Constrained peptidomimetics reveal detailed geometric requirements of covalent prolyl oligopeptidase inhibitors.

Prolyl oligopeptidases cleave peptides on the carboxy side of internal proline residues and their inhibition has potential in the treatment of human brain disorders. Using our docking program fitted, we have designed a series of constrained covalent inhibitors, built from a series of bicyclic scaffolds, to study the optimal shape required for these small molecules. These structures bear nitrile functional groups that we predicted to covalently bind to the catalytic serine of the enzyme. Synthesis and biological assays using human brain-derived astrocytic cells and endothelial cells and human fibroblasts revealed that these compounds act as selective inhibitors of prolyl oligopeptidase activity compared to prolyl-dipeptidyl-aminopeptidase activity, are able to penetrate the cells and inhibit intracellular activities in intact living cells. This integrated computational and experimental study shed light on the binding mode of inhibitors in the enzyme active site and will guide the design of future drug-like molecules.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Whole body protein synthesis and energy expenditure in very low birth weight infants.

The aim of the present work was to study whole body protein synthesis and breakdown, as well as energy metabolism, in very low birth weight premature infants (less than 1500 g) during their rapid growth phase. Ten very low birth weight infants were studied during their first and second months of life. They received a mean energy intake of 114 kcal/kg X day and 3 g protein/kg X day as breast milk or milk formula. The average weight gain was 15 g/kg X day. The apparent energy digestibility was 88%, i.e. 99 kcal/kg X day. Their resting postprandial energy expenditure was 58 kcal/kg X day, indicating that 41 kcal/kg X day was retained. The apparent protein digestibility was 89%, i.e. 2.65 g/kg X day. Their rate of protein oxidation was 0.88 g/kg X day so that protein retention was 1.76 g/kg X day. There was a linear relationship between N retention and N intake (r = 0.78, p less than 0.001). The slope of the regression line indicates a net efficiency of N utilization of 67%. Estimates of body composition from the energy balance, coupled with N balance method, showed that 25% of the gain was fat and 75% was lean tissue. Whole body protein synthesis and breakdown were determined using repeated oral administration of 15N glycine for 60-72 h, and 15N enrichment in urinary urea was measured. Protein synthesis averaged 11.2 g/kg X day and protein breakdown 9.4 g/kg X day. Muscular protein breakdown, as estimated by 3-methylhistidine excretion, contributed to 12% of the total protein breakdown.(ABSTRACT TRUNCATED AT 250 WORDS)

Correlations of glycogen synthase and phosphorylase activities with glycogen concentration in human muscle biopsies. Evidence for a double-feedback mechanism regulating glycogen synthesis and breakdown.

The purpose of this study was to verify in man the relationships of muscle glycogen synthase and phosphorylase activities with glycogen concentration that were reported in animal studies. The upper level of glycogen concentration in muscle is known to be tightly controlled, and glycogen concentration was reported to have an inhibitory effect on synthase activity and a stimulatory effect on phosphorylase activity. Glycogen synthase and phosphorylase activity and glycogen concentration were measured in muscle biopsies in a group of nine normal subjects after stimulating an increase of their muscle glycogen concentration through either an intravenous glucose-insulin infusion to stimulate glycogen synthesis, or an Intralipid (Vitrum, Stockholm, Sweden) infusion in the basal state to inhibit glycogen mobilization by favoring lipid oxidation at the expense of glucose oxidation. Phosphorylase activity increased from 71.3 +/- 21.0 to 152.8 +/- 20.0 nmol/min/mg protein (P < .005) after the glucose-insulin infusion. Phosphorylase activity was positively correlated with glycogen concentration (P = .005 and P = .0001) after the glucose-insulin and Intralipid infusions, respectively. Insulin-stimulated glycogen synthase activity was significantly negatively correlated with glycogen concentration at the end of the Intralipid infusion (P < .005). In conclusion, by demonstrating a negative correlation of glycogen concentration with glycogen synthase and a positive correlation with phosphorylase, this study might confirm in man the double-feedback mechanism by which changes in glycogen concentration regulate glycogen synthase and phosphorylase activities. It suggests that this mechanism might play an important role in the regulation of glucose storage.

Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology.

PURPOSE OF REVIEW: Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI). RECENT FINDINGS: qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function. SUMMARY: Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.

Involvement of a transforming-growth-factor-beta-like molecule in tumor-cell-derived inhibition of nitric-oxide synthesis in cerebral endothelial cells.

Nitric oxide (NO) has been shown to exert cytotoxic effects on tumor cells. We have reported that EC219 cells, a rat-brain-microvessel-derived endothelial cell line, produced NO through cytokine-inducible NO synthase (iNOS), the induction of which was significantly decreased by (a) soluble factor(s) secreted by DHD/PROb, an invasive sub-clone of a rat colon-carcinoma cell line. In this study, the DHD/PROb cell-derived NO-inhibitory factor was characterized. Northern-blot analysis demonstrated that the induction of iNOS mRNA in cytokine-activated EC219 cells was decreased by PROb-cell-conditioned medium. When DHD/PROb cell supernatant was fractionated by affinity chromatography using Con A-Sepharose or heparin-Sepharose, the NO-inhibitory activity was found only in Con A-unbound or heparin-unbound fractions, respectively, indicating that the PROb-derived inhibitory factor was likely to be a non-glycosylated and non-heparin-binding molecule. Pre-incubation of DHD/PROb-cell supernatant with anti-TGF-beta neutralizing antibody completely blocked the DHD/PROb-derived inhibition of NO production by EC219 cells. Addition of exogenous TGF-beta 1 dose-dependently inhibited NO release by EC219 cells. The presence of active TGF-beta in the DHD/PROb cell supernatant was demonstrated using a growth-inhibition assay. Moreover, heat treatment of medium conditioned by the less invasive DHD/REGb cells, which constitutively secreted very low levels of active TGF-beta, increased both TGF-beta activity and the ability to inhibit NO production in EC219 cells. Thus, DHD/PROb colon-carcinoma cells inhibited NO production in EC219 cells by secreting a factor identical or very similar to TGF-beta.