Inferring genetic interactions via a nonlinear model and an optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.</p> <p>Results</p> <p>An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in <it>S. cerevisiae</it>, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.</p> <p>Conclusions</p> <p>GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.</p

Synergistic binding of actinomycin D and echinomycin to DNA mismatch sites and their combined anti-tumour effects

Combination cancer chemotherapy is one of the most useful treatment methods to achieve a synergistic effect and reduce the toxicity of dosing with a single drug. Here, we use a combination of two well-established anticancer DNA intercalators, actinomycin D (ActD) and echinomycin (Echi), to screen their binding capabilities with DNA duplexes containing different mismatches embedded within Watson-Crick base-pairs. We have found that combining ActD and Echi preferentially stabilised thymine-related T:T mismatches. The enhanced stability of the DNA duplex-drug complexes is mainly due to the cooperative binding of the two drugs to the mismatch duplex, with many stacking interactions between the two different drug molecules. Since the repair of thymine-related mismatches is less efficient in mismatch repair (MMR)-deficient cancer cells, we have also demonstrated that the combination of ActD and Echi exhibits enhanced synergistic effects against MMR-deficient HCT116 cells and synergy is maintained in a MMR-related MLH1 gene knockdown in SW620 cells. We further accessed the clinical potential of the two-drug combination approach with a xenograft mouse model of a colorectal MMR-deficient cancer, which has resulted in a significant synergistic anti-tumour effect. The current study provides a novel approach for the development of combination chemotherapy for the treatment of cancers related to DNA-mismatches

Cyclooxygenase-2 enhances α2β1 integrin expression and cell migration via EP1 dependent signaling pathway in human chondrosarcoma cells

<p>Abstract</p> <p>Background</p> <p>Cyclooxygenase (COX)-2, the inducible isoform of prostaglandin (PG) synthase, has been implicated in tumor metastasis. Interaction of COX-2 with its specific EP receptors on the surface of cancer cells has been reported to induce cancer invasion. However, the effects of COX-2 on migration activity in human chondrosarcoma cells are mostly unknown. In this study, we examined whether COX-2 and EP interaction are involved in metastasis of human chondrosarcoma.</p> <p>Results</p> <p>We found that over-expression of COX-2 or exogenous PGE₂increased the migration of human chondrosarcoma cells. We also found that human chondrosarcoma tissues and chondrosarcoma cell lines had significant expression of the COX-2 which was higher than that in normal cartilage. By using pharmacological inhibitors or activators or genetic inhibition by the EP receptors, we discovered that the EP1 receptor but not other PGE receptors is involved in PGE₂-mediated cell migration and α2β1 integrin expression. Furthermore, we found that human chondrosarcoma tissues expressed a higher level of EP1 receptor than normal cartilage. PGE₂-mediated migration and integrin up-regulation were attenuated by phospholipase C (PLC), protein kinase C (PKC) and c-Src inhibitor. Activation of the PLCβ, PKCα, c-Src and NF-κB signaling pathway after PGE₂treatment was demonstrated, and PGE₂-induced expression of integrin and migration activity were inhibited by the specific inhibitor, siRNA and mutants of PLC, PKC, c-Src and NF-κB cascades.</p> <p>Conclusions</p> <p>Our results indicated that PGE₂enhances the migration of chondrosarcoma cells by increasing α2β1 integrin expression through the EP1/PLC/PKCα/c-Src/NF-κB signal transduction pathway.</p