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Molecular Signal Processing
Bioorganic Chemistry
Biochemistry of Plant Interactions
Cell and Metabolic Biology
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Publications - Cell and Metabolic Biology
Publications
Type III secretion (T3S) systems are essential pathogenicity factors of most Gram-negative bacteria and translocate effector proteins into plant or animal cells. T3S systems can, therefore, be used as tools for protein delivery into eukaryotic cells, for instance after transfer of the T3S gene cluster into nonpathogenic recipient strains. Here, we report the modular cloning of the T3S gene cluster from the plant-pathogenic bacterium Xanthomonas euvesicatoria. The resulting multigene construct encoded a functional T3S system and delivered effector proteins into plant cells. The modular design of the T3S gene cluster allowed the efficient replacement and rearrangement of single genes or operons and the insertion of reporter genes for functional studies. In the present study, we used the modular T3S system to analyze the assembly of a fluorescent fusion of the predicted cytoplasmic ring protein HrcQ. Our studies demonstrate the use of the modular T3S gene cluster for functional analyses and mutant approaches in X. euvesicatoria. A potential application of the modular T3S system as protein delivery tool is discussed.
Publications
Plant Synthetic Biology requires robust and efficient methods for assembling multigene constructs. Golden Gate cloning provides a precision module-based cloning technique for facile assembly of multiple genes in one construct. We present here a versatile resource for plant biologists comprising a set of cloning vectors and 96 standardized parts to enable Golden Gate construction of multigene constructs for plant transformation. Parts include promoters, untranslated sequences, reporters, antigenic tags, localization signals, selectable markers, and terminators. The comparative performance of parts in the model plant Nicotiana benthamiana is discussed.
Books and chapters
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.
Books and chapters
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.
This page was last modified on 27 Jan 2025 .
