75 Integrating Membrane Transport with Male Gametophyte ... - TAIR

203 Control of Leaf Vascular Patterning by Polar Auxin Transport
Enrico Scarpella 1 , Danielle Marcos 2 , Jiri Friml 3 , Thomas Berleth 2
1
Department of Biological Sciences, University of Alberta, CW-405 Biological Sciences Building, Edmonton AB,
T6G 2E9 Canada, 2 Department of Botany, University of Toronto, 25 Willcocks Street, Toronto ON, M5S 3B2
Canada, 3 Center for Plant Molecular Biology, Tubingen University, D-72076 Tubingen, Germany
The formation of the leaf vascular pattern has fascinated biologists for centuries. In the early leaf primordium, complex
networks of procambial cells emerge from homogeneous subepidermal tissue. The molecular nature of the underlying
positional information is unknown, but various lines of evidence implicate gradually restricted transport routes of the
plant hormone auxin in defining sites of procambium formation. Here we show that a crucial member of the Arabidopsis
AtPIN family of auxin efflux associated proteins, AtPIN1, is expressed prior to preprocambial and procambial cell fate
markers in domains that become restricted towards sites of procambium formation. Subcellular PIN1 polarity indicates
that auxin is directed to distinct convergence points in the epidermis, from where it defines the positions of major veins.
Integrated polarities in all emerging veins indicate auxin drainage towards pre-existing veins, but veins display divergent
polarities as they become connected at both ends. Auxin application and transport inhibition reveals that convergence
point positioning and PIN1 expression domain dynamics are self-organizing, auxin-transport dependent processes. We
derive a model for self-regulated, reiterative patterning of all vein orders and postulate at its onset a common epidermal
auxin-focusing mechanism for major-vein positioning and phyllotactic patterning.
204 Automated phenotyping of Arabidopsis root and shoot structures responding to light and
gravity stimulation
Nathan Miller 2, 3 , Edgar Spalding 2
2
Department of Botany, University of Wisconsin, 3 Doctorate program in Biomedical Engineering
Technologies for finding and quantifying phenotypes have not kept pace with those for producing mutants and other
genetic resources. In an effort to correct this imbalance, we have been developing software and analytical methods that
will support a high-throughput, automated platform for detailed analysis of plant morphologies and developmental
processes. Our approach begins with high-resolution electronic images of plant structures undergoing developmental
responses to changes in an environmental variable. One example with which we have been successful is the etiolated
seedling responding to first light. Another is the seedling root responding to gravistimulation. Images taken every two
to ten minutes are processed using custom computer algorithms to isolate the midline of the shoot or root at each time
point because the midline contains the key invariant shape information. From a developmental series of midlines, length,
axial curvature distribution, and rates of change in these parameters are computed. These shape parameters are sufficient
to describe the growth and development of form of these structures. The spatiotemporal resolution of the method is such
that phenotypic differences that escape detection by eye can be rigorously quantified. Examples of how the technology
has aided studies of specific photomorphogenic mutants responding to light and auxin transport mutants responding to
gravity will be presented, as will be our progress toward making a phenotype-screening platform. Methods from the field
of statistical learning are being employed to identify and categorize individuals that display non-wild-type responses. A
recent test shows that our automatic method correctly classifies a mutant that displays a very subtle curvature phenotype.
As more data are collected, the discerning power, the ability to tell mutant from wild type, will increase. A near-term goal
is to screen segregating populations of T-DNA tagged knockout mutants to identify those with subtle phenotypes without
first having to isolate homozygous individuals. The ultimate goal is to integrate ‘morpholome’ data with expression array
and other ‘omic’ level data to create a full systems level view of Arabidopsis development.