Biomedical subjects

Russ Wolfinger

Publications and source records attributed to Russ Wolfinger.

6 recordsLinked to original sources

PubMed2006

Receptor-selective coactivators as tools to define the biology of specific receptor-coactivator pairs.

In the absence of specific high-affinity agonists and antagonists, it has been difficult to define the target genes and biological responses attributable to many of the orphan nuclear receptors (ONRs). Indeed, it appears that many members of this receptor superfamily are not regulated by classical small molecules but rather their activity is controlled by interacting cofactors. Motivated by this finding, we have developed an approach to genetically isolate specific receptor-cofactor pairs in cells, allowing us to define the biological responses attributable to each complex. This is accomplished by using combinatorial peptide phage display to engineer the receptor interacting domain of each cofactor such that it interacts selectively with one nuclear receptor. In this study, we describe the customization of PGC-1alpha and its use to study the biology of the estrogen-related receptor alpha (ERRalpha) in cultured liver cells.

Cell Line, Tumor↗

PubMed2005

Analysis of variation of amplitudes in cell cycle gene expression.

BACKGROUND: Variation in gene expression among cells in a population is often considered as noise produced from gene transcription and post-transcription processes and experimental artifacts. Most studies on noise in gene expression have emphasized a few well-characterized genes and proteins. We investigated whether different cell-arresting methods have impacts on the maximum expression levels (amplitudes) of a cell cycle related gene. RESULTS: By introducing random noise, modeled by a von Mises distribution, to the phase angle in a sinusoidal model in a cell population, we derived a relationship between amplitude and the distribution of noise in maximum transcription time (phase). We applied our analysis to Whitfield's HeLa cell cycle data. Our analysis suggests that among 47 cell cycle related genes common to the 2nd experiment (thymidine-thymidine method) and the 4th experiment (thymidine-nocodazole method): (i) the amplitudes of CDC6 and PCNA, which are expressed during G1/S phase, are smaller in the 2nd experiment than in the 4th, while the amplitude of CDC20, which is expressed during G2/M phase, is smaller in the 4th experiment; and (ii) the two cell-arresting methods had little impact on the amplitudes of the other 43 genes in the 2nd and 4th experiments. CONCLUSION: Our analysis suggests that procedures that arrest cells in different stages of the cell cycle differentially affect expression of some cell cycle related genes once the cells are released from arrest. The impact of the cell-arresting method on expression of a cell cycle related gene can be quantitatively estimated from the ratio of two estimated amplitudes in two experiments. The ratio can be used to gauge the variation in the phase/peak expression time distribution involved in stochastic transcription and post-transcriptional processes for the gene. Further investigations are needed using normal, unperturbed and synchronized HeLa cells as a reference to compare how many cell cycle related genes are directly and indirectly affected by various cell-arresting methods.

Cdc20 Proteins↗

PubMed2004

Cross-site comparison of gene expression data reveals high similarity.

Consistency and coherence of gene expression data across multiple sites depends on several factors such as platform (oligo, cDNA, etc.), environmental conditions at each laboratory, and data quality. The Hepatotoxicity Working Group of the International Life Sciences Institute Health and Environmental Sciences Institute consortium on the application of genomics to mechanism-based risk assessment is investigating these factors by comparing high-density gene expression data sets generated on two sets of RNA from methapyrilene (MP) experiments conducted at Abbott Laboratories and Boehringer-Ingelheim Pharmaceuticals, Inc. using a single platform (Affymetrix Rat Genome U34A GeneChip) at seven different sites. This article focuses on the evaluation of data quality and statistical models that facilitate the comparison of such data sets at the probe level. We present methods for exploring and quantitatively assessing differences in the data, with the principal goal being the generation of lists of site-insensitive genes responsive to low and high doses of MP. A combination of numerical and graphical techniques reveals important patterns and partitions of variability in the data, including the magnitude of the site effects. Although the site effects are significantly large in the analysis results, they appear to be primarily additive and therefore can be adjusted in the statistical calculations in a way that does not bias conclusions regarding treatment differences.

Animals↗

PubMed2003

Generalizable mass spectrometry mining used to identify disease state biomarkers from blood serum.

We bring a "spectrum" of classical data mining and statistical analysis methods to bear on discrimination of two groups of spectra from 24 diseased and 17 normal patients. Our primary goal is to accurately estimate the generalizability of this small dataset. After an aggressive preprocessing step that reduces consideration to only 55 peaks, we conduct over 35 out-of-sample cross-validation simulations of logistic regression, binary decision trees, and linear discriminant analysis. Misclassification rates grow worse as the size of the holdout sample increases, with many exceeding 30 percent. The ability to generalize is clearly tempered by the statistical, instrumentation, and biophysical characteristics of the study.

Biomarkers↗

PubMed2003

Mixed models for assessing correlation in the presence of replication.

The need to assess correlation in settings where multiple measurements are available on each of the variables of interest often arises in environmental science. However, this topic is not covered in introductory statistics texts. Although several ad hoc approaches can be used, they can easily lead to invalid conclusions and to a difficult choice of an appropriate measure of the correlation. Lam et al. approached this problem by using maximum likelihood estimation in cases where the replicate measurements are linked over time, but the method requires specialized software. We reanalyze the data of Lam et al. using PROC MIXED in SAS and show how to obtain the parameter estimates of interest with just a few lines of code. We then extend Lam et al.'s method to settings where the replicate measurements are not linked. Analysis of the unlinked case is illustrated with data from a study designed to assess correlations between indoor and outdoor measurements of benzene concentration in the air.

Environmental Monitoring↗

PubMed2002

A systematic statistical linear modeling approach to oligonucleotide array experiments.

We outline and describe steps for a statistically rigorous approach to analyzing probe-level Affymetrix GeneChip data. The approach employs classical linear mixed models and operates on a gene-by-gene basis. Forgoing any attempts at gene presence or absence calls, the method simultaneously considers the data across all chips in an experiment. Primary output includes precise estimates of fold change (some as low as 1.1), their statistical significance, and measures of array and probe variability. The method can accommodate complex experiments involving many kinds of treatments and can test for their effects at the probe level. Furthermore, mismatch probe data can be incorporated in different ways or ignored altogether. Data from an ionizing radiation experiment on human cell lines illustrate the key concepts.

Cells, Cultured↗