Tumor Risks and Genotype-Phenotype-Proteotype Analysis in 358 Patients With Germline Mutations in SDHB and SDHD

Succinate dehydrogenase B (SDHB) and D (SDHD) subunit gene mutations predispose to adrenal and extraadrenal pheochromocytomas, head and neck paragangliomas (HNPGL), and other tumor types. We report tumor risks in 358 patients with SDHB (n = 295) and SDHD (n = 63) mutations. Risks of HNPGL and pheochromocytoma in SDHB mutation carriers were 29% and 52%, respectively, at age 60 years and 71% and 29%, respectively, in SDHD mutation carriers. Risks of malignant pheochromocytoma and renal tumors (14% at age 70 years) were higher in SDHB mutation carriers; 55 different mutations (including a novel recurrent exon 1 deletion) were identified. No clear genotype-phenotype correlations were detected for SDHB mutations. However, SDHD mutations predicted to result in loss of expression or a truncated or unstable protein were associated with a significantly increased risk of pheochromocytoma compared to missense mutations that were not predicted to impair protein stability (most such cases had the common p.Pro81Leu mutation). Analysis of the largest cohort of SDHB/D mutation carriers has enhanced estimates of penetrance and tumor risk and supports in silicon protein structure prediction analysis for functional assessment of mutations. The differing effect of the SDHD p.Pro81Leu on HNPGL and pheochromocytoma, risks suggests differing mechanisms of tumorigenesis in SDH-associated HNPGL and pheochromocytoma. Hum Mutat 31:41-51, 2010. (C) 2009 Wiley-Liss, Inc.

Nestor-Guillermo Progeria Syndrome: a biochemical insight into Barrier-to-Autointegration Factor 1, alanine 12 threonine mutation

Background: Premature aging syndromes recapitulate many aspects of natural aging and provide an insight into this phenomenon at a molecular and cellular level. The progeria syndromes appear to cause rapid aging through disruption of normal nuclear structure. Recently, a coding mutation (c.34G > A [p.A12T]) in the Barrier to Autointegration Factor 1 (BANF1) gene was identified as the genetic basis of Nestor-Guillermo Progeria syndrome (NGPS). This mutation was described to cause instability in the BANF1 protein, causing a disruption of the nuclear envelope structure.

Results: Here we demonstrate that the BANF1 A12T protein is indeed correctly folded, stable and that the observed phenotype, is likely due to the disruption of the DNA binding surface of the A12T mutant. We demonstrate, using biochemical assays, that the BANF1 A12T protein is impaired in its ability to bind DNA while its interaction with nuclear envelope proteins is unperturbed. Consistent with this, we demonstrate that ectopic expression of the mutant protein induces the NGPS cellular phenotype, while the protein localizes normally to the nuclear envelope.

Conclusions: Our study clarifies the role of the A12T mutation in NGPS patients, which will be of importance for understanding the development of the disease.

Prediction of the formation and stabilities of energetic salts and ionic liquids based on ab initio electronic structure calculations

A computational approach to predict the thermodynamics for forming a variety of imidazolium-based salts and ionic liquids from typical starting materials is described. The gas-phase proton and methyl cation acidities of several protonating and methylating agents, as well as the proton and methyl cation affinities of many important methyl-, nitro-, and cyano- substituted imidazoles, have been calculated reliably by using the computationally feasible DFT (B3LYP) and MP2 (extrapolated to the complete basis set limit) methods. These accurately calculated proton and methyl cation affinities of neutrals and anions are used in conjunction with an empirical approach based on molecular volumes to estimate the lattice enthalpies and entropies of ionic liquids, organic solids, and organic liquids. These quantities were used to construct a thermodynamic cycle for salt formation to reliably predict the ability to synthesize a variety of salts including ones with potentially high energetic densities. An adjustment of the gas phase thermodynamic cycle to account for solid- and liquid-phase chemistries provides the best overall assessment of salt formation and stability. This has been applied to imidazoles (the cation to be formed) with alkyl, nitro, and cyano substituents. The proton and methyl cation donors studied were as follows: HCl, HBr, HI, (HO)(2)SO2, HSO3CF3 (TfOH), and HSO3(C6H4)CH3 (TsOH); CH3Cl, CH3Br, CH3I, (CH3O)(2)SO2, CH3SO3CF3 (TfOCH3) and CH3SO3(C6H4)CH3 (TsOCH3). As substitution of the cation with electron-withdrawing groups increases, the triflate reagents appear to be the best overall choice as protonating and methylating agents. Even stronger alkylating agents should be considered to enhance the chances of synthetic success. When using the enthalpies of reaction for the gas-phase reactants (eq 6) to form a salt, a cutoff value of - 13 kcal mol(-1) or lower (more negative) should be used as the minimum value for predicting whether a salt can be synthesized.

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

P2Y(1) antagonists: Combining receptor-based modeling and QSAR for a quantitative prediction of the biological activity based on consensus scoring

P2Y(1) is an ADP-activated G protein-coupled receptor (GPCR). Its antagonists impede platelet aggregation in vivo and are potential antithrombotic agents. Combining ligand and structure-based modeling we generated a consensus model (LIST-CM) correlating antagonist structures with their potencies. We docked 45 antagonists into our rhodopsin-based human P2Y(1) homology model and calculated docking scores and free binding energies with the Linear Interaction Energy (LIE) method in continuum-solvent. The resulting alignment was also used to build QSAR based on CoMFA, CoMSIA, and molecular descriptors. To benefit from the strength of each technique and compensate for their limitations, we generated our LIST-CM with a PLS regression based on the predictions of each methodology. A test set featuring untested substituents was synthesized and assayed in inhibition of 2-MeSADP-stimulated PLC activity and in radioligand binding. LIST-CM outperformed internal and external predictivity of any individual model to predict accurately the potency of 75% of the test set.

Structure-Phenotype Correlations of Human CYP21A2 Mutations in Congenital Adrenal Hyperplasia

Congenital Adrenal Hyperplasia (CAH) is a family of autosomal recessive disorders involving impaired synthesis of cortisol from cholesterol by adrenal cortex. The predominant causes of the disorder are mutations in the CYP21A2 gene that encodes a Cytochrome P450 21-hydroxylase enzyme, which is central to steroidogenesis. The severity of the disease depends upon the extent of impaired enzymatic activity and can be classified under severe Classical form or the mild Non-Classical form, Molecular characterisation of CYP21A2 mutations can be used to predict clinical phenotype and disease severity based upon changes it brings in 21-hydroxylase enzyme structure. A humanized model of CYP21A2 has been used to map and investigate the structural role of all known disease-causing mutations. A structural explanation of clinical manifestation allows us to put forward criteria that might allow the prediction of clinical severity of the disease.

A complete analytic theory for structure and dynamics of populations and communities spanning wide ranges in body size

The prediction and management of ecosystem responses to global environmental change would profit from a clearer understanding of the mechanisms determining the structure and dynamics of ecological communities. The analytic theory presented here develops a causally closed picture for the mechanisms controlling community and population size structure, in particular community size spectra, and their dynamic responses to perturbations, with emphasis on marine ecosystems. Important implications are summarised in non-technical form. These include the identification of three different responses of community size spectra to size-specific pressures (of which one is the classical trophic cascade), an explanation for the observed slow recovery of fish communities from exploitation, and clarification of the mechanism controlling predation mortality rates. The theory builds on a community model that describes trophic interactions among size-structured populations and explicitly represents the full life cycles of species. An approximate time-dependent analytic solution of the model is obtained by coarse graining over maturation body sizes to obtain a simple description of the model steady state, linearising near the steady state, and then eliminating intraspecific size structure by means of the quasi-neutral approximation. The result is a convolution equation for trophic interactions among species of different maturation body sizes, which is solved analytically using a novel technique based on a multiscale expansion.

An Integration Methodology for Automated Recurring Cost Prediction using Digital Manufacturing Technology

The need to account for the effect of design decisions on manufacture and the impact of manufacturing cost on the life cycle cost of any product are well established. In this context, digital design and manufacturing solutions have to be further developed to facilitate and automate the integration of cost as one of the major driver in the product life cycle management. This article is to present an integration methodology for implementing cost estimation capability within a digital manufacturing environment. A digital manufacturing structure of knowledge databases are set out and the ontology of assembly and part costing that is consistent with the structure is provided. Although the methodology is currently used for recurring cost prediction, it can be well applied to other functional developments, such as process planning. A prototype tool is developed to integrate both assembly time cost and parts manufacturing costs within the same digital environment. An industrial example is used to validate this approach.

Structure and thermal properties of salicylate-based-protic ionic liquids as new heat storage media. COSMO-RS structure characterization and modeling of heat capacities

During this research, we present a study on the thermal properties, such as the melting, cold crystallization, and glass transition temperatures as well as heat capacities from 293.15 K to 323.15 K of nine in-house synthesized protic ionic liquids based on the 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate ([H-Im-C₁OC_n][Sal]) with n = 3–11. The 3D structures, surface charge distributions and COSMO volumes of all investigated ions are obtained by combining DFT calculations and the COSMO-RS methodology. The heat capacity data sets as a function of temperature of the 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate are then predicted using the methodology originally proposed in the case of ionic liquids by Ge et al. 3-(Alkoxymethyl)-1H-imidazol-3-ium salicylate based ionic liquids present specific heat capacities higher in many cases than other ionic liquids that make them suitable as heat storage media and in heat transfer processes. It was found experimentally that the heat capacity increases linearly with increasing alkyl chain length of the alkoxymethyl group of 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate as was expected and predicted using the Ge et al. method with an overall relative absolute deviation close to 3.2% for temperatures up to 323.15 K.

Low pressure methane solubility in lithium-ion batteries based solvents and electrolytes as a function of temperature. Measurement and prediction

The methane solubility in five pure electrolyte solvents and one binary solvent mixture for lithium ion batteries – such as ethylene carbonate (EC), propylene carbonate (PC), dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), diethyl carbonate (DEC) and the (50:50 wt%) mixture of EC:DMC was studied experimentally at pressures close to atmospheric and as a function of temperature between (280 and 343) K by using an isochoric saturation technique. The effect of the selected anions of a lithium salt LiX (X = hexafluorophosphate,

Numerical prediction of the low-velocity impact damage and compression after impact strength of composite laminates

Low-velocity impact damage can drastically reduce the residual mechanical properties of the composite structure even when there is barely visible impact damage. The ability to computationally predict the extent of damage and compression after impact (CAI) strength of a composite structure can potentially lead to the exploration of a larger design space without incurring significant development time and cost penalties. A three-dimensional damage model, to predict both low-velocity impact damage and compression after impact CAI strength of composite laminates, has been developed and implemented as a user material subroutine in the commercial finite element package, ABAQUS/Explicit. The virtual tests were executed in two steps, one to capture the impact damage and the other to predict the CAI strength. The observed intra-laminar damage features, delamination damage area as well as residual strength are discussed. It is shown that the predicted results for impact damage and CAI strength correlated well with experimental testing.

Numerical prediction of the low-velocity impact damage and compression after impact strength of composite laminates

Low-velocity impact damage can drastically reduce the residual mechanical properties of the composite structure even when there is barely visible impact damage. The ability to computationally predict the extent of damage and compression after impact (CAI) strength of a composite structure can potentially lead to the exploration of a larger design space without incurring significant development time and cost penalties. A three-dimensional damage model, to predict both low-velocity impact damage and compression after impact CAI strength of composite laminates, has been developed and implemented as a user material subroutine in the commercial finite element package, ABAQUS/Explicit. The virtual tests were executed in two steps, one to capture the impact damage and the other to predict the CAI strength. The observed intra-laminar damage features, delamination damage area as well as residual strength are discussed. It is shown that the predicted results for impact damage and CAI strength correlated well with experimental testing.

Impact of finite element idealisation on the prediction of welded fuselage stiffened panel buckling

Lap joints are widely used in the manufacture of stiffened panels and influence local panel sub-component stability, defining buckling unit dimensions and boundary conditions. Using the Finite Element method it is possible to model joints in great detail and predict panel buckling behaviour with accuracy. However, when modelling large panel structures such detailed analysis becomes computationally expensive. Moreover, the impact of local behaviour on global panel performance may reduce as the scale of the modelled structure increases. Thus this study presents coupled computational and experimental analysis, aimed at developing relationships between modelling fidelity and the size of the modelled structure, when the global static load to cause initial buckling is the required analysis output. Small, medium and large specimens representing welded lap-joined fuselage panel structure are examined. Two element types, shell and solid-shell, are employed to model each specimen, highlighting the impact of idealisation on the prediction of welded stiffened panel initial skin buckling.