The database of Clusters of Orthologous Groups of proteins (COGs), which represents an attempt on a phylogenetic classification of the proteins encoded in complete genomes, currently consists of 2791 COGs including 45 350 proteins from 30 genomes of bacteria, archaea and the yeast Saccharomyces cerevisiae (http://www.ncbi.nlm.nih. gov/COG). In addition, a supplement to the COGs is available, in which proteins encoded in the genomes of two multicellular eukaryotes, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster, and shared with bacteria and/or archaea were included. The new features added to the COG database include information pages with structural and functional details on each COG and literature references, improvements of the COGNITOR program that is used to fit new proteins into the COGs, and classification of genomes and COGs constructed by using principal component analysis.

To evaluate the utility of transcript profiling for prediction of protein expression levels, we compared profiles across the NCI-60 cancer cell panel, which represents nine tissues of origin. For that analysis, we present here two new NCI-60 transcript profile data sets (A based on Affymetrix HG-U95 and HG-U133A chips; Affymetrix, Santa Clara, CA) and one new protein profile data set (based on reverse-phase protein lysate arrays). The data sets are available online at http://discover.nci.nih.gov in the CellMiner program package. Using the new transcript data in combination with our previously published cDNA array and Affymetrix HU6800 data sets, we first developed a ''consensus set'' of transcript profiles based on the four different microarray platforms. Using that set, we found that 65% of the genes showed statistically significant transcript-protein correlation, and the correlations were generally higher than those reported previously for panels of mammalian cells. Using the predictive analysis of microarray nearest shrunken centroid algorithm for functional prediction of tissue of origin, we then found that (a) the consensus mRNA set did better than did data from any of the individual mRNA platforms and (b) the protein data seemed to do somewhat better (P = 0.027) on a gene-for-gene basis in this particular study than did the consensus mRNA data, but both did well. Analysis based on the Gene Ontology showed protein levels of structure-related genes to be well predicted by mRNA levels (mean r = 0.71). Because the transcript-based technologies are more mature and are currently able to assess larger numbers of genes at one time, they continue to be useful, even when the ultimate aim is information about proteins. [Mol Cancer Ther 2007;6(3):820 -32]

Background: Advances in the high-throughput omic technologies have made it possible to profile cells in a large number of ways at the DNA, RNA, protein, chromosomal, functional, and pharmacological levels. A persistent problem is that some classes of molecular data are labeled with gene identifiers, others with transcript or protein identifiers, and still others with chromosomal locations. What has lagged behind is the ability to integrate the resulting data to uncover complex relationships and patterns. Those issues are reflected in full form by molecular profile data on the panel of 60 diverse human cancer cell lines (the NCI-60) used since 1990 by the U.S. National Cancer Institute to screen compounds for anticancer activity. To our knowledge, CellMiner is the first online database resource for integration of the diverse molecular types of NCI-60 and related meta data.

For analysis of multidrug resistance, a major barrier to effective cancer chemotherapy, we profiled mRNA expression of the 48 known human ABC transporters in 60 diverse cancer cell lines (the NCI-60) used by the National Cancer Institute to screen for anticancer activity. The use of real-time RT-PCR avoided artifacts commonly encountered with microarray technologies. By correlating the results with the growth inhibitory profiles of 1,429 candidate anticancer drugs tested against the cells, we identified which transporters are more likely than others to confer resistance to which agents. Unexpectedly, we also found and validated compounds whose activity is potentiated, rather than antagonized, by the MDR1 multidrug transporter. Such compounds may serve as leads for development.

These studies identify senescence as an important process in AECII in vivo and indicate that NOX is a critical mediator of radiation-induced AECII senescence and pulmonary fibrosis.

Signal transduction events in monocyte matrix metalloproteinase (MMP) production have been shown to include a PGE2-cAMP-dependent step. To determine earlier pathway components, we examined the role of mitogen-activated protein kinases (MAPKs) in the regulation of monocyte MMP-1 and MMP-9, two major MMPs induced by LPS. Stimulation with LPS resulted in the activation of the extracellular signal-regulated kinase 1 and 2 (ERK1/2) and mitogen-activated kinase p38. The p38-specific inhibitor SB203580 suppressed p38 activity and MMP-1 mRNA and protein, but increased ERK activity and MMP-9 mRNA and protein. In contrast, the MAPK kinase 1/2-specific inhibitor PD98059 inhibited MMP-1 and MMP-9. However, both MAPK inhibitors decreased the production of cyclooxygenase-2 and PGE2, but only the inhibition of MMP-1 by SB203580 was reversed by PGE2 or dibutyryl cAMP. Examination of the effect of these MAPK inhibitors on the promoters of MMP-1 and MMP-9 revealed that PD98059 inhibited the binding of transcription factors to all of the MMP promoter-specific complementary oligonucleotides tested. However, SB203580 only inhibited the binding of MMP-1-specific CREB and SP 1 oligonucleotides, which was reversed by PGE2. Additionally, SB203580 enhanced transcription factor binding to the oligonucleotides complementary to a NF-κB site in the promoter of MMP-9. Thus, LPS induction of MMP-1 production by monocytes is regulated by both ERK1/2 and p38, whereas MMP-9 stimulation occurred mainly through the ERK1/2 pathway. Moreover, p38 regulates MMP-1 mainly through a PGE2-dependent pathway, whereas ERK1/2-mediated MMP-1 and MMP-9 production involves the activation of additional MMP promoter sites through a PGE2-independent mechanism.

• A novel germ-l i n e PHD1 mutation causing heochromocytoma/paraganglioma and polycythemia. • Increased EPOR activity and inappropriate hypersensitivity of erythroid progenitors to EPO.

The biological functions of nuclear topoisomerase I (Top1) have been difficult to study because knocking out TOP1 is lethal in metazoans. To reveal the functions of human Top1, we have generated stable Top1 small interfering RNA (siRNA) cell lines from colon and breast carcinomas (HCT116-siTop1 and MCF-7-siTop1, respectively). In those clones, Top1 is reduced f5-fold and Top2A compensates for Top1 deficiency. A prominent feature of the siTop1 cells is genomic instability, with chromosomal aberrations and histone ;-H2AX foci associated with replication defects. siTop1 cells also show rDNA and nucleolar alterations and increased nuclear volume. Genome-wide transcription profiling revealed 55 genes with consistent changes in siTop1 cells. Among them, asparagine synthetase (ASNS) expression was reduced in siTop1 cells and in cells with transient Top1 down-regulation. Conversely, Top1 complementation increased ASNS, indicating a causal link between Top1 and ASNS expression. Correspondingly, pharmacologic profiling showed L-asparaginase hypersensitivity in the siTop1 cells. Resistance to camptothecin, indenoisoquinoline, aphidicolin, hydroxyurea, and staurosporine and hypersensitivity to etoposide and actinomycin D show that Top1, in addition to being the target of camptothecins, also regulates DNA replication, rDNA stability, and apoptosis. Overall, our studies show the pleiotropic nature of human Top1 activities. In addition to its classic DNA nicking-closing functions, Top1 plays critical nonclassic roles in genomic stability, gene-specific transcription, and response to various anticancer agents. The reported cell lines and approaches described in this article provide new tools to perform detailed functional analyses related to Top1 function.