In pseudoMap, we focus on analyzing the miRNAs, post-transcriptional regulations, of TPGs. To complete the regulations of TPGs, we will analyse the transcriptional regulations of TPGs in the future. Identifying transcriptional start sties (TSS) of TPGs is the first step for deciphering transcriptional regulatory mechanism of TPGs. To archive this future aim, several high-throughput sequencing datasets will be used to analyse the TSSs. The cap analysis gene expression (CAGE) tags can be massively generated using a biotinylated cap-trapper with specific linkers to ensure that the sequences after 5’ cap of cDNAs are reserved [95]. Based on this attribute, CAGE tags are extensively and adopted to identify the TSSs of genes with 5’ cap transcripts [80]. Similar to CAGE tags, TSS Seq tags initially denominated by the database of transcriptional start site (DBTSS) are also the 5’-end sequences of human cDNAs base on use of the TSS Seq method [96]. More than 300 million TSS Seq tags were generated by integrating the oligo-capping method and Solexa sequencing technology, offering an abundant resource to detect TPG TSSs. Histone methylation significantly influences gene expression. H3K4me3, which represents histone H3 as trimethylated at its lysine 4 residue, is enriched around TSS and positively correlated with gene expression, regardless of whether or not the genes are transcribed productively. As a massive parallel signature sequencing technique, ChIP-Seq performs well in chromatin modifications and provides high-resolution profiling of histone methylations in human genome [114]. In addition, the transcription factors and binding site of TPGs need further studying.
In accordance with the studies of NOS transcript acts as a natural antisense regulator of neuronal NOS protein synthesis in snails [44, 45], we want to know how many
TPGs have indicated that the sequences of TPG with antisense complementary to its parental gene. These interesting cases need further studies.
The recently studies showed that lncRNA functions as various mechanisms, such as act as signal, decoy, guide and scaffold [43]. The similar mechanisms may involve in TPGs, as the TPGs seem to be another type of lncRNA. In this study, we have verified that TPGs act as esiRNA to regulate protein-coding genes as well as miRNAs co-regulate TPG and its parental gene. However, other mechanisms need to be study in the future. After all, no matter how computational methods are well-designed, experimental confirmation is the only way to make putative information become knowledge in the field of biological science.
Reference
1. Torrents, D., et al., A genome-wide survey of human pseudogenes. Genome Res, 2003. 13(12): p. 2559-67.
2. Mighell, A.J., et al., Vertebrate pseudogenes. FEBS Lett, 2000. 468(2-3): p.
109-14.
3. Balasubramanian, S., et al., Comparative analysis of processed ribosomal protein pseudogenes in four mammalian genomes. Genome Biol, 2009. 10(1):
p. R2.
4. Harrison, P. and Z. Yu, Frame disruptions in human mRNA transcripts, and their relationship with splicing and protein structures. BMC Genomics, 2007.
8: p. 371.
5. Harrison, P.M., et al., Transcribed processed pseudogenes in the human genome: an intermediate form of expressed retrosequence lacking protein-coding ability. Nucleic Acids Res, 2005. 33(8): p. 2374-83.
6. Vinckenbosch, N., I. Dupanloup, and H. Kaessmann, Evolutionary fate of retroposed gene copies in the human genome. Proc Natl Acad Sci U S A, 2006.
103(9): p. 3220-5.
7. Zheng, D., et al., Pseudogenes in the ENCODE regions: consensus annotation, analysis of transcription, and evolution. Genome Res, 2007. 17(6): p. 839-51.
8. Zheng, D., et al., Integrated pseudogene annotation for human chromosome 22: evidence for transcription. J Mol Biol, 2005. 349(1): p. 27-45.
9. Khachane, A.N. and P.M. Harrison, Assessing the genomic evidence for conserved transcribed pseudogenes under selection. BMC Genomics, 2009.
10: p. 435.
10. Imanishi, T., et al., Integrative annotation of 21,037 human genes validated by full-length cDNA clones. PLoS Biol, 2004. 2(6): p. e162.
11. Morozova, N., et al., Kinetic signatures of microRNA modes of action. RNA, 2012. 18(9): p. 1635-55.
12. Crick, F., Central dogma of molecular biology. Nature, 1970. 227(5258): p.
561-3.
13. Birney, E., et al., Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature, 2007. 447(7146): p.
799-816.
14. Kapranov, P., et al., RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science, 2007. 316(5830): p. 1484-8.
15. Kapranov, P., A.T. Willingham, and T.R. Gingeras, Genome-wide transcription
and the implications for genomic organization. Nat Rev Genet, 2007. 8(6): p.
413-23.
16. Alexander, R.P., et al., Annotating non-coding regions of the genome. Nat Rev Genet, 2010. 11(8): p. 559-71.
17. Cooper, D.N., et al., Genes, mutations, and human inherited disease at the dawn of the age of personalized genomics. Hum Mutat, 2010. 31(6): p.
631-55.
18. Wery, M., M. Kwapisz, and A. Morillon, Noncoding RNAs in gene regulation.
Wiley Interdiscip Rev Syst Biol Med, 2011. 3(6): p. 728-38.
19. Bernard, D., et al., A long nuclear-retained non-coding RNA regulates synaptogenesis by modulating gene expression. EMBO J, 2010. 29(18): p.
3082-93.
20. Pandey, R.R., et al., Kcnq1ot1 antisense noncoding RNA mediates lineage-specific transcriptional silencing through chromatin-level regulation.
Mol Cell, 2008. 32(2): p. 232-46.
21. Terranova, R., et al., Polycomb group proteins Ezh2 and Rnf2 direct genomic contraction and imprinted repression in early mouse embryos. Dev Cell, 2008.
15(5): p. 668-79.
22. Nagano, T., et al., The Air noncoding RNA epigenetically silences transcription by targeting G9a to chromatin. Science, 2008. 322(5908): p.
1717-20.
23. Sleutels, F., R. Zwart, and D.P. Barlow, The non-coding Air RNA is required for silencing autosomal imprinted genes. Nature, 2002. 415(6873): p. 810-3.
24. Kanduri, C., Kcnq1ot1: a chromatin regulatory RNA. Semin Cell Dev Biol, 2011. 22(4): p. 343-50.
25. Khalil, A.M., et al., Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc Natl Acad Sci U S A, 2009. 106(28): p. 11667-72.
26. Mondal, T., et al., Characterization of the RNA content of chromatin. Genome Res, 2010. 20(7): p. 899-907.
27. Hung, T., et al., Extensive and coordinated transcription of noncoding RNAs within cell-cycle promoters. Nat Genet, 2011. 43(7): p. 621-9.
28. Diederichs, S. and D.A. Haber, Dual role for argonautes in microRNA processing and posttranscriptional regulation of microRNA expression. Cell, 2007. 131(6): p. 1097-108.
29. Lee, K.J., et al., Do human transposable element small RNAs serve primarily as genome defenders or genome regulators? Mob Genet Elements, 2012. 2(1):
p. 19-25.
30. Ishizu, H., A. Nagao, and H. Siomi, Gatekeepers for Piwi-piRNA complexes to enter the nucleus. Curr Opin Genet Dev, 2011. 21(4): p. 484-90.
31. Chen, L.L. and G.G. Carmichael, Decoding the function of nuclear long non-coding RNAs. Curr Opin Cell Biol, 2010. 22(3): p. 357-64.
32. Clark, M.B. and J.S. Mattick, Long noncoding RNAs in cell biology. Semin Cell Dev Biol, 2011. 22(4): p. 366-76.
33. Seto, A.G., R.E. Kingston, and N.C. Lau, The coming of age for Piwi proteins.
Mol Cell, 2007. 26(5): p. 603-9.
34. Siomi, M.C., et al., PIWI-interacting small RNAs: the vanguard of genome defence. Nat Rev Mol Cell Biol, 2011. 12(4): p. 246-58.
35. Bartel, D.P., MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004. 116(2): p. 281-97.
36. Jovanovic, M. and M.O. Hengartner, miRNAs and apoptosis: RNAs to die for.
Oncogene, 2006. 25(46): p. 6176-87.
37. Lee, Y., et al., The nuclear RNase III Drosha initiates microRNA processing.
Nature, 2003. 425(6956): p. 415-9.
38. Lund, E., et al., Nuclear export of microRNA precursors. Science, 2004.
303(5654): p. 95-8.
39. Yi, R., et al., Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev, 2003. 17(24): p. 3011-6.
40. Kim, D.H. and J.J. Rossi, Strategies for silencing human disease using RNA interference. Nat Rev Genet, 2007. 8(3): p. 173-84.
41. Shriner, D., Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies. Front Genet, 2012. 3: p. 1.
42. Sana, J., et al., Novel classes of non-coding RNAs and cancer. J Transl Med, 2012. 10: p. 103.
43. Wang, K.C. and H.Y. Chang, Molecular mechanisms of long noncoding RNAs.
Mol Cell, 2011. 43(6): p. 904-14.
44. Korneev, S.A., J.H. Park, and M. O'Shea, Neuronal expression of neural nitric oxide synthase (nNOS) protein is suppressed by an antisense RNA transcribed from an NOS pseudogene. J Neurosci, 1999. 19(18): p. 7711-20.
45. Korneev, S. and M. O'Shea, Evolution of nitric oxide synthase regulatory genes by DNA inversion. Mol Biol Evol, 2002. 19(8): p. 1228-33.
46. Hirotsune, S., et al., An expressed pseudogene regulates the messenger-RNA stability of its homologous coding gene. Nature, 2003. 423(6935): p. 91-6.
47. Gray, T.A., et al., The putatively functional Mkrn1-p1 pseudogene is neither expressed nor imprinted, nor does it regulate its source gene in trans. Proc Natl Acad Sci U S A, 2006. 103(32): p. 12039-44.
48. Poliseno, L., et al., A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature, 2010. 465(7301): p. 1033-8.
49. Ghildiyal, M., et al., Endogenous siRNAs derived from transposons and mRNAs in Drosophila somatic cells. Science, 2008. 320(5879): p. 1077-81.
50. Brennecke, J., et al., Discrete small RNA-generating loci as master regulators of transposon activity in Drosophila. Cell, 2007. 128(6): p. 1089-103.
51. Tam, O.H., et al., Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature, 2008. 453(7194): p. 534-8.
52. Watanabe, T., et al., Endogenous siRNAs from naturally formed dsRNAs regulate transcripts in mouse oocytes. Nature, 2008. 453(7194): p. 539-43.
53. Czech, B., et al., An endogenous small interfering RNA pathway in Drosophila.
Nature, 2008. 453(7196): p. 798-802.
54. Kawamura, Y., et al., Drosophila endogenous small RNAs bind to Argonaute 2 in somatic cells. Nature, 2008. 453(7196): p. 793-7.
55. Okamura, K., et al., The Drosophila hairpin RNA pathway generates endogenous short interfering RNAs. Nature, 2008. 453(7196): p. 803-6.
56. Karro, J.E., et al., Pseudogene.org: a comprehensive database and comparison platform for pseudogene annotation. Nucleic Acids Res, 2006.
57. Khelifi, A., L. Duret, and D. Mouchiroud, HOPPSIGEN: a database of human and mouse processed pseudogenes. Nucleic Acids Res, 2005. 33(Database issue): p. D59-66.
58. Bischof, J.M., et al., Genome-wide identification of pseudogenes capable of disease-causing gene conversion. Hum Mutat, 2006. 27(6): p. 545-52.
59. Griffiths-Jones, S., et al., miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res, 2006. 34(Database issue): p. D140-4.
60. Birney, E., et al., Ensembl 2006. Nucleic Acids Res, 2006. 34(Database issue):
p. D556-61.
61. Hinrichs, A.S., et al., The UCSC Genome Browser Database: update 2006.
Nucleic Acids Res, 2006. 34(Database issue): p. D590-8.
62. Mituyama, T., et al., The Functional RNA Database 3.0: databases to support mining and annotation of functional RNAs. Nucleic Acids Res, 2009.
37(Database issue): p. D89-92.
63. Carninci, P., et al., The transcriptional landscape of the mammalian genome.
Science, 2005. 309(5740): p. 1559-63.
64. Vitali, M., et al., Analysis of the genes coding for subunit 10 and 15 of cytochrome c oxidase in Alzheimer's disease. J Neural Transm, 2009. 116(12):
p. 1635-41.
65. He, S., et al., NONCODE v2.0: decoding the non-coding. Nucleic Acids Res,
2008. 36(Database issue): p. D170-2.
66. Griffiths-Jones, S., et al., Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res, 2005. 33(Database issue): p. D121-4.
67. Pang, K.C., et al., RNAdb 2.0--an expanded database of mammalian non-coding RNAs. Nucleic Acids Res, 2007. 35(Database issue): p. D178-82.
68. Lestrade, L. and M.J. Weber, snoRNA-LBME-db, a comprehensive database of human H/ACA and C/D box snoRNAs. Nucleic Acids Res, 2006. 34(Database issue): p. D158-62.
69. Barrett, T., et al., NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res, 2006.
70. Brazma, A., et al., Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet, 2001. 29(4): p.
365-71.
71. Altschul, S.F., et al., Basic local alignment search tool. J Mol Biol, 1990.
215(3): p. 403-10.
72. Thompson, J.D., D.G. Higgins, and T.J. Gibson, CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res, 1994. 22(22): p. 4673-80.
73. Kruger, J. and M. Rehmsmeier, RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res, 2006. 34(Web Server issue): p. W451-4.
74. Lewis, B.P., et al., Prediction of mammalian microRNA targets. Cell, 2003.
115(7): p. 787-98.
75. John, B., et al., Human MicroRNA targets. PLoS Biol, 2004. 2(11): p. e363.
76. Mathews, D.H., et al., Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol, 1999.
288(5): p. 911-40.
77. Zuker, M., Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res, 2003. 31(13): p. 3406-15.
78. Costa, F.F., Non-coding RNAs: new players in eukaryotic biology. Gene, 2005.
357(2): p. 83-94.
79. Griffiths-Jones, S., et al., miRBase: tools for microRNA genomics. Nucleic Acids Res, 2008. 36(Database issue): p. D154-8.
80. Carninci, P., et al., Genome-wide analysis of mammalian promoter architecture and evolution. Nat Genet, 2006. 38(6): p. 626-35.
81. Morin, R.D., et al., Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res, 2008.
18(4): p. 610-21.
82. Seila, A.C., et al., Divergent transcription from active promoters. Science, genomes. Nucleic Acids Res, 2008. 36(Database issue): p. D165-9.
85. Friedman, R.C., et al., Most mammalian mRNAs are conserved targets of microRNAs. Genome Res, 2009. 19(1): p. 92-105.
86. Grimson, A., et al., MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell, 2007. 27(1): p. 91-105.
87. Lewis, B.P., C.B. Burge, and D.P. Bartel, Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell, 2005. 120(1): p. 15-20.
88. Barrett, T. and R. Edgar, Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol, 2006. 411: p. 352-69.
89. Su, A.I., et al., A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A, 2004. 101(16): p. 6062-7.
90. Yu, K., et al., A precisely regulated gene expression cassette potently modulates metastasis and survival in multiple solid cancers. PLoS Genet, 2008. 4(7): p. e1000129.
91. Hsu, S.D., et al., miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res, 2011. 39(Database issue): p.
D163-9.
92. Okuda, S., et al., KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res, 2008. 36(Web Server issue): p. W423-6.
93. Huang da, W., B.T. Sherman, and R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009. 4(1): p. 44-57.
94. Kozomara, A. and S. Griffiths-Jones, miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res, 2011. 39(Database issue): p. D152-7.
95. Shiraki, T., et al., Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc Natl Acad Sci U S A, 2003. 100(26): p. 15776-81.
96. Yamashita, R., et al., DBTSS provides a tissue specific dynamic view of Transcription Start Sites. Nucleic Acids Res, 2010. 38(Database issue): p.
D98-104.
97. Jover-Gil, S., H. Candela, and M.R. Ponce, Plant microRNAs and development.
Int J Dev Biol, 2005. 49(5-6): p. 733-44.
98. Chan, C.H., et al., Subcellular and functional proteomic analysis of the cellular responses induced by Helicobacter pylori. Mol Cell Proteomics, 2006.
5(4): p. 702-13.
99. Yuo, C.Y., et al., 5-(N-ethyl-N-isopropyl)-amiloride enhances SMN2 exon 7 inclusion and protein expression in spinal muscular atrophy cells. Ann Neurol, 2008. 63(1): p. 26-34.
100. Liu, T.C., et al., Epigenetic alteration of the SOCS1 gene in chronic myeloid leukaemia. Br J Haematol, 2003. 123(4): p. 654-61.
101. Zhou, X., et al., MicroRNA profiling using microParaflo microfluidic array technology. Methods Mol Biol, 2012. 822: p. 153-82.
102. Fleming, E.J., et al., What's new is old: resolving the identity of Leptothrix ochracea using single cell genomics, pyrosequencing and FISH. PLoS One, 2011. 6(3): p. e17769.
103. Rottenberg, H. and S. Wu, Quantitative assay by flow cytometry of the mitochondrial membrane potential in intact cells. Biochim Biophys Acta, 1998. 1404(3): p. 393-404.
104. Nadakavukaren, K.K., J.J. Nadakavukaren, and L.B. Chen, Increased rhodamine 123 uptake by carcinoma cells. Cancer Res, 1985. 45(12 Pt 1): p.
6093-9.
105. Darzynkiewicz, Z., et al., Interaction of rhodamine 123 with living cells studied by flow cytometry. Cancer Res, 1982. 42(3): p. 799-806.
106. de Yebenes, V.G. and A.R. Ramiro, MicroRNA activity in B lymphocytes.
Methods Mol Biol, 2010. 667: p. 177-92.
107. Lu, G., et al., A novel mitochondrial matrix serine/threonine protein phosphatase regulates the mitochondria permeability transition pore and is essential for cellular survival and development. Genes Dev, 2007. 21(7): p.
784-96.
108. O'Regan, L., J. Blot, and A.M. Fry, Mitotic regulation by NIMA-related kinases. Cell Div, 2007. 2: p. 25.
109. Bowers, A.J. and J.F. Boylan, Nek8, a NIMA family kinase member, is overexpressed in primary human breast tumors. Gene, 2004. 328: p. 135-42.
110. Tompkins, V., et al., Identification of novel ARF binding proteins by two-hybrid screening. Cell Cycle, 2006. 5(6): p. 641-6.
111. Tompkins, V.S., et al., A novel nuclear interactor of ARF and MDM2 (NIAM) that maintains chromosomal stability. J Biol Chem, 2007. 282(2): p. 1322-33.
112. Nohno, T., et al., Identification of a human type II receptor for bone
morphogenetic protein-4 that forms differential heteromeric complexes with bone morphogenetic protein type I receptors. J Biol Chem, 1995. 270(38): p.
22522-6.
113. Okamura, K., W.J. Chung, and E.C. Lai, The long and short of inverted repeat genes in animals: microRNAs, mirtrons and hairpin RNAs. Cell Cycle, 2008.
7(18): p. 2840-5.
114. Barski, A., et al., High-resolution profiling of histone methylations in the human genome. Cell, 2007. 129(4): p. 823-37.