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Epstein-Barr virus latency switch in human B-cells: a physico-chemical model.

Werner M, Ernberg I, Zou J, Almqvist J, Aurell E - BMC Syst Biol (2007)

Bottom Line: Our experimental results display significant differences in EBNA-1 and Oct-2 levels between resting and proliferating programs.We conclude that EBNA-1 and Oct-2 regulation of Cp and Qp is sufficient to establish mutually exclusive expression patterns.Our results also stress the importance of the little known regulation of human transcription factor Oct-2.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Biological Physics, School of Computer Science and Communication, Royal Institute of Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden. mariawer@kth.se

ABSTRACT

Background: The Epstein-Barr virus is widespread in all human populations and is strongly associated with human disease, ranging from infectious mononucleosis to cancer. In infected cells the virus can adopt several different latency programs, affecting the cells' behaviour. Experimental results indicate that a specific genetic switch between viral latency programs, reprograms human B-cells between proliferative and resting states. Each of these two latency programs makes use of a different viral promoter, Cp and Qp, respectively. The hypothesis tested in this study is that this genetic switch is controlled by both human and viral transcription factors; Oct-2 and EBNA-1. We build a physico-chemical model to investigate quantitatively the dynamical properties of the promoter regulation and experimentally examine protein level variations between the two latency programs.

Results: Our experimental results display significant differences in EBNA-1 and Oct-2 levels between resting and proliferating programs. With the model we identify two stable latency programs, corresponding to a resting and proliferating cell. The two programs differ in robustness and transcriptional activity. The proliferating state is markedly more stable, with a very high transcriptional activity from its viral promoter. We predict the promoter activities to be mutually exclusive in the two different programs, and our relative promoter activities correlate well with experimental data. Transitions between programs can be induced, by affecting the protein levels of our transcription factors. Simulated time scales are in line with experimental results.

Conclusion: We show that fundamental properties of the Epstein-Barr virus involvement in latent infection, with implications for tumor biology, can be modelled and understood mathematically. We conclude that EBNA-1 and Oct-2 regulation of Cp and Qp is sufficient to establish mutually exclusive expression patterns. Moreover, the modelled genetic control predict both mono- and bistable behavior and a considerable difference in transition dynamics, based on program stability and promoter activities. Both these phenomena we hope can be further investigated experimentally, to increase the understanding of this important switch. Our results also stress the importance of the little known regulation of human transcription factor Oct-2.

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Latency escapes and model robustness. Figure showing the minimum instantaneous change in EBNA-1 protein number necessary to switch from latency I to III and vice versa, as a function of the number of Oct-2+Grg/TLR proteins. For small numbers only the latency III state exists, while for large numbers only latency I state exists, compare Figure 3 and Figure 4. Left figure: red lines are at the reference parameter values, in particular Oct-2 complex binding with affinity KdOFR = 2.5 nM and an EBNA-1 dimerization dissociation constant of 1 nM. Blue lines show a five-fold weaker Oct-2 affinity (KdOFR = 12.5 nM). Similar behaviour is then displayed at approximately five-fold higher Oct-2 level. Right figure: red solid and dotted lines at reference parameter values. Blue solid and dotted lines at a tenfold stronger EBNA-1 dimerization, and green solid and dotted lines at a tenfold greater volume. The latency III state is relatively robust towards either of these changes, while the latency I state changes more. Black solid and dotted lines show both a tenfold stronger EBNA-1 dimerization and a tenfold greater volume. This influences the latency III state more, essentially because EBNA-1 concentration in the latency III state is then comparable to the EBNA-1 dimerization dissociation constant.
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Figure 5: Latency escapes and model robustness. Figure showing the minimum instantaneous change in EBNA-1 protein number necessary to switch from latency I to III and vice versa, as a function of the number of Oct-2+Grg/TLR proteins. For small numbers only the latency III state exists, while for large numbers only latency I state exists, compare Figure 3 and Figure 4. Left figure: red lines are at the reference parameter values, in particular Oct-2 complex binding with affinity KdOFR = 2.5 nM and an EBNA-1 dimerization dissociation constant of 1 nM. Blue lines show a five-fold weaker Oct-2 affinity (KdOFR = 12.5 nM). Similar behaviour is then displayed at approximately five-fold higher Oct-2 level. Right figure: red solid and dotted lines at reference parameter values. Blue solid and dotted lines at a tenfold stronger EBNA-1 dimerization, and green solid and dotted lines at a tenfold greater volume. The latency III state is relatively robust towards either of these changes, while the latency I state changes more. Black solid and dotted lines show both a tenfold stronger EBNA-1 dimerization and a tenfold greater volume. This influences the latency III state more, essentially because EBNA-1 concentration in the latency III state is then comparable to the EBNA-1 dimerization dissociation constant.

Mentions: The system volume was in our study estimated to be 2 * 10-13 l (see Methods), but was increased and decreased ten-fold in sensitivity tests. For each volume size, the stable latency I and III levels of EBNA-1 was computed for different dimerization dissociation constants for EBNA-1; 10-8 M, 10-9 M and 10-10 M, and varying levels of Oct-2. Stable steady state levels were also computed for a five-fold lower Oct-2 affinity to FR. The stability of the two latency states and their robustness to parameter changes can be quantified by the externally imposed change on EBNA-1 levels that induces the system to transit from one state to the other. This measure is of course appropriate only in the bi-stable region, where both states exist. As shown in Figure 5, for most parameters latency III is more stable than latency I, i.e., a larger change in EBNA-1 levels is needed to induce a transition from latency III to latency I than in the opposite direction. [see Additional file 1]


Epstein-Barr virus latency switch in human B-cells: a physico-chemical model.

Werner M, Ernberg I, Zou J, Almqvist J, Aurell E - BMC Syst Biol (2007)

Latency escapes and model robustness. Figure showing the minimum instantaneous change in EBNA-1 protein number necessary to switch from latency I to III and vice versa, as a function of the number of Oct-2+Grg/TLR proteins. For small numbers only the latency III state exists, while for large numbers only latency I state exists, compare Figure 3 and Figure 4. Left figure: red lines are at the reference parameter values, in particular Oct-2 complex binding with affinity KdOFR = 2.5 nM and an EBNA-1 dimerization dissociation constant of 1 nM. Blue lines show a five-fold weaker Oct-2 affinity (KdOFR = 12.5 nM). Similar behaviour is then displayed at approximately five-fold higher Oct-2 level. Right figure: red solid and dotted lines at reference parameter values. Blue solid and dotted lines at a tenfold stronger EBNA-1 dimerization, and green solid and dotted lines at a tenfold greater volume. The latency III state is relatively robust towards either of these changes, while the latency I state changes more. Black solid and dotted lines show both a tenfold stronger EBNA-1 dimerization and a tenfold greater volume. This influences the latency III state more, essentially because EBNA-1 concentration in the latency III state is then comparable to the EBNA-1 dimerization dissociation constant.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2164963&req=5

Figure 5: Latency escapes and model robustness. Figure showing the minimum instantaneous change in EBNA-1 protein number necessary to switch from latency I to III and vice versa, as a function of the number of Oct-2+Grg/TLR proteins. For small numbers only the latency III state exists, while for large numbers only latency I state exists, compare Figure 3 and Figure 4. Left figure: red lines are at the reference parameter values, in particular Oct-2 complex binding with affinity KdOFR = 2.5 nM and an EBNA-1 dimerization dissociation constant of 1 nM. Blue lines show a five-fold weaker Oct-2 affinity (KdOFR = 12.5 nM). Similar behaviour is then displayed at approximately five-fold higher Oct-2 level. Right figure: red solid and dotted lines at reference parameter values. Blue solid and dotted lines at a tenfold stronger EBNA-1 dimerization, and green solid and dotted lines at a tenfold greater volume. The latency III state is relatively robust towards either of these changes, while the latency I state changes more. Black solid and dotted lines show both a tenfold stronger EBNA-1 dimerization and a tenfold greater volume. This influences the latency III state more, essentially because EBNA-1 concentration in the latency III state is then comparable to the EBNA-1 dimerization dissociation constant.
Mentions: The system volume was in our study estimated to be 2 * 10-13 l (see Methods), but was increased and decreased ten-fold in sensitivity tests. For each volume size, the stable latency I and III levels of EBNA-1 was computed for different dimerization dissociation constants for EBNA-1; 10-8 M, 10-9 M and 10-10 M, and varying levels of Oct-2. Stable steady state levels were also computed for a five-fold lower Oct-2 affinity to FR. The stability of the two latency states and their robustness to parameter changes can be quantified by the externally imposed change on EBNA-1 levels that induces the system to transit from one state to the other. This measure is of course appropriate only in the bi-stable region, where both states exist. As shown in Figure 5, for most parameters latency III is more stable than latency I, i.e., a larger change in EBNA-1 levels is needed to induce a transition from latency III to latency I than in the opposite direction. [see Additional file 1]

Bottom Line: Our experimental results display significant differences in EBNA-1 and Oct-2 levels between resting and proliferating programs.We conclude that EBNA-1 and Oct-2 regulation of Cp and Qp is sufficient to establish mutually exclusive expression patterns.Our results also stress the importance of the little known regulation of human transcription factor Oct-2.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Biological Physics, School of Computer Science and Communication, Royal Institute of Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden. mariawer@kth.se

ABSTRACT

Background: The Epstein-Barr virus is widespread in all human populations and is strongly associated with human disease, ranging from infectious mononucleosis to cancer. In infected cells the virus can adopt several different latency programs, affecting the cells' behaviour. Experimental results indicate that a specific genetic switch between viral latency programs, reprograms human B-cells between proliferative and resting states. Each of these two latency programs makes use of a different viral promoter, Cp and Qp, respectively. The hypothesis tested in this study is that this genetic switch is controlled by both human and viral transcription factors; Oct-2 and EBNA-1. We build a physico-chemical model to investigate quantitatively the dynamical properties of the promoter regulation and experimentally examine protein level variations between the two latency programs.

Results: Our experimental results display significant differences in EBNA-1 and Oct-2 levels between resting and proliferating programs. With the model we identify two stable latency programs, corresponding to a resting and proliferating cell. The two programs differ in robustness and transcriptional activity. The proliferating state is markedly more stable, with a very high transcriptional activity from its viral promoter. We predict the promoter activities to be mutually exclusive in the two different programs, and our relative promoter activities correlate well with experimental data. Transitions between programs can be induced, by affecting the protein levels of our transcription factors. Simulated time scales are in line with experimental results.

Conclusion: We show that fundamental properties of the Epstein-Barr virus involvement in latent infection, with implications for tumor biology, can be modelled and understood mathematically. We conclude that EBNA-1 and Oct-2 regulation of Cp and Qp is sufficient to establish mutually exclusive expression patterns. Moreover, the modelled genetic control predict both mono- and bistable behavior and a considerable difference in transition dynamics, based on program stability and promoter activities. Both these phenomena we hope can be further investigated experimentally, to increase the understanding of this important switch. Our results also stress the importance of the little known regulation of human transcription factor Oct-2.

Show MeSH
Related in: MedlinePlus