Predicting development of sustained unresponsiveness to milk oral immunotherapy using epitope-specific antibody binding profiles

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Monomethylarsonous acid inhibited endogenous cholesterol biosynthesis in human skin fibroblasts

Human exposure to arsenic in drinking water is a widespread public health concern, and such exposure is known to be associated with many human diseases. The detailed molecular mechanisms about how arsenic species contribute to the adverse human health effects, however, remain incompletely understood. Monomethylarsonous acid [MMA(III)] is a highly toxic and stable metabolite of inorganic arsenic. To exploit the mechanisms through which MMA(III) exerts its cytotoxic effect, we adopted a quantitative proteomic approach, by coupling stable isotope labeling by amino acids in cell culture (SILAC) with LC-MS/MS analysis, to examine the variation in the entire proteome of GM00637 human skin fibroblasts following acute MMA(III) exposure. Among the ~ 6500 unique proteins quantified, ~ 300 displayed significant changes in expression after exposure with 2 μM MMA(III) for 24 h. Subsequent analysis revealed the perturbation of de novo cholesterol biosynthesis, selenoprotein synthesis and Nrf2 pathways evoked by MMA(III) exposure. Particularly, MMA(III) treatment resulted in considerable down-regulation of several enzymes involved in cholesterol biosynthesis. In addition, real-time PCR analysis showed reduced mRNA levels of select genes in this pathway. Furthermore, MMA(III) exposure contributed to a distinct decline in cellular cholesterol content and significant growth inhibition of multiple cell lines, both of which could be restored by supplementation of cholesterol to the culture media. Collectively, the present study demonstrated that the cytotoxicity of MMA(III) may arise, at least in part, from the down-regulation of cholesterol biosynthesis enzymes and the resultant decrease of cellular cholesterol content.     

FDR-controlling testing procedures and sample size determination for microarrays

Microarrays are used increasingly to identify genes that are truly differentially expressed in tissues under different conditions. Planning such studies requires establishing a sample size that will ensure adequate statistical power. For microarray analyses, false discovery rate (FDR) is considered to be an appropriate error measure. Several FDR-controlling procedures have been developed. How these procedures perform for such analyses has not been evaluated thoroughly under realistic assumptions. In order to develop a method of determining sample sizes for these procedures, it needs to be established whether these procedures really control the FDR below the pre-specified level so that the determined sample size indeed provides adequate power. To answer this question, we first conducted simulation studies. Our simulation results showed that these procedures do control the FDR at most situations but under-control the FDR when the proportion of positive genes is small, the most likely scenarios. Thus, these existing procedures can overestimate the power and underestimate the sample size. Accordingly, we developed a simulation-based method to provide more accurate estimates for power and sample size.     

Formal characterization and extension of the linearized diffusion tensor model

We analyzed the properties of the logarithm of the Rician distribution leading to a full characterization of the probability law of the errors in the linearized diffusion tensor model. An almost complete lack of bias, a simple relation between the variance and the signal-to-noise ratio in the original complex data, and a close approximation to normality facilitated estimation of the tensor components by an iterative weighted least squares algorithm. The theory of the linear model has also been used to derive the distribution of mean diffusivity, to develop an informative statistical test for relative lack of fit of the ellipsoidal (or spherical) model compared to an unrestricted linear model in which no specific shape is assumed for the diffusion process, and to estimate the signal-to-noise ratios in the original data. The false discovery rate (FDR) has been used to control thresholds for statistical significance in the context of multiple comparisons at voxel level. The methods are illustrated by application to three diffusion tensor imaging (DTI) datasets of clinical interest: a healthy volunteer, a patient with acute brain injury, and a patient with hydrocephalus. Interestingly, some salient features, such as a region normally comprising the basal ganglia and internal capsule, and areas of edema in patients with brain injury and hydrocephalus, had patterns of error largely independent from their mean diffusivities. These observations were made in brain regions with sufficiently large signal-to-noise ratios (>2) to justify the assumptions of the log Rician probability model. The combination of diffusivity and its error may provide added value in diagnostic DTI of acute pathologic expansion of the extracellular fluid compartment in brain parenchymal tissue.     

On the logic of hypothesis testing in functional imaging

Statistics is nowadays the customary language of functional imaging. It is common to express an experimental setting as a set of null hypotheses over complex models and to present results as maps of p-values derived from sophisticated probability distributions. However, the growing interest in the development of advanced statistical algorithms is not always paralleled by similar attention to how these techniques may regiment the ways in which users draw inferences from their data. This article investigates the logical bases of current statistical approaches in functional imaging and probes their suitability to inductive inference in neuroscience. The frequentist approach to statistical inference is reviewed with attention to its two main constituents: Fisherian "significance testing" and Neyman-Pearson "hypothesis testing". It is shown that these conceptual systems, which are similar in the univariate testing case, dissociate into two quite different methods of inference when applied to the multiple testing problem, the typical framework of functional imaging. This difference is explained with reference to specific issues, like small volume correction, which are most likely to generate confusion in the practitioner. Further insight into this problem is achieved by recasting the multiple comparison problem into a multivariate Bayesian formulation. This formulation introduces a new perspective where the inferential process is more clearly defined in two distinct steps. The first one, inductive in form, uses exploratory techniques to acquire preliminary notions on the spatial patterns and the signal and noise characteristics. The (smaller) set of likely spatial patterns generated is then tested with newer data and a more rigorous multiple hypothesis testing technique (deductive step).     

Comparison of association methods for dense marker data

While data sets based on dense genome scans are becoming increasingly common, there are many theoretical questions that remain unanswered. How can a large number of markers in high linkage disequilibrium (LD) and rare disease variants be simulated efficiently? How should markers in high LD be analyzed: individually or jointly? Are there fast and simple methods to adjust for correlation of tests? What is the power penalty for conservative Bonferroni adjustments? Assuming that association scans are adequately powered, we attempt to answer these questions. Performance of single‐point and multipoint tests, and their hybrids, is investigated using two simulation designs. The first simulation design uses theoretically derived LD patterns. The second design uses LD patterns based on real data. For the theoretical simulations we used polychoric correlation as a measure of LD to facilitate simulation of markers in LD and rare disease variants. Based on the simulation results of the two studies, we conclude that statistical tests assuming only additive genotype effects (i.e. Armitage and especially multipoint T²) should be used cautiously due to their suboptimal power in certain settings. A false discovery rate (FDR)‐adjusted combination of tests for additive, dominant and recessive effects had close to optimal power. However, the common genotypic χ² test performed adequately and could be used in lieu of the FDR combination. While some hybrid methods yield (sometimes spectacularly) higher power they are computationally intensive. We also propose an “exact” method to adjust for multiple testing, which yields nominally higher power than the Bonferroni correction. Genet. Epidemiol. 2008. © 2008 Wiley‐Liss, Inc.     

Diuron metabolites and urothelial cytotoxicity: In vivo,in vitro and molecular approaches

Diuron is carcinogenic to the rat urinary bladder at high dietary levels. The proposed mode of action (MOA) for diuron is urothelial cytotoxicity and necrosis followed by regenerative urothelial hyperplasia. Diuron-induced urothelial cytotoxicity is not due to urinary solids. Diuron is extensively metabolized, and in rats, N-(3,4-dichlorophenyl)urea (DCPU) and 4,5-dichloro-2-hydroxyphenyl urea (2-OH-DCPU) were the predominant urinary metabolites; lesser metabolites included N-(3,4-dichlorophenyl)-3-methylurea (DCPMU) and trace levels of 3,4-dichloroaniline (DCA). In humans, DCPMU and DCPU have been found in the urine after a case of product abuse. To aid in elucidating the MOA of diuron and to evaluate the metabolites that are responsible for the diuron toxicity in the bladder epithelium, we investigated the urinary concentrations of metabolites in male Wistar rats treated with 2500 ppm of diuron, the urothelial cytotoxicity in vitro of the metabolites and their gene expression profiles. DCPU was found in rat urine at concentrations substantially greater than the in vitro IC50 and induced more gene expression alterations than the other metabolites tested. 2-OH-DCPU was present in urine at a concentration approximately half of the in vitro IC50, whereas DCPMU and DCA were present in urine at concentrations well below the IC50. For the diuron-induced MOA for the rat bladder, we suggest that DCPU is the primary metabolite responsible for the urothelial cytotoxicity with some contribution also by 2-OH-DCPU. This study supports a MOA for diuron-induced bladder effects in rats consisting of metabolism to DCPU (and 2-OH-DCPU to a lesser extent), concentration and excretion in urine, urothelial cytotoxicity, and regenerative proliferation.