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Complex degradation processes lead to non-exponential decay patterns and age-dependent decay rates of messenger RNA.

Deneke C, Lipowsky R, Valleriani A - PLoS ONE (2013)

Bottom Line: Furthermore, a variety of different and complex biochemical pathways for mRNA degradation have been identified.Next, we develop a theory, formulated as a Markov chain model, that recapitulates some aspects of the multi-step nature of mRNA degradation.We apply our theory to experimental data for yeast and explicitly derive the lifetime distribution of the corresponding mRNAs.

View Article: PubMed Central - PubMed

Affiliation: Department of Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, Potsdam, Germany.

ABSTRACT
Experimental studies on mRNA stability have established several, qualitatively distinct decay patterns for the amount of mRNA within the living cell. Furthermore, a variety of different and complex biochemical pathways for mRNA degradation have been identified. The central aim of this paper is to bring together both the experimental evidence about the decay patterns and the biochemical knowledge about the multi-step nature of mRNA degradation in a coherent mathematical theory. We first introduce a mathematical relationship between the mRNA decay pattern and the lifetime distribution of individual mRNA molecules. This relationship reveals that the mRNA decay patterns at steady state expression level must obey a general convexity condition, which applies to any degradation mechanism. Next, we develop a theory, formulated as a Markov chain model, that recapitulates some aspects of the multi-step nature of mRNA degradation. We apply our theory to experimental data for yeast and explicitly derive the lifetime distribution of the corresponding mRNAs. Thereby, we show how to extract single-molecule properties of an mRNA, such as the age-dependent decay rate and the residual lifetime. Finally, we analyze the decay patterns of the whole translatome of yeast cells and show that yeast mRNAs can be grouped into three broad classes that exhibit three distinct decay patterns. This paper provides both a method to accurately analyze non-exponential mRNA decay patterns and a tool to validate different models of degradation using decay data.

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Related in: MedlinePlus

Effective degradation rate  as a function of the age a of an mRNA.The lifetime distribution of an mRNA can be translated into an age-dependent degradation rate  via Eq. (4). Here, we illustrate the change of the degradation rate during the lifetime of an mRNA for the two decay patterns shown in Fig. 3. For the mRNA encoding MGS1 (red), the degradation rate is high for young mRNAs and decreases to a constant value after some transient time. In contrast, for RPS16B mRNA (blue), the degradation rate is close to zero upon birth of the mRNA and increases gradually to a constant value. For comparison, the constant rates corresponding to a fit of the decay data with purely exponential functions (dashed lines) are also included.
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pone-0055442-g004: Effective degradation rate as a function of the age a of an mRNA.The lifetime distribution of an mRNA can be translated into an age-dependent degradation rate via Eq. (4). Here, we illustrate the change of the degradation rate during the lifetime of an mRNA for the two decay patterns shown in Fig. 3. For the mRNA encoding MGS1 (red), the degradation rate is high for young mRNAs and decreases to a constant value after some transient time. In contrast, for RPS16B mRNA (blue), the degradation rate is close to zero upon birth of the mRNA and increases gradually to a constant value. For comparison, the constant rates corresponding to a fit of the decay data with purely exponential functions (dashed lines) are also included.

Mentions: Fig. 4 illustrates the age-dependence of the degradation rates for the curves in Fig. 3. Clearly, the degradation rate varies strongly during the lifetime of the two chosen mRNAs. The two examples show that the changes of the degradation rates with age are qualitatively different for the two mRNAs. While MGS1 mRNA is initially relatively unstable, the maturation of the molecules leads either to a stabilization of the mRNAs with their age or to a selection of stable mRNAs from the pool. This form of the age-dependent degradation rate indicates that strong degradation processes are at work before or during the relatively slow process of deadenylation. Phenomena such as differential nuclear mRNA degradation, mRNA storage in cytoplasmic stress granules and transient 3′ uridylation can in principle all lead to a reduction of the decay rate[30]–[32]. In contrast, young RPS16B mRNA is very stable but aging processes lead to its destabilization. This indicates that for RPS16B a series of relatively slow steps is necessary to complete the degradation process, in agreement with the picture provided by the decapping mechanism. A similar distinction is thus relevant also for the two non-exponential categories in Fig. 5 (see below).


Complex degradation processes lead to non-exponential decay patterns and age-dependent decay rates of messenger RNA.

Deneke C, Lipowsky R, Valleriani A - PLoS ONE (2013)

Effective degradation rate  as a function of the age a of an mRNA.The lifetime distribution of an mRNA can be translated into an age-dependent degradation rate  via Eq. (4). Here, we illustrate the change of the degradation rate during the lifetime of an mRNA for the two decay patterns shown in Fig. 3. For the mRNA encoding MGS1 (red), the degradation rate is high for young mRNAs and decreases to a constant value after some transient time. In contrast, for RPS16B mRNA (blue), the degradation rate is close to zero upon birth of the mRNA and increases gradually to a constant value. For comparison, the constant rates corresponding to a fit of the decay data with purely exponential functions (dashed lines) are also included.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569439&req=5

pone-0055442-g004: Effective degradation rate as a function of the age a of an mRNA.The lifetime distribution of an mRNA can be translated into an age-dependent degradation rate via Eq. (4). Here, we illustrate the change of the degradation rate during the lifetime of an mRNA for the two decay patterns shown in Fig. 3. For the mRNA encoding MGS1 (red), the degradation rate is high for young mRNAs and decreases to a constant value after some transient time. In contrast, for RPS16B mRNA (blue), the degradation rate is close to zero upon birth of the mRNA and increases gradually to a constant value. For comparison, the constant rates corresponding to a fit of the decay data with purely exponential functions (dashed lines) are also included.
Mentions: Fig. 4 illustrates the age-dependence of the degradation rates for the curves in Fig. 3. Clearly, the degradation rate varies strongly during the lifetime of the two chosen mRNAs. The two examples show that the changes of the degradation rates with age are qualitatively different for the two mRNAs. While MGS1 mRNA is initially relatively unstable, the maturation of the molecules leads either to a stabilization of the mRNAs with their age or to a selection of stable mRNAs from the pool. This form of the age-dependent degradation rate indicates that strong degradation processes are at work before or during the relatively slow process of deadenylation. Phenomena such as differential nuclear mRNA degradation, mRNA storage in cytoplasmic stress granules and transient 3′ uridylation can in principle all lead to a reduction of the decay rate[30]–[32]. In contrast, young RPS16B mRNA is very stable but aging processes lead to its destabilization. This indicates that for RPS16B a series of relatively slow steps is necessary to complete the degradation process, in agreement with the picture provided by the decapping mechanism. A similar distinction is thus relevant also for the two non-exponential categories in Fig. 5 (see below).

Bottom Line: Furthermore, a variety of different and complex biochemical pathways for mRNA degradation have been identified.Next, we develop a theory, formulated as a Markov chain model, that recapitulates some aspects of the multi-step nature of mRNA degradation.We apply our theory to experimental data for yeast and explicitly derive the lifetime distribution of the corresponding mRNAs.

View Article: PubMed Central - PubMed

Affiliation: Department of Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, Potsdam, Germany.

ABSTRACT
Experimental studies on mRNA stability have established several, qualitatively distinct decay patterns for the amount of mRNA within the living cell. Furthermore, a variety of different and complex biochemical pathways for mRNA degradation have been identified. The central aim of this paper is to bring together both the experimental evidence about the decay patterns and the biochemical knowledge about the multi-step nature of mRNA degradation in a coherent mathematical theory. We first introduce a mathematical relationship between the mRNA decay pattern and the lifetime distribution of individual mRNA molecules. This relationship reveals that the mRNA decay patterns at steady state expression level must obey a general convexity condition, which applies to any degradation mechanism. Next, we develop a theory, formulated as a Markov chain model, that recapitulates some aspects of the multi-step nature of mRNA degradation. We apply our theory to experimental data for yeast and explicitly derive the lifetime distribution of the corresponding mRNAs. Thereby, we show how to extract single-molecule properties of an mRNA, such as the age-dependent decay rate and the residual lifetime. Finally, we analyze the decay patterns of the whole translatome of yeast cells and show that yeast mRNAs can be grouped into three broad classes that exhibit three distinct decay patterns. This paper provides both a method to accurately analyze non-exponential mRNA decay patterns and a tool to validate different models of degradation using decay data.

Show MeSH
Related in: MedlinePlus