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  • 1.
    Hägg, Sara
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Skogsberg, Josefin
    The Computational Medicine Group, Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden/Clinical Gene Networks AB, Karolinska Science Park, Stockholm, Sweden.
    Lundström, Jesper
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Noori, Peri
    The Computational Medicine Group, Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden/Clinical Gene Networks AB, Karolinska Science Park, Stockholm, Sweden.
    Nilsson, Roland
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Zhong, Hua
    Rosetta Inpharmatics, LLC, a Merck & Co., Inc, Seattle, USA.
    Maleki, Shohreh
    The Computational Medicine Group, Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    Shang, Ming-Mei
    The Computational Medicine Group, Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden/Clinical Gene Networks AB, Karolinska Science Park, Stockholm, Sweden.
    Brinne, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Bradshaw, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Bajic, Vladimir B.
    South African National Bioinformatics Institute (SANBI), University of the Western Cape, Cape Town, South Africa, and Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
    Samnegård, Ann
    Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
    Silveira, Angela
    Cardiovascular Genetics Group, Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    Kaplan, Lee M.
    Massachusetts General Hospital (MGH) Weight Center and Department of Medicine, Harvard Medical School, Boston, USA.
    Gigante, Bruna
    Department of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    Leander, Karin
    Department of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    de Faire, Ulf
    Department of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    Rosfors, Stefan
    Department of Clinical Physiology, Stockholm Söder Hospital, Karolinska Institutet, Stockholm, Sweden.
    Lockowandt, Ulf
    Department of Thoracic Surgery and Anesthesiology, Karolinska University Hospital and Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Liska, Jan
    Department of Thoracic Surgery and Anesthesiology, Karolinska University Hospital and Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Konrad, Peter
    Department of Surgery, Stockholm Söder Hospital, Karolinska Institutet, Stockholm, Sweden.
    Takolander, Rabbe
    Department of Surgery, Stockholm Söder Hospital, Karolinska Institutet, Stockholm, Sweden.
    Franco-Cereceda, Anders
    Department of Thoracic Surgery and Anesthesiology, Karolinska University Hospital and Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Schadt, Eric E.
    Rosetta Inpharmatics, LLC, a Merck & Co., Inc, Seattle, USA.
    Ivert, Torbjörn
    Department of Thoracic Surgery and Anesthesiology, Karolinska University Hospital and Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Hamsten, Anders
    Cardiovascular Genetics Group, Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    Tegnér, Jesper
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Björkegren, Johan
    The Computational Medicine Group, Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden/Clinical Gene Networks AB, Karolinska Science Park, Stockholm, Sweden 4 Rosetta Inpharmatics, LLC, a Merck & Co., Inc, Seattle, USA.
    Multi-Organ Expression Profiling Uncovers a Gene Module in Coronary Artery Disease Involving Transendothelial Migration of Leukocytes and LIM Domain Binding 2: The Stockholm Atherosclerosis Gene Expression (STAGE) Study2009In: PLoS Genetics, ISSN 1553-7390, Vol. 5, no 12, p. e1000754-Article in journal (Refereed)
    Abstract [en]

    Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n=66/tissue) and atherosclerotic and unaffected arterial wall (n=40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n=15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n=3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n=49/48) and one visceral fat (n=59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P=0.008 and P=0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n=55/54) relating to carotid stenosis (P=0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n=16/17, P<10-27and-30). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, cellular and lesion expression of LDB2, and the expression of 13 TEML genes in Ldb2-deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and together with LDB2 merits further attention in CAD research.

  • 2.
    Kovacs, Alexander
    et al.
    Atherosclerosis Research group KI.
    Tornvall, Per
    Cardiology Unit KI.
    Nilsson, Roland
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Hamsten, Anders
    Cardiology Unit KI.
    Björkegren, Johan
    Computional Medicine group KI.
    Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia2007In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 104, no 34, p. 13768-13773Article in journal (Refereed)
    Abstract [en]

    Increased baseline values of the acute-phase reactant C-reactive protein (CRP) are significantly associated with future cardiovascular disease, and some in vitro studies have claimed that human CRP (hCRP) has proatherogenic effects. In vivo studies in apolipoprotein E-deficient mouse models, however, have given conflicting results. We bred atherosclerosis-prone mice (Apob 100/100Ldlr-/-), which have human-like hypercholesterolemia, with hCRP transgenic mice (hCRP+/0) and studied lesion development at 15, 30, 40, and 50 weeks of age. Atherosclerotic lesions were smaller in hCRP+/0 Apob100/100Ldlr-/- mice than in hCRP0/0Apob100/100Ldlr-/- controls, as judged from the lesion surface areas of pinned-out aortas from mice at 40 and 50 weeks of age. In lesions from 40-week-old mice, mRNA expression levels of several genes in the proteasome degradation pathway were higher in hCRP +/0Apob100/100Ldlr-/- mice than in littermate controls, as shown by global gene expression profiles. These results were confirmed by real-time PCR, which also indicated that the activities of those genes were the same at 30 and 40 weeks in hCRP+/0Apob 100/100Ldlr-/- mice but were significantly lower at 40 weeks than at 30 weeks in controls. Our results show that hCRP is not proatherogenic but instead slows atherogenesis, possibly through proteasome-mediated protein degradation. © 2007 by The National Academy of Sciences of the USA.

  • 3.
    Nilsson, Roland
    Linköping University, Department of Physics, Chemistry and Biology, Computational Physics . Linköping University, The Institute of Technology.
    Statistical Feature Selection: With Applications in Life Science2007Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The sequencing of the human genome has changed life science research in many ways. Novel measurement technologies such as microarray expression analysis, genome-wide SNP typing and mass spectrometry are now producing experimental data of extremely high dimensions. While these techniques provide unprecedented opportunities for exploratory data analysis, the increase in dimensionality also introduces many difficulties. A key problem is to discover the most relevant variables, or features, among the tens of thousands of parallel measurements in a particular experiment. This is referred to as feature selection.

    For feature selection to be principled, one needs to decide exactly what it means for a feature to be ”relevant”. This thesis considers relevance from a statistical viewpoint, as a measure of statistical dependence on a given target variable. The target variable might be continuous, such as a patient’s blood glucose level, or categorical, such as ”smoker” vs. ”non-smoker”. Several forms of relevance are examined and related to each other to form a coherent theory. Each form of relevance then defines a different feature selection problem.

    The predictive features are those that allow an accurate predictive model, for example for disease diagnosis. I prove that finding redictive features is a tractable problem, in that consistent estimates can be computed in polynomial time. This is a substantial improvement upon current theory. However, I also demonstrate that selecting features to optimize prediction accuracy does not control feature error rates. This is a severe drawback in life science, where the selected features per se are important, for example as candidate drug targets. To address this problem, I propose a statistical method which to my knowledge is the first to achieve error control. Moreover, I show that in high dimensions, feature sets can be impossible to replicate in independent experiments even with controlled error rates. This finding may explain the lack of agreement among genome-wide association studies and molecular signatures of disease.

    The most predictive features may not always be the most relevant ones from a biological perspective, since the predictive power of a given feature may depend on measurement noise rather than biological properties. I therefore consider a wider definition of relevance that avoids this problem. The resulting feature selection problem is shown to be asymptotically intractable in the general case; however, I derive a set of simplifying assumptions which admit an intuitive, consistent polynomial-time algorithm. Moreover, I present a method that controls error rates also for this problem. This algorithm is evaluated on microarray data from case studies in diabetes and cancer.

    In some cases however, I find that these statistical relevance concepts are insufficient to prioritize among candidate features in a biologically reasonable manner. Therefore, effective feature selection for life science requires both a careful definition of relevance and a principled integration of existing biological knowledge.

  • 4.
    Nilsson, Roland
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Bajic, VB
    Suzuki, H
    di Bernardo, D
    Björkegren, J.
    Katayama, S
    Reid, JF
    Sweet, MJ
    Gariboldi, M
    Carninci, P
    Hayashizaki, Y
    Hume, DA
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Ravasi, T
    Trancriptional network dynamics in macrophage activation2006In: Genomics, ISSN 0888-7543, E-ISSN 1089-8646, Vol. 88:2, p. 133-142Article in journal (Refereed)
  • 5.
    Nilsson, Roland
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Bjorkegren, Johan
    Karolinska Institute.
    Tegnér , Jesper
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    On reliable discovery of molecular signatures2009In: BMC BIOINFORMATICS, ISSN 1471-2105 , Vol. 10, no 38Article in journal (Refereed)
    Abstract [en]

    Background: Plasmid encoded (CTX)-C-bla-M enzymes represent an important sub-group of class A beta-lactamases causing the ESBL phenotype which is increasingly found in Enterobacteriaceae including Klebsiella spp. Molecular typing of clinical ESBL-isolates has become more and more important for prevention of the dissemination of ESBL-producers among nosocomial environment.

    Methods: Multiple displacement amplified DNA derived from 20 K. pneumoniae and 34 K. oxytoca clinical isolates with an ESBL-phenotype was used in a universal CTX-M PCR amplification assay. Identification and differentiation of (CTX)-C-bla-M and (OXY)-O-bla/K1 sequences was obtained by DNA sequencing of M13-sequence-tagged CTX-M PCR-amplicons using a M13-specific sequencing primer.

    Results: Nine out of 20 K. pneumoniae clinical isolates had a (CTX)-C-bla-M genotype. Interestingly, we found that the universal degenerated primers also amplified the chromosomally located K1-gene in all 34 K. oxytoca clinical isolates. Molecular identification and differentiation between (CTX)-C-bla-M and (OXY)-O-bla/K1-genes could only been achieved by sequencing of the PCR-amplicons. In silico analysis revealed that the universal degenerated CTX-M primer-pair used here might also amplify the chromosomally located (OXY)-O-bla and K1-genes in Klebsiella spp. and K1-like genes in other Enterobacteriaceae.

    Conclusion: The PCR-based molecular typing method described here enables a rapid and reliable molecular identification of (CTX)-C-bla-M, and (OXY)-O-bla/K1-genes. The principles used in this study could also be applied to any situation in which antimicrobial resistance genes would need to be sequenced.

  • 6.
    Nilsson, Roland
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Björkegren, Johan
    Karolinska universitetssjukhuset.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    A flexible implementation for support vector machines2006In: The Mathematica journal, ISSN 1047-5974, E-ISSN 1097-1610, Vol. 10, p. 114-127Article in journal (Refereed)
  • 7.
    Nilsson, Roland
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Peña, Jose M.
    Linköping University, Department of Computer and Information Science, Database and information techniques.
    Björkegren, Johan
    Computional Medicine group KI.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Consistent feature selection for pattern recognition in polynomial time2007In: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928, Vol. 8, p. 589-612Article in journal (Refereed)
    Abstract [en]

    We analyze two different feature selection problems: finding a minimal feature set optimal for classification (MINIMAL-OPTIMAL) vs. finding all features relevant to the target variable (ALL-RELEVANT). The latter problem is motivated by recent applications within bioinformatics, particularly gene expression analysis. For both problems, we identify classes of data distributions for which there exist consistent, polynomial-time algorithms. We also prove that ALL-RELEVANT is much harder than MINIMAL-OPTIMAL and propose two consistent, polynomial-time algorithms. We argue that the distribution classes considered are reasonable in many practical cases, so that our results simplify feature selection in a wide range of machine learning tasks.

  • 8.
    Nilsson, Roland
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Peña, Jose M.
    Linköping University, Department of Computer and Information Science, Database and information techniques.
    Björkegren, Johan
    Computional Medicin group KI.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Detecting Multivariate Differentially Expressed Genes2007In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 8:150Article in journal (Refereed)
  • 9.
    Nilsson, Roland
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Peña, Jose M.
    Linköping University, Department of Computer and Information Science, Database and information techniques.
    Björkegren, Johan
    Karolinska universitetssjukhuset.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Evaluating feature selection for SVMs in high dimensions2006In: 17th European Conference on Machine Learning,2006, Berlin: Springer , 2006, p. 719-Conference paper (Refereed)
  • 10.
    Nilsson, Roland
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    al., et.
    The transcriptional landscape of the mammalian genome2005In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 309, no 5740, p. 1559-1563Article in journal (Refereed)
  • 11.
    Peña, Jose M.
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques.
    Nilsson, Roland
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Björkegren, Johan
    Karolinska universitetssjukuset.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Identifying relevant nodes without learning the model2006In: 22nd Conference on Uncertainty in Artificial Intelligence,2006, Cambridge USA: MIT Psb , 2006, p. 38-Conference paper (Refereed)
  • 12.
    Peña, Jose M.
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques.
    Nilsson, Roland
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Björkegren, Johan
    Karolinska universitetssjukhuset.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity2006In: 3rd European Workshop on Probabilistic Graphical Models PGM2006,2006, Prag: Reprostredisko UK MFF , 2006, p. 247-Conference paper (Refereed)
  • 13.
    Peña, Jose M.
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques.
    Nilsson, Roland
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Björkegren, Johan
    Computional Medicine group KI.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
    Towards scalable and data efficient learning of Markov boundaries2007In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 45, no 2, p. 211-232Article in journal (Refereed)
    Abstract [en]

    We propose algorithms for learning Markov boundaries from data without having to learn a Bayesian network first. We study their correctness, scalability and data efficiency. The last two properties are important because we aim to apply the algorithms to identify the minimal set of features that is needed for probabilistic classification in databases with thousands of features but few instances, e.g. gene expression databases. We evaluate the algorithms on synthetic and real databases, including one with 139,351 features. © 2006 Elsevier Inc. All rights reserved.

  • 14.
    Skogsberg, J.
    et al.
    The Computational Medicine Group, Karolinska Institutet, Karolinska University Hospital, Solna, Stockholm, Sweden.
    Dicker, A.
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden.
    Ryden, M.
    Rydén, M., Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden.
    Astrom, G.
    Åström, G., Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden.
    Nilsson, Roland
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Bhuiyan, H.
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Solna, Stockholm, Sweden.
    Vitols, S.
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Solna, Stockholm, Sweden.
    Mairal, A.
    Inserm, U586, Obesity Research Unit, Toulouse, France.
    Langin, D.
    Inserm, U586, Obesity Research Unit, Toulouse, France.
    Alberts, P.
    Biovitrum AB, Stockholm, Sweden.
    Walum, E.
    Biovitrum AB, Stockholm, Sweden.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Hamsten, A.
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Solna, Stockholm, Sweden.
    Arner, P.
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden.
    Bjorkegren, J.
    Björkegren, J., The Computational Medicine Group, Karolinska Institutet, Karolinska University Hospital, Solna, Stockholm, Sweden.
    ApoB100-LDL acts as a metabolic signal from liver to peripheral fat causing inhibition of lipolysis in adipocytes2008In: PLoS ONE, ISSN 1932-6203, Vol. 3, no 11Article in journal (Refereed)
    Abstract [en]

    Background: Free fatty acids released from adipose tissue affect the synthesis of apolipoprotein B-containing lipoproteins and glucose metabolism in the liver. Whether there also exists a reciprocal metabolic arm affecting energy metabolism in white adipose tissue is unknown. Methods and Findings: We investigated the effects of apoB-containing lipoproteins on catecholamine-induced lipolysis in adipocytes from subcutaneous fat cells of obese but otherwise healthy men, fat pads from mice with plasma lipoproteins containing high or intermediate levels of apoB100 or no apoB100, primary cultured adipocytes, and 3T3-L1 cells. In subcutaneous fat cells, the rate of lipolysis was inversely related to plasma apoB levels. In human primary adipocytes, LDL inhibited lipolysis in a concentration-dependent fashion. In contrast, VLDL had no effect. Lipolysis was increased in fat pads from mice lacking plasma apoB100, reduced in apoB100-only mice, and intermediate in wild-type mice. Mice lacking apoB100 also had higher oxygen consumption and lipid oxidation. In 3T3-L1 cells, apoB100-containing lipoproteins inhibited lipolysis in a dose-dependent fashion, but lipoproteins containing apoB48 had no effect. ApoB100-LDL mediated inhibition of lipolysis was abolished in fat pads of mice deficient in the LDL receptor (Ldlr-/- Apob100/100). Conclusions: Our results show that the binding of apoB100-LDL to adipocytes via the LDL receptor inhibits intracellular noradrenaline-induced lipolysis in adipocytes. Thus, apoB100-LDL is a novel signaling molecule from the liver to peripheral fat deposits that may be an important link between atherogenic dyslipidemias and facets of the metabolic syndrome. © 2008 Skogsberg et al.

  • 15.
    Skogsberg, J.
    et al.
    Computational Medicine Group, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.
    Lundstrom, J.
    Lundström, J., Computational Medicine Group, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.
    Kovacs, A.
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.
    Nilsson, Roland
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Noori, P.
    Computational Medicine Group, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.
    Maleki, S.
    Computational Medicine Group, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.
    Kohler, M.
    Köhler, M., Computational Medicine Group, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.
    Hamsten, A.
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Bjorkegren, J.
    Björkegren, J., Computational Medicine Group, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.
    Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes2008In: PLoS Genetics, ISSN 1553-7390, Vol. 4, no 3Article in journal (Refereed)
    Abstract [en]

    Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr-/-Apo 100/100 Mttpflox/flox Mx1-Cre). Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins) at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies. © 2008 Skogsberg et al.

  • 16.
    Tegnér, Jesper
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Nilsson, Roland
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Bajic, V.B.
    University of Western Cape, SANBI, Bellville, 7535, South Africa.
    Bjorkegren, J.
    Björkegren, J., Unit of Computational Medicine, King Gustaf V Research Institute, Department of Medicine, SE-171 76 Stockholm, Sweden.
    Ravasi, T.
    Genome Exploration Research Group (Genome Network Project Core Group), RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, Japan, Scripps NeuroAIDS Preclinical Studies Centre, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States, Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States.
    Systems biology of innate immunity2006In: Cellular Immunology, ISSN 0008-8749, E-ISSN 1090-2163, Vol. 244, no 2, p. 105-109Article in journal (Refereed)
    Abstract [en]

    Systems Biology has emerged as an exciting research approach in molecular biology and functional genomics that involves a systematic use of genomic, proteomic, and metabolomic technologies for the construction of network-based models of biological processes. These endeavors, collectively referred to as systems biology establish a paradigm by which to systematically interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here, we present a new systems approach, integrating DNA and transcript expression information, specifically designed to identify transcriptional networks governing the macrophage immune response to lipopolysaccharide (LPS). Using this approach, we are not only able to infer a global macrophage transcriptional network, but also time-specific sub-networks that are dynamically active across the LPS response. We believe that our system biological approach could be useful for identifying other complex networks mediating immunological responses. © 2007 Elsevier Inc. All rights reserved.

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