Canonical and non-canonical Wnt signaling in hematopoiesis and lymphocyte development

The immune system of mammals is responsible for protecting our body against pathogensand foreign substances (antigens), and it consists of two discrete lines of defense. The firstline called innate immunity and provide a quick and nonspecific defense. The innate immunityincludes different cells types, such as mast cells, macrophages, neutrophils, eosinophils,dendritic cells and natural killer (NK) cells. The second line of defense called adaptive immunityresponds in an antigen-specific manner, and comprised of B and T lymphocyte cells.This thesis focuses on one of the signals which are known to play an important role duringHSC repopulation and T cell development that is Wnt signaling pathway. Dependingon the tissue/cell types (microenvironment) and specific class of Wnt proteins binding tothe corresponding receptors on the developing lymphocytes, two discrete downstreampathways will be activated namely canonical or non-canonical Wnt pathway. The main aimof this thesis is to dissect the roles of these two distinct pathways during hematopoiesisand lymphocyte development in murine as a physiologically relevant animal model.LUMC / Geneeskunde Repositoriu

Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks

We investigate models of the mitogenactivated protein kinases (MAPK) network, with the aim of determining where in parameter space there exist multiple positive steady states. We build on recent progress which combines various symbolic computation methods for mixed systems of equalities and inequalities. We demonstrate that those techniques benefit tremendously from a newly implemented graph theoretical symbolic preprocessing method. We compare computation times and quality of results of numerical continuation methods with our symbolic approach before and after the application of our preprocessing.Comment: Accepted into Proc. CASC 201

Pancreatitis in RYR1-related disorders

Mutations in RYR1 encoding the ryanodine receptor (RyR) skeletal muscle isoform (RyR1) are a common cause of inherited neuromuscular disorders. Despite its expression in a wide range of tissues, non-skeletal muscle manifestations associated with RYR1 mutations have only been rarely reported. Here, we report three patients with a diagnosis of Central Core Disease (CCD), King-Denborough Syndrome (KDS) and Malignant Hyperthermia Susceptibility (MHS), respectively, who in addition to their (putative) RYR1-related disorder also developed symptoms and signs of acute pancreatitis. In two patients, episodes were recurrent, with severe multisystem involvement and sequelae. RyR1-mediated calcium signalling plays an important role in normal pancreatic function but has also been critically implicated in the pathophysiology of acute pancreatitis, particularly in bile acid- and ethanol-induced forms. Findings from relevant animal models indicate that pancreatic damage in these conditions may be ameliorated through administration of the specific RyR1 antagonist dantrolene and other compounds modifying pancreatic metabolism including calcium signalling. These observations suggest that patients with RYR1 gain-of-function variants may be at increased risk of developing acute pancreatitis, a condition which should therefore be considered in the health surveillance of such individuals

Functional definition of a transcription factor hierarchy regulating T cell lineage commitment

T cell factor 1 (Tcf1) is the first T cell-specific protein induced by Notch signaling in the thymus, leading to the activation of two major target genes, Gata3 and Bcl11b. Tcf1 deficiency results in partial arrests in T cell development, high apoptosis, and increased development of B and myeloid cells. Phenotypically, seemingly fully T cell-committed thymocytes with Tcf1 deficiency have promiscuous gene expression and an altered epigenetic profile and can dedifferentiate into more immature thymocytes and non-T cells. Restoring Bcl11b expression in Tcf1-deficient cells rescues T cell development but does not strongly suppress the development of non-T cells; in contrast, expressing Gata3 suppresses their development but does not rescue T cell development. Thus, T cell development is controlled by a minimal transcription factor network involving Notch signaling, Tcf1, and the subsequent division of labor between Bcl11b and Gata3, thereby ensuring a properly regulated T cell gene expression program.Molecular Technology and Informatics for Personalised Medicine and Healt

Genome-scale constraint-based modeling of Geobacter metallireducens

Background: Geobacter metallireducens was the first organism that can be grown in pure culture to completely oxidize organic compounds with Fe(III) oxide serving as electron acceptor. Geobacter species, including G. sulfurreducens and G. metallireducens, are used for bioremediation and electricity generation from waste organic matter and renewable biomass. The constraint-based modeling approach enables the development of genome-scale in silico models that can predict the behavior of complex biological systems and their responses to the environments. Such a modeling approach was applied to provide physiological and ecological insights on the metabolism of G. metallireducens. Results: The genome-scale metabolic model of G. metallireducens was constructed to include 747 genes and 697 reactions. Compared to the G. sulfurreducens model, the G. metallireducens metabolic model contains 118 unique reactions that reflect many of G. metallireducens\u27 specific metabolic capabilities. Detailed examination of the G. metallireducens model suggests that its central metabolism contains several energy-inefficient reactions that are not present in the G. sulfurreducens model. Experimental biomass yield of G. metallireducens growing on pyruvate was lower than the predicted optimal biomass yield. Microarray data of G. metallireducens growing with benzoate and acetate indicated that genes encoding these energy-inefficient reactions were up-regulated by benzoate. These results suggested that the energy-inefficient reactions were likely turned off during G. metallireducens growth with acetate for optimal biomass yield, but were up-regulated during growth with complex electron donors such as benzoate for rapid energy generation. Furthermore, several computational modeling approaches were applied to accelerate G. metallireducens research. For example, growth of G. metallireducens with different electron donors and electron acceptors were studied using the genome-scale metabolic model, which provided a fast and cost-effective way to understand the metabolism of G. metallireducens. Conclusion: We have developed a genome-scale metabolic model for G. metallireducens that features both metabolic similarities and differences to the published model for its close relative, G. sulfurreducens. Together these metabolic models provide an important resource for improving strategies on bioremediation and bioenergy generation

Towards the reconstruction of the genome-scale metabolic model of Lactobacillus acidophilus La-14

Lactobacillus acidophilus is a probiotic lactic acid bacterium used in food and dietary supplements for many years. However, despite its importance for industrial development and recognized health-promoting effects, no genome-scale metabolic model has been reported. A GSM model for L. acidophilus La-14 was developed, accounting 494 genes and 783 reactions. A genome annotation was performed to identify the metabolic potential of the bacterium. The biomass composition was determined based on information available in literature and previously published models. The model was validated by comparing in silico simulations with experimental data, regarding the aerobic and anaerobic growth. The reconstruction of the metabolic model has confirmed the fastidious requirements of L. acidophilus for amino acids, fatty acids, and vitamins. This model can be used for a better understanding of the metabolism of this bacterium and identification of industrially desirable compounds.This study was performed under the scope of the project “BIODATA.PT – Portuguese Biological Data Network” (ref. LISBOA-01-0145-FEDER-022231), funded by FCT/MCTES, through national funds of PIDDAC, Fundo Europeu de Desenvolvimento Regional (FEDER), Programa Operacional de Competitividade e Internacionalização (POCI) and Programa Operacional Regional de Lisboa (Lisboa 2020).info:eu-repo/semantics/publishedVersio

Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect

The Warburg effect - a classical hallmark of cancer metabolism - is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids

On plexus representation of dissimilarities

Correspondence analysis has found widespread application in analysing vegetation gradients. However, it is not clear how it is robust to situations where structures other than a simple gradient exist. The introduction of instrumental variables in canonical correspondence analysis does not avoid these difficulties. In this paper I propose to examine some simple methods based on the notion of the plexus (sensu McIntosh) where graphs or networks are used to display some of the structure of the data so that an informed choice of models is possible. I showthat two different classes of plexus model are available. These classes are distinguished by the use in one case of a global Euclidean model to obtain well-separated pair decomposition (WSPD) of a set of points which implicitly involves all dissimilarities, while in the other a Riemannian view is taken and emphasis is placed locally, i.e., on small dissimilarities. I showan example of each of these classes applied to vegetation data

Education as a Predictor of Chronic Periodontitis: A Systematic Review with Meta-Analysis Population-Based Studies

The impact of socioeconomic inequalities on health is well-documented. Despite the links of periodontal disease with cardiovascular diseases, adverse pregnancy outcomes and diabetes, no meta-analysis of socioeconomic variations in periodontal disease exists. This meta-analytic review was conducted to determine the extent to which education attainment influences risk of periodontitis in adults aged 35+ years in the general population.The authors searched studies published until November 2010 using EMBASE and MEDLINE databases. References listed were then scrutinised, our own files were checked, and, finally, we contacted experts in the field. The authors included only general population-based studies conducted in adults aged 35 years and more. All articles were blind reviewed by two investigators. In the case of disagreement, a third investigator arbitrated. Using PRISMA statement, two reviewers independently extracted papers of interest.Relative to the higher education group, people with low education attainment experience a greater risk of periodontitis (OR: 1.86 [1.66–2.10]; p<0.00001). The association was partially attenuated after adjustment for covariates (OR: 1.55 [1.30–1.86]; p<0.00001). Sensitivity analyses showed that methods used to assess periodontitis, definition of cases, study country and categorization of education are largely responsible for the heterogeneity between studies. No significant bias of publication was shown using both the Egger (p = 0.16) and rank correlation tests (p = 0.35).In the studies reviewed, low educational attainment was associated with an increased risk of periodontitis. Although this evidence should be cautiously interpreted due to methodological problems in selected studies, efforts to eliminate educational inequalities in periodontitis should focus on early life interventions

Parameter estimate of signal transduction pathways

BACKGROUND: The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway. RESULTS: Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions. CONCLUSION: Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data