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Quantifying the dynamics of IRES and cap translation with single-molecule resolution in live cells

Nature Structural & Molecular Biology volume 27, pages 1095–1104 (2020)Cite this article

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An Author Correction to this article was published on 27 October 2020

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Abstract

Viruses use internal ribosome entry sites (IRES) to hijack host ribosomes and promote cap-independent translation. Although they are well-studied in bulk, the dynamics of IRES-mediated translation remain unexplored at the single-molecule level. Here, we developed a bicistronic biosensor encoding distinct repeat epitopes in two open reading frames (ORFs), one translated from the 5′ cap, and the other from the encephalomyocarditis virus IRES. When combined with a pair of complementary probes that bind the epitopes cotranslationally, the biosensor lights up in different colors depending on which ORF is translated. Using the sensor together with single-molecule tracking and computational modeling, we measured the kinetics of cap-dependent versus IRES-mediated translation in living human cells. We show that bursts of IRES translation are shorter and rarer than bursts of cap translation, although the situation reverses upon stress. Collectively, our data support a model for translational regulation primarily driven by transitions between translationally active and inactive RNA states.

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Fig. 1: A multicolor biosensor to compare cap and IRES translation at the single-molecule level in living cells.
Fig. 2: IRES and cap translation sites stretch out as ribosomes load.
Fig. 3: Elongation is not a rate-limiting step lowering the efficiency of IRES-mediated translation.
Fig. 4: The cap recruits and initiates two to three times more ribosomes than does the IRES.
Fig. 5: Modeling bicistronic translation of the multicolor biosensor.

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Data availability

Raw images can be found at: https://doi.org/10.6084/m9.figshare.12751853.v1. Source data are provided with this paper.

Code availability

All codes and required data are available at: https://github.com/MunskyGroup/Koch_Aguilera_etal_2020.git

Change history

  • 27 October 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank L. Lavis for kindly providing JF646 labeled HaloTag ligand and H. Scherman for purifying Halo-MCP and GFP-scFv (anti-SunTag). We thank L. Whitehead and collaborators for the Imagej Zoom plugin. We thank all members of the Stasevich and Munsky labs for their support and helpful discussions. B.M. and L.A. were supported by a grant from the W.M. Keck Foundation and by the NIH (grant no. 5R35GM124747). T.J.S., A.K. and T.M. were supported by the National Institutes of Health (grant no. R35GM119728).

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, USA

    Amanda Koch, Tatsuya Morisaki & Timothy J. Stasevich

  2. Keck Scholars, Department of Chemical and Biological Engineering and School of Biomedical Engineering, Colorado State University, Fort Collins, CO, USA

    Luis Aguilera & Brian Munsky

  3. World Research Hub Initiative, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan

    Timothy J. Stasevich

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  1. Amanda Koch
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Contributions

A.K. and T.J.S. designed and planned all experiments. A.K. cloned all plasmids and performed all experiments. T.M. assisted A.K. with microscopy and particle tracking. B.M. and L.A. performed all modeling and fitting. T.J.S. and A.K. wrote the main manuscript, with assistance from T.M. for experimental methods related to microscopy and particle tracking and from B.M. and L.A. for computational sections. B.M. and L.A. wrote the computational details in Supplementary Note 1. A.K., L.A., T.M., B.M. and T.J.S. edited the manuscript. B.M. and T.J.S. acquired funding and designed the computational and experimental studies.

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Correspondence to Brian Munsky or Timothy J. Stasevich.

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Extended data

Extended Data Fig. 1 Controls for bleedthrough and active translation.

a-b, Five frame average of a Cap Only and IRES Only translation spots. mRNA marker dye, JF646, was not added to these cells. Cells were imaged for 3-minutes with a 6-second interval between each capture. c, Top graphs show normalized total intensity over time for Cap-dependent (left) and IRES-mediated translation spots (right), after addition of puromycin. Gray lines indicate individual cells. Thick dark line indicates the average total intensity of all cells. Red dashed lines indicate time at which puromycin was added. Cap-dependent: n = 5 cells. IRES-mediated: n = 5 cells. Bottom graphs show normalized total intensity of Cap-dependent (left) and IRES-mediated (right) translation spots without the addition of puromycin. Cap-dependent: n = 6 cells. IRES-mediated: n = 5 cells. All cells (control and drug treated) were imaged for 10-minutes with a 1-minute interval. Error bars represent S.E.M.

Extended Data Fig. 2 IRES and cap translation site localization and mobility.

a, Quantification of translating and non-translating mRNA distances in micrometers (µm) to nearest-neighbor translation spot within single cells. Each point represents the average distance per cell. n = 39 cells. b, Quantification of distance in µm from the nucleus of translating and non-translating mRNA. Each point represents the average distance from the edge of the nucleus per cell. n = 39 cells. c, Representative cell out of n = 11 cells imaged with fast imaging conditions. An example mRNA is highlighted with a white circle and a track through time of that mRNA is graphed below. d, Cumulative distribution function plot of non-translating mRNA (red), IRES Only (purple), Cap Only (yellow), and Cap+IRES (gray) species based on their diffusion coefficients (µm2 /sec). Inset shows the Mean Square Displacements (MSD) of the different species over time in seconds. n = 3771 total tracked mRNA (translating and non-translating), n = 11 cells. e, Schematic showing how the jump angles are measured. Error bars represent the standard error of the mean (S.E.M). f, Circular plots of the jump angle distributions for non-translating mRNA, Cap Only, IRES Only and Cap+IRES translation sites. For the box and whisker plots, the thick black lines indicate the medians, the boxes indicate the 25%-75% range, and the whiskers indicate the 5%-95% range. The p-values are based on a two-tailed Mann-Whitney test: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Extended Data Fig. 3 Measuring distances between Cap and IRES nascent chains in Cap+ IRES translation spots.

a, Representative data set of measured distances of Cap (light green) and IRES (blue) nascent chains to 3′UTR through time in single Cap+IRES translation tracks. b, Median distances of Cap and IRES nascent chains to 3′UTR of each Cap+IRES track. Distances are measured in nanometers (nm). 3′ UTR coordinates were fixed at (0,0) for all analyses. n = 296 translation spots.

Extended Data Fig. 4 Ribosomal run-off curves from single cells after addition of Harringtonine.

a, Harringtonine-induced ribosomal run-off curves from single cells. Each curve shows the decay in nascent chain signal intensity from all Cap-dependent and (b) IRES-mediated translation sites within a single cell post-Harringtonine. Run-off curves were phenomenologically fit to a Tanh function to align curves in time for averaging in Fig. 3. The slope of fitted curves at a normalized intensity value of 0.5 was used to estimate the elongation rate. c, Cap-dependent (n = 7 cells) and (d) IRES-mediated (n = 5 cells) translation controls in which no drugs were added. Each gray line shows the total nascent chain signal intensity from all translation sites in an individual cell. The thick black line is a representative cell. Intensity in arbitrary units (a.u.). All cells were imaged for 45 minutes with a 1-minute interval between each capture.

Extended Data Fig. 5 Original Tag comparison to Switch Tag, single mRNA selection, and polysome intensity calibrations.

a, Quantification of the percentages of each type of translation sites for the Original Tag (left, n = 39 cells) and the Switch Tag (right, n = 37 cells). Each point represents a single-cell measurement. b, Probability histograms showing distributions of mRNA intensities of non-translating mRNA (Red), Cap Only (Yellow), IRES Only (Purple), and Cap+IRES (Gray) translation sites for the Original Tag and the Switch Tag. The gray boxes represent the mRNA intensity threshold used to eliminate multiple mRNAs. Intensities in arbitrary units (a.u.). c, Translation site calibration measurements. The intensities of Cap in Cap Only translation sites (n = 20spots) in the Original Tag were compared to a 10xFlag calibration system (n = 47spots) with a known number of ribosomes. These comparisons lead to a calculated number of 14.6 ribosomes in Cap Only translation sites using the Original Tag. For the box and whisker plots, the thick black lines indicate the medians (A and C), and the dashed red line indicate the weighted means (A) the boxes indicate the 25%-75% range, and the whiskers indicate the 5%-95% range. The p-values are based on a two-tailed Mann-Whitney test: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Extended Data Fig. 6 Model of the bicistronic gene construct.

a, The most complete mathematical model considers four mutually exclusive RNA states: non-translating (SOFF), Cap-dependent (SCAP-ON), IRES-mediated (SIRES-ON), and both Cap and IRES (SCAP+IRES-ON). All transition rate values between RNA states are free-independent parameters. A cross-over mechanism (CO symbol in the figure), by which a ribosome that completes the translation of the Cap-dependent protein could immediately reinitiate translation of the IRES-mediated protein, is represented by the reaction parameter kCO. b, Comparison of 14 different sub-models. The sub-models test different hypotheses, including variations of the number of mRNA states (3 or 4 states), dependency on Cap and IRES switching states, and/or the existence of the cross-over mechanism. A complete description is given in the Supplementary Information (S.I.). c, Cross-validation is used to compare two possible mechanisms of translation inhibition under NaAs stress. The first mechanism mimics the inhibition of the Cap activation rates at the mRNA level (LNaAs-k-ON-C; that is, block of kON-C and k’ON-C). The second mechanism considers blocking ribosomal initiation for Cap (LNaAs-k-INIT-C; that is, block of kINIT-C). d, Optimization process and cross-validation for the DTT stress. The same inhibitory mechanisms described in c are tested for DTT stress. Relative Log-likelihood values for the optimization process are calculated according to (S.I.) Eq. 23 and Eq. 26 for the NaAs and DTT cross-validation experiments, respectively. A selection threshold (dashed red line) was defined by a log-likelihood of 100 worse than the most complex and best fitting model. Models above the selection threshold were discarded (gray background), and their cross-validation log-likelihood values are not shown. The best model shown (green background) was chosen as the model with fewest free parameters below the selection threshold. A complete description is given in the Statistics and Reproducibility section. e, Model simulations for the best-fit model 4SIm2 under NaAs and DTT stresses. The figure shows the effect of blocking ribosomal initiation and activation for Cap.

Source data

Supplementary information

Supplementary Information

Supplementary Note 1.

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Supplementary Video 1

Visualizing cap-dependent and IRES-mediated translation at the single-molecule level of the bicistronic biosensor (smF-KDM5B-IRES-ST-Kif18b); related to Fig. 1. A U2OS cell was beadloaded with scFv-GFP (shown in blue), Cy3-FLAG Fab (shown in green), Halo-MCP (shown in red), and the biosensor. The entire cell volume was acquired every 6 s (movie duration is 3 min) on a home-built HILO microscope. The Zoom FIJI plugin was used to show all the different modes of translation as well as non-translating mRNA within the cell. Image is 66.56 × 66.56 μm.

Supplementary Video 2

Tracking fast-moving mRNA and translation sites on the bicistronic biosensor (smF-KDM5B-IRES-ST-Kif18b); related to Fig. 2. A U2OS cell was beadloaded with scFv-GFP (shown in blue), Cy3-FLAG Fab (shown in green), Halo-MCP (shown in red), and the biosensor. A single plane of the cell was imaged at an imaging rate of 77 msec. Image is 52.39 × 42.64 μm.

Source data

Source Data Fig. 1

Percentages of modes of translation for original tag for Fig. 1d

Source Data Fig. 2

Raw intensity and xy coordinates data used to generate Fig. 2 c,d

Source Data Fig. 3

Raw intensity data before and after addition of harringtonine

Source Data Fig. 4

Intensity data for cap, IRES and cap + IRES translation spots in the original tag and switch tag

Source Data Fig. 5

Experimental and simulation data used to generate all of Fig. 5

Source Data Extended Data Fig. 6

Statistical source data

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Koch, A., Aguilera, L., Morisaki, T. et al. Quantifying the dynamics of IRES and cap translation with single-molecule resolution in live cells. Nat Struct Mol Biol 27, 1095–1104 (2020). https://doi.org/10.1038/s41594-020-0504-7

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