Abstract
Cells have evolved exquisite mechanisms to fine-tune the rate of protein synthesis in response to stress. Systemic mapping of start-codon positions and precise measurement of the corresponding initiation rate would transform our understanding of translational control. Here we present quantitative translation initiation sequencing (QTI-seq), with which the initiating ribosomes can be profiled in real time at single-nucleotide resolution. Resultant initiation maps not only delineated variations of start-codon selection but also highlighted a dynamic range of initiation rates in response to nutrient starvation. The integrated data set provided unique insights into principles of alternative translation and mechanisms controlling different aspects of translation initiation. With RiboTag mice, QTI-seq permitted tissue-specific profiling of initiating ribosomes in vivo. Liver cell–specific ribosome profiling uncovered a robust translational reprogramming of the proteasome system in fasted mice. Our findings illuminated the prevalence and dynamic nature of translational regulation pivotal to physiological adaptation in vivo.
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Acknowledgements
We thank the Qian lab members for helpful discussion; J. Parker and R. Weiss for critical reading of the manuscript; P. Walter (University of California at San Francisco) for the eIF2α(S51D) cell line; and Cornell University Life Sciences Core Laboratory Center for performing deep sequencing. This work was supported by grants to B.S. from the US National Institutes of Health (NIH CA106150) and to S.-B.Q. from the NIH (DP2 OD006449, R01AG042400), Ellison Medical Foundation (AG-NS-0605-09) and US Department of Defense (W81XWH-14-1-0068).
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X.G. and S.-B.Q. conceived of the original idea. X.G. designed experimental approaches and performed the experiments. J.W. analyzed the data. B.L. assisted data interpretation. M.M. and B.S. synthesized LTM. S.-B.Q. wrote the paper.
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Integrated supplementary information
Supplementary Figure 1 Limitations of GTI-seq in quantifying translation efficiency.
(a) Schematic of Ribo-seq (left panel) and GTI-seq (right panel) procedures. The red line indicates the inflated ribosome density when compared to the true ribosome density (blue dot line).
(b) An example of single TIS genes (RPL32) captured by Ribo-seq (grey) and GTI-seq (red) before and after amino acid starvation. The same scale is used for Y-axis and the corresponding gene structure is shown below the X-axis.
(c) A scatter plot of fold changes in LTM-associated aTIS density and CHX-associated CDS ribosome occupancy before and after amino acid starvation. Genes with single annotated TIS or multiple TISs are shown in blue and red dots respectively.
Supplementary Figure 2 Puromycin sensitivity in the absence or presence of translation inhibitors.
HEK293 cells were harvested in polysome buffer containing CHX (100 μg/mL) or LTM (5 μM). After lysis with Matrix-D, supernatant was incubated with or without 25 μM puromycin for 15 min. Puromycin-treated samples were then subjected to sucrose gradient sedimentation.
Supplementary Figure 3 Biological replicates of aTIS ribosome density captured by QTI-seq.
HEK293 cells were subject to QTI-seq and the aTIS ribosome density obtained in two biological replicates are shown in the scatter plot. The Pearson correlation value is shown.
Supplementary Figure 4 Codon compositions of TISs identified by QTI-seq.
A total of 5,099 TISs identified by QTI-seq in HEK293 cells were computed for their codon composition. Canonical start codon AUG and near-cognate start codons that differ from AUG by a single nucleotide are listed.
Supplementary Figure 5 Ribosome profiling of HEK293 cells with or without starvation.
(a) Polysome profiling of HEK293 cells with or without amino acid starvation. HEK293 cells were harvested in polysome buffer containing CHX (100 μg/mL) or LTM (5 μM). After lysis with Matrix-D, supernatant was incubated with or without 25 μM puromycin for 15 min followed by sucrose gradient sedimentation.
(b) Scatter plots of RNA abundance (left panel), Ribosome occupancy (middle panel), and aTIS ribosome density (right panel) between control and starvation conditions.
Supplementary Figure 6 Reduced elongation rate under the starvation condition.
Ribosome occupancy on CDS underestimates the translation efficiency under the starvation condition because of the reduced elongation speed. Ribosomal elongation speed was measured using harringtonine chase. Polysome profiles of HEK293 cells with or without starvation were treated with harringtonine (1ug/mL) for indicated times. Monosome (80S) and polysomes are highlighted and the P/M ratio change is quantified in the right panel.
Supplementary Figure 7 QTI-seq reveals pervasive translational regulation in response to starvation.
An example of genes (NUP88) with translational upregulation after amino acid starvation. The right panel is a bar graph indicating the relative RNA abundance, CDS ribosome occupancy, and aTIS ribosome density. The far right panel is validation of NUP88 translational control by a Fluc reporter bearing the 5’UTR of NUP88. (means ± SEM; n = 3; * p < 0.01 student t-test).
Supplementary Figure 8 QTI-seq reveals programmatic alternative translation in response to starvation.
Volcano plot showing aTIS fraction differences for genes with multiple TISs after amino acid starvation. QTI-seq was applied to MEF cells with or without amino acid starvation. FDR cut-off line was set at 0.05. Genes with significantly decreased or increased aTIS fraction after starvation are shown in blue dots and red dots respectively.
Supplementary Figure 9 QTI-seq reveals programmatic alternative translation in response to starvation.
(a) Discriminative motif analysis of sequences flanking the increased annotated TIS in response to starvation in MEF cells. Sequence log of the consensus sequence is shown at the left panel. The right panel represents the distribution of the motif over the flanking 100-nt sequence.
(b) Discriminative motif analysis of sequences flanking the increased uTIS in response to starvation in MEF cells. Sequence log of the consensus sequence is shown at the left panel. The right panel represents the distribution of the motif over the flanking 100-nt sequence.
Supplementary Figure 10 Ribosome profiling of eIF2α(S51D) cells with or without DOX-induced expression.
(a) Polysome profiling of eIF2α(S51D) cells with or without DOX (50 nM) induction for 12 hr. Cells were harvested in polysome buffer containing CHX (100 μg/mL) or LTM (5 μM). After lysis with Matrix-D, supernatant was incubated with or without 25 μM puromycin for 15 min followed by sucrose gradient sedimentation.
(b) Scatter plots of RNA abundance (left panel), Ribosome occupancy (middle panel), and aTIS ribosome density (right panel) between control and DOX-induced eIF2α(S51D) expression.
Supplementary Figure 11 Characterization of liver-specific RiboTag mice.
(a) Schematic of tissue-specific QTI-seq procedures using liver-specific RiboTag mice.
(b) Polysome profiles of liver lysates from Rpl22HA-expressing homozygous mice. Both 80S monosome and polysome peaks are indicated. The bottom panel shows the distribution of exogenous and endogenous ribosome proteins in ribosome fractions.
(c) Kidney or liver lysates from Rpl22HA-expressing homozygous mice were subjected to sucrose gradient. Ribosome fractions were digested with RNase I followed by anti-HA IP. Both the input and the immunoprecipitates were blotted for ribosomal proteins as indicated.
(d) Liver homogenates from Rpl22HA-expressing homozygous mice were immuno-precipitated using anti-HA beads. The ribosome-associated mRNAs were examined using qPCR to determine relative abundance of indicated transcripts between input and anti-HA IP after normalization with β-actin.
Supplementary Figure 12 Ribosome profiling of liver cells with or without overnight fasting.
(a) Polysome profiling of liver homogenates from mice with or without overnight fasting. Liver cells were lysed in polysome buffer containing CHX (100 μg/mL) or LTM (5 μM). After lysis with Matrix-D, supernatant was incubated with or without 25 μM puromycin for 15 min followed by sucrose gradient sedimentation.
(b) Scatter plots of RNA abundance (left panel), Ribosome occupancy (middle panel), and aTIS ribosome density (right panel) between control and fasted liver cells.
Supplementary Figure 13 Comparison of TISs identified by QTI-seq between liver and MEF cells.
A total of 5,796 TISs in MEF and 1,770 TISs in liver cells were computed for their codon composition. Canonical start codon AUG and near-cognate start codons that differ from AUG by a single nucleotide are listed.
Supplementary Figure 14 Tissue-specific QTI-seq reveals alternative translation in liver cells.
Venn diagrams depicting the overlap between TISs identified by QTI-seq in MEF (blue) and mouse liver cells (red). Highly expressed genes are of FPKM ≥ 5 in both MEF and mouse liver. The p-value for the overlapping significance between MEF and Liver is shown.
Supplementary Figure 15 Cell type–specific alternative translation between liver and MEF cells.
Examples of genes (HADH) showing alternative TIS in liver but not in MEF. The corresponding gene structure is shown below the X-axis with green triangle as the annotated start codon, black angle the stop codon, and white triangle alternative start codon. Black arrow indicates the dTIS of HADH in liver cells.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–15 and Supplementary Table 5 (PDF 2305 kb)
Supplementary Table 1
HEK293 QTI-seq data sets (XLSX 927 kb)
Supplementary Table 2
MEF QTI-seq data sets (XLSX 853 kb)
Supplementary Table 3
HEK293/eIF2α(S51D) QTI-seq data sets (XLSX 1025 kb)
Supplementary Table 4
Liver QTI-seq data sets (XLSX 305 kb)
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Gao, X., Wan, J., Liu, B. et al. Quantitative profiling of initiating ribosomes in vivo. Nat Methods 12, 147–153 (2015). https://doi.org/10.1038/nmeth.3208
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DOI: https://doi.org/10.1038/nmeth.3208
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