TLR2 and TLR4 agonists induce production of the vasoactive peptide endothelin-1 by human dendritic cells

Endothelin-1 (ET-1) is mainly secreted by endothelial cells and acts as a potent vasoconstrictor. In addition ET-1 has also been shown to have pleiotropic effects on a variety of other systems including adaptive immunity. There are two main ET-1 receptors, ET(A) and ET(B), which have different tissue and functional distributions. Dendritic cells (DC) are pivotal antigen-presenting cells linking the innate with the adaptive immune system. DC are sentinels expressing pattern-recognition receptors, e.g. the toll-like receptors (TLR) for detecting danger signals released from pathogens or tissue injury. Here we show for the first time that stimulation of human monocyte-derived DC with exogenous as well as endogenous selective TLR4 and TLR2 agonists induces the production of ET-1 in a dose- and time-dependent manner. 'Alternative' activation of DC in the presence of 1alpha,25-dihydroxyvitamin D(3) results in a marked potentiation of the endothelin response, whereas prostaglandin E(2) or dexamethasone do not increase ET-1 production. Furthermore, chetomin, an inhibitor of the transcription factor hypoxia-inducible factor 1alpha (HIF-1alpha), prevents TLR-mediated secretion of ET-1. Surprisingly, stimulation of human monocytes with LPS does not lead to secretion of detectable amounts of ET-1. These results suggest a role of ET-1 as an important player in human DC biology and innate immunity in general.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.