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Constitutive splicing and economies of scale in gene expression

Nature Structural & Molecular Biology volume 26, pages 424–432 (2019)Cite this article

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Abstract

In eukaryotic cells, many introns are constitutively, rather than alternatively, spliced and therefore do not contribute to isoform diversification. It has remained unclear what functional roles such constitutive splicing provides. To explore this issue, we asked how splicing affects the efficiency with which individual pre-messenger RNA transcripts are productively processed across different gene expression levels. We developed a quantitative single-molecule fluorescence in situ hybridization-based method to quantify splicing efficiency at transcription active sites in single cells. We found that both natural and synthetic genes in mouse and human cells exhibited an unexpected ‘economy of scale’ behavior in which splicing efficiency increased with transcription rate. Correlations between splicing efficiency and spatial proximity to nuclear speckles could explain this counterintuitive behavior. Functionally, economy of scale splicing represents a non-linear filter that amplifies the expression of genes when they are more strongly transcribed. These results indicate that constitutive splicing plays an active functional role in modulating gene expression.

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Fig. 1: Splicing efficiency requires single-cell measurement.
Fig. 2: Experimental method for quantifying splicing efficiency at the TAS in individual cells.
Fig. 3: Quantification of single-transcript intensity units in multiple channels.
Fig. 4: Splicing efficiency increases with transcript level, exhibiting ‘economy of scale’ behavior, for the two tested genes, Gli1 induced by Shh in 3T3 cells and the Tet-off minigene RG6 induced by dox in HEK293 cells.
Fig. 5: Phenomenological model for ‘economy of scale’ behavior.
Fig. 6: Splicing can act as a gene expression ‘filter’.

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Data availability

Source data for Figs. 2, 3d,e, 4 and 5d are available online. All other original data, Matlab code, DNA constructs and cell lines are available upon request.

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Acknowledgements

We thank T. Cooper for DNA constructs of minigene RG6, P. Li for providing Gli1 inducible protocol, Z. Singer and Y. Antebi for technical assistance, M. Guttman, C. Su, H. Klumpe, M. Budde and L. Bintu for critical feedbacks on the manuscript. We also thank M. Guttman, G. Seelig, D. Black, D. Baltimore, M. Moore, J.G. Ojalvo and N. Wingreen for discussion and feedback on the project. The work was funded by a Fellowship from the Schlumberger Foundation, by the Gordon and Betty Moore Foundation through Grant GBMF2809 to the Caltech Programmable Molecular Technology Initiative and the Institute for Collaborative Biotechnologies through grant W911NF-09-0001 from the U.S. Army Research Office. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. M.B.E. is a Howard Hughes Medical Institute Investigator.

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Authors and Affiliations

  1. Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA

    Fangyuan Ding & Michael B. Elowitz

  2. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Fangyuan Ding & Michael B. Elowitz

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  1. Fangyuan Ding
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F.D. conceived of the project. F.D. and M.B.E. designed experiments. F.D. performed experiments, analyzed data and did mathematical modeling. F.D. and M.B.E. wrote the manuscript.

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Correspondence to Michael B. Elowitz.

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Integrated supplementary information

Supplementary Figure 1 Detailed workflow for quantifying the number of transcripts at the TAS.

(a) The expanded version of Fig. 2b. (b) The z-axis max projection of confocal images represents the intensity of the fluorescent FISH dots. (c) We fit the dot intensity by a fit-subtraction loop. (1) Fit the dot with a 2D Gaussian intensity distribution; (2) Subtract the fitting from the original image and obtain a new image; (3) Fit the new image with another 2D Gaussian; (4) Subtract this new fitting from the new images. This fit-subtraction loop continues until the intensity of the new 2D Gaussian fitting falls below 10% of the first integrated dot intensity. Finally, the original image is fit by the sequential 2D Gaussian fit together, whose positions are constrained. (d) Analysis of dots co-occurring in multiple channels provides an alternative estimate of the single-molecule fluorescence unit. Here, each histogram includes only dots that appear in two or more channels. Poisson fitting of these intensity distributions generates similar single-molecule fluorescence units as in Fig. 3b. Color is labeled as in Fig. 2a.

Supplementary Figure 2 Splicing efficiency increases with transcription level for RG6 genes induced by dox in HEK293 cells.

Each dot is the measurement of a single TAS. Colors and labels are as in Fig. 4.

Supplementary Figure 3 DNA-FISH verifies the ‘economy of scale’ observation for Gli1.

(a) We first performed RNA-FISH, labeling intron, Exon1, and Exon2, and then ran DNA-FISH in the same cells (see SI Materials and Methods for details). In the example image, DNA-FISH identified two genomic loci, while only one has co-localized dots in the RNA-FISH images. These results indicate that one locus (circled in white) is not active, while the other one (circled in blue) is active. (b) ‘Economy of scale’ observation based only on RNA-FISH images (n = 1430). (c) The ‘economy of scale’ effect remains when considering only the TASs overlapping with DNA-FISH dots across three fluorescent channels (n = 92). Note that we have significantly fewer measurements in this plot, due to the technical difficulty of combining DNA-FISH with RNA-FISH. Data are representative of two independent experiments.

Supplementary Figure 4 Hardness of ratio correction.

(a) False-positive ‘economy of scale’ for both splicing efficiency and control measurements, due to the putative correlation between denominator and numerator, that is 1 – NI/NE1 versus NE1. (b) Mathematical methods can correct this hardness of ratio with a = 4.3. The control measurements are, as expected, constant, while splicing efficiency still maintains the ‘economy of scale’ trend. See SI text for more details.

Supplementary Figure 5

(a) Measurement of the distance between a TAS and the nearest speckle. We first defined a set of fixed distances from the TAS. Specifically, we defined distance in units of pixels. Each circle (center panel) represents a defined distance from the TAS. Then, we measured the maximum fluorescent intensity at fixed distances. Finally, we plotted the maximum intensity versus the distance and found the minimum distance where the intensity reaches a pre-set threshold (right panel). (b) Raw data (n = 2421) for Fig. 5d. Error bars represent standard error of the mean. (c) The spatial distribution of splicing factors positively correlates with nascent splicing targets in HEK293 cells. Top: Splicing factors in the nucleus by citrine labeling Serine/arginine-rich splicing factor 1 (SRSF1); Bottom: RNA from transient transfection in the nucleus by FISHing intron. The cells have stably integrated citrine labelled SRSF1 gene and transiently transfected RNA (a synthetic SRSF1 targeted transcript, see Materials and Methods). The transient transfection generates multiple transcription active sites in the nucleus. As shown in the figure, the activities of these TASs are positively correlated with the proximity to speckles: every TAS is co-localized with a speckle and higher expressed TASs are with higher amount of splicing factors. Notice that some speckles do not have correlated TAS. This is because there are other TASs in the cell apart from the transiently transfected RNAs.

Supplementary Figure 6 A phenomenological mathematical model of the ‘economy of scale’ behavior.

(a) Purple curve represents classical Michaelis-Menten model with uniform enzyme accessibility (that is constant kon). Orange curve represents the modified model in which kon is proportional to the available pre-mRNA concentration. For the classical model (purple curve), the splicing efficiency is close to 1 at low transcription levels (box 1), where enzyme levels are not limiting, and then decline at higher transcription levels (box 3) due to saturation. By comparison, for the modified model (orange curve), the splicing efficiency is close to 0 at low expression levels (box 2), where pre-mRNA concentrations are too low to recruit splicing machinery. As the transcription level increases (box 4), enzyme accessibility increases, and splicing efficiency increases to 1. This represents the observed ‘economy of scale’ effect. Further increases of transcription level eventually saturate the splicing machinery, reducing splicing efficiency (box 5). (b) ‘Economy of scale’ behavior is robust across different residence time (that is different degradation rate of unspliced and spliced isoforms, gu and gm respectively). Parameter values used here: ku = 0.1, km = 0.12, rD = 100, E0 = 1000 and K = 0.5. (c) ‘Economy of scale’ occurs across a wide range of parameters. The color scale, Δefficiency/Δb, represents the slope of splicing efficiency versus transcription level evaluated between b = 1 and b = 10. Pink to yellow (right) shows positive slope, that is ‘economy of scale’; purple to blue (left) shows negative slope, that is ‘diminishing returns.’

Supplementary Figure 7 The induction of promoter primarily affects transcriptional burst size.

The synthetic mini-gene RG6 under Tet-on CMV was induced by 32 ng/ml (left panel) and 100 ng/ml (right panel) 4-Epidoxycycline (an analogue of Doxycycline). The distribution of mRNA expression (that is the number of smFISH dots) was fit by a previous published stochastic model (Raj, A., et al., Nat. Methods 5, 877–879 2008).

Supplementary Figure 8 Examples of different TASs.

For the TAS in the bottom cell, transcripts are spreading out from the TAS, while for the TAS in the top cell, no obvious transcripts are seen in the neighborhood. Note that the two TASs have similar intensity (that is similar transcription level).

Supplementary Figure 9

(a) Ratio of the mean underestimates splicing efficiency. We calculated 1 - <Intron>/<Exon> (that is ratio of the mean) in black, as a comparison to the original 1 - <Intron/Exon> (that is mean of the ratio), same color as in Fig. 4. Averaging over heterogeneous cells mildly distorts splicing efficiency (as illustrated in Fig. 1), reducing the apparent magnitude of the ‘economy of scale’ effect. (b) Population-based measurements show the ‘economy of scale’ trend. We used qPCR to quantify the amounts of unspliced and spliced transcripts for RG6 (see Materials and Methods). Because qPCR does not provide absolute transcript abundances, we analyzed the ratio of unspliced to spliced abundances (each relative to a control gene) as a function of the relative abundance of spliced transcripts. Multiple curves represent repeats using different two different cell clones and multiple primer sets.

Supplementary Figure 10 Examples of co-localization of Exon1 and Exon2 probes.

Top: Unprocessed images of both channels. Middle: all classified dots that co-localize in both channels are marked with white boxes. Bottom: Dots that were detected in only a single channel. Overall, the fraction of all dots that were co-localized between channels is ~ 90%.

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Ding, F., Elowitz, M.B. Constitutive splicing and economies of scale in gene expression. Nat Struct Mol Biol 26, 424–432 (2019). https://doi.org/10.1038/s41594-019-0226-x

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