ENDpoiNTs

Institutions
  • FB Biologie
Publications
  Ripani, Paola; Delp, Johannes; Bode, Konstantin; Delgado, Maria E.; Dietrich, Lea; Betzler, Verena M.; von Scheven, Gudrun; Mayer, Thomas U.; Leist, Marcel; Brunner, Thomas (2019): Thiazolides promote G1 cell cycle arrest in colorectal cancer cells by targeting the mitochondrial respiratory chain Oncogene ; 2019. - ISSN 0950-9232. - eISSN 1476-5594

Thiazolides promote G1 cell cycle arrest in colorectal cancer cells by targeting the mitochondrial respiratory chain

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Systemic toxicity and tumor cell resistance still limit the efficacy of chemotherapy in colorectal cancer. Therefore, alternative treatments are desperately needed. The thiazolide Nitazoxanide (NTZ) is an FDA-approved drug for the treatment of parasite-mediated infectious diarrhea with a favorable safety profile. Interestingly, NTZ and the thiazolide RM4819—its bromo-derivative lacking antibiotic activity—are also promising candidates for cancer treatment. Yet the exact anticancer mechanism(s) of these compounds still remains unclear. In this study, we systematically investigated RM4819 and NTZ in 2D and 3D colorectal cancer culture systems. Both compounds strongly inhibited proliferation of colon carcinoma cell lines by promoting G1 phase cell cycle arrest. Thiazolide-induced cell cycle arrest was independent of the p53/p21 axis, but was mediated by inhibition of protein translation via the mTOR/c-Myc/p27 pathway, likely caused by inhibition of mitochondrial respiration. While both thiazolides demonstrated mitochondrial uncoupling activity, only RM4819 inhibited the mitochondrial respiratory chain complex III. Interestingly, thiazolides also potently inhibited the growth of murine colonic tumoroids in a comparable manner with cisplatin, while in contrast to cisplatin thiazolides did not affect the growth of primary intestinal organoids. Thus, thiazolides appear to have a tumor-selective antiproliferative activity, which offers new perspectives in the treatment of colorectal cancer.

Origin (projects)

  Krebs, Alice; Nyffeler, Johanna; Karreman, Christiaan; Schmidt, Béla Z; Kappenberg, Franziska; Mellert, Jan; Pallocca, Giorgia; Pastor, Manuel; Rahnenführer, Jörg; Leist, Marcel (2019): Determination of benchmark concentrations and their statistical uncertainty for cytotoxicity test data and functional in vitro assays Alternatives to Animal Experimentation : ALTEX ; 2019. - ISSN 1868-596X. - eISSN 1868-596X

Determination of benchmark concentrations and their statistical uncertainty for cytotoxicity test data and functional in vitro assays

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Many toxicological test methods, including assays of cell viability and function, require an evaluation of concentration-response data. This often involves curve fitting, and the resulting mathematical functions are then used to determine the concentration at which a certain deviation from the control value occurs (e.g. a decrease of cell viability by 15%). Such a threshold is called the benchmark response (BMR). For a toxicological test, it is often of interest to determine the concentration of test compound at which a pre-defined BMR of e.g. 10, 25 or 50% is reached. The concentration at which the modelled curve crosses the BMR is called the benchmark concentration (BMC). We present a user-friendly, web-based tool (BMCeasy), designed for operators without programming skills and profound statistical background, to determine BMCs and their confidence intervals. BMCeasy allows simultaneous analysis of viability plus a functional test endpoint, and it yields absolute BMCs with confidence intervals for any BMR. Besides an explanation of the algorithm underlying BMCeasy, this article also gives multiple examples of data outputs. BMCeasy was used within the EU-ToxRisk project for preparing data packages that were submitted to regulatory authorities, demonstrating the real-life applicability of the tool.

Origin (projects)

  Dreser, Nadine; Holzer, Anna-Katharina; Kapitza, Marion; Scholz, Christopher; Kranaster, Petra; Gutbier, Simon; Klima, Stefanie; Kolb, David; Dietz, Christian; Trefzer, Timo; Berthold, Michael R.; Waldmann, Tanja; Leist, Marcel (2019): Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances Archives of Toxicology ; 2019. - ISSN 0340-5761. - eISSN 1432-0738

Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances

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The first in vitro tests for developmental toxicity made use of rodent cells. Newer teratology tests, e.g. developed during the ESNATS project, use human cells and measure mechanistic endpoints (such as transcriptome changes). However, the toxicological implications of mechanistic parameters are hard to judge, without functional/morphological endpoints. To address this issue, we developed a new version of the human stem cell-based test STOP-tox<sub>(UKN)</sub>. For this purpose, the capacity of the cells to self-organize to neural rosettes was assessed as functional endpoint: pluripotent stem cells were allowed to differentiate into neuroepithelial cells for 6 days in the presence or absence of toxicants. Then, both transcriptome changes were measured (standard STOP-tox(UKN)) and cells were allowed to form rosettes. After optimization of staining methods, an imaging algorithm for rosette quantification was implemented and used for an automated rosette formation assay (RoFA). Neural tube toxicants (like valproic acid), which are known to disturb human development at stages when rosette-forming cells are present, were used as positive controls. Established toxicants led to distinctly different tissue organization and differentiation stages. RoFA outcome and transcript changes largely correlated concerning (1) the concentration-dependence, (2) the time dependence, and (3) the set of positive hits identified amongst 24 potential toxicants. Using such comparative data, a prediction model for the RoFA was developed. The comparative analysis was also used to identify gene dysregulations that are particularly predictive for disturbed rosette formation. This ‘RoFA predictor gene set’ may be used for a simplified and less costly setup of the STOP-tox<sub>(UKN)</sub> assay.

Origin (projects)

    Karreman, Christiaan; Kranaster, Petra; Leist, Marcel (2019): SUIKER : Quantification of antigens in cell organelles, neurites and cellular sub-structures by imaging Alternatives to Animal Experimentation : ALTEX ; 36 (2019), 3. - S. 518-520. - ISSN 1868-596X. - eISSN 1868-8551

SUIKER : Quantification of antigens in cell organelles, neurites and cellular sub-structures by imaging

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Quantification of fluorescence colocalization and intensity of strongly overlapping cells, e.g., neuronal cultures, is challenging for programs that use image segmentation to identify cells as individual objects. Moreover, learning to use and apply one of the large imaging packages can be very time- and/or resource-demanding. Therefore, we developed the free and highly interactive image analysis program SUIKER (program for SUperImposing KEy Regions) that quantifies colocalization of different proteins or other features over an entire image field. The software allows definition of cellular subareas by subtraction ("punching out") of structures identified in one channel from structures in a second channel. This allows, e.g., definition of neurites without cell bodies. Moreover, normalization to live or total cell numbers is possible. Providing a detailed manual that contains image analysis examples, we demonstrate how the program uses a combination of colocalization information and fluorescence intensity to quantify carbohydrate-specific stains on neurites. SUIKER can import any multichannel histology or cell culture image, builds on user-guided threshold setting, batch processes large image stacks, and exports all data (including the settings, results and metadata) in flexible formats to be used in Excel.

Origin (projects)

Funding sources
Name Project no. Description Period
Europäische Union427/19no information
Further information
Period: 01.01.2019 – 31.12.2023