SERIAL ANALYSIS OF GENE EXPRESSION (SAGE) APPLIED TO SUGARCANE (Saccharum ssp.) TRANSCRIPTION PROFILING

¹ Centro de Energia Nuclear na Agricultura - Universidade de São Paulo, Av. Centenario, 303 CP 96, 13400-970 Piracicaba SP, Brazil

Serial analysis of gene expression (SAGE) allows genome-wide expression studies based on sequencing concatemers of 9-13 bp tags, isolated from a transcript population, yielding a qualitative and quantitative snapshot of a sampled transcriptome. We used SAGE standard protocols to obtain transcriptional profiles from sugarcane leaves and stems from full-sib genotypes contrasting for Brix. Tags were analyzed using eSAGE, and annotated using SAGEmap at NCBI and Sugarcane EST cluster dataset with 43,141 clusters (sucest.lad.dcc.unicamp.br/en/) via BLASTN. Around 18,000 tags have been isolated and characterized from mature leaf and from low- or high-sucrose stem parenchyma. The leaf most frequent tags represented photosystem- and Z-scheme-related genes, but frequent transcripts from CO2 fixation; glycine-rich RNA-binding proteins; stress response and primary/secondary metabolism associated genes were also observed. Tags derived from stem parenchyma contrasting for sucrose content presented less redundancy than leaf-derived tags, with several tags presenting no identity with sugarcane clusters, or matching to clusters without positive hits with GenBank, suggesting a high level of novelty. The most abundant annotated tags represented hypothetical, structural proteins (actin factor; proline-rich cell wall protein; xyloglucan-endo-1,4-b-D-glucanase), primary/secondary metabolism (cytochrome C1; PAL; cynamoyl CoA reductase), nucleic acid binding factors (glycine-rich RNA binding protein; Zn finger transcription factor; ribosomal proteins), defense/stress (superoxide dismutase; trypsin inhibitor; metallothionein), and, noteworthy, proteins previously known as carbohydrate metabolism control factors (SPF-1 transcription factor; mannosyl-oligosaccharide glycosidase I). Sugarcane gene expression based on SAGE appears to be suitable for investigating important biological processes.