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  • 1.
    Bachelet, Delphine
    et al.
    CESP, INSERM UMR 1018, Faculty of Medicine, Paris-Sud University, UVSQ, Paris-Saclay University, Villejuif, France.
    Albert, Thilo
    Institute of Experimental Haematology and Transfusion Medicine, University Clinic Bonn, Germany.
    Mbogning, Cyprien
    CESP, INSERM UMR 1018, Faculty of Medicine, Paris-Sud University, UVSQ, Paris-Saclay University, Villejuif, France.
    Hässler, Signe
    CESP, INSERM UMR 1018, Faculty of Medicine, Paris-Sud University, UVSQ, Paris-Saclay University, Villejuif, France.
    Zhang, Yuan
    CESP, INSERM UMR 1018, Faculty of Medicine, Paris-Sud University, UVSQ, Paris-Saclay University, Villejuif, France.
    Schultze-Strasser, Stephan
    University Hospital Frankfurt, Goethe University, Department of Pediatrics, Molecular Haemostasis and Immunodeficiency, Frankfurt am Main, Germany.
    Repessé, Yohann
    CHU Caen, Hématologie Biologique, Caen, Caen, France.
    Rayes, Julie
    Sorbonne Universités, UPMC Univ Paris 06, INSERM, Université Paris Descartes, Sorbonne Paris Cité, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France.
    Pavlova, Anna
    Institute of Experimental Haematology and Transfusion Medicine, University Clinic Bonn, Bonn, Germany.
    Pezeshkpoor, Behnaz
    Institute of Experimental Haematology and Transfusion Medicine, University Clinic Bonn, Bonn, Germany.
    Liphardt, Kerstin
    Institute of Experimental Haematology and Transfusion Medicine, University Clinic Bonn, Bonn, Germany.
    Davidson, Julie E.
    GlaxoSmithKline, Uxbridge, Middlesex, United Kingdom.
    Hincelin-Méry, Agnès
    Sanofi, Chilly-Mazarin, France.
    Dönnes, Pierre
    SciCross AB, Skövde, Sweden.
    Lacroix-Desmazes, Sébastien
    Sorbonne Universités, UPMC Univ Paris 06, INSERM, Université Paris Descartes, Sorbonne Paris Cité, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France.
    Königs, Christoph
    University Hospital Frankfurt, Goethe University, Department of Pediatrics, Molecular Haemostasis and Immunodeficiency, Frankfurt am Main, Germany.
    Oldenburg, Johannes
    Institute of Experimental Haematology and Transfusion Medicine, University Clinic Bonn, Bonn, Germany.
    Broët, Philippe
    CESP, INSERM UMR 1018, Faculty of Medicine, Paris-Sud University, UVSQ, Paris-Saclay University, Villejuif, France / AP-HP, Paris-Sud University Hospitals, Villejuif, France.
    Risk stratification integrating genetic data for factor VIII inhibitor development in patients with severe hemophilia A2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 6, article id e0218258Article in journal (Refereed)
    Abstract [en]

    Replacement therapy in severe hemophilia A leads to factor VIII (FVIII) inhibitors in 30% of patients. Factor VIII gene (F8) mutation type, a family history of inhibitors, ethnicity and intensity of treatment are established risk factors, and were included in two published prediction tools based on regression models. Recently investigated immune regulatory genes could also play a part in immunogenicity. Our objective is to identify bio-clinical and genetic markers for FVIII inhibitor development, taking into account potential genetic high order interactions. The study population consisted of 593 and 79 patients with hemophilia A from centers in Bonn and Frankfurt respectively. Data was collected in the European ABIRISK tranSMART database. A subset of 125 severely affected patients from Bonn with reliable information on first treatment was selected as eligible for risk stratification using a hybrid tree-based regression model (GPLTR). In the eligible subset, 58 (46%) patients developed FVIII inhibitors. Among them, 49 (84%) were "high risk" F8 mutation type. 19 (33%) had a family history of inhibitors. The GPLTR model, taking into account F8 mutation risk, family history of inhibitors and product type, distinguishes two groups of patients: a high-risk group for immunogenicity, including patients with positive HLA-DRB1*15 and genotype G/A and A/A for IL-10 rs1800896, and a low-risk group of patients with negative HLA-DRB1*15 / HLA-DQB1*02 and T/T or G/T for CD86 rs2681401. We show associations between genetic factors and the occurrence of FVIII inhibitor development in severe hemophilia A patients taking into account for high-order interactions using a generalized partially linear tree-based approach.

  • 2.
    Delsing, Louise
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. Department of Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Institute of Neuroscience and Physiology, Gothenburg, Sweden / Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Dönnes, Pierre
    SciCross AB, Skövde, Sweden.
    Sánchez, José
    Biostatistics, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Clausen, Maryam
    Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Voulgaris, Dmitrios
    Department of Micro and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden.
    Falk, Anna
    Department of Neuroscience, Karolinska Institutet, Stockholm.
    Herland, Anna
    Department of Micro and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden / Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
    Brolén, Gabriella
    Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Zetterberg, Henrik
    Department of Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Institute of Neuroscience and Physiology, Gothenburg, Sweden / iClinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden / Department of Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom / UK Dementia Research Institute at UCL, London, United Kingdom.
    Hicks, Ryan
    Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Synnergren, Jane
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Barrier properties and transcriptome expression in human iPSC-derived models of the blood-brain barrier2018In: Stem Cells, ISSN 1066-5099, E-ISSN 1549-4918, Vol. 36, no 12, p. 1816-1827Article in journal (Refereed)
    Abstract [en]

    Cell-based models of the blood-brain barrier (BBB) are important for increasing the knowledge of BBB formation, degradation and brain exposure of drug substances. Human models are preferred over animal models because of inter-species differences in BBB structure and function. However, access to human primary BBB tissue is limited and has shown degeneration of BBB functions in vitro. Human induced pluripotent stem cells (iPSCs) can be used to generate relevant cell types to model the BBB with human tissue. We generated a human iPSC-derived model of the BBB that includes endothelial cells in co-culture with pericytes, astrocytes and neurons. Evaluation of barrier properties showed that the endothelial cells in our co-culture model have high transendothelial electrical resistance, functional efflux and ability to discriminate between CNS permeable and non-permeable substances. Whole genome expression profiling revealed transcriptional changes that occur in co-culture, including upregulation of tight junction proteins such as claudins and neurotransmitter transporters. Pathway analysis implicated changes in the WNT, TNF and PI3K-Akt pathways upon co-culture. Our data suggests that co-culture of iPSC-derived endothelial cells promotes barrier formation on a functional and transcriptional level. The information about gene expression changes in co-culture can be used to further improve iPSC-derived BBB models through selective pathway manipulation.

  • 3.
    Feldhahn, Magdalena
    et al.
    University of Tübingen, Center for Bioinformatics, Applied Bioinformatics, Sand 14, 72076 Tübingen, Germany.
    Dönnes, Pierre
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Schubert, Benjamin
    University of Tübingen, Center for Bioinformatics, Applied Bioinformatics, Sand 14, 72076 Tübingen, Germany.
    Schilbach, Karin
    University Children's Hospital, Department of, Hematology/Oncology, Hoppe-Seyler Str. 1, 72076, Tübingen, Germany.
    Rammensee, Hans-Georg
    University of Tübingen, Department of Immunology, Auf der Morgenstelle 15, 72076 Tuebingen, Germany.
    Kohlbacher, Oliver
    University of Tübingen, Center for Bioinformatics, Applied Bioinformatics, Sand 14, 72076 Tübingen, Germany / University of Tübingen, Quantitative Biology Center, Tübingen, Germany.
    miHA-Match: Computational detection of tissue-specific minor histocompatibility antigens2012In: JIM - Journal of Immunological Methods, ISSN 0022-1759, E-ISSN 1872-7905, Vol. 386, no 1-2, p. 94-100Article in journal (Refereed)
    Abstract [en]

    Allogenic stem cell transplantation has shown considerable success in a number of hematological malignancies, in particular in leukemia. The beneficial effect is mediated by donor T cells recognizing patient-specific HLA-binding peptides. These peptides are called minor histocompatibility antigens (miHAs) and are typically caused by single nucleotide polymorphisms. Tissue-specific miHAs have successfully been used in anti-tumor therapy without causing unspecific graft-versus-host reactions. However, only a small number of miHAs have been identified to date, limiting the clinical use.

    Here we present an immunoinformatics pipeline for the identification of miHAs. The pipeline can be applied to large-scale miHA screening, for example, in the development of diagnostic tests. Another interesting application is the design of personalized miHA-based cancer therapies based on patient-donor pair-specific miHAs detected by this pipeline. The suggested method covers various aspects of genetic variant detection, effects of alternative transcripts, and HLA-peptide binding. A comparison of our computational pipeline and experimentally derived datasets shows excellent agreement and coverage of the computationally predicted miHAs.

  • 4.
    Jensen, Poul Erik H.
    et al.
    Danish Multiple Sclerosis Center, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmar.
    Warnke, Clemens
    Ingenhoven, Kathleen
    Piccoli, Luca
    Gasis, Marcia
    Hermanrud, Christina
    Fernandez-Rodriguez, Blanca M.
    Ryner, Malin
    Kramer, Daniel
    Link, Jenny
    Ramanujam, Ryan
    Auer, Michael
    Buck, Dorothea
    Grummel, Verena
    Bertotti, Elisa
    Fissolo, Nicolas
    Oliver-Martos, Begoña
    Nytrova, Petra
    Khalil, Michael
    Guger, Michael
    Rathmaier, Sandra
    Sievers-Stober, Claudia
    Lindberg, Raija L. P.
    Hässler, Signe
    Bachelet, Delphine
    Aktas, Orhan
    Donnellan, Naoimh
    Lawton, Andy
    Hemmer, Bernhard
    Havrdova, Eva Kubala
    Kieseier, Bernd
    Hartung, Hans-Peter
    Comabella, Manuel
    Montalban, Xavier
    Derfuss, Tobias
    Sellebjerg, Finn
    Dönnes, Pierre
    SciCross AB, Skövde, Sweden.
    Pallardy, Marc
    Spindeldreher, Sebastian
    Broët, Philippe
    Deisenhammer, Florian
    Fogdell-Hahn, Anna
    Sorensen, Per Soelberg
    Detection and kinetics of persistent neutralizing anti-interferon-beta antibodies in patients with multiple sclerosis: Results from the ABIRISK prospective cohort study2019In: Journal of Neuroimmunology, ISSN 0165-5728, E-ISSN 1872-8421, Vol. 326, p. 19-27, article id S0165-5728(18)30332-1Article in journal (Refereed)
    Abstract [en]

    Two validated assays, a bridging ELISA and a luciferase-based bioassay, were compared for detection of anti-drug antibodies (ADA) against interferon-beta (IFN-β) in patients with multiple sclerosis. Serum samples were tested from patients enrolled in a prospective study of 18 months. In contrast to the ELISA, when IFN-β-specific rabbit polyclonal and human monoclonal antibodies were tested, the bioassay was the more sensitive to detect IFN-β ADA in patients' sera. For clinical samples, selection of method of ELISA should be evaluated prior to the use of a multi-tiered approach. A titer threshold value is reported that may be used as a predictor for persistently positive neutralizing ADA.

  • 5.
    Synnergren, Jane
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Dönnes, Pierre
    SciCross AB, Skövde, Sweden.
    Current Perspectives on Multi-Omics Data Integration With Application on Toxicity Biomarkers Discovery2018In: Open Access journal of Toxicology, ISSN 2474-7599, Vol. 2, no 5, p. 1-2, article id OAJT.MS.ID.555597Article, review/survey (Refereed)
  • 6.
    Synnergren, Jane
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Ghosheh, Nidal
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Dönnes, Pierre
    SciCross AB.
    Integration of Biomedical Big Data Requires Efficient Batch Effect Reduction2018In: 10th International Conference on Bioinformatics and Computational Biology (BICOB): Las Vegas, Nevada, USA 19 – 21 March 2018 / [ed] Hisham Al-Mubaid, Qin Ding, Oliver Eulenstein, 2018, p. 76-82Conference paper (Refereed)
    Abstract [en]

     Efficiency in dealing with batch effects will be the next frontier in large-scale biological data analysis, particularly when involving the integration of different types of datasets. Large-scale omics techniques have quickly developed during the last decade and huge amounts of data are now generated, which has started to revolutionize the area of medical research. With the increase in the volume of data across the whole spectrum of biology, problems related to data analytics are continuously increasing as analysis and interpretation of these large volumes of molecular data has become a real challenge. Tremendous efforts have been made to obtain data from various molecular levels and the most recent trends show that more and more researchers now are trying to integrate data of various molecular types to inform hypotheses and biological questions. Tightly connected to this work are the batch-related biases that commonly are apparent between different datasets, but these problems are often not tackled. In present study the ComBat algorithm was applied and evaluated on two different data integration problems. Results show that the batch effects present in the integrated datasets efficiently could be removed by applying the ComBat algorithm.

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