The causes and prevention of cancer.

Epidemiological evidence indicates that avoidance of smoking, increased consumption of fruits and vegetables, and control of infections will have a major effect on reducing rates of cancer. Other factors include avoidance of intense sun exposure, increases in physical activity, and reduction of alcohol consumption and possibly red meat. A substantial reduction in breast cancer is likely to require modification of sex hormone levels, and development of practical methods for doing so is a high research priority. Resolution of the potential protective roles of specific antioxidants and other constituents of fruits and vegetables deserves major attention. Mechanistic studies of carcinogenesis indicate an important role of endogenous oxidative damage to DNA that is balanced by elaborate defense and repair processes. Also key is the rate of cell division, which is influenced by hormones, growth, cytotoxicity, and inflammation, as this determines the probability of converting DNA lesions to mutations. These mechanisms may underlie many epidemiologic observations.

The COG database: new developments in phylogenetic classification of proteins from complete genomes

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.

Identification and classification of 16 new kinesin superfamily (KIF) proteins in mouse genome

KIF (kinesin superfamily) proteins are microtubule-dependent molecular motors that play important roles in intracellular transport and cell division. The extent to which KIFs are involved in various transporting phenomena, as well as their regulation mechanism, are unknown. The identification of 16 new KIFs in this report doubles the existing number of KIFs known in the mouse. Conserved nucleotide sequences in the motor domain were amplified by PCR using cDNAs of mouse nervous tissue, kidney, and small intestine as templates. The new KIFs were studied with respect to their expression patterns in different tissues, chromosomal location, and molecular evolution. Our results suggest that (i) there is no apparent tendency among related subclasses of KIFs of cosegregation in chromosomal mapping, and (ii) according to their tissue distribution patterns, KIFs can be divided into two classes–i.e., ubiquitous and specific tissue-dominant. Further characterization of KIFs may elucidate unknown fundamental phenomena underlying intracellular transport. Finally, we propose a straightforward nomenclature system for the members of the mouse kinesin superfamily.

Identification and classification of p53-regulated genes

Sequence-specific transactivation by p53 is essential to its role as a tumor suppressor. A modified tetracycline-inducible system was established to search for transcripts that were activated soon after p53 induction. Among 9,954 unique transcripts identified by serial analysis of gene expression, 34 were increased more than 10-fold; 31 of these had not previously been known to be regulated by p53. The transcription patterns of these genes, as well as previously described p53-regulated genes, were evaluated and classified in a panel of widely studied colorectal cancer cell lines. “Class I” genes were uniformly induced by p53 in all cell lines; “class II” genes were induced in a subset of the lines; and “class III” genes were not induced in any of the lines. These genes were also distinguished by the timing of their induction, their induction by clinically relevant chemotherapeutic agents, the absolute requirement for p53 in this induction, and their inducibility by p73, a p53 homolog. The results revealed substantial heterogeneity in the transcriptional responses to p53, even in cells derived from a single epithelial cell type, and pave the way to a deeper understanding of p53 tumor suppressor action.

Proteome Analysis Database: online application of InterPro and CluSTr for the functional classification of proteins in whole genomes

The SWISS-PROT group at EBI has developed the Proteome Analysis Database utilising existing resources and providing comparative analysis of the predicted protein coding sequences of the complete genomes of bacteria, archaea and eukaryotes (http://www.ebi.ac.uk/proteome/). The two main projects used, InterPro and CluSTr, give a new perspective on families, domains and sites and cover 31–67% (InterPro statistics) of the proteins from each of the complete genomes. CluSTr covers the three complete eukaryotic genomes and the incomplete human genome data. The Proteome Analysis Database is accompanied by a program that has been designed to carry out InterPro proteome comparisons for any one proteome against any other one or more of the proteomes in the database.

ICB database: the gyrB database for identification and classification of bacteria

The Identification and Classification of Bacteria (ICB) database (http:/www.mbio.co.jp/icb) contains currently available information about the DNA gyrase subunit B (gyrB) gene in bacteria. The database is designed to provide the scientific community with a reference point for using gyrB as an evolutionary and taxonomic marker. Nucleic and amino acid sequence data are currently available for over 850 strains, along with alignments at several different taxonomic levels and an exhaustive review of primer selection and background information.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.