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Bioorganic Chemistry
Biochemistry of Plant Interactions
Cell and Metabolic Biology
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Publications - Cell and Metabolic Biology
Publications
Pathogenicity of the Gram-negative plant-pathogenic bacterium Xanthomonas campestris pv. vesicatoria depends on a type III secretion (T3S) system which translocates effector proteins into plant cells. Effector protein delivery is controlled by the T3S chaperone HpaB, which presumably escorts effector proteins to the secretion apparatus. One intensively studied effector is the transcription activator-like (TAL) effector AvrBs3, which binds to promoter sequences of plant target genes and activates plant gene expression. It was previously reported that type III-dependent delivery of AvrBs3 depends on the N-terminal protein region. The signals that control T3S and translocation of AvrBs3, however, have not yet been characterized. In the present study, we show that T3S and translocation of AvrBs3 depend on the N-terminal 10 and 50 amino acids, respectively. Furthermore, we provide experimental evidence that additional signals in the N-terminal 30 amino acids and the region between amino acids 64 and 152 promote translocation of AvrBs3 in the absence of HpaB. Unexpectedly, in vivo translocation assays revealed that AvrBs3 is delivered into plant cells even in the absence of HrpF, which is the predicted channel-forming component of the T3S translocon in the plant plasma membrane. The presence of HpaB- and HrpF-independent transport routes suggests that the delivery of AvrBs3 is initiated during early stages of the infection process, presumably before the activation of HpaB or the insertion of the translocon into the plant plasma membrane.
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 .
