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Osteoporosis is a complex disease that affects >10 million people in the United States and results in 1.5 million fractures annually. In addition, the high prevalence of osteopenia (low bone mass) in the general population places a large number of people at risk for developing the disease. In an effort to identify genetic factors influencing bone density, we characterized a family that includes individuals who possess exceptionally dense bones but are otherwise phenotypically normal. This high–bone-mass trait (HBM) was originally localized by linkage analysis to chromosome 11q12-13. We refined the interval by extending the pedigree and genotyping additional markers. A systematic search for mutations that segregated with the HBM phenotype uncovered an amino acid change, in a predicted β-propeller module of the low-density lipoprotein receptor–related protein 5 (LRP5), that results in the HBM phenotype. During analysis of >1,000 individuals, this mutation was observed only in affected individuals from the HBM kindred. By use of in situ hybridization to rat tibia, expression of LRP5 was detected in areas of bone involved in remodeling. Our findings suggest that the HBM mutation confers a unique osteogenic activity in bone remodeling, and this understanding may facilitate the development of novel therapies for the treatment of osteoporosis.

Elucidation of the changes in gene expression associated with biological processes is a central problem in biology. Advances in molecular and computational biology have led to the development of powerful, high-thoughput methods for the analysis of differential gene expression. These tools have opened up new opportunities in disciplines ranging from cell and developmental biology to drug development and pharmacogenomics. In this review, the attributes of five commonly used differential gene expression methods are discussed: expressed sequence tag (EST) sequencing, cDNA microarray hybridization, subtractive cloning, differential display, and serial analysis of gene expression (SAGE). The application of EST sequencing and microarray hybridization is illustrated by the discovery of novel genes associated with osteoblast differentiation. The application of subtractive cloning is presented as a tool to identify genes regulated in vivo by the transcription factor pax-6. These and other examples illustrate the power of genomics for discovering novel genes that are important in biology and which also represent new targets for drug development. The central theme of the review is that each of the approaches to identifying differentially expressed genes is useful, and that the experimental context and subsequent evaluation of differentially expressed genes are the critical features that determine success. J. Cell. Biochem. Suppls. 30/31:286-296, 1998. © 1998 Wiley-Liss, Inc.

Elucidation of the changes in gene expression associated with biological processes is a central problem in biology. Advances in molecular and computational biology have led to the development of powerful, high-thoughput methods for the analysis of differential gene expression. These tools have opened up new opportunities in disciplines ranging from cell and developmental biology to drug development and pharmacogenomics. In this review, the attributes of five commonly used differential gene expression methods are discussed: expressed sequence tag (EST) sequencing, cDNA microarray hybridization, subtractive cloning, differential display, and serial analysis of gene expression (SAGE). The application of EST sequencing and microarray hybridization is illustrated by the discovery of novel genes associated with osteoblast differentiation. The application of subtractive cloning is presented as a tool to identify genes regulated in vivo by the transcription factor pax-6. These and other examples illustrate the power of genomics for discovering novel genes that are important in biology and which also represent new targets for drug development. The central theme of the review is that each of the approaches to identifying differentially expressed genes is useful, and that the experimental context and subsequent evaluation of differentially expressed genes are the critical features that determine success. J. Cell. Biochem. Suppls. 30/31:286-296, 1998. © 1998 Wiley-Liss, Inc.

The nucleotide sequence of 1.5 Mb of genomic DNA fromMycobacterium leprae was determined using computer-assisted multiplex sequencing technology. This brings the 2.8-Mb M. leprae genome sequence to ∼66% completion. The sequences, derived from 43 recombinant cosmids, contain 1046 putative protein-coding genes, 44 repetitive regions, 3 rRNAs, and 15 tRNAs. The gene density of one per 1.4 kb is slightly lower than that ofMycoplasma (1.2 kb). Of the protein coding genes, 44% have significant matches to genes with well-defined functions. Comparison of 1157 M. leprae and 1564 Mycobacterium tuberculosisproteins shows a complex mosaic of homologous genomic blocks with up to 22 adjacent proteins in conserved map order. Matches to known enzymatic, antigenic, membrane, cell wall, cell division, multidrug resistance, and virulence proteins suggest therapeutic and vaccine targets. Unusual features of the M. leprae genome include large polyketide synthase (pks) operons, inteins, and highly fragmented pseudogenes.[The sequence data described in this paper have been submitted to GenBank under accession nos. L78811–L78829,U00010–U00023, U15180–U15184, U15186, U15187, L01095, L01536, L04666, and L01263. On-line supplementary information for Table 1 is available at http://www.cshl.org/gr.]