Biomedical subjects

Rainer Breitling

Publications and source records attributed to Rainer Breitling.

27 records · Page 2Linked to original sources

PubMed2004

Biologically valid linear factor models of gene expression.

MOTIVATION: The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal component analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides a reduced-dimensional representation for subsequent study of possible biological processes responding to the particular experimental conditions. Making explicit the data assumptions underlying PCA highlights their lack of biological validity thus making biological interpretation of the principal components problematic. A microarray data representation which enables clear biological interpretation is a desirable analysis tool. RESULTS: We address this issue by employing the probabilistic interpretation of PCA and proposing alternative linear factor models which are based on refined biological assumptions. A practical study on two well-understood microarray datasets highlights the weakness of PCA and the greater biological interpretability of the linear models we have developed.

Algorithms↗

PubMed2004

Iterative Group Analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments.

BACKGROUND: The biological interpretation of even a simple microarray experiment can be a challenging and highly complex task. Here we present a new method (Iterative Group Analysis) to facilitate, improve, and accelerate this process. RESULTS: Our Iterative Group Analysis approach (iGA) uses elementary statistics to identify those functional classes of genes that are significantly changed in an experiment and at the same time determines which of the class members are most likely to be differentially expressed. iGA does not require that all members of a class change and is therefore robust against imperfect class assignments, which can be derived from public sources (e.g. GeneOntologies) or automated processes (e.g. key word extraction from gene names). In contrast to previous non-iterative approaches, iGA does not depend on the availability of fixed lists of differentially expressed genes, and thus can be used to increase the sensitivity of gene detection especially in very noisy or small data sets. In the extreme, iGA can even produce statistically meaningful results without any experimental replication. The automated functional annotation provided by iGA greatly reduces the complexity of microarray results and facilitates the interpretation process. In addition, iGA can be used as a fast and efficient tool for the platform-independent comparison of a microarray experiment to the vast number of published results, automatically highlighting shared genes of potential interest. CONCLUSIONS: By applying iGA to a wide variety of data from diverse organisms and platforms we show that this approach enhances and accelerates the interpretation of microarray experiments.

Animals↗

PubMed2004

Pathogenesis of peroxisomal deficiency disorders (Zellweger syndrome) may be mediated by misregulation of the GABAergic system via the diazepam binding inhibitor.

BACKGROUND: Zellweger syndrome (ZS) is a fatal inherited disease caused by peroxisome biogenesis deficiency. Patients are characterized by multiple disturbances of lipid metabolism, profound hypotonia and neonatal seizures, and distinct craniofacial malformations. Median live expectancy of ZS patients is less than one year. While the molecular basis of peroxisome biogenesis and metabolism is known in considerable detail, it is unclear how peroxisome deficiency leads to the most severe neurological symptoms. Recent analysis of ZS mouse models has all but invalidated previous hypotheses. HYPOTHESIS: We suggest that a regulatory rather than a metabolic defect is responsible for the drastic impairment of brain function in ZS patients. TESTING THE HYPOTHESIS: Using microarray analysis we identify diazepam binding inhibitor/acyl-CoA binding protein (DBI) as a candidate protein that might be involved in the pathogenic mechanism of ZS. DBI has a dual role as a neuropeptide antagonist of GABA(A) receptor signaling in the brain and as a regulator of lipid metabolism. Repression of DBI in ZS patients could result in an overactivation of GABAergic signaling, thus eventually leading to the characteristic hypotonia and seizures. The most important argument for a misregulation of GABA(A) in ZS is, however, provided by the striking similarity between ZS and "benzodiazepine embryofetopathy", a malformation syndrome observed after the abuse of GABA(A) agonists during pregnancy. IMPLICATIONS OF THE HYPOTHESIS: We present a tentative mechanistic model of the effect of DBI misregulation on neuronal function that could explain some of the aspects of the pathology of Zellweger syndrome.

Animals↗

PubMed2003

Embryonic expression of cholesterogenic genes is restricted to distinct domains and colocalizes with apoptotic regions in mice.

Cholesterol biosynthesis has been assumed to be an ubiquitous process in vertebrate organisms. Here we present data demonstrating that expression of key enzymes of cholesterol biosynthesis is restricted to specific tissues during embryonic development. Distinct expression starts in the dorsal neural tube at embryonic day 8 and is later detected in dorsal root and cephalic ganglia, in the pharyngeal pouches and limb buds. In the limb, expression becomes progressively restricted to interdigital regions during differentiation. Caspase3 whole mount immunostaining revealed that cholesterol biosynthesis colocalizes with apoptotic regions that are targets of the morphogenic signal Sonic hedgehog. This expression pattern correlates closely with the shared phenotypic features of cholesterol biosynthesis and hedgehog mutants.

Animals↗

PubMed2003

Closing the gap: identification of human 3-ketosteroid reductase, the last unknown enzyme of mammalian cholesterol biosynthesis.

The protein encoded by the HSD17B7 gene was originally described as a prolactin receptor-associated protein and as 17beta-hydroxysteroid dehydrogenase (HSD) type 7. Its ability to synthesize 17beta-estradiol in vitro has been reported previously. However, we demonstrate that HSD17B7 is the ortholog of the yeast 3-ketosteroid reductase Erg27p and converts zymosterone to zymosterol in vitro, using reduced nicotinamide adenine dinucleotide phosphate as cofactor. Expression of human and murine HSD17B7 in an Erg27p-deficient yeast strain complements the 3-ketosteroid reductase deficiency of the cells and restores growth on sterol-deficient medium. A fusion of HSD17B7 with green fluorescent protein is located in the endoplasmic reticulum, the site of postsqualene cholesterogenesis. Further critical evidence for a role of HSD17B7 in cholesterol metabolism is provided by the observation that its murine ortholog is a member of the same highly distinct embryonic synexpression group as hydroxymethyl-glutaryl-coenzyme A reductase, the rate-limiting enzyme of sterol biogenesis, and is specifically expressed in tissues that are involved in the pathogenesis of congenital cholesterol-deficiency disorders. We conclude that HSD17B7 participates in postsqualene cholesterol biosynthesis, thus completing the molecular cloning of all genes of this central metabolic pathway. In its function as the 3-ketosteroid reductase of cholesterol biosynthesis, HSD17B7 is a novel candidate for inborn errors of cholesterol metabolism.

3-Hydroxysteroid Dehydrogenases↗

PubMed2003

Isopentenyl-diphosphate isomerases in human and mouse: evolutionary analysis of a mammalian gene duplication.

Isopentenyl diphosphate isomerase (IDI) activates isopentenyl diphosphate (IPP) for polymerization by converting it to its highly nucleophilic isomer dimethylallyl diphosphate (DMAPP). In plants, this central reaction of isoprenoid biosynthesis is catalyzed by various highly conserved isozymes that differ in expression pattern and subcellular localization. Here we report the identification of an IDI duplication in mammals. In contrast to the situation in plants, only one of the two isoforms (IDI1) is highly conserved, ubiquitously expressed and most likely responsible for housekeeping isomerase activity. The second isoform (IDI2) is much more divergent. We demonstrate that after the initial duplication IDI2 underwent a short phase of apparently random change, during which its active center became modified. Afterwards, IDI2 was exapted for a novel function and since then has been under strong purifying selection for at least 70 million years. Molecular modeling shows that the modified IDI2 is still likely to catalyze the isomerization of IPP to DMAPP. In humans, IDI2 is expressed at high levels only in skeletal muscle, where it may be involved in the specialized production of isoprenyl diphosphates for the posttranslational modification of proteins. The significant positive fitness effect of IDI2, revealed by the pattern of sequence conservation, as well as its specific expression pattern underscores the importance of the IDI gene duplication in mammals.

Animals↗

PubMed2002

Loss of compartmentalization causes misregulation of lysine biosynthesis in peroxisome-deficient yeast cells.

To characterize the metabolic role of peroxisomes in yeast cells under physiological conditions, we performed a comprehensive meta-analysis of published microarray data. Previous studies of yeast peroxisomes have mainly been focused on the function of peroxisomes under extreme conditions, such as growth on oleate or methanol as the sole carbon source, and may therefore not be representative of the normal physiological role of yeast peroxisomes. Surprisingly, our analysis of the microarray data reveals that the only pathway responding to peroxisome deficiency in mid-log phase is lysine biosynthesis, whereas classical peroxisomal pathways such as beta-oxidation are unaffected. We show that the upregulation of lysine biosynthesis genes in peroxisome-deficient yeasts shares many characteristics with the physiological response to lysine starvation. We provide data that suggest that this is the result of a "pathological" stimulation of the Lys14p transcriptional activator by the pathway intermediate aminoadipate semialdehyde. Mistargeting of the peroxisomal lysine pathway to the cytosol increases the active concentration of aminoadipate semialdehyde, which is no longer contained in the peroxisome and can now activate Lys14p at much lower levels than in wild-type yeasts. This is the first well-documented example of pathway misregulation in response to peroxisome deficiency and will be useful in understanding the phenotypic details of human peroxisome-deficient patients (Zellweger syndrome).

Biological Transport↗

PubMed2002

A second gene for peroxisomal HMG-CoA reductase? A genomic reassessment.

HMG-CoA reductase (HMGCR) catalyzes the conversion of HMG-CoA to mevalonate, the rate-limiting step of eukaryotic isoprenoid biosynthesis, and is the main target of cholesterol-lowering drugs. The classical form of the enzyme is a transmembrane-protein anchored to the endoplasmic reticulum. However, during the last years several lines of evidence pointed to the existence of a second isoform of HMGCR localized in peroxisomes, where mevalonate is converted further to farnesyl diphosphate. This finding is relevant for our understanding of the complex regulation and compartmentalization of the cholesterogenic pathway. Here we review experimental evidence suggesting that the peroxisomal activity might be due to a second HMGCR gene in mammals. We then present a comprehensive analysis of completely sequenced eukaryotic genomes, as well as the human and mouse genome drafts. Our results provide evidence for a large number of independent duplications of HMGCR in all eukaryotic kingdoms, but not for a second gene in mammals. We conclude that the peroxisomal HMGCR activity in mammals is due to alternative targeting of the ER enzyme to peroxisomes by an as yet uncharacterized mechanism.

Animals↗

PubMedDate not supplied

The latent process decomposition of cDNA microarray data sets.

We present a new computational technique (a software implementation, data sets, and supplementary information are available at http://www.enm.bris.ac.uk/lpd/) which enables the probabilistic analysis of cDNA microarray data and we demonstrate its effectiveness in identifying features of biomedical importance. A hierarchical Bayesian model, called Latent Process Decomposition (LPD), is introduced in which each sample in the data set is represented as a combinatorial mixture over a finite set of latent processes, which are expected to correspond to biological processes. Parameters in the model are estimated using efficient variational methods. This type of probabilistic model is most appropriate for the interpretation of measurement data generated by cDNA microarray technology. For determining informative substructure in such data sets, the proposed model has several important advantages over the standard use of dendrograms. First, the ability to objectively assess the optimal number of sample clusters. Second, the ability to represent samples and gene expression levels using a common set of latent variables (dendrograms cluster samples and gene expression values separately which amounts to two distinct reduced space representations). Third, in constrast to standard cluster models, observations are not assigned to a single cluster and, thus, for example, gene expression levels are modeled via combinations of the latent processes identified by the algorithm. We show this new method compares favorably with alternative cluster analysis methods. To illustrate its potential, we apply the proposed technique to several microarray data sets for cancer. For these data sets it successfully decomposes the data into known subtypes and indicates possible further taxonomic subdivision in addition to highlighting, in a wholly unsupervised manner, the importance of certain genes which are known to be medically significant. To illustrate its wider applicability, we also illustrate its performance on a microarray data set for yeast.

Algorithms↗