Global approach to reducing lead exposure and poisoning

Lead poisoning is an important environmental disease that can have life-long adverse health effects. Most susceptible are children, and most commonly exposed are those who are poor and live in developing countries. Studies of children's blood-lead levels (BLLs) are showing cognitive impairment at increasingly lower BLLs. Lead is dangerous at all levels in children. The sources of lead exposure vary among and within countries depending on past and current uses. Sources of lead may be from historic contamination, recycling old lead products, or from manufacturing new products. In all countries that have banned leaded gasoline, average population BLLs have declined rapidly. In many developing countries where leaded gasoline is no longer used, many children and workers are exposed to fugitive emissions and mining wastes. Unexpected lead threats, such as improper disposal of electronics and children's toys contaminated with lead, continue to emerge. The only medical treatment available is chelation, which can save lives of persons with very high BLLs. However, chelating drugs are not always available in developing countries and have limited value in reducing the sequelae of chronic low dose lead exposure. Therefore, the best approach is to prevent exposure to lead. Because a key strategy for preventing lead poisoning is to identify and control or eliminate lead sources, this article highlights several major sources of lead poisoning worldwide. In addition, we recommend three primary prevention strategies for lead poisoning: identify sources, eliminate or control sources, and monitor environmental exposures and hazards.     

Enhancement of Tumour-Specific Immune Responses In Vivo by ‘MHC Loading-Enhancer’ (MLE)

Background

Class II MHC molecules (MHC II) are cell surface receptors displaying short protein fragments for the surveillance by CD4+ T cells. Antigens therefore have to be loaded onto this receptor in order to induce productive immune responses. On the cell surface, most MHC II molecules are either occupied by ligands or their binding cleft has been blocked by the acquisition of a non-receptive state. Direct loading with antigens, as required during peptide vaccinations, is therefore hindered.

Principal Findings

Here we show, that the in vivo response of CD4+ T cells can be improved, when the antigens are administered together with ‘MHC-loading enhancer’ (MLE). MLE are small catalytic compounds able to open up the MHC binding site by triggering ligand-release and stabilizing the receptive state. Their enhancing effect on the immune response was demonstrated here with an antigen from the influenza virus and tumour associated antigens (TAA) derived from the NY-ESO-1 protein. The application of these antigens in combination with adamantane ethanol (AdEtOH), an MLE compound active on human HLA-DR molecules, significantly increased the frequency of antigen-specific CD4+ T cells in mice transgenic for the human MHC II molecule. Notably, the effect was evident only with the MLE-susceptible HLA-DR molecule and not with murine MHC II molecules non-susceptible for the catalytic effect of the MLE.

Conclusion

MLE can specifically increase the potency of a vaccine by facilitating the efficient transfer of the antigen onto the MHC molecule. They may therefore open a new way to improve vaccination efficacy and tumour-immunotherapy.     

Thrombin inhibitor reduces myocardial infarction in apoE-/- x LDLR-/- mice

We have previously shown that atherosclerotic apolipoprotein E-deficient (apoE(-/-)) x LDL receptor-deficient (LDLR(-/-)) mice develop myocardial infarction when exposed to hypoxic stress. This study was performed to assess the role of thrombin and thrombosis in this process. ApoE(-/-) x LDLR(-/-) mice were fed a cholesterol-rich diet for 8 mo and were then subjected to hypoxic stress while receiving isoflurane anesthesia. One group received a bolus dose (5.6 micromol/kg) of the thrombin inhibitor melagatran, and control animals received PBS 10 min before the hypoxic stress. The mice were exposed to 10 min of hypoxia followed by normoxia. Ten minutes after the stress, Alzet pumps delivering melagatran (20 nmol x kg x (-1)min(-1)) or PBS were implanted, and the mice were allowed to recover for 48 h. The cardiac response was analyzed by histology, immunohistochemistry, and serum troponin T assay. All animals showed reversible ECG changes as a sign of ischemia during hypoxic stress, and 50% developed infarctions afterward as judged by troponin T levels. The group that received thrombin inhibitor had significantly lower troponin T and smaller myocardial infarctions than the PBS-treated group. These data show that thrombin generation is an important pathogenetic factor and suggest that coronary thrombosis is involved in myocardial infarction in atherosclerotic mice. Exposure of atherosclerotic mice to hypoxia leads to myocardial infarction through a two-phase pathway in which acute transient ischemia is followed by thrombin-dependent, irreversible, myocardial ischemia and myocardial cell death.     

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.Plants are of vital significance as a source of food (Grusak and DellaPenna, 1999; Rogalski and Carrer, 2011), feed (Lu et al., 2011), energy (Tilman et al., 2006; Parmar et al., 2011), and feedstocks for the chemical industry (Metzger and Bornscheuer, 2006; Kinghorn et al., 2011). Given the close connection between plant metabolism and the usability of plant products, there is a growing interest in understanding and predicting the behavior and regulation of plant metabolic processes. In order to increase crop quality and yield, there is a need for methods guiding the rational redesign of the plant metabolic network (Schwender, 2009).Mathematical modeling of plant metabolism offers new approaches to understand, predict, and modify complex plant metabolic processes. In plant research, the issue of metabolic modeling is constantly gaining attention, and different modeling approaches applied to plant metabolism exist, ranging from highly detailed quantitative to less complex qualitative approaches (for review, see Giersch, 2000; Morgan and Rhodes, 2002; Poolman et al., 2004; Rios-Estepa and Lange, 2007).A widely used modeling approach is flux balance analysis (FBA), which allows the prediction of metabolic capabilities and steady-state fluxes under different environmental and genetic backgrounds using (non)linear optimization (Orth et al., 2010). Assuming steady-state conditions, FBA has the advantage of not requiring the knowledge of kinetic parameters and, therefore, can be applied to model detailed, large-scale systems. In recent years, the FBA approach has been applied to several different plant species, such as maize (Zea mays; Dal’Molin et al., 2010; Saha et al., 2011), barley (Hordeum vulgare; Grafahrend-Belau et al., 2009b; Melkus et al., 2011; Rolletschek et al., 2011), rice (Oryza sativa; Lakshmanan et al., 2013), Arabidopsis (Arabidopsis thaliana; Poolman et al., 2009; de Oliveira Dal’Molin et al., 2010; Radrich et al., 2010; Williams et al., 2010; Mintz-Oron et al., 2012; Cheung et al., 2013), and rapeseed (Brassica napus; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011), as well as algae (Boyle and Morgan, 2009; Cogne et al., 2011; Dal’Molin et al., 2011) and photoautotrophic bacteria (Knoop et al., 2010; Montagud et al., 2010; Boyle and Morgan, 2011). These models have been used to study different aspects of metabolism, including the prediction of optimal metabolic yields and energy efficiencies (Dal’Molin et al., 2010; Boyle and Morgan, 2011), changes in flux under different environmental and genetic backgrounds (Grafahrend-Belau et al., 2009b; Dal’Molin et al., 2010; Melkus et al., 2011), and nonintuitive metabolic pathways that merit subsequent experimental investigations (Poolman et al., 2009; Knoop et al., 2010; Rolletschek et al., 2011). Although FBA of plant metabolic models was shown to be capable of reproducing experimentally determined flux distributions (Williams et al., 2010; Hay and Schwender, 2011b) and generating new insights into metabolic behavior, capacities, and efficiencies (Sweetlove and Ratcliffe, 2011), challenges remain to advance the utility and predictive power of the models.Given that many plant metabolic functions are based on interactions between different subcellular compartments, cell types, tissues, and organs, the reconstruction of organ-specific models and the integration of these models into interacting multiorgan and/or whole-plant models is a prerequisite to get insight into complex plant metabolic processes organized on a whole-plant scale (e.g. source-sink interactions). Almost all FBA models of plant metabolism are restricted to one cell type (Boyle and Morgan, 2009; Knoop et al., 2010; Montagud et al., 2010; Cogne et al., 2011; Dal’Molin et al., 2011), one tissue or one organ (Grafahrend-Belau et al., 2009b; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011; Mintz-Oron et al., 2012), and only one model exists taking into account the interaction between two cell types by specifying the interaction between mesophyll and bundle sheath cells in C4 photosynthesis (Dal’Molin et al., 2010). So far, no model representing metabolism at the whole-plant scale exists.Considering whole-plant metabolism raises the problem of taking into account temporal and environmental changes in metabolism during plant development and growth. Although classical static FBA is unable to predict the dynamics of metabolic processes, as the network analysis is based on steady-state solutions, time-dependent processes can be taken into account by extending the classical static FBA to a dynamic flux balance analysis (dFBA), as proposed by Mahadevan et al. (2002). The static (SOA) and dynamic optimization approaches introduced in this work provide a framework for analyzing the transience of metabolism by integrating kinetic expressions to dynamically constrain exchange fluxes. Due to the requirement of knowing or estimating a large number of kinetic parameters, so far dFBA has only been applied to a plant metabolic model once, to study the photosynthetic metabolism in the chloroplasts of C3 plants by a simplified model of five biochemical reactions (Luo et al., 2009). Integrating a dynamic model into a static FBA model is an alternative approach to perform dFBA.In this study, a multiscale metabolic modeling (MMM) approach was applied with the aim of achieving a spatiotemporal resolution of cereal crop plant metabolism. To provide a framework for the in silico analysis of the metabolic dynamics of barley on a whole-plant scale, the MMM approach integrates a static multiorgan FBA model and a dynamic whole-plant multiscale functional plant model (FPM) to perform dFBA. The performance of the novel whole-plant MMM approach was tested by studying source-sink interactions during the seed developmental phase of barley plants.     

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.     

Crucial role of aspartic acid at position 265 in the CH2 domain for murine IgG2a and IgG2b Fc-associated effector functions

Replacement of aspartic acid by alanine at position 265 (D265A) in mouse IgG1 results in a complete loss of interaction between this isotype and low-affinity IgG Fc receptors (Fc gammaRIIB and Fc gammaRIII). However, it has not yet been defined whether the D265A substitution could exhibit similar effects on the interaction with two other Fc gammaR (Fc gammaRI and Fc gammaRIV) and on the activation of complement. To address this question, 34-3C anti-RBC IgG2a and IgG2b switch variants bearing the D265A mutation were generated, and their effector functions and in vivo pathogenicity were compared with those of the respective wild-type Abs. The introduction of the D265A mutation almost completely abolished the binding of 34-3C IgG2a and IgG2b to all four classes of Fc gammaR and the activation of complement. Consequently, these mutants were hardly pathogenic. Although oligosaccharide side chains of these mutants were found to contain higher levels of sialic acids than those of wild-type Abs, the analysis of enzymatically desialylated D265A variants ruled out the possibility that very poor Fc-associated effector functions of the D265A mutants were due to an increased level of the mutant Fc sialylation. Thus, our results demonstrate that aspartic acid at position 265 is a residue critically implicated in triggering the Fc-associated effector functions of IgG, probably by defining a crucial three-dimensional structure of the Fc region.     

Genetic Structure and Molecular Variability of Cucumber mosaic virus Isolates in the United States

Cucumber mosaic virus (CMV) has a worldwide distribution and the widest host range of any known plant virus. From 2000 to 2012, epidemics of CMV severely affected the production of snap bean (Phaseulos vulgaris L.) in the Midwest and Northeastern United States. Virus diversity leading to emergence of new strains is often considered a significant factor in virus epidemics. In addition to epidemics, new disease phenotypes arising from genetic exchanges or mutation can compromise effectiveness of plant disease management strategies. Here, we captured a snapshot of genetic variation of 32 CMV isolates collected from different regions of the U.S including new field as well as historic isolates. Nucleotide diversity (π) was low for U.S. CMV isolates. Sequence and phylogenetic analyses revealed that CMV subgroup I is predominant in the US and further showed that the CMV population is a mixture of subgroups IA and IB. Furthermore, phylogenetic analysis suggests likely reassortment between subgroups IA and IB within five CMV isolates. Based on phylogenetic and computational analysis, recombination between subgroups I and II as well as IA and IB in RNA 3 was detected. This is the first report of recombination between CMV subgroups I and II. Neutrality tests illustrated that negative selection was the major force operating upon the CMV genome, although some positively selected sites were detected for all encoded proteins. Together, these data suggest that different regions of the CMV genome are under different evolutionary constraints. These results also delineate composition of the CMV population in the US, and further suggest that recombination and reassortment among strain subgroups does occur but at a low frequency, and point towards CMV genomic regions that differ in types of selection pressure.     

Genetic variation of Citrus tristeza virus isolates from California and Spain: evidence for mixed infections and recombination

We examined the population structure and genetic variation of four genomic regions within and between 30 Citrus tristeza virus (CTV) isolates from Spain and California. Our analyses showed that most isolates contained a population of sequence variants, with one being predominant. Four isolates showed two major sequence variants in some genomic regions. The two major variants of three of these isolates showed very low nucleotide identity to each other but were very similar to those of other isolates, suggesting the possibility of mixed infections with two divergent isolates. Incongruencies of phylogenetic relationships in the different genomic regions and statistical analyses suggested that the genomes of some CTV sequence variants originated by recombination events between diverged sequence variants. No correlation was observed between geographic origin and nucleotide distance, and thus from a genetic view, the Spanish and Californian isolates analyzed here could be considered members of the same population.     

M-DNA: A complex between divalent metal ions and DNA which behaves as a molecular wire

M-DNA is a complex of DNA with divalent metal ions (Zn(2+), Co(2+), or Ni(2+)) which forms at pH conditions above 8. Upon addition of these metal ions to B-DNA at pH 8.5, the pH decreases such that one proton is released per base-pair per metal ion. Together with previous NMR data, this result demonstrated that the imino proton in each base-pair of the duplex was substituted by a metal ion and that M-DNA might possess unusual conductive properties. Duplexes of 20 base-pairs were constructed with fluorescein (donor) at one end and rhodamine (acceptor) at the other. Upon formation of M-DNA (with Zn(2+)) the fluorescence of the donor was 95 % quenched. Fluorescence lifetime measurements showed the presence of a very fast component in the decay kinetics with tau相似文献   

The morphological transition of Helicobacter pylori cells from spiral to coccoid is preceded by a substantial modification of the cell wall.

The peptidoglycan (murein) of Helicobacter pylori has been investigated by high-performance liquid chromatography and mass spectrometric techniques. Murein from H. pylori corresponded to the A1gamma chemotype, but the muropeptide elution patterns were substantially different from the one for Escherichia coli in that the former produced high proportions of muropeptides with a pentapeptide side chain (about 60 mol%), with Gly residues as the C-terminal amino acid (5 to 10 mol%), and with (1-->6)anhydro-N-acetylmuramic acid (13 to 18 mol%). H. pylori murein also lacks murein-bound lipoprotein, trimeric muropeptides, and (L-D) cross-linked muropeptides. Cessation of growth and transition to coccoid shape triggered an increase in N-acetylglucosaminyl-N-acetylmuramyl-L-Ala-D-Glu (approximately 20 mol%), apparently at the expense of monomeric muropeptides with tri- and tetrapeptide side chains. Muropeptides with (1-->6)anhydro-muramic acid and with Gly were also more abundant in resting cells.