RESUMEN
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.
Asunto(s)
Análisis de Secuencia por Matrices de Oligonucleótidos , Osteoblastos/citología , Osteoblastos/fisiología , Cráneo/citología , Animales , Moléculas de Adhesión Celular/genética , Diferenciación Celular/genética , Células Cultivadas , Proteínas del Citoesqueleto/genética , Endopeptidasas/genética , Proteínas de la Matriz Extracelular/genética , Genoma , Sustancias de Crecimiento/genética , Ratones , Ratones Endogámicos , Receptores de Superficie Celular/genética , Inhibidores Tisulares de Metaloproteinasas/genética , Factores de Transcripción/genéticaRESUMEN
Several genes, such as alkaline phosphatase, osteocalcin, and Cbfa1/Osf2, are known to be regulated during osteoblastic differentiation and are commonly used as "osteoblast markers" for in vitro or in vivo studies. The number of these genes is very limited, however, and it is of major interest to identify new genes that are activated or repressed during the process of osteoblast differentiation and bone formation as well as to extend the available information on gene families relevant to this particular differentiation pathway. To identify such genes, we have implemented a genome-wide analysis by determining changes in expression levels of 27,000 genes during in vitro differentiation of primary osteoblasts isolated from mouse calvaria. This study focuses on the description of the analytical and filtering process applied; on the transcriptional analysis of well-established "bone," "adipocyte," and "muscle" pathway markers; and on a description of the regulation profiles for genes recently described in the Skeletal Gene Database. We also demonstrate that new array technologies constitute reliable and powerful tools to monitor the transcription of genes involved in osteoblastic differentiation, allowing a more integrated vision of the biological pathways regulated during osteoblast commitment, differentiation, and function.
Asunto(s)
Adipocitos/metabolismo , Perfilación de la Expresión Génica/métodos , Genoma , Mioblastos/metabolismo , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Osteoblastos/metabolismo , Cráneo/metabolismo , Adipocitos/citología , Animales , Animales Recién Nacidos , Células Cultivadas , Bases de Datos Genéticas/estadística & datos numéricos , Marcadores Genéticos/genética , Ratones , Mioblastos/citología , Osteoblastos/citología , Cráneo/citologíaRESUMEN
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.