Crystal structure of the CusBA heavy-metal efflux complex of Escherichia coli

Gram-negative bacteria, such as Escherichia coli, expel toxic chemicals through tripartite efflux pumps that span both the inner and outer membrane. The three parts are an inner membrane, substrate-binding transporter; a membrane fusion protein; and an outer-membrane-anchored channel. The fusion protein connects the transporter to the channel within the periplasmic space. A crystallographic model of this tripartite efflux complex has been unavailable because co-crystallization of the various components of the system has proven to be extremely difficult. We previously described the crystal structures of both the inner membrane transporter CusA and the membrane fusion protein CusB of the CusCBA efflux system of E. coli. Here we report the co-crystal structure of the CusBA efflux complex, showing that the transporter (or pump) CusA, which is present as a trimer, interacts with six CusB protomers and that the periplasmic domain of CusA is involved in these interactions. The six CusB molecules seem to form a continuous channel. The affinity of the CusA and CusB interaction was found to be in the micromolar range. Finally, we have predicted a three-dimensional structure for the trimeric CusC outer membrane channel and developed a model of the tripartite efflux assemblage. This CusC(3)-CusB(6)-CusA(3) model shows a 750-kilodalton efflux complex that spans the entire bacterial cell envelope and exports Cu I and Ag I ions.     

Structure and reactivity of a mononuclear non-haem iron(III)-peroxo complex

Oxygen-containing mononuclear iron species--iron(III)-peroxo, iron(III)-hydroperoxo and iron(IV)-oxo--are key intermediates in the catalytic activation of dioxygen by iron-containing metalloenzymes. It has been difficult to generate synthetic analogues of these three active iron-oxygen species in identical host complexes, which is necessary to elucidate changes to the structure of the iron centre during catalysis and the factors that control their chemical reactivities with substrates. Here we report the high-resolution crystal structure of a mononuclear non-haem side-on iron(III)-peroxo complex, [Fe(III)(TMC)(OO)](+). We also report a series of chemical reactions in which this iron(III)-peroxo complex is cleanly converted to the iron(III)-hydroperoxo complex, [Fe(III)(TMC)(OOH)](2+), via a short-lived intermediate on protonation. This iron(III)-hydroperoxo complex then cleanly converts to the ferryl complex, [Fe(IV)(TMC)(O)](2+), via homolytic O-O bond cleavage of the iron(III)-hydroperoxo species. All three of these iron species--the three most biologically relevant iron-oxygen intermediates--have been spectroscopically characterized; we note that they have been obtained using a simple macrocyclic ligand. We have performed relative reactivity studies on these three iron species which reveal that the iron(III)-hydroperoxo complex is the most reactive of the three in the deformylation of aldehydes and that it has a similar reactivity to the iron(IV)-oxo complex in C-H bond activation of alkylaromatics. These reactivity results demonstrate that iron(III)-hydroperoxo species are viable oxidants in both nucleophilic and electrophilic reactions by iron-containing enzymes.     

Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw

Permafrost contains an estimated 1672?Pg carbon (C), an amount roughly equivalent to the total currently contained within land plants and the atmosphere. This reservoir of C is vulnerable to decomposition as rising global temperatures cause the permafrost to thaw. During thaw, trapped organic matter may become more accessible for microbial degradation and result in greenhouse gas emissions. Despite recent advances in the use of molecular tools to study permafrost microbial communities, their response to thaw remains unclear. Here we use deep metagenomic sequencing to determine the impact of thaw on microbial phylogenetic and functional genes, and relate these data to measurements of methane emissions. Metagenomics, the direct sequencing of DNA from the environment, allows the examination of whole biochemical pathways and associated processes, as opposed to individual pieces of the metabolic puzzle. Our metagenome analyses reveal that during transition from a frozen to a thawed state there are rapid shifts in many microbial, phylogenetic and functional gene abundances and pathways. After one week of incubation at 5?°C, permafrost metagenomes converge to be more similar to each other than while they are frozen. We find that multiple genes involved in cycling of C and nitrogen shift rapidly during thaw. We also construct the first draft genome from a complex soil metagenome, which corresponds to a novel methanogen. Methane previously accumulated in permafrost is released during thaw and subsequently consumed by methanotrophic bacteria. Together these data point towards the importance of rapid cycling of methane and nitrogen in thawing permafrost.     

Integrating time-series and end-use methods to forecast electricity sales

Two types of forecasting methods have been receiving increasing attention by electric utility forecasters. The first type, called end-use forecasting, is recognized as an approach which is well suited for forecasting during periods characterized by technological change. The method is straightforward. The stock levels of energy-consuming equipment are forecast, as well as the energy consumption characteristics of the equipment. The final forecast is the product of the stock and usage characteristics. This approach is well suited to forecasting long time periods when technological change, equipment depletion and replacement, and other structural changes are evident. For time periods of shorter duration, these factors are static and variations are more likely to result from shocks to the environment. The shocks influence the usage of the equipment. A second forecasting approach using time-series analysis has been demonstrated to be superior for these applications. This paper discusses the integration of the two methods into a unified system. The result is a time-series model whose parameter effects become dynamic in character. An example of the models being used at the Georgia Power Company is presented. It is demonstrated that a time-series model which incorporates end-use stock and usage information is superior—even in short-term forecasting situations—to a similar time-series model which excludes the information.     

Substrate-targeting gamma-secretase modulators

Selective lowering of Abeta42 levels (the 42-residue isoform of the amyloid-beta peptide) with small-molecule gamma-secretase modulators (GSMs), such as some non-steroidal anti-inflammatory drugs, is a promising therapeutic approach for Alzheimer's disease. To identify the target of these agents we developed biotinylated photoactivatable GSMs. GSM photoprobes did not label the core proteins of the gamma-secretase complex, but instead labelled the beta-amyloid precursor protein (APP), APP carboxy-terminal fragments and amyloid-beta peptide in human neuroglioma H4 cells. Substrate labelling was competed by other GSMs, and labelling of an APP gamma-secretase substrate was more efficient than a Notch substrate. GSM interaction was localized to residues 28-36 of amyloid-beta, a region critical for aggregation. We also demonstrate that compounds known to interact with this region of amyloid-beta act as GSMs, and some GSMs alter the production of cell-derived amyloid-beta oligomers. Furthermore, mutation of the GSM binding site in the APP alters the sensitivity of the substrate to GSMs. These findings indicate that substrate targeting by GSMs mechanistically links two therapeutic actions: alteration in Abeta42 production and inhibition of amyloid-beta aggregation, which may synergistically reduce amyloid-beta deposition in Alzheimer's disease. These data also demonstrate the existence and feasibility of 'substrate targeting' by small-molecule effectors of proteolytic enzymes, which if generally applicable may significantly broaden the current notion of 'druggable' targets.     

Image statistics and the perception of surface qualities

The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.     

A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene

Type 1 diabetes (T1D) in children results from autoimmune destruction of pancreatic beta cells, leading to insufficient production of insulin. A number of genetic determinants of T1D have already been established through candidate gene studies, primarily within the major histocompatibility complex but also within other loci. To identify new genetic factors that increase the risk of T1D, we performed a genome-wide association study in a large paediatric cohort of European descent. In addition to confirming previously identified loci, we found that T1D was significantly associated with variation within a 233-kb linkage disequilibrium block on chromosome 16p13. This region contains KIAA0350, the gene product of which is predicted to be a sugar-binding, C-type lectin. Three common non-coding variants of the gene (rs2903692, rs725613 and rs17673553) in strong linkage disequilibrium reached genome-wide significance for association with T1D. A subsequent transmission disequilibrium test replication study in an independent cohort confirmed the association. These results indicate that KIAA0350 might be involved in the pathogenesis of T1D and demonstrate the utility of the genome-wide association approach in the identification of previously unsuspected genetic determinants of complex traits.