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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
Fluctuations in oxygen tension during tissue remodeling impose a major metabolic challenge in human tumors. Stem-like tumor cells in glioblastoma, the most common malignant brain tumor, possess extraordinary metabolic flexibility, enabling them to initiate growth even under non-permissive conditions. We identified a reciprocal metabolic switch between the pentose phosphate pathway (PPP) and glycolysis in glioblastoma stem-like (GS) cells. Expression of PPP enzymes is upregulated by acute oxygenation but downregulated by hypoxia, whereas glycolysis enzymes, particularly those of the preparatory phase, are regulated inversely. Glucose flux through the PPP is reduced under hypoxia in favor of flux through glycolysis. PPP enzyme expression is elevated in human glioblastomas compared to normal brain, especially in highly proliferative tumor regions, whereas expression of parallel preparatory phase glycolysis enzymes is reduced in glioblastomas, except for strong upregulation in severely hypoxic regions. Hypoxia stimulates GS cell migration but reduces proliferation, whereas oxygenation has opposite effects, linking the metabolic switch to the “go or grow” potential of the cells. Our findings extend Warburg’s observation that tumor cells predominantly utilize glycolysis for energy production, by suggesting that PPP activity is elevated in rapidly proliferating tumor cells but suppressed by acute severe hypoxic stress, favoring glycolysis and migration to protect cells against hypoxic cell damage.
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 .
