Experimental design, validation and computational modeling uncover DNA damage sensing by DNA-PK and ATM

EPHA2 Interacts with DNA-PKcs in Cell Nucleus and Controls Ionizing Radiation Responses in Non-Small Cell Lung Cancer Cells

Ephrin (EFN)/ Erythropoietin-producing human hepatocellular receptors (Eph) signaling has earlier been reported to regulate non-small cell lung cancer (NSCLC) cell survival and cell death as well as invasion and migration. Here, the role of Ephrin type-A receptor 2 (EphA2) on the DNA damage response (DDR) signaling and ionizing radiation (IR) cellular effect was studied in NSCLC cells. Silencing of EphA2 resulted in IR sensitization, with increased activation of caspase-3, PARP-1 cleavage and reduced clonogenic survival. Profiling of EphA2 expression in a NSCLC cell line panel showed a correlation to an IR refractory phenotype. EphA2 was found to be transiently and rapidly phosphorylated at Ser897 in response to IR, which was paralleled with the activation of ribosomal protein S6 kinase (RSK). Using cell fractionation, a transient increase in both total and pSer897 EphA2 in the nuclear fraction in response to IR was revealed. By immunoprecipitation and LC-MS/MS analysis of EphA2 complexes, nuclear localized EphA2 was found in a complex with DNA-PK_cs. Such complex formation rapidly increased after IR but returned back to basal level within an hour. Targeting EphA2 with siRNA or by treatment with EFNA1 ligand partly reduced phosphorylation of DNA-PK_cs at S2056 at early time points after IR. Thus, we report that EphA2 interacts with DNA-PK_cs in the cell nucleus suggesting a novel mechanism involving the EphA2 receptor in DDR signaling and IR responsiveness.

Modeling chemotherapy-induced stress to identify rational combination therapies in the DNA damage response pathway

Cells respond to DNA damage by activating complex signaling networks that decide cell fate, promoting not only DNA damage repair and survival but also cell death. We have developed a multiscale computational model that quantitatively links chemotherapy-induced DNA damage response signaling to cell fate. The computational model was trained and calibrated on extensive data from U2OS osteosarcoma cells, including the cell cycle distribution of the initial cell population, signaling data measured by Western blotting, and cell fate data in response to chemotherapy treatment measured by time-lapse microscopy. The resulting mechanistic model predicted the cellular responses to chemotherapy alone and in combination with targeted inhibitors of the DNA damage response pathway, which we confirmed experimentally. Computational models such as the one presented here can be used to understand the molecular basis that defines the complex interplay between cell survival and cell death and to rationally identify chemotherapy-potentiating drug combinations.

Helicobacter pylori induces direct activation of the lymphotoxin beta receptor and non-canonical nuclear factor-kappa B signaling

The pathogen Helicobacter pylori, which infects half of the world's population, is a major risk factor for the development of gastric diseases including chronic gastritis and gastric cancer. Among H. pylori's virulence factors is the cytotoxin-associated gene pathogenicity island (cagPAI), which encodes for a type IV secretion system (T4SS). The T4SS induces fast canonical nuclear factor-kappa B (NF-κB) signaling, a major factor increasing inflammation, supressing apoptotic cell death and thereby promoting the development of neoplasia. However, H. pylori's capability to mediate fast non-canonical NF-κB signaling is unresolved, despite a contribution of non-canonical NF-κB signaling to gastric cancer has been suggested. We analyzed signaling elements within non-canonical NF-κB in response to H. pylori in epithelial cell lines by immunoprecipitation, immunoblot, electrophoretic mobility shift assay and RNA interference knockdown. In addition, tissue samples of H. pylori-infected patients were investigated by immunohistochemistry. Here, we provide evidence for a T4SS-dependent direct activation of non-canonical NF-κB signaling. We identified the lymphotoxin beta receptor (LTβR) to elicit the fast release of NF-κB inducing kinase (NIK) from the receptor complex leading to non-canonical NF-κB signaling. Further, NIK expression was increased in human biopsies of H. pylori-associated gastritis. Thus, NIK could represent a novel target to reduce Helicobacter pylori-induced gastric inflammation and pathology.

Low doses of X-rays induce prolonged and ATM-independent persistence of γH2AX foci in human gingival mesenchymal stem cells

Diagnostic imaging delivering low doses of radiation often accompany human mesenchymal stem cells (MSCs)-based therapies. However, effects of low dose radiation on MSCs are poorly characterized. Here we examine patterns of phosphorylated histone H2AX (γH2AX) and phospho-S1981 ATM (pATM) foci formation in human gingiva-derived MSCs exposed to X-rays in time-course and dose-response experiments. Both γH2AX and pATM foci accumulated linearly with dose early after irradiation (5–60 min), with a maximum induction observed at 30–60 min (37 ± 3 and 32 ± 3 foci/cell/Gy for γH2AX and pATM, respectively). The number of γH2AX foci produced by intermediate doses (160 and 250 mGy) significantly decreased (40–60%) between 60 and 240 min post-irradiation, indicating rejoining of DNA double-strand breaks. In contrast, γH2AX foci produced by low doses (20–80 mGy) did not change after 60 min. The number of pATM foci between 60 and 240 min decreased down to control values in a dose-independent manner. Similar kinetics was observed for pATM foci co-localized with γH2AX foci. Collectively, our results suggest differential DNA double-strand break signaling and processing in response to low vs. intermediate doses of X-rays in human MSCs. Furthermore, mechanisms governing the prolonged persistence of γH2AX foci in these cells appear to be ATM-independent.

The interplay between DNA repair and autophagy in cancer therapy

DNA is the prime target of anticancer treatments. DNA damage triggers a series of signaling cascades promoting cellular survival, including DNA repair, cell cycle arrest, and autophagy. The elevated basal and/or stressful levels of both DNA repair and autophagy observed in tumor cells, in contrast to normal cells, have been identified as the most important drug-responsive programs that impact the outcome of anticancer therapy. The exact relationship between DNA repair and autophagy in cancer cells remains unclear. On one hand, autophagy has been shown to regulate some of the DNA repair proteins after DNA damage by maintaining the balance between their synthesis, stabilization, and degradation. One the other hand, some evidence has demonstrated that some DNA repair molecular have a crucial role in the initiation of autophagy. In this review, we mainly discuss the interplay between DNA repair and autophagy in anticancer therapy and expect to enlighten some effective strategies for cancer treatment.

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions

Background

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort.

Results

In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts.

Conclusions

Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0436-5) contains supplementary material, which is available to authorized users.