RESUMO
BACKGROUND: Copy number variations (CNVs) have been shown to account for substantial portions of observed genomic variation and have been associated with qualitative and quantitative traits and the onset of disease in a number of species. Information from high-resolution studies to detect, characterize and estimate population-specific variant frequencies will facilitate the incorporation of CNVs in genomic studies to identify genes affecting traits of importance. RESULTS: Genome-wide CNVs were detected in high-density single nucleotide polymorphism (SNP) genotyping data from 1,717 Nelore (Bos indicus) cattle, and in NGS data from eight key ancestral bulls. A total of 68,007 and 12,786 distinct CNVs were observed, respectively. Cross-comparisons of results obtained for the eight resequenced animals revealed that 92 % of the CNVs were observed in both datasets, while 62 % of all detected CNVs were observed to overlap with previously validated cattle copy number variant regions (CNVRs). Observed CNVs were used for obtaining breed-specific CNV frequencies and identification of CNVRs, which were subsequently used for gene annotation. A total of 688 of the detected CNVRs were observed to overlap with 286 non-redundant QTLs associated with important production traits in cattle. All of 34 CNVs previously reported to be associated with milk production traits in Holsteins were also observed in Nelore cattle. Comparisons of estimated frequencies of these CNVs in the two breeds revealed 14, 13, 6 and 14 regions in high (>20 %), low (<20 %) and divergent (NEL > HOL, NEL < HOL) frequencies, respectively. CONCLUSIONS: Obtained results significantly enriched the bovine CNV map and enabled the identification of variants that are potentially associated with traits under selection in Nelore cattle, particularly in genome regions harboring QTLs affecting production traits.
Assuntos
Variações do Número de Cópias de DNA , Genoma , Genômica , Locos de Características Quantitativas , Característica Quantitativa Herdável , Animais , Bovinos , Mapeamento Cromossômico , Biologia Computacional/métodos , Estudo de Associação Genômica Ampla , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala , Anotação de Sequência Molecular , Polimorfismo de Nucleotídeo Único , Reprodutibilidade dos TestesRESUMO
It has been shown previously that the snake venom metalloprotease-disintegrin jararhagin stimulates cell migration and cytoskeletal rearrangement, independently of its effects on cellular adhesion but possibly associated with the activation of small GTP-binding proteins from the Rho family [Costa, E.P., Santos, M.F., 2004. Toxicon 44(8), 861-870.] Here we show that jararhagin stimulates spreading, actin dynamics and neurite outgrowth in neuroblastoma cells, and that this effect is accompanied by the translocation of the Rac1 small GTPase to the membrane fraction, suggesting its activation. Stimulation of neurite outgrowth was observed within minutes and was dependent on the proteolytic activity of the toxin. These results suggest that jararhagin may stimulate neuronal differentiation, being a potential tool for neuronal regeneration studies.
Assuntos
Bothrops/fisiologia , Venenos de Crotalídeos/toxicidade , Metaloendopeptidases/toxicidade , Neuritos/efeitos dos fármacos , Neuroblastoma , Inibidores da Agregação Plaquetária/toxicidade , Proteínas rac1 de Ligação ao GTP/metabolismo , Animais , Adesão Celular/efeitos dos fármacos , Linhagem Celular Tumoral , Microscopia Confocal , Neuritos/metabolismo , Neuritos/patologia , Neuroblastoma/tratamento farmacológico , Neuroblastoma/metabolismo , Neuroblastoma/patologia , Neurônios/efeitos dos fármacos , Neurônios/metabolismo , Neurônios/patologia , Veneno de Bothrops jararacaRESUMO
BACKGROUND: Most phenomena in developmental biology involve or depend upon cell migration. This article describes a comprehensive framework for the characterization and analysis of trajectories defined by cell movement. The following two perspectives are considered: (a) the behavior of each individual cell and (b) interactions between neighboring pairs of cells. METHODS: The measurements considered for individual trajectories include the velocity magnitude and orientation, maximum spatial dispersion, displacement effectiveness, and displacement entropies. Interactions between two trajectories are characterized by comparing the respective velocities. RESULTS: The potential of the overall framework is illustrated using data of moving cells in different biological environments. The work shows that it is possible to use the new algorithm presented here to characterize cell motility. CONCLUSIONS: The features of the algorithm were successful in determining the motility changes under different experimental conditions.