Host gene expression changes in the early response to potato virus Y(NTN) interaction were compared in two differently sensitive potato cultivars: the resistant cultivar Santé and the sensitive cultivar Igor. Hybridization of potato TIGR cDNA microarrays allowed us to monitor the expression of approximately 10,000 genes simultaneously at 0.5 and 12 h post-inoculation (hpi). Microarray data, analysed by statistics and data mining, were complemented by subtraction library construction and sequence analysis to validate the findings. The expression profiles of the two cultivars were similar and faint at 0.5 hpi, but they differed substantially at 12 hpi. Although, at 0.5 hpi, cv. Santé responded by the differential expression of a greater number of genes, at 12 hpi the number was higher in cv. Igor. The majority of genes in this cultivar were down-regulated at 12 hpi, indicating a host gene shut-off. Suites of genes that exhibited altered transcript abundance in response to the virus were identified, and included genes involved in the processes of photosynthesis, perception, signalling and defence responses. The expression of the considerable number of genes associated with photosynthesis was surprisingly up-regulated as early as 0.5 hpi and down-regulated at 12 hpi in both cultivars. The expression of genes involved in perception and signalling was increased in the sensitive cultivar at 12 hpi. By contrast, a simultaneous strong defence response at the transcriptional level was evident in the resistant cultivar, as shown by the up-regulation of genes involved in brassinosteroid, polyamine and secondary metabolite biosynthesis, and of genes coding for pathogenesis-related proteins.

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Endogenous levels of free and conjugated salicylic (SA) and gentisic (GA) acids, both putative signal molecules in plant defence, were analysed in order to investigate their involvement in the resistance of four potato ( Solanum tuberosum ) genotypes with different susceptibilities to Potato virus Y NTN (PVY NTN ) infection: the highly susceptible cv. Igor and its extremely resistant transgenic line, the extremely resistant cv. Sante and the tolerant cv. Pentland Squire. The lowest levels of free and conjugated SA were observed in the extremely resistant cv. Sante, while free GA, which was detected in all the other varieties, was absent. The extremely resistant transgenic cv. Igor contained the highest basal total SA level and the lowest level of total GA of all four cultivars. In susceptible cv. Igor, but not in resistant transgenic cv. Igor, a systemic increase of free SA was measured 1 day postinfection (dpi). Even more significant increases of free and conjugated SA and GA were detected 11 dpi when systemic symptoms appeared. In inoculated but not in upper noninoculated leaves of resistant transgenic cv. Igor, significant increase of SA conjugates occurred, but not before 11 dpi. The increase of SA and GA in susceptible cv. Igor could contribute to the general elevated levels of phenolic compounds as a response to stress caused by virus infection. It appears that basal levels of SA and GA do not correlate with resistance to PVY NTN in potato plants.

Since their conception in the late 1990s, microarray techniques have become a tool of choice for monitoring pangenomic gene expression. Although there are a large number of variations on the basic methodology the general approach remains standard and involves the comparison of a "test" RNA with a "control" RNA; in this case "healthy" and "virus-infected" plants. The protocol itself can be broken down into five main parts: RNA extraction, cDNA synthesis, hybridization, array scanning, and data analysis. The method presented is optimized for use with arrays based on glass slides spotted with cDNA, in this case 15,264 cDNAs from Solanum tuberosum. The labeling technique presented involves two steps: hybridization of cDNA produced using oligo-dT linker primers to the array and hybridization with a DNA dendrimer reagent comprising sequence complementary to the linker sequence bound to a fluorescent dye. We also present the use of the R environment for data analysis, generating statistical support for differential gene expression observed.