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Nilsson, Roland
Publications (10 of 16) Show all publications
Hägg, S., Skogsberg, J., Lundström, J., Noori, P., Nilsson, R., Zhong, H., . . . Björkegren, J. (2009). 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) Study. PLoS Genetics, 5(12), e1000754
Open this publication in new window or tab >>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) Study
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2009 (English)In: PLoS Genetics, ISSN 1553-7390, Vol. 5, no 12, p. e1000754-Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
PLoS Genetics, 2009
National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-52084 (URN)10.1371/journal.pgen.1000754 (DOI)
Note
On the day of the defence day the status of this article was: In Press.Available from: 2009-12-03 Created: 2009-12-03 Last updated: 2009-12-07Bibliographically approved
Nilsson, R., Bjorkegren, J. & Tegnér , J. (2009). On reliable discovery of molecular signatures. BMC BIOINFORMATICS, 10(38)
Open this publication in new window or tab >>On reliable discovery of molecular signatures
2009 (English)In: BMC BIOINFORMATICS, ISSN 1471-2105 , Vol. 10, no 38Article in journal (Refereed) Published
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.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-17519 (URN)10.1186/1471-2105-10-38 (DOI)
Note
Original Publication:Roland Nilsson, Johan Bjorkegren and Jesper Tegnér, On reliable discovery of molecular signatures, 2009, BMC BIOINFORMATICS, (10), 38.http://dx.doi.org/10.1186/1471-2105-10-38Licensee: BioMed Centralhttp://www.biomedcentral.com/Available from: 2009-03-28 Created: 2009-03-27 Last updated: 2009-03-28Bibliographically approved
Skogsberg, J., Dicker, A., Ryden, M., Astrom, G., Nilsson, R., Bhuiyan, H., . . . Bjorkegren, J. (2008). ApoB100-LDL acts as a metabolic signal from liver to peripheral fat causing inhibition of lipolysis in adipocytes. PLoS ONE, 3(11)
Open this publication in new window or tab >>ApoB100-LDL acts as a metabolic signal from liver to peripheral fat causing inhibition of lipolysis in adipocytes
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2008 (English)In: PLoS ONE, ISSN 1932-6203, Vol. 3, no 11Article in journal (Refereed) Published
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.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-46432 (URN)10.1371/journal.pone.0003771 (DOI)
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2011-01-10
Skogsberg, J., Lundstrom, J., Kovacs, A., Nilsson, R., Noori, P., Maleki, S., . . . Bjorkegren, J. (2008). Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes. PLoS Genetics, 4(3)
Open this publication in new window or tab >>Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes
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2008 (English)In: PLoS Genetics, ISSN 1553-7390, Vol. 4, no 3Article in journal (Refereed) Published
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.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-46811 (URN)10.1371/journal.pgen.1000036 (DOI)
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2011-01-10
Nilsson, R., Peña, J. M., Björkegren, J. & Tegnér, J. (2007). Consistent feature selection for pattern recognition in polynomial time. Journal of machine learning research, 8, 589-612
Open this publication in new window or tab >>Consistent feature selection for pattern recognition in polynomial time
2007 (English)In: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928, Vol. 8, p. 589-612Article in journal (Refereed) Published
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.

National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-38405 (URN)44191 (Local ID)44191 (Archive number)44191 (OAI)
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13
Nilsson, R., Peña, J. M., Björkegren, J. & Tegnér, J. (2007). Detecting Multivariate Differentially Expressed Genes. BMC Bioinformatics, 8:150
Open this publication in new window or tab >>Detecting Multivariate Differentially Expressed Genes
2007 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 8:150Article in journal (Refereed) Published
National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-38384 (URN)10.1186/1471-2105-8-150 (DOI)44054 (Local ID)44054 (Archive number)44054 (OAI)
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13
Kovacs, A., Tornvall, P., Nilsson, R., Tegnér, J., Hamsten, A. & Björkegren, J. (2007). Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia. Proceedings of the National Academy of Sciences of the United States of America, 104(34), 13768-13773
Open this publication in new window or tab >>Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia
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2007 (English)In: 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) Published
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.

National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-38391 (URN)10.1073/pnas.0706027104 (DOI)44125 (Local ID)44125 (Archive number)44125 (OAI)
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13
Nilsson, R. (2007). Statistical Feature Selection: With Applications in Life Science. (Doctoral dissertation). : Institutionen för fysik, kemi och biologi
Open this publication in new window or tab >>Statistical Feature Selection: With Applications in Life Science
2007 (English)Doctoral 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.

Abstract [sv]

Sekvenseringen av det mänskliga genomet i början på 2000-talet tillsammans och de senare sekvenseringsprojekten för olika modellorganismer har möjliggjort revolutionerade nya biologiska mätmetoder som omfattar hela genom. Microarrayer, mass-spektrometri och SNP-typning är exempel på sådana mätmetoder. Dessa metoder genererar mycket högdimensionell data. Ett centralt problem i modern biologisk forskning är således att identifiera de relevanta variablerna bland dessa tusentals mätningar. Detta kallas f¨or variabelsökning.

För att kunna studera variabelsökning på ett systematiskt sätt är en exakt definition av begreppet ”relevans” nödvändig. I denna avhandling behandlas relevans ur statistisk synvinkel: ”relevans” innebär ett statistiskt beroende av en målvariabel ; denna kan vara kontinuerlig, till exempel en blodtrycksmätning på en patient, eller diskret, till exempel en indikatorvariabel såsom ”rökare” eller ”icke-rökare”. Olika former av relevans behandlas och en sammanhängande teori presenteras. Varje relevansdefinition ger därefter upphov till ett specifikt variabelsökningsproblem.

Prediktiva variabler är sådana som kan användas för att konstruera prediktionsmodeller. Detta är viktigt exempelvis i kliniska diagnossystem. Här bevisas att en konsistent skattning av sådana variabler kan beräknas i polynomisk tid, så att variabelssökning är möjlig inom rimlig beräkningstid. Detta är ett genombrott jämfört med tidigare forskning. Dock visas även att metoder för att optimera prediktionsmodeller ofta ger höga andelar irrelevanta varibler, vilket är mycket problematiskt inom biologisk forskning. Därför presenteras också en ny variabelsökningsmetod med vilken de funna variablernas relevans är statistiskt säkerställd. I detta sammanhang visas också att variabelsökningsmetoder inte är reproducerbara i vanlig bemärkelse i höga dimensioner, även då relevans är statistiskt säkerställd. Detta förklarar till viss del varför genetiska associationsstudier som behandlar hela genom hittills har varit svåra att reproducera.

Här behandlas också fallet där alla relevanta variabler eftersöks. Detta problem bevisas kräva exponentiell beräkningstid i det allmänna fallet. Dock presenteras en metod som löser problemet i polynomisk tid under vissa statistiska antaganden, vilka kan anses rimliga för biologisk data. Också här tas problemet med falska positiver i beaktande, och en statistisk metod presenteras som säkerställer relevans. Denna metod tillämpas på fallstudier i typ 2-diabetes och cancer.

I vissa fall är dock mängden relevanta variabler mycket stor. Statistisk behandling av en enskild datatyp är då otillräcklig. I sådana situationer är det viktigt att nyttja olika datakällor samt existerande biologisk kunskap för att för att sortera fram de viktigaste fynden.

Place, publisher, year, edition, pages
Institutionen för fysik, kemi och biologi, 2007. p. 181
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1090
Keywords
Machine learning, supervised learning, classification, dimemsionality reduction, multiple testing, gene expression, microarray, cancer
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:liu:diva-11128 (URN)978-91-85715-24-4 (ISBN)
Public defence
2007-05-04, Sal Atrium, Karolinska Institutet, Stockholm, 13:00 (English)
Opponent
Supervisors
Available from: 2008-02-26 Created: 2008-02-26 Last updated: 2018-01-13
Peña, J. M., Nilsson, R., Björkegren, J. & Tegnér, J. (2007). Towards scalable and data efficient learning of Markov boundaries. International Journal of Approximate Reasoning, 45(2), 211-232
Open this publication in new window or tab >>Towards scalable and data efficient learning of Markov boundaries
2007 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 45, no 2, p. 211-232Article in journal (Refereed) Published
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.

National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-38393 (URN)10.1016/j.ijar.2006.06.008 (DOI)44146 (Local ID)44146 (Archive number)44146 (OAI)
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13
Nilsson, R., Björkegren, J. & Tegnér, J. (2006). A flexible implementation for support vector machines. The Mathematica journal, 10, 114-127
Open this publication in new window or tab >>A flexible implementation for support vector machines
2006 (English)In: The Mathematica journal, ISSN 1047-5974, E-ISSN 1097-1610, Vol. 10, p. 114-127Article in journal (Refereed) Published
National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-34815 (URN)23352 (Local ID)23352 (Archive number)23352 (OAI)
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13
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