A fast and efficient gene-network reconstruction method from multiple over-expression experiments.

BACKGROUND: Reverse 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. RESULTS: We 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. CONCLUSION: We 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.

Inflation of the edge of chaos in a simple model of gene interaction networks.

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.

Transcriptomal comparison of human dermal lymphatic endothelial cells ex vivo and in vitro.

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.

Statistically consistent identification of differentially expressed genes in DNA chip data over the whole expression range: relative variance method.

BACKGROUND: It is a well known problem that standard techniques for analysing DNA chip data misspecify genes. In particular, genes that are confirmed to be active, often do not show up as potential candidates. This is possibly due to non-homogeneous distributions of expression levels over the whole expression range. METHODS: We introduce a method that allows the detection of genes based on a self-adaptive threshold. The threshold is determined for equally-populated expression bands by assuming a normal distribution of logarithms of expression level ratios. By specifying a significance level, the threshold is set according to 'local' expression statistics within a band. We call this method the relative variance method (RVM). We derive a test statistic for the RVM and compare it with other methods. On this statistical basis, we show that RVM is a complementary approach to the t-test, significance analysis of microarrays (SAM) or empirical Bayes analysis of microarrays (EBAM). The RVM should be particularly useful for experiments with small sample size. RESULTS: Using a clinical dataset, we demonstrate that the RVM can correctly identify known marker genes, which are not found by the t-test, SAM or EBAM. CONCLUSION: In situations with limited sample material and small number of replicates, as is often the case in clinical datasets, use of the proposed RVM provides a higher reliability of potential candidate genes.

Enhanced lymph vessel density, remodeling, and inflammation are reflected by gene expression signatures in dermal lymphatic endothelial cells in type 2 diabetes.

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.

Combined analysis of audiologic performance and the plasma biomarker stromal cell-derived factor 1a in type 2 diabetic patients.

OBJECTIVE: This study presents a potentially novel method of screening for pathogenetic factors in diabetic audiopathy by comparing the absolute plasma concentration of a microangiopathy biomarker, stromal cell-derived factor 1a (SDF-1a), with frequency-specific audiometric results. BACKGROUND: Impaired hearing function in diabetic patients has, to date, remained a controversial and poorly understood theme with sparse clinical data. This is in contrast to more established components of the disease such as diabetic retinopathy, where diabetic microangiopathy is thought to be of pathogenetic relevance, and specific molecules such as SDF-1a have been assigned a relevant role. CLINICAL SETTING: Out patient clinic, Vienna General Hospital, Medical University of Vienna. PATIENTS: 18 Type 2 diabetic patients and 18 nondiabetic controls. MATERIALS AND METHODS: Pure-tone audiometry and Freyburger number tests were used to evaluate hearing function. Blood plasma values of SDF-1a were analyzed using an enzyme-linked immunosorbent assay. Statistical comparison of functional audiometric data and the absolute SDF-1a values was performed for all frequencies. RESULTS: A significantly higher plasma SDF-1a concentration (p < 0.005) in Type 2 diabetic patients, who also presented with higher pure-tone audiometry thresholds compared with nondiabetic subjects, was noted. Furthermore, an association between SDF-1a and audiometric performance, body mass index, and duration of diabetes was observed. CONCLUSION: We hypothesize that diabetic microangiopathy and its biomarker SDF-1a should be considered as potential pathogenetic factors for altered diabetic hearing, warranting further investigation.