BackgroundMetabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis.MethodsA case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men.FindingsWe characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…).InterpretationThese findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS.FundingThe NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frost Chair funded by La Fondation de l'Université de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB French infrastructure (ANR-INBS-0010).All authors had full access to the full data in the study and accept responsibility to submit for publication.

Lung macrophages (LM) are in the first line of defense against inhaled pathogens and can undergo phenotypic polarization to the proinflammatory M1 after stimulation with Toll-like receptor agonists. The objective of the present work was to characterize the metabolic alterations occurring during the experimental M1 LM polarization. Human LM were obtained from resected lungs and cultured for 24 hrs in medium alone or with 10 ng.mL-1 lipopolysaccharide. Cells and culture supernatants were subjected to extraction for metabolomic analysis with high-resolution LC-MS (HILIC and reverse phase -RP- chromatography in both negative and positive ionization modes) and GC-MS. The data were analyzed with R and the Worklow4Metabolomics and MetaboAnalyst online infrastructures. A total of 8,741 and 4,356 features were detected in the intracellular and extracellular content, respectively, after the filtering steps. Pathway analysis showed involvement of arachidonic acid metabolism, tryptophan metabolism and Krebs cycle in the response of LM to LPS, which was confirmed by the specific quantitation of selected compounds. This refined analysis highlighted a regulation of the kynurenin pathway as well as the serotonin biosynthesis pathway, and an involvement of aspartate-arginosuccinate shunt in the malate production. Macrophages M1 polarization is accompanied by changes in the cell metabolome, with the differential expression of metabolites involved in the promotion and regulation of inflammation and antimicrobial activity. The analysis of this macrophage immunometabolome may be of interest for the understanding of the pathophysiology of lung inflammatory disesases.

IntroductionMetabolic syndrome (MetS), defined as a cluster of cardiometabolic factors, is a public health challenge because of its growing prevalence. In the context of personalized medicine, new tools are necessary to bring additional knowledge about MetS etiology, better stratify populations and customise strategies for prevention. The objective of this study was to characterize the MetS phenotypic spectrum using complementary untargeted metabolomics platforms (HRMS, RMN).Technological and methodological innovationA case-control study was designed within the Quebec NuAge cohort1. Six complementary untargetedmetabolomic/lipidomic approaches were performed on serum samples collected at recruitment and 3 years later. Procedures were set up to guaranty the inter-laboratory standardisation from sample preparation to data processing, performed using reproducible online Galaxy workflows. A full feature selection strategy was developed to build a comprehensive molecular MetS signature, stable over time.Results and impactA wide range of metabolites (lipids, carbohydrates, amino-acids, peptides…) reflecting subject stability and providing new insights about underlying mechanisms, were found to be modulated. An optimized reduced signature was proposed, allowing good prediction performances (12% misclassification, AUC=0.95, CI:[0.92-0.98]). These results demonstrated the interest of a multidimensional molecular phenotyping as part of the next generation of medicine tools in the frame of noncommunicable diseases.References[1] Gaudreau P et al., 2007. Rejuvenation Res.10(3):377-386.

Untargeted metabolomics of human plasma with mass spectrometry is of particular interest in medical research to explore pathophysiology, find disease biomarkers or for the understanding of the response to pharmacotherapy. Since analytical performances may be impacted by the laboratory environment and the acquisition method settings, the objectives of this study were to assess the role of interfering compounds and to propose an acquisition method to maximize the metabolome coverage for human plasma metabolomic analysis. Human plasma samples were processed with liquid/liquid extraction then analysed with HILIC-high resolution mass spectrometry. A method with a single m/z range was compared to four methods with different split acquisition ranges and four sets of ionization source parameters were compared. The data were analysed with the R software and on the Worklow4Metabolomics online platform. The major interfering compounds were identified in blank samples where they accounted for up to 86% of the signal intensity. Splitting the acquisition range into 3 m/z ranges improved the number of detected features, the number of features with proposed annotation in the Human Metabolome Database, as well as signal intensity throughout the whole m/z range. The method performing best was the one using three m/z ranges of approximatively the same extent. Ionization source parameters also strongly affected the number of detected features. Splitting the acquisition range into 3 m/z ranges with optimized ionization source parameters allows a comprehensive analysis of the human plasma metabolome with perspectives for applications to pathophysiological studies.

Our paper proposes a methodological strategy to select optimal sampling designs for phenotyping studies including a cocktail of drugs. A cocktail approach is of high interest to determine the simultaneous activity of enzymes responsible for drug metabolism and pharmacokinetics, therefore useful in anticipating drug-drug interactions and in personalized medicine. Phenotyping indexes, which are area under the concentration-time curves, can be derived from a few samples using nonlinear mixed effect models and maximum a posteriori estimation. Because of clinical constraints in phenotyping studies, the number of samples that can be collected in individuals is limited and the sampling times must be as flexible as possible. Therefore to optimize joint design for several drugs (i.e., to determine a compromise between informative times that best characterize each drug's kinetics), we proposed to use a compound optimality criterion based on the expected population Fisher information matrix in nonlinear mixed effect models. This criterion allows weighting different models, which might be useful to take into account the importance accorded to each target in a phenotyping test. We also computed windows around the optimal times based on recursive random sampling and Monte-Carlo simulation while maintaining a reasonable level of efficiency for parameter estimation. We illustrated this strategy for two drugs often included in phenotyping cocktails, midazolam (probe for CYP3A) and digoxin (P-glycoprotein), based on the data of a previous study, and were able to find a sparse and flexible design. The obtained design was evaluated by clinical trial simulations and shown to be efficient for the estimation of population and individual parameters.

This phase I, pilot clinical study was designed to evaluate the safety and the pharmacokinetic (PK) profiles of the CIME (Metabolic Identity Card) combination of ten drugs, with a view to its use as a phenotyping cocktail. Ten healthy Caucasian subjects were orally dosed with the CIME combination (caffeine-CYP1A2, repaglinide-CYP2C8, tolbutamide-CYP2C9, omeprazole-CYP2C19, dextromethorphan-CYP2D6, midazolam-CYP3A, acetaminophen-UGT1A1, 6&9 and 2B15, digoxin-P-gp, rosuvastatin-OATP1B1&3 and memantine-active renal transport). Blood was collected over 3 days and on day 7. CIME probes and relevant metabolites were assayed by LC-MS/MS and PK parameters were calculated. Main results were: (1) good safety with reversible mild or moderate adverse effects, (2) an analytical method able to quantify simultaneously the 10 probes and the major metabolites, (3) calculation of PK parameters for all probes in general agreed with published values, and (4) identification of the low CYP2D6 metabolizer. This pilot study showed that the CIME combination was well tolerated and that its pharmacokinetics could be accurately measured in healthy volunteers. This combination can now confidently be checked for sensitivity and specificity and for lack of interaction to be validated as a phenotyping cocktail.

PURPOSE:Cocktail approach using a combination of probes to phenotype several cytochromes P450 or transporters is of high interest in anticipating drug–drug interactions and personalized medicine. Its clinical use remains however limited by the intensive sampling scheme required to obtain phenotyping indexes (PI) which consists in calculating the area under the concentration–time curves. We proposed to use maximum a posteriori Bayesian estimation (MAPBE) that incorporates available information from the whole population to derive PI from a few individual observations. The performance of a limited sampling strategy (LSS) based on MAPBE was evaluated for a five-probe cocktail.METHODS:The studied cocktail included midazolam, tolbutamide, caffeine, dextromethorphan, omeprazole and their relevant metabolites. Prior information for MAPBE was obtained by nonlinear mixed-effect modelling of data from a pilot study. Sampling times were chosen based on optimal design theory using the Bayesian Fisher information matrix. Through a simulation study, we investigated the predicted PI in terms of bias and imprecision for varying number and timing of samples.RESULTS:Some three-point Bayesian designs gave mean prediction errors in [−5 %, 5 %], root mean square errors below 30 % for all probes, except dextromethorphan whose model should be consolidated further with additional data. This approach gave overall less outlier predicted values than single-point metrics and was more flexible to the timing of the latest sampling.CONCLUSIONS:MAPBE is accurate for predicting simultaneously several PI while being flexible in terms of integrating clinical constraints. Therefore, LSS based on MAPBE could help reduce the time of presence in hospital for individuals to be phenotyped.

Protein polymerization consists in the aggregation of single monomers into polymers that may fragment. Fibrils assembly is a key process in amyloid diseases. Up to now, protein aggregation was commonly mathematically simulated by a polymer size-structured ordinary differential equations (ODE) system, which is infinite by definition and therefore leads to high computational costs. Moreover, this Ordinary Differential Equation-based modeling approach implies biological assumptions that may be difficult to justify in the general case. For example, whereas several ordinary differential equation models use the assumption that polymerization would occur at a constant rate independently of polymer size, it cannot be applied to certain protein aggregation mechanisms. Here, we propose a novel and efficient analytical method, capable of modelling and simulating amyloid aggregation processes. This alternative approach consists of an integro-Partial Differential Equation (PDE) model of coalescence-fragmentation type that was mathematically derived from the infinite differential system by asymptotic analysis. To illustrate the efficiency of our approach, we applied it to aggregation experiments on polyglutamine polymers that are involved in Huntington's disease. Our model demonstrates the existence of a monomeric structural intermediate [Formula: see text] acting as a nucleus and deriving from a non polymerizing monomer ([Formula: see text]). Furthermore, we compared our model to previously published works carried out in different contexts and proved its accuracy to describe other amyloid aggregation processes.

Models of self-organizing bacterial communities and comparisons with experimental observations. dynamics with size dependency–strain phenomena. [4] Benoˆıt Perthame. Why hyperbolic and kinetic models for cell populations self-organization? In Hyperbolic problems: theory, numerics and applications , volume 67 of Proc. The non-local Fisher-KPP equation: travelling waves and steady states. [8] Philippe Laurençot and Benoit Perthame. Exponential decay for the growth-fragmentation/cell-division equation. [9] Gui-Qiang Chen and Benoˆıt Perthame. Large-time behavior of periodic entropy solutions to anisotropic degenerate parabolic-hyperbolic equations.

Models for the polymerization process involved in prion self-replication are well-established and studied [H. Engler, J. Pruss, and G.F. Webb, Analysis of a model for the dynamics of prions II, J. Math. Anal. Appl. 324 (2006), pp. 98-117; M.L. Greer, L. Pujo-Menjouet, and G.F. Webb, A mathematical analysis of the dynamics of prion proliferation, J. Theoret. Biol. 242 (2006), pp. 598-606; J. Pruss, L. Pujo-Menjouet, G.F. Webb, and R. Zacher, Analysis of a model for the dynamics of prions, Discrete Cont. Dyn. Sys. Ser. B 6(1) (2006), pp. 215-225] in the case where the dynamics coefficients do not depend on the size of polymers. However, several experimental studies indicate that the structure and size of the prion aggregates are determinant for their pathological effect. This motivated the analysis in Calvez et al. [Size distribution dependence of prion aggregates infectivity, Math Biosci. 217 (2009), pp. 88-99] where the authors take into account size-dependent replicative properties of prion aggregates. We first improve a result concerning the dynamics of prion aggregates when a pathological state exists (high production of the normal protein). Then we study the strain phenomena and more specifically we wonder what specific replicative properties are determinant in strain propagation. We propose to interpret it also as a dynamical property of size repartitions.

At present, there is no effective therapy for any of the neurodegenerative amyloidoses, despite renewed efforts to identify compounds active against the various implicated pathogenetic molecules. We have screened a library of 2960 natural and synthetic compounds in two cell lines chronically infected with mouse prions, and have identified eight new inhibitors of prion replication in vitro . They belong to two distinct chemical families that have not previously been recognised as effective in the field of transmissible spongiform encephalopathies: seven are 3-aminosteroids and one is a derivative of erythromycin A with an oxime functionality. Our results suggest that these aminosteroids inhibit prion replication by triggering a common target, possibly implicated in the regulatory pathways of cellular prion protein metabolism. Furthermore, using a quantitative approach for the study of protein stability, it was shown that the erythromycin A derivative altered prion protein stability by direct interaction. Such direct targeting of this amyloid precursor might provide new clues for the understanding of prion diseases and, more importantly, help to define new molecules that are active against prion diseases.

Regenerative medicine and cell therapy provide great hopes for the use of adult and stem cells. The latter are far less present in tissue than the former and must be expanded using cell culture. Stem cells culture requires the conservation of their proliferation and self-renewal capabilities. Still, the complex interaction between cell populations, for example in primary cell cultures, are not well-known and may account for part of the variability of such cultures. In order to represent and understand the evolution of cultured stem cells, we present here a mathematical model of cell proliferation and differentiation. Based on the formalism of cellular automata, this model simulates the evolution of several cell classes (which may represent either different levels of differentiation or different cell types) in an environment modeling the growth medium. We model the cell cycle as on the one hand a quiescence phase during which a cell rests, and on the other hand a division phase during which the cell starts the division process. In order to represent cell–cell interaction, the transition probability between those phases depends on the local composition of the growth medium depending itself on neighboring cells. An interaction between cellular populations is represented by a quantitative parameter which has a direct impact on cellular proliferation. Differentiation results in a change of the cell class and depends on the biological model studied : it may result from an asymmetric division or be a consequence of the local composition of the growth medium. This mathematical model aims at a better understanding of the interactions between cell populations in a culture. By defining constraints on the potential or the type of the cells at the end of a culture, it will then be possible to find optimal experimental conditions for cell production.

We consider a model for the polymerization (fragmentation) process involved in infectious prion self-replication and study both its dynamics and non-zero steady state. We address several issues. Firstly, we give conditions leading to size repartitions of PrPsc aggregates that exhibit bimodal distributions, as indicated by recent experimental studies of prion aggregates distribution. Secondly, we show stability results for this steady state for general coefficients where reduction to a system of differential equations is not possible. We use a duality method based on recent ideas developed for population models. These results underline the potential influence of the amyloid precursor production rate in promoting amyloidogenic diseases. Finally, we numerically investigate the influence of different parameters of the model on PrPsc accumulation kinetics, in the aim to study specific features of prion strains.

We consider a model for the polymerization (fragmentation) process involved in infectious prion self-replication and study both its dynamics and non-zero steady state. We address several issues. Firstly, we extend a previous study of the nucleated polymerization model [M.L. Greer, L. Pujo-Menjouet, G.F. Webb, A mathematical analysis of the dynamics of prion proliferation, J. Theoret. Biol. 242 (2006) 598; H. Engler, J. Pruss, G.F. Webb, Analysis of a model for the dynamics of prions II, J. Math. Anal. Appl. 324 (2006) 98] to take into account size dependent replicative properties of prion aggregates. This is achieved by a choice of coefficients in the model that are not constant. Secondly, we show stability results for this steady state for general coefficients where reduction to a system of differential equations is not possible. We use a duality method based on recent ideas developed for population models. These results confirm the potential influence of the amyloid precursor production rate in promoting amyloidogenic diseases. Finally, we investigate how the converting factor may depend upon the aggregate size. Besides the confirmation that size-independent parameters are unlikely to occur, the present study suggests that the PrPsc aggregate size repartition is amongst the most relevant experimental data in order to investigate this dependence. In terms of prion strain, our results indicate that the PrPsc aggregate repartition could be a constraint during the adaptation mechanism of the species barrier overcoming, that opens experimental perspectives for prion amyloid polymerization and prion strain investigation.