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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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TNF-α-mediated feedback loops in the control of ADPKD onset. a. Pathway diagram of the proteins and network involved in cytogenesis. b. Simulation of time courses of primary network components in the wild-type case. Random (Gaussian) noise is added to the expression level of each polycystin allele. Random fluctuations are damped because the expression noise in the two genes is uncorrelated. c. Heterogeneous background simulation. The level of polycystin is lower and the fluctuations are higher. d. Heterogenous background simulation, but with the TNF-α feedback loop disabled. Random fluctuations allow cyst formation to occur, but these are suppressed as soon as above-threshold expression of TNF-a is restored.
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Figure 6: TNF-α-mediated feedback loops in the control of ADPKD onset. a. Pathway diagram of the proteins and network involved in cytogenesis. b. Simulation of time courses of primary network components in the wild-type case. Random (Gaussian) noise is added to the expression level of each polycystin allele. Random fluctuations are damped because the expression noise in the two genes is uncorrelated. c. Heterogeneous background simulation. The level of polycystin is lower and the fluctuations are higher. d. Heterogenous background simulation, but with the TNF-α feedback loop disabled. Random fluctuations allow cyst formation to occur, but these are suppressed as soon as above-threshold expression of TNF-a is restored.

Mentions: Alternative to genetic changes, it is possible that some environmental or non-genetic factors, probably those embedded in the cellular networks involving polycystins, promote the dominant phenotypic expression of ADPKD. In a recent study [41], it was shown that the inflammatory cytokine, TNF-α, negatively modulates the function of PC2 and promotes cyst formation in Pkd2+/- mice. PC2, in turn, negatively regulates the level of TNF-α converting enzyme (TACE) and TNF-α receptor. An increase in TNF-α in renal tissues can also be caused by injury or infection or by cystic conditions as the cytokine was found to accumulate in cyst fluid from human ADPKD patients. These interactions form a network that connects cytokine, polycystin and cystic disease through two feedback loops belonging to loop types 3a and 3c (Figure 6a). Both of these feedback loops could potentially induce a stable disease state if a polycystin (positioned as Y in the feedback loops) is affected by heterozygosity.


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

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

TNF-α-mediated feedback loops in the control of ADPKD onset. a. Pathway diagram of the proteins and network involved in cytogenesis. b. Simulation of time courses of primary network components in the wild-type case. Random (Gaussian) noise is added to the expression level of each polycystin allele. Random fluctuations are damped because the expression noise in the two genes is uncorrelated. c. Heterogeneous background simulation. The level of polycystin is lower and the fluctuations are higher. d. Heterogenous background simulation, but with the TNF-α feedback loop disabled. Random fluctuations allow cyst formation to occur, but these are suppressed as soon as above-threshold expression of TNF-a is restored.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: TNF-α-mediated feedback loops in the control of ADPKD onset. a. Pathway diagram of the proteins and network involved in cytogenesis. b. Simulation of time courses of primary network components in the wild-type case. Random (Gaussian) noise is added to the expression level of each polycystin allele. Random fluctuations are damped because the expression noise in the two genes is uncorrelated. c. Heterogeneous background simulation. The level of polycystin is lower and the fluctuations are higher. d. Heterogenous background simulation, but with the TNF-α feedback loop disabled. Random fluctuations allow cyst formation to occur, but these are suppressed as soon as above-threshold expression of TNF-a is restored.
Mentions: Alternative to genetic changes, it is possible that some environmental or non-genetic factors, probably those embedded in the cellular networks involving polycystins, promote the dominant phenotypic expression of ADPKD. In a recent study [41], it was shown that the inflammatory cytokine, TNF-α, negatively modulates the function of PC2 and promotes cyst formation in Pkd2+/- mice. PC2, in turn, negatively regulates the level of TNF-α converting enzyme (TACE) and TNF-α receptor. An increase in TNF-α in renal tissues can also be caused by injury or infection or by cystic conditions as the cytokine was found to accumulate in cyst fluid from human ADPKD patients. These interactions form a network that connects cytokine, polycystin and cystic disease through two feedback loops belonging to loop types 3a and 3c (Figure 6a). Both of these feedback loops could potentially induce a stable disease state if a polycystin (positioned as Y in the feedback loops) is affected by heterozygosity.

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