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Commentary Blackwell Oxford, New NPH © 1469-8137 0028-646X 10.1111/j. 2565 June 5 0 Commentary Commentary 568??? 67??? The2008 Phytologist Authors UK 1469-8137.2008.02565.x Publishing (2008).Ltd Journal compilation © New Phytologist (2008)

The flowering of systems approaches in plant and crop biology The term ‘Systems Biology’ can mean many things to different people (Aderem, 2005; Kirschner, 2005). However, it is generally agreed that one of the central aims of systems biology is to understand biological processes in terms of the dynamic interactions between the components that constitute the system. Importantly, this aim is not unique to systems of biomolecules, but can apply at many different spatial and temporal scales (Aderem, 2005; Trewavas, 2006). For example, the dynamic behaviour of individual cells depends on the operation of genetic regulatory networks, while large-scale features of crop systems (such as yield and sustainability) depend on interactions between the individual plants and environmental factors (Yin & Struik, 2007). The insight that drives systems biology is that a full understanding of the role played by any one component in a biological process can be achieved only by considering it in its appropriate context in the whole system. In this sense, systems biology goes beyond a strict reductionist paradigm, in which the properties of system components are considered in isolation.

‘A key prerequisite for the systems methodology is the ability to assay over time the state of as many network components as possible.’

Despite the obvious diversity in the details of systems-level processes and their underlying components, understanding the mechanisms by which interactions between components generate the behaviours of the whole process relies on a number of steps in common: first, it is necessary to determine the identity and nature of the system components that play a significant role in generating the behaviour under study (a ‘parts list’ of the system); second, the network of interactions between these components must be mapped out, and their natures determined; and, finally, it is necessary to use this information to forge an understanding of how the dynamics

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of the system emerge from the underlying interaction network. Taken together, these three steps provide an outline ‘systems methodology’ that can be applied to systems spanning the range of biological scales. While the techniques required to achieve each stage may be different for different types of system, their integration into a coherent methodology provides a well-defined approach to tackling the difficult question of how systemslevel behaviour emerges from component interactions. The recent upsurge in interest in systems biology stems primarily from technological advances in molecular biology that have dramatically increased the speed with which it is possible to complete the first two steps, namely collating a molecular ‘parts list’ and mapping out a network of interactions (Barabási & Oltvai, 2004). High-throughput transcript and protein profiling, together with interaction screens, such as large-scale yeast-two-hybrid and ChIP-on-chip, now allow large protein and transcription interaction networks to be constructed with relative ease (reviewed in Monk, 2003; Zhu et al., 2007). While it may have become easier to generate large networks, this alone does not provide mechanistic insight into the properties of the intact system under study. Network diagrams provide only a static picture of potential interactions, while it is the dynamics of the network state that govern the behaviour of the system. A key prerequisite for systems methodology is the ability to assay, over time, the state of as many network components as possible. Given such data, statistical and mathematical analysis can be used (Monk, 2008). An example of how microarray data can be employed to infer network components is provided by Menges et al. (this issue of New Phytologist; pp. 643–662). By combining archived transcriptome data obtained under a range of different conditions using gene ontology information, the authors find new putative components of mitogen-activated protein (MAP) kinase signal transduction networks that provide a focus for further functional studies. Such information need not be generated solely by high-throughput methodologies such as transcriptomics. Gay et al. (this issue of New Phytologist; pp. 663–674) describe the use of high-resolution reflectance spectra to monitor dynamic changes in the metabolism of chlorophyll during leaf senescence. The authors present a strong argument for modelling this pathway using a systems approach, given the extensive knowledge available about the genetic and biochemical basis of chlorophyll breakdown combined with the ability to perturb this pathway and monitor its consequences noninvasively over time. Jansson & Thomas (this issue of New Phytologist; pp. 575–579) propose that leaf senescence itself can be considered a set of modelling routines, where environmental inputs influence which modules are run, loop and interact, and ultimately determine the outputs.

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Whilst systems biology naturally lends itself to model molecular to cell to organ scale processes in organisms such as Drosophila and Arabidopsis, how applicable is this approach to higher-scale processes (i.e. from population to ecosystem) or involving more complex organisms such as crops? Yin & Struik (this issue of New Phytologist; pp. 629–642) propose that there is a compelling case for crop systems biology, which builds on the rich history of modelling whole-crop physiology and recent advances in crop functional genomics. The authors argue that crop systems biology will play a crucial role in the understanding of complex crop phenotypes and subsequently crop improvement. Sheehy et al. (this issue of New Phytologist; pp. 579–582) discusses how one such complex trait – engineering the C4 pathway into rice – cannot be achieved without the use of genetic engineering and systems biology approaches. Nevertheless, this ‘grand challenge’ urgently awaits the identification of the genes that control the anatomical and biochemical pathways that confer the C4 trait. Bowen et al. (this issue of New Phytologist; pp. 583–587) argue that simply assembling a series of genes or genetic circuits to produce a desired trait (such as C4 rice) is unlikely to be successful without a detailed quantitative characterization of the network gained from systems biology. The authors argue that such information can be readily applied employing the new field of synthetic biology and significantly improves the chances of success of engineering new traits. So, is systems biology really a paradigm shift beyond the idea that we need to consider context for components? Or is it largely a technology-driven acceleration of progress towards an integrative understanding of the dynamical behaviour of complex biological systems? Marcum (this issue of New Phytologist; pp. 587–589) discusses these and other related issues, employing Kuhnian philosophy. Irrespective of whether one considers this a paradigm shift or revolution, systems biology is set to move experimental approaches from a traditional reductionist approach to more holistic treatment of complex biology phenomena. Combined with advances in mathematical and computational modelling of interaction networks (Cohen, 2004; Albert, 2007; Monk, 2008), this will facilitate progress towards an integrative understanding of the dynamical behaviour of complex biological systems. Malcolm Bennett1,2 and Nick Monk1,3* 1Centre

for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK; 2School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK; 3School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK (*Author for correspondence: tel +44 115 846 6166; fax +44 115 951 4951; email [email protected])

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References Aderem A. 2005. Systems biology: its practice and challenges. Cell 121: 511–513. Albert R. 2007. Network inference, analysis, and modeling in systems biology. The Plant Cell 19: 3327–3338. Barabási AL, Oltvai ZN. 2004. Network biology: understanding the cell’s functional organization. Nature Reviews Genetics 5: 101–113. Bowen TA, Zdunek JK, Medford JI. 2008. Cultivating plant synthetic biology from systems biology. New Phytologist 179: 583– 587. Cohen JE. 2004. Mathematics is biology’s next microscope, only better; biology is mathematics’ next physics, only better. PLoS Biology 2: e439. Gay A, Thomas H, Roca M, James C, Taylor J, Rowland J, Ougham H. 2008. Nondestructive analysis of senescence in mesophyll cells by spectral resolution of protein synthesis-dependent pigment metabolism. New Phytologist 179: 663–674. Jansson S, Thomas H. 2008. Senescence – developmental program or timetable? New Phytologist 179: 575– 579. Kirschner MW. 2005. The meaning of systems biology. Cell 121: 503–504. Marcum JA. 2008. Does systems biology represent a Kuhnian paradigm shift? New Phytologist 179: 587–589. Menges M, Dóczi R, Ökrész L, Morandini P, Mizzi L, Soloviev M, Murray JAH, Bögre L. 2008. Comprehensive gene expression atlas for the Arabidopsis MAP kinase signalling pathways. New Phytologist 179: 643– 662. Monk NAM. 2003. Unravelling nature’s networks. Biochemical Society Transactions 31: 1457–1461. Monk NAM. 2008. Using mathematical models to probe dynamic expression data. In: Hetherington A, Grierson C, eds. Practical systems biology. Abingdon, UK: Taylor and Francis, 93–112. Sheehy JE, Gunawardana D, Ferrer AB, Danila F, Tan KG, Mitchell PL. 2008. Systems biology or the biology of systems: routes to reducing hunger. New Phytologist 179: 579– 582. Trewavas A. 2006. A brief history of systems biology. The Plant Cell 18: 2420–2430. Yin X, Struik PC. 2007. Crop systems biology. In: Spiertz JHJ, Struik PC, van Laar HH, eds. Scale and complexity in plant systems research: gene–plant–crop relations. Dordrecht, Germany: Springer, 63–73. Yin X, Struik PC. 2008. Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics. New Phytologist 179: 629–642. Zhu X, Gerstein M, Snyder M. 2007. Getting connected: analysis and principles of biological networks. Genes and Development 21: 1010–1024. Key words: Arabidopsis, crop, multiscale, network, synthetic biology, systems biology. June 10.1111/j.1469-8137.2008.02550.x 2550 5 0 Commentary Commentary 568??? 67??? 2008 Commentary Commentary

The chloroplast as a regulator of Ca2+ signalling Many of the attributes associated with multicellular plant life, including a sedentary habit, a decentralized organization, signalling in the absence of a nervous system and a plastic developmental programme, can be attributed to the autotrophism facilitated by the chloroplast. In this issue of New Phytologist, Weinl et al. (pp. 675–686) identify a new role for the chloroplast in Ca2+ signalling, which suggests that the plastid can exert control over signalling events in the cytosol.

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Weinl et al. report that the 42-kDa Ca2+ receptor protein (CAS) is localized to the chloroplast and that T-DNA knockout of CAS prevents stomatal closure in response to elevated external Ca2+ ([Ca2+]ext) by abolishing oscillations of cytosolic free-Ca2+ ([Ca2+]cyt). Weinl et al. find that cas mutations act upstream of oscillations of [Ca2+]cyt because the stomata of cas mutants close in response to artificially generated oscillations of [Ca2+]cyt.

‘Recent findings suggest that a totally novel pathway that is central to Ca2+ signalling in plants awaits discovery.’

CAS was first identified by a functional screen in which pools of Arabidopsis RNA were introduced into human kidney cells loaded with the Ca2+ indicator, FURA2 (Han et al., 2003). External Ca2+ caused moderate increases of [Ca2+]cyt in kidney cells but the expression of Arabidopsis CAS in these cells resulted in large [Ca2+]ext-induced increases of [Ca2+]cyt (Ca2+induced Ca2+ increase (CICI) (Han et al., 2003)). In Arabidopsis, CAS is expressed in the shoots and is found in guard cells (Han et al., 2003). CAS binds Ca2+ at the Nterminus with low affinity and high capacity (Han et al., 2003). CAS was first proposed to be a plasma membrane receptor that senses [Ca2+]ext (Han et al., 2003). The new data of Weinl et al. suggest that CAS is not a plasma membrane protein and is localized to the chloroplast. Weinl et al. identified an N-terminal chloroplast transit peptide and found that transient expression of CAS : GREEN FLUORESCENT PROTEIN in Nicotiana benthamiana protoplasts results in a chloroplastic localization that is confirmed by subcellular fractionation experiments. These findings are consistent with those of Nomura et al. (2008), who also recently reported a chloroplastic localization for CAS, insensitivity of the stomata of cas mutants to [Ca2+]ext, reduced CICI in cas knockouts and increased stomatal closure in CAS overexpressers. The data of Weinl et al. and Nomura et al. (2008) suggest that the chloroplast has an essential role in Ca2+ signalling, with CAS as an important intermediary. How does a Ca2+ receptor in the chloroplast function to regulate oscillations of [Ca2+]cyt and CICI? One model is that CAS might sense changes in [Ca2+]cyt following Ca2+ influx across the plasma membrane and act in a feedback loop to regulate [Ca2+]cyt. However, the available data do not support this model. CAS probably senses changes in stromal [Ca2+] ([Ca2+]stroma) because CAS is localized to the thylakoid and the N-terminus Ca2+-binding domain is probably exposed on the stromal side of the membrane (Nomura et al., 2008). Further evidence that CAS does not

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sense [Ca2+]cyt comes from studies of abscisic acid (ABA) signalling in the guard cell. CAS mutations are without effect on ABA-induced stomatal closure, even though ABA causes oscillations of [Ca2+]cyt that are similar to those caused by [Ca2+]ext (Allen et al., 1999; Staxén et al., 1999; Weinl et al.). The sensing, by CAS, of changes in [Ca2+]stroma could potentially affect the release or uptake of Ca2+ by the chloroplast. It is possible that the plastids act either as Ca2+ stores that release Ca2+ into the cytosol or as a Ca2+ buffer that removes Ca2+ from the cytosol following stimulation. The plastids could have a similar role to mitochondria in Ca2+ signalling. In mammals, mitochondria act as Ca2+ buffers that take up Ca2+ from the cytosol following release from the endoplasmic reticulum (ER) and the sarcoplasmic reticulum (SR). The tight coupling between ER/SR release and mitochondrial uptake has profound effects on localized [Ca2+]cyt dynamics, and mitochondria also contain a pool of releasable Ca2+ (Hetherington & Brownlee, 2004). If plastids, like mitochondria, act as Ca2+ buffers that also have a pool of releasable Ca2+, this might explain how CAS sensing of the [Ca2+]stroma could feed back to affect oscillations of [Ca2+]cyt. There is evidence that plastids are capable of both Ca2+ uptake and release (Johnson et al., 2006), although the data from different systems conflict as to whether chloroplastic uptake of Ca2+ has consequences for [Ca2+]cyt (Miller & Sanders, 1987; Sai & Johnson, 2002; Johnson et al., 2006). Chloroplasts take up Ca2+ in the light (Miller & Sanders, 1987; Xiong et al., 2006), and CASTOR and POLLUX are required for nodulation (NOD) factor-induced Ca2+ oscillations in root hairs of Lotus japonicus and are predicted to encode plastid-localized ion channels of unknown selectivity (Imaizumi-Anraku et al., 2005). Similarly, the pea PPF1 protein localizes to the chloroplast, delays flowering when expressed in Arabidopsis and is capable of carrying Ca2+ currents (Wang et al., 2003). In addition to plastid regulation of [Ca2+]cyt, there are dark-induced increases in [Ca2+]stroma that can persist with a circadian rhythm in constant dark (Sai & Johnson, 2002). Circadian oscillations of [Ca2+]stroma appear to be independent of [Ca2+]cyt because in constant dark there are usually no oscillations of [Ca2+]cyt ( Johnson et al., 1995). Sai & Johnson (2002) proposed that the thylakoid is a dark-dischargeable Ca2+ store that releases into the stroma. The thylakoid is suggested to be filled with Ca2+ from the cytosol via the stroma as a result of the action of a Ca2+/H+ antiporter acting in the light (Ettinger et al., 1999). Lengthening the light period appears to increase the amount of Ca2+ stored in the thylakoid because dark-induced discharge is increased with longer periods of light (Sai & Johnson, 2002). It is not known if CAS affects the daily dark-induced increase in [Ca2+]stroma but CAS antisense reduces the amplitude of daily oscillations of [Ca2+]cyt in light/dark cycles (Tang et al., 2007). This appears to be related to the role of CAS in CICI because increases in [Ca2+]ext increase the amplitude of daily oscillations of [Ca2+]cyt (Tang et al., 2007). These findings

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led to a model being proposed in which the daily oscillations of [Ca2+]cyt are a consequence of similar oscillations in [Ca2+]ext caused by rhythmic water fluxes in response to daily stomatal movements. In this model, CAS was proposed to sense the rhythms of [Ca2+]ext to drive daily oscillations of [Ca2+]cyt through an inositol(1,4,5)trisphosphate-mediated pathway (Tang et al., 2007). However, recent data suggest that this may not be the case. Circadian oscillations of [Ca2+]cyt are driven by cyclic ADP ribose and are insensitive to U73182, an inhibitor of inositol(1,4,5)trisphosphate production (Dodd et al., 2007). Furthermore, circadian [Ca2+]cyt oscillations and stomatal movements are not functionally linked because the circadian rhythms of stomatal opening and [Ca2+]cyt run with different periods in both the timing of cab1-1 and zeitlupe-1 circadian mutants (Dodd et al., 2004; Xu et al., 2007). The localization of CAS to the chloroplast, and the evidence that rhythmic changes in [Ca2+]ext caused by stomatal movements are not likely to drive circadian [Ca2+]cyt oscillations, suggests that the effects of CAS should be reconsidered as evidence for the chloroplast modulating daily [Ca2+]cyt oscillations, with CAS acting in an unknown pathway. The localization of CAS to the chloroplast by Weinl et al. and Nomura et al. (2008) identifies a new aspect of Ca2+ signalling. There are essential roles for the chloroplast in the sensing of [Ca2+]ext by stomata, timing of flowering, NOD factorinduced oscillations of [Ca2+] and daily oscillations of [Ca2+]cyt. Forming a model of how CAS affects [Ca2+]cyt is difficult as so little is known about Ca2+ fluxes associated with plastids and because the biological action of CAS is unknown. The findings reported in this issue by Weinl et al., those of Nomura et al. (2008) and the work of those in the Pei laboratory, who first identified CAS along with its role in regulating CICI, oscillations of [Ca2+]cyt and Ca2+-induced stomatal closure (Han et al., 2003; Tang et al., 2007), suggest that a totally novel pathway that is central to Ca2+ signalling in plants awaits discovery. Alex A. R. Webb Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK (tel +44 1223 333948; fax +44 (0)1223 333953; email [email protected])

Ettinger WF, Clear AM, Fanning KJ, Peck ML. 1999. Identification of a Ca2+/H+ antiport in the plant chloroplast thylakoid membrane. Plant Physiology 119: 1379–1385. Han S, Tang R, Anderson LK, Woerner TE, Pei Z. 2003. A cell surface receptor mediates extracellular Ca2+ sensing in guard cells. Nature 425: 196–200. Hetherington AM, Brownlee C. 2004. The generation of Ca2+ signals in plants. Annual Review of Plant Biology 55: 401–427. Imaizumi-Anraku H, Takeda N, Charpentier M, Perry J, Miwa H, Umehara Y, Kouchi H, Murakami Y, Mulder L, Vickers K et al. 2005. Plastid proteins crucial for symbiotic fungal and bacterial entry into plant roots. Nature 433: 527–531. Johnson CH, Knight MR, Kondo T, Masson P, Sedbrook J, Haley A, Trewavas A. 1995. Circadian oscillations of cytosolic and chloroplastic free calcium in plants. Science 269: 1863–1865. Johnson CH, Shingles R, Ettinger WF. 2006. Regulation and role of calcium fluxes in the chloroplast. In: Wise RR, Hoober J, eds. The structure and function of plastids. the Netherlands: Springer, 403–416. Miller AJ, Sanders D. 1987. Depletion of cytosolic free calcium induced by photosynthesis. Nature 326: 397–400. Nomura H, Komori T, Kobori M, Nakahira Y, Shiina T. 2008. Evidence for chloroplast control of external Ca2+-induced cytosolic Ca2+ transients and stomatal closure. Plant Journal 53: 988–998. Sai JQ, Johnson CH. 2002. Dark-stimulated calcium ion fluxes in the chloroplast stroma and cytosol. Plant Cell 14: 1279–1291. Staxén I, Pical C, Montgomery LT, Gray JE, Hetherington AM, McAinsh MR. 1999. Abscisic acid induces oscillations in guard-cell cytosolic free calcium that involve phosphoinositide-specific phospholipase C. Proceedings of the National Academy of Sciences, USA 96: 1779–1784. Tang R-H, Han S, Zheng H, Cook CW, Choi CS, Woerner TE, Jackson RB, Pei Z-M 2007. Coupling diurnal cytosolic Ca2+ oscillations to the CAS-IP3 pathway in Arabidopsis. Science 315: 1423–1426. Wang D, Xu Y, Li Q, Hao X, Cui K, Sun F, Zhu Y. 2003. Transgenic expression of a putative calcium transporter affects the time of Arabidopsis flowering. Plant Journal 33: 285–292. Weinl S, Held K, Schlücking K, Steinhorst L, Kuhlgert S, Hippler M, Kudla J. 2008. A plastid protein crucial for Ca2+-regulated stomatal responses. New Phytologist 179: 675–686. Xiong T-C, Bourque S, Lecourieux D, Amelot N, Grat S, Brière C, Mazars C, Pugin A, Ranjeva R. 2006. Calcium signaling in plant cell organelles delimited by a double membrane. Biochimica et Biophysica Acta 1763: 1209–1215. Xu X, Hotta CT, Dodd AN, Love J, Sharrock R, Lee YW, Xie Q, Johnson CH, Webb AAR. 2007. Distinct light and clock modulation of cytosolic free Ca2+ ascillations and rhythmic CHLOROPHYLL A/B BINDING PROTEIN 2 promoter activity in Arabidopsis. Plant Cell 19: 3474–3490. Key words: Arabidopsis, calcium, CAS, chloroplast, guard cell, signalling, stroma, thylaroid. 10.1111/j. 2567 June 5 0 Commentary Commentary 568??? 67??? 2008 1469-8137.2008.02567.x Commentary Commentary

References Allen GJ, Kwak JM, Chu SP, Llopis J, Tsien RY, Harper JF, Schroeder JI. 1999. Cameleon calcium indicator reports cytoplasmic calcium dynamics in Arabidopsis guard cells. Plant Journal 19: 735–747. Dodd A, Parkinson K, Webb AAR. 2004. Independent circadian regulation of assimilation and stomatal conductance in the ztl-1 mutant of Arabidopsis. New Phytologist 162: 63–70. Dodd AN, Gardner MJ, Hotta CT, Hubbard KE, Dalchau N, Love J, Assie JM, Robertson FC, Kyed Jakobsen M, Gonçalves J et al. 2007. A cADPR-based feedback loop modulates the Arabidopsis circadian clock. Science 318: 1789–1792.


Great leap forward? Transposable elements, small interfering RNA and adaptive Lamarckian evolution The botanist and philosopher Lamarck famously proposed that environmental challenges suffered in one generation could


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Fig. 1 Effect of desiccation treatment on the ‘resurrection’ plant Craterostigma plantagineum: fully turgid (a), desiccated (b) and rehydrated (c). The timescale for the rehydration shown is 12 h (see Bartels et al., 1990). Image courtesy of D. Bartels, Bonn, Germany.

influence phenotypic outcomes in the next. At the turn of the 19th century, the passing of life experiences to future generations seemed part of the natural order, explaining perhaps the acclimation of new species imported from exotic locales and the perceived increase in biological complexity over generations. Lamarckian principles influenced Darwin’s vision of natural selection, but were ultimately overturned by this very vision. With the rediscovery of Mendel’s laws, Lamarckian mechanisms became less plausible, and with the advent of molecular genetics, the writing was on the wall. How could the environment influence germ cells in such a way that genes were altered in a directed and heritable way? In this issue of New Phytologist, Hilbricht et al. (pp. 877–887) provide a potential example of environmental influence on evolution and inheritance, in the desiccation-tolerant ‘resurrection’ plant Craterostigma plantagineum. They provide evidence that desiccation induces a family of non-Long Terminal Repeat (LTR) retrotransposons that encode a small RNA which promotes the expression of dehydration genes in transformed callus. They propose that transposition, on the one hand, and small RNA, on the other, have driven the evolution of this remarkable property (Fig. 1).

‘... a combination of the two stress response mechanisms – amplification of the transposon on the one hand, and triggering stress tolerance on the other – presents an interesting case for students of Lamarck.’

In most animals the germline differentiates within a few days after fertilization of the egg, long before adult cell types. As a result, any environmental influence on the adult must modify genetic material in cells that are already committed to germline fate. In long-lived flowering plants, however, germ cells can differentiate hundreds of years after embryogenesis is complete. This is because the germline is set aside very late in

development, differentiating from inflorescence meristems that in this respect resemble adult rather than germline stem cell lineages. This makes plants uniquely sensitive to environmental effects (Walbot & Evans, 2003). Even so, many of these effects are transient and are not captured in the germline. Examples include vernalization, in which adult plant cells experience cold during winter and trigger flowering the following spring. This is accomplished by silencing key regulatory genes through histone modification (Dennis & Peacock, 2007). Vernalization requires long exposure to the stimulus, and only dividing cells respond. It is thought that RNA interference may mediate some of these cues. Importantly, vernalization is erased during meiosis so that the next generation can respond to cold at the appropriate time. However, some epigenetic changes are heritable in plants: for example, many transposable elements are also very sensitive to temperature, but silent transposons can be stably inherited from generation to generation (Slotkin & Martienssen, 2007). Epigenetic mechanisms allow alternative chromosomal (and even nonchromosomal) states to be inherited from cell to cell and from generation to generation. When these states are influenced by the environment, progeny adopt their parents’ response without necessarily being subject to the same stimulus. While perhaps not the deterministic mechanism imagined by Lamarck, such epigenetic mechanisms open up the possibility of the environment directing evolution. An interesting example is provided by paramutation in maize: the R locus encodes a gene family interrupted by transposable elements. Silencing of one of these genes occurs progressively during development, but is delayed at high temperatures. By the time germ cells develop from the inflorescence meristem, few of them contain silent genes, but those that remain silent are passed on to the next generation (Chandler et al., 2000). Interestingly, this temperature-sensitive phenomenon depends on RNA interference (Chandler, 2007). Craterostigma plantagineum is a desert succulent that can lose up to 96% of its water but still recover just hours after rehydration (Fig. 1). This property is not shared by callus, which needs a supply of exogenous abscisic acid (ABA) to recover from dehydration. The authors isolated genes that could bypass this ABA requirement through activation tagging – the callus was transformed with transfer DNA (T-DNA) carrying a strong promoter and then subjected to dehydration

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in the absence of ABA. Survivors were examined to see which gene was responsible for the control of desiccation tolerance. Surprisingly, the first such gene to be identified, CDT-1, did not encode a functional protein. Worse, it was found in multiple copies and terminated with a polyA tail, flanked by direct repeats. These are hallmarks of non-LTR retrotransposons, or short interspersed elements (SINEs). One redeeming feature was that CDT-1 was induced by ABA and dehydration in normal callus, supporting its role in desiccation tolerance. How could a transposon influence desiccation tolerance? Deletions indicated that only half of the element, including the polyA tail, was required for high levels of transcript accumulation and for desiccation tolerance. A second gene detected by T-DNA insertion, CDT-2, shared this region. Abundant short interfering RNA was found on both strands and was narrowed down to a 21-nucleotide sequence reminiscent of a micro RNA or perhaps a trans-acting or tasiRNA. Protoplast transfection was used to show that this small RNA alone was capable of inducing dehydration genes, an important step in desiccation tolerance, to the same extent as exogenous application of the hormone ABA. The expression of transposons under environmental stress is well known: the resulting transposition is thought to increase chances of inheritance by the next generation, ensuring survival of the transposon (Slotkin & Martienssen, 2007). This response seems to have been co-opted during evolution, such that CDT-1 transposons now encode a small RNA that is required for desiccation tolerance and is induced by dehydration. However, a combination of the two stress response mechanisms – amplification of the transposon on the one hand, and triggering stress tolerance on the other – presents an interesting case for students of Lamarck: this is because, over generations, plants with an increased CDT copy number might be more desiccation tolerant. Unlike other transposons, non-LTR retrotransposons are difficult to remove from the genome. This is because they undergo widespread transposition but cannot undergo excision like Class II elements, or recombination between homologous LTRs like other Class I transposons. When co-opted in this way they may take their host on a journey of no return (Dover, 2002). A great leap forward indeed.

Chandler VL, Eggleston WB, Dorweiler JE. 2000. Paramutation in maize. Plant Molecular Biology 43: 121–145. Dennis ES, Peacock WJ. 2007. Epigenetic regulation of flowering. Current Opinion in Plant Biology 10: 520–527. Dover G. 2002. Molecular drive. Trends in Genetics 18: 587–589. Hilbricht T, Varotto S, Sgaramella V, Bartels D, Salamini F, Furini A. 2008. Retrotransposons and siRNA have a role in the evolution of desiccation tolerance leading to resurrection of the plant Craterostigma plantagineum. New Phytologist 179: 877–887. Slotkin RK, Martienssen R. 2007. Transposable elements and the epigenetic regulation of the genome. Nature Reviews. Genetics 8: 272–285. Walbot V, Evans MM. 2003. Unique features of the plant life cycle and their consequences. Nature Reviews. Genetics 4: 369– 379. Key words: adaptive mutagenesis, drought tolerance, Lamarck, resurrection plant, RNA interference. 10.1111/j. 2559 June 5 0 Commentary Commentary 568??? 67??? 2008 1469-8137.2008.02559.x Commentary Commentary

Genetic underpinnings of postzygotic reproductive barriers among plants The predominant causes of biological diversification – especially the formation of new species (speciation) – hold a special place in the imagination of evolutionary biologists. Much of the contemporary interest in speciation focuses on understanding the evolutionary origin and genetic basis of barriers to gene flow between closely related species (Coyne & Orr, 2004). In this issue of New Phytologist (pp. 888–900), Koide et al. present an analysis of the fine-scale structure of a transmission ratio distortion locus that causes both pollen and ovule sterility. In addition, with tester crosses, they show that this locus could contribute to F1 semi-sterility between Asian and African rice species complexes. In doing so, they make a significant contribution to the current understanding of the genetic underpinnings of loci that can contribute to postzygotic reproductive barriers among plant species.

Robert Martienssen

‘ ... the origin of postzygotic isolation (i.e. hybrid Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA (Author for correspondence: tel +1 516 367 8466; fax +1 516 367 8369; email [email protected])

inviability and sterility) was initially considered paradoxical for evolutionists, including Darwin ...’

References Bartels D, Schneider K, Terstappen G, Piatkowski D, Salamini F. 1990. Molecular cloning of absisic acid-modulated genes which are induced during desiccation of the resurrection plant Craterostigma plantagineum. Planta 181: 27– 34. Chandler VL. 2007. Paramutation: from maize to mice. Cell 128: 641–645.


Barriers to reproduction between species can act at many different stages. Classically these are divided into two classes – those that act before fertilization and those that act after


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Fig. 1 Dobzhansky–Muller (DM) model for the evolution of hybrid incompatibility (sterility and inviability). An ancestral population with genotype aabb is divided into two independently evolving lineages. New alleles arise and are fixed independently in each lineage (A and B, in lineages 1 and 2, respectively). Hybrid incompatibility is caused by dysfunctional interactions between A and B (i.e. alleles that have never been co-tested before hybridization).

fertilization (prezygotic and postzygotic barriers, respectively). It is often argued that mechanisms that act before hybridization are the most important, primarily because they exert the greatest influence on restricting gene flow between lineages by acting earlier in the life cycle (Rieseberg & Willis, 2007). Nonetheless, the nature, strength, and genetic basis of postzygotic isolation have continued to attract the attention of researchers, especially in animal systems (Coyne & Orr, 2004), but increasingly also in plants (Rieseberg & Willis, 2007). Arguably, the reasons for this interest have to do with two characteristics of postzygotic reproductive isolation. First, the origin of postzygotic isolation (i.e. hybrid inviability and sterility) was initially considered paradoxical for evolutionists, including Darwin, because natural selection should never favor the fixation of traits that reduce offspring fitness. This paradox was resolved in the form of a model (commonly called the ‘Dobzhansky–Muller’ (DM) model, after two of its originators; Fig. 1). Under the DM model, hybrid incompatibility is the result of negative interlocus epistasis; that is, dysfunctional interactions between different loci that have diverged in isolation of each other (Fig. 1). The great advantage of the DM model is that it does not require diverging populations to go through a period of reduced fitness during the evolution of genes that cause hybrid inviability or sterility. New variants can be perfectly fit in the background upon which they arose, but are dysfunctional in a genetic background where they have never been tested by natural selection. The DM model therefore proposes that genetic interactions are crucial to the evolution of postzygotic isolation, though it is silent on the specific evolutionary forces and genetic changes involved in this process. Given this, the second, and perhaps the most influential, appeal of postzygotic isolation

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is that these evolutionary forces and underlying genes are virtually unknown, even in the most well-studied genetic systems. Indeed, the molecular loci underlying hybrid sterility or inviability have been cloned and functionally characterized in only a handful of cases (Coyne & Orr, 2004; Orr et al., 2007); none of these has been in plants. Plant systems have been used to identify quantitative trait loci (QTL) associated with interspecific sterility phenotypes, either genome-wide (Li et al., 1997; Kim & Rieseberg, 1999; Moyle & Graham, 2005; Nakazato et al., 2007) or at individual interacting loci (Sweigart et al., 2006; Matsubara et al., 2007); however, the molecular genetic basis of these QTL is yet to be described. Koide et al.’s paper goes some way to bridging this gap between chromosomal regions known to be associated with hybrid incompatibility and the molecular genetic loci that underlie them. In their paper, Koide et al. combine finemapping, cytology, and tester crosses to examine the basis and possible origins of a transmission ratio distortion (TRD) locus in rice. Classical studies of this locus suggested that the distorting allele (S1) causes abortion of gametes carrying the homologous nondistorting ( S1a) allele, when both are found in heterozygotes (Sano, 1990, and references therein). By inducing semi-sterility in F1 hybrids (via abortion of male and female gametes carrying the S1a allele), this transmission ratio distorter has the potential to contribute to postzygotic reproductive isolating barriers between rice species. In their study, Koide et al. confirm that TRD at the S1 locus induces preferential abortion of both male and female gametes carrying S1a. For TRD via males (mTRD), cytology suggests that abortion is the result of arrest before the second mitotic division in microsporogenesis. For TRD via females (fTRD), abortion is caused by a broader range of phenotypes involving structural or organizational defects in the formation of eggs or embryo sacs. Using segregation ratios of aborted gametes with a linked visible marker, and fine-scale mapping, they infer the TRD locus is composed of at least two components, each influencing either mTRD or fTRD. Using fine-mapping, they narrow the mTRD to an approx. 40 kb region that contains only eight ORFs, an experimentally tractable list of candidates for further functional analysis. These data are in part exciting because they provide quite a detailed picture of the genetic components of a ‘selfish’ gene: a locus that preferentially promotes its own transmission to the fitness detriment of a carrier heterozygote (Hurst & Werren, 2001). This locus is composed of multiple components that influence male and female TRDs differently, suggesting a cluster of closely linked genes, as has been found in other segregation distorter systems. Perhaps even more interesting, however, is the apparent mechanistic link between ‘selfish’ transmission distortion, and the expression of hybrid sterility between species. Based on the results of tester crosses, Koide et al. propose that alternative alleles are fixed in Asian and African rice species complexes, indicating that the action of the distorter S1 allele found in the African rice complex



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can contribute to F1 semi-sterility (gamete abortion) between these groups. A direct link between the evolution of postzygotic isolation and active transmission ratio distorters – such as male killers, meiotic drivers, sex-ratio distorters, centromeric drivers, and other selfish genetic elements – has been proposed theoretically (see Hurst & Werren, 2001; Coyne & Orr, 2004 for reviews). Nonetheless, there is currently little empirical evidence for the predominance of active segregation distortion mechanisms as a cause of hybrid sterility or inviability. For example, while some interspecific transmission ratio distorters have been directly connected to the expression of hybrid sterility phenotypes (Coyne & Orr, 2004), in other cases there is clearly no fitness decrement associated with segregation distorters acting between species (Fishman & Willis, 2005). Other general patterns suggest that active segregation distortion is unlikely to be a ubiquitous force shaping the evolution of postzygotic isolation. For example, centromeres do not seem to be disproportionately associated with the expression of hybrid problems in species crosses (Moyle, 2007), although centromeres are genomic regions that stand to benefit most from transmission distortion under some models (Henikoff et al., 2001). While Koide et al.’s results are unlikely to resolve questions of the ubiquity of selfish genes in evolving postzygotic reproductive isolation, they do provide interesting data supporting a direct connection between genetic selfishness and the expression of hybrid sterility in this case. Gametic lethality as a result of TRD has been associated with interspecific and intervarietal rice crosses in other studies (Sano, 1990, and references therein), suggesting additional loci might behave similarly in this plant group. Clearly more data, including the dissection and description of the molecular genetic loci underlying hybrid incompatibilities in a wider range of organisms, will be instrumental in resolving how common such processes are in generating postzygotic reproductive isolation. These data will be essential for drawing any strong inferences about general patterns in the evolutionary forces or underlying genes that contribute to reproductive barriers. Finally, perhaps one of the most difficult goals in speciation research is determining whether loci that could contribute to current reproductive isolation were directly involved in the speciation process itself, rather than accumulating after diverging lineages were already well-isolated species. Genes that fall into the latter class still provide valuable insight into the range of possible mechanisms that can cause hybrid incompatibility. Arguably, however, it is the genes involved in the actual speciation event that are the most intriguing for evolutionary biologists. Koide et al. are appropriately circumspect about the possible role of the S1/ S1a locus in the actual lineage splitting of the progenitor of Asian and African rice species complexes. Evidence that the alternative alleles at this locus are fixed in the two species complexes is consistent with these alleles having diverged early in the split of these

two groups. Nonetheless, data that can more closely match the timing of the split of these species groups with the evolutionary origin of this locus (perhaps from molecular evolutionary analyses of the eventual underlying gene(s)) will be necessary to resolve this question. Regardless, this study demonstrates that plant systems, especially those with genetic, genomic, and functional tools, are very promising systems for further adding to our understanding of the genetic basis of postzygotic isolation, and the likely evolutionary forces responsible for fixing these genes. Leonie C. Moyle Indiana University, Bloomington, Department of Biology, 1001 East Third Street, Bloomington, IN 47405, USA (tel +1 812 856 7027; fax +1 812 855 6705; email [email protected])

References Coyne JA, Orr HA. 2004. Speciation. Sunderland, MA, USA: Sinauer Associates, Inc. Fishman L, Willis JH. 2005. A novel meiotic drive locus almost completely distorts segregation in Mimulus (monkeyflower) hybrids. Genetics 169: 347–353. Henikoff S, Ahmad K, Malik HS. 2001. The centromere paradox: stable inheritance with rapidly evolving DNA. Science 293: 1098–1102. Hurst GDD, Werren JH. 2001. The role of selfish genetic elements in eukaryotic evolution. Nature Reviews Genetics 2: 597– 606. Kim SC, Rieseberg LH. 1999. Genetic architecture of species differences in annual sunflowers: implications for adaptive trait introgression. Genetics 153: 965–977. Koide Y, Onishi K, Nishimoto D, Baruah AR, Kanazawa A, Sano Y. 2008. Sex-independent transmission ratio distortion system responsible for reproductive barriers between Asian and African rice species. New Phytologist 179: 888–900. Li ZK, Pinson SRM, Paterson AH, Park WD, Stansel JW. 1997. Genetics of hybrid sterility and hybrid breakdown in an intersubspecific rice (Oryza sativa L.) population. Genetics 145: 1139 –1148. Matsubara K, Ando T, Mizubayashi T, Ito S, Yano M. 2007. Identification and linkage mapping of complementary recessive genes causing hybrid breakdown in an intraspecific rice cross. Theoretical and Applied Genetics 115: 179 –186. Moyle LC. 2007. Comparative genetics of potential prezygotic and postzygotic isolating barriers in a Lycopersicon species cross. Journal of Heredity 98: 123–135. Moyle LC, Graham EB. 2005. Genetics of hybrid incompatibility between Lycopersicon esculentum and L. hirsutum. Genetics 169: 355–373. Nakazato T, Jung MK, Housworth EA, Rieseberg LH, Gastony GJ. 2007. A genomewide study of reproductive barriers between allopatric populations of a homosporous fern, Ceratopteris richardii. Genetics 177: 1141–1150. Orr HA, Masly JP, Phadnis N. 2007. Speciation in Drosophila: from phenotypes to molecules. Journal of Heredity 98: 103–110. Rieseberg LH, Willis JH. 2007. Plant speciation. Science 317: 910 –914. Sano Y. 1990. The genic nature of gamete eliminator in rice. Genetics 125: 183–191. Sweigart AL, Fishman L, Willis JH. 2006. A simple genetic incompatibility causes hybrid male sterility in Mimulus. Genetics 172: 2465–2479. Key words: angiosperm, hybrid, meiotic drive, selfish gene, speciation, sterility. ?April 0 Letters Letters ??? ?? 2008



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Letters

Letters Senescence: developmental program or timetable? The concept of the ‘program’ is widely used by developmental biologists and generally everyone knows what it means. However, with the advent of Systems Biology there is an influx into the biological sciences of researchers from other disciplines, such as computing, mathematics and engineering, in which ‘program’ is also a technical term. If Systems Biology is to keep its promises, it is important to ensure that everyone engaged in the analysis of programmed processes in living cells is talking the same language. Arising from discussions in two recent conferences (Wingler, 2007; Thomas, 2008), this Letter takes a critical look at the notion of a program as conceived and studied by plant developmental biologists, focusing particularly on our area of interest, leaf senescence. A program is a number of events that occur in a predetermined way, and developmental programs are believed to behave, by and large, like computer .exe files: signal molecules, kinases and transcription factors are often activated in sequence, leading to the development of, for example, an organ or a metabolic state. The plasticity of plant development, however, shows that developmental programs are not fixed but are instead continuously modulated by external and internal factors, to yield a plant body well adapted to its environment. Developmental programs have often been studied by analysing pathway mutants, but in recent years profiling methodologies, such as DNA microarrays, have become the techniques of choice for dissecting the sequence of events during a developmental process (Schmid et al., 2005). Every approach has its inherent problems, and we will, in this contribution, argue that, at least when leaf senescence is considered, the concept of a developmental program raises fundamental questions.

Senescence: pigment loss and differentiation without growth Leaf senescence is postmitotic and essentially a process of transdifferentiation in fully grown cells (Thomas et al., 2003). It occurs in, and uses the biochemical and cellular architecture of, mature cells and its main purpose is to degrade cellular components and remobilize them in order to re-use them elsewhere. Leaf senescence is therefore very different from the

rapidly executed process of programmed cell death (PCD); paradoxically so because apoptosis, the common name for Type I PCD, is derived from the Greek term for leaves falling from a tree (Kerr et al., 1972). Senescence involves chlorophyll loss via metabolism. Pathological bleaching, occurring after virus infection, for example, is not the same as senescence. In fact, these processes could be viewed as conflicting processes that are regulated by different sets of genes. Cell death has to be prevented until all mobilizable nutrients have been rescued (Hörtensteiner, 2004; Ougham et al., 2008). In some species, one way of distinguishing physiological and pathological yellowing is to demonstrate reversibility (ZavaletaMancera et al., 1999a,b), a characteristic of senescence that fundamentally distinguishes it from nonphysiological bleaching. Reversibility is one of the aspects of senescence that does not fit with the concept of a program (Thomas et al., 2003). Failure to make the distinction between the two possible fates of pigments (physiological and pathological) also contributes to confusion in the literature and a lack of consensus about what constitutes the core set of senescence processes. How do we know if the process under study is truly senescence? One way is to use a mutant with a lesion in physiological chlorophyll degradation. If a particular treatment results in yellowing of wild-type but not of the staygreen, it is likely to have evoked true physiological senescence. If both genotypes lose the green colour, the senescence is pathological (Thomas & Matile, 1988; Ougham et al., 2008). Physiological senescence, if not subject to suspension or reversal, will eventually be superseded by terminal cell death. Overlapping timetables in species with a rapid life cycle – such as Arabidopsis – make it difficult to identify the definitive elements in developmental programs, and encroachment of death into the senescence phase compromises the analytical separation of different patterns of gene expression and metabolism. Longer-lived species, with more extended developmental schedules and clearer temporal separation between phases, have advantages in this regard, even if they are experimentally less convenient. Mutation and pathological disturbance are exceptional circumstances; normally the photodynamic dangers inherent in chlorophyll degradation during senescence are controlled by balancing catabolism with other senescence-related metabolic mechanisms that utilize or quench incoming light energy. For this reason, yellowing is more than a cosmetic index of senescence, it is a sensitive and convenient measure of the progress of the syndrome as a whole (Kingston-Smith et al., 1997; Ougham et al., 2008).

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Ripeness to senesce A leaf has to acquire competence to senesce, and this potential may exist before it is actually evoked. This is equivalent to an old concept in developmental biology, proposed in 1918 by Klebs: ripeness to flower (see Bopp, 1996). In the same way, a seasonally quiescent species has to develop a competence to become truly dormant (endodormant; Vegis, 1964). The common feature of ripeness behaviours is that competence may be induced by different developmental and environmental influences from those that trigger the finally expressed syndromes. This imposes another level of complexity. Imagine that the environmental factor triggering senescence initiation is present, but competence has not yet been acquired (Jing et al., 2003, 2005). Senescence will not occur until conditions arise that develop competence and it will appear as if the factors that induce competence are primary inducers of senescence. Regulation can operate at many levels, from the epigenetic unmasking of promoters and genes in chromatin, to posttranslational protein modification or compartmentalization (Wingler, 2007). Early ideas about senescence were based on evidence that development of ripeness to senesce depends primarily on transcription, whereas the senescence trigger and subsequent mechanism may be largely post-transcriptional (and even post-translational) events (Thomas & Stoddart, 1980; Smart, 1994; Sullivan et al., 2003; Thompson et al., 2004; Hopkins et al., 2007). This makes the notion of a ‘senescence switch’ conceptually and experimentally difficult.

Development as an amplifier Because senescence is a terminal process, it is on the receiving end of the amplifier effect in plant development. A small perturbation early in development can have considerable consequences for the subsequent expression of senescence. This is apparent in Arabidopsis, where most growth and flowering mutants also have disturbed leaf senescence (Ellis et al., 2005; Riefler et al., 2006). This is part of the allometric control of senescence and life-history, which has been discussed by Ougham et al. (2007) and Marbà et al. (2007). Thus, genes for plastid assembly are, in the broad sense, senescence genes because a chloroplast has to be built in its characteristic way before it transdifferentiates into a gerontoplast. Arising from the early classical molecular biology approaches of differential cloning (Smart, 1994; Buchanan-Wollaston, 1997) through to contemporary omics methods (BuchananWollaston et al., 2003; Guo et al., 2004), knowledge of the variety of gene classes associated with senescence has revealed that the syndrome subsumes a wider range of cellular and physiological processes than might have been expected. Collections of senescence-associated genes typically comprise a number of transcription factors and other regulators – examples


include WRKY factors, leucine zipper proteins, SARK and SIRK receptor kinases, calmodulin-binding proteins, MYBs, zinc fingers, MADS boxes, chromatin architecture-controlling AT-hook proteins and NAC factors (Hinderhofer & Zentgraf, 2001; Buchanan-Wollaston et al., 2003; Lim et al. 2003, 2007; Lin & Wu, 2004). This adds up to a picture of the senescence program as a rather loose assemblage of transcriptional, post-transcriptional, epigenetic and allometric modules, which is difficult to convert into a coherent mechanistic framework.

Timetable or program? What is the difference between a timetable and a program? A timetable is a record of events occurring in sequence, whereas a program requires the events to occur in a given order. While mutant studies may provide data about a program, profiling techniques, such as DNA microarrays, record instead a developmental timetable. In order to obtain reproducible data, plants are passed through the developmental stages under highly controlled conditions, and consequently development follows a certain trajectory. The search for senescence-associated genes, with differential expression, by using this approach (Lin & Wu, 2004; Guo et al., 2004; Buchanan-Wollaston et al., 2005) is motivated by a hope that some of these genes may be important for senescence, or at least could be markers of certain stages of senescence. If senescence is not much of a program, even finding marker genes for senescence stages could be problematic. An illustration of this is the results from transcript profiling in autumn leaves of a free-growing aspen (Populus tremula L.). Senescence in this tree, measured as chlorophyll degradation, is initiated around 10 September, regardless of the weather conditions, and is therefore under photoperiodic control (Keskitalo et al., 2005). Further studies of a range of aspen ecotypes (see Luquez et al., 2007) have shown that the onset of senescence in the glasshouse, under natural photoperiod but otherwise controlled environmental conditions, is synchronized with free-growing ramets of the same clones, confirming that senescence in this system is triggered by the light environment alone. We performed transcript profiling using DNA microarrays over the period of initiation of senescence using leaf samples harvested from the same free-growing tree over 4 yr (Keskitalo et al., 2005, Y. Fracheboud et al., unpublished). There are indeed limitations in this approach. Leaf-to-leaf variation within a single tree, and the fact that the arrays used covered only c. 40% of the genome, will certainly reduce the precision of an analysis. Nevertheless, the data constitute a sufficiently large and representative sample of the entire senescence-associated transcriptome (Bhalerao et al., 2003) to permit conclusions to be drawn. Even if critical genes that become induced and start the senescence program are absent from this analysis, a change in expression of a significant fraction of the arrayed genes would be expected if the term

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a timetable and not a program. If senescence is initiated in leaves grown under identical environmental conditions in the laboratory, the transcriptome responds in a reproducible way, indicating that certain gene-expression patterns may correspond to specific stages of senescence and even that the expression of certain key genes could cause senescence. On the other hand, if senescence is initiated under different conditions, these relationships may not hold true.

If there is a senescence trigger, what could it be?

Fig. 1 Gene expression during initiation of autumn leaf senescence in an aspen (Populus tremula) grown at the Umeå University campus. The total pattern of gene expression from August to September in 1999, 2001, 2003 and 2004 was analysed on POP1 and POP2 DNA microarrays using a common reference (Andersson et al., 2004). Samples (a pool of > 20 leaves from each time point) were taken from a single tree at noon every day over several years. RNA preparation, microarrays and array analysis, including post-processing of the data, are described in Sjödin et al. (2006) and are stored in UPSC-BASE (www.upscbase.db.umu.se) where data are available under experiments UMA-0050 and UMA-0054. Principal component (PC) analysis was performed in SIMCA-P 11.5 (Umetrics, Umeå, Sweden). The first principal component, explaining 54% of the total variation in gene expression, is shown. This first principal component is, within each year, a description of date; numbers on the axes denote dates of leaf harvest, starting with fully green leaves (to the left) in August and ending in late September at the stage when leaves are so senescent that sufficient quantities of high-quality RNA for array analysis could not be obtained.

‘program’ is to be justified. The expectation was to find that gene expression altered during this period and that a major shift in gene expression should occur before, at, or after, the initiation of senescence. Indeed, major modulations occurred (downregulation of photosynthesis genes, for example) but, surprisingly, the shift did not coincide with a senescence stage, using extent of chlorophyll loss as the measure of physiological state between initiation and completion of senescence. Instead, the total pattern of gene expression in 2004, analysed using principal component analysis (PCA), was most similar to gene expression at later time points in the other years. In fact, the samples from 9 and 11 September 2004 had a transcriptome that were ‘later’ than those of 18 September 2003 and 17 September 2001 (Fig. 1, Y. Fracheboud et al., unpublished). Apparently, gene expression was governed by factors other than senescence and although it is obvious that, for a tree in the field, many other influences may modulate gene expression, the search for genes or gene-expression patterns that correlated with the onset of senescence was in this case unsuccessful. We believe that the explanation for this may be that transcriptional patterns during leaf senescence merely represent

If senescence is not directly invoked by changes in gene expression, what is the trigger? Changes in the leaf metabolite profile, perhaps related to sink–source relationships, may be important in this respect, especially bearing in mind the key role of leaf senescence in nutrient recycling (Diaz et al., 2005; Hikosaka, 2005; Ougham et al., 2005). If leaf senescence first evolved in annuals or in perennials in a climate that did not undergo dramatic seasonal changes, its original role would have been to move mineral nutrients out of leaves that did not contribute much to photosynthesis and into leaves better positioned, or into other strong sinks like developing seeds (Thomas et al., 2000; Thomas & Sadras, 2001). In those deciduous trees that start senescing by the calendar (Keskitalo et al., 2005), however, one must postulate that photoreceptors could influence metabolism without transcriptional changes.

Senescence: programmed, but not a program It is easy become confused about what is a program and what is programmable. Senescence is conditioned by genetic and environmental predispositions: an amplified outcome of a complex array of proximal and distant inputs. Very few of its constituent genetic, metabolic, cellular or physiological components have, however, been proven to be indispensable. We believe that our current knowledge of leaf senescence does not qualify it to be called a developmental program, like an .exe file. Perhaps senescence can instead be programmed according to the timetable set by development or the environment; that is, it behaves less like a fixed suite of propagating actions set in motion by a triggering event and more like a permissive operating system. Senescence may be better conceived of as a set of modelling routines where the nature of the inputs determine which modules are run, how they loop and interact, and which outputs follow. Alternatively, we might simply be too ignorant to see the program and the ‘Master Controller’. The search for the controller that makes leaves competent to senesce, and those that trigger senescence in competent leaves, will certainly continue. To what extent there may be a confusion between programs and timetables when other plant developmental processes are studied is hard for us to tell, but we believe that the understandable desirability of designing omics



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experiments to minimize the type of season-to-season environmental variation represented by Fig. 1 may have the unintended consequence of making it difficult to distinguish between a program and a timetable.

Acknowledgements Johanna Keskitalo and Andreas Sjödin are acknowledged for providing Fig. 1. The work in the S. J. laboratory is funded by the Swedish Research Council, the Swedish Research Council for the Environment, Agricultural Sciences and Spatial Planning, and the Swedish Foundation for Strategic Research. H. T. is grateful to the Leverhulme Trust for the award of an Emeritus Fellowship. Stefan Jansson1 and Howard Thomas2* 1

Umeå Plant Science Centre, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden; 2 Institute of Biological Sciences, Aberystwyth University, Ceredigion, SY23 3DA, UK (*Author for correspondence: tel +44 1970 628768; fax +44 1970 622350; email [email protected])

References Andersson A, Keskitalo J, Sjödin A, Bhalerao R, Sterky F, Wissel K, Tandre K, Aspeborg H, Moyle R, Ohmiya Y et al. 2004. A transcriptional timetable of autumn senescence. Genome Biology 5: R2. Bhalerao R, Keskitalo J, Sterky JF, Erlandsson R, Björkbacka H, Birve SJ, Karlsson J, Gardeström P, Gustafsson P, Lundeberg J et al. 2003. Gene expression in autumn leaves. Plant Physiology 131: 430 –442. Bopp M. 1996. The origin of developmental physiology of plants in Germany. International Journal of Developmental Biology 40: 89– 92. Buchanan-Wollaston V. 1997. The molecular biology of leaf senescence. Journal of Experimental Botany 48: 181–199. Buchanan-Wollaston V, Earl S, Harrison E, Mathas E, Navabpour S, Page T, Pink D. 2003. The molecular analysis of leaf senescence – a genomics approach. Plant Biotechnology Journal 1: 3 –22. Buchanan-Wollaston V, Page T, Harrison E, Breeze E, Lim PO, Nam HG, Lin J-F, Wu S-H, Swidzinski J, Ishizaki K et al. 2005. Comparative transcriptome analysis reveals significant differences in gene expression and signalling pathways between developmental and dark/starvation-induced senescence in Arabidopsis. Plant Journal 42: 567– 585. Diaz C, Purdy S, Christ A, Morot-Gaudry J-F, Wingler A, Masclaux-Daubresse C. 2005. Characterization of markers to determine the extent and variability of leaf senescence in Arabidopsis. A metabolic profiling approach. Plant Physiology 138: 898 –908. Ellis CM, Nagpal P, Young JC, Hagen G, Guilfoyle TJ, Reed JW. 2005. AUXIN RESPONSE FACTOR1 and AUXIN RESPONSE FACTOR2 regulate senescence and floral organ abscission in Arabidopsis thaliana. Development 132: 4563–4574. Guo Y, Cai Z, Gan S. 2004. Transcriptome of Arabidopsis leaf senescence. Plant, Cell & Environment 27: 521–549. Hikosaka K. 2005. Leaf canopy as a dynamic system: ecophysiology and optimality in leaf turnover. Annals of Botany 95: 521–533. Hinderhofer K, Zentgraf U. 2001. Identification of a transcription factor specifically expressed at the onset of leaf senescence. Planta 213: 469 – 473.


Hopkins M, Taylor C, Liu Z, Ma F, McNamara L, Wang T-W, Thompson JE. 2007. Regulation and execution of molecular disassembly and catabolism during senescence. New Phytologist 175: 201– 214. Hörtensteiner S. 2004. The loss of green color during chlorophyll degradation – a prerequisite to prevent cell death? Planta 219: 191–194. Jing H-C, Hille J, Dijkwel PP. 2003. Ageing in plants: conserved strategies and novel pathways. Plant Biology 5: 455– 464. Jing H-C, Schippers JHM, Hille J, Dijkwel PP. 2005. Ethylene-induced leaf senescence depends on age-related changes and OLD genes in Arabidopsis. Journal of Experimental Botany 56: 2915–2923. Kerr JFR, Wyllie AH, Currie AR. 1972. Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics. British Journal of Cancer 26: 239–257. Keskitalo J, Bergquist G, Gardeström P, Jansson S. 2005. A cellular timetable of autumn senescence. Plant Physiology 139:1635 –1648. Kingston-Smith AH, Thomas H, Foyer CH. 1997. Chlorophyll a fluorescence, enzyme and antioxidant analysis provide evidence for the operation of an alternative electron sink during leaf senescence in a stay-green mutant of Festuca pratensis. Plant, Cell & Environment 20: 1323– 1337. Lim PO, Kim Y, Breeze E, Koo JC, Woo HR, Ryu JS, Park DH, Beynon J, Tabrett A, Buchanan-Wollaston V et al. 2007. Overexpression of a chromatin architecture-controlling AT-hook protein extends leaf longevity and increases the post-harvest storage life of plants. Plant Journal 52: 1140– 1153. Lim PO, Woo HR, Nam HG. 2003. Molecular genetics of leaf senescence in Arabidopsis. Trends in Plant Science 8: 272–278. Lin J-F, Wu S-H. 2004. Molecular events in senescing Arabidopsis leaves. Plant Journal 39: 612–628. Luquez V, Hall D, Albrectsen BR, Karlsson J, Ingvarsson P, Jansson S. 2007. Natural phenological variation in aspen (Populus tremula): the SwAsp collection. Tree Genetics and Genomes 4: 1614– 2942. Marbà N, Duarte CM, Agustí S. 2007. Allometric scaling of plant life history. Proceedings of the National Academy of Sciences, USA 104: 15777– 15780. Ougham H, Armstead I, Howarth C, Galyuon I, Donnison I, Thomas H. 2007. The genetic control of senescence revealed by mapping quantitative trait loci. Annual Plant Reviews 26: 171–201. Ougham H, Hörtensteiner S, Armstead I, Donnison I, King I, Thomas H, Mur L. 2008. The control of chlorophyll catabolism and the status of yellowing as a biomarker of leaf senescence. Plant Biology (in press). Ougham HJ, Morris P, Thomas H. 2005. The colors of autumn leaves as symptoms of cellular recycling and defenses against environmental stresses. Current Topics in Developmental Biology 66: 135– 160. Riefler M, Novak O, Strnad M, Schmülling T. 2006. Arabidopsis cytokinin receptor mutants reveal functions in shoot growth, leaf senescence, seed size, germination, root development, and cytokinin metabolism. Plant Cell 18: 40– 54. Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, Vingron M, Schölkopf B, Weigel D, Lohmann JU. 2005. A gene expression map of Arabidopsis thaliana development. Nature Genetics 37: 501–506. Sjödin A, Bylesjo M, Skogstrom O, Eriksson D, Nilsson P, Ryden P, Jansson S, Karlsson J. 2006. UPSC-BASE – Populus transcriptomics online. Plant Journal 48: 806–817. Smart CM. 1994. Gene expression during leaf senescence. New Phytologist 126: 419 –448. Sullivan JA, Shirasu K, Deng XW. 2003. The diverse roles of ubiquitin and the 26S proteasome in the life of plants. Nature Reviews Genetics 4: 948– 958. Thomas H. 2008. Systems biology and the biology of systems: how, if at all, are they related? New Phytologist 177: 11–15. Thomas H, Matile P. 1988. Photobleaching of chloroplast pigments in leaves of a nonyellowing mutant genotype of Festuca pratensis. Phytochemistry 27: 345– 348.


Letters Thomas H, Ougham H, Thomas HM. 2000. Annuality, perenniality and cell death. Journal of Experimental Botany 51: 1 –8. Thomas H, Ougham HJ, Wagstaff C, Stead AJ. 2003. Defining senescence and death. Journal of Experimental Botany 54: 1127– 1132. Thomas H, Sadras VO. 2001. The capture and gratuitous disposal of resources by plants. Functional Ecology 15: 3–12. Thomas H, Stoddart JL. 1980. Leaf senescence. Annual Review of Plant Physiology 31: 83–111. Thompson JE, Hopkins MT, Taylor C, Wang T-W. 2004. Regulation of senescence by eukaryotic translation initiation factor 5A: implications for plant growth and development. Trends in Plant Science 9: 174 –179. Vegis A. 1964. Dormancy in higher plants. Annual Review of Plant Physiology 15: 185–224. Wingler A. 2007. Transcriptional or posttranscriptional regulation – how does a plant know when to senesce? New Phytologist 175: 1–4. Zavaleta-Mancera HA, Franklin KA, Ougham HJ, Thomas H, Scott IM. 1999a. Regreening of senescent Nicotiana leaves. I. Reappearance of NADPH-protochlorophyllide oxidoreductase and light-harvesting chlorophyll a/b-binding protein. Journal of Experimental Botany 50: 1677–1682. Zavaleta-Mancera HA, Thomas BJ, Thomas H, Scott IM. 1999b. Regreening of senescent Nicotiana leaves. II. Redifferentiation of plastids. Journal of Experimental Botany 50: 1683–1689. Key words: post-transcriptional, program, regulation, senescence, transcription. ?February 0 Letters Letters ??? ?? 2008 Letters Letters

Systems biology or the biology of systems: routes to reducing hunger New © The Phytologist Authors (2007). (2007) doi: Journal 10.1111/j.1469-8137.2007.00@@@.x compilation © New Phytologist (2007)

Introduction Each day passes with 854 million people hungry and, for that reason, the United Nations Millennium Declaration committed the world’s nations to ‘eradicate extreme poverty and hunger’. Nonetheless, developed nations are both reducing their investments in agricultural research and turning their remaining research investments away from productivity gains (Pardey et al., 2006). The elite rice cultivars, which dominate the food supplies of the millions of poor people in Asia, have approached a yield barrier (Kropff et al., 1994; Sheehy, 2001; Sheehy et al., 2007a), and the gains made from the Green Revolution technologies (centred on canopy architecture and crop nutrition) have been fully exploited (Dawe, 2007). During the coming century, climate change will probably result in more extreme variations in weather and may cause adverse shifts in the world’s existing climatic patterns, further disadvantaging the poor (Agarwal & Narain, 1991). Water scarcity will grow; and the increasing demand for biofuels will result in competition between grain for fuel and grain for food, resulting in price increases (Cassman & Liska, 2007). In the face of the above problems, an increase in rice yields of > 50%

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will be required by 2050 to keep pace with population growth in Asia (Mitchell & Sheehy, 2006). ‘Modern’ systems biology is loosely associated with the use of genomic technologies to understand specific biological processes, although ecologists and physiologists have been using a systems approach to model crops for many years (Gutierrez et al., 2005). A weakness of genetic engineering approaches (bottom-up) to crop improvement is that changes at the molecular level can be dissipated when scaled up through biochemical and physiological levels to the response of crops in the field (Sinclair et al., 2004). In this article, we address the following two issues: • can the top-down approaches of systems modelling identify a broad solution to the problem of increasing yields? • can the ‘modern’ concepts of systems biology (bottom-up) identify the details of the solution at the molecular level?

Yield: plasticity, plant community and environmental variables Here we describe briefly the factors that must be considered in a systems approach to yield improvement. The phenotype of a given genotype can vary markedly according to its interaction with the environment (Miflin, 2000). Such plasticity in plants is probably associated with their ability to succeed despite changes in weather, climate, competition for resources and soil types. In order to increase yields, plants growing in communities have to convert more solar energy into chemical energy or use the absorbed energy more efficiently in the synthesis of biomass or grain. Even something as simple as the spacing between plants can markedly alter their morphology and functionality. Crop communities are crowded neighborhoods in which leaves and stems compete for light. Full light absorption by a crop canopy is set by the leaf area per unit ground area and its angular distribution; the angular distribution also has consequences for the diurnal pattern of light interception. That pattern determines the maximum amount of radiation absorbed per unit area of a leaf, the time of day when that peak absorption occurs and the photochemical consequences of that pattern. At full light interception, the size of individual leaves is proportional to the tiller or plant number per unit ground area, and specific leaf area is determined by competition for light. Leaf photosynthesis and specific leaf area can be linearly related (Pearce et al., 1969). Heat plays a role in the efficiency with which chemical energy can be accumulated; in part it determines the length of the growing season and the rate at which panicles develop. The same daily quantity of solar energy can be delivered to a crop in both temperate and tropical environments. This is because long days in temperate environments often have less intense solar irradiance than in tropical environments, which have short days. However, the temperatures in those environments



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are usually very different. The interplay of temperature and irradiance results in growing seasons of different lengths in temperate and tropical environments, although the radiationuse efficiencies are the same. The radiation-use efficiency is the slope of the relationship between cumulative aboveground biomass and cumulative photosynthetically active radiation intercepted by the crop. Usually, irrigation ensures that water is not a limiting factor, but rainfall varies in intensity, duration and frequency in the growing season so the availability of water is a complex problem. Fertilizers are applied to ensure maximum yields, but the demand from the crop varies throughout crop development as does the quantity of fertilizer available at any given instant. In rice, more than half of the nitrogen (N) in the grain comes from the vegetative parts of the plant, although halfway through grain filling, N is diverted from grain to ratoon tillers (Sheehy et al., 2004). The availability of resources and their rate of capture have a huge influence on yield. Whenever the issue of yield increases is discussed, at some point the relative importance of source strength vs sink capacity arises. Work by Sheehy et al. (2001) showed that the sink capacity in rice was greatly in excess of that actually utilized, even at high yield, suggesting that the yield barrier was the consequence of source limitations. Experiments in which increased concentrations of CO2 were made available to rice resulted in increased yields (Yoshida, 1973; Ziska et al., 1997), suggesting that improvements in photosynthesis might have a role to play in increasing yield. In well-managed crops, in which the fraction of grain per unit of biomass has been maximized, future yield improvements must be accompanied by increases in radiation-use efficiency. Mitchell et al. (1998) showed that C4 crops had radiation-use efficiencies that were 50% greater than C3 crops and that radiation-use efficiency was a function of photosynthesis. This led to the suggestion that rice photosynthesis would have to be converted from the C3 to the C4 syndrome to achieve yield increases of 50%. Sheehy et al. (2007b) went some way to confirming this conclusion when they reported that rice and maize crops grown without limitations of water or nutrients at the International Rice Research Institute (IRRI) in the dry season of 2006 yielded 8.3 tonne ha−1 13.9 tonne ha−1 respectively. Furthermore, although C4 plants display plasticity (Sage & McKown, 2006), their C4 nature is not lost during plastic responses to the environment. The attraction of the full C4 system is not only the high productivity and yield, but also the better use made of water and N. No non-C4 solution offers this complete package of benefits.

What is a system and what does it mean for a crop scientist? A system can be defined as a number of interacting elements existing within a boundary that is surrounded by an environment. A system could be a cell, a plant, a crop, an


ecosystem or a factory; quantitative descriptions of those systems are called models. Consideration of most problems often leads to a quantitative approach and then calculations to describe what is happening or what might happen given different circumstances. The principal function of systems analysis is to understand and quantify the relationship between the inputs and outputs of materials. The analytical procedures appropriate for systems analysis are often reductionist: they are designed to analyse the individual parts of the system. Once the parts have been described, quantitatively, an integrated description of the system can be produced. In the absence of redundancy, a change to any part of the system affects the performance of the whole. The crop scientist looks at systems biology as the most coherent method of describing and understanding a complex system. The crop modeller quickly recognizes many interacting components, within and between organisms, and the hierarchical nature of the system (for example, genes–transcriptome–biochemistry–cells–organs–plants–crops). Empirical models seek to describe the system as simply as possible, whereas mechanistic models look for understanding and generality. Mechanistic models offer understanding only at one level in the hierarchy, being empirical at lower levels for the sake of making progress at the higher level of prime interest. Mechanistic models of crops tend to be based on empirical descriptions of how organs work in relation to environmental and management variables. A particular difficulty is describing mechanisms controlling assimilate partitioning between organs. Biological systems depend on control mechanisms, although they are often ill understood at a mechanistic level. If there are n interacting elements, there are n(n−1) possible interactions or routes for information exchange. If information literally flows in one direction from one element to another in a simple system containing four elements, twelve channels (actions and reactions) are required to carry the information necessary to coordinate the activities of the elements within the system. Not surprisingly, at a cellular level this rule is likely to result in a very large number of signaling pathways. Building a mechanistic model of a biological system at any scale is no easy feat, and a hypothesis, mathematics and substantial amounts of information are required (Thornley & France, 2007). Perhaps ‘modern’ systems biology is such a young branch of science that the measuring technologies have overwhelmed scientists’ ability to make quantitative models describing the way that cells work at a molecular level. In this context it is important to note that crop modelling often involves building caricatures of systems with the most important and critical features included and the fine details ignored. Crop models are often quasi-mechanistic, using factors such as radiation-use efficiency as if they were universal constants that summarize the physiological behaviour of a crop (Mitchell et al., 1998). Common to both crop systems and ‘modern’ systems approaches is the possibility that emergent properties will be found (i.e. aspects of the behaviour of the system that could not be predicted from knowledge of the


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individual components). Owing to the complexity inherent in both approaches, marrying them in a single coherent model of crop yield remains a distant prospect. However, that does not preclude a working and profitable partnership between the top-down and bottom-up approaches.

Can ‘modern’ systems biology solve crop production problems? It would seem as if the goal for plant systems biology is to describe the functioning of cells, tissues and the entire plant through molecular analysis and mathematical modelling of physical and chemical interactions between components of living plants and cells. It is an ambitious goal that will take considerable time to realize. Nonetheless, Nelson et al. (2007) suggested that a systems approach is designed to be broad and unbiased, to permit the discovery of ‘emergent’ properties that might not be revealed in hypothesis-driven experimentation that is targeted at specific genes, proteins, activities, or metabolites. By evaluating all components of the system when it is perturbed, computational approaches are able to infer networks of relationships that can then be tested. With the rice genome completely sequenced and with constantly improving annotation, Nelson et al. (2007) suggests that it now makes sense to build ‘-omics’ data sets from developing cells that will permit this computational approach to discovery. New techniques such as laser microdissection of cell types and microarray profiling may provide the comprehensive data needed for such a systems approach. Despite the current optimism, it is not yet possible to know whether ‘modern’ systems biology will play a significant role in solving global food problems in the next few decades. Nonetheless, the work of Nelson et al. (2007) is an extremely exciting approach to understanding the control of leaf development at the molecular level.

Identifying and manipulating genes responsible for important traits The techniques of genetic engineering enable genes from sexually incompatible species to be used to create transgenic crops. This development has led to progress in hypothesisdriven plant improvements. Thus far, success has generally come from inserting a single gene for increasing the tolerance to environmental pressures such as submersion, resistance to pests and diseases, as well as tolerance to herbicides. However, attempts have been made to engineer novel multigene pathways to increase photosynthesis in leaves (Suzuki et al., 2006) and to recapture CO2 from photorespiration (Kebeish et al., 2007). Thus, whole suites of genes encoding desirable traits governing yield can be introduced using the same technology. Of course, the traits have to be identified and understood. Biological N fixation is such a trait, but the genetics of the symbiosis are not yet fully understood (Ladha & Reddy, 2000). To guarantee success in genetic engineering,

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it is important to know how the trait functions at a physiological level in individual plants and in the communities of plants that form crops. Then the genes responsible for the traits have to be identified, which often involves the generation and screening of large numbers of mutants. Given the rate of progress in sequencing technologies (Service, 2006), candidate regions in the genome will be identified by sequencing the wild type and the mutant. Then, by using bioinformatics tools to compare those sequences, the specific genes of interest can eventually be identified. To create a successful transgenic using a multigene trait, an increased understanding of the regulatory networks that control the tissue-specific expression of genes will probably be needed (Gowik & Westhoff, 2007). How does a program of genetic engineering ensure that an introduced trait is not obscured during the plastic responses displayed when the plants are grown as a crop community? It is not infrequent to read that the expression or overexpression of a particular gene inserted in rice will increase yield by a large percentage (Xiao et al., 1998; Ku et al., 1999). However, a convincing quantitative assessment of its metabolic role in the context of a cell, an organ, a whole plant and a crop should accompany such claims (Fukayama et al., 2003; Sinclair et al., 2004). The possibility of resource rejection by higher plants (Thomas & Sadras, 2001) is often overlooked by molecular biologists, as is the concept of plasticity. Given the genetic complexity which underlies that plasticity, and that the ‘same’ crop is grown in geographically different regions with different climates, weather conditions and on different soil types with different histories of management, it is not surprising that in field experimentation precise repeatability, in the usual scientific sense, is the exception rather than the rule. As a result of this imprecision and the absence of universally acceptable theoretical models of crop growth, disagreements about what precisely determines both biomass and grain yield are commonplace. Consequently, even using systems approaches, it is no easy task to identify traits that will guarantee yield improvements.

Conclusions: a partnership The conclusion that to make large increases in rice yield without further damaging the environment meant introducing the C4 pathway, was reached by taking a top-down view of crop performance and using simple crop systems modeling and crop experimentation. To make C4 rice a reality, the genes controlling the anatomical and biochemical networks distinguishing the C4 syndrome from the C3 syndrome must be discovered. This cannot be undertaken without the use of genetic engineering and ‘modern’ systems biology. Two approaches are being adopted: bioinformatics coupled to the identification of genes using mutagenesis; and the emergent properties of developing cells using ‘modern’ systems biology, as proposed by Nelson et al. (2007). In hindsight, the C4 rice concept was the result of trying to solve a problem using both the top-down and bottom-up



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approaches. Installing C4 in rice may be difficult, but that is different from impossible and it is worth bearing in mind the statement of Jones (2000) with respect to the sequencing of the human genome: ‘It reaffirmed one of the most misunderstood facts in science; that it is possible to solve most problems by throwing money at them’. J. E. Sheehy1*, D. Gunawardana1, A. B. Ferrer1, F. Danila1, K. G. Tan1 and P. L. Mitchell2 1

Crop and Environmental Sciences Division, International Rice Research Institute, DAPO 7777, Metro Manila, Philippines 2 Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK (*Author for correspondence: tel +63(2) 580 5600 ext 2711; fax +63(2) 580 5699; email [email protected])

References Agarwal A, Narain S. 1991. Global warming in an unequal world: a case of environmental colonialism. New Delhi, India: Center for Science and Environment (CSE). Cassman KG, Liska AJ. 2007. Food and fuel for all: realistic or foolish?. Biofuels, Bioproducts and Biorefining 1: 18–23. Dawe D. 2007. Agricultural research, poverty alleviation, and key trends in Asia’s rice economy. In: Sheehy JE, Mitchell PL, Hardy B, eds. Charting new pathways to C4 rice. Hackensack, NJ, USA: World Scientific Publishing, 37–53. Fukayama H, Hatch M, Tamai T, Tsuchida H, Sudoh S, Furbank R, Miyao M. 2003. Activity regulation and physiological impacts of maize C4-specific phosphoenolpyruvate carboxylase overproduced in transgenic rice plants. Photosynthesis Research 77: 227–239. Gowik U, Westhoff P. 2007. Molecular evolution of C4 photosynthesis in the dicot genus Flaveria: implications for the design of a C4 plant. In: Sheehy JE, Mitchell PL, Hardy B, eds. Charting new pathways to C4 rice. Hackensack, NJ, USA: World Scientific Publishing, 175–194. Gutierrez R, Shasha D, Coruzzi G. 2005. Systems biology for the virtual plant. Plant Physiology 138: 550–554. Jones S. 2000. The language of the genes. Fulham Palace Road, Hammersmith, London, UK: Flamingo, Harper Collins Publishers. Kebeish R, Niessen M, Thiruveedhi K, Bari R, Hirsch H-J, Rosenkranz R, Stäbler N, Schönfeld B, Kreuzaler F, Peterhänsel C. 2007. Chloroplastic photorespiratory bypass increases photosynthesis and biomass production in Arabidopsis thaliana. Nature Biotechnology 25: 593–599. Kropff MJ, Cassman KG, Peng S, Matthews RB, Setter TL. 1994. Quantitative understanding of yield potential. In: Cassman KG, ed. Breaking the yield barrier. Los Baños, Philippines: International Rice Research Institute, 21–38. Ku MSB, Agarie S, Nomura M, Fukayama H, Tsuchida H, Ono K, Hirose S, Toki S, Miyao M, Matsuoka M. 1999. High-level expression of maize phosphoenolpyruvate carboxylase in transgenic rice plants. Nature Biotechnology 17: 76–80. Ladha JK, Reddy PM. 2000. The quest for nitrogen fixation in rice. Proceedings of the Third Working Group Meeting on Assessing Opportunities for Nitrogen Fixation in Rice. Makati City, Philippines: International Rice Research Institute. Miflin B. 2000. Crop improvement in the 21st Century. Journal of Experimental Botany 51: 1–8. Mitchell PL, Sheehy JE. 2006. Super charging rice photosynthesis to increase yield. New Phytologist 171: 688–693.


Mitchell PL, Sheehy JE, Woodward FI. 1998. Potential yields and the efficiency of radiation use in rice. Manila, Philippines: International Rice Research Institute. Nelson T, Tausta SL, Gandotra N, Liu T, Ceserani T, Chen M, Jiao Y, Ma L, Deng X-W, Sun N et al. 2007. The promise of systems biology for deciphering the control of C4 leaf development: transcriptome profiling of leaf cell types. In: Sheehy JE, Mitchell PL, Hardy B, eds. Charting new pathways to C4 rice. Hackensack, NJ, USA: World Scientific Publishing, 317–332. Pardey PG, Alston JM, Piggott RR. 2006. Agricultural R&D in the developing world. Washington DC, WA, USA: International Food Policy Research Institute. Pearce RB, Carlson GE, Barnes DK, Hart RH, Hanson CH. 1969. Specific leaf weight and photosynthesis in Alfalfa. Crop Science 9: 423–426. Sage RF, McKown AD. 2006. Is C4 photosynthesis less phenotypically plastic than C3 photosynthesis? Journal of Experimental Botany 57: 303–317. Service RF. 2006. Gene sequencing. The race for the $1000 genome. Science 311: 1544–1546. Sheehy JE. 2001. Will yield barriers limit future rice production? In: Nösberger J, Geiger HH, Struik PC, eds. Crop science: progress and prospects. Hamburg, Germany: CAB International, 281–305. Sheehy JE, Dionora MJA, Mitchell PL. 2001. Spikelet numbers, sink size and potential yield in rice. Field Crop Research 71: 77–85. Sheehy JE, Ferrer AB, Mitchell PL. 2007a. Harnessing photosynthesis in tomorrow’s world: humans, crop production and poverty alleviation. In: Allen JF, Gantt E, Golbeck JH, Osmond B, eds. Photosynthesis 2007. Energy from the sun. Proceedings of the 14th International Congress on Photosynthesis. Heidelberg, Germany: Springer, in press. Sheehy JE, Ferrer AB, Mitchell PL, Elmido-Mabilangan A, Pablico P, Dionora MJA. 2007b. How the rice crop works and why it needs a new engine. In: Sheehy JE, Mitchell PL, Hardy B, eds. Charting new pathways to C4 rice. Hackensack, NJ, USA: World Scientific Publishing, 3–26. Sheehy JE, Mnzava M, Cassman KG, Mitchell PL, Pablico P, Robles RP, Samonte HP, Lales JS, Ferrer AB. 2004. Temporal origin of nitrogen in the grain of irrigated rice in the dry season: the outcome of uptake, cycling, senescence and competition studied using a 15N point-placement technique. Field Crops Research 89: 337–348. Sinclair TR, Purcell LC, Sneller CH. 2004. Crop transformation and the challenge to increase yield potential. Trends in Plant Science 9: 70–75. Suzuki S, Murai N, Kasaoka K, Hiyoshi T, Imaseki H, Burnell JN, Arai M. 2006. Carbon metabolism in transgenic rice plants that express phosphoenolpyruvate carboxylase and/or phosphoenolpyruvate carboxykinase. Plant Science 170: 1010–1019. Thomas H, Sadras VO. 2001. The capture and gratuitous disposal of resources by plants. Functional Ecology 15: 3–12. Thornley JHM, France J. 2007. Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences, 2nd Edn. Wallingford, UK: CAB International. Xiao J, Li J, Grandillo S, Nag Ahn S, Yuan L, Tanksley SD, McCouch SR. 1998. Identification of trait-improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon. Genetics 150: 899–909. Yoshida S. 1973. Effects of CO2 enrichment at different stages of panicle development on yield components and yield of rice (Oryza sativa L.). Soil Science and Plant Nutrition 19: 311–316. Ziska LH, Namuco O, Moya T, Quilang J. 1997. Growth and yield response of field-grown tropical rice to increasing carbon dioxide and air temperature. Agronomy Journal 89: 45–53. Key words: breeding, C4 photosynthesis, food security, Green Revolution, modeling, systems biology, yield.


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Cultivating plant synthetic biology from systems biology ?March 0 Letters ??? ?? 2008

Introduction Systems biology has produced a wealth of information from its detailed characterization of molecular components in living organisms. However, making sense of large data sets, and how to apply systems biology data to biological systems, poses challenges. One way in which data from systems biology can be applied is in the emerging field of synthetic biology.

Synthetic biology The detailed quantitative characterizations of molecules and the behavior of their networks, gained from systems biology, provide a solid foundation for synthetic biology. Synthetic biology is a broad approach that uses tools such as rational design of genes, genetic systems and living systems for a specific purpose. For example, a synthetically designed oscillating biological clock and a bacterial ‘camera’ may seem like biological parlor tricks, but these pioneering experiments, along with others, have proved that biological systems can be engineered with a specific function and used to produce living ‘machines’ (Elowitz & Leibler, 2000; Gardner et al., 2000; Levskaya et al., 2005). A fundamental difference between synthetic biology and general genetic engineering is the collection, application and modeling of quantitative data. For example, because biological gene circuits have naturally widely varying kinetic characteristics, simply assembling a series of genes or genetic circuits to produce a desired function is unlikely to be successful. A general approach in synthetic biology is to design genetic circuits rationally, measure the steady-state and dynamic behavior of components quantitatively (e.g. mRNA synthesis and stability; protein synthesis and stability), model their behavior, and then assemble the characterized components into genetic circuits that exhibit predictable and reliable function (Andrianantoandro et al., 2006). A key concept in synthetic biology is the application of long-proven approaches from engineering. For example, one of the easiest ways to produce a functional device in classical engineering is to use standardized parts (for example, a computer can be assembled from parts such as a processor, hard drive, memory and monitor). Standardization ensures that individual working components can be used together or exchanged. To begin standardization in biology, a group of Massachusetts Institute of Technology-based researchers have assembled Biobricks, a collection of mostly bacterial regulatory components and genes with some components from bacteriophage and yeast (http://www.biobricks.org). Biobrick components have been, and are being, used for annual student

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competitions to genetically engineer machines (International Genetically Engineered Machines, http://parts.mit.edu/igem07/ index.php/Main_Page). In addition to standardization, decoupling and abstraction are central to designing complex synthetic biological systems from simple parts (Endy, 2005). Decoupling might be called ‘divide and conquer’. It allows researchers to break down a complex problem into a smaller problem that can be addressed experimentally. For example, to understand the complex process of photosynthetic electron transport, numerous studies have been carried out over many decades. Researchers first focused on questions that could be addressed experimentally, such as the effective wavelength of light, whereas studies today use detailed information about protein interactions in photosystems (Merchant & Sawaya, 2005). Collectively, such analyses could be called decoupling. A third concept that synthetic biology employs is abstraction. Organizing components of a system according to their complexity allows researchers to focus on one hierarchical level of complexity, independently of other levels (Endy, 2005). For example, to build an automobile, engineers have to design parts (pistons, tires), link these parts to form a device (engines, wheels), and assemble them together to produce an automobile. By focusing on making the best pistons, a better engine can be built and hence a better automobile. Likewise, plant synthetic engineers can optimize genes, genetic circuits and traits to produce plants with novel functions. In addition to integrating engineering concepts, as already described, synthetic biology relies heavily on the mathematical modeling of components to provide insight into rational design of genetic circuits (Drubin, 2007). The modeling of selected aspects of genetic circuits through mathematical descriptions can be simple or complex, depending on the system components. Parameters that typically concern synthetic biologists can be grouped into two broad categories: those that define network topologies (how molecules control the concentration of other molecules), and those associated with kinetic interactions of molecules within devices (Kaznessis, 2007). Examples of gene circuit parameters that are often modeled include RNA polymerase binding, transcript elongation, repressor binding, random cellular noise, and polypeptide elongation (Elowitz & Leibler, 2000; Gardner et al., 2000; Li et al., 2007). Regrettably, the information available for modeling gene expression is often limited, impeding the ability to design gene circuits rationally. A powerful means around rational design is offered by biological systems: the ability to evolve. By using directed evolution, synthetic biologists use the power of living systems to produce the best gene components based on selection of the desired behavior in vivo (Yokobayashi et al., 2002). In many cases, directed evolution has identified mutations that allow for subtle changes in the behavior of a gene or gene product that were not anticipated by modeling and rational design (Pattanaik et al., 2006; Zhou et al., 2006). For instance, researchers used directed evolution to identify mutations



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enhancing transcriptional activation of a basic helix−loop− helix transcription factor (Pattanaik et al., 2006). While most of the mutations were, as expected, in the acidic transactivation domain, one mutation was found in the N-terminal helix interaction domain. When tested, the synthetically evolved transcription factors showed significant enhancements in transcriptional activity and transactivation of an Arabidopsis promoter.

Synthetic biology accomplishments In theory, any novel gene regulatory networks can be assembled from simple genetic components to produce behavior/activities designed by the biologist. However, one of the first productions of a functional gene regulatory network (a ‘toggle-switch’) from simple genetic components required quantitative mathematical modeling (Gardner et al., 2000). A genetic toggleswitch is a synthetic, bistable gene regulatory system. Multiple genetic designs are possible for a genetic toggle-switch; for example, Gardner et al. (2000) produced such a switch using two repressible promoters arranged in a mutually inhibitory network. A functional toggle-switch required the definition of multiple genetic parameters that were mathematically modeled, and the regulatory network was assembled based on this model. The genetic switch was used to produce or not produce green fluorescent protein (GFP). The expression showed a sharp sigmoidal curve indicating bistability or the ability to exist in two states (in this case with or without GFP production). A key difference between their work and traditional genetic engineering is that they manipulated the network architecture based on theoretical parameters (Gardner et al., 2000). Genetic toggle-switches may have many applications in plant biology, such as precise ‘on switches’ for plant pharmaceutical production, or regulation of biomass accumulation. Timing is a key aspect of living systems that regulates processes such as circadian rhythms and periodicity. An oscillatory network showing that timing features can be designed synthetically was produced using a series of transcriptional repressors that controlled expression of GFP (Elowitz & Leibler, 2000). To produce the designed periodicity, Elowitz and Leibler developed a mathematical model for transcriptional/translational rates and decay rates of both mRNA and repressor proteins, and the GFP reporter. Understanding the mechanisms behind this artificial oscillatory network could reveal insight into the mechanisms of natural circadian clocks and the development of artificial clocks in living organisms. In plants, such an inducible timing mechanism could be engineered to coordinate flowering time in crop plants. Remarkably, synthetic biologists have also been able to design systems where programmed multicellular pattern formation was produced (Basu et al., 2005). Natural pattern formation typically involves cell−cell communication that is then interpreted by an intracellular genetic network. Two sets of cells were engineered: ‘sender cells’ that produced a signaling molecule, acyl-homoserine lactone (AHL), and ‘receiver


cells’ that produced a fluorescent protein in response to userdefined ranges of the AHL. By varying the spatial arrangement of sender cells and receiver cells, distinct ring-like patterns of GFP fluorescence were produced (Fig. 1). A key to accomplishing the ring pattern was the design of distinct types of receiver cells or ‘band-detect’ strains that had different genetic networks to detect and respond to a high, medium or low AHL concentration. Like previous work, the precise engineering of the genetic circuits used both theoretical and experimental analyses of various parameters (e.g. stability of proteins, strength of promoters). Mathematical models were able to define both individual cell behavior and spatiotemporal multicellular system behavior. In the receiver cells, three fluorescent proteins (GFP; red fluorescent protein, RFP; cyan fluorescent protein, CYP) were used as the output for the genetic network. From an undifferentiated lawn of receiver cells, a bullseye pattern was produced from CYP, RFP and GFP around a central sender colony (Basu et al., 2005). Synthetic pattern formation may provide quantitative understanding of natural processes, and opens doors to the possibility of engineering three-dimensional tissues (Basu et al., 2005). While pattern formation and its underlying genes have long been studied in plants, synthetic pattern formation could produce application-specific products. For example, by synthetically engineering the ability to control cell division planes, one could envision wood products that have dimensions for specific applications (e.g. a true block of wood rather than a block cut from an elongated tree). Another application of synthetic biology is producing ‘biological machines’, or living organisms designed to perform a specific task. One example is a bacterial camera that was built to produce a chemical image corresponding to an applied light pattern (Levskaya et al., 2005). This biological camera uses a photosensitve phytochrome from cyanobacteria fused to the well characterized two-component signaling system, EnvZ−OmpR. The signaling system controls expression of LacZ that enzymatically produces a black compound in the presence of β-gal-like substrate. This work showed that a simple biological machine can be produced by interfacing different, naturally occurring molecular components. A synthetic biosensor was produced in bacteria using computationally designed receptors (Looger et al., 2003). To delineate synthetic protein design, the Hellinga laboratory focused their efforts on the evolving zone, the region of ligand-receptor contact, and used periplasmic binding proteins that exhibit a hinge-binding mechanism. The hinge-binding mechanism allowed use of a fluorophore to screen computeroptimized synthetic receptors for functionality. Using these approaches, they demonstrated that a broad range of receptors can be designed: for example, receptors for an explosive, trinitrotoluene (TNT); a sugar, l-lactate; a neurotransmitter, serotonin; a nerve gas surrogate; and the metal zinc have all been designed (Dwyer et al., 2003; Looger et al., 2003; Allert et al., 2004). These receptors were shown to be highly specific,


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Fig. 1 Synthetic pattern formation from multicellular bacterial systems. (a) A schematic illustrating the communication and response between sender and receiver cells. In sender cells, the LuxI gene catalyses synthesis of the acyl-homoserine lactone (AHL) signaling molecule in response to aTc. Sender cells, also producing red fluorescent protein, become sources for the AHL signal. Receiver cells engineered with band-detect networks respond to distinct concentrations of AHL. At high AHL concentrations, green fluorescent protein (GFP) is repressed, at medium AHL concentrations GFP is expressed, and at low AHL concentrations GFP is again repressed to produce ring-like patterns. Different patterns are produced, caused by the arrangement of sender cells on lawns of various band-detect strains. To produce an ellipse (b) two discs of AHL-producing sender cells are used; to produce a heart-shaped pattern (c) three discs are used; to produce a four-leaf clover shape (d) four discs are used. (a) Modified from Basu et al. (2005); (b–d) reproduced from Basu et al. (2005) with permission.

and often detected nanomolar concentrations of their ligands. To demonstrate that these receptors function in vivo, they used a histidine kinase-signaling system with synthetic feedback to reduce background (Looger et al., 2003). In response to nanomolar levels of a specific ligand, a conformational change is induced in the computer-designed receptor. The receptor−ligand complex then develops high affinity for the extracellular domain of transmembrane histidine kinase, activates the histidine kinase, and initiates signal transduction leading to the production of GFP. The system is extremely powerful because the receptors can be computationally designed to most small molecules. Moreover, because the receptors are the first part of the histidine kinase signal transduction system, they provide a modular function. By altering the receptor, bacterial biosensors can be produced to sense molecules such as explosives, chemical agents and environmental pollutants.

Developing phytodetectors with synthetic biology Because plants naturally sense and respond to their environment, synthetic biology could be used to adapt these traits, which may lead to highly specific plant detectors or phytodetectors. A plant that could sense a substance of interest and provide a simple response could be useful in monitoring hazardous substances such as explosives, toxins/pollutants or pathogens.

Plants would serve as ideal monitors because they are ubiquitous in most places and require little maintenance. The rational design of a phytodetector would require a sensing mechanism and a transmission mechanism that ideally activates a detectable response. If the previously described computer-designed receptors could be made functional in plants, they would provide a means for simple and inexpensive detection of ligands of interest. In bacteria, the computer-designed receptors are localized in periplasmic space. While plants do not have a periplasmic space, the receptors themselves are small (e.g. the TNT receptor is 7 × 8 × 4 nm) and they could presumably diffuse freely in the apoplastic space of primary plant cell walls (Somerville et al., 2004). Moreover, the signal transduction system used in bacteria, histidine kinase, is conserved between bacteria and plants (Ferreira & Kieber, 2005; Fig. 2). Using synthetic biology approaches, it may be possible to forward engineer a system for eukaryotic synthetic signal transduction that links input from the computer-designed receptor to a response or read-out system. One response system that might be useful for plant sentinels has already been developed. Our laboratory has described a synthetic ‘degreening circuit’ that allows chlorophyll levels to be placed under control of a specific input (Antunes et al., 2006). Chlorophyll represents one of the first ‘reporter genes’ appropriate for field-level measurement. Chlorophyll levels are typically under control of genetic and environmental input



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Fig. 2 Diagrammatic comparison of histidine kinase pathways conserved between plants (Arabidopsis) and bacteria. Both pathways use a phospho-relay that is transferred from histidine to aspartate residues on various proteins. The His→Asp relay found in bacteria is elaborated in plants to His→Asp→His→Asp. The evolutionary conservation of signaling transduction components could be a basis for using the biosensor system described in the text. AHK, Arabidopsis histidine kinase; AHP, Arabidopsis histidine phosphotransferase; ARR, Arabidopsis response regulator; HK, histidine kinase; CRF, cytokinin response factor.

(Hortensteiner, 2006). To remove chlorophyll from environmental and genetic input, the synthetic degreening circuit was designed to stop synthesis and initiate breakdown simultaneously. Changes in chlorophyll fluorescence are detected in 2 h, with white plants resulting after 24–48 h (Fig. 3). The loss of chlorophyll, resulting in white plants, is a response that can be recognized by the general public. Because chlorophyll is the reporter molecule, the response can also be detected remotely with fluorescence or hyperspectral imaging (Shaw et al., 2007). When the ligand is removed, the plants regreen, allowing the plants to reset, an important aspect for any biologically based sensor. If a plant sensing system could be produced using the computer-designed receptors, some type of signal transduction, and a read-out such as provided by the synthetic degreening circuit, it could be extremely powerful, providing an inexpensive and widespread means to monitor for clean air, clean water and security.

Fig. 3 Synthetic degreening circuit in transgenic Arabidopsis plants at 0 and 48 h after induction. Chlorophyll is rapidly degraded and reactive oxygen species produced, resulting in a white phenotype (Antunes et al., 2006). ST (shoot tips) retain some pigment even after induction. Induction was with a steroid hormone transcription system (see Antunes et al., 2006 for details).

Synthetic biology and biofuels One area where synthetic biology could be particularly useful for plant biologists is biofuels. Synthetic biology could provide powerful tools for optimizing naturally occurring fuel production pathways or developing novel pathways in plants. For example,


synthetic biology approaches could be used to optimize oilproducing pathways in oilseed crops or microalgae used for biodiesel production. One challenge to biodiesel production is the presence of polyunsaturated fatty acids in some


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vegetable oils (Chisti, 2007). Synthetic biology approaches could be useful in developing crops in which fatty acid production is limited to specific fatty acids optimal for biodiesel production. Like biodiesel production, development of cellulosic ethanol production could benefit from synthetic biology. For example, design of enzymes to efficiently break down cellulose into fermentable sugars or to remove lignin would significantly reduce the cost of processing. Cost could be further reduced if production of designer enzymes could be spatially and/or temporally controlled within the plant (Himmel et al., 2007). Perhaps a network could be designed that produces the enzymes for cellulose breakdown in the cell walls, and is controlled by an artificial clock that initiates enzyme production upon senescence. Synthetic biology, the forward engineering of biological organisms for a specific purpose, is in its infancy. The enormous quantity of data from systems biology provides fertile ground to combine engineering concepts and mathematical modeling for useful purposes. Synthetic biology fundamentally describes the engineering of living systems and offers enormous potential in terms of novel materials, human health applications and energy resources. From a basic research perspective, designing synthetic systems will help us to better understand natural gene regulation mechanisms that underlie life. To date, most work on synthetic biology has been accomplished with microorganisms. However, we have described several ways in which synthetic biology may also find fruit in the green world of plants. Tessa A. Bowen†, Jeffrey K. Zdunek† and June I. Medford* Department of Biology, 1878 Campus Delivery, Colorado State University, Fort Collins, CO 80523-1878, USA (*Author for correspondence: tel +1 970 491 7865; fax +1 970 491 0649; email [email protected]) †These authors contributed equally to this work

References Andrianantoandro E, Basu S, Karig DK, Weiss R. 2006. Synthetic biology: new engineering rules for an emerging discipline. Molecular Systems Biology 2, doi: 10.1038/msb4100073. Allert M, Rizk SS, Looger LL, Hellinga HW. 2004. Computational design of receptors for an organophosphate surrogate of the nerve agent soman. Proceedings of the National Academy of Sciences, USA 101: 7907–7912. Antunes MS, Ha S-B, Tewari-Singh N, Morey KJ, Trofka AM, Kugrens P, Deyholos M, Medford JI. 2006. A synthetic de-greening gene circuit provides a reporting system that is remotely detectable and has a re-set capacity. Plant Biotechnology Journal 4: 605–622. Basu S, Gerchman Y, Collins CH, Arnold FH, Weiss R. 2005. A synthetic multicellular system for programmed pattern formation. Nature 434: 1130–1134. Chisti Y. 2007. Biodiesel from microalgae. Biotechnology Advances 25: 294–306. Drubin DA. 2007. Designing biological systems. Genes & Development 21: 242–254.

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Dwyer MA, Looger LL, Hellinga HW. 2003. Computational design of a Zn2+ receptor that controls bacterial gene expression. Proceedings of the National Academy of Sciences, USA 100: 11255–11260. Elowitz MB, Leibler S. 2000. A synthetic oscillatory network of transcriptional regulators. Nature 403: 335–338. Endy D. 2005. Foundations for engineering biology. Nature 438: 449–453. Ferreira FJ, Kieber JJ. 2005. Cytokinin signaling. Current Opinion in Plant Biology 8: 518–525. Gardner TS, Cantor CR, Collins JJ. 2000. Construction of a genetic toggle switch in Escherichia coli. Nature 403: 339–342. Himmel ME, Ding SY, Johnson DK, Adney WS, Nimlos MR, Brady JW, Foust TD. 2007. Biomass recalcitrance: engineering plants and enzymes for biofuels production. Science 315: 804–807. Hörtensteiner S. 2006. Chlorophyll degradation during senescence. Annual Review of Plant Biology 57: 675–709. Kaznessis YN. 2007. Models for synthetic biology. BMC Systems Biology 1: 47. Levskaya A, Chevalier AA, Tabor JJ, Simpson ZB, Lavery LA, Levy M, Davidson EA, Scouras A, Ellington AD, Marcotte EM et al. 2005. Synthetic biology: engineering Escherichia coli to see light. Nature 438: 441–442. Li C, Chen L, Aihara K. 2007. Stochastic synchronization of genetic oscillator networks. BMC Systems Biology 1: 6. Looger LL, Dwyer MA, Smith JJ, Hellinga HW. 2003. Computational design of receptor and sensor proteins with novel functions. Nature 423: 185–190. Merchant S, Sawaya MR. 2005. The light reactions: a guide to recent acquisitions for the picture gallery. Plant Cell 17: 648–663. Pattanaik S, Xie CH, Kong Q, Shen KA, Yuan L. 2006. Directed evolution of plant basic helix−loop−helix transcription factors for the improvement of transactivational properties. Biochimica et Biophysica Acta 1759: 308–318. Shaw AK, Medford JI, Antunes MS, McCormick WS, Wicker D. 2007. Early detection of chem−bio attacks using biosensors and hyperspectral image processing. Proceedings of SPIE 6554: 6554–6557. Somerville C, Bauer S, Brininstool G, Facette M, Hamann T, Milne J, Osborne E, Paredez A, Persson S, Raab T et al. 2004. Toward a systems approach to understanding plant cell walls. Science 306: 2206–2211. Yokobayashi Y, Weiss R, Arnold FH. 2002. Directed evolution of a genetic circuit. Proceedings of the National Academy of Sciences, USA 99: 16587–16591. Zhou M, Xu H, Wei X, Ye Z, Wei L, Gong W, Wang Y, Zhu Z. 2006. Identification of a glyphosate-resistant mutant of rice 5-enolpyruvylshikimate 3-phosphate synthase using a directed evolution strategy. Plant Physiology 140: 184–195. ?April 0 Letters Letters ??? ?? 2008 Letters Letters

Does systems biology represent a Kuhnian paradigm shift? In a recent letter, John Bothwell (2006) argues that systems biology does not represent a Kuhnian paradigm shift or revolution, as some commentators claim (Palsson, 2006; Trewavas, 2006). Rather, according to Bothwell, systems biology is best described in terms of Kuhnian normal science (Appendix A1). At first pass, this debate may seem inconsequential, especially to those who practice biology. However, closer inspection of the debate, particularly in terms of what Kuhn says about paradigm shifts and their attendant revolutions,



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reveals that systems biology may represent a fundamental or paradigmatic shift in biology’s philosophical foundation – an important element of Kuhn’s disciplinary matrix (Kuhn, 1996) (Appendix A2). That shift for many – but not all – systems biologists is from a reductionistic approach to a holistic one, for investigating and explaining biological phenomena. In response to Bothwell, I examine briefly the debate over systems biology’s potential revolutionary nature and discuss its possible consequences for the practice of 21st-century biology. The emergence of systems biology over the past decade has occurred in response to the large amounts of data generated from high-throughput genomics and proteomics (Palsson, 2006). Besides the sheer amount of data, the complexity of the biological phenomena responsible for the data beckons for a different approach than the standard reductionistic approach, in order to understand and explain these data. Reductionism involves the simplification of complex phenomena in terms of their components and comes in at least three forms (Marcum & Verschuuren, 1986). The first is theoretical reductionism, in which the terms of a complex theory are formulated in terms of a simpler one. For example, photosynthetic theoretical terms are articulated in chemical and physical theoretical terms. The second form is ontological reductionism, in which complex phenomena are simplified with respect to entities and forces (Appendix A3). Again, for example, the components comprising photosynthesis are identified with respect to chemical entities and physical forces. Methodological reductionism is the third form, in which experimental protocols are utilized to investigate complex phenomena with respect to their isolated components. As Bothwell (2006, pp. 7 and 8) acknowledges, systems biologists attempt to replace the reductionistic approach to complex biological phenomena with a holistic approach. This approach involves an epistemological or a theoretical holism, in which higher-order structures are articulated in hierarchical – rather than in reductionistic – terms (Appendix A4). He cites Hiroaki Kitano’s four components of systems biology, including system structure, system dynamics, system control method and system design method, to illustrate the epistemological or theoretical nature of the holistic approach (Kitano, 2002). Moreover, holistic ontology is not concerned solely with elemental components, such as molecules, that create complex biological phenomena but with the integrity of those phenomena at a higher level of organization (Appendix A5). Finally, systems biology’s methodological holism pertains to the integration of ‘-omics’ data through computational analysis, in an effort to identify ‘organizational’ laws or principles (Mesarovic et al., 2004). The question then is whether this move by systems biologists from a reductionistic to a holistic approach, to investigate and explain complex biological phenomena, represents a Kuhnian paradigm shift or revolution – in which scientists substitute a newer incommensurable paradigm for an older


one – or whether it represents Kuhnian normal science – in which scientists are simply ‘mopping up’ after a revolution. Bothwell claims the latter, for two reasons. The first is that although systems biologists do not reduce complex biological phenomena to chemistry, they do reduce them to engineering. In other words, systems and their emergent properties are simply additional components that supplement other elemental components comprising biological phenomena. Thus, there is no replacement of an older paradigm with a newer incommensurable one because there is significant – if not complete – overlap between them. The second reason is that systems biologists did not invent the notion of functional analysis, which has a history dating back to Aristotle and is exemplified by contemporary physiology. I think Bothwell is both correct and incorrect in his assessment of the revolutionary nature of systems biology. He is correct in that some systems biologists cling to reductionism and reject holism as a guiding principle for their trade (Sorger, 2005). In this sense these biologists are engaged in normal science in that they are mopping up after the molecular biology revolution, by articulating its paradigm (Kellenberger, 2004). He is incorrect, on the other hand, in that other systems biologists utilize a holistic approach instead of a reductionistic approach, as detailed above, and extend the standard functional analysis of physiology to include dynamical nonequilibrium analysis. Systems biology holism – as an antireductionistic approach – comes in two forms. The first is organicism, in which complex phenomena are studied strictly at higher levels of organization so that causation proceeds top-down and not, as for reductionism, bottom-up (Fujimura, 2005). This antireductionistic approach of systems biologists represents a major Kuhnian revolution or paradigm shift because the two paradigms are globally or completely incommensurable. In other words, there is little – if any – significant overlap between the two approaches in terms of their theories, experimental methodologies and problems of interest. The second form of systems biology holism represents a synthesis between the reductionism and organicism approaches, especially in terms of causation, in which bottom-up and top-down causes are integrated reciprocally (Grizzi & Chiriva-Internati, 2006). For example, as genes are expressed they modify their cellular environment, which in turn activates additional genes, which in turn further modify the cellular environment, and so on. In Kuhnian terms, this revolution is a minor one because the incommensurability is simply local or partial in nature. In other words, there is considerable – but not complete – overlap between the two paradigms. Therefore, these systems biologists utilize both upward and downward control and share, to some extent, theories, experimental methodologies and problems of interest. The appropriation of Kuhn’s philosophy of science to biology is problematic, as Bothwell points out, because Kuhn developed his notions of scientific revolution and of normal science


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using historical case studies from the physical sciences. And the systems biology case study is an excellent example of the difficulties involved in using Kuhn to understand the impact of systems biology on the future course of the biological sciences. However, apart from these problems, a Kuhnian analysis does disclose several options available for 21st-century biologists with regard to systems biology. The first option is in terms of Kuhnian normal science, in which systems biologists – who advocate a reductionistic approach – add yet another tool (e.g. computational analysis) to their toolbox for investigating and explaining complex biological phenomena. The next option is globally incommensurable organicism. The problem with this approach, unfortunately, is that there is little, if any, technology to support it. The final option is a locally incommensurable holism that integrates both reductionism and organicism, especially in terms of causal pathways. Currently, many systems biologists advocate this integrative option (Coffman, 2006). Although which of these options – if any – becomes the predominant approach for 21st-century biologists remains to be seen, systems biology does provide a feasible route for transforming 21st-century biological practice and knowledge. James A. Marcum One Bear Place #97273, Baylor University, Waco, TX 76798 USA (tel +1 254 710 3745; fax +1 254 710 3838; email [email protected])

References Bothwell JHF. 2006. The long past of systems biology. New Phytologist 170: 6–10. Coffman JA. 2006. Developmental ascendancy: from bottom-up to top-down control. Biological Theory 1: 165–178. Fujimura JH. 2005. Postgenomic futures: translations across the machinenature border in systems biology. New Genetics and Society 24: 195–225. Grizzi F, Chiriva-Internati M. 2006. Cancer: looking for simplicity and finding complexity. Cancer Cell International 6: 4–10. Kellenberger E. 2004. The evolution of molecular biology. EMBO Reports 5 546–549. Kitano H. 2002. Systems biology: a brief overview. Science 295: 1662–1664. Kuhn TS. 1996. The structure of scientific revolutions, 3rd edn. Chicago, IL, USA: University of Chicago Press. Marcum JA, Verschuuren GMN. 1986. Hemostatic regulation and Whitehead’s philosophy of organism. Acta Biotheoretica 35: 123–33. Mesarovic MD, Sreenath SN, Keene JD. 2004. Search for organizing principles: understanding in systems biology. Systems Biology 1: 19–27. Morange M. 1998. A history of molecular biology. Cambridge, MA, USA: Harvard University Press. Palsson, BO. 2006. Systems biology: properties of reconstructed networks. Cambridge, UK: Cambridge University Press. Sorger PK. 2005. A reductionist’s systems biology. Current Opinion in Cell Biology 17: 9–11. Trewavas A. 2006. A brief history of systems biology. The Plant Cell 18: 2420–2430.

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Key words: holism, Kuhn, normal science, organicism, paradigm shift, reductionism, systems biology.

Appendix A1 Kuhn (1996) reports that a particular scientific discipline begins as a preparadigmatic activity in which scientists of that discipline propose various paradigms to account for disparate data. Eventually the professional guild of that discipline agrees on one paradigm, which leads to a period of normal or paradigmatic science. During this period normal scientists ‘articulate’ the paradigm by solving puzzles that are sanctioned by the paradigm and normal science thereby advances in a cumulative manner. However, anomalous data eventually emerge over time – because no paradigm ever completely captures the complexity of natural phenomena – and lead to the proliferation of competing paradigms, a state Kuhn calls extraordinary science. As competition unfolds, the professional guild may remain loyal to or modify the old paradigm, or may adopt a completely new one. The latter move Kuhn calls a scientific revolution or paradigm shift. The move is revolutionary because the new paradigm is incommensurable with the old one. In other words there is no common ground or overlap between the two paradigms, in terms of their theories, experimental methodologies, or problems of interest. Kuhn gives the example of the notion for mass: Newton’s notion shares little (if any) commonality with Einstein’s notion. Once a revolution occurs, the guild is now guided by a new paradigm until another paradigm shift. A2 Kuhn (1996) identifies two dimensions of his notion of paradigm: exemplars and disciplinary matrix. Exemplars are the solved puzzles that act as heuristic guides for solving additional puzzles, whereas the disciplinary matrix consists of components such as symbolic generalizations, models and values. Importantly, it is within the disciplinary matrix of an embattled paradigm that philosophical adjustments are often made. A3 Traditionally the ontological refers to the nature of the material that exists within the world. For example, ontological analysis of natural phenomena (such as heredity) involves the investigation of the physical objects (such as genes). A4 Traditionally the epistemological refers to what is known or is justified in terms of a belief. For the natural sciences the epistemological often refers to theoretical knowledge, whereas for other disciplines it may refer to practical knowledge. A5 For example, even a protein’s structure, such as myoglobin, could not be deduced from structural rules based on its amino acid composition. In other words, a protein’s tertiary structure represents a whole that cannot be predicted solely on its primary or secondary structure (Morange, 1998). 10.1111/j. 2313 June 5 0 Meetings Meetings 568??? 67??? 2008 1469-8137.2008.02566.x



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Meetings Plant hydraulics: new discoveries in the pipeline Structure and Function of Plant Hydraulic Systems. A workshop at the Fullerton Arboretum, Fullerton, California, USA, March 2008 Increasing numbers of plant scientists are recognizing the importance of hydraulic design in determining plant function. Hydraulic design – which can be broadly defined as the functional properties of the plant vascular system – is a determinant not only of plant water balance but also of photosynthetic rates and ecological niche differentiation. Classic approaches (Tyree & Zimmermann, 2002) and newer concepts (Holbrook & Zwieniecki, 2005) are being applied to questions central to the evolution and ecology of plant species, ranging from organ to organism to ecosystem. A recent workshop held in southern California reflected diverse research programs but also highlighted a convergence of interest on key questions and promising approaches. Several breakout sessions focused on defining pressing questions of plant hydraulics and on addressing the critical need for standardization of practices for research on these topics.

‘The bigger question is whether repair and prevention of embolisms are distinct functions of phloem that are regulated independently from, or in addition to, the demands for sugar by cambial growth and storage.’

Xylem transport: resistance, redundancy and repair The xylem of plants has three basic functions: transport of water and minerals; mechanical support; and storage (Fig. 1). The transport function of xylem has been an area of much interest for at least 25 yr and continues to attract attention.


Much of the research presented at the conference focused on cavitation resistance or avoidance, and how both properties are affected by xylem integration. Redundancy of transport conduits and of vascular bundles can confer safety, but increased numbers of xylem conduits may involve increased construction cost and may increase the probability of mortality, depending on the connectivity among conduits and the risk of runaway embolism or of damage (Ewers et al., 2007; Sack et al., 2008). A modular hydraulic system with limited connectivity among xylem conduits and/or vascular bundles is thought to enable plants to minimize the spread of emboli between vessels. The degree of hydraulic modularity vs integration will probably affect hydraulic efficiency, resistance to hydraulic failure, embolism repair, resistance to xylem pathogens, wound repair, root-to-shoot signaling, and hydraulic redistribution within root systems. A low degree of hydraulic integration (also referred to as sectoriality) appears to be an important design feature among arid land plants, especially in desert shrubs (Schenk, 1999). An unanswered question is whether the phloem can add another layer of redundancy and act as a pathway of water transport when the xylem is completely embolized as a result of drought or freeze–thaw stress. Hydraulic redundancy and connectivity emerged as common themes in the presentations of Susana Espino (California State University (CSU), Fullerton, USA), Frank Ewers (California State Polytechnic University, Pomona, USA), Peter Kitin (Oregon State University, USA), Lawren Sack (University of California, Los Angeles (UCLA)) and Jochen Schenk (CSU, Fullerton, USA). Refilling of embolized conduits while the xylem is under considerable negative pressure has been shown in the stems and leaves of a few species (Bucci et al., 2003; Hacke & Sperry, 2003; Salleo et al., 2004). The process appears to involve living cells and to require energy. This mechanism may play an important role in the diurnal and seasonal dynamics of gas exchange and growth, and in drought responses. Yet, little is known about how widespread this process is, highlighting the need for more data from stems, leaves and roots of a wider variety of plant species. We also need to know whether refilling becomes more difficult in conduits that have previously suffered cavitation and that have a greater vulnerability to subsequent cavitation (Stiller & Sperry, 2002). Notably, refilling under tension may require phloem activity. If water that refills conduits comes from the phloem (Hölttä et al., 2006), then we need to determine the pathways by which this ‘Münch water’ moves (cambium? rays?) and to clarify how this process is regulated alongside other phloem processes, including phloem loading and unloading and the movement of cations.


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Fig. 1 Diagram of relationships between xylem structure and function with an emphasis on xylem transport safety and efficiency. Fibers and tracheids have a central role in structural support and may be important in resistance to cavitation (Jacobsen et al., 2005), parenchyma in storage and embolism repair (Salleo et al., 2004), and vessel and tracheids in efficient water transport and in providing redundancy to the transport system (Tyree & Zimmermann, 2002; Ewers et al., 2007). Münch water flowing between phloem and xylem has been hypothesized to play a role in embolism repair (Salleo et al., 2004; Hölttä et al., 2006), and may play a role in preventing embolisms, especially in species with abundant phloem parenchyma. Phloem may also act as a temporary pathway of water transport at the start of the growing season in completely embolized stems.

The bigger question is whether repair and prevention of embolisms are distinct functions of phloem that are regulated independently from, or in addition to, the demands for sugar by cambial growth and storage. Because cavitation is such a threat to sustained hydraulic transport, the question arose at the conference as to why plants would be filled with gas. Plants commonly have a large volume of gas spaces within their tissues, not only in intercellular spaces but also in fiber cells of vessel-bearing species (Utsumi et al., 1998). Having gas-filled fibers adjacent to vessels under tension would seem to create a great risk of hydraulic failure. It may be that air-seeding in some species is a design feature of xylem that staves off vessel collapse, thus preventing permanent conduit failure and possibly enabling embolism repair. Abundant air in xylem would also help to provide adequate oxygen supply to parenchyma cells (Sorz & Heitz, 2006), which may in turn be active in embolism repair.

Xylem biomechanics There are intense mechanical stresses on the xylem walls during transport of water under negative pressure. A number of mechanical traits have been correlated with cavitation resistance (e.g. vessel wall thickness to span ratio, fiber traits, xylem density, and modulus of elasticity and rupture of xylem), suggesting that transport stresses have been an important factor shaping xylem structure (Jacobsen et al., 2005). Cynthia Jones (University of Connecticut, USA) presented work on several xylem structural traits and how they vary among plant groups and across transcontinental aridity transects. The specific

functional relationships between xylem anatomy and transport stresses continue to be an active area of research. An important outstanding question is whether reversible bending of vessel walls plays an important role in air seeding. Several studies have shown that tracheids may collapse in gymnosperms (Cochard et al., 2004), and there is anatomical evidence that vessels may also collapse (Schweingruber et al., 2006). Thus, questions concerning transport-related biomechanics promise to be a fruitful avenue of future research.

Beyond stems: hydraulics of leaves, roots, flowers and fruits Species can show dramatic differences in the properties of the terminal components of the hydraulic pathways – roots and leaves – with considerable impacts on plant function. The hydraulic function of roots and leaves were the subject of presentations by Jung-Eun Lee (UC Berkeley, USA), Gretchen North (Occidental College, CA, USA), Brandon Pratt (CSU, Bakersfield, USA) and Lawren Sack (UCLA, USA). Leaves and roots show diurnal rhythms as well as dynamic responses to light, temperature and water availability, and these responses require clarification. Unanswered questions related to both leaf and root hydraulics scale from the cell to the ecosystem, with a continued need to determine contributions to overall plant hydraulic resistance. Like leaves, roots present three pathways for water flow (i.e. transmembrane, apoplastic and symplastic), and research is needed to understand how these pathways help determine overall plant hydraulic resistance. Furthermore, information is needed on the relative hydraulic



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contributions of young vs old and shallow vs deep roots, as well as the hydraulic redistribution between roots in dry and wet soil. The hydraulic properties of flowers and fruit are also of strong interest, with a particular goal of understanding how hydraulic pathways change during development and seasonally, as shown in presentations by Brendan Choat (UC Davis, USA) and Louis Santiago (UC Riverside, USA).

Ecology and phylogeny The last 20 yr have seen tremendous advances in understanding hydraulic adaptations to the environment. Examples of this progress were clearly illustrated in three presentations: Anna Jacobsen (CSU, Bakersfield, USA) detailed how three California shrub communities are divergent in their hydraulic traits; Brandon Pratt (CSU, Bakersfield, USA) discussed links between hydraulics and life history type in seedlings; and Yasuhiro Utsumi (Kyushu University, Japan) showed that the hydraulic properties of adult shoots can differ strongly from those of resprout shoots, with implications for how plants respond to disturbance. Adaptations in hydraulic design may differ considerably across plant lineages. For instance, the long-studied trade-off between xylem cavitation resistance and xylem transport efficiency varies in strength among lineages. Anna Jacobsen (CSU, Bakersfield, USA), Cynthia Jones (University of Connecticut, USA) and Lawren Sack (UCLA, USA) presented work on the divergences and convergences in hydraulic and anatomical traits in specific lineages. Comparative analyses continue to drive much hydraulics research, as shown in presentations on comparisons of urban trees (Heather McCarthy, UC Irvine, USA), conifers of different families ( Jarmila Pittermann, UC Berkeley, USA), plants differing in wood density (Calvin Threat (CSU, Fullerton, USA) and C. Jones) and species of contrasting life history (Brandon Pratt, CSU, Bakerfield, USA). Incorporating phylogenetic analyses will help to highlight the evolutionary underpinnings of the observed trends.

A critical need for common protocols and standardized methods Science moves most rapidly when the majority of researchers use similar methods and can easily and rapidly repeat and build upon each others’ discoveries. However, in hydraulics research, there appears to be much diversity in the methods and specific protocols used for given types of measurements. There is an especially pressing need for a common toolbox of methods for hydraulic measurements to increase comparability of data sets, and a need for dissemination of standardized protocols, especially as researchers increasingly wish to compile large data sets for a systems-level approach to hydraulics across communities and climate types. The meeting participants


resolved to develop a web repository of hydraulics protocols and will contact researchers around the world in creating this resource. With the increasing numbers of questions and approaches that bear on hydraulic design, common protocols and increased collaborations will lead to a stream of new discoveries from the pipeline.

Acknowledgements The organizers (J. Schenk, G. North and L. Sack) would like to thank all presenters and participants and the student volunteers. Financial support for this workshop from the Fullerton Arboretum and the Department of Biological Science at the California State University Fullerton is gratefully acknowledged. We acknowledge financial support from the National Science Foundation (IOS-0641765 to J. Schenk, IOB-0517740 to G. North and IOB-05753233 to L. Sack) and the Andrew W. Mellon Foundation to J. Schenk. We thank David Ackerly for comments that improved this report. R. Brandon Pratt1*, Anna L. Jacobsen1, Gretchen B. North2, Lawren Sack3 and H. Jochen Schenk4 1

Department of Biology, California State University, Bakersfield, CA 93311, USA; 2Department of Biology, Occidental College, Los Angeles, CA 90041, USA; 3 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA; 4Department of Biological Science California State University Fullerton, Fullerton, CA 92834, USA (*Author for correspondence: tel +1 661 654 2033; fax +1 661 654 6956; email [email protected])

References Bucci SJ, Scholz FG, Goldstein G, Meinzer FC, Sternberg LDSL. 2003. Dynamic changes in hydraulic conductivity in petioles of two savanna tree species: factors and mechanisms contributing to the refilling of embolized vessels. Plant, Cell & Environment 26: 1633–1654. Cochard H, Froux F, Mayr S, Coutand C. 2004. Xylem wall collapse in water-stressed pine needles. Plant Physiology 134: 401–408. Ewers FW, Ewers JM, Jacobsen AL, López-Portillo J. 2007. Vessel redundancy: modeling safety in numbers. International Association of Wood Anatomists Bulletin 28: 373 –388. Hacke UG, Sperry JS. 2003. Limits to xylem refilling under negative pressure in Laurus nobilis and Acer negundo. Plant, Cell & Environment 26: 303–311. Holbrook NM, Zwieniecki MA, eds. 2005. Vascular transport in plants. San Diego, CA, USA: Academic Press. Hölttä T, Vesala T, Perämäki M, Nikinmaa E. 2006. Refilling of embolised conduits as a consequence of ‘Münch water’ circulation. Functional Plant Biology 33: 949– 959. Jacobsen AL, Ewers FW, Pratt RB, Paddock WA, Davis SD. 2005. Do xylem fibers affect vessel cavitation resistance? Plant Physiology 139: 546 –556.


Meetings Sack L, Dietrich EM, Streeter CM, Sánchez-Gómez D, Holbrook NM. 2008. Leaf palmate venation and vascular redundancy confer tolerance of hydraulic disruption. Proceedings of the National Academy of Sciences, USA 105: 1567–1572. Salleo S, Lo Gullo MA, Trifilò P, Nardini A. 2004. New evidence for a role of vessel-associated cells and phloem in the rapid xylem refilling of cavitated stems of Laurus nobilis L. Plant, Cell & Environment 27: 1065–1076. Schenk HJ. 1999. Clonal splitting in desert shrubs. Plant Ecology 141: 41–52. Schweingruber FH, Börner A, Schulze E-D. 2006. Atlas of woody plant stems: evolution, structure, and environmental modifications. New York, NY, USA: Springer.

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Sorz J, Heitz P. 2006. Gas diffusion through wood: implications for oxygen supply. Trees – Structure and Function 20: 34– 41. Stiller V, Sperry JS. 2002. Cavitation fatigue and its reversal in sunflower (Helianthus annuus L.). Journal of Experimental Botany 53: 1155–1161. Tyree MT, Zimmermann MH. 2002. Xylem structure and the ascent of sap. New York, NY, USA: Springer-Verlag. Utsumi Y, Sano Y, Fujikawa S, Funada R, Ohtani J. 1998. Visualization of cavitated vessels in winter and refilled vessels in spring in diffuse-porous trees by cryo-scanning electron microscopy. Plant Physiology 117: 1463– 1471. Key words: biomechanics, cavitation, ecology, evolution, leaves, roots, stems, xylem refilling.

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