The pattern of gene expression in mouse Gr-1(+) myeloid progenitor cells

To understand the pattern of gene expression in mouse myeloid progenitor cells, we carried out a genome-wide analysis of gene expression in mouse bone marrow Gr-1(+) cells using SAGE and GLGI techniques. We identified 22,033 unique SAGE tags with quantitative information from 73,869 collected SAGE tags. Among these unique tags, 64% match known sequences, including many genes important for myeloid differentiation, and 36% have no matches to known sequences and are likely to represent novel genes. We compared the expression of mouse Gr-1(+) and human CD15(+) myeloid progenitor cells and showed that the pattern of gene expression of these two cell populations had some similarities. We also compared the expression of mouse Gr-1(+) myeloid progenitor cells with that of mouse brain tissue and found a highly tissue-specific manner of gene expression in these two samples. Our data provide a basis for studying altered gene expression in myeloid disorders using mouse models.     

Serial analysis of gene expression in the silkworm, Bombyx mori

The silkworm Bombyx mori is one of the most economically important insects and serves as a model for Lepidoptera insects. We used serial analysis of gene expression (SAGE) to derive profiles of expressed genes during the developmental life cycle of the silkworm and to create a reference for understanding silkworm metamorphosis. We generated four SAGE libraries, one from each of the four developmental stages of the silkworm. In total we obtained 257,964 SAGE tags, of which 39,485 were unique tags. Sorted by copy number, 14.1% of the unique tags were detected at a median to high level (five or more copies), 24.2% at lower levels (two to four copies), and 61.7% as single copies. Using a basic local alignment search tool on the EST database, 35% of the tags matched known silkworm expressed sequence tags. SAGE demonstrated that a number of the genes were up- or down-regulated during the four developmental phases of the egg, larva, pupa, and adult. Furthermore, we found that the generation of longer cDNA fragments from SAGE tags constituted the most efficient method of gene identification, which facilitated the analysis of a large number of unknown genes.     

Analysis of allelic differential expression in the human genome using allele-specific serial analysis of gene expression tags

Recent reports have demonstrated that a significant proportion of human genes display allelic differential expression (ADE). ADE is associated with phenotypic variability and may contribute to complex genetic diseases. Here, we present a computational analysis of ADE using allele-specific serial analysis of gene expression (SAGE) tags representing 1295 human genes. We identified 472 genes for which unequal representation (>3-fold) of allele-specific SAGE tags was observed in at least one SAGE library, suggesting the occurrence of ADE. For 235 out of these 472 genes, the difference in the expression level between both allele-specific SAGE tags was statistically significant (p < 0.05). Eleven candidate genes were then subjected to experimental validation and ADE was confirmed for 8 out of these 11 genes. Our results suggest that at least 25% of the human genes display ADE and that allele-specific SAGE tags can be efficiently used for the identification of such genes.