Rp3: Ribosome profiling-assisted proteogenomics improves coverage and confidence during microprotein discovery.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
09 Aug 2024
09 Aug 2024
Historique:
received:
10
10
2023
accepted:
08
07
2024
medline:
10
8
2024
pubmed:
10
8
2024
entrez:
9
8
2024
Statut:
epublish
Résumé
There has been a dramatic increase in the identification of non-canonical translation and a significant expansion of the protein-coding genome. Among the strategies used to identify unannotated small Open Reading Frames (smORFs) that encode microproteins, Ribosome profiling (Ribo-Seq) is the gold standard for the annotation of novel coding sequences by reporting on smORF translation. In Ribo-Seq, ribosome-protected footprints (RPFs) that map to multiple genomic sites are removed since they cannot be unambiguously assigned to a specific genomic location. Furthermore, RPFs necessarily result in short (25-34 nucleotides) reads, increasing the chance of multi-mapping alignments, such that smORFs residing in these regions cannot be identified by Ribo-Seq. Moreover, it has been challenging to identify protein evidence for Ribo-Seq. To solve this, we developed Rp3, a pipeline that integrates proteogenomics and Ribosome profiling to provide unambiguous evidence for a subset of microproteins missed by current Ribo-Seq pipelines. Here, we show that Rp3 maximizes proteomics detection and confidence of microprotein-encoding smORFs.
Identifiants
pubmed: 39122697
doi: 10.1038/s41467-024-50301-4
pii: 10.1038/s41467-024-50301-4
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
6839Informations de copyright
© 2024. The Author(s).
Références
Chothani, S. P. et al. A high-resolution map of human RNA translation. Mol. Cell 82, 2885–2899 (2022).
pubmed: 35841888
doi: 10.1016/j.molcel.2022.06.023
Martinez, T. F. et al. Accurate annotation of human protein-coding small open reading frames. Nat. Chem. Biol. 16, 458–468 (2020).
pubmed: 31819274
doi: 10.1038/s41589-019-0425-0
Mumtaz, M. A. S. & Couso, J. P. Ribosomal profiling adds new coding sequences to the proteome. Biochem. Soc. Trans. 43, 1271–1276 (2015).
pubmed: 26614672
doi: 10.1042/BST20150170
Aspden, J. L. et al. Extensive translation of small open reading frames revealed by Poly-Ribo-Seq. elife 3, e03528 (2014).
pubmed: 25144939
pmcid: 4359375
doi: 10.7554/eLife.03528
Orr, M. W., Mao, Y., Storz, G. & Qian, S. B. Alternative ORFs and small ORFs: shedding light on the dark proteome. Nucleic Acids Res. https://doi.org/10.1093/nar/gkz734 (2020).
Basrai, M. A., Hieter, P. & Boeke, J. D. Small open reading frames: beautiful needles in the haystack. Genome Res. 7, 768–771 (1997).
pubmed: 9267801
doi: 10.1101/gr.7.8.768
Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M. & Weissman, J. S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat. Protoc. 7, 1534–1550 (2012).
pubmed: 22836135
pmcid: 3535016
doi: 10.1038/nprot.2012.086
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).
pubmed: 19213877
pmcid: 2746483
doi: 10.1126/science.1168978
Ji, Z., Song, R., Regev, A. & Struhl, K. Many lncRNAs, 5’UTRs, and pseudogenes are translated and some are likely to express functional proteins. elife 4, e08890 (2015).
pubmed: 26687005
pmcid: 4739776
doi: 10.7554/eLife.08890
Xiao, Z. et al. De novo annotation and characterization of the translatome with ribosome profiling data. Nucleic Acids Res. 46, e61–e61 (2018).
pubmed: 29538776
pmcid: 6007384
doi: 10.1093/nar/gky179
Li, K., Hope, C. M., Wang, X. A. & Wang, J.-P. RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data. Nucleic Acids Res. 48, 12016–12029 (2020).
pubmed: 33211868
pmcid: 7708064
doi: 10.1093/nar/gkaa1049
Fields, A. P. et al. A regression-based analysis of ribosome-profiling data reveals a conserved complexity to mammalian translation. Mol. Cell 60, 816–827 (2015).
pubmed: 26638175
pmcid: 4720255
doi: 10.1016/j.molcel.2015.11.013
Calviello, L. et al. Detecting actively translated open reading frames in ribosome profiling data. Nat. Methods 13, 165–170 (2016).
pubmed: 26657557
doi: 10.1038/nmeth.3688
Bazzini, A. A. et al. Identification of small ORFs in vertebrates using ribosome footprinting and evolutionary conservation. EMBO J. 33, 981–993 (2014).
pubmed: 24705786
pmcid: 4193932
doi: 10.1002/embj.201488411
Malone, B. et al. Bayesian prediction of RNA translation from ribosome profiling. Nucleic Acids Res. 45, 2960–2972 (2017).
pubmed: 28126919
pmcid: 5389577
Erhard, F. et al. Improved Ribo-seq enables identification of cryptic translation events. Nat. Methods 15, 363–366 (2018).
pubmed: 29529017
pmcid: 6152898
doi: 10.1038/nmeth.4631
Deschamps-Francoeur, G., Simoneau, J. & Scott, M. S. Handling multi-mapped reads in RNA-seq. Comput. Struct. Biotechnol. J. 18, 1569–1576 (2020).
pubmed: 32637053
pmcid: 7330433
doi: 10.1016/j.csbj.2020.06.014
Wolfe, K. H. & Shields, D. C. Molecular evidence for an ancient duplication of the entire yeast genome. Nature 387, 708–713 (1997).
pubmed: 9192896
doi: 10.1038/42711
McLysaght, A., Hokamp, K. & Wolfe, K. H. Extensive genomic duplication during early chordate evolution. Nat. Genet. 31, 200–204 (2002).
pubmed: 12032567
doi: 10.1038/ng884
Ohta, T. Role of gene duplication in evolution. Genome 31, 304–310 (1989).
pubmed: 2687099
doi: 10.1139/g89-048
Kazazian, H. H. Jr Mobile elements: drivers of genome evolution. Science 303, 1626–1632 (2004).
pubmed: 15016989
doi: 10.1126/science.1089670
Magadum, S., Banerjee, U., Murugan, P., Gangapur, D. & Ravikesavan, R. Gene duplication as a major force in evolution. J. Genet. 92, 155–161 (2013).
pubmed: 23640422
doi: 10.1007/s12041-013-0212-8
Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).
pubmed: 27184599
pmcid: 10373632
doi: 10.1038/nrg.2016.49
Zhu, Y. et al. Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow. Nat. Commun. 9, 1–14 (2018).
Nesvizhskii, A. I. Proteogenomics: concepts, applications and computational strategies. Nat. Methods 11, 1114–1125 (2014).
pubmed: 25357241
pmcid: 4392723
doi: 10.1038/nmeth.3144
Martinez, T. F. et al. Profiling mouse brown and white adipocytes to identify metabolically relevant small ORFs and functional microproteins. Cell Metab. 35, 166–183 (2023).
pubmed: 36599300
pmcid: 9889109
doi: 10.1016/j.cmet.2022.12.004
Morgado-Palacin, L. et al. The TINCR ubiquitin-like microprotein is a tumor suppressor in squamous cell carcinoma. Nat. Commun. 14, 1328 (2023).
pubmed: 36899004
pmcid: 10006087
doi: 10.1038/s41467-023-36713-8
van Heesch, S. et al. The translational landscape of the human heart. Cell 178, 242–260 (2019).
pubmed: 31155234
doi: 10.1016/j.cell.2019.05.010
Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C. & Yates, J. R. III Protein analysis by shotgun/bottom-up proteomics. Chem. Rev. 113, 2343–2394 (2013).
pubmed: 23438204
pmcid: 3751594
doi: 10.1021/cr3003533
Grewal, R. N., El Aribi, H., Harrison, A. G., Siu, K. M. & Hopkinson, A. C. Fragmentation of protonated tripeptides: the proline effect revisited. J. Phys. Chem. B 108, 4899–4908 (2004).
doi: 10.1021/jp031093k
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
pubmed: 24227677
doi: 10.1093/bioinformatics/btt656
Bassani-Sternberg, M., Pletscher-Frankild, S., Jensen, L. J. & Mann, M. Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation*[S]. Mol. Cell. Proteom. 14, 658–673 (2015).
doi: 10.1074/mcp.M114.042812
Carvunis, A.-R. et al. Proto-genes and de novo gene birth. Nature 487, 370–374 (2012).
pubmed: 22722833
pmcid: 3401362
doi: 10.1038/nature11184
Suenaga, Y. et al. NCYM, a Cis-antisense gene of MYCN, encodes a de novo evolved protein that inhibits GSK3β resulting in the stabilization of MYCN in human neuroblastomas. PLoS Genet. 10, e1003996 (2014).
pubmed: 24391509
pmcid: 3879166
doi: 10.1371/journal.pgen.1003996
Van Oss, S. B. & Carvunis, A.-R. De novo gene birth. PLoS Genet. 15, e1008160 (2019).
pubmed: 31120894
pmcid: 6542195
doi: 10.1371/journal.pgen.1008160
D’Errico, I., Gadaleta, G. & Saccone, C. Pseudogenes in metazoa: origin and features. Brief. Funct. Genom. 3, 157–167 (2004).
doi: 10.1093/bfgp/3.2.157
Hancks, D. C. & Kazazian, H. H. Roles for retrotransposon insertions in human disease. Mob. DNA 7, 1–28 (2016).
doi: 10.1186/s13100-016-0065-9
Tong, G., Hah, N. & Martinez, T. F. Comparison of software packages for detecting unannotated translated small open reading frames by Ribo-seq. Brief. Bioinform. 25, bbae268 (2024).
pubmed: 38842510
pmcid: 11155197
doi: 10.1093/bib/bbae268
Ma, J., Saghatelian, A. & Shokhirev, M. N. The influence of transcript assembly on the proteogenomics discovery of microproteins. PLoS ONE 13, e0194518 (2018).
pubmed: 29584760
pmcid: 5870951
doi: 10.1371/journal.pone.0194518
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).
pubmed: 28394336
pmcid: 5409104
doi: 10.1038/nmeth.4256
Käll, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).
pubmed: 17952086
doi: 10.1038/nmeth1113
Yang, K. L. et al. MSBooster: improving peptide identification rates using deep learning-based features. Nat. Commun. 14, 4539 (2023).
pubmed: 37500632
pmcid: 10374903
doi: 10.1038/s41467-023-40129-9
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).
pubmed: 31768060
doi: 10.1038/s41592-019-0638-x
Pertea, G. & Pertea, M. GFF utilities: GffRead and GffCompare. F1000Research 9, ISCB Comm J-304 (2020).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
pubmed: 23329690
pmcid: 3603318
doi: 10.1093/molbev/mst010
Egorov, A. A. & Atkinson, G. C. uORF4u: a tool for annotation of conserved upstream open reading frames. Bioinformatics 39, btad323 (2023).
pubmed: 37184890
pmcid: 10219788
doi: 10.1093/bioinformatics/btad323
Maillet, N. Rapid Peptides Generator: fast and efficient in silico protein digestion. NAR Genom. Bioinform. 2, lqz004 (2020).
pubmed: 33575558
doi: 10.1093/nargab/lqz004
Subramanian, B., Gao, S., Lercher, M. J., Hu, S. & Chen, W.-H. Evolview v3: a webserver for visualization, annotation, and management of phylogenetic trees. Nucleic Acids Res. 47, W270–W275 (2019).
pubmed: 31114888
pmcid: 6602473
doi: 10.1093/nar/gkz357
De Souza, E. V. et al. Rp3: Ribosome Profiling-assisted Proteogenomics Improves Coverage and Confidence During Microprotein Discovery https://github.com/Eduardo-vsouza/rp3 (2024) 10.5281/zenodo.12092044.
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
pubmed: 25690850
pmcid: 4643835
doi: 10.1038/nbt.3122