Chemical modification of epibatidine causes a switch from agonist to antagonist and modifies its selectivity for neuronal nicotinic acetylcholine receptors

AbstractBackground: Studies of ligand gated channels strongly rely on the availability of compounds that can activate or inhibit with high selectivity one set or a subset of defined receptors. The alkaloid epibatidine (EPB), originally isolated from the skin of an Ecuadorian poison frog, is a very specific agonist for the neuronal nicotinic acetylcholine receptors (nAChRs). We used EPB derivatives to investigate the pharmacophore of nAChR subtypes.Results: Optically pure enantiomers of EPB analogues were synthesised. Analogues were obtained altered in the aromatic part: the chlorine was eliminated and the relative position of the pyridyl nitrogen changed. Voltage clamp electrophysiology was performed with these compounds on neuronal nAChRs reconstituted in Xenopus oocytes. The EPB derivatives show different activities towards the various nAChR subtypes.Conclusions: Small changes in the molecular structure of EPB produce marked changes in its capacity to activate the nAChRs. Subtype specificity can be obtained by changing the position of or by eliminating the pyridyl nitrogen

Functional Brain Receptor Imaging with Positron Emission Tomography

A new cocaine derivative for imaging the dopamine transporter has been developed. Measurements of radioligand binding of 11C-(+)-McN-5652 in vivo with PET suggests that ecstasy interacts directly with the serotonin reuptake sites and that a single oral dose of ecstasy (1.5 mg/kg) does not cause any changes in the serotonin transporter density in the human brain. Finally, a number of epibatidine derivatives have been developed as ligands to study the central nAChRs in vivo, however, toxicity studies prevented further clinical use

Causal Modeling of Cancer-Stromal Communication Identifies PAPPA as a Novel Stroma-Secreted Factor Activating NFκB Signaling in Hepatocellular Carcinoma

Inter-cellular communication with stromal cells is vital for cancer cells. Molecules involved in the communication are potential drug targets. To identify them systematically, we applied a systems level analysis that combined reverse network engineering with causal effect estimation. Using only observational transcriptome profiles we searched for paracrine factors sending messages from activated hepatic stellate cells (HSC) to hepatocellular carcinoma (HCC) cells. We condensed these messages to predict ten proteins that, acting in concert, cause the majority of the gene expression changes observed in HCC cells. Among the 10 paracrine factors were both known and unknown cancer promoting stromal factors, the former including Placental Growth Factor (PGF) and Periostin (POSTN), while Pregnancy-Associated Plasma Protein A (PAPPA) was among the latter. Further support for the predicted effect of PAPPA on HCC cells came from both in vitro studies that showed PAPPA to contribute to the activation of NFκB signaling, and clinical data, which linked higher expression levels of PAPPA to advanced stage HCC. In summary, this study demonstrates the potential of causal modeling in combination with a condensation step borrowed from gene set analysis [Model-based Gene Set Analysis (MGSA)] in the identification of stromal signaling molecules influencing the cancer phenotype

PAPPA expression in HSCs and HCC tissues.

<p>PAPPA protein levels in conditioned media, correlation of protein and mRNA levels, and correlation with collagen. A. PAPPA levels in conditioned media of HSCs from 15 different human donors. B. Correlation of PAPPA protein levels and mRNA levels in HSCs from 15 different human donors. C. Correlation of PAPPA and collagen I (COL1A1) mRNA expression in 51 human HCC tissues.</p

PAPPA expression in human HCC tissues of different tumor stages.

<p>PAPPA mRNA expression levels in human HCC tissues (n = 52) of tumor stages I (n = 12), II (n = 19) and III (n = 21). One-way ANOVA shows a significant effect (p = 0.008) of tumor stage on PAPPA mRNA expression level.</p

Scheme of the HSC-HCC network used in causal modeling.

<p>The network consists of three types of genes, cellular HSC genes (yellow), secreted HSC gene products (red) and HCC ‘target’ genes (blue). Individual genes are represented by nodes. Black arrows indicate dependencies among genes that were estimated from gene expression data. These can be directional, i.e. the expression level of a gene impacts the expression level of another downstream gene; or un-directed, i.e. the causal gene could not be inferred. Genes upstream of a particular gene are denoted as parents (e.g. x3 and x4 are parents of x8, and x3, x4, x7 and x8 are parents of x12). Secreted HSC gene products can be parents of other HSC genes. In contrast, HCC genes were excluded in network estimation because they cannot impact HSC genes in the chosen experimental setup. Green dashed arrows indicate estimated causal effects of secreted HSC genes on HCC cell genes. Causal effects that are stable across sub-sampling runs are reported, e.g. x10 has stable causal effects on y1, y2 and y3, whereas x13 has no stable effect on any HCC gene.</p

Most influential stromal regulators.

<p>Subset of secreted HSC gene products which best describe the expression changes observed in conditioned HCC samples. symbol: gene symbol, ensembl gene ID: ensembl gene identifier, set size: number of HCC genes influenced by HSC gene product, probability: probability from MGSA that the target gene set is active (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004293#sec011" target="_blank">Materials and Methods</a>).</p><p>Most influential stromal regulators.</p

Differentially expressed genes with large variance across HCC samples.

<p>HCC cells were stimulated with conditioned media from HSCs from 15 different human donors (Hep_1-Hep_15) while control samples (ctrl1-4) were incubated with plain medium. Of the significant differentially expressed genes upon incubation with conditioned media, only the ones with large variation across HCC samples are shown (for details please see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004293#sec011" target="_blank">Material and Methods</a>). Expression data was scaled to mean = 0 and standard deviation = 1, such that negative values (blue) indicate lower expression in the sample compared to the mean and positive values (yellow) higher expression in the sample compared to the mean.</p

Overview of the experimental and computational approach to identify secreted factors of HSCs regulating HCC gene expression.

<p>Conditioned medium of primary human HSC (n = 15) was transferred onto human Hep3B HCC cells. Gene expression data of HSC and HCC cells were filtered to reduce the dimensionality of the data and to build cause-and-effect (target) matrices. These served as input for the IDA algorithm which estimates causal effects for each cause on each target gene. Causal effects that were stable across sub-sampling runs (i.e. that were stable with respect to small perturbations of the data) were retained and subjected to Model-based Gene Set Analysis (MGSA) to extract a sparse set of HSC genes influencing HCC cell gene expression.</p