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Search Results: 1 - 10 of 296355 matches for " Rolf O Lindén "
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Quantitative maps of genetic interactions in yeast - Comparative evaluation and integrative analysis
Rolf O Lindén, Ville-Pekka Eronen, Tero Aittokallio
BMC Systems Biology , 2011, DOI: 10.1186/1752-0509-5-45
Abstract: Using large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies.We have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.The recent advances in experimental biotechnologies have made it possible to start screening genome-wide datasets of quantitative genetic interactions in model organisms such as yeast [1-3]. High-throughput genetic screening approaches, such as those based on epistatic miniarray profiling (E-MAP) [4-7], genetic interaction mapping (GIM) [8], and synthetic genetic array (SGA) [9-11], have provided systematic means to global investigation of quantitative relationship between genotype and phenotype, with potential implications for a wide range of biological phenomena, including, for instance, modularity, essentiality, redundancy, buffering, epi
Genome-Wide Scoring of Positive and Negative Epistasis through Decomposition of Quantitative Genetic Interaction Fitness Matrices
Ville-Pekka Eronen,Rolf O. Lindén,Anna Lindroos,Mirella Kanerva,Tero Aittokallio
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0011611
Abstract: Recent technological developments in genetic screening approaches have offered the means to start exploring quantitative genotype-phenotype relationships on a large-scale. What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means. To address these questions, we introduce here a computational approach for improving the quantitative discrimination power encoded in the genetic interaction screening data. Our matrix approximation model decomposes the original double-mutant fitness matrix into separate components, representing variability across the array and query mutants, which can be utilized for estimating and correcting the single-mutant fitness effects, respectively. When applied to three large-scale quantitative interaction datasets in yeast, we could improve the accuracy of scoring various interaction classes beyond that obtained with the original fitness data, especially in synthetic genetic array (SGA) and in genetic interaction mapping (GIM) datasets. In addition to the known pairs of interactions used in the evaluation of the computational approach, a number of novel interaction pairs were also predicted, along with underlying biological mechanisms, which remained undetected by the original datasets. It was shown that the optimal choice of the scoring function depends heavily on the screening approach and on the interaction class under analysis. Moreover, a simple preprocessing of the fitness matrix could further enhance the discrimination power of the epistatic miniarray profiling (E-MAP) dataset. These systematic evaluation results provide in-depth information on the optimal analysis of the future, large-scale screening experiments. In general, the modeling framework, enabling accurate identification and classification of genetic interactions, provides a solid basis for completing and mining the genetic interaction networks in yeast and other organisms.
Size distribution and structure of Barchan dune fields
O. Durán, V. Schw mmle, P. G. Lind,H. J. Herrmann
Nonlinear Processes in Geophysics (NPG) , 2011,
Abstract: Barchans are isolated mobile dunes often organized in large dune fields. Dune fields seem to present a characteristic dune size and spacing, which suggests a cooperative behavior based on dune interaction. In Duran et al. (2009), we propose that the redistribution of sand by collisions between dunes is a key element for the stability and size selection of barchan dune fields. This approach was based on a mean-field model ignoring the spatial distribution of dune fields. Here, we present a simplified dune field model that includes the spatial evolution of individual dunes as well as their interaction through sand exchange and binary collisions. As a result, the dune field evolves towards a steady state that depends on the boundary conditions. Comparing our results with measurements of Moroccan dune fields, we find that the simulated fields have the same dune size distribution as in real fields but fail to reproduce their homogeneity along the wind direction.
Metabolic Flux and Compartmentation Analysis in the Brain In vivo
Bernard Lanz,Rolf Gruetter,Jo?o M. N. Duarte
Frontiers in Endocrinology , 2013, DOI: 10.3389/fendo.2013.00156
Abstract: Through significant developments and progresses in the last two decades, in vivo localized nuclear magnetic resonance spectroscopy (MRS) became a method of choice to probe brain metabolic pathways in a non-invasive way. Beside the measurement of the total concentration of more than 20 metabolites, 1H MRS can be used to quantify the dynamics of substrate transport across the blood-brain barrier by varying the plasma substrate level. On the other hand, 13C MRS with the infusion of 13C-enriched substrates enables the characterization of brain oxidative metabolism and neurotransmission by incorporation of 13C in the different carbon positions of amino acid neurotransmitters. The quantitative determination of the biochemical reactions involved in these processes requires the use of appropriate metabolic models, whose level of details is strongly related to the amount of data accessible with in vivo MRS. In the present work, we present the different steps involved in the elaboration of a mathematical model of a given brain metabolic process and its application to the experimental data in order to extract quantitative brain metabolic rates. We review the recent advances in the localized measurement of brain glucose transport and compartmentalized brain energy metabolism, and how these reveal mechanistic details on glial support to glutamatergic and GABAergic neurons.
Compartmentalized Cerebral Metabolism of [1,6-13C]Glucose Determined by in vivo13C NMR Spectroscopy at 14.1 T
Jo?o M. N. Duarte,Bernard Lanz,Rolf Gruetter
Frontiers in Neuroenergetics , 2011, DOI: 10.3389/fnene.2011.00003
Abstract: Cerebral metabolism is compartmentalized between neurons and glia. Although glial glycolysis is thought to largely sustain the energetic requirements of neurotransmission while oxidative metabolism takes place mainly in neurons, this hypothesis is matter of debate. The compartmentalization of cerebral metabolic fluxes can be determined by 13C nuclear magnetic resonance (NMR) spectroscopy upon infusion of 13C-enriched compounds, especially glucose. Rats under light α-chloralose anesthesia were infused with [1,6-13C]glucose and 13C enrichment in the brain metabolites was measured by 13C NMR spectroscopy with high sensitivity and spectral resolution at 14.1 T. This allowed determining 13C enrichment curves of amino acid carbons with high reproducibility and to reliably estimate cerebral metabolic fluxes (mean error of 8%). We further found that TCA cycle intermediates are not required for flux determination in mathematical models of brain metabolism. Neuronal tricarboxylic acid cycle rate (VTCA) and neurotransmission rate (VNT) were 0.45 ± 0.01 and 0.11 ± 0.01 μmol/g/min, respectively. Glial VTCA was found to be 38 ± 3% of total cerebral oxidative metabolism, accounting for more than half of neuronal oxidative metabolism. Furthermore, glial anaplerotic pyruvate carboxylation rate (VPC) was 0.069 ± 0.004 μmol/g/min, i.e., 25 ± 1% of the glial TCA cycle rate. These results support a role of glial cells as active partners of neurons during synaptic transmission beyond glycolytic metabolism.
Cysteine-Rich Secretory Protein-3 (CRISP3) Is Strongly Up-Regulated in Prostate Carcinomas with the TMPRSS2-ERG Fusion Gene
Franclim R. Ribeiro,Paula Paulo,Vera L. Costa,Jo?o D. Barros-Silva,Jo?o Ramalho-Carvalho,Carmen Jerónimo,Rui Henrique,Guro E. Lind,Rolf I. Skotheim,Ragnhild A. Lothe,Manuel R. Teixeira
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0022317
Abstract: A large percentage of prostate cancers harbor TMPRSS2-ERG gene fusions, leading to aberrant overexpression of the transcription factor ERG. The target genes deregulated by this rearrangement, however, remain mostly unknown. To address this subject we performed genome-wide mRNA expression analysis on 6 non-malignant prostate samples and 24 prostate carcinomas with (n = 16) and without (n = 8) TMPRSS2-ERG fusion as determined by FISH. The top-most differentially expressed genes and their associations with ERG over-expression were technically validated by quantitative real-time PCR and biologically validated in an independent series of 200 prostate carcinomas. Several genes encoding metabolic enzymes or extracellular/transmembrane proteins involved in cell adhesion, matrix remodeling and signal transduction pathways were found to be co-expressed with ERG. Within those significantly over-expressed in fusion-positive carcinomas, CRISP3 showed more than a 50-fold increase when compared to fusion-negative carcinomas, whose expression levels were in turn similar to that of non-malignant samples. In the independent validation series, ERG and CRISP3 mRNA levels were strongly correlated (rs = 0.65, p<0.001) and both were associated with pT3 disease staging. Furthermore, immunohistochemistry results showed CRISP3 protein overexpression in 63% of the carcinomas and chromatin immunoprecipitation with an anti-ERG antibody showed that CRISP3 is a direct target of the transcription factor ERG. We conclude that ERG rearrangement is associated with significant expression alterations in genes involved in critical cellular pathways that define a subset of locally advanced PCa. In particular, we show that CRISP3 is a direct target of ERG that is strongly overexpressed in PCa with the TMPRSS2-ERG fusion gene.
Hypermethylated MAL gene – a silent marker of early colon tumorigenesis
Guro E Lind, Terje Ahlquist, Matthias Kolberg, Marianne Berg, Mette Ekn?s, Miguel A Alonso, Anne Kallioniemi, Gunn I Meling, Rolf I Skotheim, Torleiv O Rognum, Espen Thiis-Evensen, Ragnhild A Lothe
Journal of Translational Medicine , 2008, DOI: 10.1186/1479-5876-6-13
Abstract: Using methylation-specific polymerase chain reaction (MSP) the promoter methylation status of MAL was analyzed in 218 samples, including normal mucosa (n = 44), colorectal adenomas (n = 63), carcinomas (n = 65), and various cancer cell lines (n = 46). Direct bisulphite sequencing was performed to confirm the MSP results. MAL gene expression was investigated with real time quantitative analyses before and after epigenetic drug treatment. Immunohistochemical analysis of MAL was done using normal colon mucosa samples (n = 5) and a tissue microarray with 292 colorectal tumors.Bisulphite sequencing revealed that the methylation was unequally distributed within the MAL promoter and by MSP analysis a region close to the transcription start point was shown to be hypermethylated in the majority of colorectal carcinomas (49/61, 80%) as well as in adenomas (45/63, 71%). In contrast, only a minority of the normal mucosa samples displayed hypermethylation (1/23, 4%). The hypermethylation of MAL was significantly associated with reduced or lost gene expression in in vitro models. Furthermore, removal of the methylation re-induced gene expression in colon cancer cell lines. Finally, MAL protein was expressed in epithelial cells of normal colon mucosa, but not in the malignant cells of the same type.Promoter hypermethylation of MAL was present in the vast majority of benign and malignant colorectal tumors, and only rarely in normal mucosa, which makes it suitable as a diagnostic marker for early colorectal tumorigenesis.Epigenetic changes – non-sequence-based alterations that are inherited through cell division [1] – are frequently seen in human cancers, and likewise as genetic alterations they may lead to disruption of gene function. In colorectal cancer, several tumour suppressor genes have been identified to be epigenetically inactivated by CpG island promoter hypermethylation, including the DNA mismatch repair gene MLH1 [2-4], the gatekeeper APC [5], and the cell cycle inhibito
Identification of an epigenetic biomarker panel with high sensitivity and specificity for colorectal cancer and adenomas
Guro E Lind, Stine A Danielsen, Terje Ahlquist, Marianne A Merok, Kim Andresen, Rolf I Skotheim, Merete Hektoen, Torleiv O Rognum, Gunn I Meling, Geir Hoff, Michael Bretthauer, Espen Thiis-Evensen, Arild Nesbakken, Ragnhild A Lothe
Molecular Cancer , 2011, DOI: 10.1186/1476-4598-10-85
Abstract: Candidate biomarkers were subjected to quantitative methylation analysis in test sets of tissue samples from colorectal cancers, adenomas, and normal colonic mucosa. All findings were verified in independent clinical validation series. A total of 523 human samples were included in the study. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the biomarker panel.Promoter hypermethylation of the genes CNRIP1, FBN1, INA, MAL, SNCA, and SPG20 was frequent in both colorectal cancers (65-94%) and adenomas (35-91%), whereas normal mucosa samples were rarely (0-5%) methylated. The combined sensitivity of at least two positives among the six markers was 94% for colorectal cancers and 93% for adenoma samples, with a specificity of 98%. The resulting areas under the ROC curve were 0.984 for cancers and 0.968 for adenomas versus normal mucosa.The novel epigenetic marker panel shows very high sensitivity and specificity for both colorectal cancers and adenomas. Our findings suggest this biomarker panel to be highly suitable for early tumor detection.Colorectal cancer is the third most common cancer type in the US and is a major contributor to cancer-death [1]. Most cases of colorectal cancer develop from benign precursors (adenomas) during a long time interval. This provides a good opportunity for detection of colorectal cancer at an early curable stage and to screen for potentially pre-malignant adenomas [2]. Both flexible sigmoidoscopy and the Fecal Occult Blood Test (FOBT) have been tested in randomized trials and shown to reduce mortality from colorectal cancer [3]. By sigmoidoscopy adenomas may be detected and removed and thus the incidence of cancer will be reduced [4], however, this screening is invasive and cumbersome for the patient. FOBT on the other hand is non-invasive and currently the most commonly used screening test for colorectal cancer in Europe. Although the sensitivity and specificity measurements of FOBT have been
Gene methylation profiles of normal mucosa, and benign and malignant colorectal tumors identify early onset markers
Terje Ahlquist, Guro E Lind, Vera L Costa, Gunn I Meling, Morten Vatn, Geir S Hoff, Torleiv O Rognum, Rolf I Skotheim, Espen Thiis-Evensen, Ragnhild A Lothe
Molecular Cancer , 2008, DOI: 10.1186/1476-4598-7-94
Abstract: The methylation status of eleven genes (ADAMTS1, CDKN2A, CRABP1, HOXA9, MAL, MGMT, MLH1, NR3C1, PTEN, RUNX3, and SCGB3A1) was determined in 154 tissue samples including normal mucosa, adenomas, and carcinomas of the colorectum. The gene-specific and widespread methylation status among the carcinomas was related to patient gender and age, and microsatellite instability status. Possible CIMP tumors were identified by comparing the methylation profile with microsatellite instability (MSI), BRAF-, KRAS-, and TP53 mutation status.The mean number of methylated genes per sample was 0.4 in normal colon mucosa from tumor-free individuals, 1.2 in mucosa from cancerous bowels, 2.2 in adenomas, and 3.9 in carcinomas. Widespread methylation was found in both adenomas and carcinomas. The promoters of ADAMTS1, MAL, and MGMT were frequently methylated in benign samples as well as in malignant tumors, independent of microsatellite instability. In contrast, normal mucosa samples taken from bowels without tumor were rarely methylated for the same genes. Hypermethylated CRABP1, MLH1, NR3C1, RUNX3, and SCGB3A1 were shown to be identifiers of carcinomas with microsatellite instability. In agreement with the CIMP concept, MSI and mutated BRAF were associated with samples harboring hypermethylation of several target genes.Methylated ADAMTS1, MGMT, and MAL are suitable as markers for early tumor detection.Most cases of colorectal cancer (CRC) originate from adenomas. The malignant potential of adenomas increases with size, grade of dysplasia, and degree of villous components,[1] along with the number and order of genetic and epigenetic aberrations.[2] The majority (~85%) of the sporadic carcinomas are characterized by chromosomal aberrations, referred to as a chromosomal unstable (CIN) phenotype, whereas the smaller group (~15%) typically show microsatellite instability (MSI) caused by defect DNA mismatch repair.[2] Most CIN tumors are microsatellite stable (MSS). A third molecular phenotyp
Gene expression profiles of primary colorectal carcinomas, liver metastases, and carcinomatoses
Kristine Kleivi, Guro E Lind, Chieu B Diep, Gunn I Meling, Lin T Brandal, Jahn M Nesland, Ola Myklebost, Torleiv O Rognum, Karl-Erik Giercksky, Rolf I Skotheim, Ragnhild A Lothe
Molecular Cancer , 2007, DOI: 10.1186/1476-4598-6-2
Abstract: Transcriptome profiles of colorectal cancer metastases independent of tumor site, as well as separate profiles associated with primary carcinomas, liver metastases, or peritoneal carcinomatoses, were assessed by use of Bayesian statistics. Gains of chromosome arm 5p are common in peritoneal carcinomatoses and several candidate genes (including PTGER4, SKP2, and ZNF622) mapping to this region were overexpressed in the tumors. Expression signatures stratified on TP53 mutation status were identified across all tumors regardless of stage. Furthermore, the gene expression levels for the in vivo tumors were compared with an in vitro model consisting of cell lines representing all three tumor stages established from one patient.By statistical analysis of gene expression data from primary colorectal carcinomas, liver metastases, and carcinomatoses, we are able to identify genetic patterns associated with the different stages of tumorigenesis.Colorectal cancer (CRC) is the second most common cause of cancer related deaths in developed countries, including Norway [1,2]. Despite the fact that metastases are the leading cause of colorectal cancer deaths, the majority of genetic studies of colorectal carcinogenesis have focused on changes found in primary carcinomas, and the knowledge about the underlying molecular changes in more advanced disease stages remain limited. To obtain insights to this process, identification of molecular key events that distinguish primary from metastatic tumors is important. DNA microarray technology has become powerful for whole-genome investigations [3]. Recently, several reports have shown that results obtained by this technology can distinguish among subgroups of the same cancer tissue [4-7] as well as among different cancer types [8]. Additionally, genetic profiles have been identified that predict patients' clinical outcome in cancers of the breast, lung, central nervous system, digestive system, and prostate [9-15]. Several studies has invest
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