Statistics in Gene Expression, Metabolomics, and Comparative Genomics in Evolution
Doktorsavhandling, 2010

This thesis contains four papers concerning (I) the evolutionary conservation of drug targets and its potential use in environmental risk assessments, (II) RNA degradation as a control mechanism during osmotic stress in the yeast S. cerevisiae, (III) the localization and effects of the gene DDIT3 encoding a key regulator of stress response, and (IV) the integration and analysis of transcriptional and metabolic data to identify active metabolic pathways. Environmental risk assessments are needed for the approval of new pharmaceutical compounds. To date, the risk assessments have mainly been focused on organisms like algae and Daphnia. The conservation of drug targets in species relevant for ecotoxicity testing is a key aspect in developing more targeted test strategies on higher organisms like fish or amphibians. With information on predicted proteomes for a wide range of species it is possible to extract data on evolutionary conservation for drug targets. In paper I, orthology data is compiled and analyzed for a set of human drug targets in several species, and the result evaluated based on an extensive literature search. mRNA degradation can be investigated on a genome-wide scale with the use of a transcriptional inhibitor and subsequent hybridization of RNA pools, isolated at a set of time-points, to microarrays. Due to the complexity of the microarray methodology in this context, the data are in need of processing and transformation to deduce relevant information on changes in degradation rates. In paper II, mRNA degradation is investigated as a post-transcriptional control effect in connection to hyperosmotic stress. We conclude that mRNA degradation mechanisms are important regulatory keys in the stress response. The gene DDIT3 encodes a protein acting as a regulator of the stress response within human cells. For example DNA damage, hypoxia, and starvation are stress types inducing DDIT3 transcription. DDIT3 is a transcription factor and has mainly been reported as a nuclear protein. In paper III, the effects and target genes of DDIT3 are investigated using techniques like microarrays, RT-qPCR, and various bioinformatical and statistical methods. We report that DDIT3 also can be localized to the cytoplasm, and induces or represses different genes compared to the nuclear form. The cytoplasmic form of DDIT3 is involved in migration, and inhibits the migratory effects of fibrosarcoma cells. The development of different 'omics' technologies in molecular biology has resulted in several methods to characterize cells and tissues, for example microarrays to characterize the transcriptome (collection of gene transcripts) and spectrometry techniques like NMR to describe the metabolome (collection of small molecules). Interpretation of different 'omics' data is usually done separately, and often with respect to pathways, which are sets of reactions involving genes, metabolites, and proteins. A common research question is to deduce which pathways are active (regulated) when comparing two or several conditions. In paper IV, we propose a model to make such pathway level decisions by integrating transcriptomic and metabolomic data.

Gene Expression


Data Integration

Comparative genomics

Opponent: Patrik Rydén, Department of Mathematics and Mathematical Statistics , Umeå University, Sweden


Alexandra Jauhiainen

Göteborgs universitet

Chalmers, Matematiska vetenskaper

Evolutionary conservation of human drug targets in organisms used for environmental risk assessments.

Environmental Science & Technology,; Vol. 42(2008)p. 5807-13

Artikel i vetenskaplig tidskrift

mRNA stability changes precede changes in steady-state mRNA amounts during hyperosmotic stress

RNA,; Vol. 15(2009)p. 600-614

Artikel i vetenskaplig tidskrift


Biokemi och molekylärbiologi

Bioinformatik och systembiologi

Sannolikhetsteori och statistik



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 3100


Opponent: Patrik Rydén, Department of Mathematics and Mathematical Statistics , Umeå University, Sweden

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