Phylogenetic motif detection by expectation-maximization on evolutionary mixtures.

The preferential conservation of transcription factor binding sites implies that non-coding sequence data from related species will prove a powerful asset to motif discovery. We present a unified probabilistic framework for motif discovery that incorporates evolutionary information. We treat aligned DNA sequence as a mixture of evolutionary models, for motif and background, and, following the example of the MEME program, provide an algorithm to estimate the parameters by Expectation-Maximization. We examine a variety of evolutionary models and show that our approach can take advantage of phylogenic information to avoid false positives and discover motifs upstream of groups of characterized target genes. We compare our method to traditional motif finding on only conserved regions. An implementation will be made available at http://rana.lbl.gov.

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.

The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.

Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles.

The combination of genome-wide expression patterns and full genome sequences offers a great opportunity to further our understanding of the mechanisms and logic of transcriptional regulation. Many methods have been described that identify sequence motifs enriched in transcription control regions of genes that share similar gene expression patterns. Here we present an alternative approach that evaluates the transcriptional information contained by specific sequence motifs by computing for each motif the mean expression profile of all genes that contain the motif in their transcription control regions. These genome-mean expression profiles (GMEP's) are valuable for visualizing the relationship between genome sequences and gene expression data, and for characterizing the transcriptional importance of specific sequence motifs. Analysis of GMEP's calculated from a dataset of 519 whole-genome microarray experiments in Saccharomyces cerevisiae show a significant correlation between GMEP's of motifs that are reverse complements, a result that supports the relationship between GMEP's and transcriptional regulation. Hierarchical clustering of GMEP's identifies clusters of motifs that correspond to binding sites of well-characterized transcription factors. The GMEP's of these clustered motifs have patterns of variation across conditions that reflect the known activities of these transcription factors. Software that computed GMEP's from sequence and gene expression data is available under the terms of the Gnu Public License from http://rana.lbl.gov/.

Delineating developmental and metabolic pathways in vivo by expression profiling using the RIKEN set of 18,816 full-length enriched mouse cDNA arrays.

We have systematically characterized gene expression patterns in 49 adult and embryonic mouse tissues by using cDNA microarrays with 18,816 mouse cDNAs. Cluster analysis defined sets of genes that were expressed ubiquitously or in similar groups of tissues such as digestive organs and muscle. Clustering of expression profiles was observed in embryonic brain, postnatal cerebellum, and adult olfactory bulb, reflecting similarities in neurogenesis and remodeling. Finally, clustering genes coding for known enzymes into 78 metabolic pathways revealed a surprising coordination of expression within each pathway among different tissues. On the other hand, a more detailed examination of glycolysis revealed tissue-specific differences in profiles of key regulatory enzymes. Thus, by surveying global gene expression by using microarrays with a large number of elements, we provide insights into the commonality and diversity of pathways responsible for the development and maintenance of the mammalian body plan.

The Stanford Microarray Database.

The Stanford Microarray Database (SMD) stores raw and normalized data from microarray experiments, and provides web interfaces for researchers to retrieve, analyze and visualize their data. The two immediate goals for SMD are to serve as a storage site for microarray data from ongoing research at Stanford University, and to facilitate the public dissemination of that data once published, or released by the researcher. Of paramount importance is the connection of microarray data with the biological data that pertains to the DNA deposited on the microarray (genes, clones etc.). SMD makes use of many public resources to connect expression information to the relevant biology, including SGD [Ball,C.A., Dolinski,K., Dwight,S.S., Harris,M.A., Issel-Tarver,L., Kasarskis,A., Scafe,C.R., Sherlock,G., Binkley,G., Jin,H. et al. (2000) Nucleic Acids Res., 28, 77-80], YPD and WormPD [Costanzo,M.C., Hogan,J.D., Cusick,M.E., Davis,B.P., Fancher,A.M., Hodges,P.E., Kondu,P., Lengieza,C., Lew-Smith,J.E., Lingner,C. et al. (2000) Nucleic Acids Res., 28, 73-76], Unigene [Wheeler,D.L., Chappey,C., Lash,A.E., Leipe,D.D., Madden,T.L., Schuler,G.D., Tatusova,T.A. and Rapp,B.A. (2000) Nucleic Acids Res., 28, 10-14], dbEST [Boguski,M.S., Lowe,T.M. and Tolstoshev,C.M. (1993) Nature Genet., 4, 332-333] and SWISS-PROT [Bairoch,A. and Apweiler,R. (2000) Nucleic Acids Res., 28, 45-48] and can be accessed at http://genome-www.stanford.edu/microarray.

Genomic expression programs in the response of yeast cells to environmental changes.

We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.

Molecular portraits of human breast tumours.

Human breast tumours are diverse in their natural history and in their responsiveness to treatments. Variation in transcriptional programs accounts for much of the biological diversity of human cells and tumours. In each cell, signal transduction and regulatory systems transduce information from the cell's identity to its environmental status, thereby controlling the level of expression of every gene in the genome. Here we have characterized variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals, using complementary DNA microarrays representing 8,102 human genes. These patterns provided a distinctive molecular portrait of each tumour. Twenty of the tumours were sampled twice, before and after a 16-week course of doxorubicin chemotherapy, and two tumours were paired with a lymph node metastasis from the same patient. Gene expression patterns in two tumour samples from the same individual were almost always more similar to each other than either was to any other sample. Sets of co-expressed genes were identified for which variation in messenger RNA levels could be related to specific features of physiological variation. The tumours could be classified into subtypes distinguished by pervasive differences in their gene expression patterns.

Ongoing immunoglobulin somatic mutation in germinal center B cell-like but not in activated B cell-like diffuse large cell lymphomas.

B cell diffuse large cell lymphoma (B-DLCL) is a heterogeneous group of tumors, based on significant variations in morphology, clinical presentation, and response to treatment. Gene expression profiling has revealed two distinct tumor subtypes of B-DLCL: germinal center B cell-like DLCL and activated B cell-like DLCL. In a separate study, we determined that B-DLCL can also be subdivided into two groups based on the presence or absence of ongoing Ig gene hypermutation. Here, we evaluated the correlation between these B-DLCL subtypes established by the two different methods. Fourteen primary B-DLCL cases were studied by gene expression profiling using DNA microarrays and for the presence of ongoing mutations in their Ig heavy chain gene. All seven cases classified as germinal center B cell-like DLCL by gene expression showed the presence of ongoing mutations in the Ig genes. Five of the seven cases classified by gene expression as activated B cell-like DLCL had no ongoing somatic mutations, whereas, in the remaining two cases, a single point mutation was observed in only 2 of 15 and 21 examined molecular clones of variable heavy (V(H)) chain gene, respectively. These two cases were distantly related to the rest of the activated B cell-like DLCL tumors by gene expression. Our findings validate the concept that lymphoid malignancies are derived from cells at discrete stages of normal lymphocyte maturation and that the malignant cells retain the genetic program of those normal cells.

The yeast mitochondrial transport proteins: new sequences and consensus residues, lack of direct relation between consensus residues and transmembrane helices, expression patterns of the transport protein genes, and protein-protein interactions with other proteins.

Mitochondrial transport proteins (MTP) typically are homodimeric with a 30-kDa subunit with six transmembrane helices. The subunit possesses a sequence motif highly similar to Pro X Asp/Glu X X Lys/Arg X Arg within each of its three similar 10-kDa segments. Four (YNL083W, YFR045W, YPR021C, YDR470C) of the 35 yeast (S. cerevisiae) MTP genes were resequenced since the masses of their proteins deviate significantly from the typical 30 kDa. We now find these four proteins to have 545, 285, 902, and 502 residues, respectively. Together with only four other MTPs, the sequences of YPR021C and YDR470C show substitutions of some of the five residues that are absolutely conserved among the 12 MTPs with identified transport function and 17 other MTPs. We do now find these five consensus residues also in the new sequences of YNL083W and YFR045W. Additional analyses of the 35 yeast MTPs show that the location of transmembrane helix sequences do not correlate with the general consensus residues of the MTP family; protein segments connecting the six transmembrane helices and facing the intermembrane space are not uniformly short (about 20 residues) or long (about 40 residues) when facing the matrix; most MTPs have at least one transmembrane helix for which the sum of the negative hydropathy values of all residues yields a very small negative value, suggesting a membrane location bordering polar faces of other transmembrane helices or a non-transmembrane location. The extra residues of the three large MTPs are hydrophilic and at the N-terminal. The 200-residue N-terminal segment of YNL083W has four putative Ca2+-binding sites. The 500-residue N-terminal segment of YPR021C shows sequence similarity to enzymes of nucleic acid metabolism. cDNA microarray data show that YNL083W is expressed solely during sporulation, while the expressions of YFR045W, YPR021C, and YDR470C are induced by various stress situations. These results also show that the 35 MTP genes are expressed under a rather diverse set of metabolic conditions that may help identify the function of the proteins. Interestingly, yeast two-hybrid screens, that will also be useful in identifying the function of MTPs, indicate that MIR1, AAC3, YOR100C, and YPR011C do interact with non-MTPs.

Large-scale identification of secreted and membrane-associated gene products using DNA microarrays.

Membrane-associated and secreted proteins are an important class of proteins and include receptors, transporters, adhesion molecules, hormones and cytokines. Although algorithms have been developed to recognize potential amino-terminal membrane-targeting signals or transmembrane domains in protein sequences, their accuracy is limited and they require knowledge of the entire coding sequence, including the N terminus, which is not currently available for most of the genes in most organisms, including human. Several experimental approaches for identifying secreted and membrane proteins have been described, but none have taken a comprehensive genomic approach. Furthermore, none of these methods allow easy classification of clones from arrayed cDNA libraries, for which large-scale gene-expression data are now becoming available through the use of DNA microarrays. We describe here a rapid and efficient method for identifying genes that encode secreted or membrane proteins. mRNA species bound to membrane-associated polysomes were separated from other mRNAs by sedimentation equilibrium or sedimentation velocity. The distribution of individual transcripts in the 'membrane-bound' and 'cytosolic' fractions was quantitated for thousands of genes by hybridization to DNA microarrays. Transcripts known to encode secreted or membrane proteins were enriched in the membrane-bound fractions, whereas those known to encode cytoplasmic proteins were enriched in the fractions containing mRNAs associated with free and cytoplasmic ribosomes. On this basis, we identified over 275 human genes and 285 yeast genes that are likely to encode previously unrecognized secreted or membrane proteins.

Systematic variation in gene expression patterns in human cancer cell lines.

We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.

A gene expression database for the molecular pharmacology of cancer.

We used cDNA microarrays to assess gene expression profiles in 60 human cancer cell lines used in a drug discovery screen by the National Cancer Institute. Using these data, we linked bioinformatics and chemoinformatics by correlating gene expression and drug activity patterns in the NCI60 lines. Clustering the cell lines on the basis of gene expression yielded relationships very different from those obtained by clustering the cell lines on the basis of their response to drugs. Gene-drug relationships for the clinical agents 5-fluorouracil and L-asparaginase exemplify how variations in the transcript levels of particular genes relate to mechanisms of drug sensitivity and resistance. This is the first study to integrate large databases on gene expression and molecular pharmacology.

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.

Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin's lymphoma, is clinically heterogeneous: 40% of patients respond well to current therapy and have prolonged survival, whereas the remainder succumb to the disease. We proposed that this variability in natural history reflects unrecognized molecular heterogeneity in the tumours. Using DNA microarrays, we have conducted a systematic characterization of gene expression in B-cell malignancies. Here we show that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour. We identified two molecularly distinct forms of DLBCL which had gene expression patterns indicative of different stages of B-cell differentiation. One type expressed genes characteristic of germinal centre B cells ('germinal centre B-like DLBCL'); the second type expressed genes normally induced during in vitro activation of peripheral blood B cells ('activated B-like DLBCL'). Patients with germinal centre B-like DLBCL had a significantly better overall survival than those with activated B-like DLBCL. The molecular classification of tumours on the basis of gene expression can thus identify previously undetected and clinically significant subtypes of cancer.

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns.

BACKGROUND: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. RESULTS: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. CONCLUSIONS: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.

Identification of eukaryotic mRNAs that are translated at reduced cap binding complex eIF4F concentrations using a cDNA microarray.

Although most eukaryotic mRNAs need a functional cap binding complex eIF4F for efficient 5' end- dependent scanning to initiate translation, picornaviral, hepatitis C viral, and a few cellular RNAs have been shown to be translated by internal ribosome entry, a mechanism that can operate in the presence of low levels of functional eIF4F. To identify cellular mRNAs that can be translated when eIF4F is depleted or in low abundance and that, therefore, may contain internal ribosome entry sites, mRNAs that remained associated with polysomes were isolated from human cells after infection with poliovirus and were identified by using a cDNA microarray. Approximately 200 of the 7000 mRNAs analyzed remained associated with polysomes under these conditions. Among the gene products encoded by these polysome-associated mRNAs were immediate-early transcription factors, kinases, and phosphatases of the mitogen-activated protein kinase pathways and several protooncogenes, including c-myc and Pim-1. In addition, the mRNA encoding Cyr61, a secreted factor that can promote angiogenesis and tumor growth, was selectively mobilized into polysomes when eIF4F concentrations were reduced, although its overall abundance changed only slightly. Subsequent tests confirmed the presence of internal ribosome entry sites in the 5' noncoding regions of both Cyr61 and Pim-1 mRNAs. Overall, this study suggests that diverse mRNAs whose gene products have been implicated in a variety of stress responses, including inflammation, angiogenesis, and the response to serum, can use translational initiation mechanisms that require little or no intact cap binding protein complex eIF4F.

Genome-wide analysis of DNA copy-number changes using cDNA microarrays.

Gene amplifications and deletions frequently contribute to tumorigenesis. Characterization of these DNA copy-number changes is important for both the basic understanding of cancer and its diagnosis. Comparative genomic hybridization (CGH) was developed to survey DNA copy-number variations across a whole genome. With CGH, differentially labelled test and reference genomic DNAs are co-hybridized to normal metaphase chromosomes, and fluorescence ratios along the length of chromosomes provide a cytogenetic representation of DNA copy-number variation. CGH, however, has a limited ( approximately 20 Mb) mapping resolution, and higher-resolution techniques, such as fluorescence in situ hybridization (FISH), are prohibitively labour-intensive on a genomic scale. Array-based CGH, in which fluorescence ratios at arrayed DNA elements provide a locus-by-locus measure of DNA copy-number variation, represents another means of achieving increased mapping resolution. Published array CGH methods have relied on large genomic clone (for example BAC) array targets and have covered only a small fraction of the human genome. cDNAs representing over 30,000 radiation-hybrid (RH)-mapped human genes provide an alternative and readily available genomic resource for mapping DNA copy-number changes. Although cDNA microarrays have been used extensively to characterize variation in human gene expression, human genomic DNA is a far more complex mixture than the mRNA representation of human cells. Therefore, analysis of DNA copy-number variation using cDNA microarrays would require a sensitivity of detection an order of magnitude greater than has been routinely reported. We describe here a cDNA microarray-based CGH method, and its application to DNA copy-number variation analysis in breast cancer cell lines and tumours. Using this assay, we were able to identify gene amplifications and deletions genome-wide and with high resolution, and compare alterations in DNA copy number and gene expression.

Distinctive gene expression patterns in human mammary epithelial cells and breast cancers.

cDNA microarrays and a clustering algorithm were used to identify patterns of gene expression in human mammary epithelial cells growing in culture and in primary human breast tumors. Clusters of coexpressed genes identified through manipulations of mammary epithelial cells in vitro also showed consistent patterns of variation in expression among breast tumor samples. By using immunohistochemistry with antibodies against proteins encoded by a particular gene in a cluster, the identity of the cell type within the tumor specimen that contributed the observed gene expression pattern could be determined. Clusters of genes with coherent expression patterns in cultured cells and in the breast tumors samples could be related to specific features of biological variation among the samples. Two such clusters were found to have patterns that correlated with variation in cell proliferation rates and with activation of the IFN-regulated signal transduction pathway, respectively. Clusters of genes expressed by stromal cells and lymphocytes in the breast tumors also were identified in this analysis. These results support the feasibility and usefulness of this systematic approach to studying variation in gene expression patterns in human cancers as a means to dissect and classify solid tumors.