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a GDP-MT lattice. This result would explain why, in vitro, EBs can promote catastrophes [5,10,17], which are thought to be associated with the loss of the GTP cap [18]. On the other hand, in cells, the depletion of EBs leads to shorter rather than to longer EB comets and results in an increase rather than a decrease in catastrophe frequency [19]; this observation, however, might be due to the functional interplay of EBs with other MT regulators. The work of Maurer et al. raises interesting questions. If the EBs indeed preferentially bind to the GTP-MT lattice, does this mean that they provide a direct readout for the localization and size of the GTP cap? An EB-positive comet is an extended structure, which in mammalian cells easily reaches 2 micrometers in length [17,19]. In contrast, it is generally thought that a small layer of GTP-tubulin subunits with a length in the range of tens of nanometers is sufficient to stabilize growing MT ends [18]. This notion, however, does not define the actual cap length — it is possible that single dispersed tubulin subunits with non-hydrolyzed GTP persist over time in the cap, and convert only gradually to the GDP-tubulin form. This hypothesis could offer an attractive explanation for the observed exponential decay of the comet intensity [8,17,19], assuming that persisting GTP- or GDP-Pi-bound tubulin subunits could affect the structure of neighboring subunits within the cap. One way to experimentally probe the model described above would be by using an independent agent that detects GTP-tubulin subunits in MTs. In this context, a monoclonal antibody against GTPgS-tubulin has been generated [20]. However, comparison of the MT decoration pattern of EB to that of the anti GTPgS-tubulin antibody indicates that the two molecules are unlikely to recognize the same structural MT epitope: in addition to MT ends, this antibody also strongly labels small patches along the whole MT lattice, which is not the case for EBs. How do EBs distinguish between different nucleotide states of tubulin? Since EBs show no significant binding to a/b-tubulin heterodimers [5], it is tempting to speculate that they recognize a region between tubulin subunits, close to the guanine
nucleotide binding site of b-tubulin, which is expected to undergo a conformational change upon GTP hydrolysis [15]. Having static mimics of the otherwise transient structure that EBs recognize at MT tips at hand opens the way for a detailed analysis of the structural basis of this highly specific and intriguing intracellular protein localization mechanism.
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References 1. Desai, A., and Mitchison, T.J. (1997). Microtubule polymerization dynamics. Annu. Rev. Cell Dev. Biol. 13, 83–117. 2. Akhmanova, A., and Steinmetz, M.O. (2008). Tracking the ends: a dynamic protein network controls the fate of microtubule tips. Nat. Rev. Mol. Cell Biol. 9, 309–322. 3. Galjart, N. (2010). Plus-end-tracking proteins and their interactions at microtubule ends. Curr. Biol. 20, R528–R537. 4. Akhmanova, A., and Steinmetz, M.O. (2010). Microtubule +TIPs at a glance. J. Cell Sci. 123, 3415–3419. 5. Bieling, P., Laan, L., Schek, H., Munteanu, E.L., Sandblad, L., Dogterom, M., Brunner, D., and Surrey, T. (2007). Reconstitution of a microtubule plus-end tracking system in vitro. Nature 450, 1100–1105. 6. Dixit, R., Barnett, B., Lazarus, J.E., Tokito, M., Goldman, Y.E., and Holzbaur, E.L. (2009). Microtubule plus-end tracking by CLIP-170 requires EB1. Proc. Natl. Acad. Sci. USA 106, 492–497. 7. Honnappa, S., Gouveia, S.M., Weisbrich, A., Damberger, F.F., Bhavesh, N.S., Jawhari, H., Grigoriev, I., van Rijssel, F.J., Buey, R.M., Lawera, A., et al. (2009). An EB1-binding motif acts as a microtubule tip localization signal. Cell 138, 366–376. 8. Dragestein, K.A., van Cappellen, W.A., van Haren, J., Tsibidis, G.D., Akhmanova, A., Knoch, T.A., Grosveld, F., and Galjart, N. (2008). Dynamic behavior of GFP-CLIP-170 reveals fast protein turnover on microtubule plus ends. J. Cell Biol. 180, 729–737. 9. Chretien, D., Fuller, S.D., and Karsenti, E. (1995). Structure of growing microtubule ends: two-dimensional sheets close into tubes at variable rates. J. Cell Biol. 129, 1311–1328. 10. Vitre, B., Coquelle, F.M., Heichette, C., Garnier, C., Chretien, D., and Arnal, I. (2008). EB1 regulates microtubule dynamics and tubulin sheet closure in vitro. Nat. Cell Biol. 10, 415–421. 11. McIntosh, J.R., Morphew, M.K., Grissom, P.M., Gilbert, S.P., and Hoenger, A. (2009). Lattice
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structure of cytoplasmic microtubules in a cultured mammalian cell. J. Mol. Biol. 394, 177–182. Sandblad, L., Busch, K.E., Tittmann, P., Gross, H., Brunner, D., and Hoenger, A. (2006). The Schizosaccharomyces pombe EB1 homolog Mal3p binds and stabilizes the microtubule lattice seam. Cell 127, 1415–1424. des Georges, A., Katsuki, M., Drummond, D.R., Osei, M., Cross, R.A., and Amos, L.A. (2008). Mal3, the Schizosaccharomyces pombe homolog of EB1, changes the microtubule lattice. Nat. Struct. Mol. Biol. 15, 1102–1108. Zanic, M., Stear, J.H., Hyman, A.A., and Howard, J. (2009). EB1 recognizes the nucleotide state of tubulin in the microtubule lattice. PLoS One 4, e7585. Maurer, S.P., Bieling, P., Cope, J., Hoenger, A., and Surrey, T. (2011). GTPgammaS microtubules mimic the growing microtubule end structure recognised by end-binding proteins (EBs). Proc. Natl. Acad. Sci. USA 108, 3988–3993. Carlier, M.F., Didry, D., Simon, C., and Pantaloni, D. (1989). Mechanism of GTP hydrolysis in tubulin polymerization: characterization of the kinetic intermediate microtubule-GDP-Pi using phosphate analogues. Biochemistry 28, 1783–1791. Komarova, Y., De Groot, C.O., Grigoriev, I., Gouveia, S.M., Munteanu, E.L., Schober, J.M., Honnappa, S., Buey, R.M., Hoogenraad, C.C., Dogterom, M., et al. (2009). Mammalian end binding proteins control persistent microtubule growth. J. Cell Biol. 184, 691–706. Drechsel, D.N., and Kirschner, M.W. (1994). The minimum GTP cap required to stabilize microtubules. Curr. Biol. 4, 1053–1061. Komarova, Y., Lansbergen, G., Galjart, N., Grosveld, F., Borisy, G.G., and Akhmanova, A. (2005). EB1 and EB3 control CLIP dissociation from the ends of growing microtubules. Mol. Biol. Cell 16, 5334–5345. Dimitrov, A., Quesnoit, M., Moutel, S., Cantaloube, I., Pous, C., and Perez, F. (2008). Detection of GTP-tubulin conformation in vivo reveals a role for GTP remnants in microtubule rescues. Science 322, 1353–1356.
1Cell Biology, Faculty of Science, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands. 2Biomolecular Research, Paul Scherrer Insititut, CH-5232 Villigen PSI, Switzerland. E-mail:
[email protected], michel.
[email protected]
DOI: 10.1016/j.cub.2011.03.023
Alternative Strategies: The Evolution of Switch Points The evolution of conditional, alternative strategies is a major factor in adaptation. In animals, the frequency of alternative morphs, characterized by different morphologies and mating tactics, can be both condition-dependent and subject to rapid evolutionary change. Derek A. Roff The interaction of ecology and genetic variation in producing rapid evolutionary change has become an
increasingly important focus of research, particularly in the face of global warming. This process is well illustrated by a paper by Joseph Tomkins and colleagues [1] in a recent
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Figure 1. Wing dimorphism in the sand cricket. Wing dimorphism is an example of variation generated by a threshold trait. The long-winged, flight capable morph on the left possesses both large, functional wings and the associated flight musculature and flight fuels, whereas the flightless, short-wing morph on the right lacks the flight musculature and invests more in reproduction rather than flight fuels.
issue of Current Biology, which deals with the rapid evolutionary change in the frequency of alternative morphs in the mite Rhizoglyphus echinopus after a change in habitat complexity. This work is important as it comfirms a theoretical model in which the switch between two discrete morphologies is under polygenic control that is itself environmentally sensitive. The genetic model underlying this work is known as the ‘threshold model’. This model has been used to account for genetic variation in such diverse traits as disease resistance, survival in general, twinning in sheep, dimorphic variation in morphological structures such as insect wings (Figure 1), trophic morphologies, life-cycle switches (e.g. dormancy) and alternative behavioral and reproductive traits [2]. It has been hypothesized that threshold traits are based on a continuously distributed trait, called the ‘liability’, and a threshold of expression such that individuals lying above the threshold express one phenotype while those below the threshold express the alternative. Candidate traits for the liability are, for instance, hormone profiles, such as the level of juvenile hormone esterase controlling in part the expression of fully developed wings in the sand cricket at a critical time in development [3].
By now, there is abundant evidence that threshold traits are highly context-dependent and subject to rapid evolutionary change both in the wild and the lab [4–7]. The paper by Joseph Tomkins and colleagues [1] demonstrates that this can occur as a result of the interaction between habitat heterogeneity and sexual selection. In this case, increased habitat complexity favors a non-fighter male morph of R. echinopus because it is more mobile than the alternative fighter morph. The change in frequency of the two morphs was itself the result of a change in the switch point (i.e. threshold) at which the alternative morph developed. The concept of the threshold trait has a very long history. In 1889, Francis Galton in his attempt to describe disease resistance as a consequence of a normally distributed trait proposed the threshold model mentioned above, with resistance being the liability [8]. Later, Sewall Wright formalized the ‘genetical model’ in his attempt to account for extra digits in guinea pigs: in this case, Wright suggested multiple thresholds, each accounting for an extra digit [9]. The genetical model of threshold traits assumes that either or both the liability and threshold are genetically variable; for example, the
threshold might be fixed and the liability a consequence of polygenic variation. A plausible example of this model would be variation in hormone concentration. Alternatively, the liability could be fixed and the threshold genetically variable — a plausible example of this could be genetic variation in hormone receptors. Finally, both liability and threshold could vary genetically. In general, these causal models can be described by the same mathematical model. In their study, Tomkins and colleagues [1] assume a variable switch point (i.e. threshold), which is mathematically convenient but does not necessarily imply a causal biological explanation — a model in which the mean liability itself varied with the switch point remaining constant is mathematically equivalent. Functional analyses of the causal components of threshold traits are sadly lacking and represent a promising area of research by which genetic, physiological and morphological variation could be integrated. A common feature of threshold traits is that they often are condition-dependent, responsive to internal and/or external cues [10–12]. For example, the expression of protective morphological structures observed in many invertebrates is a response to critical levels of chemical released by their predators. Another example is the expression of alternative reproductive tactics found in numerous insect species: in these cases, there is one male morph that is equipped as a fighter/territorial holder by the presence of large horns, enlarged forelegs etc. and an alternative morph that lacks these structures but obtains copulations by adopting a satellite or sneaking tactic [1,13,14]. Frequently, the probability of adopting a particular tactic can be size-dependent, with the probability of developing into a fighter morph increasing with body size. This conditional response may itself be modified by another factor, such as population density [1,2,15,16]. The presence of two morphs suggests that there must be fitness differences that, in any given circumstance, are highest for one particular morph and that, overall, the two morphs are kept in balance by some form of frequency-dependent selection [17,18]. In the case of wing dimorphism in insects this difference resides in the higher fecundity and higher mating success of the flightless
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morph but the ability of the flying morph to colonize new habitats: in a heterogeneous environment frequency-dependence occurs at the meta-population scale, with local short-term advantage going to the flightless morph but the longer-term advantage shifting to the flying morph because only this morph can colonize new habitats [17]. In the case of wing dimorphism in crickets, the flightless males have an advantage over the winged males in that they can divert energy required to build and maintain the flight apparatus into calling, which is the means by which males attract females. Ability to disperse in invertebrates may reside not only in flight ability but in variation in other forms of locomotion as in the case of the mite R. echinopus that was studied by Tomkins and colleagues [1]. In this case, dispersal is accomplished by walking and the two morphs, a ‘fighter’ male morph and a ‘scrambler’ morph, are unequally equipped in this respect, the scrambler being a better disperser. On the other hand, the fighter morph, as its name implies, is equipped to displace the scrambler morph and in a head-on-head interaction typically obtains more copulations. Because the scrambler morph is better able to locate females in a complex environment, its fitness is increased in such environments and selection favors a change in the switch point such that the frequency of scrambler males is increased over time.
While the genetic architecture and physiological pathways underlying threshold traits may be complex [7,19,20] the phenotypic expression is readily apparent and thus even small evolutionary changes are easily assayed. The experiments on evolutionary changes in R. echiopus clearly demonstrate the interaction between ecological and genetic factors in rapid evolutionary change. Thus, as model systems, threshold traits hold great promise for the study of evolutionary change at multiple levels of enquiry.
References 1. Tomkins, J.L., Hazel, W.N., Penrose, M.A., Radwan, J.W., and LeBas, N.R. (2011). Habitat complexity drives the experimental evolution of a conditionally expressed secondary sexual trait. Curr. Biol. 21, 569–573. 2. Roff, D.A. (1996). The evolution of threshold traits in animals. Q. Rev. Biol. 71, 3–35. 3. Fairbairn, D.J., and Yadlowski, D.E. (1997). Coevolution of traits determining migratory tendency: correlated response of a critical enzyme, juvenile hormone esterase, to selection on wing morphology. J. Evol. Biol. 10, 495–513. 4. Roff, D.A. (1990). Selection for changes in the incidence of wing dimorphism in Gryllus firmus. Heredity 65, 163–168. 5. Moczek, A.P., and Nijhout, H.F. (2003). Rapid evolution of a polyphenic threshold. Evol. Dev. 5, 259–268. 6. Tomkins, J.L., and Brown, G.S. (2004). Population density drives the local evolution of a threshold dimorphism. Nature 431, 1099–1103. 7. Roff, D.A., and Fairbairn, D.J. (2007). Laboratory evolution of the migratory polymorphism in the sand cricket: combining physiology with quantitative genetics. Physiol. Biochem. Zool. 80, 358–369. 8. Galton, F. (1889). Natural Inheritance (London: MacMillan and Co.).
Motor Learning: Spare the Rod to Benefit the Child? A new study has found that individuals who were rewarded while they learned a motor task performed it much better one month later than those who were punished or received nothing. Long-term memories depend on events experienced during learning. John Rothwell We can learn something because we enjoy doing it, or because we are frightened of punishment for being unsuccessful. Teachers know that both approaches work, but which memories stay with us for longer? A surprising, but perhaps reassuring conclusion from the work of Abe et al. [1], reported
recently in Current Biology, is that rewarded learning stays with us better than learning through punishment. Perhaps a case of spare the rod to benefit the child? Abe et al. [1] studied motor learning: volunteers moved a small blue box on a video screen by changing the force with which they pinched a transducer between finger and thumb. When
9. Wright, S. (1934). An analysis of variability in number of digits in an inbred strain of guinea pigs. Genetics 19, 0506–0536. 10. Piche, J., Hutchings, J.A., and Blanchard, W. (2008). Genetic variation in threshold reaction norms for alternative reproductive tactics in male Atlantic salmon, Salmo salar. Proc. Roy. Soc. B 275, 1571–1575. 11. Tomkins, J.L., and Hazel, W. (2007). The status of the conditional evolutionarily stable strategy. Trends Ecol. Evol. 22, 522–528. 12. Gotthard, K., and Berger, D. (2010). The diapause decision as a cascade switch for adaptive developmental plasticity in body mass in a butterfly. J. Evol. Biol. 23, 1129–1137. 13. Emlen, D.J. (2001). Costs and the diversification of exaggerated animal structures. Science 291, 1534–1536. 14. Radwan, J. (2009). Alternative mating tactics in acarid mites. In Advances in the Study of Behavior, Volume 39 (San Diego: Elsevier Academic Press Inc), pp. 185–208. 15. Hazel, W., Smock, R., and Lively, C.M. (2004). The ecological genetics of conditional strategies. Am. Nat. 163, 888–900. 16. Gotthard, K., Berger, D., Bergman, M., and Merilaita, S. (2009). The evolution of alternative morphs: density-dependent determination of larval colour dimorphism in a butterfly. Biol. J. Linn. Soc. 98, 256–266. 17. Roff, D.A. (1994). Habitat persistence and the evolution of wing dimorphism in insects. Am. Nat. 144, 772–798. 18. Moorad, J.A., and Linksvayer, T.A. (2008). Levels of selection on threshold characters. Genetics 179, 899–905. 19. King, E.G., and Roff, D.A. (2010). Modeling the evolution of phenotypic plasticity in resource allocation in wing dimorphic insects. Am. Nat. 175, 702–716. 20. Zera, A.J., Harshman, L.G., and Williams, T.D. (2007). Evolutionary endocrinology: The developing synthesis between endocrinology and evolutionary genetics. Annu. Rev. Ecol. Evol. Syst. 38, 793–817.
Department of Biology, University of California, Riverside, CA 9521, USA. E-mail:
[email protected]
DOI: 10.1016/j.cub.2011.03.016
a larger red box appeared they had to keep their smaller box within its outline as it moved smoothly up and down. On the first few trials the volunteers could not manage to keep up. However, since the movement repeated itself on each trial, they eventually got better and better the more they practiced. After 80 trials, they had a short rest and then were tested immediately afterwards on the same task. Learning in this type of task can be defined as the improvement in tracking accuracy in these evaluation trials over and above performance in the initial trials. The experiment had a clever twist so that Abe et al. [1] could test the effect of reward and punishment. One group of subjects received monetary reward after each trial depending on how well