Biomedical subjects

Morten Krogh

Publications and source records attributed to Morten Krogh.

4 recordsLinked to original sources

PubMed2005

Folding free energies of 5'-UTRs impact post-transcriptional regulation on a genomic scale in yeast.

Using high-throughput technologies, abundances and other features of genes and proteins have been measured on a genome-wide scale in Saccharomyces cerevisiae. In contrast, secondary structure in 5'-untranslated regions (UTRs) of mRNA has only been investigated for a limited number of genes. Here, the aim is to study genome-wide regulatory effects of mRNA 5'-UTR folding free energies. We performed computations of secondary structures in 5'-UTRs and their folding free energies for all verified genes in S. cerevisiae. We found significant correlations between folding free energies of 5'-UTRs and various transcript features measured in genome-wide studies of yeast. In particular, mRNAs with weakly folded 5'-UTRs have higher translation rates, higher abundances of the corresponding proteins, longer half-lives, and higher numbers of transcripts, and are upregulated after heat shock. Furthermore, 5'-UTRs have significantly higher folding free energies than other genomic regions and randomized sequences. We also found a positive correlation between transcript half-life and ribosome occupancy that is more pronounced for short-lived transcripts, which supports a picture of competition between translation and degradation. Among the genes with strongly folded 5'-UTRs, there is a huge overrepresentation of uncharacterized open reading frames. Based on our analysis, we conclude that (i) there is a widespread bias for 5'-UTRs to be weakly folded, (ii) folding free energies of 5'-UTRs are correlated with mRNA translation and turnover on a genomic scale, and (iii) transcripts with strongly folded 5'-UTRs are often rare and hard to find experimentally.

5' Untranslated Regions↗

PubMed2005

Comprehensive regional and temporal gene expression profiling of the rat brain during the first 24 h after experimental stroke identifies dynamic ischemia-induced gene expression patterns, and reveals a biphasic activation of genes in surviving tissue.

In order to identify biological processes relevant for cell death and survival in the brain following stroke, the postischemic brain transcriptome was studied by a large-scale cDNA array analysis of three peri-infarct brain regions at eight time points during the first 24 h of reperfusion following middle cerebral artery occlusion in the rat. K-means cluster analysis revealed two distinct biphasic gene expression patterns that contained 44 genes (including 18 immediate early genes), involved in cell signaling and plasticity (i.e. MAP2K7, Sprouty2, Irs-2, Homer1, GPRC5B, Grasp). The first gene induction phase occurred at 0-3 h of reperfusion, and the second at 9-15 h, and was validated by in situ hybridization. Four gene clusters displayed a progressive increase in expression over time and included 50 genes linked to cell motility, lipid synthesis and trafficking (i.e. ApoD, NPC1, G3P-dehydrogenase1, and Choline kinase) or cell death-regulating genes such as mitochondrial CLIC. We conclude that a biphasic transcriptional up-regulation of the brain-derived neurotrophic factor (BDNF)-G-protein coupled receptor (GPCR)-mitogen-activated protein (MAP) kinase signaling pathways occurs in surviving tissue, concomitant with a progressive and persistent activation of cell proliferation signifying tissue regeneration, which provide the means for cell survival and postischemic brain plasticity.

Animals↗

PubMed2005

Signal transduction pathway profiling of individual tumor samples.

BACKGROUND: Signal transduction pathways convey information from the outside of the cell to transcription factors, which in turn regulate gene expression. Our objective is to analyze tumor gene expression data from microarrays in the context of such pathways. RESULTS: We use pathways compiled from the TRANSPATH/TRANSFAC databases and the literature, and three publicly available cancer microarray data sets. Variation in pathway activity, across the samples, is gauged by the degree of correlation between downstream targets of a pathway. Two correlation scores are applied; one considers all pairs of downstream targets, and the other considers only pairs without common transcription factors. Several pathways are found to be differentially active in the data sets using these scores. Moreover, we devise a score for pathway activity in individual samples, based on the average expression value of the downstream targets. Statistical significance is assigned to the scores using permutation of genes as null model. Hence, for individual samples, the status of a pathway is given as a sign, + or -, and a p-value. This approach defines a projection of high-dimensional gene expression data onto low-dimensional pathway activity scores. For each dataset and many pathways we find a much larger number of significant samples than expected by chance. Finally, we find that several sample-wise pathway activities are significantly associated with clinical classifications of the samples. CONCLUSION: This study shows that it is feasible to infer signal transduction pathway activity, in individual samples, from gene expression data. Furthermore, these pathway activities are biologically relevant in the three cancer data sets.

Breast Neoplasms↗

PubMed2004

Comparing functional annotation analyses with Catmap.

BACKGROUND: Ranked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis. RESULTS: We analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at http://bioinfo.thep.lu.se/Catmap), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p-values for large categories with many coexpressed genes. CONCLUSIONS: In gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.

Classification↗