Escherichia coli NemA Is an Efficient Chromate Reductase That Can Be Biologically Immobilized to Provide a Cell Free System for Remediation of Hexavalent Chromium

Hexavalent chromium is a serious and widespread environmental pollutant. Although many bacteria have been identified that can transform highly water-soluble and toxic Cr(VI) to insoluble and relatively non-toxic Cr(III), bacterial bioremediation of Cr(VI) pollution is limited by a number of issues, in particular chromium toxicity to the remediating cells. To address this we sought to develop an immobilized enzymatic system for Cr(VI) remediation. To identify novel Cr(VI) reductase enzymes we first screened cell extracts from an Escherichia coli library of soluble oxidoreductases derived from a range of bacteria, but found that a number of these enzymes can reduce Cr(VI) indirectly, via redox intermediates present in the crude extracts. Instead, activity assays for 15 candidate enzymes purified as His6-tagged proteins identified E. coli NemA as a highly efficient Cr(VI) reductase (k_cat/K_M  = 1.1×10⁵ M⁻¹s⁻¹ with NADH as cofactor). Fusion of nemA to the polyhydroxyalkanoate synthase gene phaC from Ralstonia eutropha enabled high-level biosynthesis of functionalized polyhydroxyalkanoate granules displaying stable and active NemA on their surface. When these granules were combined with either Bacillus subtilis glucose dehydrogenase or Candida boidinii formate dehydrogenase as a cofactor regenerating partner, high levels of chromate transformation were observed with only low initial concentrations of expensive NADH cofactor being required, the overall reaction being powered by consumption of the cheap sacrificial substrates glucose or formic acid, respectively. This system therefore offers promise as an economic solution for ex situ Cr(VI) remediation.     

Evolutionary food web models: effects of an additional resource

Many empirical food webs contain multiple resources, which can lead to the emergence of sub-communities—partitions—in a food web that are weakly connected with each other. These partitions interact and affect the complete food web. However, the fact that food webs can contain multiple resources is often neglected when describing food web assembly theoretically, by considering only a single resource. We present an allometric, evolutionary food web model and include two resources of different sizes. Simulations show that an additional resource can lead to the emergence of partitions, i.e. groups of species that specialise on different resources. For certain arrangements of these partitions, the interactions between them alter the food web properties. First, these interactions increase the variety of emerging network structures, since hierarchical bodysize relationships are weakened. Therefore, they could play an important role in explaining the variety of food web structures that is observed in empirical data. Second, interacting partitions can destabilise the population dynamics by introducing indirect interactions with a certain strength between predator and prey species, leading to biomass oscillations and evolutionary intermittence.     

p110γ/δ Double-Deficiency Induces Eosinophilia and IgE Production but Protects from OVA-Induced Airway Inflammation

The catalytical isoforms p110γ and p110δ of phosphatidylinositide 3-kinase γ (PI3Kγ) and PI3Kδ play an important role in the pathogenesis of asthma. Two key elements in allergic asthma are increased levels of eosinophils and IgE. Dual pharmacological inhibition of p110γ and p110δ reduces asthma-associated eosinophilic lung infiltration and ameliorates disease symptoms, whereas the absence of enzymatic activity in p110γ^KOδ^D910A mice increases IgE and basal eosinophil counts. This suggests that long-term inhibition of p110γ and p110δ might exacerbate asthma. Here, we analysed mice genetically deficient for both catalytical subunits (p110γ/δ^-/-) and determined basal IgE and eosinophil levels and the immune response to ovalbumin-induced asthma. Serum concentrations of IgE, IL-5 and eosinophil numbers were significantly increased in p110γ/δ^-/- mice compared to single knock-out and wildtype mice. However, p110γ/δ^-/- mice were protected against OVA-induced infiltration of eosinophils, neutrophils, T and B cells into lung tissue and bronchoalveolar space. Moreover, p110γ/δ^-/- mice, but not single knock-out mice, showed a reduced bronchial hyperresponsiveness. We conclude that increased levels of eosinophils and IgE in p110γ/δ^-/- mice do not abolish the protective effect of p110γ/δ-deficiency against OVA-induced allergic airway inflammation.     

Low Inbreeding and High Pollen Dispersal Distances in Populations of Two Amazonian Forest Tree Species

Recent studies suggest that tropical tree species exhibit low inbreeding and high gene dispersal levels despite the typically low density of conspecifics in tropical forests. To examine this, we undertook a study of pollen gene dispersal and mating system of two Amazonian tree species. We analyzed 341 seeds from 33 trees at four microsatellite loci in a Carapa guianensis population from Brazil, and 212 seeds from 22 trees at four microsatellite loci in a Sextonia rubra population from French Guiana. Differentiation of allele frequencies among the pollen pool of individual trees was Φ_FT= 0.053 (95% CI: 0.027–0.074) for C. guianensis and Φ_FT= 0.064 (95% CI: 0.017–0.088) for S. rubra. The mean pollen dispersal distances were estimated at 69–355 m for C. guianensis , and 86–303 m for S. rubra , depending on the pollen dispersal model and the estimate of reproductive tree density used. The multi-locus outcrossing rate was estimated at 0.918 and 0.945, and the correlation of paternity at 0.089 and 0.096, for C. guianensis and S. rubra , respectively, while no significant levels of biparental inbreeding were detected. Comparing trees with high and low local density of conspecifics, we found no evidence for differences in inbreeding levels. The results are discussed within the framework of the emerging picture of the reproductive biology of tropical forest trees.     

The Mass Distance Fingerprint: a statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry

We describe a statistical measure, Mass Distance Fingerprint, for automatic de novo detection of predominant peptide mass distances, i.e., putative protein modifications. The method's focus is to globally detect mass differences, not to assign peptide sequences or modifications to individual spectra. The Mass Distance Fingerprint is calculated from high accuracy measured peptide masses. For the data sets used in this study, known mass differences are detected at electron mass accuracy or better. The proposed method is novel because it works independently of protein sequence databases and without any prior knowledge about modifications. Both modified and unmodified peptides have to be present in the sample to be detected. The method can be used for automated detection of chemical/post-translational modifications, quality control of experiments and labeling approaches, and to control the modification settings of protein identification tools. The algorithm is implemented as a web application and is distributed as open source software.