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Translation efficiency covariation identifies conserved coordination patterns across cell types

Nature Biotechnology (2025)Cite this article

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Abstract

Characterizing shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and biological functions. However, it is unclear if such coordination extends to translation. In this study, we uniformly analyze 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of translation efficiency covariation (TEC), identifying coordinated translation patterns across cell types. We nominate candidate mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and uncovers gene functions that are not evident from RNA or protein co-expression. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish TEC as a conserved organizing principle of mammalian transcriptomes. TEC has potential as a predictive marker for gene function and may offer a framework for designing gene expression systems in synthetic biology and biotechnological applications.

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Fig. 1: RiboBase: a comprehensive ribosome profiling database with thousands of experiments.
Fig. 2: TE defined using a compositional linear regression model is conserved across cell types and species.
Fig. 3: TE covariation is conserved between human and mouse.
Fig. 4: Genes associated with certain biological functions exhibit higher similarity patterns in TE than in RNA expression.
Fig. 5: TEC enables the prediction of gene functions.
Fig. 6: Physically interacting proteins display TEC.

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Data availability

Metadata about RiboBase can be found in Supplementary Table 1. Ribo files for the HeLa cell line are accessible via Zenodo at https://doi.org/10.5281/zenodo.15660080 (ref. 103). Full TEC and RNA co-expression matrices are accessible via Zenodo at https://doi.org/10.5281/zenodo.10373032 (ref. 127). A RiboFlow configuration file and processed ribo files for RBP knockout can be accessed via Zenodo at https://doi.org/10.5281/zenodo.11388478 (ref. 135). Sequencing data and ribo files for the RBP knockout experiments are available under GEO accession code GSE269734.

Code availability

The code and data used in this study are available via Zenodo at https://doi.org/10.5281/zenodo.10373032 (ref. 127) and via GitHub at https://github.com/CenikLab/TE_model. The code and data used to generate figures can be found via Zenodo at https://doi.org/10.5281/zenodo.15337774 (ref. 136) and via GitHub at https://github.com/CenikLab/coTE_paper.

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Acknowledgements

We thank all contributors to metadata curation: H. Chiang, A. Hoffman, T. Tonn, A. Segura, C. Tante, E. Vasquez and L. Xu. We also thank Y. Shin and V. D. Chapman for their help with the experiments. We appreciate M. Miladi for providing critical feedback. The original text in this paper was written by the authors. A large language model was used to suggest edits for clarity and grammar (Open AI ChatGPT, https://chat.openai.com). The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin (http://www.tacc.utexas.edu) for providing high-performance computing and storage resources that contributed to the research results reported within this paper.

Research reported in this publication was supported in part by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) under award numbers R35GM150667 (C.C.) and R35GM138340 (E.S.C.). This work was also supported by NIH grant HD110096 (C.C.) and Welch Foundation grants F-2027-20230405 (C.C.) and F-2133-20230405 (E.S.C.). C.C. was a Cancer Prevention and Research Institute of Texas (CPRIT) Scholar in Cancer Research, supported by CPRIT grant RR180042.

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  1. Hakan Ozadam

    Present address: Sail Biomedicines, Cambridge, MA, USA

Authors and Affiliations

  1. Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA

    Yue Liu, Shilpa Rao, Ian Hoskins, Michael Geng, Qiuxia Zhao, Jonathan Chacko, Vighnesh Ghatpande, Kangsheng Qi, Logan Persyn, Yochen Zhong, Dayea Park, Elif Sarinay Cenik, Hakan Ozadam & Can Cenik

  2. mRNA Center of Excellence, Sanofi, Waltham, MA, USA

    Jun Wang, Dinghai Zheng & Vikram Agarwal

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Contributions

Y.L., I.H. and C.C. co-wrote the original manuscript. Y.L., I.H. and S.R. generated the figures for the manuscript. H.O., M.G. and J.C. downloaded all the data from the GEO and processed raw sequencing data. Y.L. and C.C. developed the TE calculation pipeline. J.C. and C.C. designed and implemented the winsorization method. Y.Z. performed the deduplication comparison. Y.L. and C.C. developed the TE covariation analysis and function prediction pipelines. Y.L., K.Q. and H.O. performed the quality control analysis for all sequencing data. Y.L. carried out covariation analysis, gene function prediction and AlphaFold2 analysis. I.H. conducted the RBP analysis. L.P., J.W., D.Z. and V.A. assessed the quality of TE measurements by developing machine learning approaches. H.O., J.W., D.Z., V.A., Q.Z. and E.S.C. provided suggestions for the manuscript. Y.L., Q.Z. and E.S.C. conducted the literature search to evaluate gene function predictions. S.R. conducted the experimental validation of LRRC28. S.R., I.H., V.G. and D.P. performed other experiments. C.C. provided study oversight, conceptualized the study and acquired funding. All authors approved the final manuscript.

Corresponding author

Correspondence to Can Cenik.

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Competing interests

D.Z., J.W. and V.A. are employees of Sanofi and may hold shares and/or stock options in the company. H.O. is an employee of Sail Biomedicines. I.H. is an employee of Monoceros Biosystems. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Sequencing quality of ribosome profiling data.

a, Distribution of read counts for 2,195 human and 1,624 mouse ribosome profiling data in RiboBase. In all figure panels, the horizontal line corresponds to the median. The box represents the interquartile range and the whiskers extend to 1.5 times of it. b, Distribution plot similar to panel a for 1,282 human and 995 mouse ribosome profiling data with matched RNA-seq. c, Distribution of the proportion of read count aligned to transcripts, read counts with high-quality alignments, and the percentage of reads remaining after PCR deduplication, relative to the total number of reads from panel a. d, Similar plot as panel c for ribosome profiling with matched RNA-seq. e, The read length distribution of RPFs aligned to coding sequences for all human experiments. The color in the heatmap represents the z-score adjusted RPF counts (Methods). Each experiment where the percentage of RPFs mapping to CDS was greater than 70% and achieving sufficient coverage of the transcript (>= 0.1X) was annotated as QC-pass. f, Similar to panel a for mouse samples.

Extended Data Fig. 2 Quality control and RPF length selection.

a, RPFs shorter than 21 nucleotides were removed, then we identified the RPF length with the highest number of reads mapping to CDS to serve as the starting point. Subsequently, we compared one nucleotide longer or shorter than the first and chose the length with the most reads again. This looping process continued until at least 85% of the total CDS mapping RPFs were included. b, We compared the usable reads selected with two different boundary cutoffs (y-axis) and the proportion of these selected reads that map to the coding regions (x-axis) for each ribosome profiling experiment. c, The percentage of ribosome profiling experiments from GEO that pass or fail quality control (the percentage of RPFs mapping to CDS was greater than 70% and achieving at least 0.1X coverage of the transcript as QC pass).

Extended Data Fig. 3 Assessment of periodicity and data matching for TE estimation.

a-d, In ribosome profiling experiments from RiboBase, samples were classified according to distinct periodicity patterns (Methods). For all figure panels, we added error bars to represent the standard deviation across samples. Statistical significance was assessed using the Wilcoxon test, and the p-values were subsequently adjusted for all 33 comparisons using the Benjamini-Hochberg method. We considered the Group 1 pattern as indicative of the expected three-nucleotide periodicity patterns. Human samples that pass quality control (a), human samples that fail quality control (b), mouse samples that pass quality control (c), mouse samples that fail quality control (d). e, We calculated the coefficient of determination (R²) between a specific ribosome profiling experiment and its corresponding RNA-seq from RiboBase. Additionally, we determined the average R² for all other pairings for the same ribosome profiling sample with other RNA-seq data from the same study. The matching score represents the difference in R² values between these two (x-axis; Methods). f, A dashed line at 0.188 serves as the threshold to identify samples with poor matching (Methods). In each figure panel containing boxplots, the horizontal line corresponds to the median. The box represents the IQR and the whiskers extend to 1.5 times of it. g, Distribution of standard error of TE values across tissue and cell lines (y-axis) for genes with polyA and without polyA tails.

Extended Data Fig. 4 Detailed workflow of data processing for TE and TEC calculations.

a, We selected ribosome profiling data with matched RNA-seq and removed duplicated reads with identical positions and lengths (PCR-deduplication). We set the RPF read length range for individual samples with our dynamic cutoff and filtered out ribosome profiling experiments that failed quality control. After selecting high-quality samples, we reprocessed all these ribosome profiling experiments using the winsorization method with non-deduplicated data. We removed genes without polyA tails and kept genes with sufficient counts per million RPFs. After obtaining RPF counts from the coding regions for both ribosome profiling and RNA-seq, we performed CLR normalization and compositional linear regression, defining the residuals as TE for each gene in each sample. We averaged this sample-level TE based on cell lines and tissues. TEC is further calculated with rho scores38. To build an RNA co-expression matrix, we transformed CDS counts from RNA-seq experiments using CLR, averaged them based on cell lines and tissue, and calculated pairwise proportionalities (rho scores).

Extended Data Fig. 5 Spearman correlation between TE and protein abundance.

a, The correlation between protein abundance and clr-transformed RPF counts from ribosome profiling (left), clr-transformed read counts from RNA-seq (middle), or TE calculated with winsorized RPFs counts using the linear regression model (right). Individual dots indicate specific experiments colored according to study (68 samples from 11 studies-HEK293, 86 samples from 10 studies-HeLa, 58 samples from 4 studies-U2OS, 29 samples from 5 studies-A549, 5 samples from 2 studies-MCF7, 7 samples from 2 studies-K562, 10 samples from 2 studies-HepG2). In the boxplot, the horizontal line corresponds to the median. The box represents the IQR and the whiskers extend to 1.5 times of this range. b, TE was calculated with winsorized RPF counts without deduplication or with deduplication based on position and fragment length. The Spearman correlation coefficient between TE calculated with winsorized RPF counts and protein abundance82 (y-axis) was plotted against “delta correlation” (x-axis) defined by subtracting the correlation values obtained with PCR deduplication from those obtained with the method using winsorized RPF counts without deduplication.

Extended Data Fig. 6 PCR vs UMI deduplication comparison for GSE144140.

a, Metagene plots centered on the start codon for samples GSM4282032 (RPFs range: 28-36 nt), GSM4282033 (RPFs range: 28-36 nt range), and GSM4282034 (RPFs range: 26-35 nt range) were plotted using three different deduplication methods: non-deduplication (ND), UMI-deduplication (UMI), and PCR-deduplication (PCR). b, Correlation of gene counts for GSM4282032 between the three deduplication methods. A blue diagonal line represents a 1:1 ratio in all figure panels. Same analysis as panel b for GSM4282033c, and GSM4282034d.

Extended Data Fig. 7 Conservation of gene expression between human and mouse.

a, The relationship between the mean RNA expressions (clr-transformed counts) of 9,194 orthologous genes across two species is plotted. Dots represent genes in all figure panels. b, The variability of genes’ RNA expression was quantified with metric standard deviation (msd; Methods) across different cell lines and tissues in either human or mouse. To account for the correlation between mean RNA expression and its variability, we adjusted the msd values with their mean values (Methods). c, The scatter plot shows the adjusted msd values (y-axis; Methods) and the average TE across different cell types (x-axis) for human genes. d, Similar analysis as in panel c for mouse genes.

Extended Data Fig. 8 Evaluation TEC calculation methods and TEC patterns.

a, The AUROCs for biological functions were calculated using the similarity scores among genes at ribosome occupancy level determined by eight distinct methods with 1,794 human ribosome profiling data (Methods). In the boxplot, the horizontal line corresponds to the median. The box represents the IQR and the whiskers extend to the largest value within 1.5 times the IQR from the hinge. The dot in this figure represents the AUROC for human 5’ TOP mRNAs. b, TE values that were randomly reassigned from the original data for each gene (shuffled) and TEC was calculated. In the figure panel, we plotted the number of orthologous gene pairs within specified ranges. Each dot represents the aggregated log10-transformed counts of these gene pairs. The dashed line captures 95% of the data. c, Distribution of absolute TEC among 110 TOP motif-containing mRNAs123 and 83 transcripts targeted by CSDE1 (Supplementary Table 22 (ref. 47); Methods) in comparison to all 11,149 human genes as background. Statistical significance between the groups was assessed using a Wilcoxon two-tailed test.

Extended Data Fig. 9 TEC and RNA co-expression among genes with shared functions.

a, A comparison between the number of human GO terms that have AUROC of 0.8 or higher with either TEC or RNA co-expression. b, Motif enrichment in human GO terms. RNA binding proteins (RBPs) from oRNAment94 or Transite95 are indicated. P-values were corrected using the Holm method and those kmers with a p-value < 0.05 are shown. c, Venn diagram for mouse GO terms that achieve an AUROC of 0.8 or higher with proportionality scores (rho) among genes at either TE or RNA expression level. d, The AUROC plot was calculated with genes associated with mannosyltransferase activity in mice. e, The connections represent absolute rho values above 0.1 in either TE pattern alone (green) from d, in both RNA co-expression and TE pattern (blue), or RNA co-expression alone (gray). f, Motif enrichment in mouse GO terms. RNA binding proteins (RBPs) from oRNAment94 or Transite95 are indicated. P-values were corrected using the Holm method and those kmers with a p-value < 0.05 are shown. g, We summarized GO terms where genes exhibit greater similarity at the TE level than at the RNA expression level (AUROC with TEC > 0.8, and different AUROC between TEC and RNA co-expression > 0.1) in mice. We visualized the distribution of absolute rho score for gene pairs within each specific GO term (bottom; gene pairs with abs(rho) > 0.1) at the TE level.

Extended Data Fig. 10 3D structure of the interaction between LRRC28 with FOXK1.

a, AlphaFold2-multimer predicted binding between LRRC28 and FOXK1. Kinetic ECAR response of b, MCF-7 cell line (n = 6, stable overexpression) and c, HEK293T cell line (n = 6; stable overexpression) overexpressing LRRC28 or LRCC42 to 10 mM glucose and 100 mM 2-DG. Unpaired two-sided Student’s t-test, (MCF-7; measurement 4 p = 0.06, 5 p = 0.1, 6 p = 0.3 & HEK293T measurement 4 p = 0.6, 5 p = 0.8, 6 p = 0.4). Panels b & c show mean ± s.d.; n shows biological independent experiments.

Supplementary information

Supplementary Information

Supplementary Information and Figs. 1–7.

Reporting Summary

Supplementary Tables1–23

Twenty-three supplementary tables support the results in this paper. The data can also serve as the source data to repeat the analysis results. Supplementary Table 1: Curated metadata for human and mouse datasets in RiboBase. Supplementary Table 2: Quality control of human ribosome profiling data. Supplementary Table 3: Quality control of mouse ribosome profiling data. Supplementary Table 4: Quality control of human RNA-seq data. Supplementary Table 5: Quality control of mouse RNA-seq data. Supplementary Table 6: RPF boundaries for human ribosome profiling data. Supplementary Table 7: RPF boundaries for mouse ribosome profiling data. Supplementary Table 8: Non-poly(A) gene list for human. Supplementary Table 9: Non-poly(A) gene list for mouse. Supplementary Table 10: Human linear regression-based TE. Supplementary Table 11: Mouse linear regression-based TE. Supplementary Table 12: Homologous genes between human and mouse. Supplementary Table 13: AUROC values for human GO terms with either ribosome profiling or RNA-seq data. Supplementary Table 14: AUROC values for mouse GO terms with either ribosome profiling or RNA-seq data. Supplementary Table 15: Literature-supported prediction of gene exhibiting TEC with genes associated with specific human and mouse GO terms. Supplementary Table 16: All predictions of a new gene exhibiting TEC with genes associated with specific human GO terms. Supplementary Table 17: All predictions of a new gene exhibiting TEC with genes associated with specific mouse GO terms. Supplementary Table 18: pDockQ and ipTM+PTM scores between human LRRC proteins and members of the forkhead family transcription factors. Supplementary Table 19: Two-sided chi-square test on the direction of similarity (same or different) among human gene pairs from GO terms and the STRING database. Supplementary Table 20: gRNA sequences for RBP validation. Supplementary Table 21: Primer sequences for human LRRC28 and LRRC42. Supplementary Table 22: Human 5′ TOP mRNA list and CSDE1 target gene list. Supplementary Table 23: Example comparing TE calculation using the canonical log-ratio method and the linear regression approach.

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Liu, Y., Rao, S., Hoskins, I. et al. Translation efficiency covariation identifies conserved coordination patterns across cell types. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02718-5

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