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posttranscriptional effects such as mRNA stability by distinguishing those genes exclusively regulated at the level of transcription. Similar approaches for multiplexing splicing reactions and translation are imaginable, and will be necessary to fully understand the posttranscriptional network operating system.

One can imagine databases in which the functional linkages between multiple mRNAs can be accessed based on their membership in one or more mRNP complexes. For example, it should be possible to account for 100% of any given mRNA within a cell whether it is a member of a structurally or functionally related group of mRNAs, or a member of a small subset of a larger set of physically clustered mRNAs. In the future, when all of the RNA-binding proteins associated with every mRNA are known, it should be possible to describe, and ultimately simulate the organization and flow of genetic information within cells. Thus, by identifying mRNAs that are members of a physically clustered mRNP subset, the functions of proteins encoded by the mRNAs in the subset may become readily apparent through “guilt by association.” As a specific case in point, growth regulatory proteins like those encoded by the mRNAs associated with ELAV/Hu proteins are believed to have related functional properties (1). In addition to the functions of the encoded proteins, mRNAs may be clustered in vivo to optimize regulatory control of their expression, including mRNA stability, translation, and localization (13). Ribonomic databases may be constructed based on physical clustering of mRNAs and the functional relationships among their protein products. Such databases would allow tracking of mRNAs although their unique nodes of information management and transfer. Therefore, being able to organize each mRNP cluster into a relational database that accounts for the functional networking among its mRNAs and their protein products may offer insights into functional genomics.

A challenge for ribonomics will be to account for a full set of cellular transcripts, and to assess the dynamics of activation, repression, and product feedback that are inherent in an mRNP network. Functional perturbations by mutation, antisense expression, RNAi, or small molecules would be expected to alter the mRNP ribonomic network with a discernable outcome in the composition of the proteome. Like genomics and proteomics, ribonomics will require sophisticated computational systems to simulate the cellular dynamics of the posttranscriptional infrastructure during development. Indeed, this is a problem suited for the complexity sciences.

Many thanks to Craig Carson and Scott Tenenbaum for intellectual input and help in the preparation of figures.

1. Tenenbaum, S.A., Carson, C.C, Lager, P.J. & Keene, J.D. (2000) Proc. Natl. Acad. Sci. USA 97, 14085–14090.

2. Levine, T.D., Gao, F., King, P.H., Andrews, L.G. & Keene, J.D. (1993) Mol. Cell. Biol. 13, 3494–3504.

3. Gao, F.B., Carson, C.C., Levine, T. & Keene, J.D. (1994) Proc. Natl. Acad. Sci. USA 91, 11207–11211.

4. Liu, J., Dalmau, J., Szabo, A., Rosenfeld, M., Huber, J. & Furneaux, H. (1995) Neurology 45, 544–550.

5. Myer, V.E., Fan, X.C. & Steitz, J.A. (1997) EMBO J. 16, 2130–2139.

6. Fan, X.C., Myer, V.E. & Steitz, J.A. (1997) Genes Dev. 11, 2557–2568.

7. Keene, J.D. (1999) Proc. Natl. Acad. Sci. USA 96, 5–7.

8. Brennan, C.M. & Steitz, J.A. (2000) Cell Mol. Life Sci., in press.

9. Antic, D. & Keene, J.D. (1997) Am. J.Hum. Genet. 61, 273–278.

10. Gao, F.B. & Keene, J.D. (1996) J. Cell Sci. 109, 579–589.

11. Antic, D. & Keene, J.D. (1998) J. Cell Sci. 111, 183–19J.

12. Jain, R.G., Andrews, L.G., McGowan, K.M., Pekala, P.H. & Keene, J.D. (1997) Mol. Cell. Biol. 17, 954–962.

13. Antic, D., Lu, N. & Keene, J.D. (1999) Genes Dev. 13, 449–461.

14. Wakamatsu, Y. & Weston, J.A. (1997) Development 124, 3449–3460.

15. Chung, S., Eckrich, M., Perrone-Bizzozero, N., Kohn, D.T. & Furneaux, H. (1997) J. Biol. Chem. 272, 6593–6598.

16. Levy, N.S., Chung, S., Furneaux, H. & Levy, A.P. (1998) J. Biol. Chem. 273, 6417–6423.

17. Peng, S.S., Chen, C.Y., Xu, N. & Shyu, A.B. (1998) EMBO J. 17, 3461–3470.

18. Fan, X.C. & Steitz, J.A. (1998) EMBO J. 17, 3448–3460.

19. Ford, L.P., Watson, J., Keene, J.D. & Wilusz, J. (1999) Genes Dev. 13, 188–201.

20. Aranda-Abreu, G.E., Behar, L., Chung, S., Furneaux, H. & Ginzburg, I. (1999) J. Neurosci. 19, 6907–6917.

21. Lockhart, D.J. & Winzeler, E.A. (2000) Nature (London) 405, 827–836.

22. Velculescu, V.E., Zhang, L., Zhou, W., Vogelstein, J. & Kinzler, K.W. (1997) Cell 88, 243–251.

23. Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. (1998) Proc. Natl. Acad. Sci. USA 95, 14863–14868.

24. Schiavi, S.C., Belasco, J.G. & Greenberg, M.E. (1992) Biochim. Biophys. Acta 1114, 95–106.

25. Ross, J. (1995) Microbiol. Rev. 59, 423–450.

26. Chen, C.Y. & Shyu, A.B. (1995) Trends Biochem. Sci. 20, 465–470.

27. Pinol-Roma, S. & Dreyfuss, G. (1993) Trends Cell Biol. 3, 151–155.

28. St. Johnston, D. (1995) Cell 81, 161–170.

29. Dreyfuss, G., Matunis, M.J., Pinol-Roma, S. & Burd, C.G. (1993) Annu. Rev. Biochem. 62, 289–321.

30. Richter, J.D. (1997) mRNA Formation and Function (Academic, New York).

31. Haynes, S.R. (1999) RNA-Protein Interactions Protocols (Humana, Totowa, NJ).

32. Savant-Bhonsale, S. & Cleveland, D.W. (1992) Genes Dev. 6, 1927–1939.

33. Zong, Q., Schummer, M., Hood, L. & Morris, D.R. (1999) Proc. Natl. Acad. Sci. USA 96, 10632–10636.

34. Gingras, A.-C., Raught, B. & Sonenberg, N. (1999) Annu. Rev. Biochem. 68, 913–963.

35. Gale, M., Tan, S.-L. & Katze, M.G. (2000) Micro. Mol. Biol. Rev. 64, 239–280.

36. Pleasure, S.J. & Lee, V.M. (1993) J. Neurosci. Res. 35, 585–602.

37. Steward, O., Wallace, C.S., Lyford, G.L. & Worley, P.F. (1998) Neuron 21, 741–751.

38. Steward, O. & Banker, G.A. (1992) Trends Neurosci. 15, 180–186.

39. Crino, P., Khodakhah, K., Becker, K., Ginsberg, S., Hemby, S. & Eberwine, J. (1998) Proc. Natl. Acad. Sci. USA 95, 2313–2318.

40. Mayford, M., Baranes, D., Podsypanina, K. & Kandel, E.R. (1996) Proc. Natl. Acad. Sci. USA 93, 13250–13255.

41. Schuman, E. (1999) Neuron 23, 645–648.

42. Bassell, G.J., Oleynikov, Y. & Singer, R.H. (1999) FASEB J. 13, 447–454.

43. King, P.H., Levine, T.D., Fremeau, R.T. & Keene, J.D. (1994) J. Neurosci. 14, 1943–1952.

44. Lindstren, T., June, C.H., Ledbetter, J.A., Stella, G. & Thompson, C.R. (1989) Science 244, 339–343.

45. Atasoy, U., Watson, J., Patel, D. & Keene, J.D. (1998) J. Cell Sci. 111, 3145–3156.

46. Zhuang, J.-Y., Chan, E.K.E., Peng, X.-X. & Tan, E.M. (1999) J. Exp. Med. 189, 1101–1110.

47. Albright, T.D., Jessell, T.M., Kandel, E.R. & Posner, M.I. (2000) Cell 100, S1-S55.

48. Rouault, T.A. & Klausner, R.D. (1997) Curr. Top. Cell. Regul. 35, 1–19.

49. Wang, Z.F., Ingledue, T.C., Dominski, Z., Sanchez, R. & Marzluff, W.F. (1999) Mol. Cell. Biol. 19, 835–845.

50. Whitfield, M.L., Zheng, L.X., Baldwin, A., Ohta, T., Hurt, M.M. & Marzluff, W.F. (2000) Mol. Cell. Biol. 20, 4188–4198.

51. Kenan, D.J., Query, C.C. & Keene, J.D. (1991) Trends Biochem. Sci. 16, 214–220.

52. Burd, C.G. & Dreyfuss, G. (1994) Science 265, 615–621.

53. Hanahan, D. & Weinberg, R.A. (2000) Cell 100, 57–70.

54. Lander, E.S. (1999) Nat. Genet. 21, 3–4.

55. Brown, P.O. & Botstein, D. (1999) Nat. Genet. 21, 38–41.



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