Furthermore, use of DAPT increased the expression of ROCK1 and 2, support ing the idea that Notch1 normally controls these genes in keratinocytes to prevent tumour progression. The transcript levels of Dasatinib Notch have been shown to be upregu lated in mouse embryos treated with trichostatin A, a po tent HDAC inhibitor. Therefore, there is evidence to suggest that Notch1 not only negatively regulates ROCK1 at the promoter level but that HDAC inhibitors upregulate Notch1 gene expression. In HD matrix, we find that Notch1 but not p53 was upregulated by MS 275 and the increase in Notch1 levels was independent of CHX. When Notch1 activation was blocked using a secretase inhibitor, DAPT, or when Notch1 levels were reduced by pooled siRNA transfec tion, the effect of MS 275 on ROCK1 activity was abro gated.

The data suggest that MS 275 directly upregulates Notch1, which in turn blocks ROCK1 expression perhaps via repressor activities on the ROCK1 promoter. Conclusion This work shows that amoeboid tumour cells migrate in stiff matrices by upregulating ROCK1 activity and cell contractility via an epigenetically derived, Notch1 dependant mechanism. However, the require ment for ROCK1 is conditional upon the availability of other mechanisms such as proteolysis assisted migration. Methods Reagents N 4 hosts over half a million single array chip expression profiles and the EBI hosts the ArrayExpress database with a similar largely overlapping number of arrays. These data cannot be compared directly as they come from different array platforms covering many different species and a variety of normalisation schemes are used.

In the overwhelming number of analyses expression profiles are compared within the given series and probed for the up or down regulation of single genes using volcano plot representations or other statistical filters. Alternatively, a larger set of responders can be scored against gene sets corresponding to pathways, interacting networks or gene ontology classes. For large series it is possible to compile correla tions of expression changes of individual gene pairs and groups of genes leading to a hierarchical clustering based network discovery and gene interaction predic tion. To this end SOURCE hosts gene expression profiles across a large collection of experimental series and profile correlations GSK-3 within a given series can be exam ined to predict genes with similar or related function. Many array analysis applications incorporate array derived network data that are valuable aids in characterising the expression profile data GeneGo. However, these analyses do not allow for a direct quantitative comparison between separate expression studies and therefore a lot of the infor mation contained in the experiment is effectively lost.