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The role of noise and positive feedback in the onset of autosomal dominant diseases.

Bosl WJ, Li R - BMC Syst Biol (2010)

Bottom Line: Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome.These diseases often do not emerge at birth.Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

View Article: PubMed Central - HTML - PubMed

Affiliation: Harvard Medical School, Boston, MA 02115, USA. william.bosl@childrens.harvard.edu

ABSTRACT

Background: Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome. These diseases often do not emerge at birth. There are presently two prevailing theories explaining the expression of AD diseases. One explanation originates from the Knudson two-hit theory of hereditary cancers, where loss of heterozygosity or occurrence of somatic mutations impairs the function of the wild-type copy. While these somatic second hits may be sufficient for stable disease states, it is often difficult to determine if their occurrence necessarily marks the initiation of disease progression. A more direct consequence of a heterozygous genetic background is haploinsufficiency, referring to a lack of sufficient gene function due to reduced wild-type gene copy number; however, haploinsufficiency can involve a variety of additional mechanisms, such as noise in gene expression or protein levels, injury and second hit mutations in other genes. In this study, we explore the possible contribution to the onset of autosomal dominant diseases from intrinsic factors, such as those determined by the structure of the molecular networks governing normal cellular physiology.

Results: First, simple models of single gene insufficiency using the positive feedback loops that may be derived from a three-component network were studied by computer simulation using Bionet software. The network structure is shown to affect the dynamics considerably; some networks are relatively stable even when large stochastic variations in are present, while others exhibit switch-like dynamics. In the latter cases, once the network switches over to the disease state it remains in that state permanently. Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

Conclusions: By identifying the intrinsic mechanisms involved in the onset of AD diseases, it may be possible to better assess risk factors as well as lead to potential new drug targets. To illustrate the applicability of this study of pathway dynamics, we simulated the primary pathways involved in two autosomal dominant diseases, Polycystic Kidney Disease (PKD) and mature onset diabetes of youth (MODY). Simulations demonstrate that some of the primary disease characteristics are consistent with the positive feedback-stochastic variation theory presented here. This has implications for new drug targets to control these diseases by blocking the positive feedback loop in the relevant pathways.

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Illustration of network response to changes in stochastic parameters. The response of network 2a to variations in stochasticity when X is deficient is used here to illustrate the effect of changing the noise added to production rate on the product concentration. These are single snapshots of the 100-simulation trials shown in table 2. Note that the normalized protein concentration is plotted, while the expression level refers to the rate of protein production; the rate is not plotted.
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Figure 5: Illustration of network response to changes in stochastic parameters. The response of network 2a to variations in stochasticity when X is deficient is used here to illustrate the effect of changing the noise added to production rate on the product concentration. These are single snapshots of the 100-simulation trials shown in table 2. Note that the normalized protein concentration is plotted, while the expression level refers to the rate of protein production; the rate is not plotted.

Mentions: When the disease state is involved in the feedback loop as in Figure 2a, a natural instability exists. The two stable states of interest are the presence or absence of disease. In all cases the initial state is assumed to be the absence of disease; D is initially zero. This prevents the situation where D is initially high and is involved in a feedback loop that completely blocks a switch to a low D state and is biologically realistic because haploinsufficiency diseases do not often exhibit the disease state immediately and deterministically. In network type 2.a, if X is initially set above a critical level, Xc0, and there is no stochastic variability, D will remain near zero, as shown in Figure 5a. Similarly, if X is set below Xc0, D will rise to its highest level and remain there. A plot of the latter case will be very similar to Figure 5b.


The role of noise and positive feedback in the onset of autosomal dominant diseases.

Bosl WJ, Li R - BMC Syst Biol (2010)

Illustration of network response to changes in stochastic parameters. The response of network 2a to variations in stochasticity when X is deficient is used here to illustrate the effect of changing the noise added to production rate on the product concentration. These are single snapshots of the 100-simulation trials shown in table 2. Note that the normalized protein concentration is plotted, while the expression level refers to the rate of protein production; the rate is not plotted.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902440&req=5

Figure 5: Illustration of network response to changes in stochastic parameters. The response of network 2a to variations in stochasticity when X is deficient is used here to illustrate the effect of changing the noise added to production rate on the product concentration. These are single snapshots of the 100-simulation trials shown in table 2. Note that the normalized protein concentration is plotted, while the expression level refers to the rate of protein production; the rate is not plotted.
Mentions: When the disease state is involved in the feedback loop as in Figure 2a, a natural instability exists. The two stable states of interest are the presence or absence of disease. In all cases the initial state is assumed to be the absence of disease; D is initially zero. This prevents the situation where D is initially high and is involved in a feedback loop that completely blocks a switch to a low D state and is biologically realistic because haploinsufficiency diseases do not often exhibit the disease state immediately and deterministically. In network type 2.a, if X is initially set above a critical level, Xc0, and there is no stochastic variability, D will remain near zero, as shown in Figure 5a. Similarly, if X is set below Xc0, D will rise to its highest level and remain there. A plot of the latter case will be very similar to Figure 5b.

Bottom Line: Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome.These diseases often do not emerge at birth.Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

View Article: PubMed Central - HTML - PubMed

Affiliation: Harvard Medical School, Boston, MA 02115, USA. william.bosl@childrens.harvard.edu

ABSTRACT

Background: Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome. These diseases often do not emerge at birth. There are presently two prevailing theories explaining the expression of AD diseases. One explanation originates from the Knudson two-hit theory of hereditary cancers, where loss of heterozygosity or occurrence of somatic mutations impairs the function of the wild-type copy. While these somatic second hits may be sufficient for stable disease states, it is often difficult to determine if their occurrence necessarily marks the initiation of disease progression. A more direct consequence of a heterozygous genetic background is haploinsufficiency, referring to a lack of sufficient gene function due to reduced wild-type gene copy number; however, haploinsufficiency can involve a variety of additional mechanisms, such as noise in gene expression or protein levels, injury and second hit mutations in other genes. In this study, we explore the possible contribution to the onset of autosomal dominant diseases from intrinsic factors, such as those determined by the structure of the molecular networks governing normal cellular physiology.

Results: First, simple models of single gene insufficiency using the positive feedback loops that may be derived from a three-component network were studied by computer simulation using Bionet software. The network structure is shown to affect the dynamics considerably; some networks are relatively stable even when large stochastic variations in are present, while others exhibit switch-like dynamics. In the latter cases, once the network switches over to the disease state it remains in that state permanently. Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

Conclusions: By identifying the intrinsic mechanisms involved in the onset of AD diseases, it may be possible to better assess risk factors as well as lead to potential new drug targets. To illustrate the applicability of this study of pathway dynamics, we simulated the primary pathways involved in two autosomal dominant diseases, Polycystic Kidney Disease (PKD) and mature onset diabetes of youth (MODY). Simulations demonstrate that some of the primary disease characteristics are consistent with the positive feedback-stochastic variation theory presented here. This has implications for new drug targets to control these diseases by blocking the positive feedback loop in the relevant pathways.

Show MeSH
Related in: MedlinePlus