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

87 High-density Arabidopsis Protein Arrays – a First Step Towards the Development of the
Arabidopsis Interactome
Sorina Popescu, George Popescu, Shawn Bachan, Zimei Zhang, Michael Snyder, S. P. Dinesh-Kumar
Department of Molecular, Cellular and Developmental Biology, 266 Whitney Avenue, New Haven, CT 06520-
8103
Much emphasis has been placed on the analysis of eukaryotic genomes at the level of nucleic acids. However, these
studies alone are not sufficient to predict the structure, function, and activity of the proteins. Proteomics, the global
analysis of proteins, is emerging as an important area of research in the post-genome era. As with the case of DNA chips,
the development of a matrix with bound purified protein in addressable grids would greatly facilitate the analysis of large
numbers of protein samples. We are generating a collection of high quality expression clones of Arabidopsis ORFs as
tandem affinity purification (TAP) tag fusions. Using these clones, we optimized a transient plant protein expression
system to produce and purify Arabidopsis proteins in a high-throughput manner. The first high-density Arabidopsis
protein arrays containing 1152 unique recombinant protein preparations produced and purified from an homologous
system, were used for the identification of binding partners for the Ca 2+ sensor protein, calmodulin.
Here we present a comparative protein interaction analysis of seven Arabidopsis calmodulin isoforms and calmodulinlike
proteins and their protein array targets. Our screen identified a complex interaction network of well-known and novel
calmodulin-interacting partners. These results expand established calmodulin functions and also suggest a broader role
for this protein in regulating cellular processes in plants. Our efforts establish protein arrays as a suitable platform for
protein-protein interaction experiments that could potentially be applied to the whole proteome.
88 Development of a Small-scale, High-performance Microarray for Detailed Characterization
of the Arabidopsis Response to Pathogen Attack
Masanao Sato 1 , Raka Mitra 1 , John Coller 2 , Dong Wang 3 , Natalie Weaver 3 , Xinnian Dong 3 , Jane Glazebrook 1 , Fumiaki
Katagiri 1
1
Dept. of Plant Biology, University of Minnesota, 2 Stanford Functional Genomics Facility, 3 Dept. of Biology,
Duke University
In response to pathogen attack, Arabidopsis undergoes dynamic transcriptional reprogramming, controlled by a
complex signaling network. To elucidate the structure of the signaling network, we need to specifically perturb this
network and then collect detailed descriptions about it's state. Gene expression profiling can be used as a highly parallel
phenotyping method to collect detailed descriptions of the network state. Current commercial or custom microarray
platforms are unsuitable for this analysis either due to the high cost or low technical reproducibility. We report the
development of a high-performance small-scale microarray platform, or "miniarray", for systems analysis.
To make large-scale systems analysis projects possible, we solved two major problems associated with current
small-scale microarray experiments: poorly established statistical approaches and the lack of expandability due to the
expression ratio-based measurements. Because statistical methods developed for large-scale microarrays are typically
inappropriate for small-scale microarrays, we developed a new normalization method and probe-sharing subarray group
pattern that allows the use of a statistical model to correct systematic biases. Conventional ratio-dependent measurements
using two-color microarrays require the comparison of paired samples in a hybridization, restricting comparison between
unpaired samples. To allow for comparison between unpaired hybridization samples, we implemented a calibration probe
to measure the expression values for a particular sample instead of the expression ratio between two samples.
The resulting microarray showed excellent performance. The correlation coefficients between technical duplicates
ranged from 0.979 to 0.996. A comparison of data obtained from the miniarray and the Affymetrix ATH1 GeneChip
showed a correlation coefficient of 0.88. The accuracy of the miniarray was further supported by quantitative RT-PCR
experiments and data from of spiked control RNAs. Using this miniarray, we initiated the analysis Arabidopsis mutants
to elucidate the signaling network of RPS2-mediated resistance, highlighting transcriptional differences between mutants
that are otherwise phenotypically indistinguishable.