Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Generation of longer cDNA fragments from serial analysis of gene expression tags for gene identification

We have developed a technique called the generation of longer cDNA fragments from serial analysis of gene expression (SAGE) tags for gene identification (GLGI), to convert SAGE tags of 10 bases into their corresponding 3′ cDNA fragments covering hundred bases. A primer containing the 10-base SAGE tag is used as the sense primer, and a single base anchored oligo(dT) primer is used as an antisense primer in PCR, together with Pfu DNA polymerase. By using this approach, a cDNA fragment extending from the SAGE tag toward the 3′ end of the corresponding sequence can be generated. Application of the GLGI technique can solve two critical issues in applying the SAGE technique: one is that a longer fragment corresponding to a SAGE tag, which has no match in databases, can be generated for further studies; the other is that the specific fragment corresponding to a SAGE tag can be identified from multiple sequences that match the same SAGE tag. The development of the GLGI method provides several potential applications. First, it provides a strategy for even wider application of the SAGE technique for quantitative analysis of global gene expression. Second, a combined application of SAGE/GLGI can be used to complete the catalogue of the expressed genes in human and in other eukaryotic species. Third, it can be used to identify the 3′ cDNA sequence from any exon within a gene. It can also be used to confirm the reality of exons predicted by bioinformatic tools in genomic sequences. Fourth, a combined application of SAGE/GLGI can be applied to define the 3′ boundary of expressed genes in the genomic sequences in human and in other eukaryotic genomes.

Gene expression, synthesis, and secretion of interleukin 18 and interleukin 1β are differentially regulated in human blood mononuclear cells and mouse spleen cells

Interleukin (IL)-18, formerly called interferon γ (IFN-γ)-inducing factor, is biologically and structurally related to IL-1β. A comparison of gene expression, synthesis, and processing of IL-18 with that of IL-1β was made in human peripheral blood mononuclear cells (PBMCs) and in human whole blood. Similar to IL-1β, the precursor for IL-18 requires processing by caspase 1. In PBMCs, mature but not precursor IL-18 induces IFN-γ; in whole human blood stimulated with endotoxin, inhibition of caspase 1 reduces IFN-γ production by an IL-1β-independent mechanism. Unlike the precursor for IL-1β, precursor for IL-18 was expressed constitutively in PBMCs and in fresh whole blood from healthy human donors. Western blotting of endotoxin-stimulated PBMCs revealed processed IL-1β in the supernatants via an caspase 1-dependent pathway. However, in the same supernatants, only unprocessed precursor IL-18 was found. Unexpectedly, precursor IL-18 was found in freshly obtained PBMCs and constitutive IL-18 gene expression was present in whole blood of healthy donors, whereas constitutive IL-1β gene expression is absent. Similar to human PBMCs, mouse spleen cells also constitutively contained the preformed precursor for IL-18 and expressed steady-state IL-18 mRNA, but there was no IL-1β protein and no spontaneous gene expression for IL-1β in these same preparations. We conclude that although IL-18 and IL-1β are likely members of the same family, constitutive gene expression, synthesis, and processing are different for the two cytokines.

A binding site for homeodomain and Pax proteins is necessary for L1 cell adhesion molecule gene expression by Pax-6 and bone morphogenetic proteins

The cell adhesion molecule L1 regulates axonal guidance and fasciculation during development. We previously identified the regulatory region of the L1 gene and showed that it was sufficient for establishing the neural pattern of L1 expression in transgenic mice. In the present study, we characterize a DNA element within this region called the HPD that contains binding motifs for both homeodomain and Pax proteins and responds to signals from bone morphogenetic proteins (BMPs). An ATTA sequence within the core of the HPD was required for binding to the homeodomain protein Barx2 while a separate paired domain recognition motif was necessary for binding to Pax-6. In cellular transfection experiments, L1-luciferase reporter constructs containing the HPD were activated an average of 4-fold by Pax-6 in N2A cells and 5-fold by BMP-2 and BMP-4 in Ng108 cells. Both of these responses were eliminated on deletion of the HPD from L1 constructs. In transgenic mice, deletion of the HPD from an L1-lacZ reporter resulted in a loss of β-galactosidase expression in the telencephalon and mesencephalon. Collectively, our experiments indicate that the HPD regulates L1 expression in neural tissues via homeodomain and Pax proteins and is likely to be a target of BMP signaling during development.