Publications by authors named "Alexandra Zerck"

Uncovering cellular responses from heterogeneous genomic data is crucial for molecular medicine in particular for drug safety. This can be realized by integrating the molecular activities in networks of interacting proteins. As proof-of-concept we challenge network modeling with time-resolved proteome, transcriptome and methylome measurements in iPSC-derived human 3D cardiac microtissues to elucidate adverse mechanisms of anthracycline cardiotoxicity measured with four different drugs (doxorubicin, epirubicin, idarubicin and daunorubicin).

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Centrosome morphology and number are frequently deregulated in cancer cells. Here, to identify factors that are functionally relevant for centrosome abnormalities in cancer cells, we established a protein-interaction network around 23 centrosomal and cell-cycle regulatory proteins, selecting the interacting proteins that are deregulated in cancer for further studies. One of these components, LGALS3BP, is a centriole- and basal body-associated protein with a dual role, triggering centrosome hypertrophy when overexpressed and causing accumulation of centriolar substructures when downregulated.

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Background: Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins.

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Targeted proteomic approaches such as multiple reaction monitoring (MRM) overcome problems associated with classical shotgun mass spectrometry experiments. Developing MRM quantitation assays can be time consuming, because relevant peptide representatives of the proteins must be found and their retention time and the product ions must be determined. Given the transitions, hundreds to thousands of them can be scheduled into one experiment run.

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Currently, the precursor ion selection strategies in LC-MS mainly choose the most prominent peptide signals for MS/MS analysis. Consequently, high-abundance proteins are identified by MS/MS of many peptides, whereas proteins of lower abundance might elude identification. We present a novel, iterative and result-driven approach for precursor ion selection that significantly increases the efficiency of an MS/MS analysis by decreasing data redundancy and analysis time.

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Background: Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data.

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