uORF-Tools—Workflow for the determination of translation-regulatory upstream open reading frames
aff001; Florian Eggenhofer
aff002; Rick Gelhausen
aff002; Björn Grüning
aff002; Kathi Zarnack
aff003; Bernhard Brüne
aff001; Rolf Backofen
aff002; Tobias Schmid
Authors place of work:
Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt am Main, Germany
aff001; Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
aff002; Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-University Frankfurt, Frankfurt am Main, Germany
aff003; Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany
Published in the journal:
PLoS ONE 14(9)
Ribosome profiling (ribo-seq) provides a means to analyze active translation by determining ribosome occupancy in a transcriptome-wide manner. The vast majority of ribosome protected fragments (RPFs) resides within the protein-coding sequence of mRNAs. However, commonly reads are also found within the transcript leader sequence (TLS) (aka 5’ untranslated region) preceding the main open reading frame (ORF), indicating the translation of regulatory upstream ORFs (uORFs). Here, we present a workflow for the identification of translation-regulatory uORFs. Specifically, uORF-Tools uses Ribo-TISH to identify uORFs within a given dataset and generates a uORF annotation file. In addition, a comprehensive human uORF annotation file, based on 35 ribo-seq files, is provided, which can serve as an alternative input file for the workflow. To assess the translation-regulatory activity of the uORFs, stimulus-induced changes in the ratio of the RPFs residing in the main ORFs relative to those found in the associated uORFs are determined. The resulting output file allows for the easy identification of candidate uORFs, which have translation-inhibitory effects on their associated main ORFs. uORF-Tools is available as a free and open Snakemake workflow at https://github.com/Biochemistry1-FFM/uORF-Tools. It is easily installed and all necessary tools are provided in a version-controlled manner, which also ensures lasting usability. uORF-Tools is designed for intuitive use and requires only limited computing times and resources.
Translation is a highly regulated cellular process, regulation occurring predominantly at the level of initiation . Global translation is initiated in a cap-dependent manner, i.e. via binding of the cap-binding protein eukaryotic initiation factor 4E (eIF4E) to the 5’ 7-methyl-guanosine (m7G) cap present in all eukaryotic mRNAs and subsequent recruitment of the eIF4F initiation complex. As cap-dependent translation initiation depends on the availability of eIF4E, sequestration of the latter by eIF4E-binding proteins (4E-BPs), which are regulated by the central mTOR kinase, provides a means to efficiently control global translation . In addition, there are numerous regulatory mechanisms that affect the translation of selected mRNAs only. Alternative modes of translational regulation commonly depend on cis-regulatory features within the 5’ untranslated region (UTR) of the respective mRNAs, e.g. specific sequences or secondary structures . Alternative modes of translational regulation, such as internal ribosome entry site (IRES)- and upstream open reading frame (uORF)-dependent initiation, are of major importance under stress conditions, when global translation is inhibited, yet the synthesis of certain proteins needs to be sustained [4–5].
The analysis of translational changes was revolutionized by the development of the ribosome profiling (ribo-seq) technology, where actively translated regions are determined across the entire transcriptome by selective sequencing of ribosome protected footprints (RPFs) . Sequencing reads in ribo-seq analyses are predominantly mapped to the protein-coding regions. Yet, while the 3’UTRs of transcripts usually lack RPFs, they are commonly observed in the 5’UTRs. Such actively translated regions are indicators for the presence of upstream open reading frames (uORFs), which represent short, peptide-coding sequences characterized by a start codon with an in-frame stop codon. Consequently, 5’UTRs are also referred to as transcript leader sequences (TLS) . With respect to their function, uORFs have been shown to affect the translation of associated main ORFs. While there are cases in which translation of a uORF positively affects the translation of the main ORF, for the most part efficient translation of a uORF is considered to restrict the translation of the respective main ORF . Of note, uORFs have been shown to play a prominent role during the integrated stress response (ISR), an adaptive response to various stress conditions aiming at restoring cellular homeostasis. During the ISR the translation initiation factor eIF2α is phosphorylated by protein kinase R (PKR), PKR-like endoplasmic reticulum stress (PERK), heme-regulated inhibitor (HRI), or general control non-repressible 2 (GCN2) kinases in response to stress conditions such as amino acid deprivation, viral infection, heme deprivation, and endoplasmic reticulum stress . Phosphorylation of eIF2α reduces global translation and at the same time enhances translation of selected, uORF-bearing mRNAs to allow for adaptation . Such stress adaptive mechanisms are of major importance in a number of disease states including cancer and inflammation .
There are various strategies to determine the presence of uORFs either based on sequence features within the TLS , or using experimental ribo-seq data to identify actively translated ORFs including uORFs (ORFscore ; RiboTaper ; RiboLace ; PRICE ; RibORF ; sORF.org ; RiboCode ; RiboWave ). With the present workflow, we aim to provide a pipeline that allows for the identification of differentially translated uORFs, which may regulate the translation of the associated main ORFs. Using ribosome profiling data, uORF-Tools determines the experiment-specific, differentially translated uORFs and compares their translation with the translation of the respective main ORFs. While a uORF annotation file is generated for each individual experiment using Ribo-TISH , a comprehensive human uORF annotation file, based on 35 data sets from nine human ribosome profiling data series, is also provided to allow for a comprehensive assessment of the translation regulatory impact of uORFs.
Implementation and workflow
uORF-Tools is provided as a free and open workflow and can be downloaded from https://github.com/Biochemistry1-FFM/uORF-Tools. It is based on Snakemake  and automatically installs all tool dependencies in a version-controlled manner via bioconda . The workflow can be run locally or in a cluster environment.
uORF-Tools is designed to receive bam files of ribosome profiling data sets as input (Fig 1). In addition, the workflow requires a genome fasta file and an annotation gtf file.
Initially, uORF-Tools generates a new genome annotation file, which is used in the subsequent steps of the workflow. For practical reasons, this annotation file contains only the validated or manually annotated (confidence levels 1 and 2 in Gencode) (www.gencodegenes.org) longest protein coding transcript variants. Based on the provided input bam files and the generated genome annotation file, an experiment-specific uORF annotation file is then generated using Ribo-TISH . Specifically, Ribo-TISH identifies translation initiation sites within ribo-seq data and uses this information to determine ORFs, i.e. regular ORFs as well as uORFs. Default settings in uORF-Tools use the canonical start codon ATG only, yet users can allow for the use of alternative start codons as well. Furthermore, as uORFs are generally considered to be short, peptide-coding ORFs, a maximal length of 400 nt was set as upper size limit within the uORF-Tools pipeline for the identification of uORFs. The minimal size limit was set to 9 nt to ensure that the potential uORFs contain at least one codon on top of the required start and stop codons . To allow for an even broader characterization of potentially active uORFs, a comprehensive human uORF annotation file (based on hg38), based on 35 ribo-seq data sets, is provided with the package (for details see S1 Table). Among other information, this file contains the exact coordinates of all uORFs (designated as ORFs in the annotation file), as well as their lengths. To use this comprehensive instead of the experiment-specific annotation file, the former needs to be selected by including its file path (uORF-Tools/comprehensive_annotation/uORF_annotation_hg38.csv) in the config.yaml file before starting the uORF-Tools workflow. Using uORF and genome annotation files, uORF-Tools creates one count file containing all reads that correspond to coding sequences (CDS) of the longest protein coding transcripts, i.e. main ORFs, and another count file which contains only reads that correspond to uORFs. To control for differences in library sizes, the count data are subsequently normalized using size factors calculated for all input libraries with DESeq2 . To determine the relative translation of a main ORF, counts of the main ORF are normalized to the corresponding uORF counts. In order to assess if the main ORF-to-uORF ratios are altered in response to a stimulus, the impact of uORFs on downstream translation is determined by comparing the main ORF-to-uORF ratios between different conditions. A stimulus-dependent increase in the ratios indicates enhanced translation of the main ORF, i.e. reduced repression by the respective uORF, conversely a decrease in the ratios indicates that an inhibitory uORF becomes more active. Of note, no translational efficiencies are determined and needed in the uORF-Tools pipeline, since both main ORF and uORF ribo-seq reads would be normalized to the same transcript abundance, which would be eliminated during the calculation of the main ORF-to-uORF ratios. We therefore decided to compare ribosome profiling reads only to minimize computing requirements. Along the same lines, uORF-Tools is designed to take bam files, i.e. processed ribosome profiling data. Nevertheless, we also provide a pre-processing pipeline (S1 File) to allow for the use of yet unprocessed fastq files.
Results and discussion
uORF-Tools is provided as a readily deployable Snakemake workflow, which comes with extensive documentation (S1 File). Running uORF-Tools on 8 test data sets, i.e. 4 replicates of control and thapsigargin-treated HEK293 cells, with about 0.36 to 4.7 million reads per file on a consumer grade laptop (Intel® Core™ i5-8265U, 256 GB NVMe-SSD, 16 GB RAM) running Ubuntu 18.04.2 LTS required as little as 1.5 hours for a complete analysis. The input data and the utilized tools are clearly defined and enable reproducible analyses (Fig 1; S1 File). Using an annotation.gtf and a genome.fa file obtained from Gencode (gencode.v28.annotation.gtf and GRCh38.p12.genome.fa), the analysis of the provided 8 test data sets (available at: ftp://biftp.informatik.uni-freiburg.de/pub/uORF-Tools/bam.tar.gz) identified 939 uORFs. In contrast, the provided, comprehensive uORF annotation file contains 1933 uORFs (Table 1, S2 Table). Interestingly, when the comprehensive annotation file was used in the analysis of the test data set, only 55 of the additional uORFs did not contain any RPF counts (S3 Table). This is likely due to the fact that the Ribo-TISH criteria for the identification of a uORF might prevent the comprehensive annotation of all translated uORFs given datasets with lower quality are analyzed. In fact, some of the uORFs showing the strongest impact on the translation of the downstream main ORF were not identified in the experiment-specific uORF annotation.
To assess how the translation of the main ORFs might be affected by the uORFs, main ORF read counts were initially normalized to those of the associated uORFs (Fig 2A). This yielded mean ratios of 23.42 ± 3.74 or 26.48 ± 3.85 for the control and 27.62 ± 8.86 or 29.85 ± 8.27 for the thapsigargin-treated samples, based on the experiment-specific or the comprehensive annotations files, respectively.
Furthermore, the main ORFs of the uORF-bearing transcripts were generally much longer (mean lengths 1509 and 1575 nt, based on experiment-specific and comprehensive annotation files, respectively) than the uORFs (mean length 38 and 40 nt, based on experiment-specific and comprehensive annotation files, respectively) (Table 1). Of note, the mean uORF lengths in either uORF annotation file were similar to the previously proposed median length of 48 nt across 11,649 predicted human uORFs .
Subsequent calculation of the stimulus-dependent changes in main ORF-to-uORF ratios (Fig 2B), provides a means to easily identify uORFs inversely correlating with their associated main ORFs with respect to the transcript-specific ribosome occupancy. Owing to the major length differences between uORFs and their associated main ORFs, the dynamic range for changes in ribo-seq reads is higher on the side of the main ORFs. Consequently, changes in the ratios can be expected to be strongly influenced by changes in main ORF translation (compare Fig 3).
In the case of the analyzed test data sets, the 5% quantile of the strongest changes in differential uORF translation was comprised of 47 transcripts based on the experiment-specific uORF annotation file, as compared to 94 transcripts in the case of the comprehensive uORF annotation file (Table 1, S3 Table). These differences underscore that it is advantageous to use comprehensive uORF annotations rather than experiment-specific ones only, as this might e.g. overcome low numbers of annotated uORFs due to ribo-seq analyses of either poor quality or containing low read numbers.
Along these lines, the experiment-specific annotation file identified only one uORF (uORF 1: 163–243 nt, 26 amino acids) within the TLS of the classical ISR target protein phosphatase 1 regulatory subunit 15A (PPP1R15A, aka growth arrest and DNA-damage-inducible 34 (GADD34)), whereas both published uORFs (uORF 2: 64–132 nt, 22 amino acids and uORF 1: 163–243 nt, 26 amino acids)  were found using the comprehensive uORF annotation file. The uORFs within the TLS of PPP1R15A were exactly annotated as previously published. They were further found to be highly translated, as becomes already apparent when looking at the relative RPF peak heights of the uORFs relative to the main ORF, wherein a shift from a largely uORF-biased distribution under control conditions to more RPF reads in the thapsigargin group can be seen (Fig 3).
Quantitative analyses of the main ORF-to-uORF ratios revealed that the 5% quantile of strongest changes showed differential uORF translations of log2FC > |1.99| / |1.88| (based on the experiment-specific and the comprehensive annotation file, respectively) (S3 Table).
In the case of PPP1R15A, translation under control conditions (main ORF-to-uORF ratios: uORF 1 = 5.51; uORF 2 = 9.40), shifted towards the main ORF under thapsigargin treatment (main ORF-to-uORF ratios: uORF 1 = 21.61; uORF 2 = 80.10). Specifically, the main ORF-to-uORF ratio of PPP1R15A displayed a log2FC increase of 1.87 and 4.26 for uORF 1 and uORF 2, respectively. This indicates that the translational repression under control conditions is relieved during the integrated stress response (ISR) and consequently the translation of the PPP1R15A main ORF increases, as previously reported. In line with previous reports, our data further suggest that uORF 2 translation is more important for the regulation of the translation of the downstream main ORF of PPP1R15A [8, 26]. As a side note, uORF-Tools determines the impact of each uORF on the translation of the associated main ORF independently. Yet, if multiple uORFs exist within the same transcript, the impact of different uORFs on the regulation of the same main ORF can be easily compared in the output as all uORF IDs contain unique specifier appended to the transcript ID (S2 and S3 Tables).
Corroborating the concept that the translation of otherwise uORF-repressed main ORFs is elevated during the ISR , only 6 of the 94 candidates within the 5% quantile of strongest changes, as identified using the comprehensive annotation file, showed reduced main ORF-to-uORF ratios, i.e. enhanced translational repression by the uORF. Furthermore, 675 candidates had main ORF-to-uORF ratios log2FC < 0 (73 candidates log2FC < -1), while 1245 had log2FC > 0 (411 candidates log2FC > 1). Along the same lines, the mean of all reduced main ORF-to-uORF ratios was log2FC = -0.50 and the mean for all elevated ones log2FC = 0.83 (S3 Table). All of these findings support the notion that thapsigargin relieves uORF-mediated translational repression of specific targets.
In addition to the identification of translation-inhibitory uORFs, the output file also contains uORFs that are regulated in the same direction as their associated main ORFs, which may indicate a translation-supportive function of the respective uORFs. The candidates within the 5% quantile of least changes display main ORF-to-uORF ratios log2FC < |0.05|. It should be noted that, while the translation-inhibitory uORFs are easily identified with uORF-Tools, the unambiguous identification of translation-supporting uORFs would require additional information. For example, translation efficiencies could be used to analyze whether main ORF-uORF pairs with unaltered ratios in fact exhibit homo-directional changes or whether these pairs are not regulated at all.
In addition to the stimulus-dependent changes in main ORF-to-uORF ratios, as indicators for the impact of the uORFs on downstream translation, the output folder contains the files ribo_norm_CDS_reads.csv and ribo_norm_uORFs_reads.csv with read counts for uORFs and main ORFs under all conditions tested. This will be informative for the assessment of the translational status of the individual transcripts and, thus, the potential relevance of the determined changes in a given data set.
uORF-Tools generates an intuitive, easy to interpret output file, containing the stimulus-dependent changes in main ORF-to-uORF ratios as an indicator for uORFs that negatively regulate the translation of their associated main ORFs. In addition, uORF-Tools provides a comprehensive human uORF annotation file based on 35 ribosome profiling data sets (S1 Table), which appeared superior to experiment-specific uORF annotation files, with respect to the identification of translation-regulatory uORFs. Future updates will incorporate comprehensive uORF annotations files for additional species.
Even with limited computing resources, uORF-Tools is a fast software solution and a valuable addition to the portfolio of methods for researchers interested in the function of uORFs.
S1 File [pdf]
A more detailed description of the workflow and its implementation.
S1 Table [pdf]
Ribo-seq data series used for the generation of the comprehensive human uORF annotation file.
S2 Table [xlsx]
Lists of uORFs in the comprehensive and in the experiment-specific annotation files.
S3 Table [xlsx]
Output files generated using the comprehensive or the experiment-specific annotation files.
1. Hinnebusch AG, Lorsch JR. The mechanism of eukaryotic translation initiation: new insights and challenges. Cold Spring Harb Perspect Biol. 2012;4(10): a011544. doi: 10.1101/cshperspect.a011544 22815232
2. Thoreen CC, Chantranupong L, Keys HR, Wang T, Gray NS, Sabatini DM. A unifying model for mTORC1-mediated regulation of mRNA translation. Nature. 2012;485(7396): 109–113. doi: 10.1038/nature11083 22552098
3. Hinnebusch AG, Ivanov IP, Sonenberg N. Translational control by 5'-untranslated regions of eukaryotic mRNAs. Science. 2016;352(6292): 1413–1416. doi: 10.1126/science.aad9868 27313038
4. Lacerda R, Menezes J, Romão L. More than just scanning: the importance of cap-independent mRNA translation initiation for cellular stress response and cancer. Cell Mol Life Sci. 2017;74(9): 1659–1680. doi: 10.1007/s00018-016-2428-2 27913822
5. Walters B, Thompson SR. Cap-Independent Translational Control of Carcinogenesis. Front Oncol. 2016;6: 128. doi: 10.3389/fonc.2016.00128 27252909
6. Ingolia NT, Ghaemmaghami S, Newman JR, Weissman JS. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science. 2009;324(5924): 218–223. doi: 10.1126/science.1168978 19213877
7. Wethmar K. The regulatory potential of upstream open reading frames in eukaryotic gene expression. Wiley Interdiscip Rev RNA. 2014;5(6): 765–778. doi: 10.1002/wrna.1245 24995549
8. Young SK, Wek RC. Upstream Open Reading Frames Differentially Regulate Gene-specific Translation in the Integrated Stress Response. J Biol Chem. 2016;291(33): 16927–16935. doi: 10.1074/jbc.R116.733899 27358398
9. Taniuchi S, Miyake M, Tsugawa K, Oyadomari M, Oyadomari S. Integrated stress response of vertebrates is regulated by four eIF2α kinases. Sci Rep. 2016;6: 32886. doi: 10.1038/srep32886 27633668
10. Pakos-Zebrucka K, Koryga I, Mnich K, Ljujic M, Samali A, Gorman AM. The integrated stress response. EMBO Rep. 2016;17(10): 1374–1395. doi: 10.15252/embr.201642195 27629041
11. Somers J, Pöyry T, Willis AE. A perspective on mammalian upstream open reading frame function. Int J Biochem Cell Biol. 2013;45(8): 1690–1700. doi: 10.1016/j.biocel.2013.04.020 23624144
12. McGillivray P, Ault R, Pawashe M, Kitchen R, Balasubramanian S, Gerstein M. A comprehensive catalog of predicted functional upstream open reading frames in humans. Nucleic Acids Res. 2018;46(7): 3326–3338. doi: 10.1093/nar/gky188 29562350
13. Bazzini AA, Johnstone TG, Christiano R, Mackowiak SD, Obermayer B, Fleming ES, et al. Identification of small ORFs in vertebrates using ribosome footprinting and evolutionary conservation. EMBO J. 2014;33(9): 981–993. doi: 10.1002/embj.201488411 24705786
14. Calviello L, Mukherjee N, Wyler E, Zauber H, Hirsekorn A, Selbach M, et al. Detecting actively translated open reading frames in ribosome profiling data. Nat Methods. 2016;13(2): 165–170. doi: 10.1038/nmeth.3688 26657557
15. Clamer M, Tebaldi T, Lauria F, Bernabò P, Gómez-Biagi RF, Marchioretto M, et al. Active Ribosome Profiling with RiboLace. Cell Rep. 2018;25(4): 1097–1108. doi: 10.1016/j.celrep.2018.09.084 30355487
16. Erhard F, Halenius A, Zimmermann C, L’Hernault A, Kowalewski DJ, Weekes MP, et al. Improved Ribo-seq enables identification of cryptic translation events. Nat Methods. 2018;15(5): 363–366. doi: 10.1038/nmeth.4631 29529017
17. Ji Z. RibORF: Identifying Genome-Wide Translated Open Reading Frames Using Ribosome Profiling. Curr Protoc Mol Biol. 2018;124(1): e67. doi: 10.1002/cpmb.67 30178897
18. Olexiouk V, Van Criekinge W, Menschaert G. An update on sORFs.org: a repository of small ORFs identified by ribosome profiling. Nucleic Acids Res. 2018;46(D1): D497–D502. doi: 10.1093/nar/gkx1130 29140531
19. Xiao Z, Huang R, Xing X, Chen Y, Deng H, Yang X. De novo annotation and characterization of the translatome with ribosome profiling data. Nucleic Acids Res. 2018;46(10): e61. doi: 10.1093/nar/gky179 29538776
20. Xu Z, Hu L, Shi B, Geng S, Xu L, Wang D, et al. Ribosome elongating footprints denoised by wavelet transform comprehensively characterize dynamic cellular translation events. Nucleic Acids Res. 2018;46(18): e109. doi: 10.1093/nar/gky533 29945224
21. Zhang P, He D, Xu Y, Hou J, Pan BF, Wang Y, et al. Genome-wide identification and differential analysis of translational initiation. Nat Commun. 2017;8(1): 1749. doi: 10.1038/s41467-017-01981-8 29170441
23. Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018;15(7): 475–476. doi: 10.1038/s41592-018-0046-7 29967506
24. Calvo SE, Pagliarini DJ, Mootha VK. Upstream open reading frames cause widespread reduction of protein expression and are polymorphic among humans. Proc Natl Acad Sci U S A. 2009;106(18): 7507–7512. doi: 10.1073/pnas.0810916106 19372376
25. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12): 550. doi: 10.1186/s13059-014-0550-8 25516281
26. Lee Y, Cevallos RC, Jan E. An Upstream Open Reading Frame Regulates Translation of GADD34 during Cellular Stresses That Induce eIF2α Phosphorylation. J Biol Chem. 2009;284: 6661–6673. doi: 10.1074/jbc.M806735200 19131336
27. Andreev DE, O’Connor PB, Fahey C, Kenny EM, Terenin IM, Dmitriev SE, et al. Translation of 5' leaders is pervasive in genes resistant to eIF2 repression. Elife. 2015;4: e03971. doi: 10.7554/eLife.03971 25621764
28. Woo YM, Kwak Y, Namkoong S, Kristjánsdóttir K, Lee SH, Lee JH, et al. TED-Seq Identifies the Dynamics of Poly(A) Length during ER Stress. Cell Rep. 2018;24(13): 3630–3641. doi: 10.1016/j.celrep.2018.08.084 30257221