Structure-activity relationship of alginate oligosaccharides in the induction of cytokine production from RAW264.7 cells

Guluronate and mannuronate oligomers with various degree of polymerization were prepared from polyguluronate (PG) and polymannuronate (PM) with an alginate lyase from a Pseudoalteromonas sp., and their activities to induce cytokine secretion from mouse macrophage cell line RAW264.7 cells were examined. Enzymatically depolymerized unsaturated alginate oligomers induced tumor necrosis factor (TNF)-alpha secretion from RAW264.7 cells in a structure-depending manner, while the activities of saturated alginate oligomers prepared by acid hydrolysis were fairly low or only trace levels. These results suggest that unsaturated end-structure of alginate oligomers was important for the TNF-alpha-inducing activity. Among the unsaturated guluronate (G3-G9) and mannuronate (M3-M9) oligomers, G8 and M7 showed the most potent activity, respectively. Bio-Plex assay revealed that interleukin (IL)-1alpha, IL-1beta, and IL-6 secretion from RAW264.7 cells were also induced by unsaturated alginate oligomers with similar structure-activity relationship profiles as seen in TNF-alpha, and the most potent activities were observed with G8 and M7. These results suggest that G8 and M7 may have the most suitable molecular size or entire structural conformation as stimulant for cytokine secretion. Since antibodies to Toll-like receptor (TLR)2 and TLR4 effectively inhibited the G8- and M7-induced production of TNF-alpha, these alginate oligomers may stimulate innate immunity through the pattern recognition receptors on macrophages similar to microbial products.     

Integrating Transgenic Vector Manipulation with Clinical Interventions to Manage Vector-Borne Diseases

Many vector-borne diseases lack effective vaccines and medications, and the limitations of traditional vector control have inspired novel approaches based on using genetic engineering to manipulate vector populations and thereby reduce transmission. Yet both the short- and long-term epidemiological effects of these transgenic strategies are highly uncertain. If neither vaccines, medications, nor transgenic strategies can by themselves suffice for managing vector-borne diseases, integrating these approaches becomes key. Here we develop a framework to evaluate how clinical interventions (i.e., vaccination and medication) can be integrated with transgenic vector manipulation strategies to prevent disease invasion and reduce disease incidence. We show that the ability of clinical interventions to accelerate disease suppression can depend on the nature of the transgenic manipulation deployed (e.g., whether vector population reduction or replacement is attempted). We find that making a specific, individual strategy highly effective may not be necessary for attaining public-health objectives, provided suitable combinations can be adopted. However, we show how combining only partially effective antimicrobial drugs or vaccination with transgenic vector manipulations that merely temporarily lower vector competence can amplify disease resurgence following transient suppression. Thus, transgenic vector manipulation that cannot be sustained can have adverse consequences—consequences which ineffective clinical interventions can at best only mitigate, and at worst temporarily exacerbate. This result, which arises from differences between the time scale on which the interventions affect disease dynamics and the time scale of host population dynamics, highlights the importance of accounting for the potential delay in the effects of deploying public health strategies on long-term disease incidence. We find that for systems at the disease-endemic equilibrium, even modest perturbations induced by weak interventions can exhibit strong, albeit transient, epidemiological effects. This, together with our finding that under some conditions combining strategies could have transient adverse epidemiological effects suggests that a relatively long time horizon may be necessary to discern the efficacy of alternative intervention strategies.     

Assessing the Feasibility of Controlling Aedes aegypti with Transgenic Methods: A Model-Based Evaluation

Suppression of dengue and malaria through releases of genetically engineered mosquitoes might soon become feasible. Aedes aegypti mosquitoes carrying a conditionally lethal transgene have recently been used to suppress local vector populations in small-scale field releases. Prior to releases of transgenic insects on a wider scale, however, most regulatory authorities will require additional evidence that suppression will be effective in natural heterogeneous habitats. We use a spatially explicit stochastic model of an Ae. aegypti population in Iquitos, Peru, along with an uncertainty analysis of its predictions, to quantitatively assess the outcome of varied operational approaches for releases of transgenic strains with conditional death of females. We show that population elimination might be an unrealistic objective in heterogeneous populations. We demonstrate that substantial suppression can nonetheless be achieved if releases are deployed in a uniform spatial pattern using strains combining multiple lethal elements, illustrating the importance of detailed spatial models for guiding genetic mosquito control strategies.     

Toxins of a Wild Candida Killer Yeast with a Novel Killer Property

The killer toxic substance of Candida SW-55 was separated into two components, I and II, by CM-Sepharose CL-6B column chromatography. They were purified 20 700-fold and 11 100-fold from the culture filtrate of SW-55, respectively. Each purified toxin gave a marked glycoprotein band with molecular mass of 36 kDa on SDS/polyacrylamide gel electrophoresis. Toxins I and II had almost the same isoelectric points, 3.4~3.7 and 3.3~3.8, respectively. Toxin I had strong killer activity against Saccharomyces cerevisiae, Candida glabrata, Hansenula anomala, and Rhodotorula rubra (MIC 0.2~0.3μg/ml), and moderate activity against Kluyveromyces lactis (MIC 2.5μg/ml) and Pichia membranaefaciens (MIC 0.6 μg/ml) but bacteria, fungi, and the other yeasts tested were not affected by toxin I even at the high concentration of 20 μg/ml. Toxin II turned out to be less active than toxin I and the MIC for S. cerevisiae Epernay was 0.4~0.5μg/ml.     

Live Imaging of Whole Mouse Embryos during Gastrulation: Migration Analyses of Epiblast and Mesodermal Cells

During gastrulation in the mouse embryo, dynamic cell movements including epiblast invagination and mesodermal layer expansion lead to the establishment of the three-layered body plan. The precise details of these movements, however, are sometimes elusive, because of the limitations in live imaging. To overcome this problem, we developed techniques to enable observation of living mouse embryos with digital scanned light sheet microscope (DSLM). The achieved deep and high time-resolution images of GFP-expressing nuclei and following 3D tracking analysis revealed the following findings: (i) Interkinetic nuclear migration (INM) occurs in the epiblast at embryonic day (E)6 and 6.5. (ii) INM-like migration occurs in the E5.5 embryo, when the epiblast is a monolayer and not yet pseudostratified. (iii) Primary driving force for INM at E6.5 is not pressure from neighboring nuclei. (iv) Mesodermal cells migrate not as a sheet but as individual cells without coordination.     

Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model

This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a posteriori estimation of high dimensional segmentations of MR within the class of generative models representing the observed MRI as a conditionally Gaussian random field, conditioned on the atlas charts and the diffeomorphic change of coordinates of each chart that generates it. The charts and their diffeomorphic correspondences are unknown and viewed as latent or hidden variables. We demonstrate that the expectation-maximization (EM) algorithm arises naturally, yielding the likelihood-fusion equation which the a posteriori estimator of the segmentation labels maximizes. The likelihoods being fused are modeled as conditionally Gaussian random fields with mean fields a function of each atlas chart under its diffeomorphic change of coordinates onto the target. The conditional-mean in the EM algorithm specifies the convex weights with which the chart-specific likelihoods are fused. The multiple atlases with the associated convex weights imply that the posterior distribution is a multi-modal representation of the measured MRI. Segmentation results for subcortical and ventricular structures of subjects, within populations of demented subjects, are demonstrated, including the use of multiple atlases across multiple diseased groups.