TY  - JOUR
A1  - Maichl, Daniela Simone
A1  - Kirner, Julius Arthur
A1  - Beck, Susanne
A1  - Cheng, Wen-Hui
A1  - Krug, Melanie
A1  - Kuric, Martin
A1  - Ade, Carsten Patrick
A1  - Bischler, Thorsten
A1  - Jakob, Franz
A1  - Hose, Dirk
A1  - Seckinger, Anja
A1  - Ebert, Regina
A1  - Jundt, Franziska
T1  - Identification of NOTCH-driven matrisome-associated genes as prognostic indicators of multiple myeloma patient survival
JF  - Blood Cancer Journal
N2  - No abstract available.
KW  - cancer microenvironment
KW  - myeloma
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357598
VL  - 13
ER  - 
TY  - JOUR
A1  - Kaltdorf, Martin
A1  - Breitenbach, Tim
A1  - Karl, Stefan
A1  - Fuchs, Maximilian
A1  - Kessie, David Komla
A1  - Psota, Eric
A1  - Prelog, Martina
A1  - Sarukhanyan, Edita
A1  - Ebert, Regina
A1  - Jakob, Franz
A1  - Dandekar, Gudrun
A1  - Naseem, Muhammad
A1  - Liang, Chunguang
A1  - Dandekar, Thomas
T1  - Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis
JF  - Scientific Reports
N2  - The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.
KW  - cellular signalling networks
KW  - computer modelling
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-313303
VL  - 13
ER  - 
TY  - JOUR
A1  - Dotterweich, Julia
A1  - Schlegelmilch, Katrin
A1  - Keller, Alexander
A1  - Geyer, Beate
A1  - Schneider, Doris
A1  - Zeck, Sabine
A1  - Tower, Robert J. J.
A1  - Ebert, Regina
A1  - Jakob, Franz
A1  - Schütze, Norbert
T1  - Contact of myeloma cells induces a characteristic transcriptome signature in skeletal precursor cells-implications for myeloma bone disease
JF  - Bone
N2  - Physical interaction of skeletal precursors with multiple myeloma cells has been shown to suppress their osteogenic potential while favoring their tumor-promoting features. Although several transcriptome analyses of myeloma patient-derived mesenchymal stem cells have displayed differences compared to their healthy counterparts, these analyses insufficiently reflect the signatures mediated by tumor cell contact, vary due to different methodologies, and lack results in lineage-committed precursors. To determine tumor cell contact-mediated changes on skeletal precursors, we performed transcriptome analyses of mesenchymal stem cells and osteogenic precursor cells cultured in contact with the myeloma cell line INA-6. Comparative analyses confirmed dysregulation of genes which code for known disease-relevant factors and additionally revealed upregulation of genes that are associated with plasma cell homing, adhesion, osteoclastogenesis, and angiogenesis. Osteoclast-derived coupling factors, a dysregulated adipogenic potential, and an imbalance in favor of anti-anabolic factors may play a role in the hampered osteoblast differentiation potential of mesenchymal stem cells. Angiopoietin-Like 4 (ANGPTL4) was selected from a list of differentially expressed genes as a myeloma cell contact-dependent target in skeletal precursor cells which warranted further functional analyses. Adhesion assays with full-length ANGPTL4-coated plates revealed a potential role of this protein in INA6 cell attachment. This study expands knowledge of the myeloma cell contact-induced signature in the stromal compartment of myelomatous bones and thus offers potential targets that may allow detection and treatment of myeloma bone disease at an early stage.
KW  - marrow stromal cells
KW  - Endothelial growth-factor
KW  - precedes multiple-myeloma
KW  - monoclonial gammopathy
KW  - in-vitro
KW  - mesenchymal stem-cells
KW  - undetermined significance
KW  - angiogenic cytokines
KW  - peripheral-blood
KW  - gene-expression
KW  - Multiple myeloma
KW  - Bone disease
KW  - Angiopoietin-like 4
KW  - Gene expression profiling
KW  - Mesenchymal stem cells
KW  - Osteogenic precursor cells
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-186688
VL  - 93
ER  -