Publikationen - Natur- und Wirkstoffchemie

A dsRNAi approach silencing a key enzyme of sinapate ester biosynthesis (UDP-glucose:sinapate glucosyltransferase, encoded by the UGT84A9 gene) in oilseed rape (Brassica napus) seeds was performed to reduce the anti-nutritive properties of the seeds by lowering the content of the major seed component sinapine (sinapoylcholine) and various minor sinapate esters. The transgenic seeds have been produced so far to the T6 generation and revealed a steady suppression of sinapate ester accumulation. HPLC analysis of the wild-type and transgenic seeds revealed, as in the previous generations, marked alterations of the sinapate ester pattern of the transformed seeds. Besides strong reduction of the amount of the known sinapate esters, HPLC analysis revealed unexpectedly the appearance of several minor hitherto unknown rapeseed constituents. These compounds were isolated and identified by mass spectrometric and NMR spectroscopic analyses. Structures of 11 components were elucidated to be 4-O-glucosides of syringate, caffeyl alcohol and its 7,8-dihydro derivative as well as of sinapate and sinapine, along with sinapoylated kaempferol glycosides, a hexoside of a cyclic spermidine alkaloid and a sinapine derivative with an ether-bridge to a C6–C3-unit. These results indicate a strong impact of the transgenic approach on the metabolic network of phenylpropanoids in B. napus seeds. Silencing of UGT84A9 gene expression disrupt the metabolic flow through sinapoylglucose and alters the amounts and nature of the phenylpropanoid endproducts.

Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring substances is an important task to investigate new potentially useful chemical compounds. Multi-dimensional NMR-spectra are relational objects like documents, but consists of continuous multi-dimensional points called peaks instead of words. We develop several mappings from continuous NMR-spectra to discrete text-like data. With the help of those mappings any text retrieval method can be applied. We evaluate the performance of two retrieval methods, namely the standard vector space model and probabilistic latent semantic indexing (PLSI). PLSI learns hidden topics in the data, which is in case of 2D-NMR data interesting in its owns rights. Additionally, we develop and evaluate a simple direct similarity function, which can detect duplicates of NMR-spectra. Our experiments show that the vector space model as well as PLSI, which are both designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI is able to find meaningful ”topics” in the NMR-data.

Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring products is an important task to investigate new potentially useful chemical compounds. We develop a set-based similarity function, which, however, does not sufficiently capture more abstract aspects of similarity. NMR-spectra are like documents, but consists of continuous multi-dimensional points instead of words. Probabilistic semantic indexing (PLSI) is an retrieval method, which learns hidden topics. We develop several mappings from continuous NMR-spectra to discrete text-like data. The new mappings include redundancies into the discrete data, which proofs helpful for the PLSI-model used afterwards. Our experiments show that PLSI, which is designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI combined with the new mappings is able to find meaningful ”topics” in the NMR-data.