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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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mRNA decay patterns and lifetime distributions.Relative mRNA number  as a function of the time  after the interruption of transcription. (A) Two different experimental decay patterns are reproduced from [23] (circles) corresponding to the genes MGS1 (red) and RPS16B (blue) of S. cerevisiae. The solid lines represent decay patterns as calculated by the Markov chain model and the corresponding rates are estimated from a non-linear regression analysis (see Models and Methods for details about the fit parameters). For comparison, we also show a fit with a simple exponential function (dashed lines) which is clearly not suitable to capture the entire information of the degradation process. In particular, the exponential fit for the red data points suggests a half-life (intersection with the horizontal line) that is almost twice as large as the true half-life. (B) The corresponding lifetime densities  are derived using the rates obtained via the fit of Eq. (1) with the data. Evidently, both densities differ strongly from an exponential distribution indicated by the dashed lines. While the red line shows a rapidly decaying lifetime distribution, the blue line is broadly distributed, with a clear maximum at an intermediate time.
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pone-0055442-g003: mRNA decay patterns and lifetime distributions.Relative mRNA number as a function of the time after the interruption of transcription. (A) Two different experimental decay patterns are reproduced from [23] (circles) corresponding to the genes MGS1 (red) and RPS16B (blue) of S. cerevisiae. The solid lines represent decay patterns as calculated by the Markov chain model and the corresponding rates are estimated from a non-linear regression analysis (see Models and Methods for details about the fit parameters). For comparison, we also show a fit with a simple exponential function (dashed lines) which is clearly not suitable to capture the entire information of the degradation process. In particular, the exponential fit for the red data points suggests a half-life (intersection with the horizontal line) that is almost twice as large as the true half-life. (B) The corresponding lifetime densities are derived using the rates obtained via the fit of Eq. (1) with the data. Evidently, both densities differ strongly from an exponential distribution indicated by the dashed lines. While the red line shows a rapidly decaying lifetime distribution, the blue line is broadly distributed, with a clear maximum at an intermediate time.

Mentions: By exploiting our Markov model, in Fig. 3A we analyzed two instructive examples from the experimental decay patterns in Fig. 1: The mRNA encoding MGS1 (Maintenance of Genome Stability 1, red) and the small ribosome subunit protein (RPS16B, blue). A comparison between the best fit with an exponential function and the best fit with a multi-step model shows that the latter is clearly more appropriate than a single exponential in describing the decay profiles over the entire time course of the experiment. Furthermore, in the case of MGS1 mRNA, the fit reveals a half-life which is substantially smaller than the estimated half-life from an exponential fit. We can thus observe that a measurement of the half-life is not, in general, a good measure of the average lifetime of the mRNAs and it fails to predict the decay rate accurately.


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)

mRNA decay patterns and lifetime distributions.Relative mRNA number  as a function of the time  after the interruption of transcription. (A) Two different experimental decay patterns are reproduced from [23] (circles) corresponding to the genes MGS1 (red) and RPS16B (blue) of S. cerevisiae. The solid lines represent decay patterns as calculated by the Markov chain model and the corresponding rates are estimated from a non-linear regression analysis (see Models and Methods for details about the fit parameters). For comparison, we also show a fit with a simple exponential function (dashed lines) which is clearly not suitable to capture the entire information of the degradation process. In particular, the exponential fit for the red data points suggests a half-life (intersection with the horizontal line) that is almost twice as large as the true half-life. (B) The corresponding lifetime densities  are derived using the rates obtained via the fit of Eq. (1) with the data. Evidently, both densities differ strongly from an exponential distribution indicated by the dashed lines. While the red line shows a rapidly decaying lifetime distribution, the blue line is broadly distributed, with a clear maximum at an intermediate time.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0055442-g003: mRNA decay patterns and lifetime distributions.Relative mRNA number as a function of the time after the interruption of transcription. (A) Two different experimental decay patterns are reproduced from [23] (circles) corresponding to the genes MGS1 (red) and RPS16B (blue) of S. cerevisiae. The solid lines represent decay patterns as calculated by the Markov chain model and the corresponding rates are estimated from a non-linear regression analysis (see Models and Methods for details about the fit parameters). For comparison, we also show a fit with a simple exponential function (dashed lines) which is clearly not suitable to capture the entire information of the degradation process. In particular, the exponential fit for the red data points suggests a half-life (intersection with the horizontal line) that is almost twice as large as the true half-life. (B) The corresponding lifetime densities are derived using the rates obtained via the fit of Eq. (1) with the data. Evidently, both densities differ strongly from an exponential distribution indicated by the dashed lines. While the red line shows a rapidly decaying lifetime distribution, the blue line is broadly distributed, with a clear maximum at an intermediate time.
Mentions: By exploiting our Markov model, in Fig. 3A we analyzed two instructive examples from the experimental decay patterns in Fig. 1: The mRNA encoding MGS1 (Maintenance of Genome Stability 1, red) and the small ribosome subunit protein (RPS16B, blue). A comparison between the best fit with an exponential function and the best fit with a multi-step model shows that the latter is clearly more appropriate than a single exponential in describing the decay profiles over the entire time course of the experiment. Furthermore, in the case of MGS1 mRNA, the fit reveals a half-life which is substantially smaller than the estimated half-life from an exponential fit. We can thus observe that a measurement of the half-life is not, in general, a good measure of the average lifetime of the mRNAs and it fails to predict the decay rate accurately.

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