BackgroundThere 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).ResultsWe 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/SignificanceOur 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.

Using one dimensional proteomic mapping (combination of one dimensional gel electrophore sis (1DE) with subsequent mass spectrometry MALDI TOF PMF) the protein profile of Danio rerio embryos has been investigated. The fish species Danio rerio is the most effective alternative model of verte brates used for studies of drug toxicity (e.g. doxorubicin) due to its high degree of homology with human genome. The proteomic profiling resulted in identification of 84 proteins, including 15 vitellogenins. Using the procedure of preparation of homogenates of Danio rerio embryos optimized by ultrasonic treatment pro moting removal of yolk basic proteins (vitellogenin) we have registered changes in the proteome profile of D. rerio embryos induced by doxorubicin (DOX). Growth D. rerio embryos in the medium with DOX caused the decrease in the number of vitellogenins, disappearance of cardiac troponins, and induction of caspase 3. All these observations are consistent with the literature data on doxorubicin induced cardiotoxicity. The pro posed method of 1D proteomic mapping may be used not only for protein identification but also for registra tion of changes in embryonic proteomic profile caused by drugs or any toxic compound for studying the mechanisms underlying induced toxicity.

In the present study, a proteomic technology combining one-dimensional gel electrophoresis (1DE) with subsequent mass spectrometry (MALDI-TOF-PMF) has been successfully applied for revelation of changes in the protein profile of zebrafish (Danio rerio) 52 hpf embryos. Prior to 1DE separation of zebrafish embryonic proteins, the procedure for obtaining embryos homogenate was optimized by ultrasonic treatment. A total of 84 proteins, including 15 vitellogenins, were identified. It was shown that growing of zebrafish embryos in the medium with doxorubicin (DOX) stimulated Caspase-3 induction and promoted the disappearance of cardiac troponins, both these findings being consistent with literature data on doxorubicin-induced cardiotoxicity. The 1DE-based proteomic mapping approach proposed herein enabled not only to identify proteins but also to register those changes in embryos' proteomic profile that were caused by doxorubicin.

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