CoordenaÃÃo de AperfeiÃoamento de NÃvel Superior / A carcinicultura marinha vem assumindo um papel de grande relevÃncia na economia mundial, nÃo somente pela geraÃÃo de empregos e rendimentos, mas especialmente por representar uma alternativa viÃvel para o atendimento da crescente demanda mundial por alimentos. No entanto, a ocorrÃncia de enfermidades nos cultivos de camarÃes, principalmente as de etiologia viral, vem comprometendo significativamente a atividade, por provocarem elevados Ãndices de mortalidade e conseqÃente perda econÃmica. A identificaÃÃo e caracterizaÃÃo de genes relacionados a enfermidades de camarÃes tÃm auxiliado significativamente na compreensÃo das respostas imunes desencadeadas nos processos infecciosos, assim como dos mecanismos relacionados à interaÃÃo patÃgeno-hospedeiro, ainda de pouco conhecimento cientÃfico. O presente trabalho teve como objetivo principal a prospecÃÃo e caracterizaÃÃo de genes relacionados a enfermidades de camarÃes da espÃcie Litopenaeus vannamei sob condiÃÃes de cultivo. A investigaÃÃo sanitÃria de 40 camarÃes por mÃtodos histopatolÃgicos e moleculares de diagnÃstico resultou na detecÃÃo da IMN, IHHN, WSS, NHP, vibrioses e gregarinas, o que permitiu o agrupamento destes camarÃes em classes diferenciais destinadas ao estudo de prospecÃÃo gÃnica atravÃs da tÃcnica de Differential display RT-PCR (DDRT-PCR). A anÃlise da expressÃo diferencial de genes nos perfis de expressÃo dos camarÃes agrupados de acordo com o status sanitÃrio resultou na identificaÃÃo de trÃs sequÃncias genÃticas inÃditas e potencialmente relacionadas à ocorrÃncia de enfermidades no camarÃo L. vannamei, sendo duas sequÃncias (882-A1R2 e 730-A4R10) de homologias indeterminada e uma (842-A2R6) com 99% de similaridade nucleotÃdica com o gene mut-7 que codifica um tipo de RNAse envolvida no mecanismo de silenciamento pÃstranscricional de genes do nematÃide Caenorhabditis elegans. Este resultado sugere que o gene 842-A2R6 pode estar relacionado ao mecanismo de silenciamento de dsRNA associadas ao IMNV. / The marine shrimp culture has achieved great importance in the global economy as an agriculture business, not only for jobs and income generation, but especially because it represents a viable alternative source of animal protein for the growing global food demand. However, the occurrence of diseases in shrimp culture, especially those of viral aetiology, has significantly compromised the activity, causing high mortality rates and consequent economic loss. The identification and characterization of genes related to shrimp diseases has helped understanding the immune responses triggered in the infectious processes, and mechanisms related to host-pathogen interaction, about which very little is known at the moment. The main objective of the present work was the prospection and characterization of genes related to diseases of the shrimp species Litopenaeus vannamei under typical rearing conditions. The health status investigation of 40 shrimps by histopathological and molecular methods of diagnosis resulted in the detection of IMN, IHHN, WSS, NHP, vibriosis and gregarines in various degrees and combinations. Shrimps have been grouped in classes for gene prospecting through the technique of Differential Display RT-CR (DDRT-PCR). The analysis of differential gene expression in the profiles produced resulted in the identification of three previously undescribed gene sequences potentially related to the occurrence of diseases in shrimp L. Vannamei. Two of these sequences (882-A1R2 and 730-A4R10) have not had homologies to any annotated invertebrate sequences. Fragment 842-A2R6 however, showed 99% nucleotide similarity with the mut-7 gene, which encodes a type of RNAse involved in the mechanism of post-transcriptional gene silencing through RNAi in the nematode Caenorhabditis elegans. It is suggested hare that this kind of mechanism may be involved in the silencing of double stranded RNA from viruses like the IMNV.

No description available.

Whole-exome and genome sequencing are widely used to diagnose individual patients. However, despite its success, this approach leaves many patients undiagnosed. This could be due to the need to discover more disease genes and variants or because disease phenotypes are novel and arise from a combination of variants of multiple known genes related to the disease. Recent rapid increases in available genomic, biomedical, and phenotypic data enable computational analyses, reducing the search space for disease-causing genes or variants and facilitating the prediction of causal variants. Therefore, artificial intelligence, data mining, machine learning, and deep learning are essential tools that have been used to identify biological interactions, including protein-protein interactions, gene-disease predictions, and variant--disease associations. Predicting these biological associations is a critical step in diagnosing patients with rare or complex diseases. In recent years, computational methods have emerged to improve gene-disease prioritization by incorporating phenotype information. These methods evaluate a patient's phenotype against a database of gene-phenotype associations to identify the closest match. However, inadequate knowledge of phenotypes linked with specific genes in humans and model organisms limits the effectiveness of the prediction. Information about gene product functions and anatomical locations of gene expression is accessible for many genes and can be associated with phenotypes through ontologies and machine-learning models. Incorporating this information can enhance gene-disease prioritization methods and more accurately identify potential disease-causing genes. This dissertation aims to address key limitations in gene-disease prediction and variant prioritization by developing computational methods that systematically relate human phenotypes that arise as a consequence of the loss or change of gene function to gene functions and anatomical and cellular locations of activity. To achieve this objective, this work focuses on crucial problems in the causative variant prioritization pipeline and presents novel computational methods that significantly improve prediction performance by leveraging large background knowledge data and integrating multiple techniques. Therefore, this dissertation presents novel approaches that utilize graph-based machine-learning techniques to leverage biomedical ontologies and linked biological data as background knowledge graphs. The methods employ representation learning with knowledge graphs and introduce generic models that address computational problems in gene-disease associations and variant prioritization. I demonstrate that my approach is capable of compensating for incomplete information in public databases and efficiently integrating with other biomedical data for similar prediction tasks. Moreover, my methods outperform other relevant approaches that rely on manually crafted features and laborious pre-processing. I systematically evaluate our methods and illustrate their potential applications for data analytics in biomedicine. Finally, I demonstrate how our prediction tools can be used in the clinic to assist geneticists in decision-making. In summary, this dissertation contributes to the development of more effective methods for predicting disease-causing variants and advancing precision medicine.