The flowering of systems approaches in plant and crop biology

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The flowering of systems approaches in plant and crop biology

The term ‘Systems Biology’ can mean many things to differentpeople (Aderem, 2005; Kirschner, 2005). However, it isgenerally agreed that one of the central aims of systemsbiology is to understand biological processes in terms of thedynamic interactions between the components that constitutethe system. Importantly, this aim is not unique to systems ofbiomolecules, but can apply at many different spatial andtemporal scales (Aderem, 2005; Trewavas, 2006). For example,the dynamic behaviour of individual cells depends on theoperation of genetic regulatory networks, while large-scalefeatures of crop systems (such as yield and sustainability)depend on interactions between the individual plants andenvironmental factors (Yin & Struik, 2007). The insight thatdrives systems biology is that a full understanding of the roleplayed by any one component in a biological process can beachieved only by considering it in its appropriate context inthe whole system. In this sense, systems biology goes beyonda strict reductionist paradigm, in which the properties ofsystem 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-levelprocesses and their underlying components, understandingthe mechanisms by which interactions between componentsgenerate the behaviours of the whole process relies on a numberof steps in common: first, it is necessary to determine theidentity and nature of the system components that play asignificant role in generating the behaviour under study (a‘parts list’ of the system); second, the network of interactionsbetween these components must be mapped out, and theirnatures determined; and, finally, it is necessary to use thisinformation to forge an understanding of how the dynamics

of the system emerge from the underlying interaction network.Taken together, these three steps provide an outline ‘systemsmethodology’ that can be applied to systems spanning the rangeof biological scales. While the techniques required to achieveeach stage may be different for different types of system, theirintegration into a coherent methodology provides a well-definedapproach to tackling the difficult question of how systems-level behaviour emerges from component interactions.

The recent upsurge in interest in systems biology stemsprimarily from technological advances in molecular biologythat have dramatically increased the speed with which it ispossible to complete the first two steps, namely collating amolecular ‘parts list’ and mapping out a network of interactions(Barabási & Oltvai, 2004). High-throughput transcript andprotein profiling, together with interaction screens, such aslarge-scale yeast-two-hybrid and ChIP-on-chip, now allow largeprotein and transcription interaction networks to be constructedwith 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 theproperties of the intact system under study. Network dia-grams provide only a static picture of potential interactions,while it is the dynamics of the network state that govern thebehaviour of the system. A key prerequisite for systems meth-odology is the ability to assay, over time, the state of as manynetwork components as possible. Given such data, statisticaland mathematical analysis can be used (Monk, 2008).

An example of how microarray data can be employed toinfer network components is provided by Menges et al. (thisissue of New Phytologist; pp. 643–662). By combining archivedtranscriptome data obtained under a range of different condi-tions using gene ontology information, the authors find newputative components of mitogen-activated protein (MAP)kinase signal transduction networks that provide a focus forfurther functional studies. Such information need not begenerated solely by high-throughput methodologies such astranscriptomics. Gay et al. (this issue of New Phytologist;pp. 663–674) describe the use of high-resolution reflectancespectra to monitor dynamic changes in the metabolism ofchlorophyll during leaf senescence. The authors present a strongargument for modelling this pathway using a systems approach,given the extensive knowledge available about the genetic andbiochemical basis of chlorophyll breakdown combined withthe ability to perturb this pathway and monitor its consequencesnoninvasively over time. Jansson & Thomas (this issue of NewPhytologist; pp. 575–579) propose that leaf senescence itself canbe considered a set of modelling routines, where environmentalinputs influence which modules are run, loop and interact,and ultimately determine the outputs.

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Whilst systems biology naturally lends itself to modelmolecular to cell to organ scale processes in organisms such asDrosophila and Arabidopsis, how applicable is this approach tohigher-scale processes (i.e. from population to ecosystem) orinvolving more complex organisms such as crops? Yin &Struik (this issue of New Phytologist; pp. 629–642) proposethat there is a compelling case for crop systems biology, whichbuilds on the rich history of modelling whole-crop physiologyand recent advances in crop functional genomics. The authorsargue that crop systems biology will play a crucial role in theunderstanding of complex crop phenotypes and subsequentlycrop 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 achievedwithout the use of genetic engineering and systems biologyapproaches. Nevertheless, this ‘grand challenge’ urgently awaitsthe identification of the genes that control the anatomical andbiochemical pathways that confer the C4 trait. Bowen et al.(this issue of New Phytologist; pp. 583–587) argue that simplyassembling a series of genes or genetic circuits to produce adesired trait (such as C4 rice) is unlikely to be successful withouta detailed quantitative characterization of the network gainedfrom systems biology. The authors argue that such informationcan be readily applied employing the new field of syntheticbiology and significantly improves the chances of success ofengineering new traits.

So, is systems biology really a paradigm shift beyond theidea that we need to consider context for components? Or isit largely a technology-driven acceleration of progress towardsan integrative understanding of the dynamical behaviourof complex biological systems? Marcum (this issue of NewPhytologist; pp. 587–589) discusses these and other relatedissues, employing Kuhnian philosophy. Irrespective of whetherone considers this a paradigm shift or revolution, systems biologyis set to move experimental approaches from a traditionalreductionist approach to more holistic treatment of complexbiology phenomena. Combined with advances in mathematicaland computational modelling of interaction networks (Cohen,2004; Albert, 2007; Monk, 2008), this will facilitate progresstowards an integrative understanding of the dynamical behaviourof complex biological systems.

Malcolm Bennett1,2 and Nick Monk1,3*

1Centre for Plant Integrative Biology, School ofBiosciences, 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])

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.255010.1111/j.1469-8137.2008.02550.xJune 200800567???568???CommentaryCommentary

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The chloroplast as a regulator of Ca2+ signalling

Many of the attributes associated with multicellular plantlife, including a sedentary habit, a decentralized organization,signalling in the absence of a nervous system and a plasticdevelopmental programme, can be attributed to the auto-trophism facilitated by the chloroplast. In this issue of NewPhytologist, Weinl et al. (pp. 675–686) identify a new rolefor the chloroplast in Ca2+ signalling, which suggests that theplastid 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 ofCAS prevents stomatal closure in response to elevated externalCa2+ ([Ca2+]ext) by abolishing oscillations of cytosolic free-Ca2+

([Ca2+]cyt). Weinl et al. find that cas mutations act upstream ofoscillations of [Ca2+]cyt because the stomata of cas mutants closein 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 poolsof Arabidopsis RNA were introduced into human kidney cellsloaded with the Ca2+ indicator, FURA2 (Han et al., 2003).External Ca2+ caused moderate increases of [Ca2+]cyt in kidneycells but the expression of Arabidopsis CAS in these cellsresulted in large [Ca2+]ext-induced increases of [Ca2+]cyt (Ca2+-induced Ca2+ increase (CICI) (Han et al., 2003)). InArabidopsis, CAS is expressed in the shoots and is found inguard cells (Han et al., 2003). CAS binds Ca2+ at the N-terminus with low affinity and high capacity (Han et al.,2003). CAS was first proposed to be a plasma membranereceptor that senses [Ca2+]ext (Han et al., 2003). The new dataof Weinl et al. suggest that CAS is not a plasma membraneprotein and is localized to the chloroplast. Weinl et al.identified an N-terminal chloroplast transit peptide and foundthat transient expression of CAS : GREEN FLUORESCENTPROTEIN in Nicotiana benthamiana protoplasts results in achloroplastic localization that is confirmed by subcellularfractionation experiments. These findings are consistent withthose of Nomura et al. (2008), who also recently reported achloroplastic localization for CAS, insensitivity of the stomataof cas mutants to [Ca2+]ext, reduced CICI in cas knockouts andincreased stomatal closure in CAS overexpressers.

The data of Weinl et al. and Nomura et al. (2008) suggestthat the chloroplast has an essential role in Ca2+ signalling, withCAS as an important intermediary. How does a Ca2+ receptorin the chloroplast function to regulate oscillations of [Ca2+]cytand CICI? One model is that CAS might sense changes in[Ca2+]cyt following Ca2+ influx across the plasma membraneand act in a feedback loop to regulate [Ca2+]cyt. However, theavailable data do not support this model. CAS probably senseschanges in stromal [Ca2+] ([Ca2+]stroma) because CAS is localizedto the thylakoid and the N-terminus Ca2+-binding domain isprobably exposed on the stromal side of the membrane(Nomura et al., 2008). Further evidence that CAS does not

sense [Ca2+]cyt comes from studies of abscisic acid (ABA) sig-nalling in the guard cell. CAS mutations are without effecton ABA-induced stomatal closure, even though ABA causesoscillations 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 couldpotentially affect the release or uptake of Ca2+ by the chloro-plast. It is possible that the plastids act either as Ca2+ storesthat release Ca2+ into the cytosol or as a Ca2+ buffer thatremoves Ca2+ from the cytosol following stimulation. Theplastids could have a similar role to mitochondria in Ca2+

signalling. In mammals, mitochondria act as Ca2+ buffers thattake up Ca2+ from the cytosol following release from theendoplasmic reticulum (ER) and the sarcoplasmic reticulum(SR). The tight coupling between ER/SR release and mito-chondrial uptake has profound effects on localized [Ca2+]cytdynamics, and mitochondria also contain a pool of releasableCa2+ (Hetherington & Brownlee, 2004). If plastids, like mito-chondria, act as Ca2+ buffers that also have a pool of releasableCa2+, this might explain how CAS sensing of the [Ca2+]stromacould feed back to affect oscillations of [Ca2+]cyt. There isevidence that plastids are capable of both Ca2+ uptake andrelease (Johnson et al., 2006), although the data from differentsystems conflict as to whether chloroplastic uptake of Ca2+ hasconsequences for [Ca2+]cyt (Miller & Sanders, 1987; Sai &Johnson, 2002; Johnson et al., 2006). Chloroplasts take upCa2+ 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 ofLotus japonicus and are predicted to encode plastid-localizedion channels of unknown selectivity (Imaizumi-Anraku et al.,2005). Similarly, the pea PPF1 protein localizes to the chlo-roplast, delays flowering when expressed in Arabidopsis andis capable of carrying Ca2+ currents (Wang et al., 2003).

In addition to plastid regulation of [Ca2+]cyt, there aredark-induced increases in [Ca2+]stroma that can persist with acircadian rhythm in constant dark (Sai & Johnson, 2002).Circadian oscillations of [Ca2+]stroma appear to be independentof [Ca2+]cyt because in constant dark there are usually nooscillations of [Ca2+]cyt ( Johnson et al., 1995). Sai & Johnson(2002) proposed that the thylakoid is a dark-dischargeableCa2+ store that releases into the stroma. The thylakoid issuggested to be filled with Ca2+ from the cytosol via thestroma as a result of the action of a Ca2+/H+ antiporter actingin the light (Ettinger et al., 1999). Lengthening the light periodappears to increase the amount of Ca2+ stored in the thylakoidbecause dark-induced discharge is increased with longer periodsof light (Sai & Johnson, 2002).

It is not known if CAS affects the daily dark-inducedincrease in [Ca2+]stroma but CAS antisense reduces the amplitudeof daily oscillations of [Ca2+]cyt in light/dark cycles (Tanget al., 2007). This appears to be related to the role of CAS inCICI because increases in [Ca2+]ext increase the amplitude ofdaily oscillations of [Ca2+]cyt (Tang et al., 2007). These findings

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led to a model being proposed in which the daily oscillationsof [Ca2+]cyt are a consequence of similar oscillations in[Ca2+]ext caused by rhythmic water fluxes in response to dailystomatal movements. In this model, CAS was proposed tosense the rhythms of [Ca2+]ext to drive daily oscillations of[Ca2+]cyt through an inositol(1,4,5)trisphosphate-mediatedpathway (Tang et al., 2007). However, recent data suggestthat this may not be the case. Circadian oscillations of[Ca2+]cyt are driven by cyclic ADP ribose and are insensitive toU73182, an inhibitor of inositol(1,4,5)trisphosphate production(Dodd et al., 2007). Furthermore, circadian [Ca2+]cyt oscillationsand stomatal movements are not functionally linked becausethe circadian rhythms of stomatal opening and [Ca2+]cyt runwith different periods in both the timing of cab1-1 andzeitlupe-1 circadian mutants (Dodd et al., 2004; Xu et al.,2007). The localization of CAS to the chloroplast, and theevidence that rhythmic changes in [Ca2+]ext caused by stomatalmovements are not likely to drive circadian [Ca2+]cyt oscilla-tions, suggests that the effects of CAS should be reconsideredas evidence for the chloroplast modulating daily [Ca2+]cytoscillations, 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 thesensing of [Ca2+]ext by stomata, timing of flowering, NOD factor-induced oscillations of [Ca2+] and daily oscillations of [Ca2+]cyt.Forming a model of how CAS affects [Ca2+]cyt is difficult asso little is known about Ca2+ fluxes associated with plastidsand because the biological action of CAS is unknown. Thefindings reported in this issue by Weinl et al., those of Nomuraet al. (2008) and the work of those in the Pei laboratory, who firstidentified CAS along with its role in regulating CICI, oscillationsof [Ca2+]cyt and Ca2+-induced stomatal closure (Han et al.,2003; Tang et al., 2007), suggest that a totally novel pathwaythat 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 protected])

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.

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.256710.1111/j. 1469-8137.2008.02567.xJune 200800567???568???CommentaryCommentary


Great leap forward? Transposable elements, small interfering RNA and adaptive Lamarckian evolution

The botanist and philosopher Lamarck famously proposedthat environmental challenges suffered in one generation could


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influence phenotypic outcomes in the next. At the turn of the19th century, the passing of life experiences to future generationsseemed part of the natural order, explaining perhaps theacclimation of new species imported from exotic locales andthe perceived increase in biological complexity over generations.Lamarckian principles influenced Darwin’s vision of naturalselection, but were ultimately overturned by this very vision.With the rediscovery of Mendel’s laws, Lamarckian mechanismsbecame less plausible, and with the advent of molecular genetics,the writing was on the wall. How could the environmentinfluence germ cells in such a way that genes were altered in adirected and heritable way? In this issue of New Phytologist,Hilbricht et al. (pp. 877–887) provide a potential exampleof environmental influence on evolution and inheritance,in the desiccation-tolerant ‘resurrection’ plant Craterostigmaplantagineum. They provide evidence that desiccation inducesa family of non-Long Terminal Repeat (LTR) retrotransposonsthat encode a small RNA which promotes the expression ofdehydration genes in transformed callus. They propose thattransposition, 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 mecha-

nisms – 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 fewdays after fertilization of the egg, long before adult cell types.As a result, any environmental influence on the adult mustmodify genetic material in cells that are already committed togermline fate. In long-lived flowering plants, however, germcells can differentiate hundreds of years after embryogenesis iscomplete. This is because the germline is set aside very late in

development, differentiating from inflorescence meristems thatin this respect resemble adult rather than germline stem celllineages. This makes plants uniquely sensitive to environmentaleffects (Walbot & Evans, 2003).

Even so, many of these effects are transient and are notcaptured in the germline. Examples include vernalization, inwhich adult plant cells experience cold during winter andtrigger flowering the following spring. This is accomplished bysilencing key regulatory genes through histone modification(Dennis & Peacock, 2007). Vernalization requires long exposureto the stimulus, and only dividing cells respond. It is thoughtthat RNA interference may mediate some of these cues.Importantly, vernalization is erased during meiosis so that thenext generation can respond to cold at the appropriate time.However, some epigenetic changes are heritable in plants: forexample, many transposable elements are also very sensitive totemperature, but silent transposons can be stably inheritedfrom generation to generation (Slotkin & Martienssen, 2007).

Epigenetic mechanisms allow alternative chromosomal (andeven nonchromosomal) states to be inherited from cell to celland from generation to generation. When these states areinfluenced by the environment, progeny adopt their parents’response without necessarily being subject to the same stimulus.While perhaps not the deterministic mechanism imagined byLamarck, such epigenetic mechanisms open up the possibilityof the environment directing evolution. An interesting exampleis provided by paramutation in maize: the R locus encodes agene family interrupted by transposable elements. Silencingof one of these genes occurs progressively during develop-ment, but is delayed at high temperatures. By the time germcells develop from the inflorescence meristem, few of themcontain silent genes, but those that remain silent are passed onto the next generation (Chandler et al., 2000). Interestingly,this temperature-sensitive phenomenon depends on RNAinterference (Chandler, 2007).

Craterostigma plantagineum is a desert succulent that canlose up to 96% of its water but still recover just hours afterrehydration (Fig. 1). This property is not shared by callus,which needs a supply of exogenous abscisic acid (ABA) torecover from dehydration. The authors isolated genes thatcould 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

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.


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in the absence of ABA. Survivors were examined to see whichgene was responsible for the control of desiccation tolerance.Surprisingly, the first such gene to be identified, CDT-1, didnot encode a functional protein. Worse, it was found in multiplecopies and terminated with a polyA tail, flanked by directrepeats. These are hallmarks of non-LTR retrotransposons, orshort interspersed elements (SINEs). One redeeming featurewas that CDT-1 was induced by ABA and dehydration innormal callus, supporting its role in desiccation tolerance.

How could a transposon influence desiccation tolerance?Deletions indicated that only half of the element, includingthe polyA tail, was required for high levels of transcript accu-mulation and for desiccation tolerance. A second gene detectedby T-DNA insertion, CDT-2, shared this region. Abundantshort interfering RNA was found on both strands and wasnarrowed down to a 21-nucleotide sequence reminiscent of amicro RNA or perhaps a trans-acting or tasiRNA. Protoplasttransfection was used to show that this small RNA alone wascapable of inducing dehydration genes, an important stepin desiccation tolerance, to the same extent as exogenousapplication of the hormone ABA.

The expression of transposons under environmental stressis well known: the resulting transposition is thought to increasechances of inheritance by the next generation, ensuring survivalof the transposon (Slotkin & Martienssen, 2007). This responseseems to have been co-opted during evolution, such thatCDT-1 transposons now encode a small RNA that is requiredfor desiccation tolerance and is induced by dehydration.However, a combination of the two stress response mechanisms– amplification of the transposon on the one hand, andtriggering stress tolerance on the other – presents an interestingcase for students of Lamarck: this is because, over generations,plants with an increased CDT copy number might be moredesiccation tolerant. Unlike other transposons, non-LTRretrotransposons are difficult to remove from the genome. Thisis because they undergo widespread transposition but cannotundergo excision like Class II elements, or recombinationbetween homologous LTRs like other Class I transposons.When co-opted in this way they may take their host on a journeyof no return (Dover, 2002). A great leap forward indeed.

Robert Martienssen

Cold Spring Harbor Laboratory, 1 Bungtown Road, ColdSpring Harbor, NY 11724, USA

(Author for correspondence: tel +1 516 367 8466;fax +1 516 367 8369; email [email protected])

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.

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.255910.1111/j. 1469-8137.2008.02559.xJune 200800567???568???CommentaryCommentary


Genetic underpinnings of postzygotic reproductive barriers among plants

The predominant causes of biological diversification –especially the formation of new species (speciation) – hold aspecial place in the imagination of evolutionary biologists.Much of the contemporary interest in speciation focuses onunderstanding the evolutionary origin and genetic basis ofbarriers 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 ofa transmission ratio distortion locus that causes both pollenand ovule sterility. In addition, with tester crosses, they showthat this locus could contribute to F1 semi-sterility betweenAsian and African rice species complexes. In doing so, theymake a significant contribution to the current understandingof the genetic underpinnings of loci that can contribute topostzygotic reproductive barriers among plant species.

‘ ... the origin of postzygotic isolation (i.e. hybrid

inviability and sterility) was initially considered

paradoxical for evolutionists, including Darwin ...’

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


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fertilization (prezygotic and postzygotic barriers, respectively).It is often argued that mechanisms that act before hybridi-zation are the most important, primarily because they exertthe greatest influence on restricting gene flow between lineagesby acting earlier in the life cycle (Rieseberg & Willis,2007). Nonetheless, the nature, strength, and genetic basis ofpostzygotic isolation have continued to attract the attention ofresearchers, 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 twocharacteristics of postzygotic reproductive isolation. First,the origin of postzygotic isolation (i.e. hybrid inviability andsterility) was initially considered paradoxical for evolutionists,including Darwin, because natural selection should neverfavor the fixation of traits that reduce offspring fitness. Thisparadox was resolved in the form of a model (commonlycalled the ‘Dobzhansky–Muller’ (DM) model, after two of itsoriginators; Fig. 1). Under the DM model, hybrid incom-patibility is the result of negative interlocus epistasis; that is,dysfunctional interactions between different loci that havediverged in isolation of each other (Fig. 1). The great advantageof the DM model is that it does not require divergingpopulations to go through a period of reduced fitness duringthe evolution of genes that cause hybrid inviability or sterility.New variants can be perfectly fit in the background uponwhich they arose, but are dysfunctional in a geneticbackground where they have never been tested by naturalselection.

The DM model therefore proposes that genetic interactionsare crucial to the evolution of postzygotic isolation, thoughit is silent on the specific evolutionary forces and geneticchanges involved in this process. Given this, the second, andperhaps the most influential, appeal of postzygotic isolation

is that these evolutionary forces and underlying genes arevirtually unknown, even in the most well-studied geneticsystems. Indeed, the molecular loci underlying hybrid sterilityor inviability have been cloned and functionally characterizedin only a handful of cases (Coyne & Orr, 2004; Orr et al.,2007); none of these has been in plants. Plant systems havebeen used to identify quantitative trait loci (QTL) associatedwith 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, themolecular genetic basis of these QTL is yet to be described.

Koide et al.’s paper goes some way to bridging this gapbetween chromosomal regions known to be associated withhybrid incompatibility and the molecular genetic loci thatunderlie them. In their paper, Koide et al. combine fine-mapping, cytology, and tester crosses to examine the basisand possible origins of a transmission ratio distortion (TRD)locus in rice. Classical studies of this locus suggested that thedistorting allele (S1) causes abortion of gametes carrying thehomologous nondistorting ( ) allele, when both are foundin heterozygotes (Sano, 1990, and references therein). Byinducing semi-sterility in F1 hybrids (via abortion of maleand female gametes carrying the allele), this transmissionratio distorter has the potential to contribute to postzygoticreproductive isolating barriers between rice species.

In their study, Koide et al. confirm that TRD at the S1locus induces preferential abortion of both male and femalegametes carrying . For TRD via males (mTRD), cytologysuggests that abortion is the result of arrest before the secondmitotic division in microsporogenesis. For TRD via females(fTRD), abortion is caused by a broader range of phenotypesinvolving structural or organizational defects in the formationof eggs or embryo sacs. Using segregation ratios of abortedgametes with a linked visible marker, and fine-scale mapping,they infer the TRD locus is composed of at least two com-ponents, each influencing either mTRD or fTRD. Usingfine-mapping, they narrow the mTRD to an approx. 40 kbregion that contains only eight ORFs, an experimentallytractable list of candidates for further functional analysis.

These data are in part exciting because they provide quitea detailed picture of the genetic components of a ‘selfish’gene: a locus that preferentially promotes its own transmissionto the fitness detriment of a carrier heterozygote (Hurst &Werren, 2001). This locus is composed of multiple com-ponents that influence male and female TRDs differently,suggesting a cluster of closely linked genes, as has been foundin other segregation distorter systems. Perhaps even moreinteresting, however, is the apparent mechanistic link between‘selfish’ transmission distortion, and the expression of hybridsterility between species. Based on the results of tester crosses,Koide et al. propose that alternative alleles are fixed in Asianand African rice species complexes, indicating that the actionof the distorter S1 allele found in the African rice complex

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).

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can contribute to F1 semi-sterility (gamete abortion) betweenthese groups.

A direct link between the evolution of postzygotic isolationand active transmission ratio distorters – such as male killers,meiotic drivers, sex-ratio distorters, centromeric drivers, andother selfish genetic elements – has been proposed theoretically(see Hurst & Werren, 2001; Coyne & Orr, 2004 for reviews).Nonetheless, there is currently little empirical evidence forthe predominance of active segregation distortion mecha-nisms as a cause of hybrid sterility or inviability. For example,while some interspecific transmission ratio distorters havebeen directly connected to the expression of hybrid sterilityphenotypes (Coyne & Orr, 2004), in other cases there isclearly no fitness decrement associated with segregationdistorters acting between species (Fishman & Willis, 2005).Other general patterns suggest that active segregation dis-tortion is unlikely to be a ubiquitous force shaping theevolution of postzygotic isolation. For example, centromeresdo not seem to be disproportionately associated with theexpression of hybrid problems in species crosses (Moyle,2007), although centromeres are genomic regions that standto benefit most from transmission distortion under somemodels (Henikoff et al., 2001).

While Koide et al.’s results are unlikely to resolve ques-tions of the ubiquity of selfish genes in evolving postzygoticreproductive isolation, they do provide interesting datasupporting a direct connection between genetic selfishnessand the expression of hybrid sterility in this case. Gameticlethality as a result of TRD has been associated with interspe-cific and intervarietal rice crosses in other studies (Sano, 1990,and references therein), suggesting additional loci mightbehave similarly in this plant group. Clearly more data,including the dissection and description of the moleculargenetic loci underlying hybrid incompatibilities in a widerrange of organisms, will be instrumental in resolving howcommon such processes are in generating postzygotic repro-ductive isolation. These data will be essential for drawing anystrong inferences about general patterns in the evolutionary forcesor underlying genes that contribute to reproductive barriers.

Finally, perhaps one of the most difficult goals in speciationresearch is determining whether loci that could contribute tocurrent reproductive isolation were directly involved in thespeciation process itself, rather than accumulating afterdiverging lineages were already well-isolated species. Genesthat fall into the latter class still provide valuable insight intothe range of possible mechanisms that can cause hybridincompatibility. Arguably, however, it is the genes involvedin the actual speciation event that are the most intriguingfor evolutionary biologists. Koide et al. are appropriatelycircumspect about the possible role of the S1/ locus in theactual lineage splitting of the progenitor of Asian and Africanrice species complexes. Evidence that the alternative alleles atthis locus are fixed in the two species complexes is consistentwith these alleles having diverged early in the split of these

two groups. Nonetheless, data that can more closely matchthe timing of the split of these species groups with the evo-lutionary origin of this locus (perhaps from molecular evo-lutionary analyses of the eventual underlying gene(s)) willbe necessary to resolve this question. Regardless, this studydemonstrates that plant systems, especially those with genetic,genomic, and functional tools, are very promising systemsfor further adding to our understanding of the genetic basisof postzygotic isolation, and the likely evolutionary forcesresponsible 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 200800??????LettersLetters

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Senescence: developmental program or timetable?

The concept of the ‘program’ is widely used by developmentalbiologists and generally everyone knows what it means.However, with the advent of Systems Biology there is aninflux into the biological sciences of researchers from otherdisciplines, such as computing, mathematics and engineering,in which ‘program’ is also a technical term. If Systems Biologyis to keep its promises, it is important to ensure thateveryone engaged in the analysis of programmed processesin living cells is talking the same language. Arising fromdiscussions in two recent conferences (Wingler, 2007;Thomas, 2008), this Letter takes a critical look at the notionof a program as conceived and studied by plant developmentalbiologists, focusing particularly on our area of interest, leafsenescence.

A program is a number of events that occur in a predeter-mined way, and developmental programs are believed tobehave, 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 ametabolic state. The plasticity of plant development, however,shows that developmental programs are not fixed but areinstead continuously modulated by external and internalfactors, to yield a plant body well adapted to its environment.

Developmental programs have often been studied byanalysing pathway mutants, but in recent years profilingmethodologies, such as DNA microarrays, have become thetechniques of choice for dissecting the sequence of eventsduring a developmental process (Schmid et al., 2005). Everyapproach has its inherent problems, and we will, in thiscontribution, argue that, at least when leaf senescence isconsidered, the concept of a developmental program raisesfundamental questions.

Senescence: pigment loss and differentiation without growth

Leaf senescence is postmitotic and essentially a process oftransdifferentiation in fully grown cells (Thomas et al., 2003).It occurs in, and uses the biochemical and cellular architectureof, mature cells and its main purpose is to degrade cellularcomponents and remobilize them in order to re-use themelsewhere. Leaf senescence is therefore very different from the

rapidly executed process of programmed cell death (PCD);paradoxically so because apoptosis, the common name forType I PCD, is derived from the Greek term for leaves fallingfrom a tree (Kerr et al., 1972). Senescence involves chlorophyllloss via metabolism. Pathological bleaching, occurring aftervirus infection, for example, is not the same as senescence. Infact, these processes could be viewed as conflicting processesthat are regulated by different sets of genes. Cell death has tobe prevented until all mobilizable nutrients have been rescued(Hörtensteiner, 2004; Ougham et al., 2008).

In some species, one way of distinguishing physiological andpathological yellowing is to demonstrate reversibility (Zavaleta-Mancera et al., 1999a,b), a characteristic of senescence thatfundamentally distinguishes it from nonphysiological bleaching.Reversibility is one of the aspects of senescence that does notfit with the concept of a program (Thomas et al., 2003).Failure to make the distinction between the two possible fatesof pigments (physiological and pathological) also contributesto confusion in the literature and a lack of consensus aboutwhat constitutes the core set of senescence processes.

How do we know if the process under study is trulysenescence? One way is to use a mutant with a lesion inphysiological chlorophyll degradation. If a particular treatmentresults in yellowing of wild-type but not of the staygreen, it islikely to have evoked true physiological senescence. If bothgenotypes lose the green colour, the senescence is pathological(Thomas & Matile, 1988; Ougham et al., 2008). Physiologicalsenescence, if not subject to suspension or reversal, willeventually be superseded by terminal cell death. Overlappingtimetables in species with a rapid life cycle – such as Arabi-dopsis – make it difficult to identify the definitive elementsin developmental programs, and encroachment of deathinto the senescence phase compromises the analytical separa-tion of different patterns of gene expression and metabolism.Longer-lived species, with more extended developmentalschedules and clearer temporal separation between phases,have advantages in this regard, even if they are experimentallyless convenient.

Mutation and pathological disturbance are exceptionalcircumstances; normally the photodynamic dangers inherentin chlorophyll degradation during senescence are controlled bybalancing catabolism with other senescence-related metabolicmechanisms that utilize or quench incoming light energy.For this reason, yellowing is more than a cosmetic index ofsenescence, it is a sensitive and convenient measure of theprogress 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 potentialmay exist before it is actually evoked. This is equivalent to anold concept in developmental biology, proposed in 1918 byKlebs: ripeness to flower (see Bopp, 1996). In the same way, aseasonally quiescent species has to develop a competence tobecome truly dormant (endodormant; Vegis, 1964). Thecommon feature of ripeness behaviours is that competence maybe induced by different developmental and environmentalinfluences from those that trigger the finally expressedsyndromes.

This imposes another level of complexity. Imagine that theenvironmental 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 thatdevelop competence and it will appear as if the factors thatinduce competence are primary inducers of senescence.

Regulation can operate at many levels, from the epigeneticunmasking of promoters and genes in chromatin, to post-translational protein modification or compartmentalization(Wingler, 2007). Early ideas about senescence were based onevidence that development of ripeness to senesce dependsprimarily on transcription, whereas the senescence trigger andsubsequent 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 receivingend of the amplifier effect in plant development. A smallperturbation early in development can have considerableconsequences for the subsequent expression of senescence.This is apparent in Arabidopsis, where most growth andflowering mutants also have disturbed leaf senescence (Elliset al., 2005; Riefler et al., 2006). This is part of the allometriccontrol of senescence and life-history, which has beendiscussed 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 inits characteristic way before it transdifferentiates into agerontoplast.

Arising from the early classical molecular biology approachesof differential cloning (Smart, 1994; Buchanan-Wollaston,1997) through to contemporary omics methods (Buchanan-Wollaston et al., 2003; Guo et al., 2004), knowledge of thevariety of gene classes associated with senescence has revealedthat the syndrome subsumes a wider range of cellular andphysiological processes than might have been expected.Collections of senescence-associated genes typically comprise anumber of transcription factors and other regulators – examples

include WRKY factors, leucine zipper proteins, SARK andSIRK receptor kinases, calmodulin-binding proteins, MYBs,zinc fingers, MADS boxes, chromatin architecture-controllingAT-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 thesenescence program as a rather loose assemblage of transcrip-tional, post-transcriptional, epigenetic and allometric modules,which is difficult to convert into a coherent mechanisticframework.

Timetable or program?

What is the difference between a timetable and a program? Atimetable is a record of events occurring in sequence, whereasa program requires the events to occur in a given order. Whilemutant studies may provide data about a program, profilingtechniques, such as DNA microarrays, record instead adevelopmental timetable. In order to obtain reproducibledata, plants are passed through the developmental stages underhighly controlled conditions, and consequently developmentfollows a certain trajectory. The search for senescence-associatedgenes, with differential expression, by using this approach(Lin & Wu, 2004; Guo et al., 2004; Buchanan-Wollastonet al., 2005) is motivated by a hope that some of these genesmay be important for senescence, or at least could be markersof certain stages of senescence.

If senescence is not much of a program, even findingmarker genes for senescence stages could be problematic. Anillustration of this is the results from transcript profiling inautumn 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 weatherconditions, and is therefore under photoperiodic control(Keskitalo et al., 2005). Further studies of a range of aspenecotypes (see Luquez et al., 2007) have shown that the onsetof senescence in the glasshouse, under natural photoperiod butotherwise controlled environmental conditions, is synchronizedwith free-growing ramets of the same clones, confirmingthat senescence in this system is triggered by the light environ-ment alone. We performed transcript profiling using DNAmicroarrays over the period of initiation of senescence usingleaf samples harvested from the same free-growing tree over4 yr (Keskitalo et al., 2005, Y. Fracheboud et al., unpublished).There are indeed limitations in this approach. Leaf-to-leafvariation within a single tree, and the fact that the arrays usedcovered only c. 40% of the genome, will certainly reduce theprecision of an analysis. Nevertheless, the data constitute asufficiently large and representative sample of the entiresenescence-associated transcriptome (Bhalerao et al., 2003) topermit conclusions to be drawn. Even if critical genes thatbecome induced and start the senescence program are absentfrom this analysis, a change in expression of a significantfraction of the arrayed genes would be expected if the term


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‘program’ is to be justified. The expectation was to find thatgene expression altered during this period and that a majorshift in gene expression should occur before, at, or after, theinitiation of senescence.

Indeed, major modulations occurred (downregulation ofphotosynthesis genes, for example) but, surprisingly, the shiftdid not coincide with a senescence stage, using extent ofchlorophyll loss as the measure of physiological state betweeninitiation and completion of senescence. Instead, the totalpattern of gene expression in 2004, analysed using principalcomponent analysis (PCA), was most similar to gene expressionat later time points in the other years. In fact, the samples from9 and 11 September 2004 had a transcriptome that were‘later’ than those of 18 September 2003 and 17 September2001 (Fig. 1, Y. Fracheboud et al., unpublished). Apparently,gene expression was governed by factors other than senescenceand although it is obvious that, for a tree in the field, manyother influences may modulate gene expression, the search forgenes or gene-expression patterns that correlated with theonset of senescence was in this case unsuccessful.

We believe that the explanation for this may be thattranscriptional patterns during leaf senescence merely represent

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

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

If senescence is not directly invoked by changes in geneexpression, what is the trigger? Changes in the leaf metaboliteprofile, perhaps related to sink–source relationships, may beimportant in this respect, especially bearing in mind the keyrole of leaf senescence in nutrient recycling (Diaz et al., 2005;Hikosaka, 2005; Ougham et al., 2005). If leaf senescencefirst evolved in annuals or in perennials in a climate that didnot undergo dramatic seasonal changes, its original rolewould have been to move mineral nutrients out of leavesthat did not contribute much to photosynthesis and intoleaves better positioned, or into other strong sinks likedeveloping seeds (Thomas et al., 2000; Thomas & Sadras,2001). In those deciduous trees that start senescing by thecalendar (Keskitalo et al., 2005), however, one must postulatethat photoreceptors could influence metabolism withouttranscriptional changes.

Senescence: programmed, but not a program

It is easy become confused about what is a program andwhat is programmable. Senescence is conditioned by geneticand environmental predispositions: an amplified outcomeof a complex array of proximal and distant inputs. Very fewof its constituent genetic, metabolic, cellular or physiologicalcomponents have, however, been proven to be indispensable.We believe that our current knowledge of leaf senescencedoes not qualify it to be called a developmental program,like an .exe file. Perhaps senescence can instead be programmedaccording to the timetable set by development or theenvironment; that is, it behaves less like a fixed suite ofpropagating actions set in motion by a triggering event andmore like a permissive operating system. Senescence may bebetter conceived of as a set of modelling routines where thenature of the inputs determine which modules are run, howthey loop and interact, and which outputs follow.

Alternatively, we might simply be too ignorant to see theprogram and the ‘Master Controller’. The search for thecontroller that makes leaves competent to senesce, and thosethat trigger senescence in competent leaves, will certainlycontinue. To what extent there may be a confusion betweenprograms and timetables when other plant developmentalprocesses are studied is hard for us to tell, but we believethat the understandable desirability of designing omics

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.


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experiments to minimize the type of season-to-seasonenvironmental variation represented by Fig. 1 may have theunintended consequence of making it difficult to distinguishbetween a program and a timetable.

Acknowledgements

Johanna Keskitalo and Andreas Sjödin are acknowledged forproviding Fig. 1. The work in the S. J. laboratory is funded bythe Swedish Research Council, the Swedish Research Councilfor the Environment, Agricultural Sciences and SpatialPlanning, and the Swedish Foundation for Strategic Research.H. T. is grateful to the Leverhulme Trust for the award of anEmeritus Fellowship.

Stefan Jansson1 and Howard Thomas2*

1Umeå Plant Science Centre, Department of PlantPhysiology, Umeå University, SE-901 87 Umeå, Sweden;2Institute of Biological Sciences, Aberystwyth University,

Ceredigion, SY23 3DA, UK(*Author for correspondence: tel +44 1970 628768;

fax +44 1970 622350; email [email protected])

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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.


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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 200800??????LettersLetters

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

Introduction

Each day passes with 854 million people hungry and, forthat reason, the United Nations Millennium Declarationcommitted the world’s nations to ‘eradicate extreme povertyand hunger’. Nonetheless, developed nations are bothreducing their investments in agricultural research andturning their remaining research investments away fromproductivity gains (Pardey et al., 2006). The elite ricecultivars, which dominate the food supplies of the millionsof 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) havebeen fully exploited (Dawe, 2007). During the comingcentury, climate change will probably result in more extremevariations in weather and may cause adverse shifts in theworld’s existing climatic patterns, further disadvantagingthe poor (Agarwal & Narain, 1991). Water scarcity willgrow; and the increasing demand for biofuels will result incompetition between grain for fuel and grain for food,resulting in price increases (Cassman & Liska, 2007). In theface of the above problems, an increase in rice yields of > 50%

will be required by 2050 to keep pace with population growthin Asia (Mitchell & Sheehy, 2006).

‘Modern’ systems biology is loosely associated with the useof genomic technologies to understand specific biologicalprocesses, although ecologists and physiologists have beenusing a systems approach to model crops for many years(Gutierrez et al., 2005). A weakness of genetic engineeringapproaches (bottom-up) to crop improvement is that changesat the molecular level can be dissipated when scaled upthrough biochemical and physiological levels to the responseof crops in the field (Sinclair et al., 2004). In this article,we address the following two issues:• can the top-down approaches of systems modellingidentify a broad solution to the problem of increasingyields?• 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 consideredin a systems approach to yield improvement. The phenotypeof a given genotype can vary markedly according to itsinteraction with the environment (Miflin, 2000). Suchplasticity in plants is probably associated with their ability tosucceed despite changes in weather, climate, competition forresources and soil types. In order to increase yields, plantsgrowing in communities have to convert more solar energyinto chemical energy or use the absorbed energy more efficientlyin the synthesis of biomass or grain. Even something assimple as the spacing between plants can markedly altertheir morphology and functionality. Crop communities arecrowded neighborhoods in which leaves and stems competefor light. Full light absorption by a crop canopy is set by theleaf area per unit ground area and its angular distribution;the angular distribution also has consequences for the diurnalpattern of light interception. That pattern determines themaximum amount of radiation absorbed per unit area of aleaf, the time of day when that peak absorption occurs andthe photochemical consequences of that pattern. At fulllight interception, the size of individual leaves is proportionalto the tiller or plant number per unit ground area, and specificleaf area is determined by competition for light. Leaf photo-synthesis and specific leaf area can be linearly related (Pearceet al., 1969).

Heat plays a role in the efficiency with which chemicalenergy can be accumulated; in part it determines the length ofthe growing season and the rate at which panicles develop.The same daily quantity of solar energy can be delivered to acrop in both temperate and tropical environments. This isbecause long days in temperate environments often have lessintense solar irradiance than in tropical environments, whichhave short days. However, the temperatures in those environments


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are usually very different. The interplay of temperature andirradiance results in growing seasons of different lengths intemperate and tropical environments, although the radiation-use efficiencies are the same. The radiation-use efficiency isthe slope of the relationship between cumulative above-ground biomass and cumulative photosynthetically activeradiation intercepted by the crop. Usually, irrigation ensuresthat water is not a limiting factor, but rainfall varies inintensity, duration and frequency in the growing season sothe availability of water is a complex problem. Fertilizers areapplied to ensure maximum yields, but the demand from thecrop varies throughout crop development as does the quantityof fertilizer available at any given instant. In rice, more thanhalf of the nitrogen (N) in the grain comes from the vegetativeparts 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 ahuge influence on yield.

Whenever the issue of yield increases is discussed, at somepoint the relative importance of source strength vs sinkcapacity arises. Work by Sheehy et al. (2001) showed that thesink capacity in rice was greatly in excess of that actually uti-lized, even at high yield, suggesting that the yield barrier wasthe consequence of source limitations. Experiments in whichincreased concentrations of CO2 were made available to riceresulted in increased yields (Yoshida, 1973; Ziska et al., 1997),suggesting that improvements in photosynthesis might have arole to play in increasing yield.

In well-managed crops, in which the fraction of grain perunit of biomass has been maximized, future yield improve-ments must be accompanied by increases in radiation-useefficiency. Mitchell et al. (1998) showed that C4 crops hadradiation-use efficiencies that were 50% greater than C3 cropsand that radiation-use efficiency was a function of photo-synthesis. This led to the suggestion that rice photosynthesiswould have to be converted from the C3 to the C4 syndrometo achieve yield increases of 50%. Sheehy et al. (2007b) wentsome way to confirming this conclusion when they reportedthat rice and maize crops grown without limitations of wateror 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 duringplastic responses to the environment. The attraction of thefull C4 system is not only the high productivity and yield, butalso the better use made of water and N. No non-C4 solutionoffers 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 elementsexisting within a boundary that is surrounded by anenvironment. A system could be a cell, a plant, a crop, an

ecosystem or a factory; quantitative descriptions of those systemsare called models. Consideration of most problems often leadsto a quantitative approach and then calculations to describewhat is happening or what might happen given differentcircumstances. The principal function of systems analysis is tounderstand and quantify the relationship between the inputsand outputs of materials. The analytical procedures appropriatefor systems analysis are often reductionist: they are designedto analyse the individual parts of the system. Once the partshave been described, quantitatively, an integrated descriptionof the system can be produced. In the absence of redundancy,a change to any part of the system affects the performance ofthe whole. The crop scientist looks at systems biology as themost coherent method of describing and understanding acomplex system. The crop modeller quickly recognizes manyinteracting 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 aspossible, whereas mechanistic models look for understandingand generality. Mechanistic models offer understanding onlyat one level in the hierarchy, being empirical at lower levels forthe sake of making progress at the higher level of prime interest.Mechanistic models of crops tend to be based on empiricaldescriptions of how organs work in relation to environmentaland management variables. A particular difficulty is describingmechanisms controlling assimilate partitioning between organs.

Biological systems depend on control mechanisms, althoughthey are often ill understood at a mechanistic level. If there aren interacting elements, there are n(n−1) possible interactionsor routes for information exchange. If information literallyflows in one direction from one element to another in a simplesystem containing four elements, twelve channels (actions andreactions) are required to carry the information necessary tocoordinate the activities of the elements within the system.Not surprisingly, at a cellular level this rule is likely to resultin a very large number of signaling pathways.

Building a mechanistic model of a biological system at anyscale is no easy feat, and a hypothesis, mathematics andsubstantial amounts of information are required (Thornley& France, 2007). Perhaps ‘modern’ systems biology is such ayoung branch of science that the measuring technologies haveoverwhelmed scientists’ ability to make quantitative modelsdescribing the way that cells work at a molecular level. In thiscontext it is important to note that crop modelling ofteninvolves building caricatures of systems with the most impor-tant and critical features included and the fine details ignored.Crop models are often quasi-mechanistic, using factors suchas radiation-use efficiency as if they were universal constantsthat summarize the physiological behaviour of a crop(Mitchell et al., 1998). Common to both crop systems and‘modern’ systems approaches is the possibility that emergentproperties will be found (i.e. aspects of the behaviour of thesystem that could not be predicted from knowledge of the


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individual components). Owing to the complexity inherentin both approaches, marrying them in a single coherent modelof crop yield remains a distant prospect. However, that doesnot preclude a working and profitable partnership betweenthe 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 todescribe the functioning of cells, tissues and the entire plantthrough molecular analysis and mathematical modelling ofphysical and chemical interactions between components ofliving plants and cells. It is an ambitious goal that will takeconsiderable time to realize. Nonetheless, Nelson et al. (2007)suggested that a systems approach is designed to be broad andunbiased, to permit the discovery of ‘emergent’ properties thatmight not be revealed in hypothesis-driven experimentationthat 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 ofrelationships that can then be tested. With the rice genomecompletely 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 thiscomputational approach to discovery. New techniques such aslaser microdissection of cell types and microarray profilingmay provide the comprehensive data needed for such asystems approach. Despite the current optimism, it is not yetpossible to know whether ‘modern’ systems biology will playa significant role in solving global food problems in the nextfew decades. Nonetheless, the work of Nelson et al. (2007) isan extremely exciting approach to understanding the controlof leaf development at the molecular level.

Identifying and manipulating genes responsible for important traits

The techniques of genetic engineering enable genes fromsexually incompatible species to be used to create transgeniccrops. This development has led to progress in hypothesis-driven plant improvements. Thus far, success has generallycome from inserting a single gene for increasing the toleranceto environmental pressures such as submersion, resistance topests and diseases, as well as tolerance to herbicides. However,attempts have been made to engineer novel multigenepathways to increase photosynthesis in leaves (Suzuki et al.,2006) and to recapture CO2 from photorespiration (Kebeishet al., 2007). Thus, whole suites of genes encoding desirabletraits governing yield can be introduced using the sametechnology. Of course, the traits have to be identified andunderstood. Biological N fixation is such a trait, but thegenetics of the symbiosis are not yet fully understood (Ladha& Reddy, 2000). To guarantee success in genetic engineering,

it is important to know how the trait functions at aphysiological level in individual plants and in the communitiesof plants that form crops. Then the genes responsible for thetraits have to be identified, which often involves the generationand screening of large numbers of mutants. Given the rate ofprogress in sequencing technologies (Service, 2006), candidateregions in the genome will be identified by sequencing thewild type and the mutant. Then, by using bioinformaticstools to compare those sequences, the specific genes of interestcan eventually be identified. To create a successful transgenicusing a multigene trait, an increased understanding of theregulatory networks that control the tissue-specific expressionof genes will probably be needed (Gowik & Westhoff, 2007).

How does a program of genetic engineering ensure that anintroduced trait is not obscured during the plastic responsesdisplayed when the plants are grown as a crop community? Itis not infrequent to read that the expression or overexpressionof a particular gene inserted in rice will increase yield by a largepercentage (Xiao et al., 1998; Ku et al., 1999). However, aconvincing quantitative assessment of its metabolic role in thecontext of a cell, an organ, a whole plant and a crop shouldaccompany 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 molecularbiologists, as is the concept of plasticity. Given the geneticcomplexity which underlies that plasticity, and that the ‘same’crop is grown in geographically different regions with differentclimates, weather conditions and on different soil types withdifferent histories of management, it is not surprising that infield experimentation precise repeatability, in the usual scientificsense, is the exception rather than the rule. As a result of thisimprecision and the absence of universally acceptable theoreticalmodels of crop growth, disagreements about what preciselydetermines both biomass and grain yield are commonplace.Consequently, even using systems approaches, it is no easytask to identify traits that will guarantee yield improvements.

Conclusions: a partnership

The conclusion that to make large increases in rice yieldwithout further damaging the environment meant introducingthe C4 pathway, was reached by taking a top-down view ofcrop performance and using simple crop systems modelingand crop experimentation. To make C4 rice a reality, thegenes controlling the anatomical and biochemical networksdistinguishing the C4 syndrome from the C3 syndrome mustbe discovered. This cannot be undertaken without the use ofgenetic engineering and ‘modern’ systems biology. Twoapproaches are being adopted: bioinformatics coupled to theidentification of genes using mutagenesis; and the emergentproperties 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 tosolve a problem using both the top-down and bottom-up


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approaches. Installing C4 in rice may be difficult, but that isdifferent from impossible and it is worth bearing in mindthe statement of Jones (2000) with respect to the sequencingof the human genome: ‘It reaffirmed one of the most mis-understood facts in science; that it is possible to solve mostproblems by throwing money at them’.

J. E. Sheehy1*, D. Gunawardana1, A. B. Ferrer1,F. Danila1, K. G. Tan1 and P. L. Mitchell2

1Crop and Environmental Sciences Division,International Rice Research Institute,

DAPO 7777, Metro Manila, Philippines2Department 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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March 200800??????LettersLetters

Cultivating plant synthetic biology from systems biology

Introduction

Systems biology has produced a wealth of information fromits detailed characterization of molecular components inliving 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 systemsbiology can be applied is in the emerging field of syntheticbiology.

Synthetic biology

The detailed quantitative characterizations of molecules andthe behavior of their networks, gained from systems biology,provide a solid foundation for synthetic biology. Syntheticbiology is a broad approach that uses tools such as rationaldesign of genes, genetic systems and living systems for a specificpurpose. For example, a synthetically designed oscillatingbiological clock and a bacterial ‘camera’ may seem like biologicalparlor tricks, but these pioneering experiments, along withothers, have proved that biological systems can be engineeredwith 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 biologyand general genetic engineering is the collection, applicationand modeling of quantitative data. For example, becausebiological gene circuits have naturally widely varying kineticcharacteristics, simply assembling a series of genes or geneticcircuits to produce a desired function is unlikely to be successful.A general approach in synthetic biology is to design geneticcircuits rationally, measure the steady-state and dynamicbehavior of components quantitatively (e.g. mRNA synthesisand stability; protein synthesis and stability), model theirbehavior, and then assemble the characterized componentsinto genetic circuits that exhibit predictable and reliablefunction (Andrianantoandro et al., 2006).

A key concept in synthetic biology is the application oflong-proven approaches from engineering. For example, oneof the easiest ways to produce a functional device in classicalengineering is to use standardized parts (for example, a com-puter can be assembled from parts such as a processor, harddrive, memory and monitor). Standardization ensures thatindividual working components can be used together orexchanged. To begin standardization in biology, a group ofMassachusetts Institute of Technology-based researchers haveassembled Biobricks, a collection of mostly bacterial regulatorycomponents and genes with some components from bacteri-ophage and yeast (http://www.biobricks.org). Biobrick com-ponents have been, and are being, used for annual student

competitions to genetically engineer machines (InternationalGenetically Engineered Machines, http://parts.mit.edu/igem07/index.php/Main_Page). In addition to standardization, decou-pling and abstraction are central to designing complex syntheticbiological systems from simple parts (Endy, 2005). Decou-pling might be called ‘divide and conquer’. It allows researchersto break down a complex problem into a smaller problem thatcan be addressed experimentally. For example, to understandthe complex process of photosynthetic electron transport,numerous studies have been carried out over many decades.Researchers first focused on questions that could be addressedexperimentally, such as the effective wavelength of light, whereasstudies today use detailed information about protein inter-actions in photosystems (Merchant & Sawaya, 2005).Collectively, such analyses could be called decoupling. A thirdconcept that synthetic biology employs is abstraction. Organ-izing components of a system according to their complexityallows researchers to focus on one hierarchical level of com-plexity, 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), andassemble them together to produce an automobile. By focus-ing on making the best pistons, a better engine can be builtand hence a better automobile. Likewise, plant syntheticengineers can optimize genes, genetic circuits and traits toproduce plants with novel functions.

In addition to integrating engineering concepts, as alreadydescribed, synthetic biology relies heavily on the mathematicalmodeling of components to provide insight into rationaldesign of genetic circuits (Drubin, 2007). The modeling ofselected aspects of genetic circuits through mathematicaldescriptions can be simple or complex, depending on thesystem components. Parameters that typically concern syn-thetic biologists can be grouped into two broad categories:those that define network topologies (how molecules controlthe concentration of other molecules), and those associated withkinetic interactions of molecules within devices (Kaznessis,2007). Examples of gene circuit parameters that are oftenmodeled include RNA polymerase binding, transcriptelongation, repressor binding, random cellular noise, andpolypeptide elongation (Elowitz & Leibler, 2000; Gardneret al., 2000; Li et al., 2007).

Regrettably, the information available for modeling geneexpression is often limited, impeding the ability to designgene circuits rationally. A powerful means around rationaldesign is offered by biological systems: the ability to evolve. Byusing directed evolution, synthetic biologists use the power ofliving systems to produce the best gene components based onselection of the desired behavior in vivo (Yokobayashi et al.,2002). In many cases, directed evolution has identified muta-tions that allow for subtle changes in the behavior of a gene orgene product that were not anticipated by modeling and rationaldesign (Pattanaik et al., 2006; Zhou et al., 2006). For instance,researchers used directed evolution to identify mutations


http://www.biobricks.org

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

Synthetic biology accomplishments

In theory, any novel gene regulatory networks can be assembledfrom simple genetic components to produce behavior/activitiesdesigned by the biologist. However, one of the first productionsof a functional gene regulatory network (a ‘toggle-switch’)from simple genetic components required quantitative mathe-matical modeling (Gardner et al., 2000). A genetic toggle-switch is a synthetic, bistable gene regulatory system. Multiplegenetic designs are possible for a genetic toggle-switch; forexample, Gardner et al. (2000) produced such a switch usingtwo repressible promoters arranged in a mutually inhibitorynetwork. A functional toggle-switch required the definition ofmultiple 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 greenfluorescent protein (GFP). The expression showed a sharpsigmoidal curve indicating bistability or the ability to existin two states (in this case with or without GFP production).A key difference between their work and traditional geneticengineering is that they manipulated the network architecturebased on theoretical parameters (Gardner et al., 2000). Genetictoggle-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 regulatesprocesses such as circadian rhythms and periodicity. An oscil-latory network showing that timing features can be designedsynthetically was produced using a series of transcriptionalrepressors that controlled expression of GFP (Elowitz &Leibler, 2000). To produce the designed periodicity, Elowitzand Leibler developed a mathematical model for transcrip-tional/translational rates and decay rates of both mRNA andrepressor proteins, and the GFP reporter. Understanding themechanisms behind this artificial oscillatory network couldreveal insight into the mechanisms of natural circadian clocksand the development of artificial clocks in living organisms.In plants, such an inducible timing mechanism could beengineered to coordinate flowering time in crop plants.

Remarkably, synthetic biologists have also been able todesign systems where programmed multicellular patternformation was produced (Basu et al., 2005). Natural patternformation typically involves cell−cell communication that isthen interpreted by an intracellular genetic network. Two setsof cells were engineered: ‘sender cells’ that produced a signal-ing molecule, acyl-homoserine lactone (AHL), and ‘receiver

cells’ that produced a fluorescent protein in response to user-defined ranges of the AHL. By varying the spatial arrangementof sender cells and receiver cells, distinct ring-like patterns ofGFP fluorescence were produced (Fig. 1). A key to accom-plishing the ring pattern was the design of distinct types ofreceiver cells or ‘band-detect’ strains that had different geneticnetworks to detect and respond to a high, medium or low AHLconcentration. Like previous work, the precise engineering ofthe genetic circuits used both theoretical and experimentalanalyses of various parameters (e.g. stability of proteins,strength of promoters). Mathematical models were able todefine both individual cell behavior and spatiotemporalmulticellular system behavior. In the receiver cells, threefluorescent proteins (GFP; red fluorescent protein, RFP;cyan fluorescent protein, CYP) were used as the output for thegenetic network. From an undifferentiated lawn of receiver cells,a bullseye pattern was produced from CYP, RFP and GFParound a central sender colony (Basu et al., 2005). Syntheticpattern formation may provide quantitative understandingof natural processes, and opens doors to the possibility ofengineering three-dimensional tissues (Basu et al., 2005). Whilepattern formation and its underlying genes have long beenstudied in plants, synthetic pattern formation could produceapplication-specific products. For example, by syntheticallyengineering the ability to control cell division planes, one couldenvision wood products that have dimensions for specificapplications (e.g. a true block of wood rather than a block cutfrom an elongated tree).

Another application of synthetic biology is producing‘biological machines’, or living organisms designed to performa specific task. One example is a bacterial camera that wasbuilt to produce a chemical image corresponding to an appliedlight pattern (Levskaya et al., 2005). This biological camerauses a photosensitve phytochrome from cyanobacteria fusedto the well characterized two-component signaling system,EnvZ−OmpR. The signaling system controls expression of LacZthat enzymatically produces a black compound in the pres-ence of β-gal-like substrate. This work showed that a simplebiological machine can be produced by interfacing different,naturally occurring molecular components.

A synthetic biosensor was produced in bacteria usingcomputationally designed receptors (Looger et al., 2003). Todelineate synthetic protein design, the Hellinga laboratoryfocused their efforts on the evolving zone, the region of lig-and-receptor contact, and used periplasmic binding proteinsthat exhibit a hinge-binding mechanism. The hinge-bindingmechanism allowed use of a fluorophore to screen computer-optimized synthetic receptors for functionality. Using theseapproaches, they demonstrated that a broad range of receptorscan 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 allbeen designed (Dwyer et al., 2003; Looger et al., 2003; Allertet al., 2004). These receptors were shown to be highly specific,


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and often detected nanomolar concentrations of their ligands.To demonstrate that these receptors function in vivo, theyused a histidine kinase-signaling system with synthetic feed-back to reduce background (Looger et al., 2003). In responseto nanomolar levels of a specific ligand, a conformationalchange is induced in the computer-designed receptor. Thereceptor−ligand complex then develops high affinity for theextracellular domain of transmembrane histidine kinase,activates the histidine kinase, and initiates signal transductionleading to the production of GFP. The system is extremelypowerful because the receptors can be computationally designedto most small molecules. Moreover, because the receptors arethe 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 suchas 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, whichmay lead to highly specific plant detectors or phytodetectors.A plant that could sense a substance of interest and provide asimple response could be useful in monitoring hazardoussubstances such as explosives, toxins/pollutants or pathogens.

Plants would serve as ideal monitors because they are ubiquitousin most places and require little maintenance. The rational designof a phytodetector would require a sensing mechanism and a trans-mission mechanism that ideally activates a detectable response.

If the previously described computer-designed receptorscould be made functional in plants, they would provide ameans for simple and inexpensive detection of ligands ofinterest. In bacteria, the computer-designed receptors arelocalized in periplasmic space. While plants do not have aperiplasmic space, the receptors themselves are small (e.g. theTNT receptor is 7 × 8 × 4 nm) and they could presumablydiffuse freely in the apoplastic space of primary plant cell walls(Somerville et al., 2004). Moreover, the signal transductionsystem used in bacteria, histidine kinase, is conserved betweenbacteria and plants (Ferreira & Kieber, 2005; Fig. 2). Usingsynthetic biology approaches, it may be possible to forwardengineer a system for eukaryotic synthetic signal transductionthat links input from the computer-designed receptor to aresponse or read-out system.

One response system that might be useful for plant sentinelshas already been developed. Our laboratory has described asynthetic ‘degreening circuit’ that allows chlorophyll levels tobe 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 levelsare typically under control of genetic and environmental input

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.


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(Hortensteiner, 2006). To remove chlorophyll from environ-mental and genetic input, the synthetic degreening circuit wasdesigned to stop synthesis and initiate breakdown simultaneously.Changes in chlorophyll fluorescence are detected in 2 h, withwhite plants resulting after 24–48 h (Fig. 3). The loss of chloro-phyll, resulting in white plants, is a response that can be recognizedby the general public. Because chlorophyll is the reporter molecule,the response can also be detected remotely with fluorescenceor hyperspectral imaging (Shaw et al., 2007). When the ligandis removed, the plants regreen, allowing the plants to reset, animportant aspect for any biologically based sensor. If a plantsensing system could be produced using the computer-designedreceptors, some type of signal transduction, and a read-outsuch as provided by the synthetic degreening circuit, it couldbe extremely powerful, providing an inexpensive and widespreadmeans to monitor for clean air, clean water and security.

Synthetic biology and biofuels

One area where synthetic biology could be particularly usefulfor plant biologists is biofuels. Synthetic biology could providepowerful tools for optimizing naturally occurring fuel productionpathways or developing novel pathways in plants. For example,

synthetic biology approaches could be used to optimize oil-producing pathways in oilseed crops or microalgae used forbiodiesel production. One challenge to biodiesel productionis the presence of polyunsaturated fatty acids in some

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.

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).


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vegetable oils (Chisti, 2007). Synthetic biology approachescould be useful in developing crops in which fatty acidproduction is limited to specific fatty acids optimal for biodieselproduction. Like biodiesel production, development of cellulosicethanol production could benefit from synthetic biology.For example, design of enzymes to efficiently break downcellulose into fermentable sugars or to remove lignin wouldsignificantly reduce the cost of processing. Cost could befurther reduced if production of designer enzymes could bespatially and/or temporally controlled within the plant(Himmel et al., 2007). Perhaps a network could be designedthat produces the enzymes for cellulose breakdown in the cellwalls, and is controlled by an artificial clock that initiatesenzyme production upon senescence.

Synthetic biology, the forward engineering of biologicalorganisms for a specific purpose, is in its infancy. The enor-mous quantity of data from systems biology provides fertileground to combine engineering concepts and mathematicalmodeling for useful purposes. Synthetic biology funda-mentally describes the engineering of living systems and offersenormous potential in terms of novel materials, human healthapplications and energy resources. From a basic researchperspective, designing synthetic systems will help us to betterunderstand natural gene regulation mechanisms that underlielife. To date, most work on synthetic biology has been accom-plished with microorganisms. However, we have describedseveral ways in which synthetic biology may also find fruit inthe green world of plants.

Tessa A. Bowen†, Jeffrey K. Zdunek†and June I. Medford*

Department of Biology, 1878 Campus Delivery, ColoradoState 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

ReferencesAndrianantoandro 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.

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.

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Does systems biology represent a Kuhnian paradigm shift?

In a recent letter, John Bothwell (2006) argues that systemsbiology does not represent a Kuhnian paradigm shift orrevolution, as some commentators claim (Palsson, 2006;Trewavas, 2006). Rather, according to Bothwell, systems biologyis best described in terms of Kuhnian normal science (AppendixA1). At first pass, this debate may seem inconsequential,especially to those who practice biology. However, closerinspection of the debate, particularly in terms of what Kuhnsays about paradigm shifts and their attendant revolutions,


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reveals that systems biology may represent a fundamental orparadigmatic shift in biology’s philosophical foundation –an important element of Kuhn’s disciplinary matrix(Kuhn, 1996) (Appendix A2). That shift for many – butnot all – systems biologists is from a reductionistic approachto a holistic one, for investigating and explaining biologicalphenomena. In response to Bothwell, I examine briefly thedebate over systems biology’s potential revolutionary natureand discuss its possible consequences for the practice of21st-century biology.

The emergence of systems biology over the past decade hasoccurred in response to the large amounts of data generatedfrom high-throughput genomics and proteomics (Palsson,2006). Besides the sheer amount of data, the complexity ofthe biological phenomena responsible for the data beckonsfor a different approach than the standard reductionisticapproach, in order to understand and explain these data.Reductionism involves the simplification of complex phe-nomena in terms of their components and comes in at leastthree forms (Marcum & Verschuuren, 1986). The first istheoretical reductionism, in which the terms of a complextheory are formulated in terms of a simpler one. For example,photosynthetic theoretical terms are articulated in chemicaland physical theoretical terms. The second form is ontologicalreductionism, in which complex phenomena are simplifiedwith respect to entities and forces (Appendix A3). Again,for example, the components comprising photosynthesis areidentified with respect to chemical entities and physical forces.Methodological reductionism is the third form, in whichexperimental protocols are utilized to investigate complexphenomena with respect to their isolated components.

As Bothwell (2006, pp. 7 and 8) acknowledges, systemsbiologists attempt to replace the reductionistic approach tocomplex biological phenomena with a holistic approach. Thisapproach involves an epistemological or a theoretical holism,in which higher-order structures are articulated in hierarchical– rather than in reductionistic – terms (Appendix A4). Hecites Hiroaki Kitano’s four components of systems biology,including system structure, system dynamics, system controlmethod and system design method, to illustrate the epistemo-logical or theoretical nature of the holistic approach (Kitano,2002). Moreover, holistic ontology is not concerned solelywith elemental components, such as molecules, that createcomplex biological phenomena but with the integrity of thosephenomena at a higher level of organization (Appendix A5).Finally, systems biology’s methodological holism pertainsto the integration of ‘-omics’ data through computationalanalysis, in an effort to identify ‘organizational’ laws orprinciples (Mesarovic et al., 2004).

The question then is whether this move by systems biolo-gists from a reductionistic to a holistic approach, to investigateand explain complex biological phenomena, represents aKuhnian paradigm shift or revolution – in which scientistssubstitute a newer incommensurable paradigm for an older

one – or whether it represents Kuhnian normal science – inwhich scientists are simply ‘mopping up’ after a revolution.Bothwell claims the latter, for two reasons. The first is thatalthough systems biologists do not reduce complex biologicalphenomena to chemistry, they do reduce them to engineering.In other words, systems and their emergent properties aresimply additional components that supplement other elementalcomponents comprising biological phenomena. Thus, thereis no replacement of an older paradigm with a newer incom-mensurable one because there is significant – if not complete– overlap between them. The second reason is that systemsbiologists did not invent the notion of functional analysis,which has a history dating back to Aristotle and is exemplifiedby contemporary physiology.

I think Bothwell is both correct and incorrect in his assess-ment of the revolutionary nature of systems biology. He is cor-rect in that some systems biologists cling to reductionism andreject holism as a guiding principle for their trade (Sorger,2005). In this sense these biologists are engaged in normalscience in that they are mopping up after the molecular biologyrevolution, by articulating its paradigm (Kellenberger, 2004).He is incorrect, on the other hand, in that other systemsbiologists utilize a holistic approach instead of a reductionisticapproach, as detailed above, and extend the standard functionalanalysis of physiology to include dynamical nonequilibriumanalysis.

Systems biology holism – as an antireductionistic approach– comes in two forms. The first is organicism, in whichcomplex phenomena are studied strictly at higher levels oforganization so that causation proceeds top-down and not, asfor reductionism, bottom-up (Fujimura, 2005). This anti-reductionistic approach of systems biologists represents a majorKuhnian revolution or paradigm shift because the two paradigmsare globally or completely incommensurable. In other words,there is little – if any – significant overlap between the twoapproaches in terms of their theories, experimental meth-odologies and problems of interest.

The second form of systems biology holism represents asynthesis between the reductionism and organicism approaches,especially in terms of causation, in which bottom-up andtop-down causes are integrated reciprocally (Grizzi &Chiriva-Internati, 2006). For example, as genes are expressedthey modify their cellular environment, which in turnactivates additional genes, which in turn further modifythe cellular environment, and so on. In Kuhnian terms,this revolution is a minor one because the incommensur-ability is simply local or partial in nature. In other words,there is considerable – but not complete – overlap between thetwo paradigms. Therefore, these systems biologists utilize bothupward and downward control and share, to some extent,theories, experimental methodologies and problems of interest.

The appropriation of Kuhn’s philosophy of science to biologyis problematic, as Bothwell points out, because Kuhn devel-oped his notions of scientific revolution and of normal science


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using historical case studies from the physical sciences. Andthe systems biology case study is an excellent example of thedifficulties involved in using Kuhn to understand the impactof systems biology on the future course of the biologicalsciences. However, apart from these problems, a Kuhniananalysis does disclose several options available for 21st-centurybiologists with regard to systems biology.

The first option is in terms of Kuhnian normal science, inwhich systems biologists – who advocate a reductionisticapproach – add yet another tool (e.g. computational analysis)to their toolbox for investigating and explaining complexbiological phenomena. The next option is globally incom-mensurable organicism. The problem with this approach,unfortunately, is that there is little, if any, technology tosupport it. The final option is a locally incommensurableholism that integrates both reductionism and organicism,especially in terms of causal pathways. Currently, manysystems biologists advocate this integrative option (Coffman,2006). Although which of these options – if any – becomesthe predominant approach for 21st-century biologists remainsto be seen, systems biology does provide a feasible routefor transforming 21st-century biological practice andknowledge.

James A. Marcum

One Bear Place #97273, Baylor University, Waco, TX 76798USA (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 machine-nature 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.

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

Appendix

A1 Kuhn (1996) reports that a particular scientific disciplinebegins as a preparadigmatic activity in which scientists of thatdiscipline propose various paradigms to account for disparatedata. Eventually the professional guild of that discipline agreeson one paradigm, which leads to a period of normal or par-adigmatic science. During this period normal scientists‘articulate’ the paradigm by solving puzzles that are sanctionedby the paradigm and normal science thereby advances ina cumulative manner. However, anomalous data eventuallyemerge over time – because no paradigm ever completelycaptures the complexity of natural phenomena – and lead tothe proliferation of competing paradigms, a state Kuhn callsextraordinary science. As competition unfolds, the professionalguild may remain loyal to or modify the old paradigm, or mayadopt a completely new one. The latter move Kuhn calls ascientific revolution or paradigm shift. The move is revo-lutionary because the new paradigm is incommensurable withthe old one. In other words there is no common ground oroverlap between the two paradigms, in terms of their theories,experimental methodologies, or problems of interest. Kuhngives the example of the notion for mass: Newton’s notionshares little (if any) commonality with Einstein’s notion. Oncea revolution occurs, the guild is now guided by a new paradigmuntil another paradigm shift.

A2 Kuhn (1996) identifies two dimensions of his notion ofparadigm: exemplars and disciplinary matrix. Exemplars arethe solved puzzles that act as heuristic guides for solvingadditional puzzles, whereas the disciplinary matrix consists ofcomponents such as symbolic generalizations, models andvalues. Importantly, it is within the disciplinary matrix of anembattled paradigm that philosophical adjustments are oftenmade.

A3 Traditionally the ontological refers to the nature of thematerial that exists within the world. For example, ontologicalanalysis of natural phenomena (such as heredity) involves theinvestigation of the physical objects (such as genes).

A4 Traditionally the epistemological refers to what is knownor is justified in terms of a belief. For the natural sciences theepistemological often refers to theoretical knowledge, whereasfor 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 aminoacid composition. In other words, a protein’s tertiary struc-ture represents a whole that cannot be predicted solely on itsprimary or secondary structure (Morange, 1998).231310.1111/j. 1469-8137.2008.02566.xJune 200800567???568???MeetingsMeetings


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Meetings

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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 theimportance of hydraulic design in determining plantfunction. Hydraulic design – which can be broadly defined asthe functional properties of the plant vascular system – is adeterminant not only of plant water balance but also ofphotosynthetic rates and ecological niche differentiation.Classic approaches (Tyree & Zimmermann, 2002) and newerconcepts (Holbrook & Zwieniecki, 2005) are being appliedto questions central to the evolution and ecology of plantspecies, ranging from organ to organism to ecosystem. Arecent workshop held in southern California reflected diverseresearch programs but also highlighted a convergence ofinterest on key questions and promising approaches. Severalbreakout sessions focused on defining pressing questions ofplant hydraulics and on addressing the critical need forstandardization 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 ofwater and minerals; mechanical support; and storage (Fig. 1).The transport function of xylem has been an area of muchinterest for at least 25 yr and continues to attract attention.

Much of the research presented at the conference focusedon cavitation resistance or avoidance, and how both propertiesare affected by xylem integration. Redundancy of transportconduits and of vascular bundles can confer safety, but increasednumbers of xylem conduits may involve increased constructioncost and may increase the probability of mortality, dependingon the connectivity among conduits and the risk of runawayembolism or of damage (Ewers et al., 2007; Sack et al., 2008).A modular hydraulic system with limited connectivity amongxylem conduits and/or vascular bundles is thought to enableplants to minimize the spread of emboli between vessels. Thedegree of hydraulic modularity vs integration will probablyaffect hydraulic efficiency, resistance to hydraulic failure,embolism repair, resistance to xylem pathogens, wound repair,root-to-shoot signaling, and hydraulic redistribution within rootsystems. A low degree of hydraulic integration (also referredto as sectoriality) appears to be an important design featureamong arid land plants, especially in desert shrubs (Schenk,1999). An unanswered question is whether the phloem canadd another layer of redundancy and act as a pathway of watertransport when the xylem is completely embolized as a resultof drought or freeze–thaw stress. Hydraulic redundancy andconnectivity emerged as common themes in the presentationsof Susana Espino (California State University (CSU),Fullerton, USA), Frank Ewers (California State PolytechnicUniversity, 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 underconsiderable negative pressure has been shown in the stemsand leaves of a few species (Bucci et al., 2003; Hacke & Sperry,2003; Salleo et al., 2004). The process appears to involve livingcells and to require energy. This mechanism may play animportant role in the diurnal and seasonal dynamics of gasexchange and growth, and in drought responses. Yet, little isknown about how widespread this process is, highlighting theneed for more data from stems, leaves and roots of a widervariety of plant species. We also need to know whether refillingbecomes more difficult in conduits that have previouslysuffered cavitation and that have a greater vulnerability to sub-sequent cavitation (Stiller & Sperry, 2002). Notably, refillingunder tension may require phloem activity. If water that refillsconduits comes from the phloem (Hölttä et al., 2006), thenwe need to determine the pathways by which this ‘Münchwater’ moves (cambium? rays?) and to clarify how this processis regulated alongside other phloem processes, includingphloem loading and unloading and the movement of cations.


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The bigger question is whether repair and prevention ofembolisms are distinct functions of phloem that are regulatedindependently from, or in addition to, the demands for sugarby cambial growth and storage.

Because cavitation is such a threat to sustained hydraulictransport, the question arose at the conference as to whyplants would be filled with gas. Plants commonly have a largevolume of gas spaces within their tissues, not only in intercellularspaces but also in fiber cells of vessel-bearing species (Utsumiet al., 1998). Having gas-filled fibers adjacent to vessels undertension would seem to create a great risk of hydraulic failure.It may be that air-seeding in some species is a design featureof xylem that staves off vessel collapse, thus preventing perma-nent conduit failure and possibly enabling embolism repair.Abundant air in xylem would also help to provide adequateoxygen 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 wallsduring transport of water under negative pressure. A numberof mechanical traits have been correlated with cavitationresistance (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 importantfactor shaping xylem structure (Jacobsen et al., 2005). CynthiaJones (University of Connecticut, USA) presented work onseveral xylem structural traits and how they vary among plantgroups and across transcontinental aridity transects. The specific

functional relationships between xylem anatomy and transportstresses continue to be an active area of research. An importantoutstanding question is whether reversible bending of vesselwalls plays an important role in air seeding. Several studieshave shown that tracheids may collapse in gymnosperms(Cochard et al., 2004), and there is anatomical evidence thatvessels may also collapse (Schweingruber et al., 2006). Thus,questions concerning transport-related biomechanics promiseto 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 theterminal components of the hydraulic pathways – roots andleaves – with considerable impacts on plant function. Thehydraulic function of roots and leaves were the subject ofpresentations by Jung-Eun Lee (UC Berkeley, USA), GretchenNorth (Occidental College, CA, USA), Brandon Pratt (CSU,Bakersfield, USA) and Lawren Sack (UCLA, USA). Leavesand roots show diurnal rhythms as well as dynamic responsesto light, temperature and water availability, and these responsesrequire clarification. Unanswered questions related to bothleaf and root hydraulics scale from the cell to the ecosystem,with a continued need to determine contributions to overallplant hydraulic resistance. Like leaves, roots present threepathways for water flow (i.e. transmembrane, apoplastic andsymplastic), and research is needed to understand how thesepathways help determine overall plant hydraulic resistance.Furthermore, information is needed on the relative hydraulic

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.


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contributions of young vs old and shallow vs deep roots, aswell as the hydraulic redistribution between roots in dry andwet soil.

The hydraulic properties of flowers and fruit are also ofstrong interest, with a particular goal of understanding howhydraulic 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 understandinghydraulic adaptations to the environment. Examples of thisprogress were clearly illustrated in three presentations: AnnaJacobsen (CSU, Bakersfield, USA) detailed how three Californiashrub communities are divergent in their hydraulic traits;Brandon Pratt (CSU, Bakersfield, USA) discussed links betweenhydraulics and life history type in seedlings; and YasuhiroUtsumi (Kyushu University, Japan) showed that the hydraulicproperties of adult shoots can differ strongly from those ofresprout shoots, with implications for how plants respond todisturbance.

Adaptations in hydraulic design may differ considerablyacross plant lineages. For instance, the long-studied trade-offbetween xylem cavitation resistance and xylem transportefficiency varies in strength among lineages. Anna Jacobsen(CSU, Bakersfield, USA), Cynthia Jones (University of Con-necticut, USA) and Lawren Sack (UCLA, USA) presentedwork on the divergences and convergences in hydraulic andanatomical traits in specific lineages. Comparative analysescontinue to drive much hydraulics research, as shown inpresentations on comparisons of urban trees (HeatherMcCarthy, UC Irvine, USA), conifers of different families( Jarmila Pittermann, UC Berkeley, USA), plants differing inwood density (Calvin Threat (CSU, Fullerton, USA) and C.Jones) and species of contrasting life history (Brandon Pratt,CSU, Bakerfield, USA). Incorporating phylogenetic analyseswill help to highlight the evolutionary underpinnings of theobserved trends.

A critical need for common protocols and standardized methods

Science moves most rapidly when the majority of researchersuse similar methods and can easily and rapidly repeat andbuild upon each others’ discoveries. However, in hydraulicsresearch, there appears to be much diversity in the methodsand specific protocols used for given types of measurements.There is an especially pressing need for a common toolbox ofmethods for hydraulic measurements to increase comparabilityof data sets, and a need for dissemination of standardizedprotocols, especially as researchers increasingly wish to compilelarge data sets for a systems-level approach to hydraulics acrosscommunities and climate types. The meeting participants

resolved to develop a web repository of hydraulics protocolsand will contact researchers around the world in creatingthis resource. With the increasing numbers of questions andapproaches that bear on hydraulic design, common protocolsand increased collaborations will lead to a stream of newdiscoveries from the pipeline.

Acknowledgements

The organizers (J. Schenk, G. North and L. Sack) wouldlike to thank all presenters and participants and the studentvolunteers. Financial support for this workshop from theFullerton Arboretum and the Department of BiologicalScience at the California State University Fullerton isgratefully acknowledged. We acknowledge financial supportfrom the National Science Foundation (IOS-0641765 toJ. Schenk, IOB-0517740 to G. North and IOB-05753233to L. Sack) and the Andrew W. Mellon Foundation to J. Schenk.We thank David Ackerly for comments that improved thisreport.

R. Brandon Pratt1*, Anna L. Jacobsen1, Gretchen B.North2, Lawren Sack3 and H. Jochen Schenk4

1Department of Biology, California State University,Bakersfield, CA 93311, USA; 2Department of Biology,

Occidental College, Los Angeles, CA 90041, USA;3Department of Ecology and Evolutionary Biology,

University of California, Los Angeles, Los Angeles, CA90095, 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.


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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.

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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Documents

The flowering of systems approaches in plant and crop biology