Publications - Cell and Metabolic Biology

Phenylpropanoid polyamine conjugates are widespread in plant species. Their presence has been established in seeds, flower buds, and pollen grains. A biosynthetic pathway proposed for hydroxycinnamoyl spermidine conjugates has been suggested for the model plant Arabidopsis thaliana with a central acyl transfer reaction performed by a BAHD-like hydroxycinnamoyl transferase. A detailed liquid chromatography (LC)–electrospray ionization–mass spectrometry- and tandem-mass-spectrometry (MS/MS)-based survey of wild-type and spermidine hydroxycinnamoyl transferase (SHT) mutants identified more than 30 different bis- and tris-substituted spermidine conjugates, five of which were glycosylated, in the methanol-soluble fraction of the pollen exine. On the basis of characterized fragmentation patterns, a high-throughput LC–MS/MS method for highly sensitive HCAA relative quantification (targeted profiling) was developed. Only minor qualitative and quantitative differences in the pattern of bis-acyl spermidine conjugates in the SHT mutant compared to wild-type plants provide strong evidence for the presence of multiple BAHD-like acyl transferases and suggest a much more complex array of enzymatic steps in the biosynthesis of these conjugates than previously anticipated.

Glycation (or non-enzymatic glycosylation) is a common non-enzymatic covalent modification of human proteins. Glucose, the highest concentrated monosaccharide in blood, can reversibly react with amino groups of proteins to form Schiff bases that can rearrange to form relatively stable Amadori products. These can be further oxidized to advanced glycation end products (AGEs). Here, we analyzed the glycation patterns of human serum albumin (HSA) in plasma samples obtained from five patients with type 2 diabetes mellitus. Therefore, glycated peptides from a tryptic digest of plasma were enriched with m-aminophenylboronic acid (mAPBA) affinity chromatography. The glycated peptides were then further separated in the second dimension by RP-HPLC coupled on-line to an electrospray ionization (ESI) tandem mass spectrometer (MS/MS). Altogether, 18 Amadori peptides, encompassing 40% of the HSA sequence, were identified. The majority of the peptides were detected and relatively quantified in all five samples with a high reproducibility among the replicas. Eleven Lys-residues were glycated at similar quantities in all samples, with glycation site Lys549 (KAm(Glc)QTALVELVK) being the most abundant. In conclusion, the established mAPBA/nanoRP-HPLC-ESI-MS/MS approach could reproducibly identify and quantify glycation sites in plasma samples, potentially useful in diagnosis and therapeutic control.

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.