The cold-induced lipokine 12,13-diHOME promotes fatty acid transport into brown adipose tissue

Brown adipose tissue (BAT) and beige adipose tissue combust fuels for heat production in adult humans, and so constitute an appealing target for the treatment of metabolic disorders such as obesity, diabetes and hyperlipidemia1,2. Cold exposure can enhance energy expenditure by activating BAT, and it has been shown to improve nutrient metabolism3–5. These therapies, however, are time consuming and uncomfortable, demonstrating the need for pharmacological interventions. Recently, lipids have been identified that are released from tissues and act locally or systemically to promote insulin sensitivity and glucose tolerance; as a class, these lipids are referred to as ‘lipokines’6–8. Because BAT is a specialized metabolic tissue that takes up and burns lipids and is linked to systemic metabolic homeostasis, we hypothesized that there might be thermogenic lipokines that activate BAT in response to cold. Here we show that the lipid 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME) is a stimulator of BAT activity, and that its levels are negatively correlated with body-mass index and insulin sensitivity. Using a global lipidomic analysis, we found that 12,13-diHOME was increased in the circulation of humans and mice exposed to cold. Furthermore, we found that the enzymes that produce 12,13-diHOME were uniquely induced in BAT by cold stimulation. The injection of 12,13-diHOME acutely activated BAT fuel uptake and enhanced cold tolerance, which resulted in decreased levels of serum triglycerides. Mechanistically, 12,13-diHOME increased fatty acid (FA) uptake into brown adipocytes by promoting the translocation of the FA transporters FATP1 and CD36 to the cell membrane. These data suggest that 12,13-diHOME, or a functional analog, could be developed as a treatment for metabolic disorders

Additional disulfide bonds in insulin:prediction, recombinant expression, receptor binding affinity, and stability

The structure of insulin, a glucose homeostasis-controlling hormone, is highly conserved in all vertebrates and stabilized by three disulfide bonds. Recently, we designed a novel insulin analogue containing a fourth disulfide bond located between positions A10-B4. The N-terminus of insulin's B-chain is flexible and can adapt multiple conformations. We examined how well disulfide bond predictions algorithms could identify disulfide bonds in this region of insulin. In order to identify stable insulin analogues with additional disulfide bonds, which could be expressed, the C(β) cut-off distance had to be increased in many instances and single X-ray structures as well as structures from MD simulations had to be used. The analogues that were identified by the algorithm without extensive adjustments of the prediction parameters were more thermally stable as assessed by DSC and CD and expressed in higher yields in comparison to analogues with additional disulfide bonds that were more difficult to predict. In contrast, addition of the fourth disulfide bond rendered all analogues resistant to fibrillation under stress conditions and all stable analogues bound to the insulin receptor with picomolar affinities. Thus activity and fibrillation propensity did not correlate with the results from the prediction algorithm

Insulin analog with additional disulfide bond has increased stability and preserved activity

Insulin is a key hormone controlling glucose homeostasis. All known vertebrate insulin analogs have a classical structure with three 100% conserved disulfide bonds that are essential for structural stability and thus the function of insulin. It might be hypothesized that an additional disulfide bond may enhance insulin structural stability which would be highly desirable in a pharmaceutical use. To address this hypothesis, we designed insulin with an additional interchain disulfide bond in positions A10/B4 based on Cα-Cα distances, solvent exposure, and side-chain orientation in human insulin (HI) structure. This insulin analog had increased affinity for the insulin receptor and apparently augmented glucodynamic potency in a normal rat model compared with HI. Addition of the disulfide bond also resulted in a 34.6°C increase in melting temperature and prevented insulin fibril formation under high physical stress even though the C-terminus of the B-chain thought to be directly involved in fibril formation was not modified. Importantly, this analog was capable of forming hexamer upon Zn addition as typical for wild-type insulin and its crystal structure showed only minor deviations from the classical insulin structure. Furthermore, the additional disulfide bond prevented this insulin analog from adopting the R-state conformation and thus showing that the R-state conformation is not a prerequisite for binding to insulin receptor as previously suggested. In summary, this is the first example of an insulin analog featuring a fourth disulfide bond with increased structural stability and retained function

A Scalable High-performance Topographic Flow Direction Algorithm for Hydrological Information Analysis

Hydrological information analyses based on Digital Elevation Models (DEM) provide hydrological properties derived from high-resolution topographic data represented as an elevation grid. Flow direction is one of the most computationally intensive functions in the current implementation of TauDEM, a broadly used high-performance hydrological analysis software in hydrology community. Hydrologic flow direction defines a flow field on the DEM that directs flow from each grid cell to one or more of its neighbors. This is a local computation for the majority of grid cells, but becomes a global calculation for the geomorphologically motivated procedure in TauDEM to route flow across flat regions. As the resolution of DEM becomes higher, the computational bottleneck of this function hinders the use of these DEM data in large-scale studies. This paper presents an efficient parallel flow direction algorithm that identifies spatial features (e.g., flats) and reduces the number of sequential and parallel iterations needed to compute their geomorphologically motivated flow direction. Numerical experiments show that our algorithm outperformed the existing parallel D8 algorithm in TauDEM by two orders of magnitude. The new parallel algorithm exhibited desirable scalability on Stampede and ROGER supercomputers

Data collection and refinement statistics.

a<p> <i>R_merge = Σ|I_i−I|/ΣI where I_i is an individual intensity measurement and I is the mean intensity for this reflection.</i></p>b<p> <i>R value = crystallographic R-factor = Σ|F_obs|−|F_calc|/Σ|F_obs|, where Fobs and Fcalc are the observed and calculated structure factors respectively. R_free value is the same as R value but calculated on 5% of the data not included in the refinement.</i></p>c<p> <i>Root-mean-square deviations of the parameters from their ideal values.</i></p

Measurements of <i>in vitro</i> activity of the B25C-dimer compared to HI.

<p><b>A:</b> Representative insulin receptor binding curves for HI(black), B25C-NEM1 (dark gray) B25C-NEM2(gray)and the B25C dimer(light gray). <b>B:</b> Representative metabolic dose response curves for HI(black) and the B25C-dimer (dark gray). Each point on the graph represents the mean ± SD, n = 4 within one assay.</p

AUC results for the B25C-dimer.

<p><b>A:</b> SV Analysis of the B25C-dimer in the presence of 2 Zn²⁺/hexamer (insulin normals). In the top part of the figure, open circles represent the g(s*)/s-curve derived from a dcdt-analysis. For clarity, only every 10^th data point is shown. The solid red line represents the fit to a model of a single ideal species, resulting in the parameters shown in Tabel 2. The bottom part of the figure represents the local deviations between the experimental and simulated data (residuals). Every data point is shown. The rmsd of the shown fit is 9.83×10⁻³. <b>B:</b> Representative data of a SE experiment used to determine the self-association model of B25C. In the top part of the figure, open circles represent experimental concentration distributions at apparent thermo- and hydrodynamic equilibrium for one concentration (out of five) at 15 krpm (black), 24 krpm (red) and 36 krpm (green). For clarity, only every 10^th data point is shown. The solid like-colored lines represent the global fit to all measured conditions to a model of a reversible monomer-dimer model, resulting in the equilibrium coefficient mentioned in the text. The bottom part of the figure represents the local deviations between the experimental and simulated data (residuals). Every data point is shown. The molar mass parameter was fixed to its expected value and the global rmsd of the fit is 7.4×10⁻³.</p

Cartoon representation of the crystal structure of the B25C-dimer.

<p><b>A:</b> The A chain is coloured in green and the B chain is shown in blue. The additional disulphide bond is shown by stick representation (yellow). An omit map was calculated by omitting the Sulphur atom of B25C. The resulting difference electron density Fo-Fc map is coloured in orange at σ-level = 3.0. It is clear from the structure that the two monomers are linked by a disulfide bond between the two adjoining B25C. <b>B:</b> Comparison of the B25C structure (blue) with that of the porcine in-sulin (PDB code 1B2E) (grey). The Cα trace shows that the two structures have a high resemblance with minor deviations in Cα positions at residue B21E and B29K.</p

Assessing the stability of the B25C-dimer compared to HI.

<p><b>A:</b> DSC of HI and the B25C-dimer. <b>B:</b> ThT fibrillation assay of 0.3 mM B25C-dimer (grey diamonds) and 0.6 mM HI (black diamonds) with incubation at 37°C and vigorous shaking as described in “<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030882#s2" target="_blank">Methods</a>”. Both samples contained 7 mM phosphate adjusted to pH 7.4.</p