Biomedical subjects

G Wesley Hatfield

Publications and source records attributed to G Wesley Hatfield.

5 recordsLinked to original sources

PubMed2003

Global gene expression profiling in Escherichia coli K12. The effects of oxygen availability and FNR.

The work presented here is a first step toward a long term goal of systems biology, the complete elucidation of the gene regulatory networks of a living organism. To this end, we have employed DNA microarray technology to identify genes involved in the regulatory networks that facilitate the transition of Escherichia coli cells from an aerobic to an anaerobic growth state. We also report the identification of a subset of these genes that are regulated by a global regulatory protein for anaerobic metabolism, FNR. Analysis of these data demonstrated that the expression of over one-third of the genes expressed during growth under aerobic conditions are altered when E. coli cells transition to an anaerobic growth state, and that the expression of 712 (49%) of these genes are either directly or indirectly modulated by FNR. The results presented here also suggest interactions between the FNR and the leucine-responsive regulatory protein (Lrp) regulatory networks. Because computational methods to analyze and interpret high dimensional DNA microarray data are still at an early stage, and because basic issues of data analysis are still being sorted out, much of the emphasis of this work is directed toward the development of methods to identify differentially expressed genes with a high level of confidence. In particular, we describe an approach for identifying gene expression patterns (clusters) obtained from multiple perturbation experiments based on a subset of genes that exhibit high probability for differential expression values.

Cell Division↗

PubMed2003

Differential analysis of DNA microarray gene expression data.

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.

Analysis of Variance↗

PubMed2002

Global gene expression profiling in Escherichia coli K12. The effects of leucine-responsive regulatory protein.

Leucine-responsive regulatory protein (Lrp) is a global regulatory protein that affects the expression of multiple genes and operons in bacteria. Although the physiological purpose of Lrp-mediated gene regulation remains unclear, it has been suggested that it functions to coordinate cellular metabolism with the nutritional state of the environment. The results of gene expression profiles between otherwise isogenic lrp(+) and lrp(-) strains of Escherichia coli support this suggestion. The newly discovered Lrp-regulated genes reported here are involved either in small molecule or macromolecule synthesis or degradation, or in small molecule transport and environmental stress responses. Although many of these regulatory effects are direct, others are indirect consequences of Lrp-mediated changes in the expression levels of other global regulatory proteins. Because computational methods to analyze and interpret high dimensional DNA microarray data are still an early stage, much of the emphasis of this work is directed toward the development of methods to identify differentially expressed genes with a high level of confidence. In particular, we describe a Bayesian statistical framework for a posterior estimate of the standard deviation of gene measurements based on a limited number of replications. We also describe an algorithm to compute a posterior estimate of differential expression for each gene based on the experiment-wide global false positive and false negative level for a DNA microarray data set. This allows the experimenter to compute posterior probabilities of differential expression for each individual differential gene expression measurement.

Base Sequence↗

PubMed2002

DNA topology-mediated control of global gene expression in Escherichia coli.

Because the level of DNA superhelicity varies with the cellular energy charge, it can change rapidly in response to a wide variety of altered nutritional and environmental conditions. This is a global alteration, affecting the entire chromosome and the expression levels of all operons whose promoters are sensitive to superhelicity. In this way, the global pattern of gene expression may be dynamically tuned to changing needs of the cell under a wide variety of circumstances. In this article, we propose a model in which chromosomal superhelicity serves as a global regulator of gene expression in Escherichia coli, tuning expression patterns across multiple operons, regulons, and stimulons to suit the growth state of the cell. This model is illustrated by the DNA supercoiling-dependent mechanisms that coordinate basal expression levels of operons of the ilv regulon both with one another and with cellular growth conditions.

Base Sequence↗

PubMed2002

The role of DNA deformation energy at individual base steps for the identification of DNA-protein binding sites.

We examine the use of deformation propensity at individual base steps for the identification of DNA-protein binding sites. We have previously demonstrated that estimates of the total energy to bend DNA to its bound conformation can partially explain indirect DNA-protein interactions. We now show that the deformation propensities at each base step are not equally informative for classifying a sequence as a binding site, and that applying non-uniform weights to the contribution of each base step to aggregate deformation propensity can greatly improve classification accuracy. We show that a perceptron can be trained to use the deformation propensity at each step in a sequence to generate such weights.

Binding Sites↗