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1、關(guān)于真核生物mRNA降解及代謝過程的計(jì)算機(jī)模擬曹丹亞力桑那大學(xué)分子細(xì)胞生物學(xué)系 Dan Cao and Roy ParkerHoward Hughes Medical Institute & Department of Molecular Cellular BiologyUniversity of ArizonaComputational modeling of mRNA turnover DNA mRNA ProteinTranscription,Splicing,transport translationdegradation degradationThe Central Dogma Wh

2、y want to model this process?1. Integration Why want to model this process(Contd)? 2. An explanatory/descriptive tool 1) correction of misconceptions 2) help interpret result correctly 3) advice suitable experiment3. Prediction from discrepancies between simulation result and experimental result4. A

3、n ideal system to start knowledge-driven simulation Plenty of data available to estimate the rates for distinct steps, including steady state distribution, half-lives, effects of various mutants Major assumptions1. Transcription is a zeroth order process, all other steps are first order processes2.

4、All steps are irreversible3. No feedback loops dttXkdttXktdX iioutiiini )()()( )(1)( rates for all steps Steady state level of each intermediate kinetics over certainexperimental time course Virtual northern gelfor direct comparisonTranscriptional shut-off exp.(decay from steady state) Transcription

5、al pulse chase exp.At steady stateInhibit transcription At the end of a short “pulse”Inhibit transcription A sample screenshot input Determine 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 h

6、as 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 v

7、ivo process. E.g: rates of terminal deadenylation and 3 to 5 decay Comparison of modeling results with experimental observations MFA2pG t1/2 (min) Full length 3 Fragment 5 Fragment Computed 3.8 51% 49% 0 Observed 3-4 53% 47% 0 PGK1pG t1/2 (min) Full length 3 Fragment 5 Fragment Computed 27 80% 19% 4

8、% of 3 fragment Observed 30 79% 21% 5% of 3 fragment The 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 chase 0% 20% 40

9、% 60% 80% 100% 0 4 8 12 16 20min after inhibition of transcription % o f in itia l m RN A Full length 3 FragmentObservedComputed 0% 20% 40% 60% 80% 100% 0 4 8 12 16 20min after repression of transcription % o f in itia l m RN A Full Length 3 Fragment Modeling of PGK1pG in transcriptional pulse chase

10、 0% 20% 40% 60% 80% 100% 0 10 20 30 40 50 60 70min after inhibition of transcription % o f in itia l m RN A Full length 3 Fragment 0% 20% 40% 60% 80% 100% 0 10 20 30 40 50 60 70 min after repression of transcription % o f in itia l m RN A Full length 3Fragment ObservedComputed The fact that we can r

11、eproduce 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 ho

12、w 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 distributio

13、n 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. A: MFA2pG 0 1 2 3 4 5 6 7 8 10W T WT 1/10 WT 1/10 0WT 1/50 0WT 1/10 00W T 1/E4 WT 1/E5 W

14、T mutant rate relative to wild type (WT) (m uta nt T1 /2) / ( WT T1 /2) deadenylation decapping 5 to 3 Exo 3 to 5 decay 0 1 2 3 4 5 6 7 8 10W T WT 1/10 WT 1/10 0WT 1/50 0WT 1/10 00W T 1/E4 WT 1/E5 WT mutant rate ralative to wild type (m uta nt T1 /2) / (W T T 1/2 ) deadenylation decapping 5 to 3 Exo

15、 3 to 5 Exo B: PGK1pG 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 rates Implication: 3 to 5 decay by exosome shows mRNA specific degradation rates that are dependent on the 5 structure of the mRNA 3 to 5 deca

16、y rate for fragment. Obtained by matching the calculated t 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

17、 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-life MFA2 0% 50% 100% 0 4 8 12 16 20 min after repression of transcription % o f in itia l m RN A tra

18、nscriptional shut-off transcriptional pulse chase half life: 3.8 min Average life span: 6 min PGK1 0% 50% 100% 0 20 40 60 80 100 min after repression of transcription % o f in itia l m RN A transcriptional shut-off transcriptional pulse chase half life: 27 min Average life span: 47 minHalf-life Aver

19、age 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 state t 1/2Transcriptional

20、pulse chase Average Life Span The measurement of measure a half-life can predominantly different steps in the decay network Different mRNAs will be affected differentially by certain change on specific step Need to be very cautious when interpreting mRNA specific effects. In silico transport mutants

21、 0 0.5 1 1.5 2 2.5 3 3.5 W T 1/4 0W T 1/8 0W T 1/1 20 W T 1/1 60 W T 1/2 00 W T mutant transport rate relative to wild type (m ut an t T 1/2 ) / (W T MFA2 T1/2 change PGK1 T1/2 changeShort-lived mRNAs (MFA2) are more responsive to changes on transport rate than long-lived mRNAs (PGK1). In silico tra

22、nsport mutants 0 200 400 600 800 1000 1200 1400 WT 1/40 WT 1/80 WT 1/12 0 W T 1/16 0 W T 1/20 0WT mutant transport rate relative to wild type (WT) am ou nt (un it) Cyto mRNA Nuclear mRNA Possible factors that disrupt the correlation between mRNA and protein levelThe increase of transcript level in t

23、he transport mutant solelycomes from the increase in the nuclear pool. The increase of transcript level in the 5 to 3 exo mutants solely comes from the increase in the cap- speciesIn silico 5 to 3 exonuclease mutants 0 200 400 600 800 WT 1/10WT 1/100 WT 1/1000 WT mutant rate relative to wild type (W

24、T) A10FL A0FL cap-A10FL cap-A0FL 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 qua

25、ntitative 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

26、 the in vivo network. Computation ExperimentationNonsense Mediated mRNA Decay(NMD) AAAAAAA70DNAtranscriptionAUG UAA UAAm7Gppp Normal decay 30Nonsense Decay3 Normal Decay and NMD ModelingPredictionExp. testing Knowledge and hypothesis based modeling. Might have multiple models: model 1, 2,3n. All mod

27、els should fit with current data.Make predications by analyzing the models and performing in silico experiments.Design and conduct critical experiments to test important predictions and distinguish the models. Some models may get invalidated. Others may need to be refined to fit well with new experi

28、mental data. May lead to new hypotheses. The most faithful model of NMD and new insights about this process. Illustration of the iterative approach.Advantage: increase the rate of hypothesis forming and testing Model 1.1Model 1.2Multiple models or different combinations of rate constants within the

29、same model can be obtained to fit with current observations. Model 1 Model 1.1 and Model 1.2Model 1.1 The entry into PAS is inhibited by more than 100 fold, the rate of DID contributes to the t1/2 of A70 Nuclear mRNATransport (slow, 52 fold down, 3 min t1/2)Cytoplasmic mRNA (A70) PAS DID (fast)Norma

30、l decay NMDModel 1.2The entry into PAS is not inhibited, the rate of transport (or maybe other unknown steps upstream of the bifurcation) is slowed down, which is usually very fast for normal transcript. Nuclear mRNATransport (fast)Cytoplasmic mRNA (A70)PAS DID (slow, 3 min t1/2)Normal decay NMDIf e

31、ntry into PAS is not inhibited, will see same decrease on steady state level, but the half-life does not change much. Distinguish Model 1.1 and Model 1.2 Relative level of nuclear transcriptModel 1.1 predicts 2% nuclear Model 1.2 predicts 90% nuclearAssumption: the rate-limiting step above the bifur

32、cation is indeed transport Decapping mutantsdcp1,dcp2 Current data is consistent with model 1.2, in which the entry into PAS is not inhibited, the rate of DID is much higher than PAS, and there might be a rate limiting step upstream of the bifurcation in silico biology Computational modeling of cell

33、ular networks, signaling pathways, metabolic pathways pathways, cells, tissues and diseases. in vivo, in vitro, in silico It is a relatively newer branch of bioinformatics, attracting great attention in post-genomic era. Examples: Academics: Virtual cell (E cell), E virus, Drosophila leg development

34、 Companies: Entelos, Physiome sciences, pharmaceutical companies working on human disease modeling. provides guidance for target selection. improved target prioritization compared with relying on empirical research alone Require very deep understanding of biology, xlnt math and computer skills, work closely with biologist. Come on, lets work on it! Acknowledgements Acknowledgements Questions? Thank you!