A genistein derivative, ITB-301, induces microtubule depolymerization and mitotic arrest in multidrug-resistant ovarian cancer

PURPOSE: To investigate the mechanistic basis of the anti-tumor effect of the compound ITB-301. METHODS: Chemical modifications of genistein have been introduced to improve its solubility and efficacy. The anti-tumor effects were tested in ovarian cancer cells using proliferation assays, cell cycle analysis, immunofluorescence, and microscopy. RESULTS: In this work, we show that a unique glycoside of genistein, ITB-301, inhibits the proliferation of SKOv3 ovarian cancer cells. We found that the 50% growth inhibitory concentration of ITB-301 in SKOv3 cells was 0.5 μM. Similar results were obtained in breast cancer, ovarian cancer, and acute myelogenous leukemia cell lines. ITB-301 induced significant time- and dose-dependent microtubule depolymerization. This depolymerization resulted in mitotic arrest and inhibited proliferation in all ovarian cancer cell lines examined including SKOv3, ES2, HeyA8, and HeyA8-MDR cells. The cytotoxic effect of ITB-301 was dependent on its induction of mitotic arrest as siRNA-mediated depletion of BUBR1 significantly reduced the cytotoxic effects of ITB-301, even at a concentration of 10 μM. Importantly, efflux-mediated drug resistance did not alter the cytotoxic effect of ITB-301 in two independent cancer cell models of drug resistance. CONCLUSION: These results identify ITB-301 as a novel anti-tubulin agent that could be used in cancers that are multidrug resistant. We propose a structural model for the binding of ITB-301 to α- and β-tubulin dimers on the basis of molecular docking simulations. This model provides a rationale for future work aimed at designing of more potent analogs

A novel method to compare protein structures using local descriptors

<p>Abstract</p> <p>Background</p> <p>Protein structure comparison is one of the most widely performed tasks in bioinformatics. However, currently used methods have problems with the so-called "difficult similarities", including considerable shifts and distortions of structure, sequential swaps and circular permutations. There is a demand for efficient and automated systems capable of overcoming these difficulties, which may lead to the discovery of previously unknown structural relationships.</p> <p>Results</p> <p>We present a novel method for protein structure comparison based on the formalism of local descriptors of protein structure - DEscriptor Defined Alignment (DEDAL). Local similarities identified by pairs of similar descriptors are extended into global structural alignments. We demonstrate the method's capability by aligning structures in difficult benchmark sets: curated alignments in the SISYPHUS database, as well as SISY and RIPC sets, including non-sequential and non-rigid-body alignments. On the most difficult RIPC set of sequence alignment pairs the method achieves an accuracy of 77% (the second best method tested achieves 60% accuracy).</p> <p>Conclusions</p> <p>DEDAL is fast enough to be used in whole proteome applications, and by lowering the threshold of detectable structure similarity it may shed additional light on molecular evolution processes. It is well suited to improving automatic classification of structure domains, helping analyze protein fold space, or to improving protein classification schemes. DEDAL is available online at <url>http://bioexploratorium.pl/EP/DEDAL</url>.</p

A Parameterized Procedure for Consensus Sequences- Validation of the Method

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Inferring Molecular Processes Heterogeneity from Transcriptional Data

RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs