6.1 Discussion
Although the current experimental evidence of virus encoded miRNAs is restricted to just three groups of viruses, computational approaches have suggested the existence of miRNAs in other viruses such as poxviruses and adenoviruses but not in most of the RNA viruses. However, there are several examples of modulation of viruses by host-encoded miRNAs. The best recent example is the remarkable positive regulation of replication of hepatitis C virus by the liver-specific miR-122. Impaired translation of target sequences from primate foamy virus 1 (PFV) by miR-32 has also been reported. The interaction of HIV with host T cells might also be modulated by miRNAs. Finally, latency type III EBV infections are associated with induction of miR-155 in B cells (M.
Zavolan, unpublished). This miRNA is also highly expressed in Hodgkin’s, primary mediastinal and diffuse large cell lymphomas, which raises the question of whether miR-155 induction might, in fact, be responsible for some of the malignant disorders associated with EBV. This work predicts putative virus miRNAs on all viruses, not only restrict to just three groups of viruses.
There are some questions in prediction of viral miRNAs. Because the conserved regions of virus genome appear to play a critical role in maintaining virus life cycle, prediction of miRNAs only focuses on conserved region in spite
of the location of microRNAs is still uncertain. Besides, few sequences can be available for every virus species, it cause only some species, not including virus which produced known microRNAs, can be predicted. These two questions are worthy to think and discuss.
The interaction between viruses and their hosts seems to involve host- and virus-encoded miRNAs, which continue to expand our knowledge about fundamental aspects of gene regulation. It will be fascinating to follow how the understanding of virus–host interactions is shaped by the discovery of the targets of these small RNAs.
6.2 Future Works
The database will be further developed as follows. (i) The database will support miRNA annotations for miRNA genes and miRNA targets on viruses for hosts other than four species, human, rat, mice and chicken, and provide more data types; (ii) Data will be further analyzed to support miRNAs involved in combinatorial control of virus expression. (iii) Complete relationships between viruses and diseases will be added, and (iv) The virus data warehouse will annotate more species and keep update to provide new information.
6.3 Conclusions
This work established databases to facilitate research the relationship
between miRNAs, virus and cancer, and stored putative, known host miRNA targets in ViTa, putative, known miRNAs in VirMiR, and also provided the regulatory relationships between miRNAs and viruses. The database can provide sufficient information to support any virus-related works. For example, miRNAs may contribute to cancer, miRNA-mediated tumorigenesis results from either down or up-regulating virus activity. For disease, such as influenza viruses and liver viruses, our work predicts host microRNA targets, result analysis and data statistics are also utilized to provide multi-directional research for users. Tissue data for viruses and miRNAs improves the accuracy of results, and consolidate the relationship between miRNAs and targeted viruses. The relationship between viruses and miRNAs participating in cancer cell regulation can be systematically identified for further experimental verification.
References
1. Ambros, V., et al., A uniform system for microRNA annotation. Rna, 2003. 9(3): p.
277-9.
2. Griffiths-Jones, S., et al., miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res, 2006. 34(Database issue): p. D140-4.
3. Washietl, S., I.L. Hofacker, and P.F. Stadler, Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci U S A, 2005. 102(7): p. 2454-9.
4. Lewis, B.P., et al., Prediction of mammalian microRNA targets. Cell, 2003. 115(7): p.
787-98.
5. John, B., et al., Human MicroRNA targets. PLoS Biol, 2004. 2(11): p. e363.
6. Rehmsmeier, M., et al., Fast and effective prediction of microRNA/target duplexes.
Rna, 2004. 10(10): p. 1507-17.
7. Krek, A., et al., Combinatorial microRNA target predictions. Nat Genet, 2005. 37(5): p.
495-500.
8. Lu, J., et al., MicroRNA expression profiles classify human cancers. Nature, 2005.
435(7043): p. 834-8.
9. Bartel, D.P., MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004.
116(2): p. 281-97.
10. He, L. and G.J. Hannon, MicroRNAs: small RNAs with a big role in gene regulation.
Nat Rev Genet, 2004. 5(7): p. 522-31.
11. Pfeffer, S., et al., Identification of virus-encoded microRNAs. Science, 2004.
304(5671): p. 734-6.
12. Cai, X., et al., Epstein-Barr Virus MicroRNAs Are Evolutionarily Conserved and Differentially Expressed. PLoS Pathog, 2006. 2(3): p. e23.
13. Berezikov, E. and R.H. Plasterk, Camels and zebrafish, viruses and cancer: a microRNA update. Hum Mol Genet, 2005. 14 Spec No. 2: p. R183-90.
14. Lecellier, C.H., et al., A cellular microRNA mediates antiviral defense in human cells.
Science, 2005. 308(5721): p. 557-60.
15. Jopling, C.L., et al., Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science, 2005. 309(5740): p. 1577-81.
16. Hariharan, M., et al., Targets for human encoded microRNAs in HIV genes. Biochem Biophys Res Commun, 2005. 337(4): p. 1214-8.
17. Schutz, S. and P. Sarnow, Interaction of viruses with the mammalian RNA interference pathway. Virology, 2006. 344(1): p. 151-7.
18. Hsu, P.W., et al., miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes. Nucleic Acids Res, 2006. 34(Database issue): p.
D135-9.
19. Sullivan, C.S., et al., SV40-encoded microRNAs regulate viral gene expression and reduce susceptibility to cytotoxic T cells. Nature, 2005. 435(7042): p. 682-6.
20. Griffiths-Jones, S., The microRNA Registry. Nucleic Acids Res, 2004. 32(Database issue): p. D109-11.
21. Karolchik, D., et al., The UCSC Genome Browser Database. Nucleic Acids Res, 2003.
31(1): p. 51-4.
22. Buchen-Osmond, C., The universal virus database ICTVdB. COMPUTING IN SCIENCE & ENGINEERING, 2003. 5(3): p. 16-25.
23. Lefkowitz, E.J., et al., Poxvirus Bioinformatics Resource Center: a comprehensive Poxviridae informational and analytical resource. Nucleic Acids Res, 2005.
33(Database issue): p. D311-6.
24. Kulkarni-Kale, U., et al., VirGen: a comprehensive viral genome resource. Nucleic Acids Res, 2004. 32(Database issue): p. D289-92.
25. Hofacker, I.L., Vienna RNA secondary structure server. Nucleic Acids Res, 2003.
31(13): p. 3429-31.
26. 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.
27. Zuker, M., Mfold web server for nucleic acid folding and hybridization prediction.
Nucleic Acids Res, 2003. 31(13): p. 3406-15.
28. Pfeffer, S., et al., Identification of microRNAs of the herpesvirus family. Nat Methods, 2005. 2(4): p. 269-76.