Dr. rer. nat.

Rüping, Stefan

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  • Publication
    POS0881 Specific AI-Generated Pattern of Tender Joints and Tenderness at Enthesial Sites are Predictive for Objective Detection of Musculoskeletal Inflammation in Psoriasis Patients
    ( 2023-05-30)
    Köhm, Michaela
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    Pick, Annika
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    Kugler, Sabine
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    Mackay, Sina
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    Antweiler, Dario orcid-logo
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    Kratz, Hannah
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    Zerweck, Lukas
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    Rüping, Stefan
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    Behrens, Frank
    Psoriasis (Pso) is one of the most common chronic inflammatory skin diseases in Europe. Psoriatic arthritis (PsA) is closely associated to Pso. Up to 30% of the Pso patients will develop PsA during skin disease course. Defined and validated approaches for early detection are still missing. Beside biomarkers from blood or imaging, clinical characteristics of the patients may be of value to detect PsA patients in the transition state early. To perform an AI-based cluster analysis in a cohort of Pso patients at-risk for development of PsA to assess clinical characteristics as markers for early PsA.
  • Publication
    A machine learning method for the identification and characterization of novel COVID-19 drug targets
    ( 2023-05-03)
    Schultz, Bruce
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    Delong, Lauren Nicole
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    Masny, Aliaksandr
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    Lentzen, Manuel
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    Raschka, Tamara
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    Dijk, David van
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    Zaliani, Andrea
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    Hansen, Anne Funck
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    Rüping, Stefan
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    Kugler, Sabine
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    Burmeister, Jan
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    Kohlhammer, Jörn orcid-logo
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    Sarau, George
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    Christiansen, Silke
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    Kannt, Aimo
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    Foldenauer, Ann Christina
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    Claussen, Carsten
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    Resch, Eduard
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    Frank, Kevin
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    Gribbon, Philip
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    Kuzikov, Maria
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    Keminer, Oliver
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    Laue, Hendrik
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    Hahn, Horst
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    Hirsch, Jochen
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    Wischnewski, Marco
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    Günther, Matthias
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    Archipovas, Saulius
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    Tom Kodamullil, Alpha
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    Gemünd, Andre
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    Fluck, Juliane
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    Steinborn, Carina
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    Ebeling, Christian
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    Domingo Fernández, Daniel
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    Hermanowski, Helena
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    Fröhlich, Holger
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    Klein, Jürgen
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    Schultz, Bruce
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    Jacobs, Marc
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    Hofmann-Apitius, Martin
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    Knieps, Meike
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    Krapp, Michael
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    Wendland, Philipp Johannes
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    Wegner, Philipp
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    Golriz Khatami, Sepehr
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    Springstubbe, Stephan
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    Linden, Thomas
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    Lentzen, Manuel
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    Fröhlich, Holger
    In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 (https://guiltytargets-covid.eu/), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.
  • Publication
    Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis
    ( 2022-09-24)
    Gurke, Robert
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    Bendes, Annika
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    Bowes, John
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    Köhm, Michaela
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    Twyman, Richard M.
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    Barton, Anne
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    Elewaut, Dirk
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    Goodyear, Phil
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    Hahnefeld, Lisa
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    Hillenbrand, Rainer
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    Hunter, Ewan
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    Ibberson, Mark
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    Ioannidis, Vassilios
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    Kugler, Sabine
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    Lories, Rik J.
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    Resch, Eduard
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    Rüping, Stefan
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    Scholich, Klaus
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    Schwenk, Jochen M.
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    Waddington, James C.
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    Whitfield, Phil
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    Geisslinger, Gerd
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    FitzGerald, Oliver
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    Behrens, Frank
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    Pennington, Stephen R.
    The definitive diagnosis and early treatment of many immune-mediated inflammatory diseases (IMIDs) is hindered by variable and overlapping clinical manifestations. Psoriatic arthritis (PsA), which develops in ~30% of people with psoriasis, is a key example. This mixed-pattern IMID is apparent in entheseal and synovial musculoskeletal structures, but a definitive diagnosis often can only be made by clinical experts or when an extensive progressive disease state is apparent. As with other IMIDs, the detection of multimodal molecular biomarkers offers some hope for the early diagnosis of PsA and the initiation of effective management and treatment strategies. However, specific biomarkers are not yet available for PsA. The assessment of new markers by genomic and epigenomic profiling, or the analysis of blood and synovial fluid/tissue samples using proteomics, metabolomics and lipidomics, provides hope that complex molecular biomarker profiles could be developed to diagnose PsA. Importantly, the integration of these markers with high-throughput histology, imaging and standardized clinical assessment data provides an important opportunity to develop molecular profiles that could improve the diagnosis of PsA, predict its occurrence in cohorts of individuals with psoriasis, differentiate PsA from other IMIDs, and improve therapeutic responses. In this review, we consider the technologies that are currently deployed in the EU IMI2 project HIPPOCRATES to define biomarker profiles specific for PsA and discuss the advantages of combining multi-omics data to improve the outcome of PsA patients.