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  1. Article ; Online: Post-translational modifications of FDA-approved plasma biomarkers in glioblastoma samples.

    Natalia A Petushkova / Victor G Zgoda / Mikhail A Pyatnitskiy / Olesya V Larina / Nadezhda B Teryaeva / Alexander A Potapov / Andrey V Lisitsa

    PLoS ONE, Vol 12, Iss 5, p e

    2017  Volume 0177427

    Abstract: Liquid chromatography-tandem mass spectrometry was used to analyze plasma proteins of volunteers (control) and patients with glioblastoma multiform (GBM). A database search was pre-set with a variable post-translational modification (PTM): ... ...

    Abstract Liquid chromatography-tandem mass spectrometry was used to analyze plasma proteins of volunteers (control) and patients with glioblastoma multiform (GBM). A database search was pre-set with a variable post-translational modification (PTM): phosphorylation, acetylation or ubiquitination. There were no significant differences between the control and the GBM groups regarding the number of protein identifications, sequence coverage or number of PTMs. However, in GBM plasma, we unambiguously observed a decreased fraction in post-translationally modified peptides identified with high quality. The disease-specific PTM patterns were extracted and mapped to the set of FDA-approved plasma protein markers. Decreases of 46% and 24% in the number of acetylated and ubiquitinated peptides, respectively, were observed in the GBM samples. Significance of capturing disease-associated patterns of protein modifications was envisaged.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Applying of hierarchical clustering to analysis of protein patterns in the human cancer-associated liver.

    Natalia A Petushkova / Mikhail A Pyatnitskiy / Vladislav A Rudenko / Olesya V Larina / Oxana P Trifonova / Julya S Kisrieva / Natalia F Samenkova / Galina P Kuznetsova / Irina I Karuzina / Andrey V Lisitsa

    PLoS ONE, Vol 9, Iss 8, p e

    2014  Volume 103950

    Abstract: Background There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training ... ...

    Abstract Background There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training dataset is not available the task can be to mine for presence of meaningful groups (clusters) of samples and to explore underlying data structure (unsupervised learning). Results We investigated the proteomic profiles of the cytosolic fraction of human liver samples using two-dimensional electrophoresis (2DE). Samples were resected upon surgical treatment of hepatic metastases in colorectal cancer. Unsupervised hierarchical clustering of 2DE gel images (n = 18) revealed a pair of clusters, containing 11 and 7 samples. Previously we used the same specimens to measure biochemical profiles based on cytochrome P450-dependent enzymatic activities and also found that samples were clearly divided into two well-separated groups by cluster analysis. It turned out that groups by enzyme activity almost perfectly match to the groups identified from proteomic data. Of the 271 reproducible spots on our 2DE gels, we selected 15 to distinguish the human liver cytosolic clusters. Using MALDI-TOF peptide mass fingerprinting, we identified 12 proteins for the selected spots, including known cancer-associated species. Conclusions/significance Our results highlight the importance of hierarchical cluster analysis of proteomic data, and showed concordance between results of biochemical and proteomic approaches. Grouping of the human liver samples and/or patients into differing clusters may provide insights into possible molecular mechanism of drug metabolism and creates a rationale for personalized treatment.
    Keywords Medicine ; R ; Science ; Q
    Subject code 004
    Language English
    Publishing date 2014-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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