygeno.2012.06.011

    Article  CAS  PubMed  Google Scholar 

  91. Cherezov, R.O. and Simonov, O.B., Overlapping genes and antisense transcription in eukaryotes, Russ. J. Genet., 2014, vol. 50, no. 7, pp. 653–666.

    Article  CAS  Google Scholar 

  92. Johnson, Z.I. and Chisholm, S.W., Properties of overlapping genes are conserved across microbial genomes, Genome Res., 2004, vol. 14, no. 11, pp. 2268–2272. doi 10.1101/gr.2433104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Uetz, P., Rajagopala, S.V., Dong, Y.-A., and Haas, J., From ORFeomes to protein interaction maps in viruses, Genome Res., 2004, vol. 14, no. 10b. doi 10.1101/gr.2583304

    Article  CAS  Google Scholar 

  94. Keese, P.K. and Gibbs, A., Origins of genes: “big bang” or continuous creation?, Proc. Natl. Acad. Sci. U.S.A., 1992, vol. 89, no. 20, pp. 9489–9493.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Yu, P., Ma, D., and Xu, M., Nested genes in the human genome, Genomics, 2005, vol. 86, no. 4, pp. 414–422. doi 10.1016/jygeno.2005.06.008

    Article  CAS  PubMed  Google Scholar 

  96. Gao, C., Xiao, M., Ren, X., et al., Characterization and functional annotation of nested transposable elements in eukaryotic genomes, Genomics, 2012, vol. 100, no. 4, pp. 222–230. doi 10.1016/jygeno.2012.07.004

    Article  CAS  PubMed  Google Scholar 

  97. Kumar, A., An overview of nested genes in eukaryotic genomes, Eukaryot. Cell, 2009, vol. 8, no. 9, pp. 1321–1329. doi 10.1128/EC.00143-09

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Wang, R.F., Parkhurst, M.R., Kawakami, Y., et al., Utilization of an alternative open reading frame of a normal gene in generating a novel human cancer antigen, J. Exp. Med., 1996, vol. 183, no. 3, pp. 1131–1140.

    Article  CAS  PubMed  Google Scholar 

  99. Ronsin, C., Chung-Scott, V., Poullion, I., et al., A non-AUG-defined alternative open reading frame of the intestinal carboxyl esterase mRNA generates an epitope recognized by renal cell carcinoma-reactive tumor-infiltrating lymphocytes in situ, J. Immunol., 1999, vol. 163, no. 1, pp. 483–490.

    CAS  PubMed  Google Scholar 

  100. Chung, W.-Y., Wadhawan, S., Szklarczyk, R., et al., A first look at ARFome: dual-coding genes in mammalian genomes, PLoS Comput. Biol., 2007, vol. 3, no. 5. e91. doi 10.1371/journalpcbi.0030091

    Article  CAS  Google Scholar 

  101. Topalian, S.L., Solomon, D., Avis, F.P., et al., Immunotherapy of patients with advanced cancer using tumor-infiltrating lymphocytes and recombinant interleukin-2: a pilot study, J. Clin. Oncol., 1988, vol. 6, no. 5, pp. 839–853.

    CAS  PubMed  Google Scholar 

  102. Wang, R.F., Robbins, P.F., Kawakami, Y., et al., Identification of a gene encoding a melanoma tumor antigen recognized by HLA-A31-restricted tumor-infiltrating lymphocytes, J. Exp. Med., 1995, vol. 181, no. 2, pp. 799–804.

    Article  CAS  PubMed  Google Scholar 

  103. Wang, R.F. and Rosenberg, S.A., Human tumor antigens recognized by T lymphocytes: implications for cancer therapy, J. Leukocyte Biol., 1996, vol. 60, no. 3, pp. 296–309.

    CAS  PubMed  Google Scholar 

  104. Vanderperre, B., Staskevicius, A.B., Tremblay, G., et al., An overlapping reading frame in the PRNP gene encodes a novel polypeptide distinct from the prion protein, FASEB J., 2011, vol. 25, no. 7, pp. 2373–2386. doi 10.1096/fj.10-173815

    Article  CAS  PubMed  Google Scholar 

  105. Orr, H.T., Chung, M.Y., Banfi, S., et al., Expansion of an unstable trinucleotide CAG repeat in spinocerebellar ataxia type 1, Nat. Genet., 1993, vol. 4, no. 3, pp. 221–226. doi 10.1038/ng0793-221

    Article  CAS  PubMed  Google Scholar 

  106. Bergeron, D., Lapointe, C., Bissonnette, C., et al., An out-of-frame overlapping reading frame in the ataxin-1 coding sequence encodes a novel ataxin-1 interacting protein, J. Biol. Chem., 2013, vol. 288, no. 30, pp. 21824–21835. doi 10.1074/jbc.M113.472654

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Somers, J., Pöyry, T., and Willis, A.E., A perspective on mammalian upstream open reading frame function, Int. J. Biochem. Cell Biol., 2013, vol. 45, no. 8, pp. 1690–1700. doi 10.1016/jbiocel.2013.04.020

    Article  CAS  PubMed  Google Scholar 

  108. Calvo, S.E., Pagliarini, D.J., and Mootha, V.K., Upstream open reading frames cause widespread reduction of protein expression and are polymorphic among humans, Proc. Natl. Acad. Sci. U.S.A., 2009, vol. 106, no. 18, pp. 7507–7512. doi 10.1073/pnas. 0810916106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Jorgensen, R.A. and Dorantes-Acosta, A.E., Conserved peptide upstream open reading frames are associated with regulatory genes in angiosperms, Front. Plant Sci., 2012, vol. 3, p. 191. doi 10.3389/fpls. 2012.00191

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Wethmar, K., Smink, J.J., and Leutz, A., Upstream open reading frames: molecular switches in (patho)physiology, BioEssays News Rev. Mol. Cell. Dev. Biol., 2010, vol. 32, no. 10, pp. 885–893. doi 10.1002/bies.201000037

    Article  CAS  Google Scholar 

  111. Wiestner, A., Schlemper, R.J., van der Maas, A.P., and Skoda, R.C., An activating splice donor mutation in the thrombopoietin gene causes hereditary thrombocythaemia, Nat. Genet., 1998, vol. 18, no. 1, pp. 49–52. doi 10.1038/ng0198-49

    Article  CAS  PubMed  Google Scholar 

  112. Liu, L., Dilworth, D., Gao, L., et al., Mutation of the CDKN2A 5’ UTR creates an aberrant initiation codon and predisposes to melanoma, Nat. Genet., 1999, vol. 21, no. 1, pp. 128–132. doi 10.1038/5082

    Article  PubMed  CAS  Google Scholar 

  113. Wen, Y., Liu, Y., Xu, Y., et al., Loss-of-function mutations of an inhibitory upstream ORF in the human hairless transcript cause Marie Unna hereditary hypotrichosis, Nat. Genet., 2009, vol. 41, no. 2, pp. 228–233. doi 10.1038/ng.276

    Article  CAS  PubMed  Google Scholar 

  114. Zhou, D., Palam, L.R., Jiang, L., et al., Phosphorylation of eIF2 directs ATF5 translational control in response to diverse stress conditions, J. Biol. Chem., 2008, vol. 283, no. 11, pp. 7064–7073. doi 10.1074/jbc.M708530200

    Article  CAS  PubMed  Google Scholar 

  115. Vattem, K.M. and Wek, R.C., Reinitiation involving upstream ORFs regulates ATF4 mRNA translation in mammalian cells, Proc. Natl. Acad. Sci. U.S.A., 2004, vol. 101, no. 31, pp. 11269–11274. doi 10.1073/pnas. 0400541101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Ghilardi, N., Wiestner, A., and Skoda, R.C., Thrombopoietin production is inhibited by a translational mechanism, Blood, 1998, vol. 92, no. 11, pp. 4023–4030.

    CAS  PubMed  Google Scholar 

  117. Stockklausner, C., Breit, S., Neu-Yilik, G., et al., The uORF-containing thrombopoietin mRNA escapes nonsense-mediated decay (NMD), Nucleic Acids Res., 2006, vol. 34, no. 8, pp. 2355–2363. doi 10.1093/nar/gkl277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Lee, C., Lai, H.-L., Lee, Y.-C., et al., The A2A adenosine receptor is a dual coding gene: a novel mechanism of gene usage and signal transduction, J. Biol. Chem., 2014, vol. 289, no. 3, pp. 1257–1270. doi 10.1074/jbc.M113.509059

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Hashimoto, Y., Kondo, T., and Kageyama, Y., Lilliputians get into the limelight: novel class of small peptide genes in morphogenesis, Dev. Growth Differ., 2008, vol. 50, suppl. 1, pp. S269–S276. doi 10.1111/j.1440169X.2008.00994x

    Article  CAS  PubMed  Google Scholar 

  120. Van Damme, P., Gawron, D., Van Criekinge, W., and Menschaert, G., N-terminal proteomics and ribosome profiling provide a comprehensive view of the alternative translation initiation landscape in mice and men, Mol. Cell. Proteomics, 2014, vol. 13, no. 5, pp. 1245–1261. doi 10.1074/mcp.M113.036442

    Article  PubMed  CAS  Google Scholar 

  121. Ma, J., Ward, C.C., Jungreis, I., et al., Discovery of human sORF-encoded polypeptides (SEPs) in cell lines and tissue, J. Proteome Res., 2014, vol. 13, no. 3, pp. 1757–1765. doi 10.1021/pr401280w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Kochetov, A.V., Prayaga, P.D., Volkova, O.A., and Sankararamakrishnan, R., Hidden coding potential of eukaryotic genomes: nonAUG started ORFs, J. Biomol. Struct. Dyn., 2013, vol. 31, no. 1, pp. 103–114. doi 10.1080/07391102.2012.691367

    Article  CAS  PubMed  Google Scholar 

  123. Pestova, T.V. and Kolupaeva, V.G., The roles of individual eukaryotic translation initiation factors in ribosomal scanning and initiation codon selection, Genes Dev., 2002, vol. 16, no. 22, pp. 2906–2922. doi 10.1101/gad.1020902

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Ivanov, I.P., Loughran, G., Sachs, M.S., and Atkins, J.F., Initiation context modulates autoregulation of eukaryotic translation initiation factor 1 (eIF1), Proc. Natl. Acad. Sci. U.S.A., 2010, vol. 107, no. 42, pp. 18056–18060. doi 10.1073/pnas.1009269107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Castellana, N.E., Payne, S.H., Shen, Z., et al., Discovery and revision of Arabidopsis genes by proteogenomics, Proc. Natl. Acad. Sci. U.S.A., 2008, vol. 105, no. 52, pp. 21034–21038. doi 10.1073/pnas. 0811066106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Hanada, K., Higuchi-Takeuchi, M., Okamoto, M., et al., Small open reading frames associated with morphogenesis are hidden in plant genomes, Proc. Natl. Acad. Sci. U.S.A., 2013, vol. 110, no. 6, pp. 2395–2400. doi 10.1073/pnas.1213958110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Vaughn, J.N., Ellingson, S.R., Mignone, F., and von Arnim, A., Known and novel post-transcriptional regulatory sequences are conserved across plant families, RNA N.Y.N., 2012, vol. 18, no. 3, pp. 368–384. doi 10.1261/rna.031179.111.

    Article  CAS  Google Scholar 

  128. Tran, M.K., Schultz, C.J., and Baumann, U., Conserved upstream open reading frames in higher plants, BMC Genomics, 2008, vol. 9, p. 361. doi 10.1186/14712164-9-361

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  129. Hayden, C.A. and Jorgensen, R.A., Identification of novel conserved peptide uORF homology groups in Arabidopsis and rice reveals ancient eukaryotic origin of select groups and preferential association with transcription factor-encoding genes, BMC Biol., 2007, vol. 5, p. 32. doi 10.1186/1741-7007-5-32

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  130. Juntawong, P., Girke, T., Bazin, J., and Bailey-Serres, J., Translational dynamics revealed by genome-wide profiling of ribosome footprints in Arabidopsis, Proc. Natl. Acad. Sci. U.S.A., 2014, vol. 111, no. 1, pp. E203–E212. doi 10.1073/pnas.1317811111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Dong, X., Wang, D., Liu, P., et al., Zm908p11, encoded by a short open reading frame (sORF) gene, functions in pollen tube growth as a profilin ligand in maize, J. Exp. Bot., 2013, vol. 64, no. 8, pp. 2359–2372. doi 10.1093/jxb/ert093

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vavilov Institute of General Genetics, Russian Academy of Science, Moscow, 119991, Russia

    E. V. Sheshukova, A. V. Shindyapina, T. V. Komarova & Yu. L. Dorokhov

  2. Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, 119991, Russia

    A. V. Shindyapina, T. V. Komarova & Yu. L. Dorokhov

Authors
  1. E. V. Sheshukova
    View author publications

    Search author on:PubMed Google Scholar

  2. A. V. Shindyapina
    View author publications

    Search author on:PubMed Google Scholar

  3. T. V. Komarova
    View author publications

    Search author on:PubMed Google Scholar

  4. Yu. L. Dorokhov
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Yu. L. Dorokhov.

Additional information

Published in Russian in Genetika, 2016, Vol. 52, No. 2, pp. 146–163.

The article was translated by the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheshukova, E.V., Shindyapina, A.V., Komarova, T.V. et al. “Matreshka” genes with alternative reading frames. Russ J Genet 52, 125–140 (2016). https://doi.org/10.1134/S1022795416020149

Download citation

  • Received:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1134/S1022795416020149

Keywords