RESUMO
Over the last years, genome-wide studies consistently showed an increased burden of rare copy number variants (CNVs) in schizophrenia patients, supporting the "common disease, rare variant" hypothesis in at least a subset of patients. We hypothesize that in families with a high burden of disease, and thus probably a high genetic load influencing disease susceptibility, rare CNVs might be involved in the etiology of schizophrenia. We performed a genome-wide CNV analysis in the index patients of eight families with multiple schizophrenia affected members, and consecutively performed a detailed family analysis for the most relevant CNVs. One index patient showed a DRD5 containing duplication. A second index patient presented with an NRXN1 containing deletion and two adjacent duplications containing MYT1L and SNTG2. Detailed analysis in the subsequent families showed segregation of the identified CNVs. With this study we show the importance of screening high burden families for rare CNVs, which will not only broaden our knowledge concerning the molecular genetic mechanisms involved in schizophrenia but also allow the use of the obtained genetic data to provide better clinical care to these families in general and to non-symptomatic causal CNV carriers in particular.
Assuntos
Variações do Número de Cópias de DNA , Esquizofrenia/genética , Adulto , Idoso , Proteínas de Ligação ao Cálcio , Moléculas de Adesão Celular Neuronais/genética , Saúde da Família , Feminino , Predisposição Genética para Doença , Variação Genética , Estudo de Associação Genômica Ampla , Humanos , Masculino , Proteínas de Membrana/genética , Pessoa de Meia-Idade , Proteínas Musculares/genética , Proteínas do Tecido Nervoso/genética , Moléculas de Adesão de Célula Nervosa , Linhagem , Biossíntese de Proteínas , Receptores de Dopamina D5/genética , Fatores de Transcrição/genéticaRESUMO
We demonstrate how a single-celled organism could undertake associative learning. Although to date only one previous study has found experimental evidence for such learning, there is no reason in principle why it should not occur. We propose a gene regulatory network that is capable of associative learning between any pre-specified set of chemical signals, in a Hebbian manner, within a single cell. A mathematical model is developed, and simulations show a clear learned response. A preliminary design for implementing this model using plasmids within Escherichia coli is presented, along with an alternative approach, based on double-phosphorylated protein kinases.
Assuntos
Escherichia coli/genética , Transdução de Sinais/fisiologia , Escherichia coli/fisiologia , Regulação Bacteriana da Expressão Gênica , Modelos Biológicos , Fosforilação , Plasmídeos/genética , Proteínas Quinases/genética , Proteínas Quinases/metabolismo , Transdução de Sinais/genéticaRESUMO
We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at http://www.biograph.be.