Análise de expressão da metiltransferase SETD4 em leucemia linfoide aguda e sua relação com a leucemogênese

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Ciências da Saúde, Programa de Pós-Graduação em Ciências da Saúde, 2016.A leucemia linfoblástica aguda (LLA) é a neoplasia com maior prevalência na infância. Melhorias muito expressivas no prognóstico da doença têm sido apresentadas nas últimas décadas, alcançando-se taxas de cura em torno de 90% em alguns casos. No entanto, a doença ainda apresenta mau prognóstico em pacientes adultos ou menores de um ano. A compreensão das neoplasias tem sido reinterpretada à luz do advento da epigenética como uma nova área do conhecimento. Constituindo um padrão de herança informacional que não depende de alterações na cadeia primária de DNA, a epigenética tem aberto o caminho para novas possibilidades de tratamento do câncer. As metiltransferases de lisina, em particular, têm estado em foco devido aos crescentes relatos de envolvimento na regulação da expressão de oncogenes ou genes supressores de tumor, bem como devido ao desenvolvimento de fármacos cujos alvos são estas mesmas enzimas. A família de metiltransferases SETD possui alguns membros que já foram descritos como tendo relacionamento com o câncer. No entanto, SETD4 ainda carece de mais profunda caracterização. Nesse sentido, este trabalho teve como objetivo descrever o perfil de transcrição de SETD4 em amostras clínicas de aspirado de medula óssea por meio de qPCR e verificar a sua relação com a leucemogênese por meio de análise de correlação com fatores clínicos e com a transcrição de outros genes. Também foi realizada uma análise exploratória de dados de expressão e transcrição disponíveis em bancos de bioinformática on-line. Foi verificado que SETD4 encontra-se com transcrição aumentada no grupo de amostras clínicas neoplásicas em relação às amostras não-neoplásicas de nossa coorte, exibindo aumento transcricional de cerca de 5.4 vezes. Durante o tratamento quimioterápico, os pacientes exibiram redução da transcrição da metiltransferase, revelando correlação com a queda no número de linfoblastos na medula óssea no 29° dia de indução quimioterápica. SETD4 teve sua transcrição fortemente correlacionada com a de duas outras metiltransferases: SETMAR e SMYD2. Foi detectada transcrição aumentada de SETD4 em dados obtidos por microarranjo e RNA-seq de outras neoplasias em plataformas disponíveis de dados públicos. Além disso, amplificações e deleções de número de cópias do gene parecem influenciar negativamente a sobrevida dos pacientes de leucemia mieloide aguda, e câncer de estômago, enquanto o nível aumentado de transcrição parece ter o mesmo efeito em LLA. Também verificamos que SETD4 apresenta mutações no domínio SET e Rubs-subs-bind que têm o potencial de impactar sua função enzimática. Juntos, estes resultados apontam para essa metiltransferase como uma enzima que pode ter um papel importante na leucemogênese e demais processos de carcinogênese, constituindo um atraente alvo terapêutico e farmacológico.Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in childhood. Very significant improvements in the prognosis of the disease have been made in recent decades, with cure rates being achieved around 90% in some cases. However, the disease still has poor prognosis in adult patients or children with less than one year. The understanding of cancer has been reinterpreted in the light of the advent of epigenetics as a new area of knowledge. Constituting a pattern of informational inheritance that does not depend on changes on primary chain of DNA, epigenetics has opened the way to new possibilities of cancer treatment. Lysine methyltransferases, in particular, have been in focus due to the increasing reports of its involvement in the regulation of oncogene or tumor suppressor gene expression, as well as to the development of drugs targeting those same proteins. The SETD methyltransferase family has some members who have been described as being related to cancer. Despite all these efforts, SETD4 still lacks deeper characterization. Therefore, this study aimed to describe SETD4 transcription profile in clinical samples of bone marrow aspirate by qPCR and to assess its relationship with leukemogenesis by means of correlation analysis with clinical factors and transcription of other genes during chemotherapy. An exploratory analysis of data available at online bioinformatics platforms was also performed. It was found that SETD4 transcription is upregulated in the neoplastic group of clinical samples relative to non-neoplastic samples, exhibiting an increase of approximately 5.4 times. During chemotherapy, patients exhibited reduced methyltransferase transcription, showing correlation with the decrease in the number of lymphoblasts in the bone marrow. SETD4 was highly correlated with the transcription of two other methyltransferases: SETMAR and SMYD2. Upregulated transcription of SETD4 was detected in data obtained by microarray and RNA-seq of other neoplasias in public data platforms. Moreover, gene copy number amplifications and deletions seems to have a negative influence on survival of acute myeloid leukemia and gastric cancer patients while upregulation of transcription in ALL seems to have the same effect. We also verified that SETD4 presents mutations in SET and Rubs-subs-bind domains that may have an impact on its enzymatic function. Together, these results point to this methyltransferase as an enzyme that may play a role in leukemogenesis and other carcinogenesis processes, constituting an attractive therapeutic and pharmacological target

Deciphering histone code of transcriptional regulation in malaria parasites by large-scale data mining

Histone modifications play a major role in the regulation of gene expression. Accumulated evidence has shown that histone modifications mediate biological processes such as transcription cooperatively. This has led to the hypothesis of 'histone code' which suggests that combinations of different histone modifications correspond to unique chromatin states and have distinct functions. In this paper, we propose a framework based on association rule mining to discover the potential regulatory relations between histone modifications and gene expression in Plasmodium falciparum. Our approach can output rules with statistical significance. Some of the discovered rules are supported by literature of experimental results. Moreover, we have also discovered de novo rules which can guide further research in epigenetic regulation of transcription. Based on our association rules we build a model to predict gene expression, which outperforms a published Bayesian network model for gene expression prediction by histone modifications. The results of our study reveal mechanisms for histone modifications to regulate transcription in large-scale. Among our findings, the cooperation among histone modifications provides new evidence for the hypothesis of histone code. Furthermore, the rules output by our method can be used to predict the change of gene expression

Deciphering histone code of transcriptional regulation in malaria parasites by large-scale data mining

Histone modifications play a major role in the regulation of gene expression. Accumulated evidence has shown that histone modifications mediate biological processes such as transcription cooperatively. This has led to the hypothesis of 'histone code' which suggests that combinations of different histone modifications correspond to unique chromatin states and have distinct functions. In this paper, we propose a framework based on association rule mining to discover the potential regulatory relations between histone modifications and gene expression in Plasmodium falciparum. Our approach can output rules with statistical significance. Some of the discovered rules are supported by literature of experimental results. Moreover, we have also discovered de novo rules which can guide further research in epigenetic regulation of transcription. Based on our association rules we build a model to predict gene expression, which outperforms a published Bayesian network model for gene expression prediction by histone modifications. The results of our study reveal mechanisms for histone modifications to regulate transcription in large-scale. Among our findings, the cooperation among histone modifications provides new evidence for the hypothesis of histone code. Furthermore, the rules output by our method can be used to predict the change of gene expression