RESUMO
Ethnic diversity has been long considered as one of the factors explaining why the severe forms of dengue are more prevalent in Southeast Asia than anywhere else. Here we take advantage of the admixed profile of Southeast Asians to perform coupled association-admixture analyses in Thai cohorts. For dengue shock syndrome (DSS), the significant haplotypes are located in genes coding for phospholipase C members (PLCB4 added to previously reported PLCE1), related to inflammation of blood vessels. For dengue fever (DF), we found evidence of significant association with CHST10, AHRR, PPP2R5E and GRIP1 genes, which participate in the xenobiotic metabolism signaling pathway. We conducted functional analyses for PPP2R5E, revealing by immunofluorescence imaging that the coded protein co-localizes with both DENV1 and DENV2 NS5 proteins. Interestingly, only DENV2-NS5 migrated to the nucleus, and a deletion of the predicted top-linking motif in NS5 abolished the nuclear transfer. These observations support the existence of differences between serotypes in their cellular dynamics, which may contribute to differential infection outcome risk. The contribution of the identified genes to the genetic risk render Southeast and Northeast Asian populations more susceptible to both phenotypes, while African populations are best protected against DSS and intermediately protected against DF, and Europeans the best protected against DF but the most susceptible against DSS.
Assuntos
Povo Asiático/genética , Vírus da Dengue/genética , Dengue/genética , Genoma Viral/genética , Estudo de Associação Genômica Ampla , Dengue Grave/genética , Adolescente , Adulto , Sudeste Asiático , Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , Proteínas de Transporte/genética , Linhagem Celular , Núcleo Celular/virologia , Pré-Escolar , Estudos de Coortes , Dengue/virologia , Feminino , Expressão Gênica , Predisposição Genética para Doença , Genótipo , Humanos , Lactente , Masculino , Proteínas do Tecido Nervoso/genética , Razão de Chances , Proteína Fosfatase 2/genética , Proteínas Repressoras/genética , Sorogrupo , Dengue Grave/etnologia , Sulfotransferases , Tailândia , Fosfolipases Tipo C/genética , Proteínas não Estruturais Virais/genética , Proteínas Virais/genética , Adulto JovemRESUMO
It is widely agreed that complex diseases are typically caused by the joint effects of multiple instead of a single genetic variation. These genetic variations may show stronger effects when considered together than when considered individually, a phenomenon known as epistasis or multilocus interaction. In this work, we explore the applicability of information interaction to discover pairwise epistatic effects related with complex diseases. We start by showing that traditional approaches such as classification methods or greedy feature selection methods (such as the Fleuret method) do not perform well on this problem. We then compare our information interaction method with BEAM and SNPHarvester in artificial datasets simulating epistatic interactions and show that our method is more powerful to detect pairwise epistatic interactions than its competitors. We show results of the application of information interaction method to the WTCCC breast cancer dataset. Our results are validated using permutation tests. We were able to find 89 statistically significant pairwise interactions with a p-value lower than 10(-3). Even though many recent algorithms have been designed to find epistasis with low marginals, we observed that all (except one) of the SNPs involved in statistically significant interactions have moderate or high marginals. We also report that the interactions found in this work were not present in gene-gene interaction network STRING.
Assuntos
Neoplasias da Mama/epidemiologia , Causalidade , Suscetibilidade a Doenças/epidemiologia , Epistasia Genética/genética , Modelos Teóricos , Biologia Computacional/métodos , Humanos , Polimorfismo de Nucleotídeo Único/genéticaRESUMO
The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perpective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-ß, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient's response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series.
