Submitted to: Meeting Abstract
Publication Type: Abstract only
Publication Acceptance Date: 4/7/2011
Publication Date: 4/11/2011
Citation: Wisniewski, M.E., Liu, J., Macarisin, D., Droby, S., Hershkovitz, V. 2011. Utilization of an apple microarray for gene expression profiling in stone fruit-postharvest pathogen interactions [abstract]. International Congress Postharvest Pathology, Lleida, Spain, p. 57. Interpretive Summary:
Technical Abstract: Brown rot disease (Monilinia fructicola Honey) is a major cause of Prunus spp. fruit losses in pre- and post-harvest settings. As part of an on-going effort to develop biological approaches for managing diseases of temperate fruit crops, we are seeking to better understand the mechanisms by which M. fructicola suppresses or overcomes defense reactions in stone fruit. To identify genes specifically induced/repressed during stone fruit-postharvest pathogen interaction, a microarray analysis was conducted of peach fruit transcriptome response to compatible (M. fructicola) and non-compatible (Penicillium digitatum) pathogens. A recently developed apple microarray consisting of ~40,000 70-mer-oligos was used to hybridize RNA extracted from fruit tissue 24 h after wound inoculation with a conidial suspension of M. fructicola, P. digitatum, or sterile water. Tissue from intact fruit was also analyzed. Sample comparison was conducted in a loop design which consisted of 4 biological replicates and included a dye swap. Statistical analysis of the data generated by a total of 16 microarrays showed that 1048 genes are differently regulated in peach fruit in response to M. fructicola and P. digitatum inoculation. From those 552 are uniquely regulated by brown rot pathogen, while P. digitatum specifically affected expression of 493 peach genes within the first 24 h after inoculation. A selection of a list of genes of known importance in plant microbial interactions is in progress to follow their expression over a time-course (3; 6; 12; 24; 36 hpi) using RT-qPCR. Results on specific genes and RT-qPCR will be presented and discussed. The current study demonstrates that a newly developed apple microarray can be successfully used for transcript profiling in stone fruits.