Quantitative profiling of initiating ribosomes in vivo

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.

Figure 1. QTI-seq captures real-time translation initiation events in a qualitative and quantitative manner

(a) Schematic of Ribo-seq (left panel) and QTI-seq (right panel) procedures. The red line represents the ribosome density on average based on regular Ribo-seq, whereas the blue line indicates the density of initiating ribosome obtained from QTI-seq. (b) Meta-gene analysis of CHX-associated ribosome density (top panel) and LTM-associated ribosome density (bottom panel) in HEK293 cells captured by QTI-seq. Normalized RPF reads are averaged across the entire transcriptome and aligned at the annotated start codon. Different reading frames are separated and color coded. (c) A stacked bar plot showing the relative ratio of different types of TIS identified by GTI-seq or QTI-seq in HEK293 cells. (d) A scatter plot showing the correlation between LTM-associated aTIS density normalized by upper quartile and CHX-associated CDS ribosome occupancy normalized by RPKM. Genes with single annotated TIS or multiple TISs are shown in blue and red dots respectively. (e) Examples of single TIS (ABCD3) and multiple TIS (GEMIN6) genes revealed by GTI-seq (top panel) and QTI-seq (bottom panel). The same scale is used for Y-axis. The corresponding gene structure is shown below the X-axis.

Figure 2. QTI-seq reveals pervasive translational regulation in response to starvation

(a) Schematic of experimental procedures for RNA-seq, Ribo-seq, and QTI-seq in cells with or without starvation. (b) A scatter plot of fold changes in LTM-associated aTIS density and CHX-associated CDS ribosome occupancy in HEK293 cells before and after amino acid starvation. Genes with single annotated TIS or multiple TISs are shown in blue and red dots respectively. Genes with more than 1.5 fold changes are marked with “Increase” or “Decrease” boxes. (c) A scatter plot of fold changes in LTM-associated aTIS density and CHX-associated CDS ribosome occupancy in MEF cells before and after amino acid starvation. (d) A heatmap of false discovery rate (FDR) of enriched GO terms (biological process) for gene groups with translational downregulation (Dec) or upregulation (Inc) in response to amino acid starvation. (e) An example of genes (RPS28) with translational downregulation after amino acid starvation. The same scale is used for Y-axis. The corresponding gene structure is shown below the X-axis. The right panel is a bar graph depicting the relative RNA abundance, CDS ribosome occupancy, and aTIS ribosome density. (f) An example of multi-TIS genes (GADD45G) showing an increased aTIS fraction after amino acid starvation. (g) Experimental validation of translational control of GADD45G by a Fluc reporter bearing the 5′UTR of GADD45G with (WT) or without the uTIS codon CUG (Del). (means ± SEM; n = 3; * p < 0.05 student t-test).

Figure 3. Distinct role of eIF2α phosphorylation in translational response to starvation

(a) Schematic of mTORC1 signaling and eIF2α phosphorylation in controlling translation initiation. Right panel shows immunoblotting results of eIF2α(S51D) cells with either amino acid starvation or Dox-induced eIF2α(S51D) expression. (b) A scatter plot of fold changes in LTM-associated aTIS density in eIF2α(S51D) cells between amino acid starvation and Dox-induced eIF2α(S51D) expression. TOP mRNAs are shown in Red dots. (c) A heatmap of fold change of enriched GO terms (biological process) for gene groups with translational downregulation (green) or upregulation (red) in response to amino acid starvation (left) or Dox-induced eIF2α(S51D) expression (right). (d) An example of genes (RPS11) showing translational repression in response to amino acid starvation but not Dox-induced eIF2α(S51D) expression. The right panel is validation of RPS11 translational control by a Fluc reporter bearing the 5′UTR of RPS11. (means ± SEM; n = 3; * p < 0.01 student t-test). (e) An example of genes (NUP43) showing translational upregulation in response to either amino acid starvation or Dox-induced eIF2α(S51D) expression. The right panel is validation of NUP43 translational control by a Fluc reporter bearing the 5′UTR of NUP43. (means ± SEM; n = 3; * p < 0.01 student t-test).

Figure 4. Liver-specific QTI-seq reveals translational reprogramming in response to fasting

(a) Schematic of tissue-specific QTI-seq procedures using liver-specific RiboTag mice. (b) Meta-gene analysis of LTM-associated ribosome density (top panel) or CHX-associated ribosome density (bottom panel) in MEF cells (blue line) and liver cells (red line). Normalized RPF reads are averaged across the entire transcriptome and aligned at the annotated start codons and stop codons. (c) A scatter plot of fold changes in LTM-associated aTIS density and CHX-associated CDS ribosome occupancy in mouse liver cells with and without fasting. (d) A heatmap of fold changes for gene groups with translational downregulation (green) or upregulation (red) in fasted liver (left) or starved MEF cells (right). (e) Reporter assay using an in vitro translation system reprogrammed from mouse liver lysates with or without fasting. The relative translation efficiency of a synthesized Fluc mRNA containing 5′UTRs of PSMA3 or PSMB4 is shown in bar graph (means ± SEM; n = 3; * p < 0.01 student t-test). (f) Mice of 8-12 week old were treated with or without overnight fasting. The chymotrypsin activity of liver homogenates was measured by Proteasome-Glo (means ± SEM; n = 3; * p < 0.01 student t-test).