@article{AssfalgSeligTolksdorfetal.2020, author = {Assfalg, Volker and Selig, Katharina and Tolksdorf, Johanna and van Meel, Marieke and de Vries, Erwin and Ramsoebhag, Anne-Marie and Rahmel, Axel and Renders, Lutz and Novotny, Alexander and Matevossian, Edouard and Schneeberger, Stefan and Rosenkranz, Alexander R. and Berlakovich, Gabriela and Ysebaert, Dirk and Knops, No{\"e}l and Kuypers, Dirk and Weekers, Laurent and Muehlfeld, Anja and Rump, Lars-Christian and Hauser, Ingeborg and Pisarski, Przemyslaw and Weimer, Rolf and Fornara, Paolo and Fischer, Lutz and Kliem, Volker and Sester, Urban and Stippel, Dirk and Arns, Wolfgang and Hau, Hans-Michael and Nitschke, Martin and Hoyer, Joachim and Thorban, Stefan and Weinmann-Menke, Julia and Heller, Katharina and Banas, Bernhard and Schwenger, Vedat and Nadalin, Silvio and Lopau, Kai and H{\"u}ser, Norbert and Heemann, Uwe}, title = {Repeated kidney re-transplantation—the Eurotransplant experience: a retrospective multicenter outcome analysis}, series = {Transplant International}, volume = {33}, journal = {Transplant International}, number = {6}, doi = {10.1111/tri.13569}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-214161}, pages = {617 -- 631}, year = {2020}, abstract = {In Eurotransplant kidney allocation system (ETKAS), candidates can be considered unlimitedly for repeated re-transplantation. Data on outcome and benefit are indeterminate. We performed a retrospective 15-year patient and graft outcome data analysis from 1464 recipients of a third or fourth or higher sequential deceased donor renal transplantation (DDRT) from 42 transplant centers. Repeated re-DDRT recipients were younger (mean 43.0 vs. 50.2 years) compared to first DDRT recipients. They received grafts with more favorable HLA matches (89.0\% vs. 84.5\%) but thereby no statistically significant improvement of patient and graft outcome was found as comparatively demonstrated in 1st DDRT. In the multivariate modeling accounting for confounding factors, mortality and graft loss after 3rd and ≥4th DDRT (P < 0.001 each) and death with functioning graft (DwFG) after 3rd DDRT (P = 0.001) were higher as compared to 1st DDRT. The incidence of primary nonfunction (PNF) was also significantly higher in re-DDRT (12.7\%) than in 1st DDRT (7.1\%; P < 0.001). Facing organ shortage, increasing waiting time, and considerable mortality on dialysis, we question the current policy of repeated re-DDRT. The data from this survey propose better HLA matching in first DDRT and second DDRT and careful selection of candidates, especially for ≥4th DDRT.}, language = {en} } @article{DirkFischerSchardtetal.2023, author = {Dirk, Robin and Fischer, Jonas L. and Schardt, Simon and Ankenbrand, Markus J. and Fischer, Sabine C.}, title = {Recognition and reconstruction of cell differentiation patterns with deep learning}, series = {PLoS Computational Biology}, volume = {19}, journal = {PLoS Computational Biology}, number = {10}, doi = {10.1371/journal.pcbi.1011582}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350167}, year = {2023}, abstract = {Abstract Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran's index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70\% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms. Author summary Mammalian embryo development relies on organized differentiation of stem cells into different lineages. Particularly at the early stages of embryogenesis, cells of different fates form three-dimensional spatial patterns that are difficult to identify by eye. Pattern quantification and mathematical modeling have produced first insights into potential mechanisms for the cell fate arrangements. However, these approaches have relied on classifications of the patterns such as inside-out or random, or used summary statistics such as pair correlation functions or cluster radii. Deep neural networks allow characterizing patterns directly. Since the tissue context can be readily reproduced by a graph, we implemented a graph neural network to characterize the patterns of embryonic stem cell organoids as a whole. In addition, we implemented a multilayer perceptron model to reconstruct the fate of a given cell based on its neighbors. To train and test the models, we used synthetic data generated by our mathematical model for cell-cell communication. This interplay of deep learning and mathematical modeling in combination with summary statistics allowed us to identify a potential mechanism for cell fate determination in mouse embryonic stem cells. Our results agree with a mechanism with a dispersion of the intercellular signal that links a cell's fate to those of the local neighborhood.}, language = {en} } @article{PetersenKuntzerFischeretal.2015, author = {Petersen, Jens A. and Kuntzer, Thierry and Fischer, Dirk and von der Hagen, Maja and Veronika, Angela and Lobrinus, Johannes A. and Kress, Wolfram and Rushing, Elisabeth J. and Sinnreich, Michael and Jung, Hans H.}, title = {Dysferlinopathy in Switzerland: clinical phenotypes and potential founder effects}, series = {BMC Neurology}, volume = {15}, journal = {BMC Neurology}, number = {182}, doi = {10.1186/s12883-015-0449-3}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-139920}, year = {2015}, abstract = {Background: Dysferlin is reduced in patients with limb girdle muscular dystrophy type 2B, Miyoshi myopathy, distal anterior compartment myopathy, and in certain Ethnic clusters. Methods: We evaluated clinical and genetic patient data from three different Swiss Neuromuscular Centers. Results: Thirteen patients from 6 non-related families were included. Age of onset was 18.8 +/- 4.3 years. In all patients, diallelic disease-causing mutations were identified in the DYSF gene. Nine patients from 3 non-related families from Central Switzerland carried the identical homozygous mutation, c.3031 + 2T>C. A possible founder effect was confirmed by haplotype analysis. Three patients from two different families carried the heterozygous mutation, c.1064_1065delAA. Two novel mutations were identified (c.2869C>T (p.Gln957Stop), c.5928G>A (p.Trp1976Stop)). Conclusions: Our study confirms the phenotypic heterogeneity associated with DYSF mutations. Two mutations (c.3031 + 2T>C, c.1064_1065delAA) appear common in Switzerland. Haplotype analysis performed on one case (c.3031 + 2T>C) suggested a possible founder effect.}, language = {en} } @article{GrossHennardMasourisetal.2012, author = {Gross, Henrik and Hennard, Christine and Masouris, Ilias and Cassel, Christian and Barth, Stephanie and Stober-Gr{\"a}sser, Ute and Mamiani, Alfredo and Moritz, Bodo and Ostareck, Dirk and Ostareck-Lederer, Antje and Neuenkirchen, Nils and Fischer, Utz and Deng, Wen and Leonhardt, Heinrich and Noessner, Elfriede and Kremmer, Elisabeth and Gr{\"a}sser, Friedrich A.}, title = {Binding of the Heterogeneous Ribonucleoprotein K (hnRNP K) to the Epstein-Barr Virus Nuclear Antigen 2 (EBNA2) Enhances Viral LMP2A Expression}, series = {PLoS One}, volume = {7}, journal = {PLoS One}, number = {8}, doi = {10.1371/journal.pone.0042106}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-133707}, year = {2012}, abstract = {The Epstein-Barr Virus (EBV) -encoded EBNA2 protein, which is essential for the in vitro transformation of B-lymphocytes, interferes with cellular processes by binding to proteins via conserved sequence motifs. Its Arginine-Glycine (RG) repeat element contains either symmetrically or asymmetrically di-methylated arginine residues (SDMA and ADMA, respectively). EBNA2 binds via its SDMA-modified RG-repeat to the survival motor neurons protein (SMN) and via the ADMA-RG-repeat to the NP9 protein of the human endogenous retrovirus K (HERV-K (HML-2) Type 1). The hypothesis of this work was that the methylated RG-repeat mimics an epitope shared with cellular proteins that is used for interaction with target structures. With monoclonal antibodies against the modified RG-repeat, we indeed identified cellular homologues that apparently have the same surface structure as methylated EBNA2. With the SDMA-specific antibodies, we precipitated the Sm protein D3 (SmD3) which, like EBNA2, binds via its SDMA-modified RG-repeat to SMN. With the ADMA-specific antibodies, we precipitated the heterogeneous ribonucleoprotein K (hnRNP K). Specific binding of the ADMA-antibody to hnRNP K was demonstrated using E. coli expressed/ADMA-methylated hnRNP K. In addition, we show that EBNA2 and hnRNP K form a complex in EBV-infected B-cells. Finally, hnRNP K, when co-expressed with EBNA2, strongly enhances viral latent membrane protein 2A (LMP2A) expression by an unknown mechanism as we did not detect a direct association of hnRNP K with DNA-bound EBNA2 in gel shift experiments. Our data support the notion that the methylated surface of EBNA2 mimics the surface structure of cellular proteins to interfere with or co-opt their functional properties.}, language = {en} } @article{PolatKaiserWohllebenetal.2017, author = {Polat, B{\"u}lent and Kaiser, Philipp and Wohlleben, Gisela and Gehrke, Thomas and Scherzad, Agmal and Scheich, Matthias and Malzahn, Uwe and Fischer, Thomas and Vordermark, Dirk and Flentje, Michael}, title = {Perioperative changes in osteopontin and TGFβ1 plasma levels and their prognostic impact for radiotherapy in head and neck cancer}, series = {BMC Cancer}, volume = {17}, journal = {BMC Cancer}, number = {6}, doi = {10.1186/s12885-016-3024-4}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157529}, year = {2017}, abstract = {Background: In head and neck cancer little is known about the kinetics of osteopontin (OPN) expression after tumor resection. In this study we evaluated the time course of OPN plasma levels before and after surgery. Methods: Between 2011 and 2013 41 consecutive head and neck cancer patients were enrolled in a prospective study (group A). At different time points plasma samples were collected: T0) before, T1) 1 day, T2) 1 week and T3) 4 weeks after surgery. Osteopontin and TGFβ1 plasma concentrations were measured with a commercial ELISA system. Data were compared to 131 head and neck cancer patients treated with primary (n = 42) or postoperative radiotherapy (n = 89; group B1 and B2). Results: A significant OPN increase was seen as early as 1 day after surgery (T0 to T1, p < 0.01). OPN levels decreased to base line 3-4 weeks after surgery. OPN values were correlated with postoperative TGFβ1 expression suggesting a relation to wound healing. Survival analysis showed a significant benefit for patients with lower OPN levels both in the primary and postoperative radiotherapy group (B1: 33 vs 11.5 months, p = 0.017, B2: median not reached vs 33.4, p = 0.031). TGFβ1 was also of prognostic significance in group B1 (33.0 vs 10.7 months, p = 0.003). Conclusions: Patients with head and neck cancer showed an increase in osteopontin plasma levels directly after surgery. Four weeks later OPN concentration decreased to pre-surgery levels. This long lasting increase was presumably associated to wound healing. Both pretherapeutic osteopontin and TGFβ1 had prognostic impact.}, language = {en} }