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Plant biology

Plumbing the pattern of roots

Nature volume 449, pages 991992 (25 October 2007) | Download Citation

The results of a powerful combination of computer modelling and experimental tests can account for the establishment of gradients of the plant molecule auxin and for major patterning elements in the plant root.

Morphogens are molecules that govern pattern formation in organisms. They are perhaps best known through the textbook models of morphogen action that can generate a pattern as simple as the tripartite French flag or as complex as a segmented fruitfly embryo1. In real flies, the gene-transcription factor Bicoid acts as a morphogen. It forms a concentration gradient across the developing embryo through diffusion from a localized site of synthesis, then instructs distinct programmes of development as a function of its concentration. On pages 1008 and 1053 of this issue, Grieneisen et al.2 and Galinha et al.3 describe how they have applied an ensemble of molecular and modelling approaches to explore a different mode of gradient formation in plant roots. It relies on the directional transport of auxin, more familiar for its action as a hormone. This directional transport may thus enable auxin to act as a morphogen.

A wealth of physiological studies has highlighted auxin's concentration-dependent effects on the division, expansion and functional fate of cells, as well as its mobility within the plant4,5,6. In the root, there is a concentration maximum that coincides with the quiescent centre (QC), a small group of cells that sits just under the root cap and that can be thought of as an organizer for the surrounding array of initial dividing cells and their more differentiated products. This organization is sensitive to chemical treatments that perturb endogenous auxin levels, as well as to mutations in genes that are thought to execute specific sets of auxin-dependent responses7.

Genes that mediate the directional movement of auxin also have a major influence, with genes encoding the PIN family of proteins having an especially prominent role. These proteins facilitate the movement of negatively ionized forms of auxin across the cell membrane, and hence promote its efflux from the cell. Once in the more acidic environment of the cell wall, auxin becomes protonated, and in an uncharged form can move across the membrane into adjacent cells via passive diffusion or auxin influx transporters8. The highly asymmetric distribution of PIN proteins in plant cells has served as the basis for 'inverted fountain' models of auxin flow, which are in rough agreement with experimentally observed fluxes9(Fig. 1). In this model, basally positioned PIN proteins in cells of the central vascular elements mediate a downward flow of auxin, whereas apical PIN localization in more superficial epidermal and cortical layers promotes an upward movement. Finally, through laterally positioned PINs in cortical and sub-epidermal layers, there is an inward reflux, or reverse flow, of auxin into the downward flows in the vascular elements.

Figure 1: The PIN-dependent formation of an auxin gradient, and patterning of plant roots.
Figure 1

a, PIN proteins control auxin efflux from cells, and they are asymmetrically localized (fluorescence in images on left) in cells of different tissues in the root depending on how central or peripheral the tissues are. b, PIN asymmetry results in polar flows of auxin and the formation of auxin gradients; higher relative levels of auxin are indicated by darker background shading. c, The auxin gradient acts in some way to maintain directly proportional levels of transcription factors of the PLETHORA group of proteins, which act to establish and maintain distinct regions of the root. The highest levels of auxin determine the quiescent centre (QC), which is characterized by pluripotent cells that divide far less frequently than surrounding cells. Lower levels determine the meristematic zone (MZ), where cells divide more frequently. Still lower levels determine the elongation zone (EZ), where the elongation and terminal differentiation of cells occurs. (Figure adapted from Figs 1 and 2 of ref. 2, in which these events are modelled.)

To explore just how well such a scheme might work and how it would affect auxin concentrations, Grieneisen et al.2 developed a simple model that predicts auxin distributions as an outcome of directional auxin flows coded at a cellular and subcellular level. Not surprisingly, like a crush of train commuters converging on the platform exit, an auxin maximum is predicted to occur where lateral and downward flows meet at the QC, before diverging to begin their upward journey. The real power of the model, however, lies with its demonstration of how robust this efflux-driven maximum can be. It can withstand a range of values for cell permeability and diffusion; floods of externally supplied auxins; and even cutting off the main source of auxin by removing the shoot.

By contrast, changes that affect the lateral movement of auxin from cell to cell have major effects. If the lateral inward movement of auxin is reversed, the QC maximum vanishes as auxin is directed to the upward flows of the superficial layers. If, instead, the inward reflux of auxin is increased, the slope of the QC-directed auxin gradient increases, and the total auxin content of the root increases. Instead of causing indigestion, reflux enables the root to behave like a capacitor, able to take up and maintain a graded charge of auxin without exogenous inputs.

Does this computerized auxin avatar have a real-life counterpart and, if so, what is it like? Grieneinsen et al.2 use fluorescence-imaging methods on roots under a variety of conditions to show that auxin distributions correlate well with those predicted by cellular distributions of PIN proteins. Recognizing the morphogenic potential of a steep auxin gradient, the authors go on to link cellular behaviours observed in the root — including division, differentiation and expansion — to auxin concentration thresholds.

It is unclear exactly how an auxin gradient determines a range of distinct cellular behaviours. But the companion paper by Galinha et al.3 highlights a role for transcription factors of the PLETHORA (PLT) group of proteins. In the model plant Arabidopsis, four genes that encode these proteins act to establish and maintain root pattern, and are expressed at both the messenger RNA and protein level in a gradient that closely parallels that of auxin. Based on changes in cellular behaviours that follow genetic perturbation of PLT levels, the authors propose that distinct cellular behaviours are evoked by the different concentration thresholds of PLT proteins, with PLT levels somehow being coupled to the PIN-mediated auxin gradient.

Many questions remain. Although auxin and PIN activities seem to reinforce each other, how are the geometrically precise PIN distributions across the root initially established? At a cellular level, asymmetric PIN localization can be understood in terms of polarized patterns of vesicle trafficking10, but how are these patterns regulated? It is unlikely that they simply reflect intracellular auxin gradients, because the polarity of PINs can be dramatically reversed during early embryo formation11. Also unclear is whether, during these early stages, auxin gradients might depend more on other classes of transporter, or on mechanisms of auxin synthesis and turnover12. With respect to PLT levels providing a graded readout of auxin levels, how are these two coupled? The transcriptional induction of PLTs depends on derepression mediated through a particular class of auxin receptor, the F-box type of receptor, but it is unclear how direct this link is or whether other classes of receptor are also involved. Finally, what are the targets of PLT regulation, and how would their regulation by graded PLT levels determine distinct cellular behaviours?

Taken together, these two papers2,3 provide a fresh look at patterning, reinforcing the potential role for auxin as a morphogen and providing a robust transport-dependent model for its graded distribution. They also illustrate the value of combining biologically grounded modelling with molecular analyses — an approach that is proving increasingly powerful for explaining biological complexity.

References

  1. 1.

    J. Theor. Biol. 25, 1–47 (1969).

  2. 2.

    , , , & Nature 449, 1008–1013 (2007).

  3. 3.

    et al. Nature 449, 1053–1057 (2007).

  4. 4.

    Science 282, 2201–2203 (1998).

  5. 5.

    & Symp. Soc. Exp. Biol. 54, 118–130 (1957).

  6. 6.

    & Curr. Opin. Genet. Dev. 17, 337–343 (2007).

  7. 7.

    et al. Cell 99, 463–472 (1999).

  8. 8.

    , & Curr. Opin. Plant Biol. 8, 494–500 (2005).

  9. 9.

    et al. Nature 433, 39–44 (2005).

  10. 10.

    et al. Cell 112, 219–230 (2003).

  11. 11.

    et al. Science 306, 862–865 (2004).

  12. 12.

    & Curr. Opin. Plant Biol. 9, 448–453 (2006).

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  1. Bruce Veit is at AgResearch Grasslands, Tennent Drive, Private Bag 11008, Palmerston North, New Zealand. bruce.veit@agresearch.co.nz

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