Biomedical subjects

Thomas Brettin

Publications and source records attributed to Thomas Brettin.

4 recordsLinked to original sources

PubMed2006

Comparative analyses of two cryptic plasmids from Haemophilus somnus (Histophilus somni).

Haemophilus somnus is an opportunistic bacterial pathogen capable of causing pneumonia, septicemia, and other systemic infections in bovines. An H. somnus isolate from bovine abortion (strain 649) was found to carry a approximately 1.3 kb plasmid (pHS649) that contained partial homology to two previously sequenced Haemophilus/Histophilus plasmids by BLAST analyses. Sequence analysis of pHS649 identified a putative RepA protein with 48% similarity to the RepA protein of Escherichia coli plasmid pKL1. A approximately 5 kb plasmid (pHS129) from H. somnus preputial isolate 129Pt was also sequenced and found to encode two copies of a putative RepB protein. Whereas pHS649 stably replicated in E. coli DH5alpha, pHS129 did not. Genetic relatedness and possible replication mechanisms of these plasmids are described.

Amino Acid Sequence↗

PubMed2005

Expression capable library for studies of Neisseria gonorrhoeae, version 1.0.

BACKGROUND: The sexually transmitted disease, gonorrhea, is a serious health problem in developed as well as in developing countries, for which treatment continues to be a challenge. The recent completion of the genome sequence of the causative agent, Neisseria gonorrhoeae, opens up an entirely new set of approaches for studying this organism and the diseases it causes. Here, we describe the initial phases of the construction of an expression-capable clone set representing the protein-coding ORFs of the gonococcal genome using a recombination-based cloning system. RESULTS: The clone set thus far includes 1672 of the 2250 predicted ORFs of the N. gonorrhoeae genome, of which 1393 (83%) are sequence-validated. Included in this set are 48 of the 61 ORFs of the gonococcal genetic island of strain MS11, not present in the sequenced genome of strain FA1090. L-arabinose-inducible glutathione-S-transferase (GST)-fusions were constructed from random clones and each was shown to express a fusion protein of the predicted size following induction, demonstrating the use of the recombination cloning system. PCR amplicons of each ORF used in the cloning reactions were spotted onto glass slides to produce DNA microarrays representing 2035 genes of the gonococcal genome. Pilot experiments indicate that these arrays are suitable for the analysis of global gene expression in gonococci. CONCLUSION: This archived set of Gateway entry clones will facilitate high-throughput genomic and proteomic studies of gonococcal genes using a variety of expression and analysis systems. In addition, the DNA arrays produced will allow us to generate gene expression profiles of gonococci grown in a wide variety of conditions. Together, the resources produced in this work will facilitate experiments to dissect the molecular mechanisms of gonococcal pathogenesis on a global scale, and ultimately lead to the determination of the functions of unknown genes in the genome.

Cloning, Molecular↗

PubMed2005

Proteomic analysis of Bacillus anthracis Sterne vegetative cells.

Mass spectrometry and proteomics have found increasing use as tools for the rapid detection of pathogenic bacteria, even when they are in a mixture of non-pathogenic relatives. The success of this technique is greatly augmented by the availability of publicly accessible proteomic databases for specific pathogenic bacteria. To aid proteomic detection analyses for the causative agent of anthrax, we have constructed a comprehensive proteomic catalogue of vegetative Bacillus anthracis Sterne cells using liquid chromatography tandem-mass spectrometry. Proteins were separated by molecular weight or isoelectric point prior to tryptic digestion. Alternatively, the whole protein extract was digested and tryptic peptides were separated by cation exchange chromatography prior to Reverse Phase-LC-MS/MS. The use of three complementary, pre-analytical separation techniques resulted in the identification of 1048 unique proteins, including 694 cytosolic, 153 membrane (including 27 cell wall), and 30 secreted proteins, accounting for 19% of the total predicted proteome. Each identified protein was functionally categorized using the gene attribute database from TIGR CMR. These results provide a large proteomic catalogue of vegetative B. anthracis cells and, coupled with the recent proteomic catalogue of B. anthracis spore proteins, form a thorough summary of proteins expressed in the active and dormant stages of this organism.

Bacillus anthracis↗

PubMed2003

Statistical analysis of microarray data: a Bayesian approach.

The potential of microarray data is enormous. It allows us to monitor the expression of thousands of genes simultaneously. A common task with microarray is to determine which genes are differentially expressed between two samples obtained under two different conditions. Recently, several statistical methods have been proposed to perform such a task when there are replicate samples under each condition. Two major problems arise with microarray data. The first one is that the number of replicates is very small (usually 2-10), leading to noisy point estimates. As a consequence, traditional statistics that are based on the means and standard deviations, e.g. t-statistic, are not suitable. The second problem is that the number of genes is usually very large (approximately 10,000), and one is faced with an extreme multiple testing problem. Most multiple testing adjustments are relatively conservative, especially when the number of replicates is small. In this paper we present an empirical Bayes analysis that handles both problems very well. Using different parametrizations, we develop four statistics that can be used to test hypotheses about the means and/or variances of the gene expression levels in both one- and two-sample problems. The methods are illustrated using experimental data with prior knowledge. In addition, we present the result of a simulation comparing our methods to well-known statistics and multiple testing adjustments.

Algorithms↗