Min-Jea Kim | Search PubMed

PubMed2006

Functional characterization of OsRacB GTPase--a potentially negative regulator of basal disease resistance in rice.

The rice genome contains at least seven expressed Rop small GTPase genes. Of these Rops, OsRac1 is the only characterized gene that has been implicated in disease resistance as a positive regulator. To our interest in finding a negative ROP regulator of disease resistance in rice, we applied a "phylogeny of function" approach to rice Rops, and identified OsRacB based on its close genetic orthologous relationship with the barley HvRacB gene, a known negative regulator of disease resistance. To determine the function of OsRacB, we isolated the OsRacB cDNA and conducted gene expression and transgenic studies. OsRacB, a single copy gene in the genome of rice, shared 98% identity with HvRacB at the amino acid level. Its mRNA was strongly expressed in leaf sheath (LS) and in panicles, but was very weakly expressed in young and mature leaves. The basal mRNA level of OsRacB in LS of two-week-old seedlings was strongly down-regulated upon wounding by cut and treatment with jasmonic acid. A dramatic down-regulation in the OsRacB transcripts was also found in plants inoculated with the blast pathogen, Magnaporthe grisea. Interestingly, transgenic rice plants over-expressing OsRacB showed increased symptom development in response to rice blast pathogens. Additionally, fluorescence microscopy of green fluorescent protein (GFP):OsRacB-transformed onion cells and Arabidopsis protoplasts revealed OsRacB association with plasma membrane (PM), suggesting that PM localization is required for proper function of OsRacB. Based on these results, we suggest that OsRacB functions as a potential regulator for a basal disease resistance pathway in rice.

Amino Acid Sequence↗

PubMed2005

Using half-normal probability plot and regression analysis to differentiate complex traits: differentiating disease response of multigenic resistance and susceptibility in tomatoes to multiple pathogen isolates.

The need for a new analytical approach was encountered in the course of characterizing newly developed tomato lines resistant to late blight. Late blight resistant tomato lines were created in independent breeding programs using the accession Solanum pimpinellifolium L. (formerly Lycopersicon pimpinellifolium (L.) Miller) L3708 as the source of the resistance. However, initial field observation suggested that the late blight resistance in the lines produced by two independent breeding programs differed. Possible causes included a partial transfer of the late blight resistance derived from S. pimpinellifolium L3708 or the possibility of race specificity of this resistance. A crucial issue was determining the most appropriate and robust analytical method to use with data from laboratory analyses of the responses of nine tomato lines against five P. infestans isolates. Prior analysis by standard ANOVA revealed significant differences across tomato lines but could not determine whether the disease responses in the CLN-R lines were different from those of the heterozygous F(1) hybrids, created by crossing susceptible tomatoes with the fixed CU-R lines. A different analytical method was needed. Therefore, sporangia numbers/leaflet and diseased area data were analyzed using a half-normal probability plot and regression analysis. The results of this analysis show its utility for genetic or pathology studies. Considering only populations of the uniform tomato lines, this method confirms the results obtained by using a standard ANOVA, but provides a clearer demonstration of the distributions of the individuals within the populations and how this distribution impacts variance and the difference among the populations. This method also allows a joint analysis of the uniform lines with an additional population that is less uniform, because it is segregating. Such an analysis would be invalid using a standard ANOVA. The results of this joint analysis determined that the additional population was divergent from the fixed CU-R lines, and, against some isolates, against the CLN-R lines as well. Half-normal probability plot analysis method would be applicable more broadly beyond analysis of disease resistance data. It could be useful for data from populations that are not normally distributed, for traits which are affected by epistatic gene action, and could be useful for selection of extremes.

Genotype↗