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Whole-Genome Expression Profiles Identify Gene Batteries in Drosophila
Developmental Cell 464
able to disrupt only CAR-CAR interactions by competitive binding. Would this disruption be sufficient to trigger breakdown of the entire complex? Could mere absence of the CAR-CAR interaction, or alternatively, presence of the fiber-knob-CAR interaction, induce signals that lead to junction disruption? Clearly, cell junctions must form, disappear, and reform according to the dictates of cell division, cell migration, and response to trauma or infection. It seems likely that viral ligands for key components of cell junctions can be useful tools for probing the signal transduction pathways that govern the dynamics of cell junction formation and disruption.
Selected Reading Barton, E.S., Forrest, J.C., Connolly, J.L., Chappell, J.D., Liu, Y., Schnell, F.J., Nusrat, A., Parkos, C.A., and Dermody, T.S. (2001). Cell 104, 441–451. Bewley, M.C., Springer, K., Zhang, Y.-B., Freimuth, P., and Flanagan, J.M. (1999). Science 286, 1579–1583. Chappell, J.D., Prota, A.E., Dermody, T.S., and Stehle, T. (2002). EMBO J. 21, 1–11. Cohen, C.J., Shieh, J.T.C., Pickles, R.J., Okegawa, T., Hsieh, J.-T., and Bergelson, J.M. (2001). Proc. Natl. Acad. Sci. USA 98, 15191– 15196. Dingwell, K.S., and Johnson, D.C. (1998). J. Virol. 72, 8933–8942. He, Y., Chipman, P.R., Howitt, J., Bator, C.M., Whitt, M.A., Baker, T.S., Kuhn, R.J., Anderson, C.W., Freimuth, P., and Rossmann, M.G. (2001). Nat. Struct. Biol. 8, 874–878.
Patricia G. Spear Department of Microbiology-Immunology The Feinberg School of Medicine Northwestern University 320 East Superior Street Chicago, Illinois 60611
Whole-Genome Expression Profiles Identify Gene Batteries in Drosophila
Microarray studies make it possible to obtain gene expression data on a whole-genome scale. Arbeitman et al. present microarray data generated at multiple time points throughout Drosophila melanogaster development, and identify new genes engaged in a broad spectrum of processes, including the patterning of the early embryo and senescence in adults. In a recent study in Science by Arbeitman et al. (2002), systematic microarray assays were conducted in an effort to obtain a comprehensive view of the temporal patterns of gene expression during the Drosophila life cycle. Approximately one-third of all genes were surveyed at different stages of embryogenesis, larval development, pupation, and the adult. Subsets of genes were organized into groups using a hierarchical clustering algorithm based on coordinate changes in gene expression (Eisen et al., 1998). Fifty clusters were defined using this approach. Some are enriched for genes encoding mitochondrial proteins, cytoskeletal/neuronal factors, and antimicrobial peptides. Other clusters appear to include new components of biochemical complexes and cellular pathways. The analysis of mutant strains helped refine some of the gene clusters, and identify a variety of tissue- and organ-specific genes. Previous microarray assays performed in S. cerevisiae and C. elegans have demonstrated that genes engaged in a common process often exhibit coordinate patterns of expression and similar responses to genetic and environmental perturbations (e.g., Eisen et al., 1998; Kim et al., 2001). Arbeitman et al. demonstrate that the same
Spear, P.G., Eisenberg, R.J., and Cohen, G.H. (2000). Virology 275, 1–8. van Raaij, M.J., Chouin, E., van der Zandt, H., Bergelson, J.M., and Cusack, S. (2000). Structure 8, 1147–1155. Walters, R.W., Freimuth, P., Moninger, T.O., Ganske, I., Zabner, J., and Welsh, M.J. (2002). Cell 110, 789–799. Yoon, M., and Spear, P.G. (2002). J. Virol. 76, 7203–7208.
seems to hold in Drosophila melanogaster, and this finding offers tremendous potential for gene discovery. For example, genes that encode uncharacterized kinases can be brought into the fold of a well-defined receptor tyrosine kinase pathway. Alternatively, coordinately expressed signaling molecules might uncover new pathways acting at various stages of development. The present study identified CG11914 (LIM domain) and CG9098 (SH2 domain) as likely muscle-specific genes based solely on the observation that their temporal expression profiles are similar to those of known “muscle genes” such as upheld, which encodes troponin T (Fyrberg et al., 1990). The proteins encoded by these newly identified genes might participate in the specification of muscle. Moreover, the use of mutant strains, tudor and eyes absent, refine the identification of genes engaged in specific processes. tudor mutants lack germ cells (Golumbeski et al., 1991) and permitted the identification of germline-specific genes that are expressed in either males or females. The eyes absent mutant, which exhibits an eyeless or reduced eye phenotype due to death of the progenitor cells in the eye disc, helped identify a set of 33 genes predicted to function in eye differentiation; 11 were known from earlier studies (e.g., Bonini et al., 1993). Here, we highlight several interesting conclusions that emerged from this analysis. First and foremost is the observation that the vast majority of all genes, nearly 90%, are expressed during embryogenesis. This observation reinforces the view, perhaps not entirely surprising to developmental biologists, that the genesis of a complex organism from the fertilized egg is the most elaborate process known in biology, and thereby depends on “every trick in the book.” Of course, we would expect to see genes that encode signaling molecules and regulatory proteins to be deployed during embryogenesis. Somewhat less obvious is the finding that genes engaged in a variety of physiological processes, such as vision, smell, and taste, are also expressed during early stages of development. Many genes that exhibit a spike of expression in the embryo also exhibit
Previews 465
a second wave of expression during the second major period of development, namely metamorphosis. For example, genes expressed during the first 3 hr of embryogenesis are often reexpressed in early pupae, whereas genes that are expressed in older embryos (9–19 hr after fertilization) are often reexpressed in late pupae. This result suggests that common gene networks might be engaged in the patterning of the embryo and adult. Arbeitman et al. identify 27 genes that exhibit strict maternal expression; 5 were known from previous studies, while 22 are new. This result suggests that there are fewer than 100 strict maternal genes in the entire Drosophila genome. These are genes that are expressed within the ovary, either the developing oocyte or associated somatic follicle cells, but are not expressed in any other tissue at any other stage during the life cycle. During the 1980s and early 1990s, geneticists recognized that the identification of such “maternal genes” would illuminate basic patterning processes in the early embryo (e.g., Anderson et al., 1985; Schupbach and Wieschaus, 1986; Ashburner et al., 1990). These studies led to the identification of determinants that establish the primary body axes, including Bicoid and Dorsal (reviewed by Nusslein-Volhard, 1996). Other genes of this class encode localized determinants (polar granules) that are essential for the formation of the germ cells (pole cells). Genetic screens for maternal genes are tedious and tricky. It is not surprising that some were missed, and there is little doubt that a number of the newly identified maternal genes will be found to participate in the patterning of the early embryo. This study provides an impressive collection of coordinate gene clusters that will foster gene discovery in a
variety of developmental and physiological processes. Drosophila has emerged as one of the most thoroughly characterized animals on Earth since its first use as a model organism over 90 years ago. There is every indication that the growing arsenal of postgenome analyses will keep it this way for the foreseeable future.
The (Theoretical) Yin and Yang of Spindle Mechanics
underlying morphogenesis of the microtubule-based mitotic spindle. In the latest paper, Ne´de´lec takes a purely theoretical approach to analyze a central aspect of spindle morphology—the stable antiparallel overlap of microtubules emanating from opposite spindle poles. Stable here refers not to the lifetime of individual overlapped microtubules, but rather to the long-term maintenance of a morphological configuration. Ne´de´lec sets up a 27parameter-based theoretical model describing symmetric asters of microtubules interacting with motor protein complexes in solution. The model is realistic in that the microtubules undergo dynamic instability, with values for the different dynamic parameters chosen from measurements in Xenopus egg extracts. The motor proteins have quantitative properties measured for conventional kinesin. This choice is not ideal, as properties of bona fide spindle motors would be more appropriate, but these have not yet been well established. By systematically varying specific properties of the motor proteins in stochastic simulations, Ne´de´lec essentially performs a “virtual visual screen” to find “solutions” that lead to stable antiparallel overlap between two asters. The results of this theoretical exercise on a highly simplified system are intriguing both for students of the spindle and for those interested in the potential use of such an approach.
Antiparallel overlap of microtubules is central to the morphogenesis of bipolar mitotic spindles. How does this overlap arise, and how is it maintained? A recent theoretical study uses computer simulations to investigate whether motor protein complexes can achieve this task. The “virtual” results reveal that a mixed polarity motor complex is needed to do the job. In a recent study, Ne´de´lec applies stochastic computer simulations to probe the morphogenetic potential of microtubule-motor protein interactions (Ne´de´lec, 2002). This work continues a theme established by Ne´de´lec, Surrey, and colleagues in which collective properties of motor protein-microtubule mixtures are quantitatively characterized by comparing computer simulations to the in vitro behavior of multicomponent mixtures (Ne´de´lec et al., 1997; Surrey et al., 2001). The broad goal of this work is to understand the self-organization principles
Angelike Stathopoulos and Michael Levine Department of Molecular and Cellular Biology Division of Genetics and Development University of California, Berkeley Berkeley, California 94720 Selected Reading Anderson, K.V., Jurgens, G., and Nusslein-Volhard, C. (1985). Cell 42, 779–789. Arbeitman, M.N., Furlong, E.E.M., Imam, F., Johnson, E., Null, B.H., Baker, B.S., Krasnow, M.A., Scott, M.P., Davis, R.W., and White, K.P. (2002). Science 297, 2270–2275. Ashburner, M., Thompson, P., Roote, J., Lasko, P.F., Grau, Y., el Messal, M., Roth, S., and Simpson, P. (1990). Genetics 126, 679–694. Bonini, N.M., Leiserson, W.M., and Benzer, S. (1993). Cell 72, 379–395. Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. (1998). Proc. Natl. Acad. Sci. USA 8, 14863–14868. Fyrberg, E., Fyrberg, C.C., Beall, C., and Saville, D.L. (1990). J. Mol. Biol. 3, 657–675. Golumbeski, G.S., Bardsley, A., Tax, F., and Boswell, R.E. (1991). Genes Dev. 5, 2060–2070. Kim, S.K., Lund, J., Kiraly, M., Duke, K., Jiang, M., Stuart, J.M., Eizinger, A., Wylie, B.N., and Davidson, G.S. (2001). Science 14, 2087–2092. Nusslein-Volhard, C. (1996). Sci. Am. 275, 54–55, 58–61. Schupbach, T., and Wieschaus, E. (1986). Dev. Biol. 113, 443–448.
able to disrupt only CAR-CAR interactions by competitive binding. Would this disruption be sufficient to trigger breakdown of the entire complex? Could mere absence of the CAR-CAR interaction, or alternatively, presence of the fiber-knob-CAR interaction, induce signals that lead to junction disruption? Clearly, cell junctions must form, disappear, and reform according to the dictates of cell division, cell migration, and response to trauma or infection. It seems likely that viral ligands for key components of cell junctions can be useful tools for probing the signal transduction pathways that govern the dynamics of cell junction formation and disruption.
Selected Reading Barton, E.S., Forrest, J.C., Connolly, J.L., Chappell, J.D., Liu, Y., Schnell, F.J., Nusrat, A., Parkos, C.A., and Dermody, T.S. (2001). Cell 104, 441–451. Bewley, M.C., Springer, K., Zhang, Y.-B., Freimuth, P., and Flanagan, J.M. (1999). Science 286, 1579–1583. Chappell, J.D., Prota, A.E., Dermody, T.S., and Stehle, T. (2002). EMBO J. 21, 1–11. Cohen, C.J., Shieh, J.T.C., Pickles, R.J., Okegawa, T., Hsieh, J.-T., and Bergelson, J.M. (2001). Proc. Natl. Acad. Sci. USA 98, 15191– 15196. Dingwell, K.S., and Johnson, D.C. (1998). J. Virol. 72, 8933–8942. He, Y., Chipman, P.R., Howitt, J., Bator, C.M., Whitt, M.A., Baker, T.S., Kuhn, R.J., Anderson, C.W., Freimuth, P., and Rossmann, M.G. (2001). Nat. Struct. Biol. 8, 874–878.
Patricia G. Spear Department of Microbiology-Immunology The Feinberg School of Medicine Northwestern University 320 East Superior Street Chicago, Illinois 60611
Whole-Genome Expression Profiles Identify Gene Batteries in Drosophila
Microarray studies make it possible to obtain gene expression data on a whole-genome scale. Arbeitman et al. present microarray data generated at multiple time points throughout Drosophila melanogaster development, and identify new genes engaged in a broad spectrum of processes, including the patterning of the early embryo and senescence in adults. In a recent study in Science by Arbeitman et al. (2002), systematic microarray assays were conducted in an effort to obtain a comprehensive view of the temporal patterns of gene expression during the Drosophila life cycle. Approximately one-third of all genes were surveyed at different stages of embryogenesis, larval development, pupation, and the adult. Subsets of genes were organized into groups using a hierarchical clustering algorithm based on coordinate changes in gene expression (Eisen et al., 1998). Fifty clusters were defined using this approach. Some are enriched for genes encoding mitochondrial proteins, cytoskeletal/neuronal factors, and antimicrobial peptides. Other clusters appear to include new components of biochemical complexes and cellular pathways. The analysis of mutant strains helped refine some of the gene clusters, and identify a variety of tissue- and organ-specific genes. Previous microarray assays performed in S. cerevisiae and C. elegans have demonstrated that genes engaged in a common process often exhibit coordinate patterns of expression and similar responses to genetic and environmental perturbations (e.g., Eisen et al., 1998; Kim et al., 2001). Arbeitman et al. demonstrate that the same
Spear, P.G., Eisenberg, R.J., and Cohen, G.H. (2000). Virology 275, 1–8. van Raaij, M.J., Chouin, E., van der Zandt, H., Bergelson, J.M., and Cusack, S. (2000). Structure 8, 1147–1155. Walters, R.W., Freimuth, P., Moninger, T.O., Ganske, I., Zabner, J., and Welsh, M.J. (2002). Cell 110, 789–799. Yoon, M., and Spear, P.G. (2002). J. Virol. 76, 7203–7208.
seems to hold in Drosophila melanogaster, and this finding offers tremendous potential for gene discovery. For example, genes that encode uncharacterized kinases can be brought into the fold of a well-defined receptor tyrosine kinase pathway. Alternatively, coordinately expressed signaling molecules might uncover new pathways acting at various stages of development. The present study identified CG11914 (LIM domain) and CG9098 (SH2 domain) as likely muscle-specific genes based solely on the observation that their temporal expression profiles are similar to those of known “muscle genes” such as upheld, which encodes troponin T (Fyrberg et al., 1990). The proteins encoded by these newly identified genes might participate in the specification of muscle. Moreover, the use of mutant strains, tudor and eyes absent, refine the identification of genes engaged in specific processes. tudor mutants lack germ cells (Golumbeski et al., 1991) and permitted the identification of germline-specific genes that are expressed in either males or females. The eyes absent mutant, which exhibits an eyeless or reduced eye phenotype due to death of the progenitor cells in the eye disc, helped identify a set of 33 genes predicted to function in eye differentiation; 11 were known from earlier studies (e.g., Bonini et al., 1993). Here, we highlight several interesting conclusions that emerged from this analysis. First and foremost is the observation that the vast majority of all genes, nearly 90%, are expressed during embryogenesis. This observation reinforces the view, perhaps not entirely surprising to developmental biologists, that the genesis of a complex organism from the fertilized egg is the most elaborate process known in biology, and thereby depends on “every trick in the book.” Of course, we would expect to see genes that encode signaling molecules and regulatory proteins to be deployed during embryogenesis. Somewhat less obvious is the finding that genes engaged in a variety of physiological processes, such as vision, smell, and taste, are also expressed during early stages of development. Many genes that exhibit a spike of expression in the embryo also exhibit
Previews 465
a second wave of expression during the second major period of development, namely metamorphosis. For example, genes expressed during the first 3 hr of embryogenesis are often reexpressed in early pupae, whereas genes that are expressed in older embryos (9–19 hr after fertilization) are often reexpressed in late pupae. This result suggests that common gene networks might be engaged in the patterning of the embryo and adult. Arbeitman et al. identify 27 genes that exhibit strict maternal expression; 5 were known from previous studies, while 22 are new. This result suggests that there are fewer than 100 strict maternal genes in the entire Drosophila genome. These are genes that are expressed within the ovary, either the developing oocyte or associated somatic follicle cells, but are not expressed in any other tissue at any other stage during the life cycle. During the 1980s and early 1990s, geneticists recognized that the identification of such “maternal genes” would illuminate basic patterning processes in the early embryo (e.g., Anderson et al., 1985; Schupbach and Wieschaus, 1986; Ashburner et al., 1990). These studies led to the identification of determinants that establish the primary body axes, including Bicoid and Dorsal (reviewed by Nusslein-Volhard, 1996). Other genes of this class encode localized determinants (polar granules) that are essential for the formation of the germ cells (pole cells). Genetic screens for maternal genes are tedious and tricky. It is not surprising that some were missed, and there is little doubt that a number of the newly identified maternal genes will be found to participate in the patterning of the early embryo. This study provides an impressive collection of coordinate gene clusters that will foster gene discovery in a
variety of developmental and physiological processes. Drosophila has emerged as one of the most thoroughly characterized animals on Earth since its first use as a model organism over 90 years ago. There is every indication that the growing arsenal of postgenome analyses will keep it this way for the foreseeable future.
The (Theoretical) Yin and Yang of Spindle Mechanics
underlying morphogenesis of the microtubule-based mitotic spindle. In the latest paper, Ne´de´lec takes a purely theoretical approach to analyze a central aspect of spindle morphology—the stable antiparallel overlap of microtubules emanating from opposite spindle poles. Stable here refers not to the lifetime of individual overlapped microtubules, but rather to the long-term maintenance of a morphological configuration. Ne´de´lec sets up a 27parameter-based theoretical model describing symmetric asters of microtubules interacting with motor protein complexes in solution. The model is realistic in that the microtubules undergo dynamic instability, with values for the different dynamic parameters chosen from measurements in Xenopus egg extracts. The motor proteins have quantitative properties measured for conventional kinesin. This choice is not ideal, as properties of bona fide spindle motors would be more appropriate, but these have not yet been well established. By systematically varying specific properties of the motor proteins in stochastic simulations, Ne´de´lec essentially performs a “virtual visual screen” to find “solutions” that lead to stable antiparallel overlap between two asters. The results of this theoretical exercise on a highly simplified system are intriguing both for students of the spindle and for those interested in the potential use of such an approach.
Antiparallel overlap of microtubules is central to the morphogenesis of bipolar mitotic spindles. How does this overlap arise, and how is it maintained? A recent theoretical study uses computer simulations to investigate whether motor protein complexes can achieve this task. The “virtual” results reveal that a mixed polarity motor complex is needed to do the job. In a recent study, Ne´de´lec applies stochastic computer simulations to probe the morphogenetic potential of microtubule-motor protein interactions (Ne´de´lec, 2002). This work continues a theme established by Ne´de´lec, Surrey, and colleagues in which collective properties of motor protein-microtubule mixtures are quantitatively characterized by comparing computer simulations to the in vitro behavior of multicomponent mixtures (Ne´de´lec et al., 1997; Surrey et al., 2001). The broad goal of this work is to understand the self-organization principles
Angelike Stathopoulos and Michael Levine Department of Molecular and Cellular Biology Division of Genetics and Development University of California, Berkeley Berkeley, California 94720 Selected Reading Anderson, K.V., Jurgens, G., and Nusslein-Volhard, C. (1985). Cell 42, 779–789. Arbeitman, M.N., Furlong, E.E.M., Imam, F., Johnson, E., Null, B.H., Baker, B.S., Krasnow, M.A., Scott, M.P., Davis, R.W., and White, K.P. (2002). Science 297, 2270–2275. Ashburner, M., Thompson, P., Roote, J., Lasko, P.F., Grau, Y., el Messal, M., Roth, S., and Simpson, P. (1990). Genetics 126, 679–694. Bonini, N.M., Leiserson, W.M., and Benzer, S. (1993). Cell 72, 379–395. Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. (1998). Proc. Natl. Acad. Sci. USA 8, 14863–14868. Fyrberg, E., Fyrberg, C.C., Beall, C., and Saville, D.L. (1990). J. Mol. Biol. 3, 657–675. Golumbeski, G.S., Bardsley, A., Tax, F., and Boswell, R.E. (1991). Genes Dev. 5, 2060–2070. Kim, S.K., Lund, J., Kiraly, M., Duke, K., Jiang, M., Stuart, J.M., Eizinger, A., Wylie, B.N., and Davidson, G.S. (2001). Science 14, 2087–2092. Nusslein-Volhard, C. (1996). Sci. Am. 275, 54–55, 58–61. Schupbach, T., and Wieschaus, E. (1986). Dev. Biol. 113, 443–448.