Biomedical subjects

Kerby Shedden

Publications and source records attributed to Kerby Shedden.

9 recordsLinked to original sources

PubMed2026

Urinary clusterin as a biomarker of human kidney disease progression and response to the endothelin receptor antagonist atrasentan: An exploratory analysis from the SONAR trial.

The endothelin receptor antagonist atrasentan improved kidney outcomes in the SONAR trial for type 2 diabetes and chronic kidney disease (NCT01858532), though individual responses varied. To identify molecular biomarkers of atrasentan response and outcome, we conducted a nested case-control proteomics study (N = 180) within the SONAR trial population and identified urinary clusterin (uCLU) as the top candidate. Transcriptomic analyses of human kidney biopsies at tissue and single cell level from independent cohorts revealed higher CLU mRNA levels associated with worse kidney function and outcomes. An endothelin signaling activation score derived from pathway genes was reduced by atrasentan in mice with diabetic kidney disease. In the SONAR trial (N = 3,060) population, higher uCLU predicted worse outcomes, while atrasentan reduced uCLU by 42.6% over six weeks. Early uCLU changes independently predict improved kidney outcomes. In summary, uCLU is associated with kidney disease progression and response to atrasentan treatment, supporting its potential as a pharmacodynamic biomarker to target therapy.

Humans↗

PubMed2003

Microarray analysis of gene expression during the cell cycle.

Microarrays have been applied to the determination of genome-wide expression patterns during the cell cycle of a number of different cells. Both eukaryotic and prokaryotic cells have been studied using whole-culture and selective synchronization methods. The published microarray data on yeast, mammalian, and bacterial cells have been uniformly interpreted as indicating that a large number of genes are expressed in a cell-cycle-dependent manner. These conclusions are reconsidered using explicit criteria for synchronization and precise criteria for identifying gene expression patterns during the cell cycle. The conclusions regarding cell-cycle-dependent gene expression based on microarray analysis are weakened by arguably problematic choices for synchronization methodology (e.g., whole-culture methods that do not synchronize cells) and questionable statistical rigor for identifying cell-cycle-dependent gene expression. Because of the uncertainties in synchrony methodology, as well as uncertainties in microarray analysis, one should be somewhat skeptical of claims that there are a large number of genes expressed in a cell-cycle-dependent manner.

Journal Article↗

PubMed2003

Expulsion of small molecules in vesicles shed by cancer cells: association with gene expression and chemosensitivity profiles.

Anticancer drug resistance results from selective pressure of chemotherapy, together with mutations or epigenetic changes that make cells refractory to treatment. In cancer cells, we report that gene expression associated with vesicle shedding correlates with chemosensitivity profiles. Experiments with doxorubicin and other small molecules confirm drug accumulation and expulsion in shed vesicles. Relative differences in the rate of vesicle shedding correspond with doxorubicin resistance across various cell lines. Moreover, accumulation of drug in membrane domains in which vesicles originate accounts for drug expulsion in shed vesicles. These observations implicate vesicle shedding as a drug efflux mechanism potentially involved in drug resistance.

Antineoplastic Agents↗

PubMed2003

A rational approach to personalized anticancer therapy: chemoinformatic analysis reveals mechanistic gene-drug associations.

PURPOSE: To predict the response of cells to chemotherapeutic agents based on gene expression profiles, we performed a chemoinformatic study of a set of standard anticancer agents assayed for activity against a panel of 60 human tumor-derived cell lines from the Developmental Therapeutics Program (DTP) at the National Cancer Institute (NCI). METHODS: Mechanistically-relevant gene:drug activity associations were identified in the scientific literature. The correlations between expression levels of drug target genes and the activity of the drugs against the NCI's 60 cell line panel were calculated across and within each tumor tissue type, using published drug activity and gene expression data. RESULTS: Compared to other mechanistically-relevant gene-drug associations, that of triciribine phosphate (TCN-P) and adenosine kinase (ADK) was exceptionally strong--overall and within tumor tissue types-across the 60 cell lines profiled for chemosensitivity (1) and gene expression (2). CONCLUSION: The results suggest ADK expression may be useful for stratifying TCN-P-responsive vs. non-responsive tumors. Based on TCN-P's mechanism of action and the observed TCN-P:ADK association, we contend that catalytic drug activation provides a rational, mechanistic basis for personalizing cancer treatment based on tumor-specific differences in the expression of drug-activating enzymes.

Acenaphthenes↗

PubMed2003

The application of rule-based methods to class prediction problems in genomics.

We propose a method for constructing classifiers using logical combinations of elementary rules. The method is a form of rule-based classification, which has been widely discussed in the literature. In this work we focus specifically on issues that arise in the context of classifying cell samples based on RNA or protein expression measurements. The basic idea is to specify elementary rules that exhibit a locally strong pattern in favor of a single class. Strict admissibility criteria are imposed to produce a manageable universe of elementary rules. Then the elementary rules are combined using a set covering algorithm to form a composite rule that achieves a perfect fit to the training data. The user has explicit control over a parameter that determines the composite rule's level of redundancy and parsimony. This built-in control, along with the simplicity of interpreting the rules, makes the method particularly useful for classification problems in genomics. We demonstrate the new method using several microarray datasets and examine its generalization performance. We also draw comparisons to other machine-learning strategies such as CART, ID3, and C4.5.

Breast Neoplasms↗

PubMed2002

Analysis of cell-cycle gene expression in Saccharomyces cerevisiae using microarrays and multiple synchronization methods.

Microarray analysis of gene expression during the yeast division cycle has led to the proposal that a significant number of genes in Saccharomyces cerevisiae are expressed in a cell-cycle-specific manner. Four different methods of synchronization were used for cell-cycle analysis. Randomized data exhibit periodic patterns of lesser strength than the experimental data. Thus the cyclicities in the expression measurements in the four experiments presented do not arise from chance fluctuations or noise in the data. However, when the degree of cyclicity for genes in different experiments are compared, a large degree of non-reproducibility is found. Re-examining the phase timing of peak expression, we find that three of the experiments (those using alpha-factor, CDC28 and CDC15 synchronization) show consistent patterns of phasing, but the elutriation synchrony results demonstrate a different pattern from the other arrest-release synchronization methods. Specific genes can show a wide range of cyclical behavior between different experiments; a gene with high cyclicity in one experiment can show essentially no cyclicity in another experiment. The elutriation experiment, possibly being the least perturbing of the four synchronization methods, may give the most accurate characterization of the state of gene expression during the normal, unperturbed cell cycle. Under this alternative explanation, the observed cyclicities in the other three experiments are a stress response to synchronization, and may not reproduce in unperturbed cells.

Cell Cycle↗

PubMed2002

Analysis of cell-cycle-specific gene expression in human cells as determined by microarrays and double-thymidine block synchronization.

Microarray analysis of gene expression patterns for thousands of human genes has led to the proposal that a large number of genes are expressed in a cell-cycle-specific manner. The identification of cyclically expressed genes was based on Affymetrix microarray analysis of gene expression after double-thymidine block synchronization. A statistical reanalysis of the original data leads to three principal findings. (i) Randomized data exhibit periodic patterns of similar or greater strength than the experimental data. This finding suggests that all apparent cyclicities in the expression measurements may arise from chance fluctuations. (ii) The presence of cyclicity and the timing of peak cyclicity in a given gene are not reproduced in two replicate experiments. This fact suggests there is an uncontrolled source of experimental variation that is stronger than the innate variation of gene expression in cells over time. (iii) The amplitude of peak expression in the second cycle is not consistently smaller than the corresponding amplitude in the first cycle. This finding places doubt on the assumption that the cells are actually synchronized. We propose that the microarray results do not support the proposal that there are numerous cell-cycle-specifically expressed genes in human cells.

Cell Cycle↗

PubMedDate not supplied

Chemoinformatic analysis of a supertargeted combinatorial library of styryl molecules.

Styryl dyes are fluorescent, lipophilic cations that have been used as specific labeling probes of mitochondria in living cells. For specific applications such as epifluorescence microscopy or flow cytometry, it is often desirable to synthesize fluorescent derivatives with optimized excitation, emission, and localization properties. Here, we present a chemoinformatic strategy suitable for multiparameter analysis of a combinatorial library of styryl molecules supertargeted to mitochondria. The strategy is based on a simple additive model relating the spectral and subcellular localization characteristics of styryl compounds to the two chemical building blocks that are used to synthesize the molecules. Using a cross-validation approach, the additive model predicts with a high degree of confidence the subcellular localization and spectral properties of the styryl product, from numerical scores that are independently associated with the individual building blocks of the molecule. The fit of the data indicates that more complex, nonadditive interactions between the two building blocks play a minor role in determining the molecule's optical or biological properties. Moreover, the observed additive relationship allows mechanistic inferences to be made regarding the structure-property relationship observed for this particular class of molecules. It points to testable, mechanistic hypotheses about how chemical structure, fluorescence, and localization properties are related.

Journal Article↗

PubMedDate not supplied

Proteomic analysis of cytokeratin isoforms uncovers association with survival in lung adenocarcinoma.

Cytokeratins (CK) are intermediate filaments whose expression is often altered in epithelial cancer. Systematic identification of lung adenocarcinoma proteins using two-dimensional polyacrylamide gel electrophoresis and mass spectrometry has uncovered numerous CK isoforms. In this study, 93 lung adenocarcinomas (64 stage I and 29 stage III) and 10 uninvolved lung samples were quantitatively examined for protein expression. Fourteen of 21 isoforms of CK 7, 8, 18, and 19 occurred at significantly higher levels (P < .05) in tumors compared to uninvolved adjacent tissue. Specific isoforms of the four types of CK identified correlated with either clinical outcome or individual clinical-pathological parameters. All five of the CK7 isoforms associated with patient survival represented cleavage products. Two of five CK7 isoforms (nos. 2165 and 2091), one of eight CK8 isoforms (no. 439), and one of three CK19 isoforms (no. 1955) were associated with survival and significantly correlated to their mRNA levels, suggesting that transcription underlies overexpression of these CK isoforms. Our data indicate substantial heterogeneity among CK in lung adenocarcinomas resulting from posttranslational modifications, some of which correlated with patient survival and other clinical parameters. Therefore, specific isoforms of individual CK may have utility as diagnostic or predictive markers in lung adenocarcinomas.

Adenocarcinoma↗