Biophysical characterization of two different stable misfolded monomeric polypeptides that are chaperone-amenable substrates.

Misfolded polypeptide monomers may be regarded as the initial species of many protein aggregation pathways, which could accordingly serve as primary targets for molecular chaperones. It is therefore of paramount importance to study the cellular mechanisms that can prevent misfolded monomers from entering the toxic aggregation pathway and moreover rehabilitate them into active proteins. Here, we produced two stable misfolded monomers of luciferase and rhodanese, which we found to be differently processed by the Hsp70 chaperone machinery and whose conformational properties were investigated by biophysical approaches. In spite of their monomeric nature, they displayed enhanced thioflavin T fluorescence, non-native β-sheets, and tertiary structures with surface-accessible hydrophobic patches, but differed in their conformational stability and aggregation propensity. Interestingly, minor structural differences between the two misfolded species could account for their markedly different behavior in chaperone-mediated unfolding/refolding assays. Indeed, only a single DnaK molecule was sufficient to unfold by direct clamping a misfolded luciferase monomer, while, by contrast, several DnaK molecules were necessary to unfold the more resistant misfolded rhodanese monomer by a combination of direct clamping and cooperative entropic pulling.

Stable isotope geochemistry and phase-equilibria of coesite-bearing whiteschists, Dora Maira massif, Western Alps

Peak metamorphic temperatures for the coesite-pyrope-bearing whiteschists from the Dora Maira Massif, western Alps were determined with oxygen isotope thermometry. The deltaO-18(SMOW) values of the quartz (after coesite) (delta O-18 = 8.1 to 8.6 parts per thousand, n = 6), phengite (6.2 to 6.4 parts per thousand, n = 3), kyanite (6.1 parts per thousand, n = 2), garnet (5.5 to 5.8 parts per thousand, n = 9), ellenbergerite (6.3 parts per thousand, n = 1) and rutile (3.3. to 3.6 parts per thousand, n = 3) reflect isotopic equilibrium. Temperature estimates based on quartz-garnet-rutile fractionation are 700-750-degrees-C. Minimum pressures are 31-32 kb based on the pressure-sensitive reaction pyrope + coesite = kyanite + enstatite. In order to stabilize pyrope and coesite by the temperature-sensitive dehydration reaction talc + kyanite = pyrope + coesite + H2O, the a(H2O) must be reduced to 0.4-0.75 at 700 750-degrees-C. The reduced a(H2O) cannot be due to dilution by CO2, as pyrope is not stable at X (CO2) > 0.02 (T = 750-degrees-C; P = 30 kb). In the absence of a more exotic fluid diluent (e.g. CH4 or N2), a melt phase is required. Granite solidus temperatures are approximately 680-degrees-C/30 kb at a(H2O) = 1.0 and are calculated to be approximately 70-degrees-C higher at a(H2O) = 0.7, consistent with this hypothesis. Kyanite-jadeite-quartz bands may represent a relict melt phase. Peak P-T-f(H2O) estimates for the whiteschist are 34 +/- 2 kb, 700-750-degrees-C and 0.4-0.75. The oxygen isotope fractionation between quartz (deltaO-18 = 11.6%.) and garnet (deltaO-18 = 8.7 parts per thousand) in the surrounding orthognesiss is identical to that in the coesite-bearing unit, suggesting that the two units shared a common, final metamorphic history. Hydrogen isotope measurements were made on primary talc and phengite (deltaD(smow) = -27 to -32 parts per thousand), on secondary talc and chlorite after pyrope (deltaD = - 39 to - 44 parts per thousand) and on the surrounding biotite (deltaD = -64 parts per thousand) and phengite (deltaD = -44 parts per thousand) gneiss. All phases appear to be in near-equilibrium. The very high deltaD values for the primary hydrous phases is consistent with an initial oceanic-derived/connate fluid source. The fluid source for the retrograde talc + chlorite after pyrope may be fluids evolved locally during retrograde melt crystallization. The similar deltaD, but dissimilar deltaO-18 values of the coesite-bearing whiteschists and hosting orthogneiss suggest that the two were in hydrogen isotope equilibrium, but not oxygen isotope equilibrium. The unusual hydrogen and oxygen isotope compositions of the coesite-bearing unit can be explained as the result of metasomatism from slab-derived fluids at depth.

MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status.

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.

Physical activity in free-living, overweight white and black women: divergent responses by race to diet-induced weight loss.

BACKGROUND: Black women are at greater risk of obesity than are white women, perhaps because of their lower levels of physical activity. OBJECTIVE: We compared free-living activity energy expenditure (AEE) in sedentary white and black women (in overweight and normal-weight states) and in never-overweight control subjects. DESIGN: Subjects included 46 women (23 white, 23 black) studied while overweight and after reaching a normal weight and 38 female control subjects (23 white, 15 black). Diet, without exercise training, resulted in a mean weight loss of 13 kg and a body mass index (in kg/m(2)) < 25. Body composition, sleeping energy expenditure, free-living total energy expenditure, and the energy cost of activity and aerobic capacity were assessed before and after weight loss under 4-wk, diet-controlled, weight-stable conditions and in the control subjects. AEE was defined as above-sleep energy expenditure. RESULTS: No significant racial differences in body composition, before or after weight loss, were found. After weight loss, AEE and aerobic capacity increased in the white women and decreased in the black women (P < 0.05 and P < 0.02, respectively). After weight loss, but not before, the white women had a significantly higher mean AEE than did the black women (2448 +/- 979 and 1728 +/- 1373 kJ/d, respectively; P < 0.05), approximating AEEs in the white (2314 +/- 1105) and black (2310 +/- 1251) control subjects. CONCLUSIONS: Relative to the responses of the white women to diet-induced weight loss, the black women became less fit and less physically active. Induction of a normal body weight in overweight black women appeared to produce a more obesity-prone state, favoring weight relapse.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.