关于真核生物降解及代谢过程的计算机模拟

关于真核生物 mRNA降解及代谢过程的计算机模拟曹丹亚力桑那大学分子细胞生物学系Dan Cao and Roy ParkerHoward Hughes Medical Institute & Department of Molecular Cellular BiologyUniversity of ArizonaComputational modeling of mRNA turnoverDNA mRNA ProteinTranscription,Splicing,transporttranslationdegradation degradationThe Central DogmaWhy want to model this process?1. IntegrationWhy want to model this process(Contd)? An explanatory/descriptive tool1) correction of misconceptions2) help interpret result correctly3) advice suitable experiment Predictionfrom discrepancies between simulation result and experimental result An ideal system to start knowledge-driven simulationPlenty of data available to estimate the rates for distinct steps, including steady state distribution, half-lives, effects of various mutantsMajor assumptions Transcription is a zeroth order process, all other steps are first order processes All steps are irreversible No feedback loopsrates for all stepsSteady state level of each intermediatekinetics over certainexperimental time courseVirtual northern gelfor direct comparisonTranscriptional shut-off exp.(decay from steady state) Transcriptional pulse chase exp.At steady stateInhibit transcription At the end of a short “pulse”Inhibit transcriptionA sample screenshotinputDetermine the fitness of the model with the in vivo decay network: MFA2pG & PGK1pG They represent a unstable (MFA2) and stable (PGK1) mRNA in yeast Their degradation has been extensively characterized, with lots of data available to estimate the rates. E.g: transcriptional pulse chase gel can give information for the rates of deadenylation and decapping. Strong poly (G) structure at the 3UTR can trap the decay intermediates, give additional information about in vivo process. E.g: rates of terminal deadenylation and 3 to 5 decayComparison of modeling results with experimental observationsThe simulated steady state poly (A) distribution, pulse chase gel pattern,previously characterized mutants (transport, decapping, 3 to 5 decay) are also consistent with exp. observations.Modeling of MFA2pG in transcriptional pulse chaseObservedComputedModeling of PGK1pG in transcriptional pulse chaseObservedComputed The fact that we can reproduce the experimental results by modeling suggests that our model is quite accurate, and we have a relatively robust understanding of the in vivo process. The obtaining of a good model for both MFA2pG and PGK1pG allows us to further analyze the whole network. We have used our model to examine how transcripts respond to a variety of perturbations by performing a series of in silico experiments. E.g.: increase or decrease the rate of transport, deadenylation, decapping, 5 to 3 exonucleolytic decay, 3 to 5 exonucleolytic decay, see how the transcript level, half-life, steady state distribution are affected. What have we learned?Comparison of in silico mutants Deadenylation is a key step in controlling mRNA turnover. Provide explanation for why many decay elements identified affecting deadenylation. 3 to 5 decay rate for full length. Obtained by matching the calculated t with the observed t in dcp1 mutant.The calculated 3 to 5 decay ratesImplication: 3 to 5 decay by exosome shows mRNA specific degradation rates that are dependent on the 5 structure of the mRNA 3 to 5 decay rate for fragment. Obtained by matching the calculatedt of fragment with the observed t when decapping is blocked. Half-life. Measured by transcriptional shut-off experiment. At the point where transcript reaches half of its initial level. t is thought to represent how long the mRNA is persist in the cell. Average life span. Calculated from the simulation for transcriptional pulse chase experiment. More accurate representation of the average time the mRNA is present in the cell.View on half-lifeHalf-life Average life span. The traditional way of measuring t1/2 may underestimate the life span of an mRNA. The difference is due to the distribution of mRNA at steady state. Certain % of mRNAs has already passed several decay steps.Why Average life span half-life?Decay from steady statet 1/2Transcriptional pulse chaseAverage Life Span The measurement of measure a half-life can predominantly different steps in the decay networkDifferent mRNAs will be affected differentially by certain change on specific stepNeed to be very cautious when interpreting mRNA specific effects.Short-lived mRNAs (MFA2) are more responsive to changes on transport rate than long-lived mRNAs (PGK1).Possible factors that disrupt the correlation between mRNA and protein levelThe increase of transcript levelin the transport mutant solelycomes from the increase in the nuclear pool.The increase of transcript levelin the 5 to 3 exo mutantssolely comes from the increasein the cap- species We are able to simulate MFA2 and PGK1, which suggests that we have a relatively robust understanding about yeast mRNA turnover. This program can be adaptable to other eukaryotic mRNAs that follow the same degradation scheme. This is a useful, explanatory tool for quantitative analysis of the process and regulation of mRNA turnover in eukaryotic cells. Some In silico experiments performed might be able to suggest the best exp. for a particular purpose. E.g: decapping mutants. Discrepancies between in silico results and real results might lead to new insights for the in vivo network.Computation ExperimentationNonsense Mediated mRNA Decay(NMD)AAAAAAA70DNAtranscriptionAUG UAA UAAm7GpppNormal decay 30Nonsense Decay3Normal Decay and NMDModelingPredictionExp. testingKnowledge and hypothesis based modeling. Might have multiple models: model 1, 2,3n. All models should fit with current data.Ma