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Tumour-specific proline vulnerability uncovered by differential ribosome codon reading


Tumour growth and metabolic adaptation may restrict the availability of certain amino acids for protein synthesis. It has recently been shown that certain types of cancer cells depend on glycine, glutamine, leucine and serine metabolism to proliferate and survive1,2,3,4. In addition, successful therapies using L-asparaginase-induced asparagine deprivation have been developed for acute lymphoblastic leukaemia5. However, a tailored detection system for measuring restrictive amino acids in each tumour is currently not available. Here we harness ribosome profiling6 for sensing restrictive amino acids, and develop diricore, a procedure for differential ribosome measurements of codon reading. We first demonstrate the functionality and constraints of diricore using metabolic inhibitors and nutrient deprivation assays. Notably, treatment with L-asparaginase elicited both specific diricore signals at asparagine codons and high levels of asparagine synthetase (ASNS). We then applied diricore to kidney cancer and discover signals indicating restrictive proline. As for asparagine, this observation was linked to high levels of PYCR1, a key enzyme in proline production7, suggesting a compensatory mechanism allowing tumour expansion. Indeed, PYCR1 is induced by shortage of proline precursors, and its suppression attenuated kidney cancer cell proliferation when proline was limiting. High PYCR1 is frequently observed in invasive breast carcinoma. In an in vivo model system of this tumour, we also uncover signals indicating restrictive proline. We further show that CRISPR-mediated knockout of PYCR1 impedes tumorigenic growth in this system. Thus, diricore has the potential to reveal unknown amino acid deficiencies, vulnerabilities that can be used to target key metabolic pathways for cancer treatment.

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Figure 1: Ribosome positions as readout for amino acid availability for protein synthesis.
Figure 2: Estimation of global changes in translation and specific codon signals by diricore.
Figure 3: Diricore detects limitations in proline availability in ccRCC.
Figure 4: Diricore uncovers tumour-specific proline vulnerability.

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Gene Expression Omnibus

Data deposits

Data were deposed in GEO with accession code GSE59821.


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We thank R. Kerkhoven, R. Kluin and M. Nieuwland from the NKI-AVL Genomics Core Facility for assistance with deep-sequencing experiments, P. Snaebjornsson for valuable help with the pathology studies, and K. Verhoef for advice. We acknowledge the assistance of the NKI-AVL core facilities of Radionuclides Centre, Flow Cytometry, Molecular Pathology, and Biobanking (CFMPB). We thank R. Elkon for technical assistance, and all the members of the Agami group for valuable discussions. This work was supported by funds from the Human Frontier Science Program (LT000640/2013) to A.P.U, and the European Research Council (ERC-PoC EEG-CEC / EU665317), the Netherlands Organization for Scientific Research (NWO-VICI 918.11.601 ), and the Dutch Cancer Society (KWF NKI-2013-5814 ) to R.A.

Author information

Authors and Affiliations



F.L.-P., K.R., and R.A. conceived the project, designed all the experiments, and wrote the manuscript. F.L.-P. and J.Z. performed ribosome profiling and most of the experiments on the study. K.R. developed the diricore analysis and performed all the bioinformatic studies. L.C.M.B. and O.v.T. performed the in vivo studies. J.F.O.V. and P.v.B. prepared the plasmid constructs and established cell line models. R.L. designed and established the PYCR1 knockout models. A.P.U. prepared RNA-seq libraries. I.H. and J.W. performed the pathological studies. A.B. performed the nephrectomies on kidney cancer patients.

Corresponding author

Correspondence to Reuven Agami.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Robustness analysis.

Diricore analysis was performed on data sets where the reads were displaced −5/+5 nucleotides randomly, for details see material and methods section. For each panel we present RPF density analyses to the right (note the randomization of the signal at −5 to +5 nucleotides) and to the left subsequence analyses following the randomization process. a refers to Fig. 1b, c; b refers to Fig. 1d, e; c refers to Fig. 1f; d refers to Fig. 3a; e refers to Fig. 3i; f refers to Fig. 4c. g, h, Illustrative overviews of the diricore method.

Extended Data Figure 2 Supporting evidence for the asparagine signal in diricore.

a, Diricore analysis of PC3 cells treated for 48 h with L-asparaginase reveals a specific signal in asparagine codons at position 15, two replicates are shown. b, Subsequence shift data from Fig. 1f, and a, in which significant shifts at the amino-acid level are displayed (***FDR ≤ 0.001). c, Subsequence shift at in-frame and out-of-frame positions (8–21 nucleotides from RPF 5′-ends). d, Volcano plot showing gene expression changes (as determined by ribosome profiling) upon ASNase treatment. The horizontal axis shows log-fold-changes, while the vertical axis shows the statistical significance of the differential expression. Blue dots indicate genes annotated with GO term “cellular amino acid metabolic process” (GO:0006520), while red dots indicate genes annotated with GO term “asparagine metabolic process” (GO:0006528). e, Schematic diagram of a protocol based on Zaborske et al.13, to compare aminoacylated and non-aminoacylated tRNAs by qRT–PCR. f, Relative total tRNA levels from Fig. 1h, Leu-CAG tRNA was used to normalize the values. The error bars in f represent s.d.; n = 3 independent biological replicates; NS, non significant by Student’s t-test.

Extended Data Figure 3 Quality control of ribosome profiling from tumour and normal ccRCC tissue, and gene expression analysis of key ccRCC tumour markers.

a, Left, gross presentation of ccRCC. Depicted areas indicate normal and tumour tissue. Arrows indicate regions where the samples were taken. Right, representative microscopic images of tumour sections stained with haematoxylin and eosin (H&E). Pathological analysis confirmed a clear cell renal cell carcinoma (ccRCC). b, Quality control of the Ribo-seq data obtained from Normal and Tumour tissues. Left, correlation of Ribo-seq data from independent samples. Right, RPF periodicity from all the samples. c, Gene expression analysis by Ribo-seq confirms high level of EGFR, VIM, and CA9 in the tumour samples compared with the normal tissues, as was reported for renal cell carcinomas (Cancer Genome Atlas Research, 2013). d, Schematic diagram of the proline synthesis pathway (upper panel). Changes in gene expression in the proline synthesis pathway based on Ribo-seq.

Extended Data Figure 4 Diricore analysis of additional tissue samples of ccRCC tumour 1, and a combined statistical analysis of signals at positions 12 and 15.

a, Diricore analysis as in Fig. 3a. b, Subsequence shifts calculated by counting codon occurrences in specific positions within RPFs, normalized within and averaged over genes, with respect to untreated controls. A mixed linear model, in which amino acids are fixed effects and codons are random effects, is used to test for deviating shifts at the amino acid level (***FDR ≤ 0.001, **FDR ≤ 0.01, *FDR ≤ 0.1). c, Diricore analysis between samples N1 and N2 from normal kidney (upper panel) and between samples T2 and T4 (lower panel). d, Diricore analysis between samples T1 and N2 does not show a consistent signal at Ala codons. e, f, Relative total tRNA levels from Fig. 3b, j, respectively. Leu-CAG tRNA was used to normalize the values. For e and f, error bars represent s.d.; n = 3 independent biological replicates.

Extended Data Figure 5 Supporting evidence for global stimulation of translation in ccRCC tumours in study.

a, Immunohistochemistry performed on a section containing tumour and normal tissues (T and N, respectively) using anti-p4EBP1 threonine 37/46 and anti-PYCR1 antibodies. b, Diricore analysis of MCF10a cells treated with either Torin 1 or deprived of nutrients for 2 h. Methionine codons at the Start and CDS are shown. c, Proline codons content was interrogated using RNA-seq data from the tumour presented in Fig. 3. The analysis was performed with a subset of genes significantly (FDR < 0.05) altered in the tumour compared with normal tissue (up or down). The box indicates the 25th/50th/75th percentiles (bottom/middle/top bar), the extent of the whiskers indicate the most extreme values that are within 1.5 times the IQR (inter-quartile range) of the hinge (as per R package ggplot2/geom_boxplot). d, qRT–PCR analysis of the relative expression of PYCR1 in 786-O and A498 ccRCC cell lines. e, qRT–PCR analysis of the relative expression of PYCR2 and PYCRL in the A498 ccRCC cell line after 48 h of glutamine deprivation. f, Immunohistochemistry analysis detects high levels of p4EBP1 threonine 37/46 but no PYCR1 signal. g, A scheme of the common tumour changes in gene expression. In particular, while down regulation of PRODH2 and ALDH4A1 was clearly observed, no strong activation of PYCR1 was noted. h, Immunohistochemistry analysis using anti-CD31 antibodies. i, Knockdown of PYCR1 in 786-O cells expressing high levels of PYCR1 (d), expression is normalized to GAPDH mRNA. Error bars represent s.d.; scale bar, 100 μm.

Extended Data Figure 6 Supporting evidence for the link between the proline signals in diricore and high PYCR1 expression level.

a, b, Two additional ccRCC and normal samples were subjected to diricore analysis. Tumour specific signals at the methionine and proline codons are highlighted. c, e, Gene expression analysis based on Ribo-seq shows that the selected tumours express high levels of PYCR1. d, f, Analysis of the relative levels of non-aminoacylated tRNAs in ccRCC tumours. For d and f, error bars represent s.d.; n = 3 independent biological replicates; n = 3 independent biological replicates; *P < 0.05 by Student’s t-test. Cutoff for subsequence shift plots in this figure was 25 reads per gene. Cutoff for RPF density shift plots in this figure was 25 reads per window.

Extended Data Figure 7 Usage of eGFP–RPL10a fusion protein for ribosome profiling in a high PYCR1 expressing ccRCC cell line.

a, PYCR1 mRNA levels measured by qRT–PCR in a panel of breast cancer cell lines. Values were normalized to GAPDH mRNA. b, Immunoblot analysis from sucrose gradients shows the incorporation of the eGFP–RPL10a fusion protein in polysomes. c, Immunoprecipitation experiments with beads coated with antibodies against GFP followed by western blot analysis in SUM159PT cells expressing eGFP–RPL10a. d, Correlations between independent Ribo-seq experiments where ribosomes where purified by sucrose gradients (SG) or by GFP immunoprecipitation. MCF7 eGFP–RPL10a cells were used in this experiment. e, RPF reads profiles from Ribo-seq experiments generated from sucrose gradients or GFP immunoprecipitation. Error bars represent s.d.

Extended Data Figure 8 Additional tumour vs cultured eGFP–RPL10a-expressing SUM159PT conditions.

a, b, Diricore analysis of an independent experiment of tumours derived from eGFP–RPL10a-expressing SUM159PT. A specific signal in proline codons at position 15 is observed.

Extended Data Figure 9 Additional data supporting a role for proline production pathway in tumour growth in vivo.

a, Tumour volume of clonally derived SUM159PT cells expressing a non-targeting sgRNA (control) or two independent sgRNAs against PYCR1 (KO). b, Gene expression analysis by RNA-seq shows high levels of eiF5A in the tumour samples compared to normal tissues. c, RPF density shift plots at amino acid motifs PPG and PPP (left panels) on the in vivo ribosome profiling data set of Fig. 4c, d. The first nucleotide of the first codon of the motif is located at the 0-position. RPF density shift plots at proline and cysteine codons are shown in the middle and right panels, respectively. For the PPG and PPP motifs the cutoff of RPFs per window was lowered to 10 (from the usual 50) in order to compensate for the fewer windows with sufficient coverage. n = 6. Error bars represent s.d.; n refers to the number of xenografts per condition.

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Extended Data Figure 10 A schematic diagram modelling our findings.

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This file contains the uncropped images of immunoblots for Figure 2b (a+b) and Figure 4e (c+d). (PDF 1599 kb)

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Loayza-Puch, F., Rooijers, K., Buil, L. et al. Tumour-specific proline vulnerability uncovered by differential ribosome codon reading. Nature 530, 490–494 (2016).

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