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Kinetic competition during the transcription cycle results in stochastic RNA processing.

Coulon A, Ferguson ML, de Turris V, Palangat M, Chow CC, Larson DR - Elife (2014)

Bottom Line: We find that kinetic competition results in multiple competing pathways for pre-mRNA splicing.Splicing of the terminal intron occurs stochastically both before and after transcript release, indicating there is not a strict quality control checkpoint.The majority of pre-mRNAs are spliced after release, while diffusing away from the site of transcription.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, United States.

ABSTRACT
Synthesis of mRNA in eukaryotes involves the coordinated action of many enzymatic processes, including initiation, elongation, splicing, and cleavage. Kinetic competition between these processes has been proposed to determine RNA fate, yet such coupling has never been observed in vivo on single transcripts. In this study, we use dual-color single-molecule RNA imaging in living human cells to construct a complete kinetic profile of transcription and splicing of the β-globin gene. We find that kinetic competition results in multiple competing pathways for pre-mRNA splicing. Splicing of the terminal intron occurs stochastically both before and after transcript release, indicating there is not a strict quality control checkpoint. The majority of pre-mRNAs are spliced after release, while diffusing away from the site of transcription. A single missense point mutation (S34F) in the essential splicing factor U2AF1 which occurs in human cancers perturbs this kinetic balance and defers splicing to occur entirely post-release.

No MeSH data available.


Related in: MedlinePlus

Counting transcripts at the transcription site.(A–B) The average intensity of the single-RNA particle diffusing in the nucleus detected in one channel of a confocal video (e.g., Figure 4A or Video 5) was used to normalize the intensities of all the spots found in that channel. Panels (A) and (B) show the distribution of normalized intensities of all the single RNAs (centered around 1) as well as the transcription site (TS) for the red and the green channels respectively. This allows estimating the average number of RNAs at the TS that are labeled in red and green. (C) Repeating this analysis for multiple cells (N = 9) and averaging shows that there are more red RNAs than green RNAs. (D) The average of the ratio between the number of red and green RNAs at the TS is very close to the expected value of 1.4 calculated from the fitting parameters shown in Table 1. Errors: SEM over cells.DOI:http://dx.doi.org/10.7554/eLife.03939.015
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fig2s6: Counting transcripts at the transcription site.(A–B) The average intensity of the single-RNA particle diffusing in the nucleus detected in one channel of a confocal video (e.g., Figure 4A or Video 5) was used to normalize the intensities of all the spots found in that channel. Panels (A) and (B) show the distribution of normalized intensities of all the single RNAs (centered around 1) as well as the transcription site (TS) for the red and the green channels respectively. This allows estimating the average number of RNAs at the TS that are labeled in red and green. (C) Repeating this analysis for multiple cells (N = 9) and averaging shows that there are more red RNAs than green RNAs. (D) The average of the ratio between the number of red and green RNAs at the TS is very close to the expected value of 1.4 calculated from the fitting parameters shown in Table 1. Errors: SEM over cells.DOI:http://dx.doi.org/10.7554/eLife.03939.015

Mentions: The preceding conclusions are general and make no reference to a specific model. To gain further insight, we developed mathematical models which relate the shape of the correlation functions to the timing of the underlying molecular processes (see ‘Materials and methods’). We generated five different mechanistic schemes: (I) purely post-release splicing, (II) independence between splicing and elongation/release, (III) polymerase pausing at the 3′ splice site (ss) until splicing is complete, (IV) splicing only during 3′ end retention of the transcript, and (V) release only after splicing is complete (Figure 2—figure supplement 4 and ‘Materials and methods’). For each one of these general schemes, different time distributions were tested for elongation, splicing, and release (Supplementary file 2). Since by construction, the intron-to-exon cross-correlation at 0 delay is necessarily in scheme III and have a slope in scheme I, these two schemes can be ruled out (See Figure 2—figure supplement 5A,D for fits). The three other schemes were better at fitting the correlation curves but the best model is one from scheme II, that is where splicing is independent of elongation and transcript release (See Figure 2—figure supplement 5 and discussion on Model comparison in ‘Materials and methods’). In this 3-parameter model (Table 1), splicing occurs a fixed amount of time after the 3′ss has been transcribed, and transcript release involves a stochastic delay after the poly(A) site is reached. No pause at the 3′ ss was needed to fit the data. This observation does not rule out pausing at these sites but suggests that such a pause would be much shorter than the other timescales observed. Notably, our data are fit better with a model having a fixed time for intron removal rather than a stochastic (exponential) one, arguing for several sequential kinetic steps (Aitken et al., 2011; Schmidt et al., 2011). In total, the β-globin-terminal intron splicing time was 267 ± 9 s after the polymerase passes the 3′ ss. This measurement of splicing time is consistent with previous estimates either in vivo on cell populations (Singh and Padgett, 2009) or in vitro at the single-molecule level (Hoskins et al., 2011), suggesting that PP7 stem loops do not perturb splicing kinetics of this intron, contrary to MS2 stem loops (Aitken et al., 2011; Schmidt et al., 2011). As an independent validation of our modeling results, we counted the number of red and green RNAs at transcription sites using a normalized ratiometric approach (Zenklusen et al., 2008) (See ‘Materials and methods’ and Figure 2—figure supplement 6). The average red-to-green ratio of 1.41 is indistinguishable from the expected 1.4 value predicted by our modeling analysis of the correlation functions.10.7554/eLife.03939.019Table 1.


Kinetic competition during the transcription cycle results in stochastic RNA processing.

Coulon A, Ferguson ML, de Turris V, Palangat M, Chow CC, Larson DR - Elife (2014)

Counting transcripts at the transcription site.(A–B) The average intensity of the single-RNA particle diffusing in the nucleus detected in one channel of a confocal video (e.g., Figure 4A or Video 5) was used to normalize the intensities of all the spots found in that channel. Panels (A) and (B) show the distribution of normalized intensities of all the single RNAs (centered around 1) as well as the transcription site (TS) for the red and the green channels respectively. This allows estimating the average number of RNAs at the TS that are labeled in red and green. (C) Repeating this analysis for multiple cells (N = 9) and averaging shows that there are more red RNAs than green RNAs. (D) The average of the ratio between the number of red and green RNAs at the TS is very close to the expected value of 1.4 calculated from the fitting parameters shown in Table 1. Errors: SEM over cells.DOI:http://dx.doi.org/10.7554/eLife.03939.015
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4210818&req=5

fig2s6: Counting transcripts at the transcription site.(A–B) The average intensity of the single-RNA particle diffusing in the nucleus detected in one channel of a confocal video (e.g., Figure 4A or Video 5) was used to normalize the intensities of all the spots found in that channel. Panels (A) and (B) show the distribution of normalized intensities of all the single RNAs (centered around 1) as well as the transcription site (TS) for the red and the green channels respectively. This allows estimating the average number of RNAs at the TS that are labeled in red and green. (C) Repeating this analysis for multiple cells (N = 9) and averaging shows that there are more red RNAs than green RNAs. (D) The average of the ratio between the number of red and green RNAs at the TS is very close to the expected value of 1.4 calculated from the fitting parameters shown in Table 1. Errors: SEM over cells.DOI:http://dx.doi.org/10.7554/eLife.03939.015
Mentions: The preceding conclusions are general and make no reference to a specific model. To gain further insight, we developed mathematical models which relate the shape of the correlation functions to the timing of the underlying molecular processes (see ‘Materials and methods’). We generated five different mechanistic schemes: (I) purely post-release splicing, (II) independence between splicing and elongation/release, (III) polymerase pausing at the 3′ splice site (ss) until splicing is complete, (IV) splicing only during 3′ end retention of the transcript, and (V) release only after splicing is complete (Figure 2—figure supplement 4 and ‘Materials and methods’). For each one of these general schemes, different time distributions were tested for elongation, splicing, and release (Supplementary file 2). Since by construction, the intron-to-exon cross-correlation at 0 delay is necessarily in scheme III and have a slope in scheme I, these two schemes can be ruled out (See Figure 2—figure supplement 5A,D for fits). The three other schemes were better at fitting the correlation curves but the best model is one from scheme II, that is where splicing is independent of elongation and transcript release (See Figure 2—figure supplement 5 and discussion on Model comparison in ‘Materials and methods’). In this 3-parameter model (Table 1), splicing occurs a fixed amount of time after the 3′ss has been transcribed, and transcript release involves a stochastic delay after the poly(A) site is reached. No pause at the 3′ ss was needed to fit the data. This observation does not rule out pausing at these sites but suggests that such a pause would be much shorter than the other timescales observed. Notably, our data are fit better with a model having a fixed time for intron removal rather than a stochastic (exponential) one, arguing for several sequential kinetic steps (Aitken et al., 2011; Schmidt et al., 2011). In total, the β-globin-terminal intron splicing time was 267 ± 9 s after the polymerase passes the 3′ ss. This measurement of splicing time is consistent with previous estimates either in vivo on cell populations (Singh and Padgett, 2009) or in vitro at the single-molecule level (Hoskins et al., 2011), suggesting that PP7 stem loops do not perturb splicing kinetics of this intron, contrary to MS2 stem loops (Aitken et al., 2011; Schmidt et al., 2011). As an independent validation of our modeling results, we counted the number of red and green RNAs at transcription sites using a normalized ratiometric approach (Zenklusen et al., 2008) (See ‘Materials and methods’ and Figure 2—figure supplement 6). The average red-to-green ratio of 1.41 is indistinguishable from the expected 1.4 value predicted by our modeling analysis of the correlation functions.10.7554/eLife.03939.019Table 1.

Bottom Line: We find that kinetic competition results in multiple competing pathways for pre-mRNA splicing.Splicing of the terminal intron occurs stochastically both before and after transcript release, indicating there is not a strict quality control checkpoint.The majority of pre-mRNAs are spliced after release, while diffusing away from the site of transcription.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, United States.

ABSTRACT
Synthesis of mRNA in eukaryotes involves the coordinated action of many enzymatic processes, including initiation, elongation, splicing, and cleavage. Kinetic competition between these processes has been proposed to determine RNA fate, yet such coupling has never been observed in vivo on single transcripts. In this study, we use dual-color single-molecule RNA imaging in living human cells to construct a complete kinetic profile of transcription and splicing of the β-globin gene. We find that kinetic competition results in multiple competing pathways for pre-mRNA splicing. Splicing of the terminal intron occurs stochastically both before and after transcript release, indicating there is not a strict quality control checkpoint. The majority of pre-mRNAs are spliced after release, while diffusing away from the site of transcription. A single missense point mutation (S34F) in the essential splicing factor U2AF1 which occurs in human cancers perturbs this kinetic balance and defers splicing to occur entirely post-release.

No MeSH data available.


Related in: MedlinePlus