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Long intervening non-coding RNA 00320 is human brain-specific and highly expressed in the cortical white matter

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Abstract

Pervasive transcription of the genome produces a diverse array of functional non-coding RNAs (ncRNAs). One particular class of ncRNAs, long intervening non-coding RNAs (lincRNAs) are thought to play a role in regulating gene expression and may be a major contributor to organism and tissue complexity. The human brain with its heterogeneous cellular make-up is a rich source of lincRNAs; however, the functions of the majority of lincRNAs are unknown. Recently, by completing RNA sequencing (RNA-Seq) of the human frontal cortex, we identified linc00320 as being highly expressed in the white matter compared to grey matter in multiple system atrophy (MSA) brain. Here, we further investigate the expression patterns of linc00320 and conclude that it is involved in specific brain regions rather than having involvement in the MSA disease process. We also show that the full-length linc00320 is only expressed in human brain tissue and not in other primates, suggesting that it may be involved in improved functional connectivity for higher human brain cognition.

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References

  1. Vinogradov AE, Anatskaya OV (2007) Organismal complexity, cell differentiation and gene expression: human over mouse. Nucleic Acids Res 35(19):6350–6356. doi:10.1093/nar/gkm723

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Nolte J, Sundsten J (2002) The human brain: an introduction to its functional anatomy, 5th edn. Mosby, China

    Google Scholar 

  3. Saladin K (2010) Anatomy and physiology: the unity of form and function, 5th edn. McGraw-Hill, New York

    Google Scholar 

  4. Martin J, Howard J, Leonard M (2004) Neuroanatomy: text and atlas, vol., 3rd edn. McGraw-Hill, USA

  5. Underwood J, Cross S (2009) General and systematic pathology, 5th edn. Elsevier Limited, China

    Google Scholar 

  6. Schoenemann PT, Sheehan MJ, Glotzer LD (2005) Prefrontal white matter volume is disproportionately larger in humans than in other primates. Nat Neurosci 8(2):242–252. doi:10.1038/nn1394

    Article  CAS  PubMed  Google Scholar 

  7. Smaers JB, Schleicher A, Zilles K, Vinicius L (2010) Frontal white matter volume is associated with brain enlargement and higher structural connectivity in anthropoid primates. PLoS One 5(2):e9123. doi:10.1371/journal.pone.0009123

    Article  PubMed Central  PubMed  Google Scholar 

  8. Zhang K, Sejnowski TJ (2000) A universal scaling law between gray matter and white matter of cerebral cortex. Proc Natl Acad Sci U S A 97(10):5621–5626. doi:10.1073/pnas.090504197

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Mills JD, Kavanagh T, Kim WS, Chen BJ, Kawahara Y, Halliday GM, Janitz M (2013) Unique transcriptome patterns of the white and grey matter corroborate structural and functional heterogeneity in the human frontal lobe. PLoS One 8(10):e78480. doi:10.1371/journal.pone.0078480

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Mattick JS (2011) The central role of RNA in human development and cognition. FEBS Lett 585(11):1600–1616. doi:10.1016/j.febslet.2011.05.001

    Article  CAS  PubMed  Google Scholar 

  11. Mattick JS (2001) Non-coding RNAs: the architects of eukaryotic complexity. EMBO Rep 2(11):986–991. doi:10.1093/embo-reports/kve230

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Qureshi IA, Mehler MF (2012) Emerging roles of non-coding RNAs in brain evolution, development, plasticity and disease. Nat Rev Neurosci 13(8):528–541. doi:10.1038/nrn3234

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Qureshi IA, Mattick JS, Mehler MF (2010) Long non-coding RNAs in nervous system function and disease. Brain Res 1338:20–35. doi:10.1016/j.brainres.2010.03.110

    Article  CAS  PubMed  Google Scholar 

  14. Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, Rinn JL (2011) Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev 25(18):1915–1927. doi:10.1101/gad.17446611

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Duret L, Chureau C, Samain S, Weissenbach J, Avner P (2006) The Xist RNA gene evolved in eutherians by pseudogenization of a protein-coding gene. Science 312(5780):1653–1655. doi:10.1126/science.1126316

    Article  CAS  PubMed  Google Scholar 

  16. Ulitsky I, Shkumatava A, Jan CH, Sive H, Bartel DP (2011) Conserved function of lincRNAs in vertebrate embryonic development despite rapid sequence evolution. Cell 147(7):1537–1550. doi:10.1016/j.cell.2011.11.055

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Sone M, Hayashi T, Tarui H, Agata K, Takeichi M, Nakagawa S (2007) The mRNA-like noncoding RNA Gomafu constitutes a novel nuclear domain in a subset of neurons. J Cell Sci 120(Pt 15):2498–2506. doi:10.1242/jcs.009357

    Article  CAS  PubMed  Google Scholar 

  18. Rapicavoli NA, Poth EM, Blackshaw S (2010) The long noncoding RNA RNCR2 directs mouse retinal cell specification. BMC Dev Biol 10:49. doi:10.1186/1471-213X-10-49

    Article  PubMed Central  PubMed  Google Scholar 

  19. Mills JD, Kim WS, Halliday GM, Janitz M (2014) Transcriptome analysis of grey and white matter cortical tissue in multiple system atrophy. Neurogenetics. doi:10.1007/s10048-014-0430-0

    PubMed  Google Scholar 

  20. Mills JD, Kavanagh T, Kim WS, Chen B, Waters PD, Halliday GM, Janitz M (2015) High expression of long intervening non-coding RNA OLMALINC in the human cortical white matter is associated with regulation of oligodendrocyte maturation. Mol Brain 8(1):2. doi:10.1186/s13041-014-0091-9

    Article  PubMed Central  PubMed  Google Scholar 

  21. Gilman S, Wenning G, Low P, Brooks D, Mathias C, Trojanowski J, Wood NW, Colosimo C, Dürr A, Fowler C, Kaufmann H, Klockgether T, Lees A, Poewe W, Quinn N, Revesz T, Robertson D, Sandroni P, Seppi K, Vidailhet M (2008) Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71(9):670–676

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Lu CF, Soong BW, Wu HM, Teng S, Wang PS, Wu YT (2013) Disrupted cerebellar connectivity reduces whole‐brain network efficiency in multiple system atrophy. Mov Disord 28(3):362–369

    Article  PubMed  Google Scholar 

  23. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578. doi:10.1038/nprot.2012.016

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Iyer A, Zurolo E, Prabowo A, Fluiter K, Spliet WG, van Rijen PC, Gorter JA, Aronica E (2012) MicroRNA-146a: a key regulator of astrocyte-mediated inflammatory response. PLoS One 7(9):e44789. doi:10.1371/journal.pone.0044789

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Wenning GK, Tison F, Seppi K, Sampaio C, Diem A, Yekhlef F, Ghorayeb I, Ory F, Galitzky M, Scaravilli T, Bozi M, Colosimo C, Gilman S, Shults CW, Quinn NP, Rascol O, Poewe W, Multiple System Atrophy Study G (2004) Development and validation of the Unified Multiple System Atrophy Rating Scale (UMSARS). Mov Disord 19(12):1391–1402. doi:10.1002/mds.20255

    Article  PubMed  Google Scholar 

  26. Aronica E, Boer K, Becker A, Redeker S, Spliet WG, van Rijen PC, Wittink F, Breit T, Wadman WJ, Lopes da Silva FH, Troost D, Gorter JA (2008) Gene expression profile analysis of epilepsy-associated gangliogliomas. Neuroscience 151(1):272–292. doi:10.1016/j.neuroscience.2007.10.036

    Article  CAS  PubMed  Google Scholar 

  27. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) Method. Methods 25(4):402–408. doi:10.1006/meth.2001.1262

    Article  CAS  PubMed  Google Scholar 

  28. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079. doi:10.1093/bioinformatics/btp352

    Article  PubMed Central  PubMed  Google Scholar 

  29. Brawand D, Soumillon M, Necsulea A, Julien P, Csardi G, Harrigan P, Weier M, Liechti A, Aximu-Petri A, Kircher M, Albert FW, Zeller U, Khaitovich P, Grutzner F, Bergmann S, Nielsen R, Paabo S, Kaessmann H (2011) The evolution of gene expression levels in mammalian organs. Nature 478(7369):343–348. doi:10.1038/nature10532

    Article  CAS  PubMed  Google Scholar 

  30. Gruber AR, Lorenz R, Bernhart SH, Neubock R, Hofacker IL (2008) The Vienna RNA websuite. Nucleic Acids Res 36(Web Server issue):W70–W74. doi:10.1093/nar/gkn188

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Lorenz R, Bernhart SH, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler PF, Hofacker IL (2011) ViennaRNA package 2.0. Algorithms Mol Biol 6:26. doi:10.1186/1748-7188-6-26

    Article  PubMed Central  PubMed  Google Scholar 

  32. Hamada M, Kiryu H, Sato K, Mituyama T, Asai K (2009) Prediction of RNA secondary structure using generalized centroid estimators. Bioinformatics 25(4):465–473. doi:10.1093/bioinformatics/btn601

    Article  CAS  PubMed  Google Scholar 

  33. Agostini F, Zanzoni A, Klus P, Marchese D, Cirillo D, Tartaglia GG (2013) catRAPID omics: a web server for large-scale prediction of protein-RNA interactions. Bioinformatics 29(22):2928–2930. doi:10.1093/bioinformatics/btt495

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Bellucci M, Agostini F, Masin M, Tartaglia GG (2011) Predicting protein associations with long noncoding RNAs. Nat Methods 8(6):444–445. doi:10.1038/nmeth.1611

    Article  CAS  PubMed  Google Scholar 

  35. Kellis M, Wold B, Snyder MP, Bernstein BE, Kundaje A, Marinov GK, Ward LD, Birney E, Crawford GE, Dekker J (2014) Defining functional DNA elements in the human genome. Proc Natl Acad Sci U S A 111(17):6131–6138

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Hangauer MJ, Vaughn IW, McManus MT (2013) Pervasive transcription of the human genome produces thousands of previously unidentified long intergenic noncoding RNAs. PLoS Genet 9(6):e1003569. doi:10.1371/journal.pgen.1003569

  37. Mercer TR, Mattick JS (2013) Structure and function of long noncoding RNAs in epigenetic regulation. Nat Struct Mol Biol 20(3):300–307. doi:10.1038/nsmb.2480

  38. Schaeffer C, Bardoni B, Mandel JL, Ehresmann B, Ehresmann C, Moine H (2001) The fragile X mental retardation protein binds specifically to its mRNA via a purine quartet motif. EMBO J 20(17):4803–4813. doi:10.1093/emboj/20.17.4803

  39. Royce-Tolland ME, Andersen AA, Koyfman HR, Talbot DJ, Wutz A, Tonks ID, Kay GF, Panning B (2010) The A-repeat links ASF/SF2-dependent Xist RNA processing with random choice during X inactivation. Nat Struct Mol Biol 17(8):948–954. doi:10.1038/nsmb.1877

  40. Gautier-Courteille C, Le Clainche C, Barreau C, Audic Y, Graindorge A, Maniey D, Osborne HB, Paillard L (2004) EDEN-BP-dependent post-transcriptional regulation of gene expression in Xenopus somitic segmentation. Development 131(24):6107–6117. doi:10.1242/dev.01528

  41. Blech-Hermoni Y, Stillwagon SJ, Ladd AN (2013) Diversity and conservation of CELF1 and CELF2 RNA and protein expression patterns during embryonic development. Dev Dyn 242(6):767–777. doi:10.1002/dvdy.23959

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Kress C, Gautier-Courteille C, Osborne HB, Babinet C, Paillard L (2007) Inactivation of CUG-BP1/CELF1 causes growth, viability, and spermatogenesis defects in mice. Mol Cell Biol 27(3):1146–1157. doi:10.1128/MCB. 01009-06

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Campos AR, Grossman D, White K (1985) Mutant alleles at the locus elav in Drosophila melanogaster lead to nervous system defects. A developmental-genetic analysis. J Neurogenet 2(3):197–218

    Article  CAS  PubMed  Google Scholar 

  44. Chagnovich D, Fayos BE, Cohn SL (1996) Differential activity of ELAV-like RNA-binding proteins in human neuroblastoma. J Biol Chem 271(52):33587–33591

    Article  CAS  PubMed  Google Scholar 

  45. Heck MV, Azizov M, Stehning T, Walter M, Kedersha N, Auburger G (2014) Dysregulated expression of lipid storage and membrane dynamics factors in Tia1 knockout mouse nervous tissue. Neurogenetics 15(2):135–144. doi:10.1007/s10048-014-0397-x

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. Jin K, Li W, Nagayama T, He X, Sinor AD, Chang J, Mao X, Graham SH, Simon RP, Greenberg DA (2000) Expression of the RNA-binding protein TIAR is increased in neurons after ischemic cerebral injury. J Neurosci Res 59(6):767–774

    Article  CAS  PubMed  Google Scholar 

  47. Beck AR, Miller IJ, Anderson P, Streuli M (1998) RNA-binding protein TIAR is essential for primordial germ cell development. Proc Natl Acad Sci U S A 95(5):2331–2336

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  48. Tapia-Paez I, Tammimies K, Massinen S, Roy AL, Kere J (2008) The complex of TFII-I, PARP1, and SFPQ proteins regulates the DYX1C1 gene implicated in neuronal migration and dyslexia. FASEB J 22(8):3001–3009. doi:10.1096/fj.07-104455

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  49. Anczukow O, Rosenberg AZ, Akerman M, Das S, Zhan L, Karni R, Muthuswamy SK, Krainer AR (2012) The splicing factor SRSF1 regulates apoptosis and proliferation to promote mammary epithelial cell transformation. Nat Struct Mol Biol 19(2):220–228. doi:10.1038/nsmb.2207

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Vance C, Rogelj B, Hortobagyi T, De Vos KJ, Nishimura AL, Sreedharan J, Hu X, Smith B, Ruddy D, Wright P, Ganesalingam J, Williams KL, Tripathi V, Al-Saraj S, Al-Chalabi A, Leigh PN, Blair IP, Nicholson G, de Belleroche J, Gallo JM, Miller CC, Shaw CE (2009) Mutations in FUS, an RNA processing protein, cause familial amyotrophic lateral sclerosis type 6. Science 323(5918):1208–1211. doi:10.1126/science.1165942

    Article  CAS  PubMed  Google Scholar 

  51. Timchenko NA, Cai ZJ, Welm AL, Reddy S, Ashizawa T, Timchenko LT (2001) RNA CUG repeats sequester CUGBP1 and alter protein levels and activity of CUGBP1. J Biol Chem 276(11):7820–7826. doi:10.1074/jbc.M005960200

    Article  CAS  PubMed  Google Scholar 

  52. Guttman M, Garber M, Levin JZ, Donaghey J, Robinson J, Adiconis X, Fan L, Koziol MJ, Gnirke A, Nusbaum C (2010) Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol 28(5):503–510

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28(5):511–515

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Mercer TR, Dinger ME, Sunkin SM, Mehler MF, Mattick JS (2008) Specific expression of long noncoding RNAs in the mouse brain. Proc Natl Acad Sci U S A 105(2):716–721. doi:10.1073/pnas.0706729105

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Pan C, Kumar C, Bohl S, Klingmueller U, Mann M (2009) Comparative proteomic phenotyping of cell lines and primary cells to assess preservation of cell type-specific functions. Mol Cell Proteomics 8(3):443–450. doi:10.1074/mcp. M800258-MCP200

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Burdall SE, Hanby AM, Lansdown MR, Speirs V (2003) Breast cancer cell lines: friend or foe? Breast Cancer Res 5(2):89–95

    Article  PubMed Central  PubMed  Google Scholar 

  57. Peters A (2004) A fourth type of neuroglial cell in the adult central nervous system. J Neurocytol 33(3):345–357. doi:10.1023/B:NEUR.0000044195.64009.27

    Article  PubMed  Google Scholar 

  58. Ishizawa K, Komori T, Sasaki S, Arai N, Mizutani T, Hirose T (2004) Microglial activation parallels system degeneration in multiple system atrophy. J Neuropathol Exp Neurol 63(1):43–52

    PubMed  Google Scholar 

  59. Stefanova N, Reindl M, Neumann M, Kahle PJ, Poewe W, Wenning GK (2007) Microglial activation mediates neurodegeneration related to oligodendroglial alpha-synucleinopathy: implications for multiple system atrophy. Mov Disord 22(15):2196–2203. doi:10.1002/mds.21671

    Article  PubMed  Google Scholar 

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Acknowledgments

Tissues were received from the Sydney Brain Bank at Neuroscience Research Australia and the New South Wales Tissue Resource Centre at the University of Sydney which are supported by the National Health and Medical Research Council of Australia (NHMRC), University of New South Wales, Neuroscience Research Australia, Schizophrenia Research Institute, and National Institute of Alcohol Abuse and Alcoholism (NIH (NIAAA) R24AA012725). This research was supported by the National Health & Medical Research Council of Australia (Project grant #1022325 to WSK and Fellowship #630434 to GMH) and Brain Foundation Australia (to MJ). EA is supported by the Framework Programme FP7/2007-2013 under the project EPISTOP (grant agreement n°: 602391) and (NeuroGeM grant 733051052).

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Authors and Affiliations

  1. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia

    James D. Mills, Jieqiong Chen, Paul D. Waters & Michael Janitz

  2. Neuroscience Research Australia, Sydney, NSW, 2031, Australia

    Woojin S. Kim & Glenda M. Halliday

  3. School of Medical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia

    Woojin S. Kim & Glenda M. Halliday

  4. Department (Neuro) Pathology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

    Avanita S. Prabowo & Eleonora Aronica

  5. Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, Amsterdam, The Netherlands

    Avanita S. Prabowo & Eleonora Aronica

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  1. James D. Mills
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Supplementary Figure 1

Conservation and expression of linc00320 in mammals. For each species a brain RNA-seq read coverage track across the locus is shaded blue. Read depth scales are different between species. On the human track the linc00320 exons were numbered and highlighted in green. Expression of the last exon was only observed in human. Light blue shaded tracks (for species other than human) are BLASTN hits of each human linc00320 (ENST00000416768) exon to the respective genomes. Exons are colored differently, and the percentage of homology shared with the human linc00320 is listed after the exon names. (PDF 1585 kb)

Supplementary Figure 2

RNA secondary structure as predicted by RNAfold and region that interacts with the selected RNA-binding proteins. The color of the bases indicates how likely those bases are to pair, with red indicated maximum likely hood and blue indicating unlikely. The common 3’ exon of each isoforms results in a common secondary structure for all of the linc00320 isoforms. The area of RNA-binding protein interaction is highlighted in purple. Interestingly the RNA-binding protein interaction area comprises a similar structural motif in each isoform. The starting point of each exon is indicated with a black bar and indication of the exon number, as well as the 5’ and 3’ end of the folded transcript. A. linc00320-002 B. linc00320-006 C. linc00320-007. (GIF 5 kb)

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Supplementary Figure 3

Interaction profile of each linc00320 transcript and selected RNA-binding proteins. A. Linc00320-006 B. Linc00320-007. The x-axis indicates the nucleotide position along the transcript. The interaction score on the y-axis indicates how likely it is for the RNA-binding protein to interact with this region of the transcript. The peak of 3.5 between approximately 150–300 indicates that this is a likely area of interaction. Two experimentally validated predictions, the interaction between the Fmr1 protein and its mRNA and the interaction between Srsf1 and the long non-coding RNA Xist give interaction scores of close to 4 [30, 31]. The prediction is based on structural properties, further analysis also showed that sequence motifs are present for each of the RNA-binding proteins. (GIF 478 kb)

(GIF 472 kb)

High Resolution Image

(TIFF 6665 kb)

High Resolution Image

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Mills, J.D., Chen, J., Kim, W.S. et al. Long intervening non-coding RNA 00320 is human brain-specific and highly expressed in the cortical white matter. Neurogenetics 16, 201–213 (2015). https://doi.org/10.1007/s10048-015-0445-1

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