Distinct functions of human cohesin-SA1 and cohesin-SA2 in double-strand break repair.

Cohesin is an essential multiprotein complex that mediates sister chromatid cohesion critical for proper segregation of chromosomes during cell division. Cohesin is also involved in DNA double-strand break (DSB) repair. In mammalian cells, cohesin is involved in both DSB repair and the damage checkpoint response, although the relationship between these two functions is unclear. Two cohesins differing by one subunit (SA1 or SA2) are present in somatic cells, but their functional specificities with regard to DNA repair remain enigmatic. We found that cohesin-SA2 is the main complex corecruited with the cohesin-loading factor NIPBL to DNA damage sites in an S/G(2)-phase-specific manner. Replacing the diverged C-terminal region of SA1 with the corresponding region of SA2 confers this activity on SA1. Depletion of SA2 but not SA1 decreased sister chromatid homologous recombination repair and affected repair pathway choice, indicating that DNA repair activity is specifically associated with cohesin recruited to damage sites. In contrast, both cohesin complexes function in the intra-S checkpoint, indicating that cell cycle-specific damage site accumulation is not a prerequisite for cohesin's intra-S checkpoint function. Our findings reveal the unique ways in which cohesin-SA1 and cohesin-SA2 participate in the DNA damage response, coordinately protecting genome integrity in human cells.

Rule extraction in gene-disease relationship discovery.

BACKGROUND: Biomedical data available to researchers and clinicians have increased dramatically over the past years because of the exponential growth of knowledge in medical biology. It is difficult for curators to go through all of the unstructured documents so as to curate the information to the database. Associating genes with diseases is important because it is a fundamental challenge in human health with applications to understanding disease properties and developing new techniques for prevention, diagnosis and therapy. METHODS: Our study uses the automatic rule-learning approach to gene-disease relationship extraction. We first prepare the experimental corpus from MEDLINE and OMIM. A parser is applied to produce some grammatical information. We then learn all possible rules that discriminate relevant from irrelevant sentences. After that, we compute the scores of the learned rules in order to select rules of interest. As a result, a set of rules is generated. RESULTS: We produce the learned rules automatically from the 1000 positive and 1000 negative sentences. The test set includes 400 sentences composed of 200 positives and 200 negatives. Precision, recall and F-score served as our evaluation metrics. The results reveal that the maximal precision rate is 77.8% and the maximal recall rate is 63.5%. The maximal F-score is 66.9% where the precision rate is 70.6% and the recall rate is 63.5%. CONCLUSIONS: We employ the rule-learning approach to extract gene-disease relationships. Our main contributions are to build rules automatically and to support a more complete set of rules than a manually generated one. The experiments show exhilarating results and some improving efforts will be made in the future.