Biomedical subjects

Ingrid Lönnstedt

Publications and source records attributed to Ingrid Lönnstedt.

4 recordsLinked to original sources

PubMed2006

Normalization and expression changes in predefined sets of proteins using 2D gel electrophoresis: a proteomic study of L-DOPA induced dyskinesia in an animal model of Parkinson's disease using DIGE.

BACKGROUND: Two-Dimensional Difference In Gel Electrophoresis (2D-DIGE) is a powerful tool for measuring differences in protein expression between samples or conditions. However, to remove systematic variability within and between gels the data has to be normalized. In this study we examined the ability of four existing and four novel normalization methods to remove systematic bias in data produced with 2D-DIGE. We also propose a modification of an existing method where the statistical framework determines whether a set of proteins shows an association with the predefined phenotypes of interest. This method was applied to our data generated from a monkey model (Macaca fascicularis) of Parkinson's disease. RESULTS: Using 2D-DIGE we analysed the protein content of the striatum from 6 control and 21 MPTP-treated monkeys, with or without de novo or long-term L-DOPA administration. There was an intensity and spatial bias in the data of all the gels examined in this study. Only two of the eight normalization methods evaluated ('2D loess+scale' and 'SC-2D+quantile') successfully removed both the intensity and spatial bias. In 'SC-2D+quantile' we extended the commonly used loess normalization method against dye bias in two-channel microarray systems to suit systems with three or more channels.Further, by using the proposed method, Differential Expression in Predefined Proteins Sets (DEPPS), several sets of proteins associated with the priming effects of L-DOPA in the striatum in parkinsonian animals were identified. Three of these sets are proteins involved in energy metabolism and one set involved proteins which are part of the microtubule cytoskeleton. CONCLUSION: Comparison of the different methods leads to a series of methodological recommendations for the normalization and the analysis of data, depending on the experimental design. Due to the nature of 2D-DIGE data we recommend that the p-values obtained in significance tests should be used as rankings only. Individual proteins may be interesting as such, but by studying sets of proteins the interpretation of the results are probably more accurate and biologically informative.

Animals↗

PubMed2005

Empirical bayes microarray ANOVA and grouping cell lines by equal expression levels.

In the exploding field of gene expression techniques such as DNA microarrays, there are still few general probabilistic methods for analysis of variance. Linear models and ANOVA are heavily used tools in many other disciplines of scientific research. The usual F-statistic is unsatisfactory for microarray data, which explore many thousand genes in parallel, with few replicates. We present three potential one-way ANOVA statistics in a parametric statistical framework. The aim is to separate genes that are differently regulated across several treatment conditions from those with equal regulation. The statistics have different features and are evaluated using both real and simulated data. Our statistic B1 generally shows the best performance, and is extended for use in an algorithm that groups cell lines by equal expression levels for each gene. An extension is also outlined for more general ANOVA tests including several factors. The methods presented are implemented in the freely available statistical language R. They are available at http://www.math.uu.se/staff/pages/?uname=ingrid.

Journal Article↗

PubMed2005

Hierarchical Bayes models for cDNA microarray gene expression.

cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000-20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.

Animals↗

PubMed2004

Growth hormone-mediated alteration of fuel metabolism in the aged rat as determined from transcript profiles.

Age-related changes in body composition and serum lipids resemble symptoms of adult-onset growth hormone (GH) deficiency. GH treatment has been shown to normalize these changes in both GH-deficient adult patients and elderly subjects. The aim of this study was to identify GH-responsive genes that might mediate positive effects of GH treatment on fuel metabolism and body composition. cDNA microarrays were used to analyze age- and GH-induced changes in gene expression patterns in male rats. Tissues analyzed were liver, adipose tissue, and skeletal muscle from animals on or off GH treatment. A value of 1.5 was chosen to denote differences (increased or decreased expression) in the level of mRNA expression. In the liver, 7.3% of the expressed genes were affected by age and 6.5% by GH. Similar values for the other tissues were 8.3% and 5.3% (fat), and 7.9% and 9.6% (muscle), respectively. Among the differentially expressed genes, we identified several that encode proteins involved in fuel metabolism. Old rats were shown to have induced expression of genes involved in hepatic glucose oxidation and lipid synthesis, whereas these pathways were reduced in adipose tissue. GH treatment induced the expression of genes for lipid oxidation in liver and for glucose oxidation in skeletal muscle. In adipose tissue, GH reduced the expression of genes involved in lipogenesis even further. Changes in transcript levels were reflected in serum in terms of altered lipid profiles. Serum levels of triglycerides, high-density lipoprotein (HDL) cholesterol, and total cholesterol were higher in the old animals than in the young and normalized by GH treatment.

Adipose Tissue↗