Toxoplasma gondii Chitinase Induces Macrophage Activation

Toxoplasma gondii is an obligate intracellular protozoan parasite found worldwide that is able to chronically infect almost all vertebrate species, especially birds and mammalians. Chitinases are essential to various biological processes, and some pathogens rely on chitinases for successful parasitization. Here, we purified and characterized a chitinase from T. gondii. The enzyme, provisionally named Tg_chitinase, has a molecular mass of 13.7 kDa and exhibits a Km of 0.34 mM and a Vmax of 2.64. The optimal environmental conditions for enzymatic function were at pH 4.0 and 50°C. Tg_chitinase was immunolocalized in the cytoplasm of highly virulent T. gondii RH strain tachyzoites, mainly at the apical extremity. Tg_chitinase induced macrophage activation as manifested by the production of high levels of pro-inflammatory cytokines, a pathogenic hallmark of T. gondii infection. In conclusion, to our knowledge, we describe for the first time a chitinase of T. gondii tachyzoites and provide evidence that this enzyme might influence the pathogenesis of T. gondii infection.     

Modeling the Impact of Alternative Immunization Strategies: Using Matrices as Memory Lanes

Existing modeling approaches are divided between a focus on the constitutive (micro) elements of systems or on higher (macro) organization levels. Micro-level models enable consideration of individual histories and interactions, but can be unstable and subject to cumulative errors. Macro-level models focus on average population properties, but may hide relevant heterogeneity at the micro-scale. We present a framework that integrates both approaches through the use of temporally structured matrices that can take large numbers of variables into account. Matrices are composed of several bidimensional (time×age) grids, each representing a state (e.g. physiological, immunological, socio-demographic). Time and age are primary indices linking grids. These matrices preserve the entire history of all population strata and enable the use of historical events, parameters and states dynamically in the modeling process. This framework is applicable across fields, but particularly suitable to simulate the impact of alternative immunization policies. We demonstrate the framework by examining alternative strategies to accelerate measles elimination in 15 developing countries. The model recaptured long-endorsed policies in measles control, showing that where a single routine measles-containing vaccine is employed with low coverage, any improvement in coverage is more effective than a second dose. It also identified an opportunity to save thousands of lives in India at attractively low costs through the implementation of supplementary immunization campaigns. The flexibility of the approach presented enables estimating the effectiveness of different immunization policies in highly complex contexts involving multiple and historical influences from different hierarchical levels.     

Computational analysis of pediatric ventricular assist device implantation to decrease cerebral particulate embolization

Stroke is the most devastating complication after ventricular assist device (VAD) implantation with a 19% incidence and 65% mortality in the pediatric population. Current pediatric VAD technology and anticoagulation strategies alone are suboptimal. VAD implantation assisted by computational methods (CFD) may contribute reducing the risk of cerebral embolization. Representative three-dimensional aortic arch models of an infant and a child were generated. An 8 mm VAD outflow-graft (VAD-OG) anastomosed to the aorta was rendered and CFD was applied to study blood flow patterns. Particle tracks, originating in the VAD, were computed with a Lagrangian phase model and the percentage of particles entering the cerebral vessels was calculated. Eight implantation configurations (infant = 5 and child = 3) and 5 particle sizes (0.5, 1, 2, 3, and 4 mm) were considered. For the infant model, percentage of particles entering the cerebral vessels ranged from 15% for a VAD-OG anastomosed at 90° to the aorta, to 31% for 30° VAD-OG anastomosis (overall percentages: X² = 10,852, p < 0.0001). For the child model, cerebral embolization ranged from 9% for the 30° VAD-OG anastomosis to 15% for the 60° anastomosis (overall percentages: χ² = 10,323, p < 0.0001). Using detailed CFD calculations, we demonstrate that the risk of stroke depends significantly on the VAD implantation geometry. In turn, the risk probably depends on patient-specific anatomy. CFD can be used to optimize VAD implantation geometry to minimize stroke risk.     

Interfacial water and its potential role in the function of sericin against biofouling

Abstract

Silk sericin is a globular protein whose resistance against fouling is important for applications in biomaterials and water-purification membranes. Here it is shown how sericin generates a water-exclusion zone that may facilitate antifouling behavior. Negatively charged microspheres were used to mimic the surface charge and hydrophobic domains in bacteria. Immersed in water, regenerated silk sericin formed a 100-µm-sized exclusion zone (for micron-size foulants), along with a proton gradient with a decrease of >2?pH-units. Thus, when in contact with sericin, water molecules near the surface restructure to form a physical exclusionary barrier that might prevent biofouling. The decreased pH turns the aqueous medium unviable for neutrophilic bacteria. Therefore, resistance to biofouling seems explainable, among other factors, on the basis of water-exclusionary phenomena. Furthermore, sericin may play a role in triggering the fibroin assembly process by lowering the pH to the required value.     

Phytoplankton Growth and Microzooplankton Grazing in the Subtropical Northeast Atlantic

Dilution experiments were performed to estimate phytoplankton growth and microzooplankton grazing rates during two Lagrangian surveys in inner and eastern locations of the Eastern North Atlantic Subtropical Gyre province (NAST-E). Our design included two phytoplankton size fractions (0.2–5 µm and >5 µm) and five depths, allowing us to characterize differences in growth and grazing rates between size fractions and depths, as well as to estimate vertically integrated measurements. Phytoplankton growth rates were high (0.11–1.60 d⁻¹), especially in the case of the large fraction. Grazing rates were also high (0.15–1.29 d⁻¹), suggesting high turnover rates within the phytoplankton community. The integrated balances between phytoplankton growth and grazing losses were close to zero, although deviations were detected at several depths. Also, O₂ supersaturation was observed up to 110 m depth during both Lagrangian surveys. These results add up to increased evidence indicating an autotrophic metabolic balance in oceanic subtropical gyres.     

Majority Rules with Random Tie-Breaking in Boolean Gene Regulatory Networks

We consider threshold Boolean gene regulatory networks, where the update function of each gene is described as a majority rule evaluated among the regulators of that gene: it is turned ON when the sum of its regulator contributions is positive (activators contribute positively whereas repressors contribute negatively) and turned OFF when this sum is negative. In case of a tie (when contributions cancel each other out), it is often assumed that the gene keeps it current state. This framework has been successfully used to model cell cycle control in yeast. Moreover, several studies consider stochastic extensions to assess the robustness of such a model.Here, we introduce a novel, natural stochastic extension of the majority rule. It consists in randomly choosing the next value of a gene only in case of a tie. Hence, the resulting model includes deterministic and probabilistic updates. We present variants of the majority rule, including alternate treatments of the tie situation. Impact of these variants on the corresponding dynamical behaviours is discussed. After a thorough study of a class of two-node networks, we illustrate the interest of our stochastic extension using a published cell cycle model. In particular, we demonstrate that steady state analysis can be rigorously performed and can lead to effective predictions; these relate for example to the identification of interactions whose addition would ensure that a specific state is absorbing.