Molecular characterization of cat factor XII gene and identification of a mutation causing factor XII deficiency in a domestic shorthair cat colony.

Coagulation factor XII (FXII) may be important in cardiovascular and inflammatory diseases. We have identified and characterized a naturally occurring mutation in the feline FXII gene that results in a mutant protein and enzymatic loss of activity. Feline intron/exon gene structure and sequence were acquired by comparing DNA sequences obtained from a fragmented Felis catus genomic sequence and the National Center for Biotechnology Information's Cross Species Megablast of multiple species' FXII gene sequences. Fourteen exons ranging in size from 57 to 222 base pairs were confirmed spanning 8 Kb on chromosome A1. The 1828-base pair feline FXII messenger RNA (mRNA) sequence contains an open reading frame that encodes a protein of 609 amino acids with high homology to human FXII protein. Total RNA and mRNA purified from liver tissue of 4 wild-type/normal and 8 FXII-deficient cats confirmed the predicted mRNA sequence and identified one important single-nucleotide polymorphism (SNP). A single base deletion in exon 11 of the FXII coding gene in our colony of cats results in deficient FXII activity. Translation of the mRNA transcript shows a frame shift at L441 (C441fsX119) resulting in a nonsense mutation and a premature stop codon with a predicted 560-amino acid protein. The mutant FXII protein is truncated in the 3' proteolytic light chain region of the C-terminus, explaining its loss of enzymatic activity. This study is the first molecular characterization of the feline FXII gene and the first identification of an FXII mutation in the domestic cat, providing insights into the origin and nature of feline FXII deficiency.

Database resources of the National Center for Biotechnology Information.

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources that operate on the data in GenBank and a variety of other biological data made available through NCBI's Web site. NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq, UniGene, HomoloGene, Database of Single Nucleotide Polymorphisms (dbSNP), Human Genome Sequencing, Human MapViewer, GeneMap'99, Human-Mouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, Cancer Genome Anatomy Project (CGAP), SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheri-tance in Man (OMIM), the Molecular Modeling Database (MMDB) and the Conserved Domain Database (CDD). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih. gov.

Detecting AIDS restriction genes: from candidate genes to genome-wide association discovery.

The screening of common genetic polymorphisms among candidate genes for AIDS pathology in HIV exposed cohort populations has led to the description of 20 AIDS restriction genes (ARGs), variants that affect susceptibility to HIV infection or to AIDS progression. The combination of high-throughput genotyping platforms and the recent HapMap annotation of some 3 million human SNP variants has been developed for and applied to gene discovery in complex and multi-factorial diseases. Here, we explore novel computational approaches to ARG discovery which consider interacting analytical models, various genetic influences, and SNP-haplotype/LD structure in AIDS cohort populations to determine if these ARGs could have been discovered using an unbiased genome-wide association approach. The procedures were evaluated by tracking the performance of haplotypes and SNPs within ARG regions to detect genetic association in the same AIDS cohort populations in which the ARGs were originally discovered. The methodology captures the signals of multiple non-independent AIDS-genetic association tests of different disease stages and uses association signal strength (odds ratio or relative hazard), statistical significance (p-values), gene influence, internal replication, and haplotype structure together as a multi-facetted approach to identifying important genetic associations within a deluge of genotyping/test data. The complementary approaches perform rather well and predict the detection of a variety of undiscovered ARGs that affect different stages of HIV/AIDS pathogenesis using genome-wide association analyses.