Computational modeling of eukaryotic mRNA turnover
- PMID: 11565744
- PMCID: PMC1370166
- DOI: 10.1017/s1355838201010330
Computational modeling of eukaryotic mRNA turnover
Abstract
The process of eukaryotic gene expression involves a diverse number of steps including transcription, RNA processing, transport, translation, and mRNA turnover. A critical step in understanding this process will be the development of mathematical models that quantitatively describe and predict the behavior of this complex system. We have simulated eukaryotic mRNA turnover in a linear multicomponent model based on the known mRNA decay pathways in yeast. Using rate constants based on experimental data for the yeast unstable MFA2 and stable PGK1 transcripts, the computational modeling reproduces experimental observations after minor adjustments. Subsequent analysis and a series of in silico experiments led to several conclusions. First, we demonstrate that mRNA half-life as commonly measured underestimates the average life span of an mRNA. Second, due to the properties of the pathways, the measurement of a half-life can predominantly measure different steps in the decay network. A corollary of this fact is that different mRNAs will be affected differentially by changes in specific rate constants. Third, the way to obtain the largest change of levels of mRNA for the smallest changes in rate is by changing the rate of deadenylation, where a large amount of regulation of mRNA decay occurs. Fourth, the 3'-to-5' degradation of mRNA shows mRNA-specific rates of degradation that are dependent on the 5' structure of the mRNA. These programs can be run over the Web, are adaptable to other eukaryotes, and provide outputs as graphs and virtual northern gels, which can be directly compared to experimental data. Therefore, this model constitutes a useful tool for the quantitative analysis of the process and control of mRNA degradation in eukaryotic cells.
Similar articles
-
Mechanisms and control of mRNA decapping in Saccharomyces cerevisiae.Annu Rev Biochem. 2000;69:571-95. doi: 10.1146/annurev.biochem.69.1.571. Annu Rev Biochem. 2000. PMID: 10966469 Review.
-
The cis acting sequences responsible for the differential decay of the unstable MFA2 and stable PGK1 transcripts in yeast include the context of the translational start codon.RNA. 1999 Mar;5(3):420-33. doi: 10.1017/s1355838299981748. RNA. 1999. PMID: 10094310 Free PMC article.
-
Deadenylation of the unstable mRNA encoded by the yeast MFA2 gene leads to decapping followed by 5'-->3' digestion of the transcript.Genes Dev. 1994 Apr 1;8(7):855-66. doi: 10.1101/gad.8.7.855. Genes Dev. 1994. PMID: 7926773
-
Turnover mechanisms of the stable yeast PGK1 mRNA.Mol Cell Biol. 1995 Apr;15(4):2145-56. doi: 10.1128/MCB.15.4.2145. Mol Cell Biol. 1995. PMID: 7891709 Free PMC article.
-
Degradation of mRNA in eukaryotes.Cell. 1995 Apr 21;81(2):179-83. doi: 10.1016/0092-8674(95)90326-7. Cell. 1995. PMID: 7736570 Review.
Cited by
-
Solving stochastic gene-expression models using queueing theory: A tutorial review.Biophys J. 2024 May 7;123(9):1034-1057. doi: 10.1016/j.bpj.2024.04.004. Epub 2024 Apr 9. Biophys J. 2024. PMID: 38594901 Review.
-
Distilling dynamical knowledge from stochastic reaction networks.Proc Natl Acad Sci U S A. 2024 Apr 2;121(14):e2317422121. doi: 10.1073/pnas.2317422121. Epub 2024 Mar 26. Proc Natl Acad Sci U S A. 2024. PMID: 38530895
-
The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.Sci Adv. 2023 Aug 9;9(32):eadh5138. doi: 10.1126/sciadv.adh5138. Epub 2023 Aug 9. Sci Adv. 2023. PMID: 37556551 Free PMC article.
-
Contributions of Ccr4 and Gcn2 to the Translational Response of C. neoformans to Host-Relevant Stressors and Integrated Stress Response Induction.mBio. 2023 Apr 25;14(2):e0019623. doi: 10.1128/mbio.00196-23. Epub 2023 Apr 5. mBio. 2023. PMID: 37017529 Free PMC article.
-
The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.bioRxiv [Preprint]. 2023 Mar 8:2023.03.06.531283. doi: 10.1101/2023.03.06.531283. bioRxiv. 2023. Update in: Sci Adv. 2023 Aug 9;9(32):eadh5138. doi: 10.1126/sciadv.adh5138. PMID: 36945401 Free PMC article. Updated. Preprint.
References
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
Miscellaneous