Dissection of two soybean QTL conferring partial resistance to Phytophthora sojae through sequence and gene expression analysis.

BACKGROUND: Phytophthora sojae is the primary pathogen of soybeans that are grown on poorly drained soils. Race-specific resistance to P. sojae in soybean is gene-for-gene, although in many areas of the US and worldwide there are populations that have adapted to the most commonly deployed resistance to P. sojae ( Rps) genes. Hence, this system has received increased attention towards identifying mechanisms and molecular markers associated with partial resistance to this pathogen. Several quantitative trait loci (QTL) have been identified in the soybean cultivar 'Conrad' that contributes to the expression of partial resistance to multiple P. sojae isolates. RESULTS: In this study, two of the Conrad QTL on chromosome 19 were dissected through sequence and expression analysis of genes in both resistant (Conrad) and susceptible ('Sloan') genotypes. There were 1025 single nucleotide polymorphisms (SNPs) in 87 of 153 genes sequenced from Conrad and Sloan. There were 304 SNPs in 54 genes sequenced from Conrad compared to those from both Sloan and Williams 82, of which 11 genes had SNPs unique to Conrad. Eleven of 19 genes in these regions analyzed with qRT-PCR had significant differences in fold change of transcript abundance in response to infection with P. sojae in lines with QTL haplotype from the resistant parent compared to those with the susceptible parent haplotype. From these, 8 of the 11 genes had SNPs in the upstream, untranslated region, exon, intron, and/or downstream region. These 11 candidate genes encode proteins potentially involved in signal transduction, hormone-mediated pathways, plant cell structural modification, ubiquitination, and basal resistance. CONCLUSIONS: These findings may indicate a complex defense network with multiple mechanisms underlying these two soybean QTL conferring resistance to P. sojae. SNP markers derived from these candidate genes can contribute to fine mapping of QTL and marker assisted breeding for resistance to P. sojae.

Infection and genotype remodel the entire soybean transcriptome.

BACKGROUND: High throughput methods, such as high density oligonucleotide microarray measurements of mRNA levels, are popular and critical to genome scale analysis and systems biology. However understanding the results of these analyses and in particular understanding the very wide range of levels of transcriptional changes observed is still a significant challenge. Many researchers still use an arbitrary cut off such as two-fold in order to identify changes that may be biologically significant. We have used a very large-scale microarray experiment involving 72 biological replicates to analyze the response of soybean plants to infection by the pathogen Phytophthora sojae and to analyze transcriptional modulation as a result of genotypic variation. RESULTS: With the unprecedented level of statistical sensitivity provided by the high degree of replication, we show unambiguously that almost the entire plant genome (97 to 99% of all detectable genes) undergoes transcriptional modulation in response to infection and genetic variation. The majority of the transcriptional differences are less than two-fold in magnitude. We show that low amplitude modulation of gene expression (less than two-fold changes) is highly statistically significant and consistent across biological replicates, even for modulations of less than 20%. Our results are consistent through two different normalization methods and two different statistical analysis procedures. CONCLUSION: Our findings demonstrate that the entire plant genome undergoes transcriptional modulation in response to infection and genetic variation. The pervasive low-magnitude remodeling of the transcriptome may be an integral component of physiological adaptation in soybean, and in all eukaryotes.

Global transcription profiling reveals multiple sugar signal transduction mechanisms in Arabidopsis.

Complex and interconnected signaling networks allow organisms to control cell division, growth, differentiation, or programmed cell death in response to metabolic and environmental cues. In plants, it is known that sugar and nitrogen are critical nutrient signals; however, our understanding of the molecular mechanisms underlying nutrient signal transduction is very limited. To begin unraveling complex sugar signaling networks in plants, DNA microarray analysis was used to determine the effects of glucose and inorganic nitrogen source on gene expression on a global scale in Arabidopsis thaliana. In whole seedling tissue, glucose is a more potent signal in regulating transcription than inorganic nitrogen. In fact, other than genes associated with nitrate assimilation, glucose had a greater effect in regulating nitrogen metabolic genes than nitrogen itself. Glucose also regulated a broader range of genes, including genes associated with carbohydrate metabolism, signal transduction, and metabolite transport. In addition, a large number of stress responsive genes were also induced by glucose, indicating a role of sugar in environmental responses. Cluster analysis revealed significant interaction between glucose and nitrogen in regulating gene expression because glucose can modulate the effects of nitrogen and vise versa. Intriguingly, cycloheximide treatment appeared to disrupt glucose induction more than glucose repression, suggesting that de novo protein synthesis is an intermediary event required before most glucose induction can occur. Cross talk between sugar and ethylene signaling may take place on the transcriptional level because several ethylene biosynthetic and signal transduction genes are repressed by glucose, and the repression is largely unaffected by cycloheximide. Collectively, our global expression data strongly support the idea that glucose and inorganic nitrogen act as both metabolites and signaling molecules.