Aleksandra Gruca

Synthetic conjugates of genistein affecting proliferation and mitosis of cancer cells

This paper describes the synthesis and antiproliferative activity of conjugates of genistein (1) and unsaturated pyranosides. Constructs linking genistein with a sugar moiety through an alkyl chain were obtained in a two-step synthesis: in a first step genistein was converted into an intermediate bearing an ω-hydroxyalkyl substituent, containing two, three or five carbon atoms, at position 7, while the second step involved Ferrier glycosylation reaction, employing glycals. Antiproliferative activity of several genistein derivatives was tested in cancer cell lines in vitro. The most potent derivative, Ram-3 inhibited the cell cycle, interacted with mitotic spindles and caused apoptotic cell death. Neither genistein nor the sugar alone were able to influence the mitotic spindle organization. Our results indicate, that conjugation of genistein with certain sugars may render the interaction of derivatives with new molecular targets.

Induction and selection of the most interesting Gene Ontology based multiattribute rules for descriptions of gene groups

A rules induction algorithm dedicated to describe groups of genes with similar expression profiles by means of Gene Ontology terms is discussed in the paper. The presented algorithm takes into consideration information contained in the Gene Ontology graph. A huge number of created rules requires defining the rules quality and similarity measures, thus the paper presents such measures and proposes a new method of the most interesting rules selection. Features reduction method based on the rough sets theory is adopted and applied in order to reduce the number of Gene Ontology terms occurring in rules. The paper presents results of performed experiments and describes shortly the internet application RuleGO in which the proposed methods were implemented.

Unsaturated genistein disaccharide glycoside as a novel agent affecting microtubules.

Genistein, due to its recognized chemopreventive and antitumor potential, is a molecule of interest as a lead compound in drug design. While multiple molecular targets for genistein have been identified, so far neither for this isoflavonoid nor for its natural or synthetic derivatives disruption of microtubules and mitotic spindles has been reported. Here we describe such properties of the synthetic glycosidic derivative of genistein significantly more cytotoxic than genistein, 7-O-(2,3,4,6-tetra-O-acetyl-beta-D-galactopyranosyl)-(1-->4)-(6-O-acetyl-hex-2-ene-alpha-D-erythro-pyranosyl)genistein, shortly named G21. We found that G21 causes significant mitotic delay, frequent appearance of multipolar spindles, and alteration of the interphase microtubule array.

Synthetic genistein glycosides inhibiting EGFR phosphorylation enhance the effect of radiation in HCT 116 colon cancer cells.

The need to find new EGFR inhibitors for use in combination with radiotherapy in the treatment of solid tumors has drawn our attention to compounds derived from genistein, a natural isoflavonoid. The antiproliferative potential of synthetic genistein derivatives used alone or in combination with ionizing radiation was evaluated in cancer cell lines using clonogenic assay. EGFR phosphorylation was assessed with western blotting. Genistein derivatives inhibited clonogenic growth of HCT 116 cancer cells additively or synergistically when used in combination with ionizing radiation, and decreased EGFR activation. Our preclinical evaluation of genistein-derived EGFR inhibitors suggests that these compounds are much more potent sensitizers of cells to radiation than the parent isoflavonoid, genistein and indicate that these compounds may be useful in the treatment of colon cancer with radiation therapy.

RuleGO: a logical rules-based tool for description of gene groups by means of Gene Ontology

Genome-wide expression profiles obtained with the use of DNA microarray technology provide abundance of experimental data on biological and molecular processes. Such amount of data need to be further analyzed and interpreted in order to obtain biological conclusions on the basis of experimental results. The analysis requires a lot of experience and is usually time-consuming process. Thus, frequently various annotation databases are used to improve the whole process of analysis. Here, we present RuleGO—the web-based application that allows the user to describe gene groups on the basis of logical rules that include Gene Ontology (GO) terms in their premises. Presented application allows obtaining rules that reflect coappearance of GO-terms describing genes supported by the rules. The ontology level and number of coappearing GO-terms is adjusted in automatic manner. The user limits the space of possible solutions only. The RuleGO application is freely available at http://rulego.polsl.pl/.

Image Quality Assessment Using Phase Spectrum Correlation

This paper presents the method of evaluating the image quality using similarity of images phase spectrum. The authors introduce phase correlation coefficient as an objective measure of an image quality index which is compared to the subjective distortion evaluation using stimulus impairment scale. Artificially distorted images produced by proportional mixing of images phase spectra with a noise were used for testing purposes. The results are the mean correlation coefficient values related to the mean opinion score grades. The obtained relation between human responses and phase correlation is linear.

Evaluation of semantic term and gene similarity measures

In this paper we present the results of the research verifying how the functional description of genes contained in Gene Ontology database is related to genes expression values recorded during biological experiments. We compare several different gene similarity measures and semantic term similarity measures, and evaluate how the similarity of genes based on Gene Ontology terms is correlated with similarity of genes based on expression profiles. The analysis are preformed on three different datasets and we show that there is no single term similarity measure that always gives the best correlation results. The choice of the best term similarity measure depends on dataset characteristic.

Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rules

Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rules In this paper we present a method for evaluating the importance of GO terms which compose multi-attribute rules. The rules are generated for the purpose of biological interpretation of gene groups. Each multi-attribute rule is a combination of GO terms and, based on relationships among them, one can obtain a functional description of gene groups. We present a method which allows evaluating the influence of a given GO term on the quality of a rule and the quality of a whole set of rules. For each GO term, we compute how big its influence on the quality of generated set of rules and therefore the quality of the obtained description is. Based on the computed quality of GO terms, we propose a new algorithm of rule induction in order to obtain a more synthetic and more accurate description of gene groups than the description obtained by initially determined rules. The obtained GO terms ranking and newly obtained rules provide additional information about the biological function of genes that compose the analyzed group of genes.

Fuzzy Clustering and Gene Ontology Based Decision Rules for Identification and Description of Gene Groups

The paper presents results of the research verifying whether gene clustering that takes under consideration both gene expression values and similarity of GO terms improves a quality of rule-based description of the gene groups. The obtained results show that application of the Conditional Robust Fuzzy C-Medoids algorithm enables to obtain gene groups similar to the groups determined by domain experts. However, the differences observed in clustering influences a description quality of the groups. The rules determined cover more genes retaining their statistical significance. The rules induction and post-processing method presented in the paper takes under consideration, among others, a hierarchy of GO terms and a compound measure that evaluates the generated rules. The approach presented is unique, it makes possible to limit a number of rules determined considerably and to obtain rules that reflect varied biological knowledge even if they cover the same genes.

Data- and expert-driven rule induction and filtering framework for functional interpretation and description of gene sets

BackgroundHigh-throughput methods in molecular biology provided researchers with abundance of experimental data that need to be interpreted in order to understand the experimental results. Manual methods of functional gene/protein group interpretation are expensive and time-consuming; therefore, there is a need to develop new efficient data mining methods and bioinformatics tools that could support the expert in the process of functional analysis of experimental results.ResultsIn this study, we propose a comprehensive framework for the induction of logical rules in the form of combinations of Gene Ontology (GO) terms for functional interpretation of gene sets. Within the framework, we present four approaches: the fully automated method of rule induction without filtering, rule induction method with filtering, expert-driven rule filtering method based on additive utility functions, and expert-driven rule induction method based on the so-called seed or expert terms – the GO terms of special interest which should be included into the description. These GO terms usually describe some processes or pathways of particular interest, which are related to the experiment that is being performed. During the rule induction and filtering processes such seed terms are used as a base on which the description is build.ConclusionWe compare the descriptions obtained with different algorithms of rule induction and filtering and show that a filtering step is required to reduce the number of rules in the output set so that they could be analyzed by a human expert. However, filtering may remove information from the output rule set which is potentially interesting for the expert. Therefore, in the study, we present two methods that involve interaction with the expert during the process of rule induction. Both of them are able to reduce the number of rules, but only in the case of the method based on seed terms, each of the created rule includes expert terms in combination with the other terms. Further analysis of such combinations may provide new knowledge about biological processes and their combination with other pathways related to genes described by the rules. A suite of Matlab scripts that provide the functionality of a comprehensive framework for the rule induction and filtering presented in this study is available free of charge at: http://rulego.polsl.pl/framework.