Biomedical subjects

Satish Patel

Publications and source records attributed to Satish Patel.

8 recordsLinked to original sources

PubMed2006

Glycogen synthase kinase-3--an overview of an over-achieving protein kinase.

Glycogen synthase kinase-3 (GSK-3) has attracted much scrutiny due to its plethora of cellular functions, novel mechanisms of regulation and its potential as a therapeutic target for several common diseases. In mammals, GSK-3 is encoded by two genes, termed GSK-3alpha and GSK-3beta, that yield related but distinct protein-serine kinases. GSK-3 is unusual in that its protein kinase activity tends to be high in resting cells and cellular stimuli, such as hormones and growth factors, result in its catalytic inactivation. Further, many of the substrate proteins of GSK-3 are functionally inhibited by phosphorylation. Thus, signals that inhibit GSK-3 often cause activation of its diverse array of target proteins. Regulation of GSK-3 is important for normal development, regulation of metabolism, neuronal growth and differentiation and modulation of cell death. Dysregulation of GSK-3 activity has been implicated in human pathologies such as neurodegenerative diseases and type-2 diabetes. In this introductory chapter we provide a primer on the modes of GSK-3 regulation and a description of the various signaling pathways and cellular processes in which GSK-3 is an active participant.

Animals↗

PubMed2004

Glycogen synthase kinase-3 regulates IGFBP-1 gene transcription through the thymine-rich insulin response element.

BACKGROUND: Hepatic expression of several gene products involved in glucose metabolism, including phosphoenolpyruvate carboxykinase (PEPCK), glucose-6-phosphatase (G6Pase) and insulin-like growth factor binding protein-1 (IGFBP-1), is rapidly and completely inhibited by insulin. This inhibition is mediated through the regulation of a DNA element present in each of these gene promoters, that we call the Thymine-rich Insulin Response Element (TIRE). The insulin signalling pathway that results in the inhibition of these gene promoters requires the activation of phosphatidylinositol 3-kinase (PI 3-kinase). However, the molecules that connect PI 3-kinase to these gene promoters are not yet fully defined. Glycogen Synthase Kinase 3 (GSK-3) is inhibited following activation of PI 3-kinase. We have shown previously that inhibitors of GSK-3 reduce the activity of two TIRE-containing gene promoters (PEPCK and G6Pase), whose products are required for gluconeogenesis. RESULTS: In this report we demonstrate that in H4IIE-C3 cells, four distinct classes of GSK-3 inhibitor mimic the effect of insulin on a third TIRE-containing gene, IGFBP-1. We identify the TIRE as the minimum requirement for inhibition by these agents, and demonstrate that the target of GSK-3 is unlikely to be the postulated TIRE-binding protein FOXO-1. Importantly, overexpression of GSK-3 in cells reduces the insulin regulation of TIRE activity as well as endogenous IGFBP-1 expression. CONCLUSIONS: These results implicate GSK-3 as an intermediate in the pathway from the insulin receptor to the TIRE. Indeed, this is the first demonstration of an absolute requirement for GSK-3 inhibition in insulin regulation of gene transcription. These data support the potential use of GSK-3 inhibitors in the treatment of insulin resistant states such as Type 2 diabetes mellitus, but suggest that it will be important to identify all TIRE-containing genes to assess potential side effects of these agents.

Animals↗

PubMed2004

Tests for finding complex patterns of differential expression in cancers: towards individualized medicine.

BACKGROUND: Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g., comparison of sample means) to identify differentially expressed genes. This leads to the discovery of 'population-level' markers, i.e., genes with the expression patterns A > B and B > A. We introduce the PPST test that identifies genes where a significantly large subset of cases exhibit expression values beyond upper and lower thresholds observed in the control samples. RESULTS: Interestingly, the test identifies A > B and B < A pattern genes that are missed by population-level approaches, such as the t-test, and many genes that exhibit both significant overexpression and significant underexpression in statistically significantly large subsets of cancer patients (ABA pattern genes). These patterns tend to show distributions that are unique to individual genes, and are aptly visualized in a 'gene expression pattern grid'. The low degree of among-gene correlations in these genes suggests unique underlying genomic pathologies and high degree of unique tumor-specific differential expression. We compare the PPST and the ABA test to the parametric and non-parametric t-test by analyzing two independently published data sets from studies of progression in astrocytoma. CONCLUSIONS: The PPST test resulted findings similar to the nonparametric t-test with higher self-consistency. These tests and the gene expression pattern grid may be useful for the identification of therapeutic targets and diagnostic or prognostic markers that are present only in subsets of cancer patients, and provide a more complete portrait of differential expression in cancer.

Delivery of Health Care↗

PubMed2004

caGEDA: a web application for the integrated analysis of global gene expression patterns in cancer.

The explosion of microarray data from pilot studies, basic research and large-scale clinical trials requires the development of integrative computational tools that can not only analyse gene expression patterns but that can also evaluate the methods of analysis adopted and then provide a boost to post-analysis translational interpretation of those patterns. We have developed a web application called caGEDA (cancer gene expression data analyzer) that can: (1) upload gene expression profiles from cDNA or oligonucleotide microarrays; (2) conduct a diverse range of serial linear normalisations; (3) identify differentially expressed genes using a variety of tests - either threshold or permutation tests; (4) produce tables of literature references to papers reporting that specific genes (identified by accession numbers) are up- or down-regulated in specific cancers; (5) estimate the error of sample class prediction using the significant gene set for features; (6) perform low-bias and accurate validated learning using three computational validation techniques (leave-one out validation, k-fold validation, random re-sampling validation); and (7) validate a classifier with a randomly selected or user-defined validation set. Significant genes are reported in a table of links to entries in the following databases: Locus Link, Genome View, UCSC, Ensembl, UniGene, dbSNP, AmiGO and OMIM. caGEDA is seamlessly integrated via embedded forms with UCSD's (University of California at San Diego) 2HAPI server (for medical subject heading (MeSH) term exploration) and EZ-Retrieve (to identify common transcription factors located upstream of sets of genes that exhibit similar modes of differential expression). caGEDA offers a variety of previously described and novel tests for differentially expressed genes, most notably the permutation percentile separability test, which is most appropriate for identifying genes that are significantly differentially expressed in a subset of patients. caGEDA, which is open source and free to academic users, will soon be greatly enhanced by operating with the components of the National Cancer Institute's new cancer bioinformatics grid (caBIG).

Biomarkers, Tumor↗

PubMed2003

Different mechanisms are used by insulin to repress three genes that contain a homologous thymine-rich insulin response element.

Insulin rapidly and completely inhibits expression of the hepatic insulin-like growth factor binding protein-1 (IGFBP-1), phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase) genes. This inhibition is mediated through a phosphatidyl inositol 3-kinase-dependent regulation of a DNA element, termed the thymine-rich insulin response element, found within the promoters of each of these genes. This has led to the conclusion that these three promoters are regulated by insulin using the same molecular mechanism. However, we recently found that the regulation of the IGFBP1 but not the PEPCK or G6Pase genes by insulin was sensitive to rapamycin, an inhibitor of mTOR. Here, we present further evidence that different regulatory pathways mediate the insulin regulation of these promoters. Importantly, we identify a protein phosphatase activity in the pathway connecting mTOR to the IGFBP-1 promoter. These data have major implications for the development of molecular therapeutics for the treatment of insulin-resistant states such as diabetes and hypertension.

Animals↗

PubMed2003

A classification-based machine learning approach for the analysis of genome-wide expression data.

Three important areas of data analysis for global gene expression analysis are class discovery, class prediction, and finding dysregulated genes (biomarkers). The clinical application of microarray data will require marker genes whose expression patterns are sufficiently well understood to allow accurate predictions on disease subclass membership. Commonly used methods of analysis include hierarchical clustering algorithms, t-, F-, and Z-tests, and machine learning approaches. We describe an approach called the maximum difference subset (MDSS) algorithm that combines classification algorithms, classical statistics, and elements of machine learning and provides a coherent framework. By integrating prediction accuracy, the MDSS algorithm learns the critical threshold of statistical significance (the alpha or P-value), eliminating the arbitrariness of setting a threshold of statistical significance and minimizing the effect of the normality assumptions. To reduce the false positive rate and to increase external validity of the predictive gene set, a jackknife step is used. This step identifies and removes genes in the initial MDSS with low combined predictive utility. The overall MDSS provides a prediction that is less dependent on an arbitrary study design (sample inclusion or exclusion) and should thus have high external validity. We demonstrate that this approach, unlike other published methods, identifies biomarkers capable of predicting the outcome of anthracycline-cytarabine chemotherapy in cases of acute myeloid leukemia. By incorporating two criteria-statistical significance and predictive utility-the approach learns the significance level relevant for a given data set. The MDSS approach can be used with any test and classifier operator pair.

Acute Disease↗

PubMed2002

Insulin regulation of hepatic insulin-like growth factor-binding protein-1 (IGFBP-1) gene expression and mammalian target of rapamycin (mTOR) signalling is impaired by the presence of hydrogen peroxide.

Hepatic expression of insulin-like growth factor-binding protein-1 (IGFBP-1) is rapidly and completely inhibited by insulin. The signalling pathway that mediates this effect of insulin requires the activation of phosphoinositide 3-kinase (PI 3-kinase). Many of the cellular actions of insulin, including activation of PI 3-kinase, can be 'mimicked' by oxidative stresses, such as H(2)O(2). In the present study, we demonstrate that H(2)O(2) does not 'mimic' but rather antagonizes insulin repression of IGFBP-1 gene expression in H4IIE cells. This effect is accompanied by a decrease in the insulin-induced activation of mammalian target of rapamycin (mTOR)-dependent signalling. However, insulin-induced phosphorylation and regulation of protein kinase B, glycogen synthase kinase-3 and FKHR (forkhead in rhabdomyosarcoma) are not affected by H(2)O(2) in the same cells. In addition, H(2)O(2) strongly activates the p42/p44 mitogen-activated protein kinases, but the presence of PD184352 (an inhibitor of this pathway) does not block the effect of H(2)O(2) on IGFBP-1 gene expression. Our results support the view that the insulin-mediated repression of IGFBP-1 gene expression is partly mTOR-dependent, and demonstrate that H(2)O(2) selectively antagonizes mTOR-dependent insulin action. The implications for the use of H(2)O(2)-generating agents as therapeutics for the treatment of insulin resistance, as well as the role of oxidative stress in the development of insulin resistance, are discussed.

Animals↗

PubMed2002

Insulin regulation of insulin-like growth factor-binding protein-1 gene expression is dependent on the mammalian target of rapamycin, but independent of ribosomal S6 kinase activity.

Insulin inhibits the expression of the hepatic insulin-like growth factor-binding protein-1 (IGFBP-1) and glucose-6-phosphatase (G6Pase) genes. The signaling pathway that mediates these events requires the activation of phosphatidylinositol 3-kinase, whereas transfection studies have suggested an involvement of Akt (protein kinase B) and FKHR, a transcription factor regulated by Akt. We now demonstrate that insulin repression of endogenous IGFBP-1 gene transcription was blocked by rapamycin or by amino acid starvation. Rapamycin inhibited the mammalian target of rapamycin (mTOR) and the subsequent activation of p70/p85 S6 protein kinase-1 (S6K1) by insulin, whereas amino acid depletion prevented insulin induction of these signaling molecules. Importantly, we demonstrate that insulin regulation of the thymine-rich insulin response element of the IGFBP-1 promoter was also inhibited by rapamycin. However, sustained activation of S6K1 did not repress this promoter. In addition, rapamycin did not affect insulin regulation of G6Pase expression or Akt activation. We propose that these observations indicate that an mTOR-dependent, but S6K-independent mechanism regulates the suppression of IGFBP-1 (but not G6Pase) gene expression by insulin. Therefore, although the insulin-responsive sequence of the G6Pase gene promoter is related to that of the IGFBP-1 promoter, the signaling pathways that mediate suppression of these genes are distinct.

Adenoviridae↗