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Dynamics of p53 and NF-κB regulation in response to DNA damage and identification of target proteins suitable for therapeutic intervention.

Poltz R, Naumann M - BMC Syst Biol (2012)

Bottom Line: Simulating therapeutic intervention by agents causing DNA single-strand breaks (SSBs) or DNA double-strand breaks (DSBs) we identified candidate target proteins for sensitization of carcinomas to therapeutic intervention.Further, we enlightened the DDR in different genetic diseases, and by failure mode analysis we defined molecular defects putatively contributing to carcinogenesis.By logic modelling we identified candidate target proteins that could be suitable for radio- and chemotherapy, and contributes to the design of more effective therapies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Experimental Internal Medicine, Otto von Guericke University, Leipziger Str, 44, Magdeburg, 39120, Germany.

ABSTRACT

Background: The genome is continuously attacked by a variety of agents that cause DNA damage. Recognition of DNA lesions activates the cellular DNA damage response (DDR), which comprises a network of signal transduction pathways to maintain genome integrity. In response to severe DNA damage, cells undergo apoptosis to avoid transformation into tumour cells, or alternatively, the cells enter permanent cell cycle arrest, called senescence. Most tumour cells have defects in pathways leading to DNA repair or apoptosis. In addition, apoptosis could be counteracted by nuclear factor kappa B (NF-κB), the main anti-apoptotic transcription factor in the DDR. Despite the high clinical relevance, the interplay of the DDR pathways is poorly understood. For therapeutic purposes DNA damage signalling processes are induced to induce apoptosis in tumour cells. However, the efficiency of radio- and chemotherapy is strongly hampered by cell survival pathways in tumour cells. In this study logical modelling was performed to facilitate understanding of the complexity of the signal transduction networks in the DDR and to provide cancer treatment options.

Results: Our comprehensive discrete logical model provided new insights into the dynamics of the DDR in human epithelial tumours. We identified new mechanisms by which the cell regulates the dynamics of the activation of the tumour suppressor p53 and NF-κB. Simulating therapeutic intervention by agents causing DNA single-strand breaks (SSBs) or DNA double-strand breaks (DSBs) we identified candidate target proteins for sensitization of carcinomas to therapeutic intervention. Further, we enlightened the DDR in different genetic diseases, and by failure mode analysis we defined molecular defects putatively contributing to carcinogenesis.

Conclusion: By logic modelling we identified candidate target proteins that could be suitable for radio- and chemotherapy, and contributes to the design of more effective therapies.

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Related in: MedlinePlus

Dependence of the network dynamics on p53 and NF-κB. A-E: Interaction graphs of the core network comprising only the regulatory components ‘DSBs early‘, DSBs late‘, ‘RPA-P-ATR-ATRIP-P‘, ‘ATM-P‘, p53-P‘, and ‘nuclear p50-p65-P‘. Shown are the networks for the wildtype (A), the mutant with constitutively active p50-p65-P (B), p53-deficiency (C), both constitutively active p50-p65-P and p53-deficiency (D), and constitutively active p53 (E). a-e: corresponding state transition graphs, which illustrate the dynamical behaviour of the networks. The nodes of the state transition graphs gives the activity levels of ‘DSBs early‘ (first digit), ‘DSBs late‘ (second digit), ‘RPA-P-ATR-ATRIP-P‘,(third digit), ‘ATM-P‘ (fourth digit), p53-P‘ (fifth digit), and ‘nuclear p50-p65-P‘ (sixth digit). State transition graphs a-c show only attractors. In d and e, complete state transition graphs are given, wherein the logical steady states are marked with ellipses.
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Figure 4: Dependence of the network dynamics on p53 and NF-κB. A-E: Interaction graphs of the core network comprising only the regulatory components ‘DSBs early‘, DSBs late‘, ‘RPA-P-ATR-ATRIP-P‘, ‘ATM-P‘, p53-P‘, and ‘nuclear p50-p65-P‘. Shown are the networks for the wildtype (A), the mutant with constitutively active p50-p65-P (B), p53-deficiency (C), both constitutively active p50-p65-P and p53-deficiency (D), and constitutively active p53 (E). a-e: corresponding state transition graphs, which illustrate the dynamical behaviour of the networks. The nodes of the state transition graphs gives the activity levels of ‘DSBs early‘ (first digit), ‘DSBs late‘ (second digit), ‘RPA-P-ATR-ATRIP-P‘,(third digit), ‘ATM-P‘ (fourth digit), p53-P‘ (fifth digit), and ‘nuclear p50-p65-P‘ (sixth digit). State transition graphs a-c show only attractors. In d and e, complete state transition graphs are given, wherein the logical steady states are marked with ellipses.

Mentions: In order to study the terminal fate (attractors) of the network, we reduced it to a model with conserved attractors. Previously, a method has been proposed to reduce Boolean models to their functional interactions. However, this method is only applicable to models of intermediate dimension (i.e. maximal 20 variables) [53]. Therefore, we used a different network reduction technique, which is applicable to large-scale models (see Methods). The reduced model contains only the regulatory components DSBs early, DSBs late, RPA-ATR-ATRIP-P, ATM-P, p53-P and NF-κB (nuclear p50-p65-P) (Figure 4). We calculated the state transition graph of the reduced model by using an asynchronous updating schedule with three priority classes. The state transitions that were assigned to priority classes 1, 2, and 3 coincide with the interactions of time scale values 1, 2, and 3, respectively. Hence, state transitions involving activations of RPA-ATR-ATRIP-P, ATM-P, p53-P or nuclear NF-κB were assigned to priority class 1; priority class 2 embraces the subsequent state transitions leading to activation of ‘DSBs late’ by ‘DSBs early’. State transitions coinciding with the initiation of the inactivation of signal transduction pathways, i.e., the downregulation of RPA-ATR-ATRIP-P, ATM-P, p53-P and NF-κB, constitute priority class 3.


Dynamics of p53 and NF-κB regulation in response to DNA damage and identification of target proteins suitable for therapeutic intervention.

Poltz R, Naumann M - BMC Syst Biol (2012)

Dependence of the network dynamics on p53 and NF-κB. A-E: Interaction graphs of the core network comprising only the regulatory components ‘DSBs early‘, DSBs late‘, ‘RPA-P-ATR-ATRIP-P‘, ‘ATM-P‘, p53-P‘, and ‘nuclear p50-p65-P‘. Shown are the networks for the wildtype (A), the mutant with constitutively active p50-p65-P (B), p53-deficiency (C), both constitutively active p50-p65-P and p53-deficiency (D), and constitutively active p53 (E). a-e: corresponding state transition graphs, which illustrate the dynamical behaviour of the networks. The nodes of the state transition graphs gives the activity levels of ‘DSBs early‘ (first digit), ‘DSBs late‘ (second digit), ‘RPA-P-ATR-ATRIP-P‘,(third digit), ‘ATM-P‘ (fourth digit), p53-P‘ (fifth digit), and ‘nuclear p50-p65-P‘ (sixth digit). State transition graphs a-c show only attractors. In d and e, complete state transition graphs are given, wherein the logical steady states are marked with ellipses.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3473366&req=5

Figure 4: Dependence of the network dynamics on p53 and NF-κB. A-E: Interaction graphs of the core network comprising only the regulatory components ‘DSBs early‘, DSBs late‘, ‘RPA-P-ATR-ATRIP-P‘, ‘ATM-P‘, p53-P‘, and ‘nuclear p50-p65-P‘. Shown are the networks for the wildtype (A), the mutant with constitutively active p50-p65-P (B), p53-deficiency (C), both constitutively active p50-p65-P and p53-deficiency (D), and constitutively active p53 (E). a-e: corresponding state transition graphs, which illustrate the dynamical behaviour of the networks. The nodes of the state transition graphs gives the activity levels of ‘DSBs early‘ (first digit), ‘DSBs late‘ (second digit), ‘RPA-P-ATR-ATRIP-P‘,(third digit), ‘ATM-P‘ (fourth digit), p53-P‘ (fifth digit), and ‘nuclear p50-p65-P‘ (sixth digit). State transition graphs a-c show only attractors. In d and e, complete state transition graphs are given, wherein the logical steady states are marked with ellipses.
Mentions: In order to study the terminal fate (attractors) of the network, we reduced it to a model with conserved attractors. Previously, a method has been proposed to reduce Boolean models to their functional interactions. However, this method is only applicable to models of intermediate dimension (i.e. maximal 20 variables) [53]. Therefore, we used a different network reduction technique, which is applicable to large-scale models (see Methods). The reduced model contains only the regulatory components DSBs early, DSBs late, RPA-ATR-ATRIP-P, ATM-P, p53-P and NF-κB (nuclear p50-p65-P) (Figure 4). We calculated the state transition graph of the reduced model by using an asynchronous updating schedule with three priority classes. The state transitions that were assigned to priority classes 1, 2, and 3 coincide with the interactions of time scale values 1, 2, and 3, respectively. Hence, state transitions involving activations of RPA-ATR-ATRIP-P, ATM-P, p53-P or nuclear NF-κB were assigned to priority class 1; priority class 2 embraces the subsequent state transitions leading to activation of ‘DSBs late’ by ‘DSBs early’. State transitions coinciding with the initiation of the inactivation of signal transduction pathways, i.e., the downregulation of RPA-ATR-ATRIP-P, ATM-P, p53-P and NF-κB, constitute priority class 3.

Bottom Line: Simulating therapeutic intervention by agents causing DNA single-strand breaks (SSBs) or DNA double-strand breaks (DSBs) we identified candidate target proteins for sensitization of carcinomas to therapeutic intervention.Further, we enlightened the DDR in different genetic diseases, and by failure mode analysis we defined molecular defects putatively contributing to carcinogenesis.By logic modelling we identified candidate target proteins that could be suitable for radio- and chemotherapy, and contributes to the design of more effective therapies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Experimental Internal Medicine, Otto von Guericke University, Leipziger Str, 44, Magdeburg, 39120, Germany.

ABSTRACT

Background: The genome is continuously attacked by a variety of agents that cause DNA damage. Recognition of DNA lesions activates the cellular DNA damage response (DDR), which comprises a network of signal transduction pathways to maintain genome integrity. In response to severe DNA damage, cells undergo apoptosis to avoid transformation into tumour cells, or alternatively, the cells enter permanent cell cycle arrest, called senescence. Most tumour cells have defects in pathways leading to DNA repair or apoptosis. In addition, apoptosis could be counteracted by nuclear factor kappa B (NF-κB), the main anti-apoptotic transcription factor in the DDR. Despite the high clinical relevance, the interplay of the DDR pathways is poorly understood. For therapeutic purposes DNA damage signalling processes are induced to induce apoptosis in tumour cells. However, the efficiency of radio- and chemotherapy is strongly hampered by cell survival pathways in tumour cells. In this study logical modelling was performed to facilitate understanding of the complexity of the signal transduction networks in the DDR and to provide cancer treatment options.

Results: Our comprehensive discrete logical model provided new insights into the dynamics of the DDR in human epithelial tumours. We identified new mechanisms by which the cell regulates the dynamics of the activation of the tumour suppressor p53 and NF-κB. Simulating therapeutic intervention by agents causing DNA single-strand breaks (SSBs) or DNA double-strand breaks (DSBs) we identified candidate target proteins for sensitization of carcinomas to therapeutic intervention. Further, we enlightened the DDR in different genetic diseases, and by failure mode analysis we defined molecular defects putatively contributing to carcinogenesis.

Conclusion: By logic modelling we identified candidate target proteins that could be suitable for radio- and chemotherapy, and contributes to the design of more effective therapies.

Show MeSH
Related in: MedlinePlus