Database of two-dimensional polyacrylamide gel electrophoresis of proteins labeled with CyDye DIGE Fluor saturation dye

CyDye DIGE Fluor saturation dye (saturation dye, GE Healthcare Amersham Biosciences) enables highly sensitive 2-D PAGE. As the dye reacts with all reduced cysteine thiols, 2-D PAGE can be performed with a lower amount of protein, compared with CyDye DIGE Fluor minimal dye (GE Healthcare Amersham Biosciences), the sensitivity of which is equivalent to that of silver staining. We constructed a 2-D map of the saturation dye-labeled proteins of a liver cancer cell line (HepG2) and identified by MS 92 proteins corresponding to 123 protein spots. Functional classification revealed that the identified proteins had chaperone, protein binding, nucleotide binding, metal ion binding, isomerase activity, and motor activity. The functional distribution and the cysteine contents of the proteins were similar to those in the most comprehensive 2-D database of hepatoma cells (Seow et al.., Electrophoresis 2000, 21, 1787-1813), where silver staining was used for protein visualization. Hierarchical clustering on the basis of the quantitative expression profiles of the 123 characterized spots labeled with two charge- and mass-matched saturation dyes (Cy3 and Cy5) discriminated between nine hepatocellular carcinoma cell lines and primary cultured hepatocytes from five individuals, suggesting the utility of saturation dye and our database for proteomic studies of liver cancer.     

Meeting Report of the 2nd International Workshop on 2‐D DIGE Applications in Proteomics

The second international workshop on “2‐D DIGE Applications in Proteomics” took place at the Medizinisches Proteom‐Center, Ruhr‐Universität Bochum, from February 27^th to March 2^nd, 2007. The four day “hands‐on” course was addressed to all interested scientists from the field of Proteomics, inter alia to members of HUPO and DGPF, with a greater focus on image analysis and statistical analysis of 2‐D DIGE experiments.     

利用线性混合效应模型模拟杉木人工林枝条生物量

基于福建省将乐林场45株人工杉木解析木的572组枝条生物量数据,采用线性混合效应模型方法,建立杉木人工林枝条总生物量和枝、叶生物量的预测模型,并利用独立样本数据对模型进行检验.结果表明： 线性混合效应模型比传统多元线性回归模型的拟合精度高.不同随机效应参数的组合,其混合模型的精度不同.考虑异方差结构的混合模型能够消除数据间的异方差性,其精度更高,其中,对于枝条总生物量和叶生物量模型,以指数函数作为异方差结构时的模型精度最高;对于枝生物量模型,以常数加幂函数作为异方差结构时的模型精度最高.模型检验结果表明：对于杉木人工林枝条生物量预测模型,考虑随机效应和异方差结构的线性混合模型的检验精度比传统多元线性回归模型的精度有明显提高.     

Logistic regression when covariates are random effects from a non‐linear mixed model

In many studies, the association of longitudinal measurements of a continuous response and a binary outcome are often of interest. A convenient framework for this type of problems is the joint model, which is formulated to investigate the association between a binary outcome and features of longitudinal measurements through a common set of latent random effects. The joint model, which is the focus of this article, is a logistic regression model with covariates defined as the individual‐specific random effects in a non‐linear mixed‐effects model (NLMEM) for the longitudinal measurements. We discuss different estimation procedures, which include two‐stage, best linear unbiased predictors, and various numerical integration techniques. The proposed methods are illustrated using a real data set where the objective is to study the association between longitudinal hormone levels and the pregnancy outcome in a group of young women. The numerical performance of the estimating methods is also evaluated by means of simulation.     

Identification by 2-D DIGE of apoplastic proteins regulated by oligogalacturonides in Arabidopsis thaliana

Oligogalacturonides (OGs) are elicitors of plant defence responses released from the homogalacturonan of the plant cell wall during the attack by pathogenic micro-organisms. The signalling pathway mediated by OGs remains poorly understood, and no proteins involved in their signal perception and transduction have yet been identified. In order to shed light into the molecular pathways regulated by OGs, a differential proteomic analysis has been carried out in Arabidopsis. Proteins from the apoplastic compartment were isolated and their expression compared between control and OG-treated seedlings. 2-D gels and difference in gel electrophoresis (DIGE) techniques were used to compare control and treated proteomes in the same gel. The analysis of subcellular proteomes from seedlings allowed the identification of novel and low abundance proteins that otherwise remain masked when total cellular extracts are investigated. The DIGE technique showed to be a powerful tool to overcome the high interexperiment variation of 2-D gels. Differentially expressed apoplastic proteins were identified by MS and included proteins putatively involved in recognition as well as proteins whose PTMs are regulated by OGs. Our findings underscore the importance of cell wall as a source of molecules playing a role in the perception of pathogens and provide candidate proteins involved in the response to OGs.     

Predictive cross-validation for the choice of linear mixed-effects models with application to data from the Swiss HIV Cohort Study

Model choice in linear mixed-effects models for longitudinal data is a challenging task. Apart from the selection of covariates, also the choice of the random effects and the residual correlation structure should be possible. Application of classical model choice criteria such as Akaike information criterion (AIC) or Bayesian information criterion is not obvious, and many versions do exist. In this article, a predictive cross-validation approach to model choice is proposed based on the logarithmic and the continuous ranked probability score. In contrast to full cross-validation, the model has to be fitted only once, which enables fast computations, even for large data sets. Relationships to the recently proposed conditional AIC are discussed. The methodology is applied to search for the best model to predict the course of CD4+ counts using data obtained from the Swiss HIV Cohort Study.     

An estimation method for the semiparametric mixed effects model

A semiparametric mixed effects regression model is proposed for the analysis of clustered or longitudinal data with continuous, ordinal, or binary outcome. The common assumption of Gaussian random effects is relaxed by using a predictive recursion method (Newton and Zhang, 1999) to provide a nonparametric smooth density estimate. A new strategy is introduced to accelerate the algorithm. Parameter estimates are obtained by maximizing the marginal profile likelihood by Powell's conjugate direction search method. Monte Carlo results are presented to show that the method can improve the mean squared error of the fixed effects estimators when the random effects distribution is not Gaussian. The usefulness of visualizing the random effects density itself is illustrated in the analysis of data from the Wisconsin Sleep Survey. The proposed estimation procedure is computationally feasible for quite large data sets.     

Some alternatives to asymptotic tests for the analysis of pharmacogenetic data using nonlinear mixed effects models

Nonlinear mixed effects models allow investigating individual differences in drug concentration profiles (pharmacokinetics) and responses. Pharmacogenetics focuses on the genetic component of this variability. Two tests often used to detect a gene effect on a pharmacokinetic parameter are (1) the Wald test, assessing whether estimates for the gene effect are significantly different from 0 and (2) the likelihood ratio test comparing models with and without the genetic effect. Because those asymptotic tests show inflated type I error on small sample size and/or with unevenly distributed genotypes, we develop two alternatives and evaluate them by means of a simulation study. First, we assess the performance of the permutation test using the Wald and the likelihood ratio statistics. Second, for the Wald test we propose the use of the F-distribution with four different values for the denominator degrees of freedom. We also explore the influence of the estimation algorithm using both the first-order conditional estimation with interaction linearization-based algorithm and the stochastic approximation expectation maximization algorithm. We apply these methods to the analysis of the pharmacogenetics of indinavir in HIV patients recruited in the COPHAR2-ANRS 111 trial. Results of the simulation study show that the permutation test seems appropriate but at the cost of an additional computational burden. One of the four F-distribution-based approaches provides a correct type I error estimate for the Wald test and should be further investigated.     

Testing random effects in the linear mixed model using approximate bayes factors

Summary .  Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance and introduce a parameter that controls the relative contribution of each random effect free of the scale of the data. We integrate out the random effects and the variance components using closed-form solutions. The resulting integrals needed to calculate the Bayes factor are low-dimensional integrals lacking variance components and can be efficiently approximated with Laplace's method. We propose a default prior distribution on the parameter controlling the contribution of each random effect and conduct simulations to show that our method has good properties for model selection problems. Finally, we illustrate our methods on data from a clinical trial of patients with bipolar disorder and on data from an environmental study of water disinfection by-products and male reproductive outcomes.     

Robust linear mixed models using the skew t distribution with application to schizophrenia data

We consider an extension of linear mixed models by assuming a multivariate skew t distribution for the random effects and a multivariate t distribution for the error terms. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously among continuous longitudinal data. We present an efficient alternating expectation‐conditional maximization (AECM) algorithm for the computation of maximum likelihood estimates of parameters on the basis of two convenient hierarchical formulations. The techniques for the prediction of random effects and intermittent missing values under this model are also investigated. Our methodologies are illustrated through an application to schizophrenia data.     

Linear mixed models with flexible distributions of random effects for longitudinal data

Normality of random effects is a routine assumption for the linear mixed model, but it may be unrealistic, obscuring important features of among-individual variation. We relax this assumption by approximating the random effects density by the seminonparameteric (SNP) representation of Gallant and Nychka (1987, Econometrics 55, 363-390), which includes normality as a special case and provides flexibility in capturing a broad range of nonnormal behavior, controlled by a user-chosen tuning parameter. An advantage is that the marginal likelihood may be expressed in closed form, so inference may be carried out using standard optimization techniques. We demonstrate that standard information criteria may be used to choose the tuning parameter and detect departures from normality, and we illustrate the approach via simulation and using longitudinal data from the Framingham study.     

High performance CCD camera system for digitalisation of 2D DIGE gels

An essential step in 2D DIGE‐based analysis of differential proteome profiles is the accurate and sensitive digitalisation of 2D DIGE gels. The performance progress of commercially available charge‐coupled device (CCD) camera‐based systems combined with light emitting diodes (LED) opens up a new possibility for this type of digitalisation. Here, we assessed the performance of a CCD camera system (Intas Advanced 2D Imager) as alternative to a traditionally employed, high‐end laser scanner system (Typhoon 9400) for digitalisation of differential protein profiles from three different environmental bacteria. Overall, the performance of the CCD camera system was comparable to the laser scanner, as evident from very similar protein abundance changes (irrespective of spot position and volume), as well as from linear range and limit of detection.     

Analysis of left-censored longitudinal data with application to viral load in HIV infection

The classical model for the analysis of progression of markers in HIV-infected patients is the mixed effects linear model. However, longitudinal studies of viral load are complicated by left censoring of the measures due to a lower quantification limit. We propose a full likelihood approach to estimate parameters from the linear mixed effects model for left-censored Gaussian data. For each subject, the contribution to the likelihood is the product of the density for the vector of the completely observed outcome and of the conditional distribution function of the vector of the censored outcome, given the observed outcomes. Values of the distribution function were computed by numerical integration. The maximization is performed by a combination of the Simplex algorithm and the Marquardt algorithm. Subject-specific deviations and random effects are estimated by modified empirical Bayes replacing censored measures by their conditional expectations given the data. A simulation study showed that the proposed estimators are less biased than those obtained by imputing the quantification limit to censored data. Moreover, for models with complex covariance structures, they are less biased than Monte Carlo expectation maximization (MCEM) estimators developed by Hughes (1999) Mixed effects models with censored data with application to HIV RNA Levels. Biometrics 55, 625-629. The method was then applied to the data of the ALBI-ANRS 070 clinical trial for which HIV-1 RNA levels were measured with an ultrasensitive assay (quantification limit 50 copies/ml). Using the proposed method, estimates obtained with data artificially censored at 500 copies/ml were close to those obtained with the real data set.     

Use of 2-D DIGE analysis reveals altered phosphorylation in a tropomyosin mutant (Glu54Lys) linked to dilated cardiomyopathy

Current electrophoretic methods have not been optimized to fully separate post-translationally modified mutant forms of tropomyosin (Tm) from wild-type cardiac samples. We describe here a method employing a modified 2-D PAGE/2-D DIGE protocol, to fully separate native, mutant (E54K), and phosphorylated forms of Tm. Our data demonstrate the first evidence of a significant (approximately 40%) decrease in Tm phosphorylation in transgenic compared to non-transgenic mouse hearts, and indicate that altered phosphorylation may be a significant factor in the linkage of the E54K mutation to dilated cardiomyopathy.     

2‐D DIGE analysis of the proteome of extracts from peanut variants reveals striking differences in major allergen contents

Over the last decade, an increasing prevalence of peanut allergies was observed worldwide. Peanuts are meanwhile categorized among the most dangerous food allergens. This is particularly relevant since peanut‐derived ingredients are widely used in industrial food production. To minimize the problem of hidden food allergens causing severe anaphylactic reactions, pre‐packaged food containing peanut components needs to be classified according to European ruling since 2005. Food companies search for strategies to reduce the allergenicity of peanut‐derived food additives either by genetically altering the allergen content or by identifying peanut varieties with low levels of major allergens. In our study, we focused on peanut extracts from Indonesia that apparently contain lower levels of the major Arachis hypogaea allergen 1 (Ara h 1). Basic extracts of Virginia‐type and Indonesian peanuts were compared by 1‐ and 2‐DE. We identified more than hundred individual components in these extracts by MS and provide a high‐resolution allergen map that also includes so far unknown fragments of major peanut allergens. The reduced level of Ara h 1 associated with a significantly lower abundance of the most potent peanut allergen Ara h 2 in various Indonesian peanuts was also confirmed by Western blotting with monoclonal antibodies and sera of allergic patients.     

Identification of proteins differentially expressed between capillary endothelial cells of hepatocellular carcinoma and normal liver in an orthotopic rat tumor model using 2‐D DIGE

Hepatocellular carcinoma (HCC) is one of the deadliest cancers with few treatment options. It is a hypervascular tumor in which angiogenesis plays a critical role in its progression. Tumor capillary endothelial cells (TECs) in HCC are known to originate from liver sinusoid endothelial cells, which then go through a capillarization process to become morphologically as well as functionally different TECs. In this work, we investigated proteins differentially expressed between freshly isolated TECs and sinusoid endothelial cells from well‐formed rat HCC using 2‐D DIGE coupled with MALDI‐TOF/TOF MS. Thirty‐eight unique proteins were identified to be differentially expressed more than twofold between the two endothelial cell types. Amongst the differentially expressed proteins, two novel endothelial markers, EH domain‐containing protein 3 and galectin‐3, were confirmed by Western blot and immunohistochemistry in both rat and human HCC samples. We showed that EH domain‐containing protein 3 is significantly down‐regulated in TECs, but galectin‐3 is up‐regulated. We propose possible roles of these two proteins in tumor vessel development in HCC.     

Estimating data transformations in nonlinear mixed effects models

A routine practice in the analysis of repeated measurement data is to represent individual responses by a mixed effects model on some transformed scale. For example, for pharmacokinetic, growth, and other data, both the response and the regression model are typically transformed to achieve approximate within-individual normality and constant variance on the new scale; however, the choice of transformation is often made subjectively or by default, with adoption of a standard choice such as the log. We propose a mixed effects framework based on the transform-both-sides model, where the transformation is represented by a monotone parametric function and is estimated from the data. For this model, we describe a practical fitting strategy based on approximation of the marginal likelihood. Inference is complicated by the fact that estimation of the transformation requires modification of the usual standard errors for estimators of fixed effects; however, we show that, under conditions relevant to common applications, this complication is asymptotically negligible, allowing straightforward implementation via standard software.     

Application of 2-D DIGE to formalin-fixed, paraffin-embedded tissues

The ability to investigate the proteome of formalin-fixed, paraffin-embedded (FFPE) tissues can be considered a major recent achievement in the field of clinical proteomics. However, gel-based approaches to the investigation of FFPE tissue proteomes have lagged behind, mainly because of insufficient quality of full-length protein extracts. Here, the 2-D DIGE technology was investigated for applicability to FFPE proteins, for internal reproducibility among replicate FFPE extracts, and for comparability between FFPE and fresh-frozen tissue profiles. The 2-D DIGE patterns obtained upon labeling and electrophoresis of replicate FFPE tissue extracts were highly reproducible, with satisfactory resolution and complexity. Moreover, the implementation of DIGE enabled to highlight and characterize the consistent differences found in the FFPE profiles compared with fresh-frozen profiles, represented by an acidic shift, directly correlated to the protein pI value, and by a reduction in spot signal intensity, directly correlated to molecular weight and percentage of lysine residues. Being constantly and reproducibly present in all FFPE tissue extract replicates at similar extents, these modifications do not appear to hinder the comparative analysis of FFPE tissue extracts by 2-D DIGE, opening the way to its application for the differential proteomic investigation of archival tissue repositories.     

Application of 2-D DIGE to survey the quality of biological medicines

2-D DIGE was used to investigate 'fingerprint proteins' in biological medicines. A presumably non-originator human albumin was analysed, and the 2-D DIGE patterns of the non-genuine and the authentic product were compared. The products could be clearly distinguished based on the pattern of minor components, which represent plasma proteins and degradation products remaining in the final products after fractionation and purification. The approach demonstrated that 2-D DIGE is an excellent tool for the analysis of biologicals of different sources and for ensuring the identity and quality of blood products.