Molecular Mechanisms of the DYRK1A-regulated DNA Repair

Molecular Mechanisms of the DYRK1A-regulated DNA Repair Polina Bukina, Dept. of Biology, with Dr. Sarah Golding, Dept. of Biology The functions of human Dual-specificity tyrosine (Y)-Regulated Kinase 1A, or DYRK1A, include cell cycle control and differentiation. DYRK1A is required for assembly of the DREAM complex and repression of the cell cycle-dependent genes, such as BRCA1 and RAD51, in quiescence. Our lab previously reported that overexpression of DYRK1A inhibits the accumulation of a DNA repair protein 53BP1, at the DNA double-stranded breaks (DSB). Accumulation of 53BP1 is attributed to repair by non-homologous end joining (NHEJ) over homologous recombination (HRR). The function of 53BP1 is opposed by RNF169, a ubiquitin-binding protein that also accumulates at the DSB sites and promotes HRR. It was found that DYRK1A interacts with RNF169 to regulate the displacement of 53BP1 from the DSB sites. This study focuses on RNF169 in order to understand the role of DYRK1A in DNA damage response. We used the Multi-Dimensional Protein Identification Technology (MudPIT) proteomic analysis to identify RNF169-interacting proteins. Human cancer U-2 OS cells stably expressing HA-tagged RNF169, as well as control cells were used for immunoprecipitation. The samples were sent to Stowers Institute for Medical Research for MudPIT proteomic analysis. In order to understand the regulation of DNA repair by DYRK1A, the RNA sequencing dataset was analyzed as part of other studies in the lab. The expression of the mRNA for repair factors RAD51 and BRCA1 was found to be regulated by DYRK1A. To determine the significance of this finding, an experiment was designed to assess BRCA1 and RAD51 protein levels in the normal U-2 OS cells and in the cells lacking DYRK1A (U-2 OS DYRK1A knockout cells) after inducing DNA damage by gamma irradiation. It was found that the levels of RAD51, BRCA1 and 53BP1 levels were increased with DYRK1A KO. These results were consistent with the finding that DNA repair efficiency is increased with DYRK1A KO. Further studies can help to understand if these effects are mediated by DYRK1A-regulated DREAM complex.https://scholarscompass.vcu.edu/uresposters/1357/thumbnail.jp

TRANSLATION METHODOLOGY BASED ON META- MODEL OF COMPLEX STRUCTURE FOR DATABASE DESIGN

ABSTRACT We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle (mitochondria and chloroplasts) complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation

New Strategies to Develop Hidden Markov Models Combining KEGG and Pfam

Profile Hidden Markov Models utilize the information stored in a multiple sequence alignment such as its residues and conserved regions to assign probabilities at each column. This indicates areas in the sequences where similar functionality is observed. These probability weights are then combined to assign a sequence score, which is then able to better detect distant protein sequences. Current databases take advantage of the Profile HMMs such as Pfam and KEGG. However, where Pfam has a lot of sequences, it lacks specific annotation, making it harder to detect distant sequences that share the same functionality. Additionally, where KEGG has specific annotation, it lacks in number of sequences. Our goal is then to leverage the unannotated sequences from Pfam to generate KO-annotated level HMMs

Aplicação da inteligência artificial na anotação automática de genomas bacterianos

Orientador : Prof. Dr. Fábio de Oliveira PedrosaCo-Orientador: Prof. Dr. Roberto Tadeu RaittzDissertação (mestrado) - Universidade Federal do Paraná, Setor de Educação Profissional e Tecnológica, Programa de Pós-Graduação em Bioinformática. Defesa: Curitiba, 16/02/2012Bibliografia: fls. 81-86Resumo: O propósito da anotação é identificar sequências de DNA codificadoras de RNAs ou proteínas, esse processo é importante porque atribuem funções moleculares aos produtos gênicos. Para isso, são utilizadas ferramentas computacionais de anotação de genes que usam alinhamentos de sequência de proteína ou de DNA com o propósito de identificar genes homólogos e utilizar as informações de banco de dados de domínio público para inferir a função do gene. Embora sejam técnicas eficientes, elas podem estar sujeitas a erros quando realizada sem curadoria de um perito, em particular quando ocorre inexistência de grau de similaridade significativo de uma sequência comparada com outras sequências ou quando o banco de dados é composto por sequências parciais. Além disso, a taxa de erro de anotação pode ser significativamente aumentada quando a sequência de proteína de consulta é nova, compartilhando nenhuma semelhança com qualquer sequência disponível em bases de dados. Por esses motivos, neste trabalho desenvolveu-se uma ferramenta para verificar anotação de genes em genomas completos de bactérias, o programa Bioinformatics Tool Based on Bacterial Genomes Comparison (BOBBLES). Ele realiza a verificação da predição de genes computacionalmente propostos pelo programa Hybrid-Gene Finder (HGF). O programa BOBBLES compara a anotação de um genoma de referência completo de bactérias com os genes identificados pelo programa HGF. Este programa utiliza duas abordagens de comparação de sequências, uma utilizando pesquisas de similaridade de sequência através do programa BlastP e a outra utilizando o programa SILA. Ambas as abordagens servem para decidir se as sequências sugeridas pelo programa HGF foram anotadas corretamente. Para testar a ferramenta BOBBLES, utilizou-se um conjunto composto por 14 genomas bacterianos completos. Foram encontrados 365 novos genes e 101 genes com melhor ou similar grau alinhamento em fase de leitura diferente do genoma de referência, resultando em uma porcentagem de acerto de aproximadamente 76 % para esse conjunto de genomas, utilizando o alinhamento das sequências com o programa SILA. Já com o alinhamento realizado pelo programa Blastp obteve-se 529 novos genes. No entanto, o tempo médio estimado de execução do programa BOBBLES tendo em seu algoritmo a ferramenta SILA é de pelo menos cinco vezes mais rápido do que utilizando o programa BlastP. Essa diferença de tempo é justificada pelo fato do programa SILA realizar os alinhamentos das sequências com indexação recursiva em um banco de dados local, o banco de dados de proteínas não redundantes do NCBI, conhecido por NR.Abstract: The annotation purpose is to identify DNA sequences coding for proteins or RNAs, this process is important because it gives the molecular function for the genes products. For that, it's used Gene Annotation tools using protein or DNA sequences alignments to identify homologous genes and use information from the public database to infer gene function. Although these are efficient techniques, they can be error-prone when performed without curation of an expert, particularly in cases of similarity sequence with no degree of similarity with other sequences that may be relevant or when the database is composed by partial sequences. In addition, annotation error rate can be significantly increased when it's a new query protein sequence, sharing no similarity with any available sequence in databases. Therefore, this work has developed a tool to verify genes annotation in complete bacterial genomes, the Bioinformatics Tool Based on Bacterial Genomes Comparison program (BOBBLES). It realizes the computationally gene prediction performed by Hybrid-Gene Finder (HGF). The BOBBLES compares a previous complete bacterial genome annotation with the genes identified by HGF program. This program uses two sequence comparison approaches, the first one using the BlastP program, and another approach using the SILA program, to decide whether they were recorded correctly. The BOBBLES was tested using a set composed of 14 complete bacterial genomes. These tests obtained 365 new genes and 101 genes with better or similar alignment in process of reading different from the reference genome, resulting in 76% of correct results for genomes set which used the alignment of sequences with the SILA program. But using the BlastP program, 529 new genes were obtained. However, the estimated average execution time for the BOBBLES program using SILA program was at least five times faster than using the BlastP program. This time difference is justified by the fact that the SILA program performs the alignments of the sequences with recursive indexing into a local database, the NCBI's non-redundant protein sequence (NR) database