Previously, using an inbred strain screen and QTL mapping strategies, we demonstrated the presence of loci in the mouse genome that significantly influenced the ability of a transgene-induced mammary tumor to metastasize to the lung. Here we present data supporting the signal transduction molecule, Sipa1, as a candidate for the metastasis efficiency modifier locus Mtes1. Sequence analysis of genes in a candidate haplotype block revealed a non-synonymous animo acid polymorphism in the Sipa1 PDZ protein-protein interaction domain. Biochemical analysis indicates that the missense substitution had a significant effect on the Sipa1 RapGAP function. Spontaneous metastasis assays using cells expressing ectopic Sipa1 or Sipa1 shRNA to modulate the expression of Sipa1demonstrate that the metastatic capacity of a highly aggressive mouse mammary tumor cell line is correlated with cellular Sipa1 levels. Examination of human gene expression data is consistent with the role of Sipa1 concentration in metastatic progression. Together these data suggest that the PDZ domain polymorphism is likely to be at least one of the underlying genetic polymorphisms responsible for the Mtes1 locus. This is also, to the best of our knowledge, the first demonstration of a constitutional genetic polymorphism having a significant impact on tumor metastasis.

Breast cancer mortality is primarily due to the occurrence of metastatic disease. We have identified a novel potential therapeutic agent derived from an edible root of the plant Colocasia esculenta, commonly known as taro, that has demonstrable activity in a preclinical model of metastatic breast cancer and that should have minimal toxicity. We have shown for the first time that a water-soluble extract of taro (TE) potently inhibits lung colonizing ability as well as spontaneous metastasis from mammary gland-implanted tumors, in a murine model of highly metastatic ER, PR and Her-2/neu negative breast cancer. TE modestly inhibits proliferation of some, but not all, breast and prostate cancer cell lines. Morphologic changes including cell rounding were observed. Tumor cell migration was completely blocked by TE. TE treatment also inhibited prostaglandin E2 (PGE2) synthesis and downregulated cyclooxygenase (COX) 1 and 2 mRNA expression. We purified the active compound(s) to near homogeneity with antimetastatic activity comparable to stock TE. The active compound with a native size of approximately 25 kD contains two fragments of nearly equal size. The N-terminal amino acid sequencing of both fragments reveals that the active compound is highly related to three taro proteins; 12 kD storage protein, tarin and lectin. All are similar in terms of amino acid sequence, post-translational processing and all contain a carbohydrate-binding domain. This is the first report describing a compound(s) derived from taro, that potently and specifically inhibits tumor metastasis.

Electronic educational games can be highly entertaining, but studies have shown that they do not always trigger learning. To enhance the effectiveness of educational games, we propose intelligent pedagogical agents that can provide individualized instruction integrated with the entertaining nature of the games. In this paper, we describe one such agent, that we have developed for Prime Climb, an educational game on number factorization. The Prime Climb agent relies on a probabilistic student model to generate tailored interventions aimed at helping students learn number factorization through the game. After describing the functioning of the agent and the underlying student model, we report the results of an empirical study that we performed to test the agent's effectiveness.

Genomic sequencing techniques introduce experimental errors into reads which can mislead sequence assembly efforts and complicate the diagnostic process. Here we present a method for detecting and removing sequencing errors from reads generated in genomic shotgun sequencing projects prior to sequence assembly. For each input read, the set of all length k substrings (k-mers) it contains are calculated. The read is evaluated based on the frequency with which each k-mer occurs in the complete data set (k-count). For each read, k-mers are clustered using the variable-bandwidth mean-shift algorithm. Based on the k-count of the cluster center, clusters are classified as error regions or non-error regions. For the 23 real and simulated data sets tested (454 and Solexa), our algorithm detected error regions that cover 99% of all errors. A heuristic algorithm is then applied to detect the location of errors in each putative error region. A read is corrected by removing the errors, thereby creating two or more smaller, error-free read fragments. After performing error removal, the error-rate for all data sets tested decreased (∼35-fold reduction, on average). EDAR has comparable accuracy to methods that correct rather than remove errors and when the error rate is greater than 3% for simulated data sets, it performs better. The performance of the Velvet assembler is generally better with error-removed data. However, for short reads, splitting at the location of errors can be problematic. Following error detection with error correction, rather than removal, may improve the assembly results.

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