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Peptidomics Methods Applied to the Study of Flower Development

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Flower Development

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2686))

Abstract

Understanding the global and dynamic nature of plant developmental processes requires not only the study of the transcriptome, but also of the proteome, including its largely uncharacterized peptidome fraction. Recent advances in proteomics and high-throughput analyses of translating RNAs (ribosome profiling) have begun to address this issue, evidencing the existence of novel, uncharacterized, and possibly functional peptides. To validate the accumulation in tissues of sORF-encoded polypeptides (SEPs), the basic setup of proteomic analyses (i.e., LC-MS/MS) can be followed. However, the detection of peptides that are small (up to ~100 aa, 6–7 kDa) and novel (i.e., not annotated in reference databases) presents specific challenges that need to be addressed both experimentally and with computational biology resources. Several methods have been developed in recent years to isolate and identify peptides from plant tissues. In this chapter, we outline two different peptide extraction protocols and the subsequent peptide identification by mass spectrometry using the database search or the de novo identification methods.

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References

  1. Tavormina P, De Coninck B, Nikonorova N, De Smet I, Cammuea BPA (2015) The plant peptidome: an expanding repertoire of structural features and biological functions. Plant Cell 27(8):2095–2118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Hsu PY, Benfey PN (2018) Small but mighty: functional peptides encoded by small ORFs in plants. Proteomics 18:1700038

    Article  Google Scholar 

  3. Brunet MA, Leblanc S, Roucou X (2020) Reconsidering proteomic diversity with functional investigation of small ORFs and alternative ORFs. Exp Cell Res 393(1):112057

    Article  CAS  PubMed  Google Scholar 

  4. Brunet MA, Levesque SA, Hunting DJ, Cohen AA, Roucou X (2018) Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship. Genome Res 28(5):609–624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mudge JM, Ruiz-Orera J, Prensner JR, Brunet MA, Calvet F, Jungreis I et al (2022) Standardized annotation of translated open reading frames. Nat Biotechnol 40(7):994–999

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lyapina I, Ivanov V, Fesenko I (2021) Peptidome: chaos or inevitability. Int J Mol Sci 22:13128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hellens RP, Brown CM, Chisnall MAW, Waterhouse PM, Macknight RC (2016) The emerging world of small ORFs. Trends Plant Sci 21(4):317–328

    Article  CAS  PubMed  Google Scholar 

  8. Takahashi F, Hanada K, Kondo T, Shinozaki K (2019) Hormone-like peptides and small coding genes in plant stress signaling and development. Curr Opin Plant Biol 51:88–95

    Article  CAS  PubMed  Google Scholar 

  9. Andrews SJ, Rothnagel JA (2014) Emerging evidence for functional peptides encoded by short open reading frames. Nat Rev Genet 15(3):193–204

    Article  CAS  PubMed  Google Scholar 

  10. Couso JP, Patraquim P (2017) Classification and function of small open reading frames. Nat Rev Mol Cell Biol 18(9):575–589

    Article  CAS  PubMed  Google Scholar 

  11. Plaza S, Menschaert G, Payre F (2017) In search of lost small peptides. Annu Rev Cell Dev Biol 33:391–416

    Article  CAS  PubMed  Google Scholar 

  12. Wright BW, Yi Z, Weissman JS, Chen J (2022) The dark proteome: translation from noncanonical open reading frames. Trends Cell Biol 32(3):243–258

    Article  CAS  PubMed  Google Scholar 

  13. Orr MW, Mao Y, Storz G, Qian SB (2021) Alternative ORFs and small ORFs: shedding light on the dark proteome. Nucleic Acids Res 48(3):1029–1042

    Article  Google Scholar 

  14. Ruiz-Orera J, Hernandez-Rodriguez J, Chiva C, Sabidó E, Kondova I, Bontrop R et al (2015) Origins of de novo genes in human and chimpanzee. PLoS Genet 11(12):e1005721

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ruiz-Orera J, Verdaguer-Grau P, Villanueva-Cañas JL, Messeguer X, Albà MM (2018) Translation of neutrally evolving peptides provides a basis for de novo gene evolution. Nat Ecol Evol 2(5):890–896

    Article  PubMed  Google Scholar 

  16. Ruiz-Orera J, Albà MM (2019) Translation of small open reading frames: roles in regulation and evolutionary innovation. Trends Genet 35(3):186–198

    Article  CAS  PubMed  Google Scholar 

  17. Ruiz-Orera J, Villanueva-Cañas JL, Albà MM (2020) Evolution of new proteins from translated sORFs in long non-coding RNAs. Exp Cell Res 391(1):111940

    Article  CAS  PubMed  Google Scholar 

  18. Blevins WR, Ruiz-Orera J, Messeguer X, Blasco-Moreno B, Villanueva-Cañas JL, Espinar L et al (2021) Uncovering de novo gene birth in yeast using deep transcriptomics. Nat Commun 12(1):604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Fesenko I, Shabalina SA, Mamaeva A, Knyazev A, Glushkevich A, Lyapina I et al (2021) A vast pool of lineage-specific microproteins encoded by long non-coding RNAs in plants. Nucleic Acids Res 49(18):10328–10346

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Goto H, Okuda S, Mizukami A, Mori H, Sasaki N, Kurihara D et al (2011) Chemical visualization of an attractant peptide, LURE. Plant Cell Physiol 52(1):49–58

    Article  CAS  PubMed  Google Scholar 

  21. Santiago J, Brandt B, Wildhagen M, Hohmann U, Hothorn LA, Butenko MA et al (2016) Mechanistic insight into a peptide hormone signaling complex mediating floral organ abscission. eLife 5:e15075

    Article  PubMed  PubMed Central  Google Scholar 

  22. Covey PA, Subbaiah CC, Parsons RL, Pearce G, Lay FT, Anderson MA et al (2019) A pollen-specific RALF from tomato that regulates pollen tube elongation. Plant Physiol 153:703–715

    Article  Google Scholar 

  23. Hsu PY, Calviello L, Wu HYL, Li FW, Rothfels CJ, Ohler U et al (2016) Super-resolution ribosome profiling reveals unannotated translation events in Arabidopsis. Proc Natl Acad Sci U S A 113(45):E7126–E7135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Juntawong P, Girke T, Bazin J, Bailey-Serres J (2014) Translational dynamics revealed by genome-wide profiling of ribosome footprints in Arabidopsis. Proc Natl Acad Sci U S A 111(1):E203–E212

    Article  CAS  PubMed  Google Scholar 

  25. Bazin J, Baerenfaller K, Gosai SJ, Gregory BD, Crespi M, Bailey-Serres J (2017) Global analysis of ribosome-associated noncoding RNAs unveils new modes of translational regulation. Proc Natl Acad Sci U S A 114(46):E10018–E10027

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Slavoff SA, Mitchell AJ, Schwaid AG, Cabili MN, Ma J, Levin JZ et al (2013) Peptidomic discovery of short open reading frame-encoded peptides in human cells. Nat Chem Biol 9(1):59–64

    Article  CAS  PubMed  Google Scholar 

  27. Vanderperre B, Lucier JF, Bissonnette C, Motard J, Tremblay G, Vanderperre S et al (2013) Direct detection of alternative open reading frames translation products in human significantly expands the proteome. PLoS One 8(8):e70698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Aspden JL, Eyre-Walker YC, Phillips RJ, Amin U, Mumtaz MAS, Brocard M et al (2014) Extensive translation of small open reading frames revealed by poly-ribo-seq. eLife 3:e03528

    Article  PubMed  PubMed Central  Google Scholar 

  29. Huang JZ, Chen M, Chen D, Gao XC, Zhu S, Huang H et al (2017) A peptide encoded by a putative lncRNA HOXB-AS3 suppresses colon cancer growth. Mol Cell 68(1):171–184

    Article  CAS  PubMed  Google Scholar 

  30. Nelson BR, Makarewich CA, Anderson DM, Winders BR, Troupes CD, Wu F et al (2016) A peptide encoded by a transcript annotated as long noncoding RNA enhances SERCA activity in muscle. Science 351:271–275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Makarewich CA, Baskin KK, Munir AZ, Bezprozvannaya S, Sharma G, Khemtong C et al (2018) MOXI is a mitochondrial micropeptide that enhances fatty acid β-oxidation. Cell Rep 23(13):3701–3709

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Prensner JR, Enache OM, Luria V, Krug K, Clauser KR, Dempster JM et al (2021) Noncanonical open reading frames encode functional proteins essential for cancer cell survival. Nat Biotechnol 39(6):697–704

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Boix O, Martinez M, Vidal S, Giménez-Alejandre M, Palenzuela L, Lorenzo-Sanz L et al (2022) pTINCR microprotein promotes epithelial differentiation and suppresses tumor growth through CDC42 SUMOylation and activation. Nat Commun 13(1):6840

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lin MF, Jungreis I, Kellis M (2011) PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions. Bioinformatics 27(13):i275–i282

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hanada K, Zhang X, Borevitz JO, Li WH, Shiu SH (2007) A large number of novel coding small open reading frames in the intergenic regions of the Arabidopsis thaliana genome are transcribed and/or under purifying selection. Genome Res 17(5):632–640

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Miravet-Verde S, Ferrar T, Espadas-García G, Mazzolini R, Gharrab A, Sabido E et al (2019) Unraveling the hidden universe of small proteins in bacterial genomes. Mol Syst Biol 15(2):e8290

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hanada K, Higuchi-Takeuchi M, Okamoto M, Yoshizumi T, Shimizu M, Nakaminami K et al (2013) Small open reading frames associated with morphogenesis are hidden in plant genomes. Proc Natl Acad Sci U S A 110(6):2395–2400

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wang S, Tian L, Liu H, Li X, Zhang J, Chen X et al (2020) Large-scale discovery of non-conventional peptides in maize and Arabidopsis through an integrated peptidogenomic pipeline. Mol Plant 13(7):1078–1093

    Article  CAS  PubMed  Google Scholar 

  39. Hazarika RR, De Coninck B, Yamamoto LR, Martin LR, Cammue BPA, Van Noort V (2017) ARA-PEPs: a repository of putative SORF-encoded peptides in Arabidopsis thaliana. BMC Bioinformatics 18(1):37

    Article  PubMed  PubMed Central  Google Scholar 

  40. Couzigou J-M, Lauressergues D, Bécard G, Combier J-P, Ecard GB (2015) miRNA-encoded peptides (miPEPs): a new tool to analyze the roles of miRNAs in plant biology. RNA Biol 12:1178–1180

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ruiz-Orera J, Messeguer X, Subirana JA, Alba MM (2014) Long non-coding RNAs as a source of new peptides. eLife 3:e03523

    Article  PubMed  PubMed Central  Google Scholar 

  42. Hartford CCR, Lal A (2020) When long noncoding becomes protein coding. Mol Cell Biol 40(6):e00528–e00519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kurihara Y, Makita Y, Shimohira H, Fujita T, Iwasaki S, Matsui M (2020) Translational landscape of protein-coding and non-protein-coding RNAs upon light exposure in Arabidopsis. Plant Cell Physiol 61(3):536–545

    Article  CAS  PubMed  Google Scholar 

  44. Liang Y, Zhu W, Chen S, Qian J, Li L (2021) Genome-wide identification and characterization of small peptides in maize. Front Plant Sci 12:695439

    Article  PubMed  PubMed Central  Google Scholar 

  45. Wu HYL, Song G, Walley JW, Hsu PY (2019) The tomato translational landscape revealed by transcriptome assembly and ribosome profiling. Plant Physiol 181(1):367–380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Mergner J, Frejno M, List M, Papacek M, Chen X, Chaudhary A et al (2020) Mass-spectrometry-based draft of the Arabidopsis proteome. Nature 579:409–414

    Article  CAS  PubMed  Google Scholar 

  47. Wang P, Yao S, Kosami K-I, Guo T, Li J, Zhang Y et al (2020) Identification of endogenous small peptides involved in rice immunity through transcriptomics-and proteomics-based screening. Plant Biotechnol J 18:415–428

    Article  CAS  PubMed  Google Scholar 

  48. Jorge GL, Balbuena TS (2021) Identification of novel protein-coding sequences in Eucalyptus grandis plants by high-resolution mass spectrometry. Biochim Biophys Acta Proteins Proteom 1869:140594

    Article  CAS  PubMed  Google Scholar 

  49. Fesenko I, Kirov I, Kniazev A, Khazigaleeva R, Lazarev V, Kharlampieva D et al (2019) Distinct types of short open reading frames are translated in plant cells. Genome Res 29(9):1464–1477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ouspenskaia T, Law T, Clauser KR, Klaeger S, Sarkizova S, Aguet F et al (2021) Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer. Nat Biotechnol 40:209–217

    Article  PubMed  PubMed Central  Google Scholar 

  51. Chen J, Brunner AD, Cogan JZ, Nuñez JK, Fields AP, Adamson B et al (2020) Pervasive functional translation of noncanonical human open reading frames. Science 367:140–146

    Article  Google Scholar 

  52. Ma J, Ward CC, Jungreis I, Slavoff SA, Schwaid AG, Neveu J et al (2014) Discovery of human sORF-encoded polypeptides (SEPs) in cell lines and tissue. J Proteome Res 13(3):1757–1765

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Flower CT, Chen L, Jung HJ, Raghuram V, Knepper MA, Yang CR (2020) Genetic and genomics investigation of structure and function of the kidney: an integrative proteogenomics approach reveals peptides encoded by annotated lincRNA in the mouse kidney inner medulla. Physiol Genomics 52(10):485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Luo W, Xiao Y, Liang Q, Su Y, Xiao L (2019) Identification of potential auxin-responsive small signaling peptides through a peptidomics approach in arabidopsis thaliana. Molecules 24:3146

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Barashkova AS, Rogozhin EA (2020) Isolation of antimicrobial peptides from different plant sources: does a general extraction method exist? Plant Methods 16:143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Damerval C, De Vienne D, Zivy M, Thiellement H (1986) Technical improvements in two-dimensional electrophoresis increase the level of genetic variation detected in wheat-seedling proteins. Electrophoresis 7(1):52–54

    Article  CAS  Google Scholar 

  57. Chatterjee M, Gupta S, Bhar A, Das S (2012) Optimization of an efficient protein extraction protocol compatible with two-dimensional electrophoresis and mass spectrometry from recalcitrant phenolic rich roots of chickpea (Cicer arietinum L.). Int J Proteomics 2012:536963

    Article  PubMed  PubMed Central  Google Scholar 

  58. Shi Y, Li J, Li L, Lin G, Bilal AM, Smagghe G et al (2021) Genomics, transcriptomics, and peptidomics of Spodoptera frugiperda (Lepidoptera, Noctuidae) neuropeptides. Arch Insect Biochem Physiol 106:e21740

    Article  CAS  PubMed  Google Scholar 

  59. Culver KD, Allen JL, Shaw LN, Hicks LM (2021) Too hot to handle: antibacterial peptides identified in ghost pepper. J Nat Prod 84:2200–2208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kuljanin M, Dieters-Castator DZ, Hess DA, Postovit L-M, Lajoie GA (2017) Comparison of sample preparation techniques for large-scale proteomics. Proteomics 17(1–2):1600337

    Article  Google Scholar 

  61. Flower CT, Chen L, Jung HJ, Raghuram V, Knepper MA, Yang C-R (2020) An integrative proteogenomics approach reveals peptides encoded by annotated lincRNA in the mouse kidney inner medulla. Physiol Genomics 52:485–491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Cao S, Liu X, Huang Y, Yan Y, Zhou C, Shao C et al (2021) Proteogenomic discovery of sORF-encoded peptides associated with bacterial virulence in Yersinia pestis. Commun Biol 4:1248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Grossmann J, Roschitzki B, Panse C, Fortes C, Barkow-Oesterreicher S, Rutishauser D et al (2010) Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods. J Proteome 73(9):1740–1746

    Article  CAS  Google Scholar 

  64. Eng JK, Mccormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:977–989

    Article  Google Scholar 

  65. Perkins DN, Pappin DJ, Creasy DM, Cottrell JS (1999) Probablity-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551–3557

    Article  CAS  PubMed  Google Scholar 

  66. Colinge J, Masselot A, Giron M, Dessingy T, Magnin J (2003) OLAV: towards high-throughput tandem mass spectrometry data identification. Proteomics 3(8):1454–1463

    Article  CAS  PubMed  Google Scholar 

  67. Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20(9):1466–1467

    Article  CAS  PubMed  Google Scholar 

  68. Geer LY, Markey SP, Kowalak JA, Wagner L, Xu M, Maynard DM et al (2004) Open mass spectrometry search algorithm. J Proteome Res 3:958–964

    Article  CAS  PubMed  Google Scholar 

  69. Fu Y, Yang Q, Sun R, Li D, Zeng R, Ling CX et al (2004) Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry. Bioinformatics 20(12):1948–1954

    Article  CAS  PubMed  Google Scholar 

  70. Tanner S, Shu H, Frank A, Wang LC, Zandi E, Mumby M et al (2005) InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. Anal Chem 77(14):4626–4639

    Article  CAS  PubMed  Google Scholar 

  71. Bern M, Cai Y, Goldberg D (2007) Lookup peaks: a hybrid of de novo sequencing and database search for protein identification by tandem mass spectrometry. Anal Chem 79(4):1393–1400

    Article  CAS  PubMed  Google Scholar 

  72. Eng JK, Jahan TA, Hoopmann MR (2013) Comet: an open-source MS/MS sequence database search tool. Proteomics 13(1):22–24

    Article  CAS  PubMed  Google Scholar 

  73. Kim S, Pevzner PA (2014) MS-GF+ makes progress towards a universal database search tool for proteomics. Nat Commun 5(1):5277

    Article  CAS  PubMed  Google Scholar 

  74. Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11(12):2301–2319

    Article  CAS  PubMed  Google Scholar 

  75. Zeng X, Ma B (2021) MSTracer: a machine learning software tool for peptide feature detection from liquid chromatography-mass spectrometry data. J Proteome Res 20(7):3455–3462

    Article  CAS  PubMed  Google Scholar 

  76. Hanada K, Akiyama K, Sakurai T, Toyoda T, Shinozaki K, Shiu S-H (2010) sORF finder: a program package to identify small open reading frames with high coding potential. Bioinformatics 26(3):399–400

    Article  CAS  PubMed  Google Scholar 

  77. Yang X, Jensen SI, Wulff T, Harrison SJ, Long KS (2016) Identification and validation of novel small proteins in Pseudomonas putida. Environ Microbiol Rep 8(6):966–674

    Article  CAS  PubMed  Google Scholar 

  78. Ma B, Zhang K, Hendrie C, Liang C, Li M, Doherty-Kirby A et al (2003) PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom 17:2337–2342

    Article  CAS  PubMed  Google Scholar 

  79. Han Y, Ma B, Zhang K (2005) Spider: software for protein identification from sequence tags with de novo sequencing error. J Bioinforma Comput Biol 3(3):697–716

    Article  CAS  Google Scholar 

  80. Jeong K, Kim S, Pevzner PA (2013) UniNovo: a universal tool for de novo peptide sequencing. Bioinformatics 29(16):1953–1962

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Chi H, Chen H, He K, Wu L, Yang B, Sun R-X et al (2013) pNovo+: de novo peptide sequencing using complementary HCD and ETD tandem mass spectra. J Proteome Res 12:615–625

    Article  CAS  PubMed  Google Scholar 

  82. Ma B (2015) Novor: real-time peptide de novo sequencing software. J Am Soc Mass Spectrom 26:1885–1894

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Tran NH, Zhang X, Xin L, Shan B, Li M (2017) De novo peptide sequencing by deep learning. Proc Natl Acad Sci U S A 114(31):8247–8252

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Tran NH, Qiao R, Xin L, Chen X, Liu C, Zhang X et al (2019) Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry. Nat Methods 16(1):63–66

    Article  PubMed  Google Scholar 

  85. Pathan M, Samuel M, Keerthikumar S, Mathivanan S (2017) Unassigned MS/MS spectra: who am I? In: Keerthikumar S, Mathivanan S (eds) Proteome bioinformatics. Methods in molecular biology, vol 1549. Humana Press, New York, pp 67–74

    Chapter  Google Scholar 

  86. Muth T, Renard BY (2018) Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification? Brief Bioinform 19(5):954–970

    Article  CAS  PubMed  Google Scholar 

  87. Wu H, Johnson MC, Lu CH, Fritsche KL, Thomas AL, Lai Y et al (2015) Peptidomics study of anthocyanin-rich juice of elderberry. Talanta 131:640–644

    Article  CAS  PubMed  Google Scholar 

  88. Gemperline E, Keller C, Jayaraman D, Maeda J, Sussman MR, Ané J-MA et al (2016) Examination of endogenous peptides in Medicago truncatula using mass spectrometry imaging. J Proteome Res 15:4403–4411

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Gemperline E, Keller C, Li L (2016) Mass spectrometry in plant-omics. Anal Chem 88(7):3422–3434

    Article  CAS  PubMed  Google Scholar 

  90. Ye X, Zhao N, Yu X, Han X, Gao H, Zhang X (2016) Extensive characterization of peptides from Panax ginseng C. A. Meyer using mass spectrometric approach. Proteomics 16:2788–2791

    Article  CAS  PubMed  Google Scholar 

  91. Zhang K, Mckinlay C, Hocart CH, Djordjevic MA (2006) The Medicago truncatula small protein proteome and peptidome. J Proteome Res 12:3355–3367

    Article  Google Scholar 

  92. Wang X, Li Y, Wu Z, Wang H, Tan H, Peng J (2014) JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy. Mol Cell Proteomics 13(12):3663–3673

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Röst HL, Rosenberger G, Navarro P, Gillet L, Miladinović SM, Schubert OT et al (2014) OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 32:219–223

    Article  PubMed  Google Scholar 

  94. Wilhelm M, Zolg DP, Graber M, Gessulat S, Schmidt T, Schnatbaum K et al (2021) Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nat Commun 12:3346

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Gessulat S, Schmidt T, Zolg DP, Samaras P, Schnatbaum K, Zerweck J et al (2019) Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods 16:509–518

    Article  CAS  PubMed  Google Scholar 

  96. Ekvall M, Truong P, Gabriel W, Wilhelm M, Käll L (2022) Prosit transformer: a transformer for prediction of MS2 spectrum intensities. J Proteome Res 21(5):1359–1364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Gabriels R, Martens L, Degroeve S (2019) Updated MS2PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques. Nucleic Acids Res 47(W1):W295–W299

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Beer LA, Liu P, Ky B, Barnhart KT, Speicher DW (2017) Efficient quantitative comparisons of plasma proteomes using label-free analysis with MaxQuant. Methods Mol Biol 1619:339–352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Gerster S, Kwon T, Ludwig C, Matondo M, Vogel C, Marcotte EM et al (2014) Statistical approach to protein quantification. Mol Cell Proteomics 13(2):666–677

    Article  CAS  PubMed  Google Scholar 

  100. Fabre B, Lambour T, Bouyssié D, Menneteau T, Monsarrat B, Burlet-Schiltz O et al (2014) Comparison of label-free quantification methods for the determination of protein complexes subunits stoichiometry. EuPA Open Proteom 4:82–86

    Article  CAS  Google Scholar 

  101. Yeung YG, Stanley ER (2010) Rapid detergent removal from peptide samples with ethyl acetate for mass spectrometry analysis. Curr Protoc Protein Sci 16(16):12

    Google Scholar 

  102. Michel AM, Fox G, Kiran A M, De Bo C, O’Connor PBF, Heaphy SM et al (2014) GWIPS-viz: development of a ribo-seq genome browser. Nucleic Acids Res 42:D859–D864

    Article  CAS  PubMed  Google Scholar 

  103. Wang H, Yang L, Wang Y, Chen L, Li H, Xie Z (2019) RPFdb v2.0: an updated database for genome-wide information of translated mRNA generated from ribosome profiling. Nucleic Acids Res 47:D230–D234

    Article  CAS  PubMed  Google Scholar 

  104. Chen Y, Li D, Fan W, Zheng X, Zhou Y, Ye H et al (2020) PsORF: a database of small ORFs in plants. Plant Biotechnol J 18:2158–2160

    Article  PubMed  PubMed Central  Google Scholar 

  105. Wethmar K, Barbosa-Silva A, Andrade-Navarro MA, Leutz A (2014) uORFdb--a comprehensive literature database on eukaryotic uORF biology. Nucleic Acids Res 42:D60–D67

    Article  CAS  PubMed  Google Scholar 

  106. Calviello L, Mukherjee N, Wyler E, Zauber H, Hirsekorn A, Selbach M et al (2016) Detecting actively translated open reading frames in ribosome profiling data. Nat Methods 13(2):165–170

    Article  CAS  PubMed  Google Scholar 

  107. Erhard F, Halenius A, Zimmermann C, L’Hernault A, Kowalewski DJ, Weekes MP et al (2018) Improved Ribo-seq enables identification of cryptic translation events. Nat Methods 15(5):363–366

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Xiao Z, Huang R, Xing X, Chen Y, Deng H, Yang X (2018) De novo annotation and characterization of the translatome with ribosome profiling data. Nucleic Acids Res 46(10):e61

    Article  PubMed  PubMed Central  Google Scholar 

  109. Perkins P, Mazzoni-Putman S, Stepanova A, Alonso J, Heber S (2019) RiboStreamR: a web application for quality control, analysis, and visualization of Ribo-seq data. BMC Genomics 20:422

    Article  PubMed  PubMed Central  Google Scholar 

  110. Larry Wu H-Y, Yingshan Hsu P (2021) RiboPlotR: a visualization tool for periodic Ribo-seq reads. Plant Methods 17:124

    Article  Google Scholar 

  111. Song B, Jiang M, Gao L (2021) RiboNT: a noise-tolerant predictor of open reading frames from ribosome-protected footprints. Life (Basel) 11(7):701

    CAS  PubMed  Google Scholar 

  112. Zhou P, Silverstein KAT, Gao L, Walton JD, Nallu S, Guhlin J et al (2013) Detecting small plant peptides using SPADA (small peptide alignment discovery application). BMC Bioinformatics 14(1):335

    Article  PubMed  PubMed Central  Google Scholar 

  113. Zhu M, Gribskov M (2019) MiPepid: MicroPeptide identification tool using machine learning. BMC Bioinformatics 20(1):559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Tong X, Hong X, Xie J, Liu S (2020) CPPred-sORF: coding potential prediction of sORF based on non-AUG. bioRxiv. https://doi.org/10.1101/2020.03.31.017525

  115. Zhao S, Meng J, Luan Y (2022) LncRNA-encoded short peptides identification using feature subset recombination and ensemble learning. Interdiscip Sci 14(1):101–112

    Article  CAS  PubMed  Google Scholar 

  116. Zhang Y, Jia C, Fullwood MJ, Kwoh CK (2021) DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction. Brief Bioinform 22(2):2073–2084

    Article  CAS  PubMed  Google Scholar 

  117. Kersten RD, Yang Y, Xu Y, Cimermancic P, Nam S-J, Fenical W et al (2011) A mass spectrometry-guided genome mining approach for natural product peptidogenomics. Nat Chem Biol 7(11):794–802

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Cao X, Slavoff SA (2020) Non-AUG start codons: expanding and regulating the small and alternative ORFeome. Exp Cell Res 391(1):111973

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Na CH, Barbhuiya MA, Kim MS, Verbruggen S, Eacker SM, Pletnikova O et al (2018) Discovery of noncanonical translation initiation sites through mass spectrometric analysis of protein N termini. Genome Res 28(1):25–36

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Li YR, Liu MJ (2020) Prevalence of alternative AUG and non-AUG translation initiators and their regulatory effects across plants. Genome Res 30(10):1418–1433

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S et al (2022) The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res 50(D1):D543–D552

    Article  CAS  PubMed  Google Scholar 

  122. Patel N, Mohd-Radzman NA, Corcilius L, Crossett B, Connolly A, Cordwell SJ et al (2018) Diverse peptide hormones affecting root growth identified in the Medicago truncatula secreted peptidome. Mol Cell Proteomics 17(1):160–174

    Article  CAS  PubMed  Google Scholar 

  123. Chen YL, Lee CY, Cheng KT, Chang WH, Huang RN, Nam HG et al (2014) Quantitative peptidomics study reveals that a wound-induced peptide from PR-1 regulates immune signaling in tomato. Plant Cell 26(10):4135–4148

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Das D, Jaiswal M, Khan FN, Ahamad S, Kumar S (2020) PlantPepDB: a manually curated plant peptide database. Sci Rep 10(1):2194

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Szcześniak MW, Bryzghalov O, Ciomborowska-Basheer J, Makałowska I (2019) CANTATAdb 2.0: expanding the collection of plant long noncoding RNAs. In: Chekanova JA, Wang HLV (eds) Plant long non-coding RNAs, Methods in molecular biology, vol 1933. Humana Press, New York, pp 415–429

    Chapter  Google Scholar 

  126. Singh A, Vivek AT, Kumar S (2021) AlnC: an extensive database of long non-coding RNAs in angiosperms. PLoS One 16(4):e0247215

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Niu R, Zhou Y, Zhang Y, Mou R, Tang Z, Wang Z et al (2020) uORFlight: a vehicle toward uORF-mediated translational regulation mechanisms in eukaryotes. Database 2020:baaa007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Niarchou A, Alexandridou A, Athanasiadis E, Spyrou G (2013) C-PAmP: large scale analysis and database construction containing high scoring computationally predicted antimicrobial peptides for all the available plant species. PLoS One 8(11):e79728

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Wang J, Yin T, Xiao X, He D, Xue Z, Jiang X et al (2018) StraPep: a structure database of bioactive peptides. Database 2018:bay038

    Article  PubMed  PubMed Central  Google Scholar 

  130. Shi G, Kang X, Dong F, Liu Y, Zhu N, Hu Y et al (2022) DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides. Nucleic Acids Res 50(D1):D488–D496

    Article  CAS  PubMed  Google Scholar 

  131. Boschiero C, Dai X, Lundquist PK, Roy S, de Bang TC, Zhang S et al (2020) MtSSPdb: the Medicago truncatula small secreted peptide database. Plant Physiol 183(1):399–413

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Lin X, Lin W, Ku YS, Wong FL, Li MW, Lam HM et al (2020) Analysis of soybean long non-coding RNAs reveals a subset of small peptide-coding transcripts. Plant Physiol 182(3):1359–1374

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

Our work on peptidomics was supported by grant BFU2014-58289-P (funded by MICIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”) and by grant 2017SGR718 (from the Agencia de Gestió d’Ajuts Universitaris I de Recerca) to JLR, and by institutional grant SEV-2015-0533 (funded by MCIN/AEI/10.13039/501100011033) and by the CERCA Programme/Generalitat de Catalunya. R.A. is supported by fellowship PRE2018-084278 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future.” The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech). We also acknowledge “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” (2017SGR595) and support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Programme/Generalitat de Catalunya.

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Authors and Affiliations

  1. Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, Cerdanyola del Vallès, Barcelona, Spain

    Raquel Álvarez-Urdiola, José Tomás Matus & José Luis Riechmann

  2. Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain

    Eva Borràs & Eduard Sabidó

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Eva Borràs & Eduard Sabidó

  4. Institute for Plant Biochemistry and Photosynthesis CSIC – University of Seville, Seville, Spain

    Federico Valverde

  5. Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, Valencia, Spain

    José Tomás Matus

  6. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    José Luis Riechmann

Authors
  1. Raquel Álvarez-Urdiola
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  2. Eva Borràs
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  3. Federico Valverde
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  4. José Tomás Matus
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  5. Eduard Sabidó
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  6. José Luis Riechmann
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Corresponding author

Correspondence to José Luis Riechmann .

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Editors and Affiliations

  1. Centre for Research in Agricultural Genomics CSIC-IRTA-UAB-UB, Barcelona, Spain

    José Luis Riechmann

  2. Instituto de Biología Molecular y Celular de Plantas CSIC-UPV, Valencia, Spain

    Cristina Ferrándiz

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Álvarez-Urdiola, R., Borràs, E., Valverde, F., Matus, J.T., Sabidó, E., Riechmann, J.L. (2023). Peptidomics Methods Applied to the Study of Flower Development. In: Riechmann, J.L., Ferrándiz, C. (eds) Flower Development . Methods in Molecular Biology, vol 2686. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3299-4_24

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  • DOI: https://doi.org/10.1007/978-1-0716-3299-4_24

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