A high-density screen for linkage in multiple sclerosis

To provide a definitive linkage map for multiple sclerosis, we have genotyped the Illumina BeadArray linkage mapping panel (version 4) in a data set of 730 multiplex families of Northern European descent. After the application of stringent quality thresholds, data from 4,506 markers in 2,692 individuals were included in the analysis. Multipoint nonparametric linkage analysis revealed highly significant linkage in the major histocompatibility complex (MHC) on chromosome 6p21 (maximum LOD score [MLS] 11.66) and suggestive linkage on chromosomes 17q23 (MLS 2.45) and 5q33 (MLS 2.18). This set of markers achieved a mean information extraction of 79.3% across the genome, with a Mendelian inconsistency rate of only 0.002%. Stratification based on carriage of the multiple sclerosis–associated DRB1*1501 allele failed to identify any other region of linkage with genomewide significance. However, ordered-subset analysis suggested that there may be an additional locus on chromosome 19p13 that acts independent of the main MHC locus. These data illustrate the substantial increase in power that can be achieved with use of the latest tools emerging from the Human Genome Project and indicate that future attempts to systematically identify susceptibility genes for multiple sclerosis will have to involve large sample sizes and an association-based methodology.     

Current perspectives on cartilage and chondrocyte mechanobiology

It is well known that physiological forces are essential for the maintenance of normal composition and structure of articular cartilage. Although some of the mechanisms of mechanotransduction are known today, there are certainly many others left unrevealed. In order to understand the complicated systems present in articular cartilage, we have to bring together the data from all fields of cartilage mechanobiology. The 3rd Symposium on Mechanobiology of Cartilage and Chondrocyte was a good effort towards that goal.     

Mechano-acoustic determination of Young's modulus of articular cartilage

The compressive stiffness of an elastic material is traditionally characterized by its Young's modulus. Young's modulus of articular cartilage can be directly measured using unconfined compression geometry by assuming the cartilage to be homogeneous and isotropic. In isotropic materials, Young's modulus can also be determined acoustically by the measurement of sound speed and density of the material. In the present study, acoustic and mechanical techniques, feasible for in vivo measurements, were investigated to quantify the static and dynamic compressive stiffness of bovine articular cartilage in situ. Ultrasound reflection from the cartilage surface, as well as the dynamic modulus were determined with the recently developed ultrasound indentation instrument and compared with the reference mechanical and ultrasound speed measurements in unconfined compression (n=72). In addition, the applicability of manual creep measurements with the ultrasound indentation instrument was evaluated both experimentally and numerically. Our experimental results indicated that the sound speed could predict 47% and 53% of the variation in the Young's modulus and dynamic modulus of cartilage, respectively. The dynamic modulus, as determined manually with the ultrasound indentation instrument, showed significant linear correlations with the reference Young's modulus (r(2)=0.445, p<0.01, n=70) and dynamic modulus (r(2)=0.779, p<0.01, n=70) of the cartilage. Numerical analyses indicated that the creep measurements, conducted manually with the ultrasound indentation instrument, were sensitive to changes in Young's modulus and permeability of the tissue, and were significantly influenced by the tissue thickness. We conclude that acoustic parameters, i.e. ultrasound speed and reflection, are indicative to the intrinsic mechanical properties of the articular cartilage. Ultrasound indentation instrument, when further developed, provides an applicable tool for the in vivo detection of cartilage mechano-acoustic properties. These techniques could promote the diagnostics of osteoarthrosis.     

Problem formulation in the environmental risk assessment for genetically modified plants

Problem formulation is the first step in environmental risk assessment (ERA) where policy goals, scope, assessment endpoints, and methodology are distilled to an explicitly stated problem and approach for analysis. The consistency and utility of ERAs for genetically modified (GM) plants can be improved through rigorous problem formulation (PF), producing an analysis plan that describes relevant exposure scenarios and the potential consequences of these scenarios. A properly executed PF assures the relevance of ERA outcomes for decision-making. Adopting a harmonized approach to problem formulation should bring about greater uniformity in the ERA process for GM plants among regulatory regimes globally. This paper is the product of an international expert group convened by the International Life Sciences Institute (ILSI) Research Foundation.     

On the mechanism of accumulation of cholestanol in the brain of mice with a disruption of sterol 27-hydroxylase

The rare disease cerebrotendinous xanthomatosis (CTX) is due to a lack of sterol 27-hydroxylase (CYP27A1) and is characterized by cholestanol-containing xanthomas in brain and tendons. Mice with the same defect do not develop xanthomas. The driving force in the development of the xanthomas is likely to be conversion of a bile acid precursor into cholestanol. The mechanism behind the xanthomas in the brain has not been clarified. We demonstrate here that female cyp27a1^−/− mice have an increase of cholestanol of about 2.5- fold in plasma, 6-fold in tendons, and 12-fold in brain. Treatment of cyp27a1^−/− mice with 0.05% cholic acid normalized the cholestanol levels in tendons and plasma and reduced the content in the brain. The above changes occurred in parallel with changes in plasma levels of 7α-hydroxy-4-cholesten-3-one, a precursor both to bile acids and cholestanol. Injection of a cyp27a1^−/− mouse with ²H₇-labeled 7α-hydroxy-4-cholesten-3-one resulted in a significant incorporation of ²H₇-cholestanol in the brain. The results are consistent with a concentration-dependent flux of 7α-hydroxy-4-cholesten-3-one across the blood-brain barrier in cyp27a1^−/− mice and subsequent formation of cholestanol. It is suggested that the same mechanism is responsible for accumulation of cholestanol in the brain of patients with CTX.     

Extended Bayesian LASSO for Multiple Quantitative Trait Loci Mapping and Unobserved Phenotype Prediction

The Bayesian LASSO (BL) has been pointed out to be an effective approach to sparse model representation and successfully applied to quantitative trait loci (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. However, the BL relies on a single parameter known as the regularization parameter to simultaneously control the overall model sparsity and the shrinkage of individual covariate effects. This may be idealistic when dealing with a large number of predictors whose effect sizes may differ by orders of magnitude. Here we propose the extended Bayesian LASSO (EBL) for QTL mapping and unobserved phenotype prediction, which introduces an additional level to the hierarchical specification of the BL to explicitly separate out these two model features. Compared to the adaptiveness of the BL, the EBL is “doubly adaptive” and thus, more robust to tuning. In simulations, the EBL outperformed the BL in regard to the accuracy of both effect size estimates and phenotypic value predictions, with comparable computational time. Moreover, the EBL proved to be less sensitive to tuning than the related Bayesian adaptive LASSO (BAL), which introduces locus-specific regularization parameters as well, but involves no mechanism for distinguishing between model sparsity and parameter shrinkage. Consequently, the EBL seems to point to a new direction for QTL mapping, phenotype prediction, and GBV estimation.REGULARIZATION or shrinkage methods are gaining increasing recognition as a valuable alternative to variable selection techniques in dealing with oversaturated or otherwise ill-defined regression problems in both the classical and Bayesian frameworks (e.g., O''hara and Sillanpää 2009). Many studies (e.g., Xu 2003; Wang et al. 2005; Zhang and Xu 2005; De los Campos et al. 2009; Usai et al. 2009; Wu et al. 2009; Xu et al. 2009) have documented the potential of shrinkage methods for quantitative trait locus (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. Lee et al. (2008) make a clear connection between phenotype prediction and GBV estimation, suggesting that methods developed for one are also applicable to the other. We thus use the two concepts interchangeably throughout this article.Regularized regression methods, such as ridge regression (Hoerl and Kennard 1970) or the least absolute shrinkage and selection operator (LASSO) (Tibshirani 1996), are essentially penalized likelihood procedures, where suitable penalty functions are added to the negative log-likelihood to automatically shrink spurious effects (effects of redundant covariates) toward zero, while allowing relevant effects to take values farther from zero.It has been pointed out that these non-Bayesian shrinkage methods are not suitable for oversaturated models. Zou and Hastie (2005) and Park and Casella (2008) noted that the LASSO cannot select a number of nonzero effects exceeding the sample size. Xu (2003) found that for ridge regression to work, the number of model effects should be in the same order as the number of observations. This is impractical for genomic selection, which capitalizes on the variation due to small-marker effects, the number of which can exceed the sample size, by contrast to QTL mapping where interest lies mostly in a small subset of loci with large effects on the focal phenotype. In connection with the LASSO, the Bayesian LASSO (BL) (Park and Casella 2008; Yi and Xu 2008) has been proposed to overcome this limitation by imposing a selective shrinkage across regression parameters. Xu (2003) also proposed a Bayesian shrinkage method for QTL mapping, which extends ridge regression in a similar fashion.Although the BL has been successfully applied to QTL mapping (e.g., Yi and Xu 2008) and to GBV estimation (e.g., De los Campos et al. 2009), it relies on a single parameter known as the regularization parameter to simultaneously regulate the overall model sparsity and the extent to which individual regression coefficients are shrunken. However, this is unrealistic when dealing with a large number of predictors whose effect sizes may differ by orders of magnitude. It is therefore natural to ask whether this practice can be relaxed and how such an attempt may impinge on the model performance (e.g., Sun et al. 2010).Here we propose an extension to the Bayesian LASSO for QTL mapping and unobserved phenotype prediction. Our method, the extended Bayesian LASSO (EBL), introduces locus-specific regularization parameters and utilizes a parameterization that clearly separates the overall model sparsity from the degree of shrinkage of individual regression parameters. We use simulated data to investigate the performance of the EBL relative to the Bayesian LASSO in mapping QTL and in predicting unobserved phenotypes. We also compare the performance of the EBL to the Bayesian adaptive LASSO (BAL) recently proposed by Sun et al. (2010), which also assumes locus-specific regularization parameters.     

Interaction between mammalian glyceraldehyde-3-phosphate dehydrogenase and L-lactate dehydrogenase from heart and muscle

The exceptionally high protein concentration in living cells can favor functional protein-protein interactions that can be difficult to detect with purified proteins. In this study we describe specific interactions between mammalian D-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and L-lactate dehydrogenase (LDH) isozymes from heart and muscle. We use poly(ethylene-glycol) (PEG)-induced coprecipitation and native agarose electrophoresis as two independent methods uniquely suited to mimic some of the conditions that can favor protein-protein interaction in living cells. We found that GAPDH interacts with heart or muscle isozymes of LDH with approximately one-to-one stoichiometry. The interaction is specific; GAPDH shows interaction with two LDH isozymes that have very different net charge and solubility in PEG solution, while no interaction is observed with GAPDH from other species, other NAD(H) dehydrogenases, or other proteins that have very similar net charge and molecular mass. Analytical ultracentrifugation showed that the LDH and GAPDH complex is insoluble in PEG solution. The interaction is abolished by saturation with NADH, but not by saturation with NAD(+) in correlation with GAPDH solubility in PEG solution. The crystal structures show that GAPDH and LDH isozymes share complementary size, shape, and electric potential surrounding the active sites. The presented results suggest that GAPDH and LDH have a functional interaction that can affect NAD(+)/NADH metabolism and glycolysis in living cells.