Universal stress proteins in Escherichia coli.
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Bacterial Proteins↗
Biomedical subjects
Deborah A Siegele
Publications and source records attributed to Deborah A Siegele.
Proteome analysis of Escherichia coli K-12 by two-dimensional native-state chromatography and MALDI-MS.
To identify proteins expressed in Escherichia coli K-12 MG1655 during exponential growth in defined medium, we separated soluble proteins of E. coli over two dimensions of native-state high-performance liquid chromatography, and examined the components of the protein mixtures in each of 380 fractions by peptide mass fingerprinting. To date, we have identified the products of 310 genes covering a wide range of cellular functions. Validation of protein assignments was made by comparing the assignments of proteins to specific first-dimension fractions to proteins visualized by two-dimensional gel electrophoresis. Co-fractionation of proteins suggests the possible identities of components of multiprotein complexes. This approach yields high-throughput gel-independent identification of proteins. It can also be used to assign identities to spots visualized by two-dimensional gels, and should be useful to evaluate differences in expressed proteome content and protein complexes among strains or between different physiological states.
Chromatography, High Pressure Liquid↗
MOPAC: motif finding by preprocessing and agglomerative clustering from microarrays.
We propose a novel strategy for discovering motifs from gene expression data. The gene expression data in our experiments comes from DNA Microarray analysis of the bacterium E. coli in response to recovery from nutrient starvation. We have annotated the data and identified the upregulated genes. Our interest is to find common regulatory motifs that are responsible for the upregulation of these specific genes. We assume that a common motif that a regulatory protein can bind to will be present in the upstream region of the upregulated genes and will not be present in the upstream regions of genes that showed a constant level of expression over time. Our objective is to find the common motifs that are present in at least some of the upstream sequences of upregulated genes and not present in the control set, which is the set of genes whose expression remained the same. Because it is possible that there could be several subsets of co-regulated genes under different control mechanisms among the co-expressed genes, we do not want to require motifs to be present in all upregulated sequences. Therefore, we propose a new algorithm for finding such motifs through stages of pre-processing, denoising, agglomerative clustering and consensus checking. Through this process, we have found some motifs that are good candidates for further validation.
Algorithms↗