Fast and sensitive supercritical fluid chromatography - tandem mass spectrometry multi-class screening method for the determination of doping agents in urine.

This study shows the possibility offered by modern ultra-high performance supercritical fluid chromatography combined with tandem mass spectrometry in doping control analysis. A high throughput screening method was developed for 100 substances belonging to the challenging classes of anabolic agents, hormones and metabolic modulators, synthetic cannabinoids and glucocorticoids, which should be detected at low concentrations in urine. To selectively extract these doping agents from urine, a supported liquid extraction procedure was implemented in a 48-well plate format. At the tested concentration levels ranging from 0.5 to 5 ng/mL, the recoveries were better than 70% for 48-68% of the compounds and higher than 50% for 83-87% of the tested substances. Due to the numerous interferences related to isomers of steroids and ions produced by the loss of water in the electrospray source, the choice of SFC separation conditions was very challenging. After careful optimization, a Diol stationary phase was employed. The total analysis time for the screening assay was only 8 min, and interferences as well as susceptibility to matrix effect (ME) were minimized. With the developed method, about 70% of the compounds had relative ME within the range ±20%, at a concentration of 1 and 5 ng/mL. Finally, limits of detection achieved with the above-described strategy including 5-fold preconcentration were below 0.1 ng/mL for the majority of the tested compounds. Therefore, LODs were systematically better than the minimum required performance levels established by the World anti-doping agency, except for very few metabolites.

Direct binding of peptides to MHC class I molecules on living cells. Analysis at the single cell level.

To directly assess the binding of exogenous peptides to cell surface-associated MHC class I molecules at the single cell level, we examined the possibility of combining the use of biotinylated peptide derivatives with an immunofluorescence detection system based on flow cytometry. Various biotinylated derivatives of the adenovirus 5 early region 1A peptide 234-243, an antigenic peptide recognized by CTL in the context of H-2Db, were first screened in functional assays for their ability to bind efficiently to Db molecules on living cells. Suitable peptide derivatives were then tested for their ability to generate positive fluorescence signals upon addition of phycoerythrin-labeled streptavidin to peptide derivative-bearing cells. Strong fluorescent staining of Db-expressing cells was achieved after incubation with a peptide derivative containing a biotin group at the C-terminus. Competition experiments using the unmodified parental peptide as well as unrelated peptides known to bind to Kd, Kb, or Db, respectively, established that binding of the biotinylated peptide to living cells was Db-specific. By using Con A blasts derived from different H-2 congenic mouse strains, it could be shown that the biotinylated peptide bound only to Db among > 20 class I alleles tested. Moreover, binding of the biotinylated peptide to cells expressing the Dbm13 and Dbm14 mutant molecules was drastically reduced compared to Db. Binding of the biotinylated peptide to freshly isolated Db+ cells was readily detectable, allowing direct assessment of the relative amount of peptide bound to distinct lymphocyte subpopulations by three-color flow cytometry. While minor differences between peripheral T and B cells could be documented, thymocytes were found to differ widely in their peptide binding activity. In all cases, these differences correlated positively with the differential expression of Db at the cell surface. Finally, kinetic studies at different temperatures strongly suggested that the biotinylated peptide first associated with Db molecules available constitutively at the cell surface and then with newly arrived Db molecules.

Structural prediction of peptides bound to MHC class I.

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

Specific binding of antigenic peptides to cell-associated MHC class I molecules.

T lymphocytes recognize antigen in the form of peptides that associate with specific alleles of class I or class II major histocompatibility (MHC) molecules. By contrast with the clear MHC allele-specific binding of peptides to purified class II molecules purified solubilized class I molecules either bind relatively poorly or show degenerate specificity. Using photo-affinity labelling, we demonstrate here the specific interaction of peptides with cell-associated MHC class I molecules and show that this involves metabolically active processes.

Molecular modeling of peptide-MHC class I complexes : applications to peptide based immunotherapy

Summary The specific CD8+ T cell immune response against tumors relies on the recognition by the T cell receptor (TCR) on cytotoxic T lymphocytes (CTL) of antigenic peptides bound to the class I major histocompatibility complex (MHC) molecule. Such tumor associated antigenic peptides are the focus of tumor immunotherapy with peptide vaccines. The strategy for obtaining an improved immune response often involves the design of modified tumor associated antigenic peptides. Such modifications aim at creating higher affinity and/or degradation resistant peptides and require precise structures of the peptide-MHC class I complex. In addition, the modified peptide must be cross-recognized by CTLs specific for the parental peptide, i.e. preserve the structure of the epitope. Detailed structural information on the modified peptide in complex with MHC is necessary for such predictions. In this thesis, the main focus is the development of theoretical in silico methods for prediction of both structure and cross-reactivity of peptide-MHC class I complexes. Applications of these methods in the context of immunotherapy are also presented. First, a theoretical method for structure prediction of peptide-MHC class I complexes is developed and validated. The approach is based on a molecular dynamics protocol to sample the conformational space of the peptide in its MHC environment. The sampled conformers are evaluated using conformational free energy calculations. The method, which is evaluated for its ability to reproduce 41 X-ray crystallographic structures of different peptide-MHC class I complexes, shows an overall prediction success of 83%. Importantly, in the clinically highly relevant subset of peptide-HLAA*0201 complexes, the prediction success is 100%. Based on these structure predictions, a theoretical approach for prediction of cross-reactivity is developed and validated. This method involves the generation of quantitative structure-activity relationships using three-dimensional molecular descriptors and a genetic neural network. The generated relationships are highly predictive as proved by high cross-validated correlation coefficients (0.78-0.79). Together, the here developed theoretical methods open the door for efficient rational design of improved peptides to be used in immunotherapy. Résumé La réponse immunitaire spécifique contre des tumeurs dépend de la reconnaissance par les récepteurs des cellules T CD8+ de peptides antigéniques présentés par les complexes majeurs d'histocompatibilité (CMH) de classe I. Ces peptides sont utilisés comme cible dans l'immunothérapie par vaccins peptidiques. Afin d'augmenter la réponse immunitaire, les peptides sont modifiés de façon à améliorer l'affinité et/ou la résistance à la dégradation. Ceci nécessite de connaître la structure tridimensionnelle des complexes peptide-CMH. De plus, les peptides modifiés doivent être reconnus par des cellules T spécifiques du peptide natif. La structure de l'épitope doit donc être préservée et des structures détaillées des complexes peptide-CMH sont nécessaires. Dans cette thèse, le thème central est le développement des méthodes computationnelles de prédiction des structures des complexes peptide-CMH classe I et de la reconnaissance croisée. Des applications de ces méthodes de prédiction à l'immunothérapie sont également présentées. Premièrement, une méthode théorique de prédiction des structures des complexes peptide-CMH classe I est développée et validée. Cette méthode est basée sur un échantillonnage de l'espace conformationnel du peptide dans le contexte du récepteur CMH classe I par dynamique moléculaire. Les conformations sont évaluées par leurs énergies libres conformationnelles. La méthode est validée par sa capacité à reproduire 41 structures des complexes peptide-CMH classe I obtenues par cristallographie aux rayons X. Le succès prédictif général est de 83%. Pour le sous-groupe HLA-A*0201 de complexes de grande importance pour l'immunothérapie, ce succès est de 100%. Deuxièmement, à partir de ces structures prédites in silico, une méthode théorique de prédiction de la reconnaissance croisée est développée et validée. Celle-ci consiste à générer des relations structure-activité quantitatives en utilisant des descripteurs moléculaires tridimensionnels et un réseau de neurones couplé à un algorithme génétique. Les relations générées montrent une capacité de prédiction remarquable avec des valeurs de coefficients de corrélation de validation croisée élevées (0.78-0.79). Les méthodes théoriques développées dans le cadre de cette thèse ouvrent la voie du design de vaccins peptidiques améliorés.

Rgtsp: a generalized top scoring pairs package for class prediction.

SUMMARY: A top scoring pair (TSP) classifier consists of a pair of variables whose relative ordering can be used for accurately predicting the class label of a sample. This classification rule has the advantage of being easily interpretable and more robust against technical variations in data, as those due to different microarray platforms. Here we describe a parallel implementation of this classifier which significantly reduces the training time, and a number of extensions, including a multi-class approach, which has the potential of improving the classification performance. AVAILABILITY AND IMPLEMENTATION: Full C++ source code and R package Rgtsp are freely available from http://lausanne.isb-sib.ch/~vpopovic/research/. The implementation relies on existing OpenMP libraries.

CT dose optimization when changing to CT multi-detector row technology

The purpose of this article was to review the strategies to control patient dose in adult and pediatric computed tomography (CT), taking into account the change of technology from single-detector row CT to multi-detector row CT. First the relationships between computed tomography dose index, dose length product, and effective dose in adult and pediatric CT are revised, along with the diagnostic reference level concept. Then the effect of image noise as a function of volume computed tomography dose index, reconstructed slice thickness, and the size of the patient are described. Finally, the potential of tube current modulation CT is discussed.

Monoclonal antibodies to murine lipopolysaccharide (LPS)-binding protein (LBP) protect mice from lethal endotoxemia by blocking either the binding of LPS to LBP or the presentation of LPS/LBP complexes to CD14.

Cellular responses to LPS, the major lipid component of the outer membrane of Gram-negative bacteria, are enhanced markedly by the LPS-binding protein (LBP), a plasma protein that transfers LPS to the cell surface CD14 present on cells of the myeloid lineage. LBP has been shown previously to potentiate the host response to LPS. However, experiments performed in mice with a disruption of the LBP gene have yielded discordant results. Whereas one study showed that LBP knockout mice were resistant to endotoxemia, another study did not confirm an important role for LBP in the response of mice challenged in vivo with low doses of LPS. Consequently, we generated rat mAbs to murine LBP to investigate further the contribution of LBP in experimental endotoxemia. Three classes of mAbs were obtained. Class 1 mAbs blocked the binding of LPS to LBP; class 2 mAbs blocked the binding of LPS/LBP complexes to CD14; class 3 mAbs bound LBP but did not suppress LBP activity. In vivo, class 1 and class 2 mAbs suppressed LPS-induced TNF production and protected mice from lethal endotoxemia. These results show that the neutralization of LBP accomplished by blocking either the binding of LPS to LBP or the binding of LPS/LBP complexes to CD14 protects the host from LPS-induced toxicity, confirming that LBP is a critical component of innate immunity.

In-situ revascularisation for secondary aorto-enteric fistulae: the success of silver-coated Dacron is closely linked to a suitable bowel repair.

OBJECTIVES: The purpose of this study was to assess short- and mid-term results of in-situ revascularisation (ISR) using silver-coated Dacron prostheses and bowel repair for management of secondary aorto-enteric fistulae (SAEF). DESIGN: Single-centre retrospective chart review. MATERIAL AND METHODS: This study includes all the patients treated by ISR using silver-coated Dacron for SAEF between 2006 and 2010. Primary end points were mortality and survival rates. Secondary end points were reinfection-free survival and secondary patency rates. RESULTS: Eighteen male patients with SAEF with a median age of 64 years were operated by ISR using silver-coated Dacron during the study period without operative death. The 30-day mortality was 22% and the in-hospital mortality rate was 39%. Indeed, during hospitalisation, a duodenal leak was observed in four patients including three who died. Four others patients died due to multi-system organ failure. Median follow-up was 16 months (range 1-66). The survival rate at 12 months was 55%. One duodenal leak was observed leading to death. The reinfection-free survival and the secondary patency rates at 12 months were 60% and 89%, respectively. CONCLUSION: In-situ revascularisation with silver-coated Dacron provides acceptable results in terms of mortality. This treatment may be useful for simple vascular reconstruction and allow greater attention to bowel repair that is a determinant in short- and mid-term survival.

PPAR-mediated activity of phthalates: A link to the obesity epidemic?

The endocrine disruption hypothesis asserts that exposure to small amounts of some chemicals in the environment may interfere with the endocrine system and lead to harmful effects in wildlife and humans. Many of these chemicals may interact with members of the nuclear receptor superfamily. Peroxisome proliferator-activated receptors (PPARs) are such candidate members, which interact with many different endogenous and exogenous lipophilic compounds. More particularly, the roles of PPARs in lipid and carbohydrate metabolism raise the question of their activation by a sub-class of pollutants, tentatively named "metabolic disrupters". Phthalates are abundant environmental micro-pollutants in Europe and North America and may belong to this class. Mono-ethyl-hexyl-phthalate (MEHP), a metabolite of the widespread plasticizer di-ethyl-hexyl-phthalate (DEHP), has been found in exposed organisms and interacts with all three PPARs. A thorough analysis of its interactions with PPARgamma identified MEHP as a selective PPARgamma modulator, and thus a possible contributor to the obesity epidemic.

Adherence as a predictor of the development of class-specific resistance mutations: the Swiss HIV Cohort Study.

BACKGROUND: Non-adherence is one of the strongest predictors of therapeutic failure in HIV-positive patients. Virologic failure with subsequent emergence of resistance reduces future treatment options and long-term clinical success. METHODS: Prospective observational cohort study including patients starting new class of antiretroviral therapy (ART) between 2003 and 2010. Participants were naïve to ART class and completed ≥1 adherence questionnaire prior to resistance testing. Outcomes were development of any IAS-USA, class-specific, or M184V mutations. Associations between adherence and resistance were estimated using logistic regression models stratified by ART class. RESULTS: Of 314 included individuals, 162 started NNRTI and 152 a PI/r regimen. Adherence was similar between groups with 85% reporting adherence ≥95%. Number of new mutations increased with increasing non-adherence. In NNRTI group, multivariable models indicated a significant linear association in odds of developing IAS-USA (odds ratio (OR) 1.66, 95% confidence interval (CI): 1.04-2.67) or class-specific (OR 1.65, 95% CI: 1.00-2.70) mutations. Levels of drug resistance were considerably lower in PI/r group and adherence was only significantly associated with M184V mutations (OR 8.38, 95% CI: 1.26-55.70). Adherence was significantly associated with HIV RNA in PI/r but not NNRTI regimens. CONCLUSION: Therapies containing PI/r appear more forgiving to incomplete adherence compared with NNRTI regimens, which allow higher levels of resistance, even with adherence above 95%. However, in failing PI/r regimens good adherence may prevent accumulation of further resistance mutations and therefore help to preserve future drug options. In contrast, adherence levels have little impact on NNRTI treatments once the first mutations have emerged.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline.

On the transcriptional role of NLRC5 and the function of NLRC5-driven MHC class I expression in the immune system

Major histocompatibility complex (MHC) molecules are of crucial importance for the immune system to recognize and defend the body against external attacks. Foreign antigens are presented by specialized cells, called antigen presenting cells, to T lymphocytes in the context of MHC molecules, thereby inducing T cell activation. In addition, MHC molecules are essential for Natural Killer (NK) cell biology, playing a role in NK cell education and activation. Recently, the NOD-like receptor (NLR) family member NLRC5 (NLR caspase recruitment domain containing protein 5) was found to act as transcriptional regulator of MHC class I, in particular in T and NK cells. Its role in MHC class I expression is however minor in dendritic cells (DCs). This raised the question of whether inflammatory conditions, which augment the levels of NLRC5 in DCs, could increase its contribution to MHC class I expression. Our work shows that MHC class I transcript and intracellular levels depend on NLRC5, while its role in MHC class I surface expression is instead negligible. We describe however a general salvage mechanism that enables cells with low intracellular MHC class I levels to nevertheless maintain relatively high MHC class I on the cell surface. In addition, we lack a thorough understanding of NLRC5 target gene specificity and mechanism of action. Our work delineates the unique consensus sequence in MHC class I promoters required for NLRC5 recruitment and pinpoints conserved features conferring its specificity. Furthermore, through genome-wide analyses, we confirm that NLRC5 regulates classical MHC class I genes and identify novel target genes all encoding non-classical MHC class I molecules exerting an array of functions in immunity and tolerance. We finally asked why a dedicated factor co-regulates MHC class I expression specifically in T and NK lymphocytes. We show that deregulated NLRC5 expression affects the education of NK cells and alters the crosstalk between T and NK cells, leading to NK cell-mediated killing of T lymphocytes. Altogether this thesis work brings insights into molecular and physiological aspects of NLRC5 function, which might help understand certain aspects of immune responses and disorders. -- Les molécules du complexe majeur d'histocompatibilité (CMH) sont essentielles au système immunitaire pour l'initiation de la réponse immunitaire. En effet, l'activation des lymphocytes T nécessite la reconnaissance d'un antigène étranger présenté par les cellules présentatrices d'antigènes sur une molécule du CMH. Les molécules du CMH ont également un rôle fondamental pour la fonction des cellules Natural Killer (NK) puisqu'elles sont nécessaires à leur processus d'éducation et d'activation. Récemment, NLRC5 (NLR caspase recruitment domain containing protein 5), un membre de la famille des récepteurs de type NOD (NLRs), a été décrit comme un facteur de transactivation de l'expression des gènes du CMH de classe I. A l'état basai, cette fonction transcriptionnelle est essentielle dans les lymphocytes T et NK, alors que ce rôle reste mineur pour l'expression des molécules du CMH de classe I dans les cellules dendritiques (DCs). Dans des conditions inflammatoires, l'expression de NLRC5 augmente dans les DCs. Notre travail démontre que, dans ces conditions, les transcrits et les niveaux intracellulaires des molécules du CMH de classe I augmentent aussi d'une façon dépendante de NLRC5. A contrario, le rôle de NLRC5 sur les niveaux de molécules de surface reste minoritaire. Cette observation nous a conduits à l'identification d'un mécanisme général de compensation qui permet aux cellules de maintenir des niveaux relativement élevés de molécules de CMH de class I à leur surface malgré de faibles niveaux intracellulaires. De plus, il semblait nécessaire de s'orienter vers une approche plus globale afin de déterminer l'étendue de la fonction transcriptionnelle de NLRC5. Par une approche du génome entier, nous avons pu décrire une séquence consensus conservée présente dans les promoteurs des gènes du CMH de classe I, sur laquelle NLRC5 est spécifiquement recruté. Nous avons pu également identifier de nouveaux gènes cibles codant pour des molécules de CMH de classe I non classiques impliqués dans l'immunité et la tolérance. Finalement, nous nous sommes demandé quel est l'intérêt d'avoir un facteur transcriptionnel, en l'occurrence NLRC5, qui orchestre l'expression du CMH de classe I dans les lymphocytes T et NK. Nous montrons que la dérégulation de l'expression de NLRC5 affecte l'éducation des cellules NK et conduit à la mort cellulaire des lymphocytes T médiée par les cellules NK. Dans l'ensemble ce travail de thèse contribue à la caractérisation du rôle de NLRC5, tant au niveau moléculaire que physiologique, ce qui présente un intérêt dans le cadre de la compréhension de certains aspects physiopathologique de la réponse immunitaire.

Using digital RNA counting and flow cytometry to compare mRNA with protein expression in acute leukemias.

BACKGROUND: The diagnosis of malignant hematologic diseases has become increasingly complex during the last decade. It is based on the interpretation of results from different laboratory analyses, which range from microscopy to gene expression profiling. Recently, a method for the analysis of RNA phenotypes has been developed, the nCounter technology (Nanostring® Technologies), which allows for simultaneous quantification of hundreds of RNA molecules in biological samples. We evaluated this technique in a Swiss multi-center study on eighty-six samples from acute leukemia patients. METHODS: mRNA and protein profiles were established for normal peripheral blood and bone marrow samples. Signal intensities of the various tested antigens with surface expression were similar to those found in previously performed Affymetrix microarray analyses. Acute leukemia samples were analyzed for a set of twenty-two validated antigens and the Pearson Correlation Coefficient for nCounter and flow cytometry results was calculated. RESULTS: Highly significant values between 0.40 and 0.97 were found for the twenty-two antigens tested. A second correlation analysis performed on a per sample basis resulted in concordant results between flow cytometry and nCounter in 44-100% of the antigens tested (mean = 76%), depending on the number of blasts present in a sample, the homogeneity of the blast population, and the type of leukemia (AML or ALL). CONCLUSIONS: The nCounter technology allows for fast and easy depiction of a mRNA profile from hematologic samples. This technology has the potential to become a valuable tool for the diagnosis of acute leukemias, in addition to multi-color flow cytometry.