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Biomedical subjects

I Jurisica

Publications and source records attributed to I Jurisica.

12 recordsLinked to original sources
PubMed2006

Differentially androgen-modulated genes in ovarian epithelial cells from BRCA mutation carriers and control patients predict ovarian cancer survival and disease progression.

Epidemiological studies have implicated androgens in the etiology and progression of epithelial ovarian cancer. We previously reported that some androgen responses were dysregulated in malignant ovarian epithelial cells relative to control, non-malignant ovarian surface epithelial (OSE) cells. Moreover, dysregulated androgen responses were observed in OSE cells derived from patients with germline BRCA-1 or -2 mutations (OSEb), which account for the majority of familial ovarian cancer predisposition, and such altered responses may be involved in ovarian carcinogenesis or progression. In the present study, gene expression profiling using cDNA microarrays identified 17 genes differentially expressed in response to continuous androgen exposure in OSEb cells and ovarian cancer cells as compared to OSE cells derived from control patients. A subset of these differentially affected genes was selected and verified by quantitative real-time reverse transcription-polymerase chain reaction. Six of the gene products mapped to the OPHID protein-protein interaction database, and five were networked within two interacting partners. Basic leucine zipper transcription factor 2 (BACH2) and acetylcholinesterase (ACHE), which were upregulated by androgen in OSEb cells relative to OSE cells, were further investigated using an ovarian cancer tissue microarray from a separate set of 149 clinical samples. Both cytoplasmic ACHE and BACH2 immunostaining were significantly increased in ovarian cancer relative to benign cases. High levels of cytoplasmic ACHE staining correlated with decreased survival, whereas nuclear BACH2 staining correlated with decreased time to disease recurrence. The finding that products of genes differentially responsive to androgen in OSEb cells may predict survival and disease progression supports a role for altered androgen effects in ovarian cancer. In addition to BACH2 and ACHE, this study highlights a set of potentially functionally related genes for further investigation in ovarian cancer.

Acetylcholinesterase↗
PubMed2006

Efficient estimation of graphlet frequency distributions in protein-protein interaction networks.

MOTIVATION: Algorithmic and modeling advances in the area of protein-protein interaction (PPI) network analysis could contribute to the understanding of biological processes. Local structure of networks can be measured by the frequency distribution of graphlets, small connected non-isomorphic induced subgraphs. This measure of local structure has been used to show that high-confidence PPI networks have local structure of geometric random graphs. Finding graphlets exhaustively in a large network is computationally intensive. More complete PPI networks, as well as PPI networks of higher organisms, will thus require efficient heuristic approaches. RESULTS: We propose two efficient and scalable heuristics for finding graphlets in high-confidence PPI networks. We show that both PPI and their model geometric random networks, have defined boundaries that are sparser than the 'inner parts' of the networks. In addition, these networks exhibit 'uniformity' of local structure inside the networks. Our first heuristic exploits these two structural properties of PPI and geometric random networks to find good estimates of graphlet frequency distributions in these networks up to 690 times faster than the exhaustive searches. Our second heuristic is a variant of a more standard sampling technique and it produces accurate approximate results up to 377 times faster than the exhaustive searches. We indicate how the combination of these approaches may result in an even better heuristic. AVAILABILITY: Supplementary information is available at http://www.cs.toronto.edu/~natasha/BIOINF-2005-0946/Supplementary.pdf. Software implementing the algorithms is available at http://www.cs.toronto.edu/~natasha/BIOINF-2005-0946/estimate_grap-hlets.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2006

Transcriptional targets of hepatocyte growth factor signaling and Ki-ras oncogene activation in colorectal cancer.

Both Ki-ras mutation and hepatocyte growth factor (HGF) receptor Met overexpression occur at high frequency in colon cancer. This study investigates the transcriptional changes induced by Ki-ras oncogene and HGF/Met signaling activation in colon cancer cell lines in vitro and in vivo. The model system used in these studies included the DLD-1 colon cancer cell line with a mutated Ki-ras allele, and the DKO-4 cell line generated from DLD-1, with its mutant Ki-ras allele inactivated by targeted disruption. These cell lines were transduced with cDNAs of full-length Met receptor. Microarray transcriptional profiling was conducted on cell lines stimulated with HGF, as well as on tumor xenograft tissues. Overlapping genes between in vitro and in vivo microarray data sets were selected as a subset of HGF/Met and Ki-ras oncogene-regulated targets. Using the Online Predicted Human Interaction Database, novel HGF/Met and Ki-ras regulated proteins with putative functional linkage were identified. Novel proteins identified included histone acetyltransferase 1, phosphoribosyl pyrophosphate synthetase 2, chaperonin containing TCP1, subunit 8, CSE1 chromosome segregation 1-like (yeast)/cellular apoptosis susceptibility (mammals), CCR4-NOT transcription complex, subunit 8, and cyclin H. Transcript levels for these Met-signaling targets were correlated with Met expression levels, and were significantly elevated in both primary and metastatic human colorectal cancer samples compared to normal colorectal mucosa. These genes represent novel Met and/or Ki-ras transcriptionally coregulated genes with a high degree of validation in human colorectal cancers.

Alleles↗
PubMed2005

The histone deacetylase inhibitor valproic acid alters sensitivity towards all trans retinoic acid in acute myeloblastic leukemia cells.

Acute myeloblastic leukemia (AML) may be classified in a number of ways. Using the French American British classification, the M3 form of the disease or acute promyelocytic leukemia (APL) has been found to be sensitive in vitro and in vivo to the retinoid all trans retinoic acid (ATRA). The mechanism for this is by restoration of normal gene expression through the release of histone deacetylase complexes (HDACs). In contrast to APL, other forms of AML are either nonresponsive or show blunted responses to ATRA. We evaluated if the inhibitor of HDAC activity, valproic acid (VPA), could mimic or enhance retinoid sensitivity in the AML cell line, OCI/AML-2, and clinical samples derived from patients with AML. An Affymetrix GeneChip experiment demonstrated that VPA modulated the expression of numerous genes in OCI/AML-2 cells that were not affected by ATRA including p21, a retinoid responsive gene in APL. VPA induced p21 expression in OCI/AML-2 cells and the majority of the AML samples tested; this was associated with cell cycle arrest and apoptosis not seen with ATRA alone. The addition of ATRA to VPA accentuated many of these responses, supporting the potential beneficial combination of these drugs in the treatment of AML.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2004

Modeling interactome: scale-free or geometric?

MOTIVATION: Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have as accurate a model as possible. As a result, new techniques and models for analyzing and modeling real-world networks have recently been introduced. RESULTS: One example of large and complex networks involves protein-protein interaction (PPI) networks. We analyze PPI networks of yeast Saccharomyces cerevisiae and fruitfly Drosophila melanogaster using a newly introduced measure of local network structure as well as the standardly used measures of global network structure. We examine the fit of four different network models, including Erdos-Renyi, scale-free and geometric random network models, to these PPI networks with respect to the measures of local and global network structure. We demonstrate that the currently accepted scale-free model of PPI networks fails to fit the data in several respects and show that a random geometric model provides a much more accurate model of the PPI data. We hypothesize that only the noise in these networks is scale-free. CONCLUSIONS: We systematically evaluate how well-different network models fit the PPI networks. We show that the structure of PPI networks is better modeled by a geometric random graph than by a scale-free model. SUPPLEMENTARY INFORMATION: Supplementary information is available at http://www.cs.utoronto.ca/~juris/data/data/ppiGRG04/

Algorithms↗
PubMed2004

Protein complex prediction via cost-based clustering.

MOTIVATION: Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein-protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach to protein complex identification. RESULTS: We have developed the Restricted Neighborhood Search Clustering Algorithm (RNSC) to efficiently partition networks into clusters using a cost function. We applied this cost-based clustering algorithm to PPI networks of Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans to identify and predict protein complexes. We have determined functional and graph-theoretic properties of true protein complexes from the MIPS database. Based on these properties, we defined filters to distinguish between identified network clusters and true protein complexes. CONCLUSIONS: Our application of the cost-based clustering algorithm provides an accurate and scalable method of detecting and predicting protein complexes within a PPI network.

Algorithms↗
PubMed2004

Functional topology in a network of protein interactions.

MOTIVATION: The building blocks of biological networks are individual protein-protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells. RESULTS: We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database. CONCLUSIONS: Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure-function relationships observable in a large PPI network.

Algorithms↗
PubMed2004

Alterations in mitochondrial membrane potential during preimplantation stages of mouse and human embryo development.

Mitochondria are cellular organelles regulating metabolism and cell death pathways. This study examined changes in mitochondrial membrane potential (deltapsim) throughout the stages of preimplantation development in mouse embryos conceived either in vivo or in vitro and human embryos donated to research from IVF. Embryos stained with the deltapsim-sensitive dye (JC-1) were quantified for the ratio of high- to low-polarized mitochondria using a deconvolution microscope. Overall, mouse zygotes and early embryos contain a subset of high-polarized mitochondria with a progressive increase in the ratio of deltapsim observed with increasing cleavage. A transient increase in the ratio of high to low deltapsim was observed in in vivo fertilized 2-cell stage embryos, coincident with embryonic genome activation in the mouse, but not in 2-cell embryos obtained through IVF. We further observed that arrested mouse 2-cell embryos possessed an increased ratio of deltapsim compared with non-arrested embryos. In human 8-cell embryos we observed an increased ratio of high- to low-polarized mitochondria with increasing degrees of embryo fragmentation. We concluded that the pattern of mitochondrial membrane potential progressively changes throughout preimplantation development, and that an aberrant shift in deltapsim could contribute to, or is associated with, decreased developmental potential.

Animals↗
PubMed2002

Binary tree-structured vector quantization approach to clustering and visualizing microarray data.

MOTIVATION: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. RESULTS: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach.

Algorithms↗
PubMed2001

Mining mouse microarray data.

Microarrays of mouse genes are now available from several sources, and they have so far given new insights into gene expression in embryonic development, regions of the brain and during apoptosis. Microarray data posted on the internet can be reanalyzed to study a range of questions.

Animals↗
PubMed1998

Case-based reasoning in IVF: prediction and knowledge mining.

In vitro fertilization (IVF) is a medically-assisted reproduction technique, enabling infertile couples to achieve successful pregnancy. Given the unpredictability of the task, we propose to use a case-based reasoning system that exploits past experiences to suggest possible modifications to an IVF treatment plan in order to improve overall success rates. Once the system's knowledge base is populated with a sufficient number of past cases, it can be used to explore and discover interesting relationships among data, thereby achieving a form of knowledge mining. The article describes the TA3IVF system--a case-based reasoning system which relies on context-based relevance assessment to assist in knowledge visualization, interactive data exploration and discovery in this domain. The system can be used as an advisor to the physician during clinical work and during research to help determine what knowledge sources are relevant for a treatment plan.

Artificial Intelligence↗