Dephosphorylation of Centrins by Protein Phosphatase 2C α and β

In the present study, we identified protein phosphatases dephosphorylating centrins previously phosphorylated by protein kinase CK2. The following phosphatases known to be present in the retina were tested: PP1, PP2A, PP2B, PP2C, PP5, and alkaline phosphatase. PP2C α and β were capable of dephosphorylating P-Thr138-centrin1 most efficiently. PP2Cδ was inactive and the other retinal phosphatases also had much less or no effect. Similar results were observed for centrins 2 and 4. Centrin3 was not a substrate for CK2. The results suggest PP2C α and β to play a significant role in regulating the phosphorylation status of centrins in vivo

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods

A Framework for Efficient Process Development Using Optimal Experimental Designs

Introduction The aim of this study was to develop and demonstrate a framework assuring efficient process development using fewer experiments than standard experimental designs. Methods A novel optimality criterion for experimental designs (Iw criterion) is defined that leads to more efficient process development because: (a) prior knowledge is used in the experimental design to focus on optimal processing conditions and (b) a lean design is used which can dramatically reduce the number of experiments compared to standard designs. In this way, the criterion serves as a framework to connect a series of screening and optimization designs. Results The philosophy behind the Iw criterion is explained including a detailed step-wise discussion how to apply it in practice. Moreover, its advantages were shown in an industrial process development case using a screening and an optimization design that were not explicitly connected. In this paper, a reduction of 21% of experiments could be obtained compared to the traditional approach using standard experimental designs and no framework. Conclusions The Iw criterion is a valuable tool to increase accuracy and to speed up research that contain sets of experiments and where prior knowledge is already available or will be derived using screening designs

Improving the analysis of designed studies by combining statistical modelling with study design information

Abstract Background In the fields of life sciences, so-called designed studies are used for studying complex biological systems. The data derived from these studies comply with a study design aimed at generating relevant information while diminishing unwanted variation (noise). Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects. Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects. Results The benefit of this approach was demonstrated with an analysis that combines multivariate PLS (Partial Least Squares) regression with data decomposition from ANOVA (Analysis of Variance): ANOVA-PLS. As a case, a nutritional intervention study is used on Apoliprotein E3-Leiden (APOE3Leiden) transgenic mice to study the relation between liver lipidomics and a plasma inflammation marker, Serum Amyloid A. The ANOVA-PLS performance was compared to PLS regression on the non-decomposed data with respect to the quality of the modelled relation, model reliability, and interpretability. Conclusion It was shown that ANOVA-PLS leads to a better statistical model that is more reliable and better interpretable compared to standard PLS analysis. From a following biological interpretation, more relevant metabolites were derived from the model. The concept of combining data composition with a subsequent statistical analysis, as in ANOVA-PLS, is however not limited to PLS regression in metabolomics but can be applied for many statistical methods and many different types of data.</p