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Steroidal glycoalkaloids(SGAs) are nitrogen-containing secondary metabolites of the Solanum species,which are known to have large chemical and bioactive diversity in nature.While recent effort and development on LC/MS techniques for SGA profiling have elucidated the main pathways of SGA metabolism in tomato,the problem of peak annotation still remains due to the vast diversity of chemical structure and similar on overlapping of chemical formula.Here we provide a case study of peak classification and annotation approach by integration of species and tissue specificities of SGA accumulation for provision of comprehensive pathways of SGA biosynthesis.In order to elucidate natural diversity of SGA biosynthesis,a total of 169 putative SGAs found in eight tomato accessions(Soianum lycopersicum,S.pimpinellifolium,S.cheesmaniae,S.chmielewskii,S.neorickii,S.peruvianum,S.habrochaites,S.pennellii) and four tissue types were used for correlation analysis.The results obtained in this study contribute annotation and classification of SGAs as well as detecting putative novel biosynthetic branch points.As such this represents a novel strategy for peak annotation for plant secondary metabolites.
Steroidal glycoalkaloids (SGAs) are nitrogen-containing secondary metabolites of the Solanum species, which are known to have large chemical and bioactive diversity in nature. Still in effort and development on LC / MS techniques for SGA profiling have elucidated the main pathways of SGA metabolism in tomato, the problem of peak annotation still remains due to the vast diversity of chemical structure and similar on overlapping of chemical formula. Here we provide a case study of peak classification and annotation approach by integration of species and tissue specificities of SGA accumulation for provision of comprehensive pathways of SGA biosynthesis. In order to elucidate natural diversity of SGA biosynthesis, a total of 169 putative SGAs found in eight tomato accessions (Soianum lycopersicum, S. pimpinellifolium, S. cheesmaniae, S. chmielewskii, S. neorickii, S. peruvianum, S. habrochaites, S. pennellii) and four tissue types were used for correlation analysis. The results obtained in this study contribu te annotation and classification of SGAs as well as detecting putative novel biosynthetic branch points. As such this represents a novel strategy for peak annotation for plant secondary metabolites.