Biomedical subjects

Gregory E Gonye

Publications and source records attributed to Gregory E Gonye.

6 recordsLinked to original sources

PubMed2006

A universal reference sample derived from clone vector for improved detection of differential gene expression.

BACKGROUND: Using microarrays by co-hybridizing two samples labeled with different dyes enables differential gene expression measurements and comparisons across slides while controlling for within-slide variability. Typically one dye produces weaker signal intensities than the other often causing signals to be undetectable. In addition, undetectable spots represent a large problem for two-color microarray designs and most arrays contain at least 40% undetectable spots even when labeled with reference samples such as Stratagene's Universal Reference RNAs. RESULTS: We introduce a novel universal reference sample that produces strong signal for all spots on the array, increasing the average fraction of detectable spots to 97%. Maximizing detectable spots on the reference image channel also decreases the variability of microarray data allowing for reliable detection of smaller differential gene expression changes. The reference sample is derived from sequence contained in the parental EST clone vector pT7T3D-Pac and is called vector RNA (vRNA). We show that vRNA can also be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This reference sample can be made inexpensively in large quantities as a renewable resource that is consistent across experiments. CONCLUSION: Results of this study show that vRNA provides a useful universal reference that yields high signal for almost all spots on a microarray, reduces variation and allows for comparisons between experiments and laboratories. Further, it can be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This type of reference allows for detection of small changes in differential expression while reference designs in general allow for large-scale multivariate experimental designs. vRNA in combination with reference designs enable systems biology microarray experiments of small physiologically relevant changes.

Calibration↗

PubMed2005

Kinetics of cardiac thin-filament activation probed by fluorescence polarization of rhodamine-labeled troponin C in skinned guinea pig trabeculae.

A genetically engineered cardiac TnC mutant labeled at Cys-84 with tetramethylrhodamine-5-iodoacetamide dihydroiodide was passively exchanged for the endogenous form in skinned guinea pig trabeculae. The extent of exchange averaged nearly 70%, quantified by protein microarray of individual trabeculae. The uniformity of its distribution was verified by confocal microscopy. Fluorescence polarization, giving probe angle and its dispersion relative to the fiber long axis, was monitored simultaneously with isometric tension. Probe angle reflects underlying cTnC orientation. In steady-state experiments, rigor cross-bridges and Ca2+ with vanadate to inhibit cross-bridge formation produce a similar change in probe orientation as that observed with cycling cross-bridges (no Vi). Changes in probe angle were found at [Ca2+] well below those required to generate tension. Cross-bridges increased the Ca2+ dependence of angle change (cooperativity). Strong cross-bridge formation enhanced Ca2+ sensitivity and was required for full change in probe position. At submaximal [Ca2+], the thin filament regulatory system may act in a coordinated fashion, with the probe orientation of Ca2+-bound cTnC significantly affected by Ca2+ binding at neighboring regulatory units. The time course of the probe angle change and tension after photolytic release [Ca2+] by laser photolysis of NP-EGTA was Ca2+ sensitive and biphasic: a rapid component approximately 10 times faster than that of tension and a slower rate similar to that of tension. The fast component likely represents steps closely associated with Ca2+ binding to site II of cTnC, whereas the slow component may arise from cross-bridge feedback. These results suggest that the thin filament activation rate does not limit the tension time course in cardiac muscle.

Actin Cytoskeleton↗

PubMed2004

Erythroid-induced commitment of K562 cells results in clusters of differentially expressed genes enriched for specific transcription regulatory elements.

Understanding regulation of fetal and embryonic hemoglobin expression is critical, since their expression decreases clinical severity in sickle cell disease and beta-thalassemia. K562 cells, a human erythroleukemia cell line, can differentiate along erythroid or megakaryocytic lineages and serve as a model for regulation of fetal/embryonic globin expression. We used microarray expression profiling to characterize transcriptomes from K562 cells treated for various times with hemin, an inducer of erythroid commitment. Approximately 5,000 genes were expressed irrespective of treatment. Comparative expression analysis (CEA) identified 899 genes as differentially expressed; analysis by the self-organizing map (SOM) algorithm clustered 425 genes into 8 distinct expression patterns, 322 of which were shared by both analyses. Differential expression of a subset of genes was validated by real-time RT-PCR. Analysis of 5'-flanking regions from differentially expressed genes by PAINT v3.0 software showed enrichment in specific transcription regulatory elements (TREs), some localizing to different expression clusters. This finding suggests coordinate regulation of cluster members by specific TREs. Finally, our findings provide new insights into rate-limiting steps in the appearance of heme-containing hemoglobin tetramers in these cells.

5' Flanking Region↗

PubMed2003

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Angiotensin II↗

PubMed2003

Continuous-time identification of gene expression models.

One objective of systems biology is to create predictive, quantitative models of the transcriptional regulation networks that govern numerous cellular processes. Gene expression measurements, as provided by microarrays, are commonly used in studies that attempt to infer the regulation underlying these processes. At present, most gene expression models that have been derived from microarray data are based in discrete-time, which have limited applicability to common biological data sets, and may impede the integration of gene expression models with other models of biological processes that are formulated as ordinary differential equations (ODEs). To overcome these difficulties, a continuous-time approach for process identification to identify gene expression models based in ODEs was developed. The approach utilizes the modulating functions method of parameter identification. The method was applied to three simulated systems: (1) a linear gene expression model, (2) an autoregulatory gene expression model, and (3) simulated microarray data from a nonlinear transcriptional network. In general, the approach was well suited for identifying models of gene expression dynamics, capable of accurately identifying parameters for small numbers of data samples in the presence of modest experimental noise. Additionally, numerous insights about gene expression modeling were revealed by the case studies.

Data Interpretation, Statistical↗

PubMed2003

Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.

Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

Computational Biology↗