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  • 1.
    Barrenäs, Fredrik
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Bruhn, Sören
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Jörnsten, Rebecka
    Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden.
    Langston, Michael A
    Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA.
    Nestor, Colm
    Östergötlands Läns Landsting.
    Rogers, Gary
    Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA .
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Disease-Associated MRNA Expression Differences in Genes with Low DNA Methylation2012Manuscript (preprint) (Other academic)
    Abstract [en]

    Although the importance of DNA methylation for mRNA expression has been shown for individualgenes in several complex diseases, such a relation has been difficult to show on a genome-wide scale.Here, we used microarrays to examine the relationship between DNA methylation and mRNAexpression in CD4+ T cells from patients with seasonal allergic rhinitis (SAR) and healthy controls.SAR is an optimal disease model because the disease process can be studied by comparing allergenchallengedCD4+ T cells obtained from patients and controls, and mimicked in Th2 polarised T cellsfrom healthy controls. The cells from patients can be analyzed to study relations between methylationand mRNA expression, while the Th2 cells can be used for functional studies. We found that DNAmethylation, but not mRNA expression clearly separated patients from controls. Similar to studies ofother complex diseases, we found no general relation between DNA methylation and mRNAexpression. However, when we took into account the absence or presence of CpG islands in thepromoters of disease associated genes an association was found: low methylation genes without CpGislands had significantly higher expression levels of disease-associated genes. This association wasconfirmed for genes whose expression levels were regulated by a transcription factor of knownrelevance for allergy, IRF4, using combined ChIP-chip and siRNA mediated silencing of IRF4expression. In summary, disease-associated increases of mRNA expression were found in lowmethylation genes without CpG islands in CD4+ T cells from patients with SAR. Further studies arewarranted to examine if a similar association is found in other complex diseases.

  • 2.
    Björnsson, Bergthor
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Surgery in Linköping.
    Borrebaeck, Carl
    Lund Univ, Sweden.
    Elander, Nils
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences.
    Gasslander, Thomas
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Surgery in Linköping.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Jornsten, Rebecka
    Univ Gothenburg, Sweden; Chalmers Univ Technol, Sweden.
    Jung Lee, Eun Jung
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Yonsei Univ, South Korea.
    Li, Xinxiu
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Lilja, Sandra
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Martinez, David
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Matussek, Andreas
    Karolinska Univ Hosp, Sweden; Dept Lab Med, Sweden.
    Sandström, Per
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Surgery in Linköping.
    Schäfer, Samuel
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Stenmarker, Margaretha
    Futurum Acad Hlth and Care, Sweden; Inst Clin Sci, Sweden.
    Sun, Xiao-Feng
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Oncology.
    Sysoev, Oleg
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, H.K.H. Kronprinsessan Victorias barn- och ungdomssjukhus Linköping/Motala.
    Digital twins to personalize medicine2019In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 12, no 1, article id 4Article in journal (Refereed)
    Abstract [en]

    Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

  • 3.
    Bruhn, Sören
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Fang, Yu
    Guiyang Medical Coll, Peoples R China University of Gothenburg, Sweden .
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Konstantinell, Aelita
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Kronke, Andrea
    Cenix BioScience GmbH, Germany .
    Sonnichsen, Birte
    Cenix BioScience GmbH, Germany .
    Bresnick, Anne
    Albert Einstein Coll Med, NY 10461 USA .
    Dulyaninova, Natalya
    Albert Einstein Coll Med, NY 10461 USA .
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhao, Yelin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Klingelhofer, Jorg
    University of Copenhagen, Denmark .
    Ambartsumian, Noona
    University of Copenhagen, Denmark .
    Beck, Mette K.
    Technical University of Denmark, Denmark .
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Bona, Elsa
    Boras Hospital, Sweden .
    Xiang, Zou
    University of Gothenburg, Sweden .
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    A Generally Applicable Translational Strategy Identifies S100A4 as a Candidate Gene in Allergy2014In: Science Translational Medicine, ISSN 1946-6234, E-ISSN 1946-6242, Vol. 6, no 218Article in journal (Refereed)
    Abstract [en]

    The identification of diagnostic markers and therapeutic candidate genes in common diseases is complicated by the involvement of thousands of genes. We hypothesized that genes co-regulated with a key gene in allergy, IL13, would form a module that could help to identify candidate genes. We identified a T helper 2 (T(H)2) cell module by small interfering RNA-mediated knockdown of 25 putative IL13-regulating transcription factors followed by expression profiling. The module contained candidate genes whose diagnostic potential was supported by clinical studies. Functional studies of human TH2 cells as well as mouse models of allergy showed that deletion of one of the genes, S100A4, resulted in decreased signs of allergy including TH2 cell activation, humoral immunity, and infiltration of effector cells. Specifically, dendritic cells required S100A4 for activating T cells. Treatment with an anti-S100A4 antibody resulted in decreased signs of allergy in the mouse model as well as in allergen-challenged T cells from allergic patients. This strategy, which may be generally applicable to complex diseases, identified and validated an important diagnostic and therapeutic candidate gene in allergy.

  • 4.
    Bruhn, Sören
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Katzenellenbogen, Mark
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Krönke, Andrea
    Cenix BioScience GmbH, Dresden, Germany.
    Sönnichsen, Birte
    Cenix BioScience GmbH, Dresden, Germany.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Combining gene expression microarray- and cluster analysis with sequence-based predictions to identify regulators of IL-13 in allergy2012In: Cytokine, ISSN 1043-4666, E-ISSN 1096-0023, Vol. 60, no 3, p. 736-740Article in journal (Refereed)
    Abstract [en]

    The Th2 cytokine IL-13 plays a key role in allergy, by regulating IgE, airway hyper secretion, eosinophils and mast cells. In this study, we aimed to identify novel transcription factors (TFs) that potentially regulated IL-13. We analyzed Th2 polarized naïve T cells from four different blood donors with gene expression microarrays to find clusters of genes that were correlated or anti-correlated with IL13. These clusters were further filtered, by selecting genes that were functionally related. In these clusters, we identified three transcription factors (TFs) that were predicted to regulate the expression of IL13, namely CEBPB, E2F6 and AHR. siRNA mediated knockdowns of these TFs in naïve polarized T cells showed significant increases of IL13, following knockdown of CEBPB and E2F6, but not AHR. This suggested an inhibitory role of CEBPB and E2F6 in the regulation of IL13 and allergy. This was supported by analysis of E2F6, but not CEBPB, in allergen-challenged CD4+ T cells from six allergic patients and six healthy controls, which showed decreased expression of E2F6 in patients. In summary, our findings indicate an inhibitory role of E2F6 in the regulation of IL-13 and allergy. The analytical approach may be generally applicable to elucidate the complex regulatory patterns in Th2 cell polarization and allergy.

  • 5.
    Carlstrom, Karl E.
    et al.
    Karolinska Inst, Sweden.
    Ewing, Ewoud
    Karolinska Inst, Sweden.
    Granqvist, Mathias
    Karolinska Inst, Sweden.
    Gyllenberg, Alexandra
    Karolinska Inst, Sweden.
    Aeinehband, Shahin
    Karolinska Inst, Sweden.
    Enoksson, Sara Lind
    Karolinska Univ Hosp, Sweden.
    Checa, Antonio
    Karolinska Inst, Sweden.
    Badam, Tejaswi
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. Univ Skovde, Sweden.
    Huang, Jesse
    Karolinska Inst, Sweden.
    Gomez-Cabrero, David
    Univ Publ Nevarra UPNA, Spain.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Al Nimer, Faiez
    Karolinska Inst, Sweden.
    Wheelock, Craig E.
    Karolinska Inst, Sweden.
    Kockum, Ingrid
    Karolinska Inst, Sweden.
    Olsson, Tomas
    Karolinska Inst, Sweden.
    Jagodic, Maja
    Karolinska Inst, Sweden.
    Piehl, Fredrik
    Karolinska Inst, Sweden.
    Therapeutic efficacy of dimethyl fumarate in relapsing-remitting multiple sclerosis associates with ROS pathway in monocytes2019In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 10, article id 3081Article in journal (Refereed)
    Abstract [en]

    Dimethyl fumarate (DMF) is a first-line-treatment for relapsing-remitting multiple sclerosis (RRMS). The redox master regulator Nrf2, essential for redox balance, is a target of DMF, but its precise therapeutic mechanisms of action remain elusive. Here we show impact of DMF on circulating monocytes and T cells in a prospective longitudinal RRMS patient cohort. DMF increases the level of oxidized isoprostanes in peripheral blood. Other observed changes, including methylome and transcriptome profiles, occur in monocytes prior to T cells. Importantly, monocyte counts and monocytic ROS increase following DMF and distinguish patients with beneficial treatment-response from non-responders. A single nucleotide polymorphism in the ROS-generating NOX3 gene is associated with beneficial DMF treatment-response. Our data implicate monocyte-derived oxidative processes in autoimmune diseases and their treatment, and identify NOX3 genetic variant, monocyte counts and redox state as parameters potentially useful to inform clinical decisions on DMF therapy of RRMS.

  • 6.
    Das, Jyotirmoy
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology, Infection and Inflammation. Linköping University, Faculty of Medicine and Health Sciences.
    Verma, Deepti
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Lerm, Maria
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology, Infection and Inflammation. Linköping University, Faculty of Medicine and Health Sciences.
    Identification of DNA methylation patterns predisposing for an efficient response to BCG vaccination in healthy BCG-naive subjects2019In: Epigenetics, ISSN 1559-2294, E-ISSN 1559-2308, Vol. 14, no 6, p. 589-601Article in journal (Refereed)
    Abstract [en]

    The protection against tuberculosis induced by the Bacille Calmette Guerin (BCG) vaccine is unpredictable. In our previous study, altered DNA methylation pattern in peripheral blood mononuclear cells (PBMCs) in response to BCG was observed in a subgroup of individuals, whose macrophages killed mycobacteria effectively (responders). These macrophages also showed production of Interleukin-1 beta (IL-1 beta) in response to mycobacterial stimuli before vaccination. Here, we hypothesized that the propensity to respond to the BCG vaccine is reflected in the DNA methylome. We mapped the differentially methylated genes (DMGs) in PBMCs isolated from responders/non-responders at the time point before vaccination aiming to identify possible predictors of BCG responsiveness. We identified 43 DMGs and subsequent bioinformatic analyses showed that these were enriched for actin-modulating pathways, predicting differences in phagocytosis. This could be validated by experiments showing that phagocytosis of mycobacteria, which is an event preceding mycobacteria-induced IL-1 beta production, was strongly correlated with the DMG pattern.

  • 7.
    Edström, Måns
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences.
    Dahle, Charlotte
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Vrethem, Magnus
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuroscience. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping. Huddinge University Hospital.
    Jenmalm, Maria
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences.
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Regulatory T cells in Multiple Sclerosis – Indications of impaired function of suppressive capacity and a role for chemokines2014Manuscript (preprint) (Other academic)
    Abstract [en]

    BACKGROUND Regulatory T cells (Treg) are critical for immune regulation and homeostasis. In multiple sclerosis (MS), the function of these cells has been shown to be impaired, although the underlying mechanism has yet to be shown. In the current study, we aimed to characterize and assess the phenotypical, functional and transcriptional characteristics of memory and naïve Treg in MS patients and controls.

    MATERIAL AND METHODS 27 patients with relapsing-remitting disease were included, along with 29 healthy controls. Flow cytometry was used for detailed phenotyping of Treg subpopulations CD4+CD45RA+/- and CD4dimCD25++ and their expression of FOXP3, CD39 and HELIOS. CFSE (proliferation marker) and CD69 (activation marker) were used to investigate the functional capacity of Treg. A microarray was employed for genome-wide transcriptional characterization of isolated Treg.

    RESULTS CD4+CD45RA–CD25++ activated Treg displayed a higher expression of FOXP3 and CD39 than resting CD4+CD45RA+CD25+ Treg, while no significant phenotypical differences were observed in Treg subpopulations between patients and controls. However, a lower anti-proliferative capacity was observed in activated Treg of MS patients compared with those of controls (p<0.05), while suppression of activation was similar to controls. Gene set enrichment analysis (GSEA) of microarray data revealed enrichment for the GO gene set ‘chemokine receptor binding’ in MS Treg.

    CONCLUSION Although numerical phenotypical assessment of resting and activated Tregs did not reveal any significant difference between patients and controls, functional co-culturing experiments showed an impaired function in activated Treg of MS patients. Furthermore, GSEA revealed immune-related gene sets overexpressed in Treg of MS patients, possibly containing clues to the functional impairment. In particular over-activity in chemokine signalling in Treg would be of interest for further investigation.

  • 8.
    Gawel, Danuta
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    James, A. Rani
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Liljenstrom, R.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Muraro, A.
    Padua University Hospital, Italy .
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    The Allergic Airway Inflammation Repository - a user-friendly, curated resource of mRNA expression levels in studies of allergic airways2014In: Allergy. European Journal of Allergy and Clinical Immunology, ISSN 0105-4538, E-ISSN 1398-9995, Vol. 69, no 8, p. 1115-1117Article in journal (Refereed)
    Abstract [en]

    Public microarray databases allow analysis of expression levels of candidate genes in different contexts. However, finding relevant microarray data is complicated by the large number of available studies. We have compiled a user-friendly, open-access database of mRNA microarray experiments relevant to allergic airway inflammation, the Allergic Airway Inflammation Repository (AAIR, http://aair.cimed.ike.liu.se/). The aim is to allow allergy researchers to determine the expression profile of their genes of interest in multiple clinical data sets and several experimental systems quickly and intuitively. AAIR also provides quick links to other relevant information such as experimental protocols, related literature and raw data files.

  • 9.
    Gawel, Danuta
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Serra-Musach, Jordi
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Lilja, Sandra
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Aagesen, Jesper
    Reg Jonkoping Cty, Sweden.
    Arenas, Alex
    Univ Rovira and Virgili, Spain.
    Asking, Bengt
    Reg Jonkoping Cty, Sweden.
    Bengner, Malin
    Reg Jonkoping Cty, Sweden.
    Bjorkander, Janne
    Reg Jonkoping Cty, Sweden.
    Biggs, Sophie
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Hjortswang, Henrik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Karlsson, Jan-Erik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Reg Jonkoping Cty, Sweden.
    Köpsén, Mattias
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Jung Lee, Eun Jung
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Yonsei Univ, South Korea.
    Lentini, Antonio
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Li, Xinxiu
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Magnusson, Mattias
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Martinez, David
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Matussek, Andreas
    Reg Jonkoping Cty, Sweden; Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Schafer, Samuel
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Seifert, Oliver
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Reg Jonkoping Cty, Sweden.
    Sonmez, Ceylan
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences.
    Stjernman, Henrik
    Reg Jonkoping Cty, Sweden.
    Tjärnberg, Andreas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Wu, Simon
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Åkesson, Karin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Reg Jonkoping Cty, Sweden.
    Shalek, Alex K.
    MIT, MA 02139 USA; Broad Inst MIT and Harvard, MA 02142 USA; Ragon Inst MGH MIT and Harvard, MA USA.
    Stenmarker, Margaretha
    Reg Jonkoping Cty, Sweden; Inst Clin Sci, Sweden.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, H.K.H. Kronprinsessan Victorias barn- och ungdomssjukhus Linköping/Motala.
    A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases2019In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 11, article id 47Article in journal (Refereed)
    Abstract [en]

    Background

    Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs.

    Methods

    The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs.

    Results

    We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model.

    Conclusions

    Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.

  • 10. Order onlineBuy this publication >>
    Gustafsson, Mika
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gene networks from high-throughput data: Reverse engineering and analysis2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Experimental innovations starting in the 1990’s leading to the advent of high-throughput experiments in cellular biology have made it possible to measure thousands of genes simultaneously at a modest cost. This enables the discovery of new unexpected relationships between genes in addition to the possibility of falsify existing. To benefit as much as possible from these experiments the new inter disciplinary research field of systems biology have materialized. Systems biology goes beyond the conventional reductionist approach and aims at learning the whole system under the assumption that the system is greater than the sum of its parts. One emerging enterprise in systems biology is to use the high-throughput data to reverse engineer the web of gene regulatory interactions governing the cellular dynamics. This relatively new endeavor goes further than clustering genes with similar expression patterns and requires the separation of cause of gene expression from the effect. Despite the rapid data increase we then face the problem of having too few experiments to determine which regulations are active as the number of putative interactions has increased dramatic as the number of units in the system has increased. One possibility to overcome this problem is to impose more biologically motivated constraints. However, what is a biological fact or not is often not obvious and may be condition dependent. Moreover, investigations have suggested several statistical facts about gene regulatory networks, which motivate the development of new reverse engineering algorithms, relying on different model assumptions. As a result numerous new reverse engineering algorithms for gene regulatory networks has been proposed. As a consequent, there has grown an interest in the community to assess the performance of different attempts in fair trials on “real” biological problems. This resulted in the annually held DREAM conference which contains computational challenges that can be solved by the prosing researchers directly, and are evaluated by the chairs of the conference after the submission deadline.

    This thesis contains the evolution of regularization schemes to reverse engineer gene networks from high-throughput data within the framework of ordinary differential equations. Furthermore, to understand gene networks a substantial part of it also concerns statistical analysis of gene networks. First, we reverse engineer a genome-wide regulatory network based solely on microarray data utilizing an extremely simple strategy assuming sparseness (LASSO). To validate and analyze this network we also develop some statistical tools. Then we present a refinement of the initial strategy which is the algorithm for which we achieved best performer at the DREAM2 conference. This strategy is further refined into a reverse engineering scheme which also can include external high-throughput data, which we confirm to be of relevance as we achieved best performer in the DREAM3 conference as well. Finally, the tools we developed to analyze stability and flexibility in linearized ordinary differential equations representing gene regulatory networks is further discussed.

    List of papers
    1. Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation
    Open this publication in new window or tab >>Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation
    2005 (English)In: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964, Vol. 2, no 3, p. 254-261Article in journal (Refereed) Published
    Abstract [en]

    We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-29432 (URN)10.1109/TCBB.2005.35 (DOI)000235704200008 ()14778 (Local ID)14778 (Archive number)14778 (OAI)
    Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2017-12-13
    2. Comparison and validation of community structures in complex networks
    Open this publication in new window or tab >>Comparison and validation of community structures in complex networks
    2006 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 367, p. 559-576Article in journal (Refereed) Published
    Abstract [en]

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information.

    Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

    Keywords
    Network, Community, Validation, Distance measure, Hierarchical clustering, K-means, GO
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-32261 (URN)10.1016/j.physa.2005.12.017 (DOI)000238236700049 ()18142 (Local ID)18142 (Archive number)18142 (OAI)
    Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2017-12-13
    3. Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions
    Open this publication in new window or tab >>Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions
    Show others...
    2009 (English)In: CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY, ISSN 0077-8923 , Vol. 1158, p. 265-275Article in journal (Refereed) Published
    Abstract [en]

    The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series kind steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed net-work, in which each edge has been assigned a score front it bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSillico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.

    Keywords
    reverse engineering, network inference, nonlinear, DREAM conference, LARS, LASSO
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-18289 (URN)10.1111/j.1749-6632.2008.03764.x (DOI)
    Note
    This is the authors’ version of the following article: Mika Gustafsson, Michael Hörnquist, Jesper Lundstrom, Johan Bjorkegren and Jesper Tegnér, Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions, 2009, Annals of the New York Academy of Sciences, Volume 1158 Issue, The Challenges of Systems Biology Community Efforts to Harness Biological Complexity, 265-275. which has been published in final form at: http://dx.doi.org/10.1111/j.1749-6632.2008.03764.x Copyright: Blackwell Publishing Ltd http://www.blackwellpublishing.com/ Available from: 2009-05-25 Created: 2009-05-15 Last updated: 2013-09-12Bibliographically approved
    4. Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
    Open this publication in new window or tab >>Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
    2009 (English)In: IET SYSTEMS BIOLOGY, ISSN 1751-8849, Vol. 3, no 4, p. 219-228Article in journal (Refereed) Published
    Abstract [en]

    Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.

    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-19799 (URN)10.1049/iet-syb.2008.0112 (DOI)
    Note
    This paper is a postprint of a paper submitted to and accepted for publication in IET SYSTEMS BIOLOGY and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library Original Publication: Mika Gustafsson, Michael Hörnquist, J Bjorkegren and Jesper Tegnér, Genome-wide system analysis reveals stable yet flexible network dynamics in yeast, 2009, IET SYSTEMS BIOLOGY, (3), 4, 219-228. http://dx.doi.org/10.1049/iet-syb.2008.0112 Copyright: The Institution of Engineering and Technology http://www.theiet.org/ Available from: 2009-08-28 Created: 2009-08-10 Last updated: 2013-12-12Bibliographically approved
    5. Integrating various data sources for improved quality in reverse engineering of gene regulatory networks
    Open this publication in new window or tab >>Integrating various data sources for improved quality in reverse engineering of gene regulatory networks
    2009 (English)In: Handbook of Research on Computational Methodologies in Gene Regulatory Networks / [ed] Sanjoy Das, Doina Caragea, Stephen M. Welch and William H. Hsu, IGI Global , 2009, 1, p. 476-496Chapter in book (Other academic)
    Abstract [en]

    In this chapter we outline a methodology to reverse engineer GRNs from various data sources within an ODE framework. The methodology is generally applicable and is suitable to handle the broad error distribution present in microarrays. The main effort of this chapter is the exploration of a fully data driven approach to the integration problem in a “soft evidence” based way. Integration is here seen as the process of incorporation of uncertain a priori knowledge and is therefore only relied upon if it lowers the prediction error. An efficient implementation is carried out by a linear programming formulation. This LP problem is solved repeatedly with small modifications, from which we can benefit by restarting the primal simplex method from nearby solutions, which enables a computational efficient execution. We perform a case study for data from the yeast cell cycle, where all verified genes are putative regulators and the a priori knowledge consists of several types of binding data, text-mining and annotation knowledge.

    Place, publisher, year, edition, pages
    IGI Global, 2009 Edition: 1
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-54096 (URN)978-1-60566-685-3 (ISBN)978-1-60566-686-0 (ISBN)
    Available from: 2010-02-23 Created: 2010-02-23 Last updated: 2013-09-12Bibliographically approved
    6. Gene Expression Prediction by Soft Integration and the Elastic Net: Best Performance of the DREAM3 Gene Expression Challenge
    Open this publication in new window or tab >>Gene Expression Prediction by Soft Integration and the Elastic Net: Best Performance of the DREAM3 Gene Expression Challenge
    2010 (English)In: PLoS ONE, ISSN 1932-6203, Vol. 5, no 2, p. e9134-Article in journal (Refereed) Published
    Abstract [en]

    Background: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance.

    Methodology/Principal Findings: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance.

    Conclusions/Significance: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.

    Keywords
    elastic net
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-54001 (URN)10.1371/journal.pone.0009134 (DOI)000274590500002 ()
    Projects
    CENIIT
    Available from: 2010-02-23 Created: 2010-02-18 Last updated: 2014-10-06Bibliographically approved
    7. System Analysis of Gene Regulatory Networks
    Open this publication in new window or tab >>System Analysis of Gene Regulatory Networks
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    The inference of genome-wide regulatory networks in cells from high-throughput data sets has revealed a diverse picture of only partly overlapping descriptions. Nevertheless, several conclusions of the large-scale properties in the organization of these networks are possible. For example, the presence of hubs, a modular structure and certain motifs are recurrent phenomena.

    Several authors have recently claimed cell systems to be stable against perturbations and random errors, but still able to rapidly switch between different states from specific stimuli. Since inferred genome-wide systems need to be extremely simple to avoid overfitting, these two features are hard to attain simultaneously in a mathematical model. Here we review and discuss possible measures of how system stability and flexibility may be manifested and measured for linear ODE models. Furthermore, we review how different network properties contribute to these systems level properties. It turns out that the presence of repressed hubs, together with other phenomena of topological nature such as motifs and modules, contributes to the overall stability and/or flexibility of the system.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-54097 (URN)
    Available from: 2010-02-23 Created: 2010-02-23 Last updated: 2013-09-12
  • 11.
    Gustafsson, Mika
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Large-scale topology, stability and biology of gene networks2006Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Experimental innovations in cell biology have provided a huge amount of genomescale data sets, settling the stage for understanding organisms on a system level. Recently, complex networks have been widely adopted and serve as a unifying language for widely different systems, including social, technological and biological systems. Still- in most biological cases-the number of interacting units vastly exceeds the number of measurements, hence large-scale models must still be very simple or non-specific. In this thesis we explore the limits of a linear (Lasso) network model on a genomic-scale for the Saccharomyces cerevisae organism and the limits of some analysis tools from the research field of complex networks. The former study (Paper I and Paper III) mainly regards validation issues, but also stipulate novel statistical system hypotheses, e.g., the system is significantly more stable than random, but still flexible to target stimuli. The latter study (Paper II) explores different heuristics in the search for communities (i.e., dense sub-graphs) within large complex networks and how the concept relates to functional modules.

    List of papers
    1. Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation
    Open this publication in new window or tab >>Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation
    2005 (English)In: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964, Vol. 2, no 3, p. 254-261Article in journal (Refereed) Published
    Abstract [en]

    We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-29432 (URN)10.1109/TCBB.2005.35 (DOI)000235704200008 ()14778 (Local ID)14778 (Archive number)14778 (OAI)
    Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2017-12-13
    2. Comparison and validation of community structures in complex networks
    Open this publication in new window or tab >>Comparison and validation of community structures in complex networks
    2006 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 367, p. 559-576Article in journal (Refereed) Published
    Abstract [en]

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information.

    Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

    Keywords
    Network, Community, Validation, Distance measure, Hierarchical clustering, K-means, GO
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-32261 (URN)10.1016/j.physa.2005.12.017 (DOI)000238236700049 ()18142 (Local ID)18142 (Archive number)18142 (OAI)
    Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2017-12-13
    3. Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
    Open this publication in new window or tab >>Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
    2009 (English)In: IET SYSTEMS BIOLOGY, ISSN 1751-8849, Vol. 3, no 4, p. 219-228Article in journal (Refereed) Published
    Abstract [en]

    Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.

    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-19799 (URN)10.1049/iet-syb.2008.0112 (DOI)
    Note
    This paper is a postprint of a paper submitted to and accepted for publication in IET SYSTEMS BIOLOGY and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library Original Publication: Mika Gustafsson, Michael Hörnquist, J Bjorkegren and Jesper Tegnér, Genome-wide system analysis reveals stable yet flexible network dynamics in yeast, 2009, IET SYSTEMS BIOLOGY, (3), 4, 219-228. http://dx.doi.org/10.1049/iet-syb.2008.0112 Copyright: The Institution of Engineering and Technology http://www.theiet.org/ Available from: 2009-08-28 Created: 2009-08-10 Last updated: 2013-12-12Bibliographically approved
  • 12.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Edström, Måns
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Tojo, James
    Karolinska Institute, Sweden Centre Molecular Med, Sweden .
    Kockum, Ingrid
    Karolinska Institute, Sweden Centre Molecular Med, Sweden .
    Olsson, Tomas
    Karolinska Institute, Sweden Centre Molecular Med, Sweden .
    Serra-Musach, Jordi
    IDIBELL, Spain .
    Bonifaci, Nuria
    IDIBELL, Spain .
    Angel Pujana, Miguel
    IDIBELL, Spain .
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment2014In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 6, no 17Article in journal (Refereed)
    Abstract [en]

    Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.

  • 13.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Alfredsson, Lars
    Karolinska Institute, Sweden.
    Baranzini, Sergio
    University of Calif San Francisco, CA, USA.
    Bjorkander, Janne
    County Council Jonköping, Sweden.
    Blomgran, Robert
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Hellberg, Sandra
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Eklund, Daniel
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Kockum, Ingrid
    Karolinska Institute, Sweden; Centre Molecular Med, Sweden.
    Konstantinell, Aelita
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Arctic University of Norway, Norway.
    Lahesmaa, Riita
    University of Turku, Finland; Abo Akad University, Finland.
    Lentini, Antonio
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Liljenström, H. Robert I.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Mattson, Lina
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Matussek, Andreas
    County Council Jonköping, Sweden.
    Mellergård, Johan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Neurology.
    Mendez, Melissa
    University of Peruana Cayetano Heredia, Peru.
    Olsson, Tomas
    Karolinska Institute, Sweden; Centre Molecular Med, Sweden.
    Pujana, Miguel A.
    Catalan Institute Oncol, Spain.
    Rasool, Omid
    University of Turku, Finland; Abo Akad University, Finland.
    Serra-Musach, Jordi
    Catalan Institute Oncol, Spain.
    Stenmarker, Margaretha
    County Council Jonköping, Sweden.
    Tripathi, Subhash
    University of Turku, Finland; Abo Akad University, Finland.
    Viitala, Miro
    University of Turku, Finland; Abo Akad University, Finland.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. University of Texas MD Anderson Cancer Centre, TX 77030 USA.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    A validated gene regulatory network and GWAS identifies early regulators of T cell-associated diseases2015In: Science Translational Medicine, ISSN 1946-6234, E-ISSN 1946-6242, Vol. 7, no 313, article id 313ra178Article in journal (Refereed)
    Abstract [en]

    Early regulators of disease may increase understanding of disease mechanisms and serve as markers for presymptomatic diagnosis and treatment. However, early regulators are difficult to identify because patients generally present after they are symptomatic. We hypothesized that early regulators of T cell-associated diseases could be found by identifying upstream transcription factors (TFs) in T cell differentiation and by prioritizing hub TFs that were enriched for disease-associated polymorphisms. A gene regulatory network (GRN) was constructed by time series profiling of the transcriptomes and methylomes of human CD4(+) T cells during in vitro differentiation into four helper T cell lineages, in combination with sequence-based TF binding predictions. The TFs GATA3, MAF, and MYB were identified as early regulators and validated by ChIP-seq (chromatin immunoprecipitation sequencing) and small interfering RNA knockdowns. Differential mRNA expression of the TFs and their targets in T cell-associated diseases supports their clinical relevance. To directly test if the TFs were altered early in disease, T cells from patients with two T cell-mediated diseases, multiple sclerosis and seasonal allergic rhinitis, were analyzed. Strikingly, the TFs were differentially expressed during asymptomatic stages of both diseases, whereas their targets showed altered expression during symptomatic stages. This analytical strategy to identify early regulators of disease by combining GRNs with genome-wide association studies may be generally applicable for functional and clinical studies of early disease development.

  • 14.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gene Expression Prediction by Soft Integration and the Elastic Net: Best Performance of the DREAM3 Gene Expression Challenge2010In: PLoS ONE, ISSN 1932-6203, Vol. 5, no 2, p. e9134-Article in journal (Refereed)
    Abstract [en]

    Background: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance.

    Methodology/Principal Findings: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance.

    Conclusions/Significance: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.

  • 15.
    Gustafsson, Mika
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Hörnquist, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Gene expression prediction by the elastic net2008In: DREAM,2008, 2008, p. 48-48Conference paper (Refereed)
  • 16.
    Gustafsson, Mika
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Hörnquist, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    In-silico network predictions by ODE and lasso2008In: DREAM,2008, 2008, p. 138-138Conference paper (Refereed)
  • 17.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Integrating various data sources for improved quality in reverse engineering of gene regulatory networks2009In: Handbook of Research on Computational Methodologies in Gene Regulatory Networks / [ed] Sanjoy Das, Doina Caragea, Stephen M. Welch and William H. Hsu, IGI Global , 2009, 1, p. 476-496Chapter in book (Other academic)
    Abstract [en]

    In this chapter we outline a methodology to reverse engineer GRNs from various data sources within an ODE framework. The methodology is generally applicable and is suitable to handle the broad error distribution present in microarrays. The main effort of this chapter is the exploration of a fully data driven approach to the integration problem in a “soft evidence” based way. Integration is here seen as the process of incorporation of uncertain a priori knowledge and is therefore only relied upon if it lowers the prediction error. An efficient implementation is carried out by a linear programming formulation. This LP problem is solved repeatedly with small modifications, from which we can benefit by restarting the primal simplex method from nearby solutions, which enables a computational efficient execution. We perform a case study for data from the yeast cell cycle, where all verified genes are putative regulators and the a priori knowledge consists of several types of binding data, text-mining and annotation knowledge.

  • 18.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    System Analysis of Gene Regulatory NetworksManuscript (preprint) (Other academic)
    Abstract [en]

    The inference of genome-wide regulatory networks in cells from high-throughput data sets has revealed a diverse picture of only partly overlapping descriptions. Nevertheless, several conclusions of the large-scale properties in the organization of these networks are possible. For example, the presence of hubs, a modular structure and certain motifs are recurrent phenomena.

    Several authors have recently claimed cell systems to be stable against perturbations and random errors, but still able to rapidly switch between different states from specific stimuli. Since inferred genome-wide systems need to be extremely simple to avoid overfitting, these two features are hard to attain simultaneously in a mathematical model. Here we review and discuss possible measures of how system stability and flexibility may be manifested and measured for linear ODE models. Furthermore, we review how different network properties contribute to these systems level properties. It turns out that the presence of repressed hubs, together with other phenomena of topological nature such as motifs and modules, contributes to the overall stability and/or flexibility of the system.

  • 19.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Bjorkegren, J
    Karolinska University Sjukhuset.
    Tegnér, Jesper
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Genome-wide system analysis reveals stable yet flexible network dynamics in yeast2009In: IET SYSTEMS BIOLOGY, ISSN 1751-8849, Vol. 3, no 4, p. 219-228Article in journal (Refereed)
    Abstract [en]

    Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.

  • 20.
    Gustafsson, Mika
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Hörnquist, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Björkegren, Johan
    Karolinska Institutet.
    Tegnér, Jesper
    Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
    Soft Integration of Data for Reverse Engineering2008In: International Conference on Systems Biology,2008, 2008, p. 127-127Conference paper (Refereed)
  • 21.
    Gustafsson, Mika
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Hörnquist, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Lombardi, Anna
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Combination of gene clusterings enhances biological validity2004In: Reglermöte,2004, 2004Conference paper (Other academic)
  • 22.
    Gustafsson, Mika
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Hörnquist, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Lombardi, Anna
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Community structure in "gene-to-gene" networks2004In: BioInformatics Conference,2004, 2004, p. 41-41Conference paper (Other academic)
  • 23.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Lombardi, Anna
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Comparison and validation of community structures in complex networks2006In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 367, p. 559-576Article in journal (Refereed)
    Abstract [en]

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information.

    Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

  • 24.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Lombardi, Anna
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation2005In: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964, Vol. 2, no 3, p. 254-261Article in journal (Refereed)
    Abstract [en]

    We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.

  • 25.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Lundstrom, Jesper
    Karolinska University Sjukhuset.
    Bjorkegren, Johan
    Karolinska University Sjukhuset.
    Tegnér , Jesper
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions2009In: CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY, ISSN 0077-8923 , Vol. 1158, p. 265-275Article in journal (Refereed)
    Abstract [en]

    The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series kind steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed net-work, in which each edge has been assigned a score front it bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSillico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.

  • 26.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Tegnér, Jesper
    Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
    et al. 155 external authors,
    The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line2009In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 41, p. 553-562Article in journal (Refereed)
    Abstract [en]

    Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

  • 27.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Barabasi, Albert-Laszlo
    Northeastern University, MA 02115 USA.
    Baranzini, Sergio
    University of Calif San Francisco, CA 94143 USA.
    Brunak, Soeren
    Technical University of Denmark, Denmark; University of Copenhagen, Denmark.
    Fan Chung, Kian
    University of London Imperial Coll Science Technology and Med, England.
    Federoff, Howard J.
    Georgetown University, DC 20057 USA.
    Gavin, Anne-Claude
    European Molecular Biol Lab, Germany.
    Meehan, Richard R.
    University of Edinburgh, Scotland.
    Picotti, Paola
    University of Zurich, Switzerland.
    Angel Pujana, Miguel
    Bellvitge Biomed Research Institute IDIBELL, Spain.
    Rajewsky, Nikolaus
    Max Delbruck Centre Molecular Med, Germany.
    Smith, Kenneth G. C.
    University of Cambridge, England; University of Cambridge, England.
    Sterk, Peter J.
    University of Amsterdam, Netherlands.
    Villoslada, Pablo
    Hospital Clin Barcelona, Spain; Hospital Clin Barcelona, Spain.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Modules, networks and systems medicine for understanding disease and aiding diagnosis2014In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 6, no 82Article, review/survey (Refereed)
    Abstract [en]

    Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.

  • 28.
    Hellberg, Sandra
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Eklund, Daniel
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Köpsén, Mattias
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Kockum, Ingrid
    Karolinska Institute, Department Clin Neurosci, Neuroimmunol Unit, S-17177 Linkoping, Sweden.
    Olsson, Tomas
    Karolinska Institute, Department Clin Neurosci, Neuroimmunol Unit, S-17177 Linkoping, Sweden.
    Skogh, Thomas
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Rheumatology.
    Kastbom, Alf
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Rheumatology.
    Sjöwall, Christopher
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Rheumatology.
    Vrethem, Magnus
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Neurology.
    Håkansson, Irene
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    Jenmalm, Maria
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Dynamic Response Genes in CD4+T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis2016In: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247, Vol. 16, no 11, p. 2928-2939Article in journal (Refereed)
    Abstract [en]

    Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.

  • 29.
    Hörnquist, Michael
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gustafsson, Mika
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Stability and Flexibility from a System Analysis of Gene RegulatoryNetworks Based on Ordinary Differential Equations2011In: The Open Bioinformatics Journal, ISSN 1875-0362, Vol. 5, p. 26-33Article in journal (Refereed)
    Abstract [en]

    The inference of large-scale gene regulatory networks from high-throughput data sets has revealed a diverse picture of only partially overlapping descriptions. Nevertheless, several properties in the organization of these networks are recurrent, such as hubs, a modular structure and certain motifs. Several authors have recently claimed cell systems to be stable against perturbations and random errors, but still able to rapidly switch between different states from specific stimuli. Since inferred mathematical models of large-scale systems need to be extremely simple to avoid overfitting, these two features are hard to attain simultaneously for a model. Here we review and discuss possible measures of how system stability and flexibility may be manifested and measured for linearized models based on systems of ordinary differential equations. Furthermore, we review how the network properties mentioned above together with the nature of the interactions contribute to these systems level properties. It turns out that the presence of repressed hubs, together with other phenomena of topological nature such as motifs and modules, contribute to the overall stability and/or flexibility of the model.

  • 30.
    Magnusson, Rasmus
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. CVMD iMedical DMPK AstraZeneca RandD, Sweden.
    Cross-talks via mTORC2 can explain enhanced activation in response to insulin in diabetic patients2017In: Bioscience Reports, ISSN 0144-8463, E-ISSN 1573-4935, Vol. 37, article id BSR20160514Article in journal (Refereed)
    Abstract [en]

    The molecular mechanisms of insulin resistance in Type 2 diabetes have been extensively studied in primary human adipocytes, and mathematical modelling has clarified the central role of attenuation of mammalian target of rapamycin (mTOR) complex 1 (mTORC1) activity in the diabetic state. Attenuation of mTORC1 in diabetes quells insulin-signalling network-wide, except for the mTOR in complex 2 (mTORC2)-catalysed phosphorylation of protein kinase B (PKB) at Ser(473) (PKB-S473P), which is increased. This unique increase could potentially be explained by feedback and interbranch cross-talk signals. To examine if such mechanisms operate in adipocytes, we herein analysed data from an unbiased phosphoproteomic screen in 3T3-L1 adipocytes. Using a mathematical modelling approach, we showed that a negative signal from mTORC1-p70 S6 kinase (S6K) to rictor-mTORC2 in combination with a positive signal from PKB to SIN1-mTORC2 are compatible with the experimental data. This combined cross-branch signalling predicted an increased PKB-S473P in response to attenuation of mTORC1 - a distinguishing feature of the insulin resistant state in human adipocytes. This aspect of insulin signalling was then verified for our comprehensive model of insulin signalling in human adipocytes. Introduction of the cross-branch signals was compatible with all data for insulin signalling in human adipocytes, and the resulting model can explain all data network-wide, including the increased PKB-S473P in the diabetic state. Our approach was to first identify potential mechanisms in data from a phosphoproteomic screen in a cell line, and then verify such mechanisms in primary human cells, which demonstrates how an unbiased approach can support a direct knowledge-based study.

  • 31.
    Magnusson, Rasmus
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Mariotti, Guido
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Köpsén, Mattias
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Lövfors, William
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Jornsten, Rebecka
    University of Gothenburg, Sweden.
    Linde, Joerg
    Hans Knoell Institute, Germany; Hans Knoell Institute, Germany.
    Nordling, Torbjorn
    National Cheng Kung University, Taiwan; Science Life Lab, Sweden.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Schulze, Sylvie
    Hans Knoell Institute, Germany.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Zhang, Hanmin
    Linköping University, Department of Physics, Chemistry and Biology. Linköping University, The Institute of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    Tjärnberg, Andreas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    LASSIM-A network inference toolbox for genome-wide mechanistic modeling2017In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 6, article id e1005608Article in journal (Refereed)
    Abstract [en]

    Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naive Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

  • 32.
    Mattson, Lina
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Lentini, Antonio
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Badam, Tejaswi
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    Ledin, Torbjörn
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Otorhinolaryngology in Linköping.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Serra I Musach, Jordi
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Björkander, Janne
    County Council Jonköping, Sweden.
    Xiang, Zou
    Hong Kong Polytech University, Peoples R China.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Potential Involvement of Type I Interferon Signaling in Immunotherapy in Seasonal Allergic Rhinitis2016In: Journal of Immunology Research, ISSN 2314-8861, E-ISSN 2314-7156, article id 5153184Article in journal (Refereed)
    Abstract [en]

    Specific immunotherapy (SIT) reverses the symptoms of seasonal allergic rhinitis (SAR) in most patients. Recent studies report type I interferons shifting the balance between type I T helper cell (Th1) and type II T helper cells (Th2) towards Th2 dominance by inhibiting the differentiation of naive Tcells into Th1 cells. As SIT is thought to cause a shift towardsTh1 dominance, we hypothesized that SIT would alter interferon type I signaling. To test this, allergen and diluent challenged CD4(+) T cells from healthy controls and patients from different time points were analyzed. The initial experiments focused on signature genes of the pathway and found complex changes following immunotherapy, which were consistent with our hypothesis. As interferon signaling involves multiple genes, expression profiling studies were performed, showing altered expression of the pathway. These findings require validation in a larger group of patients in further studies.

  • 33.
    Nestor, Colm
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Lentini, Antonio
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Bruhn, Sören
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Jornsten, Rebecka
    University of Gothenburg, Sweden .
    Langston, Michael A.
    University of Tennessee, TN USA .
    Rogers, Gary
    University of Tennessee, TN USA .
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    DNA Methylation Changes Separate Allergic Patients from Healthy Controls and May Reflect Altered CD4(+) T-Cell Population Structure2014In: PLoS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 10, no 1, p. e1004059-Article in journal (Refereed)
    Abstract [en]

    Altered DNA methylation patterns in CD4(+) T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its welldefined phenotype and etiology. We generated genome-wide DNA methylation (N-patients = 8, N-controls = 8) and gene expression (N-patients = 9, N-controls = 10) profiles of CD4(+) T-cells from SAR patients and healthy controls using Illuminas HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (N-patients = 12, N-controls = 12), but not by gene expression (N-patients = 21, N-controls = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (N-patients = 35) and controls (N-controls = 12), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4(+) T cells.

  • 34.
    Nestor, Colm E
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Dadfa, Elham
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Björkander, Jan Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Sublingual immunotherapy alters expression of IL-4 and its soluble and membrane-bound receptors2014In: Allergy. European Journal of Allergy and Clinical Immunology, ISSN 0105-4538, E-ISSN 1398-9995, Vol. 69, no 11, p. 1564-1566Article in journal (Refereed)
    Abstract [en]

    Seasonal allergic rhinitis (SAR) is a disease of increasing prevalence, which results from an inappropriate T-helper cell, type 2 (Th2) response to pollen. Specific immunotherapy (SIT) involves repeated treatment with small doses of pollen and can result in complete and lasting reversal of SAR. Here, we assayed the key Th2 cytokine, IL-4, and its soluble and membrane-bound receptor in SAR patients before and after SIT. Using allergen-challenge assays, we found that SIT treatment decreased IL-4 cytokine levels, as previously reported. We also observed a significant decrease in the IL-4 membrane-bound receptor (mIL4R) at both the level of mRNA and protein. SIT treatment resulted in a significant increase in the inhibitory soluble IL-4 receptor (sIL4R). Reciprocal changes in mIL4R and sIL4R were also observed in patient serum. Altered mIL4R and sIL4R is a novel explanation for the positive effects of immunotherapy with potential basic and clinical research implications.

  • 35.
    Nestor, Colm
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Lentini, Antonio
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Hägg Nilsson, Cathrine
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Mattson, Lina
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Wang, Hui
    MD Anderson Cancer Centre, TX 77030 USA.
    Rundquist, Olof
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Meehan, Richard R.
    University of Edinburgh, Scotland.
    Klocke, Bernward
    Genomatix Software GmbH, Germany.
    Seifert, Martin
    Genomatix Software GmbH, Germany.
    Hauck, Stefanie M.
    German Research Centre Environm Health GmbH, Germany.
    Laumen, Helmut
    Technical University of Munich, Germany; Technical University of Munich, Germany; Helmholtz Zentrum Munchen, Germany; Technical University of Munich, Germany; Technical University of Munich, Germany.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    5-Hydroxymethylcytosine Remodeling Precedes Lineage Specification during Differentiation of Human CD4(+) T Cells2016In: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247, Vol. 16, no 2, p. 559-570Article in journal (Refereed)
    Abstract [en]

    5-methylcytosine (5mC) is converted to 5-hydroxymethylcytosine (5hmC) by the TET family of enzymes as part of a recently discovered active DNA de-methylation pathway. 5hmC plays important roles in regulation of gene expression and differentiation and has been implicated in T cell malignancies and autoimmunity. Here, we report early and widespread 5mC/5hmC remodeling during human CD4(+) T cell differentiation ex vivo at genes and cell-specific enhancers with known T cell function. We observe similar DNA de-methylation in CD4(+) memory T cells in vivo, indicating that early remodeling events persist long term in differentiated cells. Underscoring their important function, 5hmC loci were highly enriched for genetic variants associated with T cell diseases and T-cell-specific chromosomal interactions. Extensive functional validation of 22 risk variants revealed potentially pathogenic mechanisms in diabetes and multiple sclerosis. Our results support 5hmC-mediated DNA de-methylation as a key component of CD4(+) T cell biology in humans, with important implications for gene regulation and lineage commitment.

  • 36.
    Schleicher, Jana
    et al.
    Hans Knöll Institute (HKI) in Jena, Germany.
    Conrad, Theresia
    Hans Knöll Institute (HKI) in Jena, Germany.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Guthke, Reinhard
    Systems Biology at Jena University,Germany.
    Linde, Jörg
    Jena University,Germany.
    Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions2017In: Briefings in Functional Genomics & Proteomics, ISSN 2041-2649, E-ISSN 2041-2657, Vol. 16, no 2, p. 57-69Article in journal (Refereed)
    Abstract [en]

    Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.

  • 37.
    Serra-Musach, Jordi
    et al.
    Bellvitge Institute Biomed Research IDIBELL, Spain.
    Mateo, Francesca
    Bellvitge Institute Biomed Research IDIBELL, Spain.
    Capdevila-Busquets, Eva
    Barcelona Institute Science and Technology, Spain.
    Ruiz de Garibay, Gorka
    Bellvitge Institute Biomed Research IDIBELL, Spain.
    Zhang, Xiaohu
    NIH, MD 20850 USA.
    Guha, Raj
    NIH, MD 20850 USA.
    Thomas, Craig J.
    NIH, MD 20850 USA.
    Grueso, Judit
    VHIO, Spain.
    Villanueva, Alberto
    Bellvitge Institute Biomed Research IDIBELL, Spain.
    Jaeger, Samira
    Barcelona Institute Science and Technology, Spain.
    Heyn, Holger
    IDIBELL, Spain.
    Vizoso, Miguel
    IDIBELL, Spain.
    Perez, Hector
    IDIBELL, Spain.
    Cordero, Alex
    IDIBELL, Spain.
    Gonzalez-Suarez, Eva
    IDIBELL, Spain.
    Esteller, Manel
    IDIBELL, Spain; University of Barcelona, Spain; University of Barcelona, Spain; ICREA, Spain.
    Moreno-Bueno, Gema
    Autonomous University of Madrid, Spain; MD Anderson Int Fdn, Spain.
    Tjärnberg, Andreas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Lazaro, Conxi
    IDIBELL, Spain.
    Serra, Violeta
    VHIO, Spain.
    Arribas, Joaquin
    ICREA, Spain; VHIO, Spain; Autonomous University of Barcelona, Spain.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Ferrer, Marc
    NIH, MD 20850 USA.
    Aloy, Patrick
    Barcelona Institute Science and Technology, Spain; ICREA, Spain.
    Angel Pujana, Miquel
    Bellvitge Institute Biomed Research IDIBELL, Spain.
    Cancer network activity associated with therapeutic response and synergism2016In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 8, no 88Article in journal (Refereed)
    Abstract [en]

    Background: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods: A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations.

  • 38.
    Sjogren, A-K M
    et al.
    University of Gothenburg.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Muraro, A
    Padua Gen University Hospital.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Saetrom, P
    University of Gothenburg.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Monozygotic twins discordant for intermittent allergic rhinitis differ in mRNA and protein levels2012In: Allergy. European Journal of Allergy and Clinical Immunology, ISSN 0105-4538, E-ISSN 1398-9995, Vol. 67, no 6, p. 831-833Article in journal (Refereed)
    Abstract [en]

    Monozygotic (MZ) twins discordant for complex diseases may help to find disease mechanisms that are not due to genetic variants. Intermittent allergic rhinitis (IAR) is an optimal disease model because it occurs at defined time points each year, owing to known external antigens. We hypothesized that MZ twins discordant for IAR could help to find gene expression differences that are not dependent on genetic variants. We collected blood outside of the season from MZ twins discordant for IAR, challenged their peripheral blood mononuclear cells (PBMC) with pollen allergen in vitro, collected supernatants and isolated CD4+ T cells. We identified disease-relevant mRNAs and proteins that differed between the discordant MZ twins. By contrast, no differences in microRNA expression were found. Our results indicate that MZ twins discordant for IAR is an optimal model to identify disease mechanisms that are not due to genetic variants.

  • 39.
    Vlaic, Sebastian
    et al.
    Leibniz Inst Nat Prod Res and Infect Biol, Germany.
    Conrad, Theresia
    Leibniz Inst Nat Prod Res and Infect Biol, Germany.
    Tokarski-Schnelle, Christian
    Leibniz Inst Nat Prod Res and Infect Biol, Germany.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Dahmen, Uta
    Friedrich Schiller Univ, Germany.
    Guthke, Reinhard
    Leibniz Inst Nat Prod Res and Infect Biol, Germany.
    Schuster, Stefan
    Friedrich Schiller Univ, Germany.
    ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 433Article in journal (Refereed)
    Abstract [en]

    The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPINs community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jene.de/index.php/0/2/490 (Others/ModuleDiscoverer).

  • 40.
    Zhang, Huan
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Nestor, Colm E
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Chung, Kian Fan
    Experimental Studies, National Heart and Lung Institute, Imperial College London, London, UK / NIHR Respiratory Biomedical Research Unit at the Royal Brompton NHS Foundation Trust and Imperial College London, London, UK; Royal Brompton NHS Fdn Trust, NIHR Resp Biomed Res Unit, London, England.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Pediatrics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Targeted omics and systems medicine: personalising care2014In: The Lancet Respiratory Medicine, ISSN 2213-2600, E-ISSN 2213-2619, Vol. 2, no 10, p. 785-787Article in journal (Other academic)
  • 41.
    Zhao, Yelin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Muraro, Antonella
    University of Padua, Italy .
    Bruhn, Sören
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Combined Multivariate and Pathway Analyses Show That Allergen-Induced Gene Expression Changes in CD4(+) T Cells Are Reversed by Glucocorticoids2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 6Article in journal (Refereed)
    Abstract [en]

    Background: Glucocorticoids (GCs) play a key role in the treatment of allergy. However, the genome-wide effects of GCs on gene expression in allergen-challenged CD4(+) T cells have not been described. The aim of this study was to perform a genome-wide analysis to investigate whether allergen-induced gene expression changes in CD4(+) T cells could be reversed by GCs. Methodology/Principal Findings: Gene expression microarray analysis was performed to profile gene expression in diluent( D), allergen- (A), and allergen + hydrocortisone- (T) challenged CD4(+) T cells from patients with seasonal allergic rhinitis. Principal component analysis (PCA) showed good separation of the three groups. To identify the correlation between changes in gene expression in allergen-challenged CD4(+) T cells before and after GC treatment, we performed orthogonal partial least squares discriminant analysis (OPLS-DA) followed by Pearson correlation analysis. This revealed that allergen-induced genes were widely reversed by GC treatment (r = -0.77, Pless than0.0001). We extracted 547 genes reversed by GC treatment from OPLS-DA models based on their high contribution to the discrimination and found that those genes belonged to several different inflammatory pathways including TNFR2 Signalling, Interferon Signalling, Glucocorticoid Receptor Signalling and T Helper Cell Differentiation. The results were supported by gene expression microarray analyses of two independent materials. Conclusions/Significance: Allergen-induced gene expression changes in CD4(+) T cells were reversed by treatment with glucocorticoids. The top allergen-induced genes that reversed by GC treatment belonged to several inflammatory pathways and genes of known or potential relevance for allergy.

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