Inoculation of soybean (Glycine max) plants with Phakopsora pachyrhizi, the causal organism of Asian soybean rust, elicits a biphasic response characterized by a burst of differential gene expression in the first 12 h. A quiescent period occurs from 24 to 48 h after inoculation, in which P. pachyrhizi continues to develop but does not elicit strong host responses, followed by a second phase of intense gene expression. To correlate soybean responses with P. pachyrhizi growth and development, we inoculated the soybean cultivar Ankur (accession PI462312), which carries the Rpp3 resistance gene, with avirulent and virulent isolates of P. pachyrhizi. The avirulent isolate Hawaii 94-1 elicits hypersensitive cell death that limits fungal growth on Ankur and results in an incompatible response, while the virulent isolate Taiwan 80-2 grows extensively, sporulates profusely, and produces a compatible reaction. Inoculated leaves were collected over a 288-h time course for microarray analysis of soybean gene expression and microscopic analysis of P. pachyrhizi growth and development. The first burst in gene expression correlated with appressorium formation and penetration of epidermal cells, while the second burst of gene expression changes followed the onset of haustoria formation in both compatible and incompatible interactions. The proliferation of haustoria coincided with the inhibition of P. pachyrhizi growth in the incompatible interaction or the beginning of accelerated growth in the compatible interaction. The temporal relationships between P. pachyrhizi growth and host responses provide an important context in which to view interacting gene networks that mediate the outcomes of their interactions.

Soybean rust caused by Phakopsora pachyrhizi Sydow is a devastating foliar disease that has spread to most soybean growing regions throughout the world, including the USA. Four independent rust resistance genes, Rpp1-Rpp4, have been identified in soybean that recognize specific isolates of P. pachyrhizi. A suppressive subtraction hybridization (SSH) complementary DNA (cDNA) library was constructed from the soybean accession PI200492, which contains Rpp1, after inoculation with two different isolates of P. pachyrhizi that result in susceptible or immune reactions. Both forward and reverse SSH were performed using cDNA from messenger RNA pooled from 1, 6, 12, 24, and 48 h post-inoculation. A total of 1,728 SSH clones were sequenced and compared to sequences in GenBank for similarity. Microarray analyses were conducted on a custom 7883 soybean-cDNA clone array encompassing all of the soybean-rust SSH clones and expressed sequence tags from four other soybean cDNA libraries. Results of the microarray revealed 558 cDNA clones differentially expressed in the immune reaction. The majority of the upregulated cDNA clones fell into the functional category of defense. In particular, cDNA clones with similarity to peroxidases and lipoxygenases were prevalent. Downregulated cDNA clones included those with similarity to cell-wall-associated protein, such as extensins, proline-rich proteins, and xyloglucan endotransglycosylases.

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.