Mesenchymal cells reactivate Snail1 expression to drive three-dimensional invasion programs

Epithelial–mesenchymal transition (EMT) is required for mesodermal differentiation during development. The zinc-finger transcription factor, Snail1, can trigger EMT and is sufficient to transcriptionally reprogram epithelial cells toward a mesenchymal phenotype during neoplasia and fibrosis. Whether Snail1 also regulates the behavior of terminally differentiated mesenchymal cells remains unexplored. Using a Snai1 conditional knockout model, we now identify Snail1 as a regulator of normal mesenchymal cell function. Snail1 expression in normal fibroblasts can be induced by agonists known to promote proliferation and invasion in vivo. When challenged within a tissue-like, three-dimensional extracellular matrix, Snail1-deficient fibroblasts exhibit global alterations in gene expression, which include defects in membrane type-1 matrix metalloproteinase (MT1-MMP)-dependent invasive activity. Snail1-deficient fibroblasts explanted atop the live chick chorioallantoic membrane lack tissue-invasive potential and fail to induce angiogenesis. These findings establish key functions for the EMT regulator Snail1 after terminal differentiation of mesenchymal cells

Application of multidisciplinary analysis to gene expression.

Molecular analysis of cancer, at the genomic level, could lead to individualized patient diagnostics and treatments. The developments to follow will signal a significant paradigm shift in the clinical management of human cancer. Despite our initial hopes, however, it seems that simple analysis of microarray data cannot elucidate clinically significant gene functions and mechanisms. Extracting biological information from microarray data requires a complicated path involving multidisciplinary teams of biomedical researchers, computer scientists, mathematicians, statisticians, and computational linguists. The integration of the diverse outputs of each team is the limiting factor in the progress to discover candidate genes and pathways associated with the molecular biology of cancer. Specifically, one must deal with sets of significant genes identified by each method and extract whatever useful information may be found by comparing these different gene lists. Here we present our experience with such comparisons, and share methods developed in the analysis of an infant leukemia cohort studied on Affymetrix HG-U95A arrays. In particular, spatial gene clustering, hyper-dimensional projections, and computational linguistics were used to compare different gene lists. In spatial gene clustering, different gene lists are grouped together and visualized on a three-dimensional expression map, where genes with similar expressions are co-located. In another approach, projections from gene expression space onto a sphere clarify how groups of genes can jointly have more predictive power than groups of individually selected genes. Finally, online literature is automatically rearranged to present information about genes common to multiple groups, or to contrast the differences between the lists. The combination of these methods has improved our understanding of infant leukemia. While the complicated reality of the biology dashed our initial, optimistic hopes for simple answers from microarrays, we have made progress by combining very different analytic approaches

Developing position structure based framework for Chinese entity relation extraction

Relation extraction is the task of finding semantic relations between two entities in text, and is often cast as a classification problem. In contrast to the significant achievements on English language, research progress in Chinese relation extraction is relatively limited. In this article, we present a novel Chinese relation extraction framework, which is mainly based on a 9-position structure. The design of this proposed structure is motivated by the fact that there are some obvious connections between relation types/subtypes and position structures of two entities. The 9-position structure can be captured with less effort than applying deep natural language processing, and is effective to relieve the class imbalance problem which often hurts the classification performance. In our framework, all involved features do not require Chinese word segmentation, which has long been limiting the performance of Chinese language processing. We also utilize some correction and inference mechanisms to further improve the classified results. Experiments on the ACE 2005 Chinese data set show that the 9-position structure feature can provide strong support for Chinese relation extraction. As well as this, other strategies are also effective to further improve the performance

Adsorption of Nitrogen Dioxide in a Redox-Active Vanadium MetalOrganic Framework Material

International audienceNitrogen dioxide (NO2) is a toxic air pollutant, and efficient abatement technologies are important to mitigate the many associated health and environmental problems. Here, we report the reactive adsorption of NO2 in a redox-active metal-organic framework (MOF), MFM-300(V). Adsorption of NO2 induces the oxidation of V(III) to V(IV) centers in MFM-300(V), and this is accompanied by the reduction of adsorbed NO2 to NO and the release of water via deprotonation of the framework hydroxyl groups, as confirmed by synchrotron X-ray diffraction and various experimental techniques. The efficient packing of {NO2 center dot N2O4}(infinity) chains in the pores of MFM-300(V-IV) results in a high isothermal NO2 uptake of 13.0 mmol g(-1) at 298 K and 1.0 bar and is retained for multiple adsorption-desorption cycles. This work will inspire the design of redox-active sorbents that exhibit reductive adsorption of NO2 for the elimination of air pollutants