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Quantification of translation uncovers the functions of the alternative transcriptome

Nature Structural & Molecular Biology volume 27, pages 717–725 (2020)Cite this article

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Abstract

Translation has a fundamental function in defining the fate of the transcribed genome. RNA-sequencing (RNA-seq) data enable the quantification of complex transcript mixtures, often detecting several transcript isoforms of unknown functions for one gene. Here, we describe ORFquant, a method to annotate and quantify translation at the level of single open reading frames (ORFs), using information from Ribo-seq data. By developing an approach for transcript filtering, we quantify translation transcriptome-wide, revealing translated ORFs on multiple isoforms per gene. For most genes, one ORF represents the dominant translation product, but we also detect genes with translated ORFs on multiple transcript isoforms, including targets of RNA surveillance mechanisms. Measuring translation across human cell lines reveals the extent of gene-specific differences in protein production, supported by steady-state protein abundance estimates. Computational analysis of Ribo-seq data with ORFquant (https://github.com/lcalviell/ORFquant) provides insights into the heterogeneous functions of complex transcriptomes.

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Fig. 1: The ORFquant strategy to quantify translation on selected transcripts.
Fig. 2: Quantification of translation on cytoplasmic mRNAs.
Fig. 3: De novo annotation of NMD candidates.
Fig. 4: Diversity in gene translation across cell lines.
Fig. 5: Agreement of protein synthesis with steady-state protein abundance estimates.

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Data availability

Ribo-seq data for HepG2 and K562 are available at GEO under the accession code GSE129061. Public Ribo-seq data sets for other cell lines were downloaded under the accession codes GSE79664 (HeLa), GSE73136 (HEK293), GSE74279 (Jurkat) and GSE56924 (U2OS). Nuclear and cytoplasmic RNA-seq data were accessed at the European Nucleotide Archive using the accession code PRJEB4197. TriP-seq data were downloaded from GEO using the accession code GSE69352. Bigwig tracks of 5′ ends were accessed using the accession code GSE57433. Proteomics data were downloaded from the PRIDE repository under accession code PXD002395. The list of P-site positions and junction reads and a list of quantified ORFs in the cell types analyzed are available in Supplementary Data 1. The final protein database is available in Supplementary Data 2, together with the parameters used to perform the MaxQuant search and the set of identified peptides and proteins. Source data are provided with this paper.

Code availability

ORFquant is available at https://github.com/lcalviell/ORFquant.

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Acknowledgements

The authors acknowledge funding from the German Federal Ministry of Education and Research (BMBF grant 031 A538A RBC) and the German Research Foundation (DFG grant TR175). L.C. thanks Stephen Floor (UCSF) for support and feedback during the preparation of this manuscript.

Author information

Author notes

  1. Lorenzo Calviello

    Present address: University of California San Francisco, Department of Cell & Tissue Biology, San Francisco, CA, USA

Authors and Affiliations

  1. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Lorenzo Calviello, Antje Hirsekorn & Uwe Ohler

  2. Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany

    Lorenzo Calviello & Uwe Ohler

  3. Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany

    Uwe Ohler

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Contributions

Initial study was conceived by L.C. and U.O. L.C. ideated and implemented the ORFquant pipeline, with supervision from U.O. All data analysis and visualization was performed by L.C. Ribosome footprinting libraries in K562 and HepG2 were performed by A.H. The manuscript was written by L.C. and U.O., with additional input by A.H.

Corresponding authors

Correspondence to Lorenzo Calviello or Uwe Ohler.

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The authors declare no competing interests.

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Peer review information Anke Sparmann was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Transcript selection and ORF quantification statistics.

In a) the number of selected transcripts per gene (x-axis) against the number of genes and their TPM levels. In b) Number of genes and their TPM values are plotted against the number of detected ORFs. In c) the number of genes (top), their TPM values, the number of unique ORF features and the variability of RSEM quantification estimates (averaged across all the ORFs per gene) are plotted against the contribution (in percentages) of the major ORF. As for b), the maximum width of each violin plot is the same for each panel, and the median value is shown as a black bar. In d) RSEM quantification (on the x axis, IsoPct_from_PME_TPMs) plotted against ORFquant quantification estimates (ORF_pct_P_sites_pN). Both values correspond to length-normalized quantification estimates.

Extended Data Fig. 2 ORFquant quantifies translation on alternatively spliced isoforms.

Aggregate plots of Ribo-seq coverage (normalized 0–1 per each region) and ORF coverage (ORF_pct_P_sites_pN) over different candidate alternative splice sites. No mixture indicates the presence of a single ORF only, while other lines indicate the presence of additional ORFs, divided by their translation values. Explanatory schemes are depicted at the bottom of each plot, with blue representing the major ORF and red the additional ORF(s).

Extended Data Fig. 3 Polysome profiles of alternative isoform with different translation output.

Distributions of exonic log2 fold changes between different polysome profiles and cytoplasmic abundance. Lowly translated ORFs are depleted in heavier polysome fractions, while highly translated ORFs show signal in all fractions.

Extended Data Fig. 4 Statistics on ORFs detection and quantification in the assayed cell lines.

In a) number of genes and average Ribo-seq signal per gene (y-axes) against the number of detected ORFs (x-axis), for the assayed cell lines. In b) number of genes and average Ribo-seq signal per gene (y-axes) against the translation of the major ORF (x-axis), for the assayed cell lines. As in a), the maximum width of each violin plot is the same for each panel, and the median value is shown as a black bar.

Extended Data Fig. 5 Genes expressing one or multiple major ORFs across different cell lines.

In a) average length-normalized translation of the major ORF and average Ribo-seq gene signal per gene (y-axes), plotted against the number of cell lines where the gene harbored a detected ORF. Colors indicate the gene biotype. Values were plotted dividing genes according the number of district major ORF detected across cell lines. The maximum width of each violin plot is the same for each panel, and the median value is shown as a black bar. In b) top enriched GO categories for genes translating one (right) or multiple (left) major ORFs across cell lines.

Extended Data Fig. 6 Examples of genes exhibiting translation of multiple major ORFs.

Translation on multiple major ORFs per gene for the a) EP400NL, b) GAS7, and c) IFRD1 genes, representing examples of alternative splicing, transcription start site or small ORF usage. Displayed tracks represent, in descending order: gene annotation and (for each cell line): P-sites positions, junction reads, ORF coverage (defined as % of gene translation) and quantified ORFs. In b) and c) intron sizes were scaled to a maximum of 300 and 150 nt respectively.

Extended Data Fig. 7 Ribo-seq assisted protein detection and quantification across cell lines.

In a) the overlap between in-silico generated tryptic peptides using the full GENCODE25 database or ORFquant-derived protein sequences in the 6 cell lines analyzed. Up to two missed cleavage events were allowed. In b) iBAQ values (y-axis) are plotted against length-normalized translation quantification estimates, for each cell line. Each plot is divided by number of unique peptides (from proteomics) and unique features with reads (from Ribo-seq).

Extended Data Fig. 8 Agreement between transcript and protein abundance.

iBAQ values (y-axis) plotted against (x-axis) gene-level TPM values from RNA-seq. Plots are divided according to the number of unique peptides detected. Number of proteins, together with Pearson and Spearman correlations, is shown for each plot.

Extended Data Fig. 9 Translation quantification and protein abundance for multiple gene products.

Pearson and Spearman correlation coefficients between ORFquant-derived % of gene translation and % of gene protein abundance. In each plot, translation quantification is show with (right) and without (left) adjusting for the presence of multiple ORFs. Size and color of each data point indicate the number of unique peptides detected.

Extended Data Fig. 10 Increased agreement between translation and protein abundance using signal from all reads.

Correlation between translation quantification and protein abundance using all reads, as shown in Fig. 5 using uniquely mapping reads only. Values are shown for all proteins (left), or as percentages of gene output for protein isoforms (right).

Supplementary information

Reporting Summary

Supplementary Table 1

Summary of Ribo-seq datasets analyzed in this study.

Supplementary Data 1

Archive containing all P-site positions and junction reads (using uniquely mapping reads), together with the set of ORFquant identified ORFs, for each cell line.

Supplementary Data 2

Archive containing the set of identified peptides and proteins, including their Ribo-seq statistics, the parameters used for the MaxQuant run and the custom protein database.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

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Calviello, L., Hirsekorn, A. & Ohler, U. Quantification of translation uncovers the functions of the alternative transcriptome. Nat Struct Mol Biol 27, 717–725 (2020). https://doi.org/10.1038/s41594-020-0450-4

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