Search

Your search keyword '"Bakke P"' showing total 44 results
Start Over Author "Bakke P" Publisher biomed central
44 results on '"Bakke P"'

39. Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease

Author

Guerra, Beniamino, Haile, Sarah R., Lamprecht, Bernd, Ramírez, Ana S., Martinez-Camblor, Pablo, Kaiser, Bernhard, Alfageme, Inmaculada, Almagro, Pere, Casanova, Ciro, Esteban-González, Cristóbal, Soler-Cataluña, Juan J., de-Torres, Juan P., Miravitlles, Marc, Celli, Bartolome R., Marin, Jose M., ter Riet, Gerben, Sobradillo, Patricia, Lange, Peter, Garcia-Aymerich, Judith, Antó, Josep M., Turner, Alice M., Han, Meilan K., Langhammer, Arnulf, Leivseth, Linda, Bakke, Per, Johannessen, Ane, Oga, Toru, Cosio, Borja, Ancochea-Bermúdez, Julio, Echazarreta, Andres, Roche, Nicolas, Burgel, Pierre-Régis, Sin, Don D., Soriano, Joan B., Puhan, Milo A., and for the 3CIA collaboration

Published
2018
Full Text
View/download PDF

41. A computational framework for complex disease stratification from multiple large-scale datasets

Author

De Meulder, B., Lefaudeux, D., Bansal, A. T., Mazein, A., Chaiboonchoe, A., Ahmed, H., Balaur, I., Saqi, M., Pellet, J., Ballereau, S., Lemonnier, N., Sun, K., Pandis, I., Yang, X., Batuwitage, M., Kretsos, K., van Eyll, J., Bedding, A., Davison, T., Dodson, P., Larminie, C., Postle, A., Corfield, J., Djukanovic, R., Chung, K. F., Adcock, I. M., Guo, Y. -K., Sterk, P. J., Manta, A., Rowe, A., Baribaud, F., Auffray, C., Gibeon, D., Hoda, U., Kuo, S., Meah, S., Meiser, A., Fleming, L. J., Hu, S., Pavlidis, S., Rossios, C., Russel, K., Wiegman, C., Nezhad, A. T., Oehmichen, A., O'Malley, D., Guitton, F., Emam, I., Agapow, P., Rice, P., Miles, S., Elyasigomari, V., Bel, E., Brinkman, P., Dekker, T., Dijkhuis, A., Hashimoto, S., Hekking, P. -P., Lone-Latif, S., Lutter, R., Ravanetti, L., Smids, B., van Aalderen, W., van de Pol, M., van Drunen, K., van Drunen, M., Wagener, A., Zwinderman, K., Adriaens, N., Carusi, A. M., Richard, F., Nogueira, M. M., Taibi, N., Brasier, O., Aliprantis, A., Alving, K., Faulenbach, C., Braun, A., Hohlfeld, J., Krug, N., Badorrek, P., Bakke, P., Berglind, A., Chaleckis, R., Dahlen, B., Delin, I., Gallart, H., Gomez, C., Hedlin, G., Henriksson, E., James, A. J., Kolmert, J., Konradsen, J., Kupczyk, M., Lantz, A. -S., Lazarinis, L., Mathon, C., Middelveld, R., Naz, S., Nordlund, B., Petren, A., Reinke, S., Sjodin, M., Soderman, P., Strandberg, K., Wheelock, C. E., Zetterquist, W., Balgoma, D., Brandsma, J., Burg, D., Dennison, P., Nicholas, B., Schofield, J. P. R., Skipp, P. J., Staykova, D., Tariq, K., Ward, J., Wilson, S. J., Barber, C., Loza, M. J., Bautmans, A., Sandstrom, T., Behndig, A. F., De Alba, J., Beleta, J., Berton, A., de Verdier, M. G., Nihlen, U., Ostling, J., Dalentoft, T., Lindgren, E., Boedigheimer, M. J., Hu, R., Hu, X., Yu, W., Bigler, J., Bonnelykke, K., Thorsen, J., Vising, N., Bisgaard, H., Bochenek, G., Caruso, M., Emma, R., Campagna, D., Thornton, B., Carayannopoulos, L., Gent, J., Manzies-Gow, A., Sogbesan, A., da Purificacao Rocha, P. C., Pedro, J., Chanez, P., Edwards, J., Flood, B., Hudson, V., Kennington, E. J., Metcalf, L., Rahman-Amin, M., Reynolds, L., Roberts, A., Smith, J., Supple, D., Versnel, J., Walker, S., Coleman, C., Hasan, S., Compton, C., Myles, D., Riley, J., Sousa, A. R., Yeyasingham, E., Pennazza, G., Santoninco, M., D'Amico, A., Dahlen, S. -E., de Boer, P., Robberechts, M., De Lepeleire, I., Fitch, N., Garret, T., Wagers, S., Draper, A., Thorngren, J. -O., Ericsson, M., Erpenbeck, V., Kluglich, M., Nething, K., Riemann, K., Schoelch, C., Seibold, W., Sigmund, R., Wald, F., Wetzel, K., Fichtner, K., Erzen, D., Galffy, G., Horvath, I., Szentkereszty, M., Tamasi, L., Fowler, S. J., Krueger, L., Singer, F., Frey, U., Gahlemann, M., Geiser, T., Hewitt, L., Howarth, P., Marouzet, L., Martin, J., Pink, S., Ray, E., Roberts, G., Smith, C., Gove, K., Gozzard, N., Williams, S., Haughney, J., Higgenbottam, T., Matthews, J. G., Holweg, C., Rutgers, M., Kamphuis, J., Kerry, D., Vink, A., Knobel, H., Knowles, R., Shaw, D. E., Smith, K. M., Know, A., Kots, M., Lambrecht, B., Masefield, S., Nilsson, P., Mikus, M., Miralpeix, M., Monk, P., Mores, N., Valente, S., Montuschi, P., Murray, C. S., Musial, J., Pacino, A., Pahus, L., Palkonen, S., Powel, P., Rao, N., Santini, G., Vestbo, J., von Garnier, C., Weiszhart, Z., Woodcock, A., Biryukov, M., Schneider, R., Herzinger, S., Satagopam, V., Gu, W., da Silva, A. B., Tielmann, A., Bergeron, J., Gaudette, A., Silberberg, A., Henderson, D., Hayat, S., Elefsinioti, A., Moltzen, E. K., Harbo, I. S., Birgitte, J., Bratfalean, D., Houston, P., Kisler, B., Capdevila, F. B., Verbeeck, D., Marchetti, G., Rahal, G., Schuermann, H. D., Mazuranok, L., Hendlich, M., Painell'S, L., Marren, D., Martasek, J., Rimell, J., Romacker, M., Braxenthaler, M., Sansone, S. -A., Rocca-Serra, P., Commission of the European Communities, Pulmonology, Graduate School, Experimental Immunology, Paediatric Pulmonology, Ear, Nose and Throat, Epidemiology and Data Science, APH - Methodology, ARD - Amsterdam Reproduction and Development, Consortium, U-Biopred Study Group And The Etriks, Rocca-Serra, P, Sansone, S, De Meulder, Bertrand [0000-0002-2108-7657], and Apollo - University of Cambridge Repository

Subjects
Quality Control, 0301 basic medicine, Computer science, Bioinformatics, Systems biology, Big data, Environmental data, Machine Learning, Set (abstract data type), 03 medical and health sciences, Structural Biology, Modelling and Simulation, Cluster Analysis, U-BIOPRED Study Group and the eTRIKS Consortium, Disease, False Positive Reactions, Cluster analysis, Molecular signatures, Molecular Biology, lcsh:QH301-705.5, ‘Omics data, 'Omics data, business.industry, Systems Biology, Applied Mathematics, 1199 Other Medical And Health Sciences, Data science, 3. Good health, Computer Science Applications, Systems medicine, 030104 developmental biology, lcsh:Biology (General), Feature (computer vision), Modeling and Simulation, Stratification, Scale (map), business, Biomarkers, Research Article
Abstract

Background Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-‘omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-‘omics signatures of disease states. Methods The framework is divided into four major steps: dataset subsetting, feature filtering, ‘omics-based clustering and biomarker identification. Results We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-‘omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions This framework will help health researchers plan and perform multi-‘omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine. Electronic supplementary material The online version of this article (10.1186/s12918-018-0556-z) contains supplementary material, which is available to authorized users.

Published
2018

42. A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry

Author

Lutz, Sharon M., Cho, Michael H., Young, Kendra, Hersh, Craig P., Castaldi, Peter J., McDonald, Merry-Lynn, Regan, Elizabeth, Mattheisen, Manuel, DeMeo, Dawn L., Parker, Margaret, Foreman, Marilyn, Make, Barry J., Jensen, Robert L., Casaburi, Richard, Lomas, David A., Bhatt, Surya P., Bakke, Per, Gulsvik, Amund, Crapo, James D., Beaty, Terri H., Laird, Nan M., Lange, Christoph, Hokanson, John E., and Silverman, Edwin K.

Subjects
Chronic obstructive pulmonary disease, DBH, FEV, Genome-wide association study, Spirometry
Abstract

Background: Pulmonary function decline is a major contributor to morbidity and mortality among smokers. Post bronchodilator FEV1 and FEV1/FVC ratio are considered the standard assessment of airflow obstruction. We performed a genome-wide association study (GWAS) in 9919 current and former smokers in the COPDGene study (6659 non-Hispanic Whites [NHW] and 3260 African Americans [AA]) to identify associations with spirometric measures (post-bronchodilator FEV1 and FEV1/FVC). We also conducted meta-analysis of FEV1 and FEV1/FVC GWAS in the COPDGene, ECLIPSE, and GenKOLS cohorts (total n = 13,532). Results: Among NHW in the COPDGene cohort, both measures of pulmonary function were significantly associated with SNPs at the 15q25 locus [containing CHRNA3/5, AGPHD1, IREB2, CHRNB4] (lowest p-value = 2.17 × 10−11), and FEV1/FVC was associated with a genomic region on chromosome 4 [upstream of HHIP] (lowest p-value = 5.94 × 10−10); both regions have been previously associated with COPD. For the meta-analysis, in addition to confirming associations to the regions near CHRNA3/5 and HHIP, genome-wide significant associations were identified for FEV1 on chromosome 1 [TGFB2] (p-value = 8.99 × 10−9), 9 [DBH] (p-value = 9.69 × 10−9) and 19 [CYP2A6/7] (p-value = 3.49 × 10−8) and for FEV1/FVC on chromosome 1 [TGFB2] (p-value = 8.99 × 10−9), 4 [FAM13A] (p-value = 3.88 × 10−12), 11 [MMP3/12] (p-value = 3.29 × 10−10) and 14 [RIN3] (p-value = 5.64 × 10−9). Conclusions: In a large genome-wide association study of lung function in smokers, we found genome-wide significant associations at several previously described loci with lung function or COPD. We additionally identified a novel genome-wide significant locus with FEV1 on chromosome 9 [DBH] in a meta-analysis of three study populations. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0299-4) contains supplementary material, which is available to authorized users.

Published
2015
Full Text
View/download PDF

43. Nitric oxide synthase polymorphisms, gene expression and lung function in chronic obstructive pulmonary disease

Author

Aminuddin, Farzian, Hackett, Tillie-Louise, Stefanowicz, Dorota, Saferali, Aabida, Paré, Peter D, Gulsvik, Amund, Bakke, Per, Cho, Michael H, Litonjua, Augusto, Lomas, David A, Anderson, Wayne H, Beaty, Terri H, Silverman, Edwin K, and Sandford, Andrew J

Subjects
Chronic obstructive pulmonary disease, Nitric oxide synthase, Polymorphism, Gene expression
Abstract

Background: Due to the pleiotropic effects of nitric oxide (NO) within the lungs, it is likely that NO is a significant factor in the pathogenesis of chronic obstructive pulmonary disease (COPD). The aim of this study was to test for association between single nucleotide polymorphisms (SNPs) in three NO synthase (NOS) genes and lung function, as well as to examine gene expression and protein levels in relation to the genetic variation. Methods: One SNP in each NOS gene (neuronal NOS (NOS1), inducible NOS (NOS2), and endothelial NOS (NOS3)) was genotyped in the Lung Health Study (LHS) and correlated with lung function. One SNP (rs1800779) was also analyzed for association with COPD and lung function in four COPD case–control populations. Lung tissue expression of NOS3 mRNA and protein was tested in individuals of known genotype for rs1800779. Immunohistochemistry of lung tissue was used to localize NOS3 expression. Results: For the NOS3 rs1800779 SNP, the baseline forced expiratory volume in one second in the LHS was significantly higher in the combined AG + GG genotypic groups compared with the AA genotypic group. Gene expression and protein levels in lung tissue were significantly lower in subjects with the AG + GG genotypes than in AA subjects. NOS3 protein was expressed in the airway epithelium and subjects with the AA genotype demonstrated higher NOS3 expression compared with AG and GG individuals. However, we were not able to replicate the associations with COPD or lung function in the other COPD study groups. Conclusions: Variants in the NOS genes were not associated with lung function or COPD status. However, the G allele of rs1800779 resulted in a decrease of NOS3 gene expression and protein levels and this has implications for the numerous disease states that have been associated with this polymorphism.

Published
2013
Full Text
View/download PDF

44. Mutations in TP53 increase the risk of SOX2 copy number alterations and silencing of TP53 reduces SOX2 expression in non-small cell lung cancer.

Author

Samulin Erdem J, Skaug V, Bakke P, Gulsvik A, Haugen A, and Zienolddiny S

Subjects
Adult, Aged, Aged, 80 and over, Carcinoma, Non-Small-Cell Lung pathology, Cell Line, Tumor, DNA Copy Number Variations, Female, Gene Expression Regulation, Neoplastic, Gene Silencing, Humans, Male, MicroRNAs genetics, Middle Aged, Mutation, Prognosis, Risk, SOXB1 Transcription Factors genetics, Tumor Suppressor Protein p53 antagonists & inhibitors, Carcinoma, Non-Small-Cell Lung genetics, MicroRNAs biosynthesis, SOXB1 Transcription Factors biosynthesis, Tumor Suppressor Protein p53 genetics
Abstract

Background: Amplifications of the transcription factor, SRY (sex determining region Y)-box 2 (SOX2), are common in non-small cell lung cancer (NSCLC). SOX2 signaling is important in maintaining the stem cell-like phenotype of cancer cells and contributes to the pathogenesis of lung cancer. TP53 is known to inhibit gene amplifications and to repress many stem cell-associated genes following DNA damage. The aim of this study was to investigate if TP53 mutational status affected SOX2 copy number variation and gene expression in early-stage NSCLC patients; moreover, to assess if TP53 regulates SOX2 expression in human lung cancer cells., Methods: 258 early-stage lung cancer patients were included in the study. Exons 4-9 in the TP53 gene were sequenced for mutations in tumor tissues. SOX2 copy number as well as TP53 and SOX2 gene expression were analyzed in tumor and in adjacent non-tumorous tissues by qPCR. TP53 and SOX2 were silenced using gene-specific siRNAs in human lung adenocarcinoma A427 cells, and the expression of TP53, SOX2 and subset of selected miRNAs was analyzed by qPCR. The odds ratios (ORs) for associations between copy number variation and lung cancer were estimated by conditional logistic regression, and the correlation between gene status and clinicopathological characteristics was assessed by Chi-square or Fisher's exact test. Gene expression data was analyzed using nonparametric Mann-Whitney test., Results: TP53 mutations were associated with an increased risk of acquiring a SOX2 copy number alteration (OR = 2.08, 95% CI: 1.14-3.79, p = 0.017), which was more frequently occurring in tumor tissues (34%) than in adjacent non-tumorous tissues (3%). Moreover, SOX2 and TP53 expression levels were strongly correlated in tumor tissues. In vitro studies showed that a reduction in TP53 was associated with decreased SOX2 expression in A427 cells. Furthermore, TP53 knockdown reduced the miRNA hsa-miR-145, which has previously been shown to regulate SOX2 expression., Conclusions: TP53 signaling may be important in the regulation of SOX2 copy number and expression in NSCLC tumors, and the miRNA hsa-miR-145-5p may be one potential driver. This prompts for further studies on the mechanisms behind the TP53-induced regulation of SOX2 expression and the possible importance of hsa-miR-145 in lung cancer.

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results