Search PubMed⌕ Search

Biomedical subjects

James Lyons-Weiler

Publications and source records attributed to James Lyons-Weiler.

14 recordsLinked to original sources
PubMed2006

Proteomic analysis of urine in kidney transplant patients with BK virus nephropathy.

The differentiation of BK virus-associated renal allograft nephropathy (BKVAN) from acute allograft rejection (AR) in renal transplant recipients is an important clinical problem because the treatment can be diametrically opposite for the two conditions. The aim of this discovery-phase biomarker development study was to examine feasibility of developing a noninvasive method to differentiate BKVAN from AR. Surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry analysis was used to compare proteomic profiles of urine samples of 21 patients with BKVAN, 28 patients with AR (Banff Ia to IIb), and 29 patients with stable graft function. SELDI analysis showed proteomic profiles that were significantly different in the BKVAN group versus the AR and stable transplant groups. Peaks that corresponded to m/z values of 5.872, 11.311, 11.929, 12.727, and 13.349 kD were significantly higher in patients with BKVAN. Bioinformatics analyses allowed distinction of profiles of patients with BKVAN from patients with AR and stable patients. SELDI profiles also showed a high degree of reproducibility. Proteomic analysis of urine may offer a noninvasive way to differentiate BKVAN from AR in clinical practice. The identification of individual proteomic peaks can improve further the clinical utility of this screening method.

Adult↗
PubMed2006

Molecular overlap of fly circadian rhythms and human pancreatic cancer.

Circumstantial evidence demonstrating a role for circadian rhythms in cancer has been presented but there is little direct molecular evidence to support this idea in human cancer. Herein, we report a significant similarity between fly genes with strong circadian rhythms and human genes under expressed in pancreatic cancer. The list of genes includes both circadian regulator genes, such as period 1 and DEC1, and downstream effectors, such as ubiquitin specific protease 30. This observation may indicate that the pancreas peripheral clock is disrupted in pancreatic cancer and are consistent with the recent proposals that circadian genes act as tumor suppressors.

Animals↗
PubMed2006

Hepatic gene expression response to acute indomethacin exposure.

BACKGROUND: Rising morbidity and mortality related to the use of NSAIDs has led to the withdrawal of some of these agents and reconsideration of the adverse effects and usage paradigms of commonly available NSAIDs. Our objective in this study was to assay molecular indicators of acute hepatic injury associated with the administration of indomethacin, a prototypical NSAID, metabolized by the liver that undergoes enterohepatic circulation with associated gastrointestinal adverse effects. METHODS: Analysis of gene expression, using high-throughput, ADME (absorption, distribution, metabolism, excretion)-specific microarrays, was performed on RNA extracted from the livers of control or indomethacin treated rats, in parallel with serum enzyme tests and histological analysis of paraffin-embedded liver specimens. Male Sprague-Dawley rats (n = 45) were administered intraperitoneal injections of indomethacin for 3 days at the recommended normal dose (6.7 mg/kg), indomethacin at a high dose (20 mg/kg) or vehicle alone (controls). RESULTS: Upon termination of the study on day 4, serum gamma-glutamyl transferase activity and alkaline phosphatase/alanine aminotransferase ratios were significantly elevated in both high- and normal-dose cohorts compared with vehicle-treated animals. Diffuse microvascular steatosis was present in hepatic serial sections obtained from all animals subjected to the high-dosage regimen. High-resolution microarray analysis (six replicates/gene/animal) identified 256 genes, after outlier removal, in 17 functional classifications that were significantly altered by the high, but not by the normal dosage. These included depression of 10 of 11 cytochrome P450 genes (2B3, 2C70, 1A2-P2, 4F1, 2E1, 3A1, 2F1, 3AP7, 2C11, phenobarb-inducible P6) and 7 of 9 genes involved in the response to reactive oxygen species (e.g. glutathione reductase, glutathione transferase, and superoxide dismutase). Of 16 genes associated with toxin removal, nine exhibited significantly decreased transcripts. There was a marked shift away from lipid metabolism (decreased expression of eight genes) towards glucose utilization associated with steatosis. Despite the compromise of detoxification programs and a shift in metabolic substrate utilization, a compensatory remodeling response was activated, including genes for metalloproteases (ADAM10, MMP10, MMP11), integrins (integrin alpha-1 and alpha-E1), and extracellular matrix molecules (platelet/endothelial cell adhesion molecule-1 and heparan sulfate proteoglycan, perlecan), as well as transcripts associated with cell proliferation. The expression levels of only five genes were significantly altered among animals receiving the normal indomethacin dosage. CONCLUSION: These data confirmed that even brief exposure to indomethacin altered serum enzymatic activities and that high levels significantly altered gene expression in the liver and hepatic histology (by interfering with the clearance of toxins and xenobiotic substrates) and the regulation of basal metabolism.

Animals↗
PubMed2005

Prediction of lymph node metastasis by analysis of gene expression profiles in primary lung adenocarcinomas.

PURPOSE: Lymph node status is a strong predictor of outcome for lung cancer patients. Recently, several reports have hinted that gene expression profiles of primary tumor may be able to predict node status. The goals of this study were to determine if microarray data could be used to accurately classify patients with regard to pathologic lymph node status, and to determine if this analysis could identify patients at risk for occult disease and worse survival. EXPERIMENTAL DESIGN: Two previously published lung adenocarcinoma microarray data sets were reanalyzed. Patients were separated into two groups based on pathologic lymph node positive (pN+) or negative (pN0) status, and prediction analysis of microarray (PAM) was used for training and validation to classify nodal status. Overall survival analysis was performed based on PAM classifications. RESULTS: In the training phase, a 318-gene set gave classification accuracy of 88.4% when compared with pathology. Survival was significantly worse in PAM-positive compared with PAM-negative patients overall (P < 0.0001) and also when confined to pN0 patients only (P = 0.0037). In the validation set, classification accuracy was again 94.1% in the pN+ patients but only 21.2% in the pN0 patients. However, among the pN0 patients, recurrence rates and overall survival were significantly worse in the PAM-positive compared with PAM-negative patients (P = 0.0258 and 0.0507). CONCLUSIONS: Analysis of gene expression profiles from primary tumor may predict lymph node status but frequently misclassifies pN0 patients as node positive. Recurrence rates and overall survival are worse in these "misclassified" patients, implying that they may in fact have occult disease spread.

Adenocarcinoma↗
PubMed2005

Identification of ATF-3, caveolin-1, DLC-1, and NM23-H2 as putative antitumorigenic, progesterone-regulated genes for ovarian cancer cells by gene profiling.

Although progesterone (P4) has been implicated to offer protection against ovarian cancer (OCa), little is known of its mechanism of action. The goal of this study was to identify P4-regulated genes that have anti-OCa action. Three immortalized nontumorigenic human ovarian surface epithelial (HOSE) cell lines and three OCa (OVCA) cell lines were subjected to 5 days of P4 treatment. Transcriptional profiling with a cDNA microarray containing approximately 2400 known genes was used to identify genes (1) whose expression was consistently downregulated in OVCA cell lines compared to HOSE cell lines, and (2) whose expression was restored in OCa cell lines by P4 treatment. From the candidates selected, activating transcription factor-3 (ATF-3), caveolin-1, deleted in liver cancer-1 (DLC-1), and nonmetastatic clone 23 (NM23-H2) were chosen for post hoc functional studies based on their previously reported action as tumor suppressors or apoptosis inducers. Semiquantitative RT-PCR analyses confirmed loss of or reduced transcription of these genes in OVCA cells when compared to HOSE cells and their upregulation following P4 treatment. Hormonal specificity was demonstrated by blockade experiments with a progestin antagonist RU 38486. Ectopic expression of caveolin-1, DLC-1, and NM23-H2 caused growth inhibition in OVCA cell cultures, but not in HOSE cell cultures, while forced expression of ATF-3 suppressed growth in both. Overexpression of AFT-3 also enhanced caspase-3 activity in both HOSE and OVCA cells, whereas ectopic expression of caveolin-1 and DLC-1 only activated this enzyme in OCa cells. In contrast, NM23-H2 overexpression was ineffective in activating caspase-3. Overexpression of any of the four genes in OCa cells reduced soft-agar colony formation and cell invasiveness. Taken together, we have identified four new P4-regulated, antitumor genes for OCa. However, their modes of action differ significantly; ATF-3 primarily functions as an apoptosis inducer, NM23-H2 as a suppressor of cell motility, and caveolin-1 and DLC-1 exhibiting features of classical tumor suppressors. To the best of our knowledge, except for NM23-H2, this is the first report linking P4 to the regulation of these tumor suppressor/proapoptotic genes, which could serve as future therapeutic targets.

Activating Transcription Factor 3↗
PubMed2005

Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles.

BACKGROUND: Proteomic peptide profiling is an emerging technology harbouring great expectations to enable early detection, enhance diagnosis and more clearly define prognosis of many diseases. Although previous research work has illustrated the ability of proteomic data to discriminate between cases and controls, significantly less attention has been paid to the analysis of feature selection strategies that enable learning of such predictive models. Feature selection, in addition to classification, plays an important role in successful identification of proteomic biomarker panels. METHODS: We present a new, efficient, multivariate feature selection strategy that extracts useful feature panels directly from the high-throughput spectra. The strategy takes advantage of the characteristics of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) profiles and enhances widely used univariate feature selection strategies with a heuristic based on multivariate de-correlation filtering. We analyse and compare two versions of the method: one in which all feature pairs must adhere to a maximum allowed correlation (MAC) threshold, and another in which the feature panel is built greedily by deciding among best univariate features at different MAC levels. RESULTS: The analysis and comparison of feature selection strategies was carried out experimentally on the pancreatic cancer dataset with 57 cancers and 59 controls from the University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA. The analysis was conducted in both the whole-profile and peak-only modes. The results clearly show the benefit of the new strategy over univariate feature selection methods in terms of improved classification performance. CONCLUSION: Understanding the characteristics of the spectra allows us to better assess the relative importance of potential features in the diagnosis of cancer. Incorporation of these characteristics into feature selection strategies often leads to a more efficient data analysis as well as improved classification performance.

Journal Article↗
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

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↗
PubMed2003

Overcoming confounded controls in the analysis of gene expression data from microarray experiments.

A potential limitation of data from microarray experiments exists when improper control samples are used. In cancer research, comparisons of tumour expression profiles to those from normal samples is challenging due to tissue heterogeneity (mixed cell populations). A specific example exists in a published colon cancer dataset, in which tissue heterogeneity was reported among the normal samples. In this paper, we show how to overcome or avoid the problem of using normal samples that do not derive from the same tissue of origin as the tumour. We advocate an exploratory unsupervised bootstrap analysis that can reveal unexpected and undesired, but strongly supported, clusters of samples that reflect tissue differences instead of tumour versus normal differences. All of the algorithms used in the analysis, including the maximum difference subset algorithm, unsupervised bootstrap analysis, pooled variance t-test for finding differentially expressed genes and the jackknife to reduce false positives, are incorporated into our online Gene Expression Data Analyzer ( http:// bioinformatics.upmc.edu/GE2/GEDA.html ).

Algorithms↗
PubMed2002

Homocysteine, folate deprivation and Alzheimer neuropathology.

Increased levels of homocysteine (HC), arising in some situations via deficiencies in folate--an essential cofactor in metabolic regulation of HC--have long been known to contribute to cardiovascular disorders and stroke. More recently, clinical studies implicate increased HC and reduced folate with neurodegenerative conditions including Alzheimer's disease. It has remained unclear from clinical studies whether the neurotoxicity of increased HC and/or reduced folate is derived from direct detrimental effects on neurons themselves, or is instead derived indirectly following perturbation of nervous system vasculature. However, recent reports from several laboratories provide evidence that HC not only induces direct neurotoxicity, but also potentiates both amyloid-beta and glutamate neurotoxicity. These latter studies leave open the possibility that even mild elevations in HC may place neurons at risk for additional trauma. The potential contribution of folate deficiency and resultant increases in HC to neurodegeneration in AD, and therapeutic approaches to alleviate their impact, is discussed.

Alzheimer Disease↗
PubMedDate not supplied

Gene expression patterns in isolated keloid fibroblasts.

Keloid scars after skin trauma are a significant clinical problem, especially in black populations, in which the incidence of keloids has been estimated at 4-16%. Keloids are abnormal dermal proliferative scars secondary to dysregulated wound healing. Despite several biochemical studies on the role of extracellular matrix proteins and growth factors during keloid formation, we still do not know what molecules and signals induce this change. Fibroblasts are thought to be the major inductive cell for keloid scar formation. The aim of this study was to identify gene expression patterns that characterize keloid fibroblasts; identifying such genetic disequilibrium may shed light on the molecular signaling events responsible for keloid formation. In this study, we performed gene expression analysis of fibroblasts isolated from keloid lesions from three individuals in comparison with the fibroblasts isolated from normal skin using the Affymetrix U133a chip (22,284 genes and expression sequence tags). We found through J5 test score expression analysis that among 22,284 genes, there were 43 genes that were overexpressed and five genes were underexpressed in keloid fibroblasts when compared with dermal fibroblasts from persons without keloids. The overexpression of three genes not previously reported as being up-regulated in keloids (annexin A2, Transgelin, and RPS18) was confirmed by real-time polymerase chain reaction. Certain overexpressed genes were similar to previous biochemical observations on the protein levels of these overexpressed genes during keloid formation. We also report for the first time that a few tumor-related genes are overexpressed in keloid fibroblasts.

Adult↗