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Tilevik, Andreas
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Publications (10 of 23) Show all publications
Kokkonen, A., Tilevik, D., Pernestig, A.-K., Tilevik, A., Fagerlind, M. & Enroth, H. (2019). Clinical use of 16SrRNA Ion TorrentNext-generation sequencing and bioinformatics pipeline. In: : . Paper presented at 14th Annual Workshop in Systems Biology, University of Skövde, Sweden, 21 November 2019.
Open this publication in new window or tab >>Clinical use of 16SrRNA Ion TorrentNext-generation sequencing and bioinformatics pipeline
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2019 (English)Conference paper, Poster (with or without abstract) (Other academic)
National Category
Medical and Health Sciences Infectious Medicine
Research subject
INF502 Biomarkers
Identifiers
urn:nbn:se:his:diva-18275 (URN)
Conference
14th Annual Workshop in Systems Biology, University of Skövde, Sweden, 21 November 2019
Projects
BioMine
Funder
Knowledge Foundation
Available from: 2020-03-03 Created: 2020-03-03 Last updated: 2020-03-13Bibliographically approved
Irani Shemirani, M., Tilevik, A., Tilevik, D., Pernestig, A.-K. & Enroth, H. (2019). Comparison of Whole Genome Sequencing Pipelines for Analysis of Staphylococcus aureus Isolates from Sepsis Patients. In: : . Paper presented at 14th Annual Workshop in Systems Biology, University of Skövde, Sweden, 21 November 2019.
Open this publication in new window or tab >>Comparison of Whole Genome Sequencing Pipelines for Analysis of Staphylococcus aureus Isolates from Sepsis Patients
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2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical and Health Sciences Infectious Medicine
Research subject
INF502 Biomarkers; Infection Biology
Identifiers
urn:nbn:se:his:diva-18274 (URN)
Conference
14th Annual Workshop in Systems Biology, University of Skövde, Sweden, 21 November 2019
Projects
BioMine - Data-mining för identifiering, selektion och validering av biomarkörer
Funder
Knowledge Foundation, 20160330
Available from: 2020-03-03 Created: 2020-03-03 Last updated: 2020-03-13Bibliographically approved
Tilevik, D., Saxenborn, P., Tilevik, A., Fagerlind, M., Lubovac-Pilav, Z., Pernestig, A.-K. & Enroth, H. (2019). Using next-generation sequencing to study biodiversity in Klebsiella spp. isolated from patients with suspected sepsis. In: : . Paper presented at 29th European Congress of Clinical Microbiology and Infectious Diseases, ECCMID, Amsterdam, Netherlands, 13-16 April, 2019.
Open this publication in new window or tab >>Using next-generation sequencing to study biodiversity in Klebsiella spp. isolated from patients with suspected sepsis
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2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical and Health Sciences Infectious Medicine Microbiology
Research subject
Infection Biology; Bioinformatics
Identifiers
urn:nbn:se:his:diva-18200 (URN)
Conference
29th European Congress of Clinical Microbiology and Infectious Diseases, ECCMID, Amsterdam, Netherlands, 13-16 April, 2019
Available from: 2020-02-07 Created: 2020-02-07 Last updated: 2020-03-12Bibliographically approved
Wallner, F. K., Hultquist Hopkins, M., Woodworth, N., Lindvall Bark, T., Olofsson, P. & Tilevik, A. (2018). Correlation and cluster analysis of immunomodulatory drugs based on cytokine profiles. Pharmacological Research, 128, 244-251
Open this publication in new window or tab >>Correlation and cluster analysis of immunomodulatory drugs based on cytokine profiles
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2018 (English)In: Pharmacological Research, ISSN 1043-6618, E-ISSN 1096-1186, Vol. 128, p. 244-251Article in journal (Refereed) Published
Abstract [en]

Drug discovery is a constant struggle to overcome hurdles posed by the complexity of biological systems. One of these hurdles is to find and understand the molecular target and the biological mechanism of action. Although the molecular target has been determined, the true biological effect may be unforeseen also for well-established drugs. Hence, there is a need for novel ways to increase the knowledge of the biological effects of drugs in the developmental process. In this study, we have determined cytokine profiles for 26 non-biological immunomodulatory drugs or drug candidates and used these profiles to cluster the compounds according to their effect in a preclinical ex vivo culture model of arthritis. This allows for prediction of functions and drug target of a novel drug candidate based on profiles obtained in this study. Results from the study showed that the JAK inhibitors tofacitinib and ruxolitinib formed a robust cluster and were found to have a distinct cytokine profile compared to the other drugs. Another robust cluster included the calcineurin inhibitors cyclosporine A and tacrolimus and the protein kinase inhibitors fostamatinib disodium and sotrastaurin acetate, which caused a strong overall inhibition of the cytokine production. The results of this methodology indicate that cytokine profiles can be used to provide a fingerprint-like identification of a drug as a tool to benchmark novel drugs and to improve descriptions of mode of action.

Keywords
Clustering, Cytokines, Immunosuppressive, PCA
National Category
Pharmacology and Toxicology
Research subject
Infection Biology; INF502 Biomarkers
Identifiers
urn:nbn:se:his:diva-14564 (URN)10.1016/j.phrs.2017.10.012 (DOI)000435626500025 ()29079427 (PubMedID)2-s2.0-85032948174 (Scopus ID)
Projects
A novel framework for facilitating drug discovery
Funder
Carl Tryggers foundation , CTS15:232
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2019-11-19Bibliographically approved
Wallner, F. K., Hultqvist Hopkins, M., Lindvall, T., Olofsson, P. & Tilevik, A. (2017). Cytokine correlation analysis based on drug perturbation. Cytokine, 90, 73-79
Open this publication in new window or tab >>Cytokine correlation analysis based on drug perturbation
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2017 (English)In: Cytokine, ISSN 1043-4666, E-ISSN 1096-0023, Vol. 90, p. 73-79Article in journal (Refereed) Published
Abstract [en]

Cytokines and chemokines play a crucial role in regulating the immune system. Understanding how these molecules are co-regulated is important to understand general immunology, and particularly their role in clinical applications such as development and evaluation of novel drug therapies. Cytokines are today widely used as therapeutic targets and as biomarkers to monitor effects of drug therapies and for prognosis and diagnosis of diseases. Therapies that target a specific cytokine are also likely to affect the production of other cytokines due to their cross-regulatory functions and because the cytokines are produced by common cell types. In this study, we have perturbated the production of 17 different cytokines in a preclinical rat model of autoimmune arthritis, using 55 commercially available immunomodulatory drugs and clinical candidates. The majority of the studied drugs was selected for their anti-inflammatory role and was confirmed to inhibit the production of IL-2 and IFN-γ in this model but was also found to increase the production of other cytokines compared to the untreated control. Correlation analysis identified 58 significant pairwise correlations between the cytokines. The strongest correlations found in this study were between IL-2 and IFN-γ (r=0.87) and between IL-18 and EPO (r=0.84). Cluster analysis identified two robust clusters: (1) IL-7, IL-18 and EPO, and (2) IL-2, IL-17 and IFN-γ. The results show that cytokines are highly co-regulated, which provide valuable information for how a therapeutic drug might affect clusters of cytokines. In addition, a cytokine that is used as a therapeutic biomarker could be combined with its related cytokines into a biomarker panel to improve diagnostic accuracy.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Clustering, PCA, Profile, Compound, T cell
National Category
Immunology
Research subject
Infection Biology; INF502 Biomarkers
Identifiers
urn:nbn:se:his:diva-13195 (URN)10.1016/j.cyto.2016.10.015 (DOI)000394396100011 ()27816795 (PubMedID)2-s2.0-84994342354 (Scopus ID)
Funder
Carl Tryggers foundation Knowledge FoundationEU, Horizon 2020VINNOVA
Available from: 2016-12-06 Created: 2016-12-06 Last updated: 2018-06-01Bibliographically approved
Li, K., Cheng, X., Tilevik, A., Davis, S. J. & Zhu, C. (2017). In situ and in silico kinetic analyses of programmed cell death-1 (PD-1) receptor, programmed cell death ligands, and B7-1 protein interaction network. Journal of Biological Chemistry, 292(16), 6799-6809
Open this publication in new window or tab >>In situ and in silico kinetic analyses of programmed cell death-1 (PD-1) receptor, programmed cell death ligands, and B7-1 protein interaction network
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2017 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 292, no 16, p. 6799-6809Article in journal (Refereed) Published
Abstract [en]

Programmed cell death-1 (PD-1) is an inhibitory receptor with an essential role in maintaining peripheral tolerance and is among the most promising immunotherapeutic targets for treating cancer, autoimmunity, and infectious diseases. A complete understanding of the consequences of PD-1 engagement by its ligands, PD-L1 and PD-L2, and of PD-L1 binding to B7-1 requires quantitative analysis of their interactions at the cell surface. We present here the first complete in situ kinetic analysis of the PD-1/PD-ligands/B7-1 system. Consistent with previous solution measurements, we observed higher in situ affinities for human (h) than murine (m) PD-1 interactions, stronger binding of hPD-1 to hPD-L2 than hPD-L1, and comparable binding of mPD-1 to both ligands. However, in contrast to the relatively weak solution affinities, the in situ affinities of PD-1 are as high as those of the T cell receptor for agonist pMHC and of LFA-1 (lymphocyte function-associated antigen 1) for ICAM-1 (intercellular adhesion molecule 1) but significantly lower than that of the B7-1/CTLA-4 interaction, suggesting a distinct basis for PD-1- versus CTLA-4-mediated inhibition. Notably, the in situ interactions of PD-1 are much stronger than that of B7-1 with PD-L1. Overall, the in situ affinity ranking greatly depends on the on-rate instead of the off-rate. In silico simulations predict that PD-1/PD-L1 interactions dominate at interfaces between activated T cells and mature dendritic cells and that these interactions will be highly sensitive to the dynamics of PD-L1 and PD-L2 expression. Our results provide a kinetic framework for better understanding inhibitory PD-1 activity in health and disease.

Place, publisher, year, edition, pages
American Society for Biochemistry and Molecular Biology, Inc., 2017
Keywords
T-cell, cell surface protein, kinetics, mathematical modeling, protein-protein interaction
National Category
Immunology
Research subject
Infection Biology; INF000
Identifiers
urn:nbn:se:his:diva-13534 (URN)10.1074/jbc.M116.763888 (DOI)000399813400030 ()28270509 (PubMedID)2s2.0-85018570744 (Scopus ID)
Available from: 2017-04-28 Created: 2017-04-28 Last updated: 2019-11-25Bibliographically approved
Cheng, X., Veverka, V., Radhakrishnan, A., Waters, L. C., Muskett, F. W., Morgan, S. H., . . . Davis, S. J. (2013). Structure and Interactions of the Human Programmed Cell Death 1 Receptor. Journal of Biological Chemistry, 288(17), 11771-11785
Open this publication in new window or tab >>Structure and Interactions of the Human Programmed Cell Death 1 Receptor
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2013 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 288, no 17, p. 11771-11785Article in journal (Refereed) Published
Abstract [en]

PD-1, a receptor expressed by T cells, B cells, and monocytes, is a potent regulator of immune responses and a promising therapeutic target. The structure and interactions of human PD-1 are, however, incompletely characterized. We present the solution nuclear magnetic resonance (NMR)-based structure of the human PD-1 extracellular region and detailed analyses of its interactions with its ligands, PD-L1 and PD-L2. PD-1 has typical immunoglobulin superfamily topology but differs at the edge of the GFCC' sheet, which is flexible and completely lacks a C '' strand. Changes in PD-1 backbone NMR signals induced by ligand binding suggest that, whereas binding is centered on the GFCC' sheet, PD-1 is engaged by its two ligands differently and in ways incompletely explained by crystal structures of mouse PD-1.ligand complexes. The affinities of these interactions and that of PD-L1 with the costimulatory protein B7-1, measured using surface plasmon resonance, are significantly weaker than expected. The 3-4-fold greater affinity of PD-L2 versus PD-L1 for human PD-1 is principally due to the 3-fold smaller dissociation rate for PD-L2 binding. Isothermal titration calorimetry revealed that the PD-1/PD-L1 interaction is entropically driven, whereas PD-1/PD-L2 binding has a large enthalpic component. Mathematical simulations based on the biophysical data and quantitative expression data suggest an unexpectedly limited contribution of PD-L2 to PD-1 ligation during interactions of activated T cells with antigen-presenting cells. These findings provide a rigorous structural and biophysical framework for interpreting the important functions of PD-1 and reveal that potent inhibitory signaling can be initiated by weakly interacting receptors.

Place, publisher, year, edition, pages
American Society for Biochemistry and Molecular Biology, 2013
National Category
Natural Sciences
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-8400 (URN)10.1074/jbc.M112.448126 (DOI)000318157600014 ()23417675 (PubMedID)2-s2.0-84876924130 (Scopus ID)
Available from: 2013-08-13 Created: 2013-08-13 Last updated: 2017-12-06Bibliographically approved
Jansson, A., Pernestig, A.-K., Nilsson, P., Jirstrand, M. & Hornquist, E. H. (2013). Toward Quantifying the Thymic Dysfunctional State in Mouse Models of Inflammatory Bowel Disease. Inflammatory Bowel Diseases, 19(4), 881-888
Open this publication in new window or tab >>Toward Quantifying the Thymic Dysfunctional State in Mouse Models of Inflammatory Bowel Disease
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2013 (English)In: Inflammatory Bowel Diseases, ISSN 1078-0998, E-ISSN 1536-4844, Vol. 19, no 4, p. 881-888Article, review/survey (Refereed) Published
Abstract [en]

Inflammatory bowel disease is characterized by a number of immunological alterations, not the least in the T-cell compartment. Numerous animal models of colitis have revealed aberrant thymocyte dynamics associated with skewed thymocyte development. The recent advancements in quantitative methods have proposed critical kinetic alterations in the thymocyte development during the progression of colitis. This review focuses on the aberrant thymocyte dynamics in G alpha i2-deficient mice as this mouse model provides most quantitative data of the thymocyte development associated with colitis. Herein, we discuss several dynamic changes during the progression of colitis and propose a hypothesis for the underlying causes for the skewed proportions of the thymocyte populations seen in the G alpha i2-deficient mice and in other mouse models of colitis. (Inflamm Bowel Dis 2013; 19: 881-888)

Place, publisher, year, edition, pages
Oxford University Press, 2013
Keywords
kinetics, thymocyte, thymus, IBD, colitis
National Category
Natural Sciences
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-8399 (URN)10.1097/MIB.0b013e3182802c58 (DOI)000316451600037 ()23448795 (PubMedID)2-s2.0-84884651875 (Scopus ID)
Available from: 2013-08-13 Created: 2013-08-13 Last updated: 2019-11-25Bibliographically approved
Elgbratt, K., Jansson, A. & Hultgren-Hornquist, E. (2012). A Quantitative Study of the Mechanisms behind Thymic Atrophy in G alpha i2-Deficient Mice during Colitis Development. PLoS ONE, 7(5), e36726
Open this publication in new window or tab >>A Quantitative Study of the Mechanisms behind Thymic Atrophy in G alpha i2-Deficient Mice during Colitis Development
2012 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 5, p. e36726-Article in journal (Refereed) Published
Abstract [en]

Mice deficient for the G protein subunit G alpha i2 spontaneously develop colitis, a chronic inflammatory disease associated with dysregulated T cell responses. We and others have previously demonstrated a thymic involution in these mice and an aberrant thymocyte dynamics. The G alpha i2(-/-) mice have a dramatically reduced fraction of double positive thymocytes and an increased fraction of single positive (SP) thymocytes. In this study, we quantify a number of critical parameters in order to narrow down the underlying mechanisms that cause the dynamical changes of the thymocyte development in the G alpha i2(-/-) mice. Our data suggest that the increased fraction of SP thymocytes results only from a decreased number of DP thymocytes, since the number of SP thymocytes in the Gai2(-/-) mice is comparable to the control littermates. By measuring the frequency of T cell receptor excision circles (TRECs) in the thymocytes, we demonstrate that the number of cell divisions the G alpha i2(-/-) SP thymocytes undergo is comparable to SP thymocytes from control littermates. In addition, our data show that the mature SP CD4(+) and CD8(+) thymocytes divide to the same extent before they egress from the thymus. By estimating the number of peripheral TREC+ T lymphocytes and their death rate, we could calculate the daily egression of thymocytes. G alpha i2(-/-) mice with no/mild and moderate colitis were found to have a slower export rate in comparison to the control littermates. The quantitative measurements in this study suggest a number of dynamical changes in the thymocyte development during the progression of colitis.

National Category
Natural Sciences
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-6192 (URN)10.1371/journal.pone.0036726 (DOI)000305336400031 ()22590596 (PubMedID)2-s2.0-84861002198 (Scopus ID)
Available from: 2012-08-08 Created: 2012-08-08 Last updated: 2017-12-07Bibliographically approved
Fagerlind, M. G., Webb, J. S., Barraud, N., McDougald, D., Jansson, A., Nilsson, P., . . . Rice, S. A. (2012). Dynamic modelling of cell death during biofilm development. Journal of Theoretical Biology, 295, 23-36
Open this publication in new window or tab >>Dynamic modelling of cell death during biofilm development
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2012 (English)In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 295, p. 23-36Article in journal (Refereed) Published
Abstract [en]

Biofilms are currently recognised as the predominant bacterial life-style and it has been suggested that biofilm development is influenced by a number of different processes such as adhesion, detachment, mass transport, quorum sensing, cell death and active dispersal. One of the least understood processes and its effects on biofilm development is cell death. However, experimental studies suggest that bacterial death is an important process during biofilm development and many studies show a relationship between cell death and dispersal in microbial biofilms. We present a model of the process of cell death during biofilm development, with a particular focus on the spatial localisation of cell death or cell damage. Three rules governing cell death or cell damage were evaluated which compared the effects of starvation, damage accumulation, and viability during biofilm development and were also used to design laboratory based experiments to test the model. Results from model simulations show that actively growing biofilms develop steep nutrient gradients within the interior of the biofilm that affect neighbouring microcolonies resulting in cell death and detachment. Two of the rules indicated that high substrate concentrations lead to accelerated cell death, in contrast to the third rule, based on the accumulation of damage, which predicted earlier cell death for biofilms grown with low substrate concentrations. Comparison of the modelling results with experimental results suggests that cell death is favoured under low nutrient conditions and that the accumulation of damage may be the main cause of cell death during biofilm development. (C) 2011 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Academic Press, 2012
Keywords
Biofilm, Individual based CA, Cell death
National Category
Natural Sciences
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-5452 (URN)10.1016/j.jtbi.2011.10.007 (DOI)000299408500003 ()2-s2.0-82255169356 (Scopus ID)
Available from: 2012-02-23 Created: 2012-02-22 Last updated: 2017-12-07Bibliographically approved
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