The in vivo functions of lymphatic endothelial cells depend on their microenvironment, which cannot be fully reproduced in vitro. Because of technical limitations, gene expression in uncultured, "ex vivo" lymphatic endothelial cells has not been characterized at the molecular level. We combined tissue micropreparation and direct cell isolation with DNA chip experiments to identify 159 genes differentiating human lymphatic endothelial cells from blood vascular endothelial cells ex vivo. The same analysis performed with cultured primary cells revealed that only 19 genes characteristic for lymphatic endothelium ex vivo retained this property upon culture, while 27 marker genes were newly induced. In addition, a set of panendothelial genes could be recognized. The propagation of lymphatic endothelial cells in culture stimulated transcription of genes associated with cell turnover, basic metabolism, and the cytoskeleton. On the other hand, there was downregulation of genes encoding extracellular matrix components, signaling via transmembrane tyrosine kinase pathways and the chemokine (C-C) ligand 21. Direct ex vivo analysis of the lymphatic endothelial cell transcriptome is helpful for the understanding of the physiology of the lymphatic vascular system and of the pathogenesis of its diseases.

We study a set of linearized catalytic reactions to model gene and protein interactions. The model is based on experimentally motivated interaction network topologies and is designed to capture some key properties of gene expression statistics. We impose a nonlinearity to the system by enforcing a boundary condition which guarantees non-negative concentrations of chemical substances. System stability is quantified by maximum Lyapunov exponents. We find that the non-negativity constraint leads to a drastic inflation of those regions in parameter space where the Lyapunov exponent exactly vanishes. Within the model this finding can be fully explained as a result of a symmetry breaking mechanism induced by the positivity constraint. The robustness of this finding with respect to network topologies and the role of intrinsic molecular and external noise is discussed. We argue that systems with inflated "edges of chaos" could be much more easily favored by natural selection than systems where the Lyapunov exponent vanishes only on a parameter set of measure zero.

Type 2 diabetes is associated with microvascular damage that causes frequent infections in the skin and chronic ulcers as a result of impaired wound healing. To trace the pathological changes, we performed a comprehensive analysis of lymphatic vessels in the skin of type 2 diabetic versus nondiabetic patients. The dermis revealed enhanced lymphatic vessel density, and transcriptional profiling of ex vivo isolated lymphatic endothelial cells (LECs) identified 160 genes differentially expressed between type 2 diabetic and nondiabetic LECs. Bioinformatic analysis of deregulated genes uncovered sets functionally related to inflammation, lymphatic vessel remodeling, lymphangiogenesis, and lipid and small molecule transport. Furthermore, we traced CD68+ macrophage accumulation and concomitant upregulation of tumor necrosis factor-α (TNF-α) levels in type 2 diabetic skin. TNF-α treatment of LECs and its specific blockade in vitro reproduced differential regulation of a gene set that led to enhanced LEC mobility and macrophage attachment, which was mediated by the LEC-derived chemokine CXCL10. This study identifies lymph vessel gene signatures directly correlated with type 2 diabetes skin manifestations. In addition, we provide evidence for paracrine cross-talk fostering macrophage recruitment to LECs as one pathophysiological process that might contribute to aberrant lymphangiogenesis and persistent inflammation in the skin.

BackgroundReverse engineering of gene regulatory networks presents one of the big challenges in systems biology. Gene regulatory networks are usually inferred from a set of single-gene over-expressions and/or knockout experiments. Functional relationships between genes are retrieved either from the steady state gene expressions or from respective time series.ResultsWe present a novel algorithm for gene network reconstruction on the basis of steady-state gene-chip data from over-expression experiments. The algorithm is based on a straight forward solution of a linear gene-dynamics equation, where experimental data is fed in as a first predictor for the solution. We compare the algorithm's performance with the NIR algorithm, both on the well known E. coli experimental data and on in-silico experiments.ConclusionWe show superiority of the proposed algorithm in the number of correctly reconstructed links and discuss computational time and robustness. The proposed algorithm is not limited by combinatorial explosion problems and can be used in principle for large networks.