RESUMO
Enhanced proteolysis and altered tight junction (TJ) proteins associate with carcinoma invasion. We hypothesized that trypsin-2, a tumor-associated serine proteinase, induces tongue carcinoma invasion by activating pro-membrane type-1 matrix metalloproteinase (MT1-MMP) and disturbing the TJs. The effects of invasion were analyzed using trypsin-2 over-expressing human tongue squamous cell carcinoma cells (Try2-HSC-3) in vitro and in vivo. The invasion of Try2-HSC-3 cells was increased in mouse xenografts and human organotypic model. Trypsin-2 activated proMT1-MMP, as well as altered the expression of TJ protein claudin-7. In conclusion, trypsin-2 over-expression enhanced tongue carcinoma cell invasion by various genetic and proteolytic mechanisms.
Assuntos
Carcinoma de Células Escamosas/metabolismo , Carcinoma de Células Escamosas/patologia , Metaloproteinase 14 da Matriz/metabolismo , Precursores de Proteínas/metabolismo , Junções Íntimas/metabolismo , Neoplasias da Língua/metabolismo , Neoplasias da Língua/patologia , Tripsina/metabolismo , Tripsinogênio/metabolismo , Animais , Carcinoma de Células Escamosas/genética , Linhagem Celular Tumoral , Movimento Celular/genética , Claudinas/genética , Claudinas/metabolismo , Ativação Enzimática , Feminino , Expressão Gênica , Perfilação da Expressão Gênica , Humanos , Camundongos , Camundongos Nus , Invasividade Neoplásica , Precursores de Proteínas/genética , Neoplasias da Língua/genética , Tripsina/genética , Tripsinogênio/genética , Carga Tumoral/genética , Ensaios Antitumorais Modelo de XenoenxertoRESUMO
Here we describe the Genome Variation Format (GVF) and the 10Gen dataset. GVF, an extension of Generic Feature Format version 3 (GFF3), is a simple tab-delimited format for DNA variant files, which uses Sequence Ontology to describe genome variation data. The 10Gen dataset, ten human genomes in GVF format, is freely available for community analysis from the Sequence Ontology website and from an Amazon elastic block storage (EBS) snapshot for use in Amazon's EC2 cloud computing environment.
Assuntos
Bases de Dados de Ácidos Nucleicos , Genoma Humano/genética , Armazenamento e Recuperação da Informação , Sequência de Bases , Variação Genética , Humanos , InternetRESUMO
The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database (HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes.
Assuntos
Alelos , Mapeamento Cromossômico/métodos , Biologia Computacional/métodos , Doença/genética , Especiação Genética , Sequência de Aminoácidos/genética , Técnicas de Laboratório Clínico , Bases de Dados Genéticas , Evolução Molecular , Frequência do Gene , Marcadores Genéticos , Genoma Humano , Humanos , Desequilíbrio de Ligação , Fenótipo , Polimorfismo de Nucleotídeo Único , Proteínas/genética , Alinhamento de SequênciaRESUMO
The VBI Microbial Database (VMD) is a database system designed to host a range of microbial genome sequences. At present, the database contains genome sequence and annotation data of two plant pathogens Phytophthora sojae and Phytophthora ramorum. With the completion of the draft genome sequences of these pathogens in collaboration with the DOE Joint Genome Institute (JGI), we have created this resource to make the sequences publicly available. The genome sequences (95 MB for P.sojae and 65 MB for P.ramorum) were annotated with approximately 19,000 and approximately 16,000 gene models, respectively. We used two different statistical methods to validate these gene models, Fickett's and a log-likelihood method. Functional annotation of the gene models is based on results from BlastX and InterProScan screens. From the InterProScan results, we could assign putative functions to 17,694 genes in P.sojae and 14,700 genes in P.ramorum. We created an easy-to-use genome browser to view the genome sequence data, which opens to detailed annotation pages for each gene model. A community annotation interface is available for registered community members to add or edit annotations. There are approximately 1600 gene models for P.sojae and approximately 700 models for P.ramorum that have already been manually curated. A toolkit is provided as an additional resource for users to perform a variety of sequence analysis jobs. The database is publicly available at http://phytophthora.vbi.vt.edu/.
