Cluster analysis and display of genome-wide expression patterns

A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.

Analysis of gene expression during myc oncogene-induced lymphomagenesis in the bursa of Fabricius

The transcriptional effects of deregulated myc gene overexpression are implicated in tumorigenesis in a spectrum of experimental and naturally occurring neoplasms. In follicles of the chicken bursa of Fabricius, myc induction of B-cell neoplasia requires a target cell population present during early bursal development and progresses through preneoplastic transformed follicles to metastatic lymphomas. We developed a chicken immune system cDNA microarray to analyze broad changes in gene expression that occur during normal embryonic B-cell development and during myc-induced neoplastic transformation in the bursa. The number of mRNAs showing at least 3-fold change was greater during myc-induced lymphomagenesis than during normal development, and hierarchical cluster analysis of expression patterns revealed that levels of several hundred mRNAs varied in concert with levels of myc overexpression. A set of 41 mRNAs were most consistently elevated in myc-overexpressing preneoplastic and neoplastic cells, most involved in processes thought to be subject to regulation by Myc. The mRNAs for another cluster of genes were overexpressed in neoplasia independent of myc expression level, including a small subset with the expression signature of embryonic bursal lymphocytes. Overexpression of myc, and some of the genes overexpressed with myc, may be important for generation of preneoplastic transformed follicles. However, expression profiles of late metastatic tumors showed a large variation in concert with myc expression levels, and some showed minimal myc overexpression. Therefore, high-level myc overexpression may be more important in the early induction of these lymphomas than in maintenance of late-stage metastases.

Hydrophobic cluster analysis predicts an amino-terminal domain of varicella-zoster virus open reading frame 10 required for transcriptional activation.

Varicella-zoster virus open reading frame 10 (ORF10) protein, the homolog of the herpes simplex virus protein VP16, can transactivate immediate-early promoters from both viruses. A protein sequence comparison procedure termed hydrophobic cluster analysis was used to identify a motif centered at Phe-28, near the amino terminus of ORF10, that strongly resembles the sequence of the activating domain surrounding Phe-442 of VP16. With a series of GAL4-ORF10 fusion proteins, we mapped the ORF10 transcriptional-activation domain to the amino-terminal region (aa 5-79). Extensive mutagenesis of Phe-28 in GAL4-ORF10 fusion proteins demonstrated the importance of an aromatic or bulky hydrophobic amino acid at this position, as shown previously for Phe-442 of VP16. Transactivation by the native ORF10 protein was abolished when Phe-28 was replaced by Ala. Similar amino-terminal domains were identified in the VP16 homologs of other alphaherpesviruses. Hydrophobic cluster analysis correctly predicted activation domains of ORF10 and VP16 that share critical characteristics of a distinctive subclass of acidic activation domains.

Global analysis of gene expression in pulmonary fibrosis reveals distinct programs regulating lung inflammation and fibrosis

The molecular mechanisms of pulmonary fibrosis are poorly understood. We have used oligonucleotide arrays to analyze the gene expression programs that underlie pulmonary fibrosis in response to bleomycin, a drug that causes lung inflammation and fibrosis, in two strains of susceptible mice (129 and C57BL/6). We then compared the gene expression patterns in these mice with 129 mice carrying a null mutation in the epithelial-restricted integrin β6 subunit (β6−/−), which develop inflammation but are protected from pulmonary fibrosis. Cluster analysis identified two distinct groups of genes involved in the inflammatory and fibrotic responses. Analysis of gene expression at multiple time points after bleomycin administration revealed sequential induction of subsets of genes that characterize each response. The availability of this comprehensive data set should accelerate the development of more effective strategies for intervention at the various stages in the development of fibrotic diseases of the lungs and other organs.

High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral blood

Early detection is an effective means of reducing cancer mortality. Here, we describe a highly sensitive high-throughput screen that can identify panels of markers for the early detection of solid tumor cells disseminated in peripheral blood. The method is a two-step combination of differential display and high-sensitivity cDNA arrays. In a primary screen, differential display identified 170 candidate marker genes differentially expressed between breast tumor cells and normal breast epithelial cells. In a secondary screen, high-sensitivity arrays assessed expression levels of these genes in 48 blood samples, 22 from healthy volunteers and 26 from breast cancer patients. Cluster analysis identified a group of 12 genes that were elevated in the blood of cancer patients. Permutation analysis of individual genes defined five core genes (P ≤ 0.05, permax test). As a group, the 12 genes generally distinguished accurately between healthy volunteers and patients with breast cancer. Mean expression levels of the 12 genes were elevated in 77% (10 of 13) untreated invasive cancer patients, whereas cluster analysis correctly classified volunteers and patients (P = 0.0022, Fisher's exact test). Quantitative real-time PCR confirmed array results and indicated that the sensitivity of the assay (1:2 × 108 transcripts) was sufficient to detect disseminated solid tumor cells in blood. Expression-based blood assays developed with the screening approach described here have the potential to detect and classify solid tumor cells originating from virtually any primary site in the body.