- Email: [email protected]
Group Dynamics: Predators and Prey Get a Little Help from Their Friends
Dispatch R531
5. Roy, S.W., and Irimia, M. (2009). Splicing in the eukaryotic ancestor: form, function and dysfunction. Trends Ecol. Evol. 24, 447–455. 6. Worden, A.Z., Lee, J.H., Mock, T., Rouze´, P., Simmons, M.P., Aerts, A.L., Allen, A.E., Cuvelier, M.L., Derelle, E., Everett, M.V., et al. (2009). Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes Micromonas. Science 324, 268–272. 7. Rogozin, I.B., Carmel, L., Csuros, M., and Koonin, E.V. (2012). Origin and evolution of spliceosomal introns. Biol. Direct 7, 11.
8. Li, W., Tucker, A.E., Sung, W., Thomas, W.K., and Lynch, M. (2009). Extensive, recent intron gains in Daphnia populations. Science 326, 1260–1262. 9. Denoeud, F., Henriet, S., Mungpakdee, S., Aury, J.M., Da Silva, C., Brinkmann, H., Mikhaleva, J., Olsen, L.C., Jubin, C., Can˜estro, C., et al. (2010). Plasticity of animal genome architecture unmasked by rapid evolution of a pelagic tunicate. Science 330, 1381–1385. 10. Torriani, S.F., Stukenbrock, E.H., Brunner, P.C., McDonald, B.A., and Croll, D. (2011). Evidence for extensive recent intron transposition
Group Dynamics: Predators and Prey Get a Little Help from Their Friends Transfer of information about predatory attacks between individuals allows schooling or flocking prey to evade predation without disrupting group integrity. But, predators can mitigate this effect by working together themselves. Graeme D. Ruxton You and I will probably not meet a violent end: in the USA, less than four out of a thousand people end up murdered [1]. Things are less cosy in the natural world: for example, some studies suggest that most zebras end their days in the grasp of a lion [2]. Hence, predation is a very potent selective force, and animals show a huge diversity of adaptations that can be understood in terms of managing their predation risk. One widespread and intensively-studied adaptation is group living. There are a number of mechanisms by which grouping can reduce predation risk. If predators can only catch one individual at a time, the risk for group members can be diluted as most will escape when a group is attacked. Moreover, this benefit can increase with group size more steeply than the costs of larger groups, for instance, being more obvious [3]. A group of prey has many eyes to watch out for surprise attacks, and sometimes the facility to mount a collective defence, e.g. when water buffalo form a circle with their horns facing outward and their vulnerable rumps protected in the centre. Furthermore, if the group is moving then predators appear to suffer a confusion effect where they have difficulty tracking a particular moving individual against the distractive background of other
similar moving alternative targets [4]. This last mechanism in particular has often been suggested to explain the extraordinary coordinated displays of schooling fish and some flocking birds. However, it would be surprising if predators had not co-evolved countermeasures, and in this issue of Current Biology Handegard et al. [5] provide a fascinating demonstration of such countermeasures in predaceous spotted seatrout (Cynoscion nebulosus) attacking schools of juvenile Gulf menhaden (Brevoortia patronus). Their observations on naturally occurring attacks in the Gulf of Mexico were made possible by high-frequency imaging sonar giving 2 cm spatial resolution across a 24 m2 area and 8 Hz temporal resolution. Handegard et al. [5] first of all demonstrated the prey’s defensive measures. When a seatrout mounts an attack towards the school, there is a coordinated response of school members to maintain a safe distance from the approaching predator, so a vacuole of empty space in the school opens up in front of the predator and closes behind it (Figure 1). This coordinated movement of individuals requires information transfer over greater distances than those at which fish can detect the predator in the turbid water, and over faster timescales than a fish can swim. Such group-level responses
in closely related fungi. Curr. Biol. 21, 2017–2022. 1Department of Biology, 1600 Holloway Avenue, San Francisco State University, San Francisco, CA 94132, USA. 2160 College Street, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada. *E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2012.05.017
can be understood as emerging from individuals reacting to the acceleration of their near neighbours only [6]. Seatrout, however, often do not attack alone, but in a coordinated group of individuals attacking in line astern. This tactic prevents the closing off of the vacuole behind the first attacker. Furthermore, different parts of the school respond to the multiple threats such that the coherence of movement across the whole school breaks down, which in extreme cases can lead to a breaking up of the school into smaller parts. The sonar did not allow individual predation events to be recorded, but predators were able to get much nearer to fish when schools were smaller and within-school movements were less coherent. Such close proximity is very likely to lead to predation: the predators are faster than their prey in a straight line and it is only their better manoeuvrability that normally lets prey stay out of close proximity to the predators. Our understanding the dynamics of coordinated group movement has made great strides over the last decade. This was mainly driven by observation of the emergent patterns from computer models of individuals that react to their neighbours according to rules that the modellers can specify. These models have had conspicuous success in demonstrating that the apparent complexity of coherent group-level movement can be generated by very simple local interactions without centralised control or special sensory or cognitive powers [6–8]. However, in the last few years computational and technological improvements have also allowed empirical work to make dramatic strides [9–14]. These studies suggest
Current Biology Vol 22 No 13 R532
Figure 1. Predator in a bubble. A predator attacking a fish school, and the corresponding response of the prey school. The image is taken using an acoustic camera, similar to that used in [5], and the image is filtered to enhance the typical school response to the predator. Image: Simon P. Leblanc.
that there is now a need to return to the models and revisit their assumptions in the light of emerging empirical evidence. For example, these models generally are based on the assumption that when fish respond to the positioning and movement of near-neighbours, a neighbourhood is described by a physical distance metric — such as the visual range at which neighbours can be seen. But it seems that, in at least some groups, it is topological rather than metric distance that matters, with perhaps a cognitive rather than sensory constraint causing individuals to respond only to a fixed number of nearest neighbours regardless of variation in local density of individuals [9]. Furthermore, for understandable reasons of Occam’s razor, most models have assumed that the reaction fields around individuals are isotropic, with distance (rather than direction) between neighbours governing interactions. However, recent empirical evidence suggests that shoaling fish might respond differently in response to the same movement by fish ahead of them as opposed to fish behind them [10]. However, perhaps the most dramatic challenge to current theory comes from recent evidence of multi-body responses [10]. Most previous theory is based on the assumption that although a focal individual can respond to several near neighbours
simultaneously, the effect of these can be understood by averaging the responses to each of the neighbours as if they existed in isolation. However, recent work on fish shoaling [AU reference] suggests that three-body interactions make a substantial contribution to collective dynamics. That is, the response of fish A to the proximity and movements of fish B and C cannot be predicted from its responses to each of these in the absence of the other. Development of three-body rules for simulation models will require very close interaction between theory and data collection, with experiments designed to test contrasting predictions of alternative model formulations testing candidate rules. Aircraft designers face a trade-off between stability and manoeuvrability: they design airliners to be insensitive to turbulence, whereas jet fighters can only attain their great manoeuvrability at a cost of instability that requires continual correction of deviations from the intended flight path by computer control. A similar trade-off must exist in the responsiveness of collective school dynamics. Thus, the local rules must confer a robustness, such that noise generated by environmental micro-scale turbulence or simple mistakes by individuals does not get propagated across the whole group; yet, valuable information about predator attack needs to be effectively
communicated. It may be in managing this challenge that we find the selective pressures that have driven the adoption of use of topological space rules and multi-body interactions. When a single predator attacks the group, the trade-off been sensitivity and robustness becomes easier to manage because useful initial information about the attack is likely to come from a single spatially concentrated part of the group, whereas noise will likely be generated more diffusely. However, the work of Handegard et al. [5] suggests that coordinated attacks can remove this easy means of separating signal from noise, and require responses to information about a number of simultaneous relevant events. There is still much we have to uncover about how local interaction rules can produce complex group behaviours, but recent technological breakthroughs and increasingly tight connection between theory and data suggest that we have all the tools to considerably improve our understanding in the next few years. Such progress may be of more than academic value, and should improve our ability to manage human crowds on our increasingly crowded planet [15]. References 1. FBI Crime in the USA Report 2011: http://www. fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/ preliminary-annual-ucr-jan-jun-2011. 2. Sinclair, A.R.E., Mduma, S., and Brashare, J.S. (2003). Patterns of predation in a diverse predator-prey system. Nature 425, 288–290. 3. Turner, G.F., and Pitcher, T.J. (1986). Attack abatement: a model of group protection by combined avoidance and dilution. Am. Nat. 128, 228–240. 4. Neill, S.R., St, J., and Cullen, J.M. (1974). Experiments on whether schooling in prey affects hunting behaviour in cephalopod and fish predators. J. Zool. 172, 549–569. 5. Handegard, N.O., Boswell, K.M., Ioannou, C.C., Leblanc, S.P., Tjøstheim, D.B., and Couzin, I.D. (2012). The dynamics of coordinated group hunting and collective information transfer among schooling prey. Curr. Biol. 22, 1213–1217. 6. Couzin, I.D., Krause, J., James, R., and Ruxton, G.D. (2002). Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11. 7. Huth, A., and Wissel, C. (1992). The simulation of the movement of fish schools. J. Theor. Biol. 156, 365–385. 8. Buhl, J., Sumpter, D.J.T., Couzin, I.D., Hale, J.J., Despland, E., Miller, E.R., and Simpson, S.J. (2006). From disorder to order in marching locusts. Science 312, 1402–1406. 9. Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, I., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., et al. (2008). Interaction ruling animal collective behaviour depends on topological rather than metric distance: evidence from a field study. Proc. Natl. Acad. Sci. USA 105, 1232–1237.
Dispatch R533
10. Katz, Y., Tunstrøm, K., Ioannou, C.C., Huepe, C., and Couzin, I.D. (2011). Inferring the structure and dynamics of interactions in schooling fish. Proc. Natl. Acad. Sci. USA 108, 18720–18725. 11. Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., and Viale, M. (2010). Scale-free correlations in starling flocks. Proc. Natl. Acad. Sci. USA 107, 11865–11870. 12. Bialek, W., Cavagna, A., Giardina, I., Mora, T., Silvestri, E., Viale, M., and Walczak, M.
(2012). Statistical mechanics for natural bird flocks. Proc. Natl. Acad. Sci. USA 109, 4786–4791. 13. Herbert-Read, J.E., Perna, A., Mann, R.P., Schaef, T.M., Sumpter, D.J.T., and Ward, A.J.W. (2011). Inferring the rules of interaction of shoaling fish. Proc. Natl. Acad. Sci. USA 108, 18726–18731. 14. Lukeman, R., Li, Y.-X., and Edelstein-Keshet, L. (2010). Inferring individuals rules from collective behavior. Proc. Natl. Acad. Sci. USA 107, 12576–12580.
Plant Cell Biology: The ABC of Monolignol Transport Lignins are complex aromatic heteropolymers that reinforce the cell walls of terrestrial plants. A new study identifies an ATP-binding cassette ABC transporter that pumps a monolignol lignin precursor across the plasma membrane. Richard Sibout and Herman Ho¨fte* Cell wall lignification has been a critical innovation in the evolution of terrestrial plants from their aquatic ancestors over 450 million years ago [1]. Lignins impermeabilize and consolidate cell walls, provide resistance to negative pressure in water-conducting tissues and provide strength to organs, thus allowing an erect growth habit. Lignins are also important targets for plant biotechnology; for instance, they interfere with enzymatic depolymerization of polysaccharides and hence are a main obstacle for biorefinery applications. Lignins are highly complex polymers of phenylpropanoid precursors, the monolignols. These building blocks are synthesized in the cytosol, but end up at various subcellular locations — the cell wall, the vacuole or the Golgi apparatus (Figure 1). Monolignol biosynthesis is well understood and progress has been made in our understanding of the oxidative polymerization process [2]. However, until recently, it was not known how monolignols are transported across membranes. In this issue of Current Biology, an important breakthrough is reported with the identification of a transporter of the ATP-binding cassette (ABC) family, which pumps the monolignol p-coumaryl alcohol across the plasma membrane [3]. The three main monolignols are p-coumaryl alcohol, coniferyl alcohol,
and sinapyl alcohol, which differ by their degree of methoxylation (Figure 1) and which after polymerization are referred to as p-hydroxyphenyl (H), guaiacyl (G) or syringyl (S) units, respectively. Monolignol biosynthesis takes place in the cytosol. The phenylalanine precursor is exported from the chloroplast and successively de-aminated, hydroxylated, o-methylated and reduced to produce the monolignols. Three cytochrome P450 enzymes are anchored in the endoplasmic reticulum membrane facing the cytosol. The other enzymes don’t have membrane anchors, and little is known about their sub-cellular localization (Figure 1). It is likely that many of the enzymes form multiprotein complexes as was recently demonstrated for the endoplasmic reticulum-associated enzymes [4]. Upon arrival in the cell wall, monolignols undergo oxidative crosslinking, promoted by large families of cell wall-associated peroxidases and laccases [2,5]. Monolignols also accumulate in the vacuole as glucoconjugates [6,7]. How do monolignols cross membranes? Different hypotheses have been proposed: passive diffusion of the hydrophobic monolignols across the lipid bilayer; vesicular trafficking; post-mortem release or through specialized membrane transporters [7,8]. A recent indication of how this process occurs came from the demonstration that monolignol
15. Krause, J., and Ruxton, G.D. (2011). The dynamics of collective human behaviour. Lancet 377, 903–904.
School of Biology, University of St Andrews, St Andrews KY16 9TH, United Kingdom. E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2012.05.013
transport is an ATP-dependent process [9]. This result, combined with the pharmacology, suggests the involvement of ABC-type transporters. Interestingly, plasma membrane transport shows selectivity for hydroxycinnamyl alcohols and aldehydes, and does not accept ferulic acid or monolignol glucosides. The vacuolar transport instead is highly selective for monolignol 4-O-glucosides, suggesting that glucoconjugation is a prerequisite for selective import into the vacuole. Santiago and colleagues [3] now report the identification of an ABC transporter as a p-coumaryl alcohol transporter. ABC transporters belong to a large superfamily present in all kingdoms, and the majority of its members are ATP-driven pumps involved in the transport across membranes of a wide range of molecules (primary and secondary metabolites, signaling molecules, lipids, proteins, etc.). Functional pumps consist of two membrane-spanning pores and two nucleotide-binding domains. Plant genomes contain large ABC transporter gene families (e.g. the Arabidopsis genome encodes 130 family members, only 22 of which have been functionally characterized [10]). The authors of this new study used public microarray data to identify a putative ABC transporter gene (AtABC29) that was co-expressed with other phenylpropanoid biosynthetic genes [3]. AtABC29 was highly expressed in roots and anthers and a GFP-fusion protein accumulated in the plasma membrane in Arabidopsis. To study the function of this gene, it was expressed in an adapted yeast strain. In the control strain, growth was inhibited by p-coumaryl alcohol and coniferyl alcohol, but not by sinapyl alcohol. Interestingly, the expression
5. Roy, S.W., and Irimia, M. (2009). Splicing in the eukaryotic ancestor: form, function and dysfunction. Trends Ecol. Evol. 24, 447–455. 6. Worden, A.Z., Lee, J.H., Mock, T., Rouze´, P., Simmons, M.P., Aerts, A.L., Allen, A.E., Cuvelier, M.L., Derelle, E., Everett, M.V., et al. (2009). Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes Micromonas. Science 324, 268–272. 7. Rogozin, I.B., Carmel, L., Csuros, M., and Koonin, E.V. (2012). Origin and evolution of spliceosomal introns. Biol. Direct 7, 11.
8. Li, W., Tucker, A.E., Sung, W., Thomas, W.K., and Lynch, M. (2009). Extensive, recent intron gains in Daphnia populations. Science 326, 1260–1262. 9. Denoeud, F., Henriet, S., Mungpakdee, S., Aury, J.M., Da Silva, C., Brinkmann, H., Mikhaleva, J., Olsen, L.C., Jubin, C., Can˜estro, C., et al. (2010). Plasticity of animal genome architecture unmasked by rapid evolution of a pelagic tunicate. Science 330, 1381–1385. 10. Torriani, S.F., Stukenbrock, E.H., Brunner, P.C., McDonald, B.A., and Croll, D. (2011). Evidence for extensive recent intron transposition
Group Dynamics: Predators and Prey Get a Little Help from Their Friends Transfer of information about predatory attacks between individuals allows schooling or flocking prey to evade predation without disrupting group integrity. But, predators can mitigate this effect by working together themselves. Graeme D. Ruxton You and I will probably not meet a violent end: in the USA, less than four out of a thousand people end up murdered [1]. Things are less cosy in the natural world: for example, some studies suggest that most zebras end their days in the grasp of a lion [2]. Hence, predation is a very potent selective force, and animals show a huge diversity of adaptations that can be understood in terms of managing their predation risk. One widespread and intensively-studied adaptation is group living. There are a number of mechanisms by which grouping can reduce predation risk. If predators can only catch one individual at a time, the risk for group members can be diluted as most will escape when a group is attacked. Moreover, this benefit can increase with group size more steeply than the costs of larger groups, for instance, being more obvious [3]. A group of prey has many eyes to watch out for surprise attacks, and sometimes the facility to mount a collective defence, e.g. when water buffalo form a circle with their horns facing outward and their vulnerable rumps protected in the centre. Furthermore, if the group is moving then predators appear to suffer a confusion effect where they have difficulty tracking a particular moving individual against the distractive background of other
similar moving alternative targets [4]. This last mechanism in particular has often been suggested to explain the extraordinary coordinated displays of schooling fish and some flocking birds. However, it would be surprising if predators had not co-evolved countermeasures, and in this issue of Current Biology Handegard et al. [5] provide a fascinating demonstration of such countermeasures in predaceous spotted seatrout (Cynoscion nebulosus) attacking schools of juvenile Gulf menhaden (Brevoortia patronus). Their observations on naturally occurring attacks in the Gulf of Mexico were made possible by high-frequency imaging sonar giving 2 cm spatial resolution across a 24 m2 area and 8 Hz temporal resolution. Handegard et al. [5] first of all demonstrated the prey’s defensive measures. When a seatrout mounts an attack towards the school, there is a coordinated response of school members to maintain a safe distance from the approaching predator, so a vacuole of empty space in the school opens up in front of the predator and closes behind it (Figure 1). This coordinated movement of individuals requires information transfer over greater distances than those at which fish can detect the predator in the turbid water, and over faster timescales than a fish can swim. Such group-level responses
in closely related fungi. Curr. Biol. 21, 2017–2022. 1Department of Biology, 1600 Holloway Avenue, San Francisco State University, San Francisco, CA 94132, USA. 2160 College Street, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada. *E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2012.05.017
can be understood as emerging from individuals reacting to the acceleration of their near neighbours only [6]. Seatrout, however, often do not attack alone, but in a coordinated group of individuals attacking in line astern. This tactic prevents the closing off of the vacuole behind the first attacker. Furthermore, different parts of the school respond to the multiple threats such that the coherence of movement across the whole school breaks down, which in extreme cases can lead to a breaking up of the school into smaller parts. The sonar did not allow individual predation events to be recorded, but predators were able to get much nearer to fish when schools were smaller and within-school movements were less coherent. Such close proximity is very likely to lead to predation: the predators are faster than their prey in a straight line and it is only their better manoeuvrability that normally lets prey stay out of close proximity to the predators. Our understanding the dynamics of coordinated group movement has made great strides over the last decade. This was mainly driven by observation of the emergent patterns from computer models of individuals that react to their neighbours according to rules that the modellers can specify. These models have had conspicuous success in demonstrating that the apparent complexity of coherent group-level movement can be generated by very simple local interactions without centralised control or special sensory or cognitive powers [6–8]. However, in the last few years computational and technological improvements have also allowed empirical work to make dramatic strides [9–14]. These studies suggest
Current Biology Vol 22 No 13 R532
Figure 1. Predator in a bubble. A predator attacking a fish school, and the corresponding response of the prey school. The image is taken using an acoustic camera, similar to that used in [5], and the image is filtered to enhance the typical school response to the predator. Image: Simon P. Leblanc.
that there is now a need to return to the models and revisit their assumptions in the light of emerging empirical evidence. For example, these models generally are based on the assumption that when fish respond to the positioning and movement of near-neighbours, a neighbourhood is described by a physical distance metric — such as the visual range at which neighbours can be seen. But it seems that, in at least some groups, it is topological rather than metric distance that matters, with perhaps a cognitive rather than sensory constraint causing individuals to respond only to a fixed number of nearest neighbours regardless of variation in local density of individuals [9]. Furthermore, for understandable reasons of Occam’s razor, most models have assumed that the reaction fields around individuals are isotropic, with distance (rather than direction) between neighbours governing interactions. However, recent empirical evidence suggests that shoaling fish might respond differently in response to the same movement by fish ahead of them as opposed to fish behind them [10]. However, perhaps the most dramatic challenge to current theory comes from recent evidence of multi-body responses [10]. Most previous theory is based on the assumption that although a focal individual can respond to several near neighbours
simultaneously, the effect of these can be understood by averaging the responses to each of the neighbours as if they existed in isolation. However, recent work on fish shoaling [AU reference] suggests that three-body interactions make a substantial contribution to collective dynamics. That is, the response of fish A to the proximity and movements of fish B and C cannot be predicted from its responses to each of these in the absence of the other. Development of three-body rules for simulation models will require very close interaction between theory and data collection, with experiments designed to test contrasting predictions of alternative model formulations testing candidate rules. Aircraft designers face a trade-off between stability and manoeuvrability: they design airliners to be insensitive to turbulence, whereas jet fighters can only attain their great manoeuvrability at a cost of instability that requires continual correction of deviations from the intended flight path by computer control. A similar trade-off must exist in the responsiveness of collective school dynamics. Thus, the local rules must confer a robustness, such that noise generated by environmental micro-scale turbulence or simple mistakes by individuals does not get propagated across the whole group; yet, valuable information about predator attack needs to be effectively
communicated. It may be in managing this challenge that we find the selective pressures that have driven the adoption of use of topological space rules and multi-body interactions. When a single predator attacks the group, the trade-off been sensitivity and robustness becomes easier to manage because useful initial information about the attack is likely to come from a single spatially concentrated part of the group, whereas noise will likely be generated more diffusely. However, the work of Handegard et al. [5] suggests that coordinated attacks can remove this easy means of separating signal from noise, and require responses to information about a number of simultaneous relevant events. There is still much we have to uncover about how local interaction rules can produce complex group behaviours, but recent technological breakthroughs and increasingly tight connection between theory and data suggest that we have all the tools to considerably improve our understanding in the next few years. Such progress may be of more than academic value, and should improve our ability to manage human crowds on our increasingly crowded planet [15]. References 1. FBI Crime in the USA Report 2011: http://www. fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/ preliminary-annual-ucr-jan-jun-2011. 2. Sinclair, A.R.E., Mduma, S., and Brashare, J.S. (2003). Patterns of predation in a diverse predator-prey system. Nature 425, 288–290. 3. Turner, G.F., and Pitcher, T.J. (1986). Attack abatement: a model of group protection by combined avoidance and dilution. Am. Nat. 128, 228–240. 4. Neill, S.R., St, J., and Cullen, J.M. (1974). Experiments on whether schooling in prey affects hunting behaviour in cephalopod and fish predators. J. Zool. 172, 549–569. 5. Handegard, N.O., Boswell, K.M., Ioannou, C.C., Leblanc, S.P., Tjøstheim, D.B., and Couzin, I.D. (2012). The dynamics of coordinated group hunting and collective information transfer among schooling prey. Curr. Biol. 22, 1213–1217. 6. Couzin, I.D., Krause, J., James, R., and Ruxton, G.D. (2002). Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11. 7. Huth, A., and Wissel, C. (1992). The simulation of the movement of fish schools. J. Theor. Biol. 156, 365–385. 8. Buhl, J., Sumpter, D.J.T., Couzin, I.D., Hale, J.J., Despland, E., Miller, E.R., and Simpson, S.J. (2006). From disorder to order in marching locusts. Science 312, 1402–1406. 9. Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, I., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., et al. (2008). Interaction ruling animal collective behaviour depends on topological rather than metric distance: evidence from a field study. Proc. Natl. Acad. Sci. USA 105, 1232–1237.
Dispatch R533
10. Katz, Y., Tunstrøm, K., Ioannou, C.C., Huepe, C., and Couzin, I.D. (2011). Inferring the structure and dynamics of interactions in schooling fish. Proc. Natl. Acad. Sci. USA 108, 18720–18725. 11. Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., and Viale, M. (2010). Scale-free correlations in starling flocks. Proc. Natl. Acad. Sci. USA 107, 11865–11870. 12. Bialek, W., Cavagna, A., Giardina, I., Mora, T., Silvestri, E., Viale, M., and Walczak, M.
(2012). Statistical mechanics for natural bird flocks. Proc. Natl. Acad. Sci. USA 109, 4786–4791. 13. Herbert-Read, J.E., Perna, A., Mann, R.P., Schaef, T.M., Sumpter, D.J.T., and Ward, A.J.W. (2011). Inferring the rules of interaction of shoaling fish. Proc. Natl. Acad. Sci. USA 108, 18726–18731. 14. Lukeman, R., Li, Y.-X., and Edelstein-Keshet, L. (2010). Inferring individuals rules from collective behavior. Proc. Natl. Acad. Sci. USA 107, 12576–12580.
Plant Cell Biology: The ABC of Monolignol Transport Lignins are complex aromatic heteropolymers that reinforce the cell walls of terrestrial plants. A new study identifies an ATP-binding cassette ABC transporter that pumps a monolignol lignin precursor across the plasma membrane. Richard Sibout and Herman Ho¨fte* Cell wall lignification has been a critical innovation in the evolution of terrestrial plants from their aquatic ancestors over 450 million years ago [1]. Lignins impermeabilize and consolidate cell walls, provide resistance to negative pressure in water-conducting tissues and provide strength to organs, thus allowing an erect growth habit. Lignins are also important targets for plant biotechnology; for instance, they interfere with enzymatic depolymerization of polysaccharides and hence are a main obstacle for biorefinery applications. Lignins are highly complex polymers of phenylpropanoid precursors, the monolignols. These building blocks are synthesized in the cytosol, but end up at various subcellular locations — the cell wall, the vacuole or the Golgi apparatus (Figure 1). Monolignol biosynthesis is well understood and progress has been made in our understanding of the oxidative polymerization process [2]. However, until recently, it was not known how monolignols are transported across membranes. In this issue of Current Biology, an important breakthrough is reported with the identification of a transporter of the ATP-binding cassette (ABC) family, which pumps the monolignol p-coumaryl alcohol across the plasma membrane [3]. The three main monolignols are p-coumaryl alcohol, coniferyl alcohol,
and sinapyl alcohol, which differ by their degree of methoxylation (Figure 1) and which after polymerization are referred to as p-hydroxyphenyl (H), guaiacyl (G) or syringyl (S) units, respectively. Monolignol biosynthesis takes place in the cytosol. The phenylalanine precursor is exported from the chloroplast and successively de-aminated, hydroxylated, o-methylated and reduced to produce the monolignols. Three cytochrome P450 enzymes are anchored in the endoplasmic reticulum membrane facing the cytosol. The other enzymes don’t have membrane anchors, and little is known about their sub-cellular localization (Figure 1). It is likely that many of the enzymes form multiprotein complexes as was recently demonstrated for the endoplasmic reticulum-associated enzymes [4]. Upon arrival in the cell wall, monolignols undergo oxidative crosslinking, promoted by large families of cell wall-associated peroxidases and laccases [2,5]. Monolignols also accumulate in the vacuole as glucoconjugates [6,7]. How do monolignols cross membranes? Different hypotheses have been proposed: passive diffusion of the hydrophobic monolignols across the lipid bilayer; vesicular trafficking; post-mortem release or through specialized membrane transporters [7,8]. A recent indication of how this process occurs came from the demonstration that monolignol
15. Krause, J., and Ruxton, G.D. (2011). The dynamics of collective human behaviour. Lancet 377, 903–904.
School of Biology, University of St Andrews, St Andrews KY16 9TH, United Kingdom. E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2012.05.013
transport is an ATP-dependent process [9]. This result, combined with the pharmacology, suggests the involvement of ABC-type transporters. Interestingly, plasma membrane transport shows selectivity for hydroxycinnamyl alcohols and aldehydes, and does not accept ferulic acid or monolignol glucosides. The vacuolar transport instead is highly selective for monolignol 4-O-glucosides, suggesting that glucoconjugation is a prerequisite for selective import into the vacuole. Santiago and colleagues [3] now report the identification of an ABC transporter as a p-coumaryl alcohol transporter. ABC transporters belong to a large superfamily present in all kingdoms, and the majority of its members are ATP-driven pumps involved in the transport across membranes of a wide range of molecules (primary and secondary metabolites, signaling molecules, lipids, proteins, etc.). Functional pumps consist of two membrane-spanning pores and two nucleotide-binding domains. Plant genomes contain large ABC transporter gene families (e.g. the Arabidopsis genome encodes 130 family members, only 22 of which have been functionally characterized [10]). The authors of this new study used public microarray data to identify a putative ABC transporter gene (AtABC29) that was co-expressed with other phenylpropanoid biosynthetic genes [3]. AtABC29 was highly expressed in roots and anthers and a GFP-fusion protein accumulated in the plasma membrane in Arabidopsis. To study the function of this gene, it was expressed in an adapted yeast strain. In the control strain, growth was inhibited by p-coumaryl alcohol and coniferyl alcohol, but not by sinapyl alcohol. Interestingly, the expression