Steven Bushnell

Identification of genes regulated during osteoblastic differentiation by genome-wide expression analysis of mouse calvaria primary osteoblasts in vitro

Although several independent studies of gene expression patterns during osteoblast differentiation in cultures from calvaria and other in vitro models have been reported, only a small portion of the mRNAs expressed in osteoblasts have been characterized. We have previously analyzed the behavior of several known markers in osteoblasts, using Affymetrix GeneChip murine probe arrays (27,000 genes). In the present study we report larger groups of transcripts displaying significant expression modulation during the culture of osteoblasts isolated from mice calvaria. The expression profiles of 601 such regulated genes, classified in distinct functional families, are presented and analyzed here. Although some of these genes have previously been shown to play important roles in bone biology, the large majority of them have never been demonstrated to be regulated during osteoblast differentiation. Despite the fact that the precise involvement of these genes in osteoblast differentiation and function needs to be evaluated, the data presented herein will aid in the identification of genes that play a significant role in osteoblasts. This will provide a better understanding of the regulation of osteoblast differentiation and maturation.

Bayesian estimation of fold-changes in the analysis of gene expression: the PFOLD algorithm.

A general and detailed noise model for the DNA microarray measurement of gene expression is presented and used to derive a Bayesian estimation scheme for expression ratios, implemented in a program called PFOLD, which provides not only an estimate of the fold-change in gene expression, but also confidence limits for the change and a P-value quantifying the significance of the change. Although the focus is on oligonucleotide microarray technologies, the scheme can also be applied to cDNA based technologies if parameters for the noise model are provided. The model unifies estimation for all signals in that it provides a seamless transition from very low to very high signal-to-noise ratios, an essential feature for current microarray technologies for which the median signal-to-noise ratios are always moderate. The dual use, as decision statistics in a two-dimensional space, of the P-value and the fold-change is shown to be effective in the ubiquitous problem of detecting changing genes against a background of unchanging genes, leading to markedly higher sensitivities, at equal selectivity, than detection and selection based on the fold-change alone, a current practice until now.

GECKO: a complete large-scale gene expression analysis platform

BackgroundGecko (Gene Expression: Computation and Knowledge Organization) is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community.ResultsBased on a client-server architecture, with a centralized repository of typically many tens of thousands of Affymetrix scans, Gecko includes automatic processing pipelines for uploading data from remote sites, a data base, a computational engine implementing ~ 50 different analysis tools, and a client application. Among available analysis tools are clustering methods, principal component analysis, supervised classification including feature selection and cross-validation, multi-factorial ANOVA, statistical contrast calculations, and various post-processing tools for extracting data at given error rates or significance levels. On account of its open architecture, Gecko also allows for the integration of new algorithms. The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. A unique feature of the Gecko architecture is the concept of the Analysis Tree (actually, a directed acyclic graph), in which all successive results in ongoing analyses are saved. This approach has proven invaluable in allowing a large (~ 100 users) and distributed community to share results, and to repeatedly return over a span of years to older and potentially very complex analyses of gene expression data.ConclusionsThe Gecko system is being made publicly available as free software http://sourceforge.net/projects/geckoe. In totality or in parts, the Gecko framework should prove useful to users and system developers with a broad range of analysis needs.

Bayesian estimation of fold-changes in gene expression: the PFOLD algorithm and its uses in the analysis of complex expression profiles

Bayesian estimation of fold-changes in gene expression: the PFOLD algorithm and its uses in the analysis of complex expression profiles

GECKO: a software system for the analysis and organization of gene expression information

We have established a cDNA microarray platform for use in research focused on atherosclerosis and heart disease. Our objective is the elucidation of signalling pathways with an impact on cholesterol metabolism regulation. Specifically, we aim to discover novel genes and their encoded products that could serve as drugs, drug targets or diagnostic markers for this indication. Our specific focus is on modulating gene function to promote cholesterol efflux from peripheral tissues (reverse cholesterol transport) via HDL, thereby alleviating the potentially deleterious accumulation and oxidation of cholesterol, most notably in macrophage foam cells, that is strongly correlated with atherosclerosis. Macrophages mediate a limited-specificity immune response that includes recognition of ‘foreign’ lipid derivatives. Macrophages display a diverse set of ‘scavenger receptors’ that bind bacterial lipopolysaccharides (LPS), phospholipids inappropriately exposed on apoptotic cells and oxidized forms of LDL cholesterol. The resulting signal transduction events and transcriptional programs are overlapping but distinct. Whereas LPS induces a transcriptional program constituting an inflammatory response, the binding and phagocytosis of apoptotic cells results in a non(or anti-) inflammatory response. It appears that oxidized LDL or oxidized cholesterol metabolites stimulate an inflammatory response that becomes chronic and pathological as they accumulate to high levels in macrophage foam cells. A full understanding of the transcriptional programs and signalling cascades mediated by related scavenger receptor ligands will enable the identification of unique components of each signalling program, which may provide opportunities for drug development. In addition to targeting genes involved in an abherent inflammatory response to cholesterol accumulation, target genes may regulate cholesterol metabolism and have an impact on efflux mechanisms. Our current microarray analyses cover approximately 50% of the human genome (~57,000 cDNAs from IMAGE, distributed by Genome Systems). We are collecting data on gene expression in cultured and primary monocytes/macrophage and those accompanying foam cell formation and stimulation by bacterial lipids and apoptotic cells. A future objective is the analysis of monocyte/macrophage-specific arrays for a more targeted gene discovery effort. Novel candidate genes will be confirmed and validated in physiological assays of cholesterol metabolism and macrophage function. Bushnell, Steven