Skip to main content

Advertisement

SpringerLink
Log in

Application of Proteomics Technologies in Oil Palm Research

  • Published:
The Protein Journal Aims and scope Submit manuscript

Abstract

Proteomics technologies were first applied in the oil palm research back in 2008. Since proteins are the gene products that are directly correspond to phenotypic traits, proteomic tools hold a strong advantage above other molecular tools to comprehend the biological and molecular mechanisms in the oil palm system. These emerging technologies have been used as non-overlapping tools to link genome-wide transcriptomics and metabolomics-based studies to enhance the oil palm yield and quality through sustainable plant breeding. Many efforts have also been made using the proteomics technologies to address the oil palm’s Ganoderma disease; the cause and management. At present, the high-throughput screening technologies are being applied to identify potential biomarkers involved in metabolism and cellular development through determination of protein expression changes that correlate with oil production and disease. This review highlights key elements in proteomics pipeline, challenges and some examples of their implementations in plant studies in the context of oil palm in particular. We foresee that the proteomics technologies will play more significant role to address diverse issues related to the oil palm in the effort to improve the oil crop.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Woittiez LS, van Wijk MT, Slingerland M, van Noordwijk M, Giller KE (2017) Yield gaps in oil palm: a quantitative review of contributing factors. Eur J Agron 83(Supplement C):57–77. https://doi.org/10.1016/j.eja.2016.11.002

    Article  Google Scholar 

  2. Kushairi A, Singh R, Ong-Abdullah M (2017) The oil palm industry in Malaysia: thriving with transformative technologies. J Oil Palm Res 29(4):431–439. https://doi.org/10.21894/jopr.2017.00017

    Article  Google Scholar 

  3. Low E-T, Alias H, Boon S-H, Shariff E, Tan C-Y, Ooi L, Cheah S-C, Raha A-R, Wan K-L, Singh R (2008) Oil palm (Elaeis guineensis Jacq.) tissue culture ESTs: identifying genes associated with callogenesis and embryogenesis. BMC Plant Biol 8(1):62

    Article  PubMed  PubMed Central  Google Scholar 

  4. Low E-TL, Rosli R, Jayanthi N, Mohd-Amin AH, Azizi N, Chan K-L, Maqbool NJ, Maclean P, Brauning R, McCulloch A, Moraga R, Ong-Abdullah M, Singh R (2014) Analyses of hypomethylated oil palm gene space. PLoS ONE 9(1):e86728. https://doi.org/10.1371/journal.pone.0086728

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Singh R, Low ET, Ooi LC, Ong-Abdullah M, Ting NC, Nagappan J, Nookiah R, Amiruddin MD, Rosli R, Manaf MA, Chan KL, Halim MA, Azizi N, Lakey N, Smith SW, Budiman MA, Hogan M, Bacher B, Van Brunt A, Wang C, Ordway JM, Sambanthamurthi R, Martienssen RA (2013) The oil palm SHELL gene controls oil yield and encodes a homologue of SEEDSTICK. Nature 500(7462):340–344. https://doi.org/10.1038/nature12356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Singh R, Low E-TL, Ooi LC-L, Ong-Abdullah M, Nookiah R, Ting N-C, Marjuni M, Chan P-L, Ithnin M, Manaf MAA, Nagappan J, Chan K-L, Rosli R, Halim MA, Azizi N, Budiman MA, Lakey N, Bacher B, Van Brunt A, Wang C, Hogan M, He D, MacDonald JD, Smith SW, Ordway JM, Martienssen RA, Sambanthamurthi R (2014) The oil palm VIRESCENS gene controls fruit colour and encodes a R2R3-MYB. Nat Commun 5:4106. https://doi.org/10.1038/ncomms5106

    Article  CAS  PubMed  Google Scholar 

  7. Singh R, Ong-Abdullah M, Low ET, Manaf MA, Rosli R, Nookiah R, Ooi LC, Ooi SE, Chan KL, Halim MA, Azizi N, Nagappan J, Bacher B, Lakey N, Smith SW, He D, Hogan M, Budiman MA, Lee EK, DeSalle R, Kudrna D, Goicoechea JL, Wing RA, Wilson RK, Fulton RS, Ordway JM, Martienssen RA, Sambanthamurthi R (2013) Oil palm genome sequence reveals divergence of interfertile species in Old and New worlds. Nature 500(7462):335–339. https://doi.org/10.1038/nature12309

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Singh R, Tan SG, Panandam JM, Rahman RA, Ooi LCL, Low E-TL, Sharma M, Jansen J, Cheah S-C (2009) Mapping quantitative trait loci (QTLs) for fatty acid composition in an interspecific cross of oil palm. BMC Plant Biol 9:114–114. https://doi.org/10.1186/1471-2229-9-114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ting N-C, Yaakub Z, Kamaruddin K, Mayes S, Massawe F, Sambanthamurthi R, Jansen J, Low LET, Ithnin M, Kushairi A, Arulandoo X, Rosli R, Chan K-L, Amiruddin N, Sritharan K, Lim CC, Nookiah R, Amiruddin MD, Singh R (2016) Fine-mapping and cross-validation of QTLs linked to fatty acid composition in multiple independent interspecific crosses of oil palm. BMC Genomics 17:289. https://doi.org/10.1186/s12864-016-2607-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ting N-C, Zaki N, Rosli R, Low E-T, Ithnin M, Cheah S-C, Tan S-G, Singh R (2010) SSR mining in oil palm EST database: application in oil palm germplasm diversity studies. J Genet 89(2):135–145. https://doi.org/10.1007/s12041-010-0053-7

    Article  PubMed  Google Scholar 

  11. Lau BY, Deb-Choudhury S, Morton JD, Clerens S, Dyer JM, Ramli US (2015) Method developments to extract proteins from oil palm chromoplast for proteomic analysis. SpringerPlus 4(1):791. https://doi.org/10.1186/s40064-015-1576-4

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hassan H, Lau B, Ramli U (2014) Extraction methods for analysis of oil palm leaf and root proteins by two-dimensional gel electrophoresis. J Oil Palm Res 26:54–61

    CAS  Google Scholar 

  13. Daim LD, Ooi TE, Yusof HM, Majid NA, Karsani SA (2015) Optimization of protein extraction and two-dimensional electrophoresis protocols for oil palm leaf. Protein J 34(4):304–312. https://doi.org/10.1007/s10930-015-9626-x

    Article  CAS  PubMed  Google Scholar 

  14. Ooi T, Yeap W, Daim L, Ng B, Lee F, Othman A, Appleton D, Chew F, Kulaveerasingam H (2015) Differential abundance analysis of mesocarp protein from high- and low-yielding oil palms associates non-oil biosynthetic enzymes to lipid biosynthesis. Proteome Sci 13(1):28

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lau BYC, Morton DJ, Deb-Choudhury S, Clerens S, Dyer JM, Ramli US (2017) Differential expression analysis of oil palm fatty acid biosynthetic enzymes with gel-free quantitative proteomics. J Oil Palm Res 29(1):23–34

    Article  Google Scholar 

  16. Loei H, Lim J, Tan M, Lim TK, Lin QS, Chew FT, Kulaveerasingam H, Chung MC (2013) Proteomic analysis of the oil palm fruit mesocarp reveals elevated oxidative phosphorylation activity is critical for increased storage oil production. J Proteome Res 12(11):5096–5109. https://doi.org/10.1021/pr400606h

    Article  CAS  PubMed  Google Scholar 

  17. Lau BY, Clerens S, Morton JD, Dyer JM, Deb-Choudhury S, Ramli US (2016) Application of a mass spectrometric approach to detect the presence of fatty acid biosynthetic phosphopeptides. Protein J 35(2):163–170. https://doi.org/10.1007/s10930-016-9655-0

    Article  CAS  PubMed  Google Scholar 

  18. de Carvalho Silva R, Carmo LS, Luis ZG, Silva LP, Scherwinski-Pereira JE, Mehta A (2014) Proteomic identification of differentially expressed proteins during the acquisition of somatic embryogenesis in oil palm (Elaeis guineensis Jacq.). J Proteomics 104(0):112–127. https://doi.org/10.1016/j.jprot.2014.03.013

    Article  CAS  Google Scholar 

  19. Tan HS, Jacoby RP, Ong-Abdullah M, Taylor NL, Liddell S, Chee WW, Chin CF (2017) Proteomic profiling of mature leaves from oil palm (Elaeis guineensis Jacq.). Electrophoresis 38(8):1147–1153. https://doi.org/10.1002/elps.201600506

    Article  CAS  PubMed  Google Scholar 

  20. Al-Obaidi JR, Saidi NB, Usuldin SR, Hussin SN, Yusoff NM, Idris AS (2016) Comparison of different protein extraction methods for gel-based proteomic analysis of Ganoderma spp. Protein J 35(2):100–106. https://doi.org/10.1007/s10930-016-9656-z

    Article  CAS  PubMed  Google Scholar 

  21. Al-Obaidi JR, Mohd-Yusuf Y, Razali N, Jayapalan JJ, Tey CC, Md-Noh N, Junit SM, Othman RY, Hashim OH (2014) Identification of proteins of altered abundance in oil palm infected with Ganoderma boninense. Int J Mol Sci 15(3):5175–5192. https://doi.org/10.3390/ijms15035175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Syahanim S, Abrizah O, Mohamad Arif AM, Idris AS, Mohd Din A (2013) Identification of differentially expressed proteins in oil palm seedlings artificially infected with Ganoderma: a proteomics approach. J Oil Palm Res 25(3):298–304

    CAS  Google Scholar 

  23. Al-Obaidi JR, Hussin SNIS, Saidi NB, Rahmad N, Idris AS (2017) Comparative proteomic analysis of Ganoderma species during in vitro interaction with oil palm root. Physiol Mol Plant Pathol 99(Supplement C):16–24. https://doi.org/10.1016/j.pmpp.2017.02.001

    Article  Google Scholar 

  24. Daim LDJ, Ooi TE, Ithnin N, Mohd Yusof H, Kulaveerasingam H, Abdul Majid N, Karsani SA (2015) Comparative proteomic analysis of oil palm leaves infected with Ganoderma boninense revealed changes in proteins involved in photosynthesis, carbohydrate metabolism, and immunity and defense. Electrophoresis 36(15):1699–1710. https://doi.org/10.1002/elps.201400608

    Article  CAS  Google Scholar 

  25. Carpentier SC, Panis B, Vertommen A, Swennen R, Sergeant K, Renaut J, Laukens K, Witters E, Samyn B, Devreese B (2008) Proteome analysis of non-model plants: a challenging but powerful approach. Mass Spectrom Rev 27(4):354–377. https://doi.org/10.1002/mas.20170

    Article  CAS  PubMed  Google Scholar 

  26. Gooding PS, Bird C, Robinson SP (2001) Molecular cloning and characterisation of banana fruit polyphenol oxidase. Planta 213(5):748–757. https://doi.org/10.1007/s004250100553

    Article  CAS  PubMed  Google Scholar 

  27. Wuyts N, De Waele D, Swennen R (2006) Extraction and partial characterization of polyphenol oxidase from banana (Musa acuminata Grande naine) roots. Plant Physiol Biochem 44(5–6):308–314. https://doi.org/10.1016/j.plaphy.2006.06.005

    Article  CAS  PubMed  Google Scholar 

  28. Amalraj RS, Selvaraj N, Veluswamy GK, Ramanujan RP, Muthurajan R, Palaniyandi M, Agrawal GK, Rakwal R, Viswanathan R (2010) Sugarcane proteomics: establishment of a protein extraction method for 2-DE in stalk tissues and initiation of sugarcane proteome reference map. Electrophoresis 31(12):1959–1974. https://doi.org/10.1002/elps.200900779

    Article  CAS  PubMed  Google Scholar 

  29. McCabe MS, Garratt LC, Schepers F, Jordi WJRM, Stoopen GM, Davelaar E, van Rhijn JHA, Power JB, Davey MR (2001) Effects of PSAG12-IPT gene expression on development and senescence in transgenic lettuce. Plant Physiol 127(2):505–516. https://doi.org/10.1104/pp.010244

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jensen ON (2004) Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr Opin Chem Biol 8(1):33–41. https://doi.org/10.1016/j.cbpa.2003.12.009

    Article  CAS  PubMed  Google Scholar 

  31. Corthals GL, Wasinger VC, Hochstrasser DF, Sanchez JC (2000) The dynamic range of protein expression: a challenge for proteomic research. Electrophoresis 21 (6):1104–1115. https://doi.org/10.1002/(SICI)1522-2683(20000401)21:6%3C1104::AID-ELPS1104%3E3.0.CO;2-C

    Article  CAS  PubMed  Google Scholar 

  32. van Wijk KJ, Baginsky S (2011) Plastid proteomics in higher plants: current state and future goals. Plant Physiol 155(4):1578–1588. https://doi.org/10.1104/pp.111.172932

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hey J, Posch A, Cohen A, Liu N, Harbers A (2008) Fractionation of complex protein mixtures by liquid-phase isoelectric focusing. In: Posch A (ed) 2D PAGE: sample preparation and fractionation, vol 424. Methods in Molecular Biology™. Humana Press, Totowa, pp 225–239. https://doi.org/10.1007/978-1-60327-064-9_19

    Google Scholar 

  34. Horth P, Miller CA, Preckel T, Wenz C (2006) Efficient fractionation and improved protein identification by peptide OFFGEL electrophoresis. Mol Cell Proteomics 5(10):1968–1974. https://doi.org/10.1074/mcp.T600037-MCP200

    Article  CAS  PubMed  Google Scholar 

  35. Bayer RG, Stael S, Csaszar E, Teige M (2011) Mining the soluble chloroplast proteome by affinity chromatography. Proteomics 11(7):1287–1299. https://doi.org/10.1002/pmic.201000495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Mamone G, Picariello G, Ferranti P, Addeo F (2010) Hydroxyapatite affinity chromatography for the highly selective enrichment of mono- and multi-phosphorylated peptides in phosphoproteome analysis. Proteomics 10(3):380–393. https://doi.org/10.1002/pmic.200800710

    Article  CAS  PubMed  Google Scholar 

  37. Aryal UK, Krochko JE, Ross ARS (2011) Identification of phosphoproteins in Arabidopsis thaliana leaves using polyethylene glycol fractionation, immobilized metal-ion affinity chromatography, two-dimensional gel electrophoresis and mass spectrometry. J Proteome Res 11(1):425–437. https://doi.org/10.1021/pr200917t

    Article  CAS  PubMed  Google Scholar 

  38. Tan HS, Liddell S, Ong Abdullah M, Wong WC, Chin CF (2016) Differential proteomic analysis of embryogenic lines in oil palm (Elaeis guineensis Jacq). J Proteomics 143:334–345. https://doi.org/10.1016/j.jprot.2016.04.039

    Article  CAS  PubMed  Google Scholar 

  39. Schulze WX, Usadel B (2010) Quantitation in mass-spectrometry-based proteomics. Annu Rev Plant Biol 61(1):491–516. https://doi.org/10.1146/annurev-arplant-042809-112132

    Article  CAS  PubMed  Google Scholar 

  40. Remmerie N, De Vijlder T, Laukens K, Dang TH, Lemière F, Mertens I, Valkenborg D, Blust R, Witters E (2011) Next generation functional proteomics in non-model plants: a survey on techniques and applications for the analysis of protein complexes and post-translational modifications. Phytochemistry 72(10):1192–1218. https://doi.org/10.1016/j.phytochem.2011.01.003

    Article  CAS  PubMed  Google Scholar 

  41. Timms JF, Cramer R (2008) Difference gel electrophoresis. Proteomics 8(23–24):4886–4897. https://doi.org/10.1002/pmic.200800298

    Article  CAS  PubMed  Google Scholar 

  42. Unlu M, Morgan ME, Minden JS (1997) Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis 18(11):2071–2077. https://doi.org/10.1002/elps.1150181133

    Article  CAS  PubMed  Google Scholar 

  43. Bindschedler LV, Cramer R (2011) Quantitative plant proteomics. Proteomics 11(4):756–775. https://doi.org/10.1002/pmic.201000426

    Article  CAS  PubMed  Google Scholar 

  44. Gerber IB, Laukens K, De Vijlder T, Witters E, Dubery IA (2008) Proteomic profiling of cellular targets of lipopolysaccharide-induced signalling in Nicotiana tabacum BY-2 cells. Biochim Biophys Acta 1784(11):1750–1762. https://doi.org/10.1016/j.bbapap.2008.06.012

    Article  CAS  PubMed  Google Scholar 

  45. Chivasa S, Hamilton JM, Pringle RS, Ndimba BK, Simon WJ, Lindsey K, Slabas AR (2006) Proteomic analysis of differentially expressed proteins in fungal elicitor-treated Arabidopsis cell cultures. J Exp Bot 57(7):1553–1562. https://doi.org/10.1093/jxb/erj149

    Article  CAS  PubMed  Google Scholar 

  46. Amey RC, Schleicher T, Slinn J, Lewis M, Macdonald H, Neill SJ, Spencer-Phillips PTN (2008) Proteomic analysis of a compatible interaction between Pisum sativum (pea) and the downy mildew pathogen Peronospora viciae. In: Lebeda A, Spencer-Phillips PN, Cooke BM (eds) The downy mildews—genetics, molecular biology and control. Springer, The Netherlands, pp 41–55. https://doi.org/10.1007/978-1-4020-8973-2_5

    Chapter  Google Scholar 

  47. Schenkluhn L, Hohnjec N, Niehaus K, Schmitz U, Colditz F (2010) Differential gel electrophoresis (DIGE) to quantitatively monitor early symbiosis- and pathogenesis-induced changes of the Medicago truncatula root proteome. J Proteomics 73(4):753–768. https://doi.org/10.1016/j.jprot.2009.10.009

    Article  CAS  PubMed  Google Scholar 

  48. Renaut J, Hausman J-F, Wisniewski ME (2006) Proteomics and low-temperature studies: bridging the gap between gene expression and metabolism. Physiol Plant 126(1):97–109. https://doi.org/10.1111/j.1399-3054.2006.00617.x

    Article  CAS  Google Scholar 

  49. Casati P, Zhang X, Burlingame AL, Walbot V (2005) Analysis of leaf proteome after UV-B irradiation in maize lines giffering in sensitivity. Mol Cell Proteomics 4(11):1673–1685. https://doi.org/10.1074/mcp.M500173-MCP200

    Article  CAS  PubMed  Google Scholar 

  50. Zhou S, Sauvé R, Thannhauser TW (2009) Proteome changes induced by aluminium stress in tomato roots. J Exp Bot 60(6):1849–1857. https://doi.org/10.1093/jxb/erp065

    Article  PubMed  Google Scholar 

  51. Gomez A, Lopez JA, Pintos B, Camafeita E, Bueno MA (2009) Proteomic analysis from haploid and diploid embryos of Quercus suber L. identifies qualitative and quantitative differential expression patterns. Proteomics 9(18):4355–4367. https://doi.org/10.1002/pmic.200900179

    Article  CAS  PubMed  Google Scholar 

  52. Gerber IB, Laukens K, Witters E, Dubery IA (2006) Lipopolysaccharide-responsive phosphoproteins in Nicotiana tabacum cells. Plant Physiol Biochem 44(5–6):369–379. https://doi.org/10.1016/j.plaphy.2006.06.015

    Article  CAS  PubMed  Google Scholar 

  53. Barsan C, Sanchez-Bel P, Rombaldi C, Egea I, Rossignol M, Kuntz M, Zouine M, Latché A, Bouzayen M, Pech J-C (2010) Characteristics of the tomato chromoplast revealed by proteomic analysis. J Exp Bot 61(9):2413–2431. https://doi.org/10.1093/jxb/erq070

    Article  CAS  PubMed  Google Scholar 

  54. Agrawal G, Thelen J (2009) A high-resolution two dimensional gel- and Pro-Q DPS-based proteomics workflow for phosphoprotein identification and quantitative profiling. In: Graauw M (ed) Phospho-proteomics, vol 527. Methods in Molecular Biology™. Humana Press, Totowa, pp 3–19. https://doi.org/10.1007/978-1-60327-834-8_1

    Chapter  Google Scholar 

  55. Agrawal GK, Thelen JJ (2006) Large scale identification and quantitative profiling of phosphoproteins expressed during seed filling in oilseed rape. Mol Cell Proteomics 5(11):2044–2059. https://doi.org/10.1074/mcp.M600084-MCP200

    Article  CAS  PubMed  Google Scholar 

  56. Chitteti BR, Peng Z (2007) Proteome and phosphoproteome dynamic change during cell dedifferentiation in Arabidopsis. Proteomics 7(9):1473–1500. https://doi.org/10.1002/pmic.200600871

    Article  CAS  PubMed  Google Scholar 

  57. Steinberg TH, Agnew BJ, Gee KR, Leung WY, Goodman T, Schulenberg B, Hendrickson J, Beechem JM, Haugland RP, Patton WF (2003) Global quantitative phosphoprotein analysis using multiplexed proteomics technology. Proteomics 3(7):1128–1144. https://doi.org/10.1002/pmic.200300434

    Article  CAS  PubMed  Google Scholar 

  58. Niittylä T, Fuglsang AT, Palmgren MG, Frommer WB, Schulze WX (2007) Temporal analysis of sucrose-induced phosphorylation changes in plasma membrane proteins of Arabidopsis. Mol Cell Proteomics 6(10):1711–1726. https://doi.org/10.1074/mcp.M700164-MCP200

    Article  CAS  PubMed  Google Scholar 

  59. Stulemeijer IJE, Joosten MHAJ, Jensen ON (2009) Quantitative phosphoproteomics of tomato mounting a hypersensitive response reveals a swift suppression of photosynthetic activity and a differential role for Hsp90 isoforms. J Proteome Res 8(3):1168–1182. https://doi.org/10.1021/pr800619h

    Article  CAS  PubMed  Google Scholar 

  60. Kito K, Ito T (2008) Mass spectrometry-based approaches toward absolute quantitative proteomics. Curr Genomics 9(4):263–274. https://doi.org/10.2174/138920208784533647

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Chahrour O, Cobice D, Malone J (2015) Stable isotope labelling methods in mass spectrometry-based quantitative proteomics. J Pharm Biomed Anal 113:2–20. https://doi.org/10.1016/j.jpba.2015.04.013

    Article  CAS  PubMed  Google Scholar 

  62. Kieffer P, Dommes J, Hoffmann L, Hausman JF, Renaut J (2008) Quantitative changes in protein expression of cadmium-exposed poplar plants. Proteomics 8(12):2514–2530. https://doi.org/10.1002/pmic.200701110

    Article  CAS  PubMed  Google Scholar 

  63. Zhang X, Asara JM, Adamec J, Ouzzani M, Elmagarmid AK (2005) Data pre-processing in liquid chromatography–mass spectrometry-based proteomics. Bioinformatics 21(21):4054–4059. https://doi.org/10.1093/bioinformatics/bti660

    Article  CAS  PubMed  Google Scholar 

  64. Palmblad M, Mills DJ, Bindschedler LV, Cramer R (2007) Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction. J Am Soc Mass Spectrom 18(10):1835–1843. https://doi.org/10.1016/j.jasms.2007.07.018

    Article  CAS  PubMed  Google Scholar 

  65. Reiland S, Messerli G, Baerenfaller K, Gerrits B, Endler A, Grossmann J, Gruissem W, Baginsky S (2009) Large-scale Arabidopsis phosphoproteome profiling teveals novel vhloroplastkinase dubstrates and phosphorylation networks. Plant Physiol 150(2):889–903. https://doi.org/10.1104/pp.109.138677

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lilley KS, Dupree P (2007) Plant organelle proteomics. Curr Opin Plant Biol 10(6):594–599. https://doi.org/10.1016/j.pbi.2007.08.006

    Article  CAS  PubMed  Google Scholar 

  67. Abdallah C, Dumas-Gaudot E, Renaut J, Sergeant K (2012) Gel-based and gel-free quantitative proteomics approaches at a glance. Int J Plant Genomics 2012:494572. https://doi.org/10.1155/2012/494572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky JR, Resing KA, Ahn NG (2005) Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteomics 4(10):1487–1502. https://doi.org/10.1074/mcp.M500084-MCP200

    Article  CAS  PubMed  Google Scholar 

  69. Friso G, Majeran W, Huang M, Sun Q, van Wijk KJ (2010) Reconstruction of metabolic pathways, protein expression, and homeostasis machineries across maize bundle sheath and mesophyll chloroplasts: large-scale quantitative proteomics using the first maize genome assembly. Plant Physiol 152(3):1219–1250. https://doi.org/10.1104/pp.109.152694

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Gammulla CG, Pascovici D, Atwell BJ, Haynes PA (2010) Differential metabolic response of cultured rice (Oryza sativa) cells exposed to high- and low-temperature stress. Proteomics 10(16):3001–3019. https://doi.org/10.1002/pmic.201000054

    Article  CAS  PubMed  Google Scholar 

  71. Lee J, Feng J, Campbell KB, Scheffler BE, Garrett WM, Thibivilliers S, Stacey G, Naiman DQ, Tucker ML, Pastor-Corrales MA, Cooper B (2009) Quantitative proteomic analysis of bean plants infected by a virulent and avirulent obligate rust fungus. Mol Cell Proteomics 8(1):19–31. https://doi.org/10.1074/mcp.M800156-MCP200

    Article  CAS  PubMed  Google Scholar 

  72. Zybailov B, Friso G, Kim J, Rudella A, Rodríguez VR, Asakura Y, Sun Q, van Wijk KJ (2009) Large scale comparative proteomics of a chloroplast Clp protease mutant reveals folding stress, altered protein homeostasis, and feedback regulation of metabolism. Mol Cell Proteomics 8(8):1789–1810. https://doi.org/10.1074/mcp.M900104-MCP200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Benschop JJ, Mohammed S, O’Flaherty M, Heck AJR, Slijper M, Menke FLH (2007) Quantitative phosphoproteomics of early elicitor signaling in Arabidopsis. Mol Cell Proteomics 6(7):1198–1214. https://doi.org/10.1074/mcp.M600429-MCP200

    Article  CAS  PubMed  Google Scholar 

  74. Jones AME, Bennett MH, Mansfield JW, Grant M (2006) Analysis of the defence phosphoproteome of Arabidopsis thaliana using differential mass tagging. Proteomics 6(14):4155–4165. https://doi.org/10.1002/pmic.200500172

    Article  CAS  PubMed  Google Scholar 

  75. Nuhse TS, Bottrill AR, Jones AM, Peck SC (2007) Quantitative phosphoproteomic analysis of plasma membrane proteins reveals regulatory mechanisms of plant innate immune responses. Plant J 51(5):931–940. https://doi.org/10.1111/j.1365-313X.2007.03192.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17(10):994–999. https://doi.org/10.1038/13690

    Article  CAS  PubMed  Google Scholar 

  77. Majeran W, Cai Y, Sun Q, van Wijk KJ (2005) Functional differentiation of bundle sheath and mesophyll maize chloroplasts determined by comparative proteomics. Plant Cell 17(11):3111–3140. https://doi.org/10.1105/tpc.105.035519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Dunkley TPJ, Dupree P, Watson RB, Lilley KS (2004) The use of isotope-coded affinity tags (ICAT) to study organelle proteomes in Arabidopsis thaliana. Biochem Soc Trans 32:520–523

    Article  CAS  PubMed  Google Scholar 

  79. Hagglund P, Bunkenborg J, Yang F, Harder LM, Finnie C, Svensson B (2010) Identification of thioredoxin target disulfides in proteins released from barley aleurone layers. J Proteomics 73(6):1133–1136. https://doi.org/10.1016/j.jprot.2010.01.007

    Article  CAS  PubMed  Google Scholar 

  80. Miles GP, Samuel MA, Ranish JA, Donohoe SM, Sperrazzo GM, Ellis BE (2009) Quantitative proteomics identifies oxidant-induced, AtMPK6-dependent changes in Arabidopsis thaliana protein profiles. Plant Signal Behav 4(6):497–505. https://doi.org/10.4161/psb.4.6.8538

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Ow SY, Salim M, Noirel J, Evans C, Rehman I, Wright PC (2009) iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J Proteome Res 8(11):5347–5355. https://doi.org/10.1021/pr900634c

    Article  CAS  PubMed  Google Scholar 

  82. Perkel JM (2009) iTRAQ gets put to the test. J Proteome Res 8(11):4885–4885. https://doi.org/10.1021/pr900897d

    Article  CAS  PubMed  Google Scholar 

  83. Zhang L, Elias JE (2017) Relative protein quantification using tandem mass tag mass spectrometry. In: Comai L, Katz JE, Mallick P (eds) Proteomics: methods and protocols. Springer, New York, pp 185–198. https://doi.org/10.1007/978-1-4939-6747-6_14

    Chapter  Google Scholar 

  84. Melo-Braga MN, Verano-Braga T, Leon IR, Antonacci D, Nogueira FC, Thelen JJ, Larsen MR, Palmisano G (2012) Modulation of protein phosphorylation, N-glycosylation and Lys-acetylation in grape (Vitis vinifera) mesocarp and exocarp owing to Lobesia botrana infection. Mol Cell Proteomics 11(10):945–956. https://doi.org/10.1074/mcp.M112.020214

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Marsh E, Alvarez S, Hicks LM, Barbazuk WB, Qiu W, Kovacs L, Schachtman D (2010) Changes in protein abundance during powdery mildew infection of leaf tissues of Cabernet Sauvignon grapevine (Vitis vinifera L.). Proteomics 10(10):2057–2064. https://doi.org/10.1002/pmic.200900712

    Article  CAS  PubMed  Google Scholar 

  86. Fan J, Chen C, Yu Q, Brlansky RH, Li Z-G, Gmitter FG (2011) Comparative iTRAQ proteome and transcriptome analyses of sweet orange infected by “Candidatus Liberibacter asiaticus”. Physiol Plant 143(3):235–245. https://doi.org/10.1111/j.1399-3054.2011.01502.x

    Article  CAS  PubMed  Google Scholar 

  87. Mohammadi M, Anoop V, Gleddie S, Harris LJ (2011) Proteomic profiling of two maize inbreds during early gibberella ear rot infection. Proteomics 11(18):3675–3684. https://doi.org/10.1002/pmic.201100177

    Article  CAS  PubMed  Google Scholar 

  88. Kaffarnik FAR, Jones AME, Rathjen JP, Peck SC (2009) Effector proteins of the bacterial pathogen Pseudomonas syringae alter the extracellular proteome of the host plant, Arabidopsis thaliana. Mol Cell Proteomics 8(1):145–156. https://doi.org/10.1074/mcp.M800043-MCP200

    Article  CAS  PubMed  Google Scholar 

  89. Zhao Z, Stanley BA, Zhang W, Assmann SM (2010) ABA-regulated G protein signaling in Arabidopsis guard cells: a proteomic perspective. J Proteome Res 9(4):1637–1647. https://doi.org/10.1021/pr901011h

    Article  CAS  PubMed  Google Scholar 

  90. Lucker J, Laszczak M, Smith D, Lund S (2009) Generation of a predicted protein database from EST data and application to iTRAQ analyses in grape (Vitis vinifera cv. Cabernet Sauvignon) berries at ripening initiation. BMC Genomics 10(1):50

    Article  PubMed  PubMed Central  Google Scholar 

  91. Liu X, Dekker LJ, Wu S, Vanduijn MM, Luider TM, Tolic N, Kou Q, Dvorkin M, Alexandrova S, Vyatkina K, Pasa-Tolic L, Pevzner PA (2014) De novo protein sequencing by combining top-down and bottom-up tandem mass spectra. J Proteome Res 13(7):3241–3248. https://doi.org/10.1021/pr401300m

    Article  CAS  PubMed  Google Scholar 

  92. Kumaravel M, Uma S, Backiyarani S, Saraswathi MS, Vaganan MM, Muthusamy M, Sajith KP (2017) Differential proteome analysis during early somatic embryogenesis in Musa spp. AAA cv. Grand Naine. Plant Cell Rep 36(1):163–178. https://doi.org/10.1007/s00299-016-2067-y

    Article  CAS  PubMed  Google Scholar 

  93. Sghaier-Hammami B, Drira N, Jorrin-Novo JV (2009) Comparative 2-DE proteomic analysis of date palm (Phoenix dactylifera L.) somatic and zygotic embryos. J Proteomics 73(1):161–177. https://doi.org/10.1016/j.jprot.2009.07.003

    Article  CAS  PubMed  Google Scholar 

  94. Rahman MA, Ren L, Wu W, Yan Y (2015) Proteomic analysis of PEG-induced drought stress responsive protein in TERF1 overexpressed sugarcane (Saccharum officinarum) Leaves. Plant Mol Biol Rep 33(3):716–730. https://doi.org/10.1007/s11105-014-0784-3

    Article  CAS  Google Scholar 

  95. Chen X, Zhang W, Zhang B, Zhou J, Wang Y, Yang Q, Ke Y, He H (2011) Phosphoproteins regulated by heat stress in rice leaves. Proteome Sci 9(1):37. https://doi.org/10.1186/1477-5956-9-37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Luís IM, Alexandre BM, Oliveira MM, Abreu IA (2016) Selection of an appropriate protein extraction method to study the phosphoproteome of maize photosynthetic tissue. PLoS ONE 11(10):e0164387. https://doi.org/10.1371/journal.pone.0164387

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Lau BYC (2015) Proteomic profiling of fatty acid biosynthetic enzymes from oil palm chromoplast. Lincoln University, Lincoln

    Google Scholar 

  98. Cui D, Wu D, Liu J, Li D, Xu C, Li S, Li P, Zhang H, Liu X, Jiang C, Wang L, Chen T, Chen H, Zhao L (2015) Proteomic analysis of seedling roots of two maize inbred lines that differ significantly in the salt stress response. PLoS ONE 10(2):e0116697. https://doi.org/10.1371/journal.pone.0116697

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Ji W, Cong R, Li S, Li R, Qin Z, Li Y, Zhou X, Chen S, Li J (2016) Comparative proteomic analysis of soybean leaves and roots by iTRAQ provides insights into response mechanisms to short-term salt stress. Front Plant Sci 7:573. https://doi.org/10.3389/fpls.2016.00573

    Article  PubMed  PubMed Central  Google Scholar 

  100. Lin Q, Zhou Z, Luo W, Fang M, Li M, Li H (2017) Screening of proximal and interacting proteins in rice protoplasts by proximity-dependent biotinylation. Front Plant Sci 8:749. https://doi.org/10.3389/fpls.2017.00749

    Article  PubMed  PubMed Central  Google Scholar 

  101. Wang S, Chen W, Yang C, Yao J, Xiao W, Xin Y, Qiu J, Hu W, Yao H, Ying W, Fu Y, Tong J, Chen Z, Ruan S, Ma H (2016) Comparative proteomic analysis reveals alterations in development and photosynthesis-related proteins in diploid and triploid rice. BMC Plant Biol 16(1):199. https://doi.org/10.1186/s12870-016-0891-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Rattanakan S, George I, Haynes PA, Cramer GR (2016) Relative quantification of phosphoproteomic changes in grapevine (Vitis vinifera L.) leaves in response to abscisic acid. Hortic Res 3:16029. https://doi.org/10.1038/hortres.2016.29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Khan MN, Komatsu S (2016) Proteomic analysis of soybean root including hypocotyl during recovery from drought stress. J Proteomics 144:39–50. https://doi.org/10.1016/j.jprot.2016.06.006

    Article  CAS  PubMed  Google Scholar 

  104. Timabud T, Yin X, Pongdontri P, Komatsu S (2016) Gel-free/label-free proteomic analysis of developing rice grains under heat stress. J Proteomics 133:1–19. https://doi.org/10.1016/j.jprot.2015.12.003

    Article  CAS  PubMed  Google Scholar 

  105. Wu Y, Mirzaei M, Pascovici D, Chick JM, Atwell BJ, Haynes PA (2016) Quantitative proteomic analysis of two different rice varieties reveals that drought tolerance is correlated with reduced abundance of photosynthetic machinery and increased abundance of ClpD1 protease. J Proteomics 143:73–82. https://doi.org/10.1016/j.jprot.2016.05.014

    Article  CAS  PubMed  Google Scholar 

  106. Heringer AS, Reis RS, Passamani LZ, de Souza-Filho GA, Santa-Catarina C, Silveira V (2017) Comparative proteomics analysis of the effect of combined red and blue lights on sugarcane somatic embryogenesis. Acta Physiol Plant 39(2):52. https://doi.org/10.1007/s11738-017-2349-1

    Article  CAS  Google Scholar 

  107. Martinez M (2016) Computational tools for genomic studies in plants. Curr Genomics 17(6):509–514. https://doi.org/10.2174/1389202917666160520103447

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. International Brachypodium I (2010) Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature 463(7282):763–768. https://doi.org/10.1038/nature08747

    Article  CAS  Google Scholar 

  109. Rine J (2014) A future of the model organism model. Mol Biol Cell 25(5):549–553. https://doi.org/10.1091/mbc.E12-10-0768

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Uthaipaisanwong P, Chanprasert J, Shearman JR, Sangsrakru D, Yoocha T, Jomchai N, Jantasuriyarat C, Tragoonrung S, Tangphatsornruang S (2012) Characterization of the chloroplast genome sequence of oil palm (Elaeis guineensis Jacq.). Gene 500(2):172–180. https://doi.org/10.1016/j.gene.2012.03.061

    Article  CAS  PubMed  Google Scholar 

  111. Yang L, Luo Y, Wei J, Ren C, Zhou X, He S (2010) Methods for protein identification using expressed sequence tags and peptide mass fingerprinting for seed crops without complete genome sequences. Seed Sci Res 20(04):257–262. https://doi.org/10.1017/S0960258510000243

    Article  CAS  Google Scholar 

  112. Pedretti K, Scheetz T, Braun T, Roberts C, Robinson N, Casavant T (2001) A parallel expressed sequence tag (EST) clustering program. In: Malyshkin V (ed) Parallel computing technologies, vol 2127. Lecture notes in computer science. Springer, Berlin, pp 490–497. https://doi.org/10.1007/3-540-44743-1_51

    Google Scholar 

  113. Hoff KJ (2009) The effect of sequencing errors on metagenomic gene prediction. BMC Genomics 10(1):520. https://doi.org/10.1186/1471-2164-10-520

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Rowley A, Choudhary JS, Marzioch M, Ward MA, Weir M, Solari RCE, Blackstock WP (2000) Applications of protein mass spectrometry in cell biology. Methods 20(4):383–397. https://doi.org/10.1006/meth.2000.0951

    Article  CAS  PubMed  Google Scholar 

  115. Gabaldon T (2007) Evolution of proteins and proteomes: a phylogenetics approach. Evol Bioinform 1(1):51–61. https://doi.org/10.4137/ebo.s0

    Article  Google Scholar 

  116. Fitch WM (1970) Distinguishing homologous from analogous proteins. Syst Zool 19(2):99–113. https://doi.org/10.2307/2412448

    Article  CAS  PubMed  Google Scholar 

  117. Perkins DN, Pappin DJC, Creasy DM, Cottrell JS (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20 (18):3551–3567. https://doi.org/10.1002/(sici)1522-2683(19991201)20:18%3C3551::aid-elps3551%3E3.0.co;2-2

    Article  CAS  PubMed  Google Scholar 

  118. Peltier J-B, Friso G, Kalume DE, Roepstorff P, Nilsson F, Adamska I, van Wijk KJ (2000) Proteomics of the chloroplast: systematic identification and targeting analysis of lumenal and peripheral thylakoid proteins. Plant Cell 12(3):319–342. https://doi.org/10.1105/tpc.12.3.319

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Chang WWP, Huang L, Shen M, Webster C, Burlingame AL, Roberts JKM (2000) Patterns of protein synthesis and tolerance of anoxia in root tips of maize seedlings acclimated to a low-oxygen environment, and identification of proteins by mass spectrometry. Plant Physiol 122(2):295–318. https://doi.org/10.1104/pp.122.2.295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Medzihradszky KF, Chalkley RJ (2015) Lessons in de novo peptide sequencing by tandem mass spectrometry. Mass Spectrom Rev 34(1):43–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Shevchenko A, Sunyaev S, Loboda A, Bork P, Ens W, Standing KG (2001) Charting the proteomes of organisms with unsequenced genomes by MALDI-quadrupole time-of-flight mass spectrometry and BLAST homology searching. Anal Chem 73(9):1917–1926. https://doi.org/10.1021/ac0013709

    Article  CAS  PubMed  Google Scholar 

  122. Heazlewood JL, Verboom RE, Tonti-Filippini J, Small I, Millar AH (2007) SUBA: the Arabidopsis subcellular database. Nucleic Acids Res 35(Database issue):D213–D218. https://doi.org/10.1093/nar/gkl863

    Article  CAS  PubMed  Google Scholar 

  123. Hooper CM, Castleden IR, Tanz SK, Aryamanesh N, Millar AH (2017) SUBA4: the interactive data analysis centre for Arabidopsis subcellular protein locations. Nucleic Acids Res 45(D1):D1064–D1074. https://doi.org/10.1093/nar/gkw1041

    Article  CAS  PubMed  Google Scholar 

  124. Negi S, Pandey S, Srinivasan SM, Mohammed A, Guda C (2015) LocSigDB: a database of protein localization signals. Database. https://doi.org/10.1093/database/bav003

    Article  PubMed  PubMed Central  Google Scholar 

  125. Sun Q, Zybailov B, Majeran W, Friso G, Olinares PDB, van Wijk KJ (2009) PPDB, the plant proteomics database at Cornell. Nucleic Acids Res 37(suppl 1):D969–D974. https://doi.org/10.1093/nar/gkn654

    Article  CAS  PubMed  Google Scholar 

  126. Palagi PM, Walther D, Quadroni M, Catherinet S, Burgess J, Zimmermann-Ivol CG, Sanchez JC, Binz PA, Hochstrasser DF, Appel RD (2005) MSight: an image analysis software for liquid chromatography-mass spectrometry. Proteomics 5(9):2381–2384. https://doi.org/10.1002/pmic.200401244

    Article  CAS  PubMed  Google Scholar 

  127. Pluskal T, Castillo S, Villar-Briones A, Orešič M (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform 11(1):395. https://doi.org/10.1186/1471-2105-11-395

    Article  CAS  Google Scholar 

  128. Sturm M, Kohlbacher O (2009) TOPPView: an open-source viewer for mass spectrometry data. J Proteome Res 8(7):3760–3763. https://doi.org/10.1021/pr900171m

    Article  CAS  PubMed  Google Scholar 

  129. Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11:2301. https://doi.org/10.1038/nprot.2016.136

    Article  CAS  PubMed  Google Scholar 

  130. Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26(12):1367–1372. https://doi.org/10.1038/nbt.1511

    Article  CAS  PubMed  Google Scholar 

  131. Tyanova S, Cox J (2018) Perseus: a bioinformatics platform for integrative analysis of proteomics data in cancer research. In: von Stechow L (ed) Cancer systems biology: methods and protocols. Springer, New York, pp 133–148. https://doi.org/10.1007/978-1-4939-7493-1_7

    Chapter  Google Scholar 

  132. Wenig P, Odermatt J (2010) OpenChrom: a cross-platform open source software for the mass spectrometric analysis of chromatographic data. BMC Bioinform 11:405–405. https://doi.org/10.1186/1471-2105-11-405

    Article  CAS  Google Scholar 

  133. Muth T, Weilnböck L, Rapp E, Huber CG, Martens L, Vaudel M, Barsnes H (2014) DeNovoGUI: an open source graphical user interface for de novo sequencing of tandem mass spectra. J Proteome Res 13(2):1143–1146. https://doi.org/10.1021/pr4008078

    Article  CAS  PubMed  Google Scholar 

  134. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26(7):966–968. https://doi.org/10.1093/bioinformatics/btq054

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8(9):R183. https://doi.org/10.1186/gb-2007-8-9-r183

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C (2017) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 45(Database issue):D362–D368. https://doi.org/10.1093/nar/gkw937

    Article  CAS  PubMed  Google Scholar 

  137. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43(Database issue):D447–D452. https://doi.org/10.1093/nar/gku1003

    Article  CAS  PubMed  Google Scholar 

  138. Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B, Jupe S, Kalatskaya I, Mahajan S, May B, Ndegwa N, Schmidt E, Shamovsky V, Yung C, Birney E, Hermjakob H, D’Eustachio P, Stein L (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 39(Database issue):D691–D697. https://doi.org/10.1093/nar/gkq1018

    Article  CAS  PubMed  Google Scholar 

  139. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B, Milacic M, Roca CD, Rothfels K, Sevilla C, Shamovsky V, Shorser S, Varusai T, Viteri G, Weiser J, Wu G, Stein L, Hermjakob H, D’Eustachio P (2018) The reactome pathway knowledge base. Nucleic Acids Res 46(D1):D649–D655. https://doi.org/10.1093/nar/gkx1132

    Article  PubMed  Google Scholar 

  140. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27(1):29–34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Kutmon M, van Iersel MP, Bohler A, Kelder T, Nunes N, Pico AR, Evelo CT (2015) PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol 11(2):e1004085. https://doi.org/10.1371/journal.pcbi.1004085

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, Salazar GA, Tate J, Bateman A (2016) The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 44(D1):D279–D285. https://doi.org/10.1093/nar/gkv1344

    Article  CAS  PubMed  Google Scholar 

  145. Finn RD, Attwood TK, Babbitt PC, Bateman A, Bork P, Bridge AJ, Chang H-Y, Dosztányi Z, El-Gebali S, Fraser M, Gough J, Haft D, Holliday GL, Huang H, Huang X, Letunic I, Lopez R, Lu S, Marchler-Bauer A, Mi H, Mistry J, Natale DA, Necci M, Nuka G, Orengo CA, Park Y, Pesseat S, Piovesan D, Potter SC, Rawlings ND, Redaschi N, Richardson L, Rivoire C, Sangrador-Vegas A, Sigrist C, Sillitoe I, Smithers B, Squizzato S, Sutton G, Thanki N, Thomas PD, Tosatto Silvio CE, Wu CH, Xenarios I, Yeh L-S, Young S-Y, Mitchell AL (2017) InterPro in 2017—beyond protein family and domain annotations. Nucleic Acids Res 45(Database issue):D190–D199. https://doi.org/10.1093/nar/gkw1107

    Article  CAS  PubMed  Google Scholar 

  146. Jungblut P, Thiede B, Zimny-Arndt U, Muller EC, Scheler C, Wittmann-Liebold B, Otto A (1996) Resolution power of two-dimensional electrophoresis and identification of proteins from gels. Electrophoresis 17(5):839–847. https://doi.org/10.1002/elps.1150170505

    Article  CAS  PubMed  Google Scholar 

  147. Schluter H, Apweiler R, Holzhutter H-G, Jungblut P (2009) Finding one’s way in proteomics: a protein species nomenclature. Chem Cent J 3(1):11. https://doi.org/10.1186/1752-153X-3-11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Bond AE, Row PE, Dudley E (2011) Post-translation modification of proteins; methodologies and applications in plant sciences. Phytochemistry 72(10):975–996. https://doi.org/10.1016/j.phytochem.2011.01.029

    Article  CAS  PubMed  Google Scholar 

  149. Mann M, Jensen ON (2003) Proteomic analysis of post-translational modifications. Nat Biotechnol 21(3):255–261. https://doi.org/10.1038/nbt0303-255

    Article  CAS  PubMed  Google Scholar 

  150. Endler A, Baginsky S (2011) Use of phosphoproteomics to study posttranslational protein modifications in Arabidopsis chloroplasts. In: Jarvis RP (ed) Chloroplast research in arabidopsis, vol 775. Methods in Molecular Biology. Humana Press, Totowa, pp 283–296. https://doi.org/10.1007/978-1-61779-237-3_15

    Chapter  Google Scholar 

  151. Bienvenut WV, Espagne C, Martinez A, Majeran W, Valot B, Zivy M, Vallon O, Adam Z, Meinnel T, Giglione C (2011) Dynamics of post-translational modifications and protein stability in the stroma of Chlamydomonas reinhardtii chloroplasts. Proteomics 11(9):1734–1750. https://doi.org/10.1002/pmic.201000634

    Article  CAS  PubMed  Google Scholar 

  152. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N, Levine AJ, Roberts RJ, Simon M, Slayman C, Hunkapiller M, Bolanos R, Delcher A, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, Hannenhalli S, Kravitz S, Levy S, Mobarry C, Reinert K, Remington K, Abu-Threideh J, Beasley E, Biddick K, Bonazzi V, Brandon R, Cargill M, Chandramouliswaran I, Charlab R, Chaturvedi K, Deng Z, Di Francesco V, Dunn P, Eilbeck K, Evangelista C, Gabrielian AE, Gan W, Ge W, Gong F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke Z, Ketchum KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin X, Lu F, Merkulov GV, Milshina N, Moore HM, Naik AK, Narayan VA, Neelam B, Nusskern D, Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Wang Z, Wang A, Wang X, Wang J, Wei M, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M, Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Zhong W, Zhu S, Zhao S, Gilbert D, Baumhueter S, Spier G, Carter C, Cravchik A, Woodage T, Ali F, An H, Awe A, Baldwin D, Baden H, Barnstead M, Barrow I, Beeson K, Busam D, Carver A, Center A, Cheng ML, Curry L, Danaher S, Davenport L, Desilets R, Dietz S, Dodson K, Doup L, Ferriera S, Garg N, Gluecksmann A, Hart B, Haynes J, Haynes C, Heiner C, Hladun S, Hostin D, Houck J, Howland T, Ibegwam C, Johnson J, Kalush F, Kline L, Koduru S, Love A, Mann F, May D, McCawley S, McIntosh T, McMullen I, Moy M, Moy L, Murphy B,Nelson K, Pfannkoch C, Pratts E, Puri V, Qureshi H, Reardon M, Rodriguez R, Rogers YH, Romblad D, Ruhfel B, Scott R, Sitter C, Smallwood M, Stewart E, Strong R, Suh E, Thomas R, Tint NN, Tse S, Vech C, Wang G, Wetter J, Williams S, Williams M, Windsor S, Winn-Deen E, Wolfe K, Zaveri J, Zaveri K, Abril JF, Guigo R, Campbell MJ, Sjolander KV, Karlak B, Kejariwal A, Mi H, Lazareva B, Hatton T, Narechania A, Diemer K, Muruganujan A, Guo N, Sato S, Bafna V, Istrail S, Lippert R, Schwartz R, Walenz B, Yooseph S, Allen D, Basu A, Baxendale J, Blick L, Caminha M, Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Dahlke C, Mays A, Dombroski M, Donnelly M, Ely D, Esparham S, Fosler C, Gire H, Glanowski S, Glasser K, Glodek A, Gorokhov M, Graham K, Gropman B, Harris M, Heil J, Henderson S, Hoover J, Jennings D, Jordan C, Jordan J, Kasha J, Kagan L,Kraft C, Levitsky A, Lewis M, Liu X, Lopez J, Ma D, Majoros W, McDaniel J, Murphy S, Newman M, Nguyen T, Nguyen N, Nodell M, Pan S, Peck J, Peterson M, Rowe W, Sanders R, Scott J, Simpson M, Smith T, Sprague A, Stockwell T, Turner R, Venter E, Wang M,Wen M, Wu D, Wu M, Xia A, Zandieh A, Zhu X (2001) The sequence of the human genome. Science 291(5507):1304–1351. https://doi.org/10.1126/science.1058040

    Article  CAS  PubMed  Google Scholar 

  153. Adams JA (2001) Kinetic and catalytic mechanisms of protein kinases. Chem Rev 101(8):2271–2290. https://doi.org/10.1021/cr000230w

    Article  CAS  PubMed  Google Scholar 

  154. Johnson SA, Hunter T (2005) Kinomics: methods for deciphering the kinome. Nat Methods 2(1):17–25. https://doi.org/10.1038/nmeth731

    Article  CAS  PubMed  Google Scholar 

  155. Cohen P (2001) The role of protein phosphorylation in human health and disease. Eur J Biochem 268(19):5001–5010. https://doi.org/10.1046/j.0014-2956.2001.02473.x

    Article  CAS  PubMed  Google Scholar 

  156. Lim YP (2005) Mining the tumor phosphoproteome for cancer markers. Clin Cancer Res 11(9):3163–3169. https://doi.org/10.1158/1078-0432.ccr-04-2243

    Article  CAS  PubMed  Google Scholar 

  157. Reinders J, Sickmann A (2005) State-of-the-art in phosphoproteomics. Proteomics 5(16):4052–4061. https://doi.org/10.1002/pmic.200401289

    Article  CAS  PubMed  Google Scholar 

  158. Vissers JPC, Pons S, Hulin A, Tissier R, Berdeaux A, Connolly JB, Langridge JI, Geromanos SJ, Ghaleh B (2009) The use of proteome similarity for the qualitative and quantitative profiling of reperfused myocardium. J Chromatogr B 877(13):1317–1326. https://doi.org/10.1016/j.jchromb.2008.10.024

    Article  CAS  Google Scholar 

  159. Emes MJ (2009) Oxidation of methionine residues: the missing link between stress and signalling responses in plants. Biochem J 422:e1–e2. https://doi.org/10.1042/BJ20091063

    Article  CAS  PubMed  Google Scholar 

  160. O’Donovan C, Apweiler R, Bairoch A (2001) The human proteomics initiative (HPI). Trends Biotechnol 19(5):178–181. https://doi.org/10.1016/s0167-7799(01)01598-0

    Article  PubMed  Google Scholar 

  161. Prabakaran S, Lippens G, Steen H, Gunawardena J (2012) Post-translational modification: nature’s escape from genetic imprisonment and the basis for dynamic information encoding. Wiley Interdiscip Rev 4(6):565–583. https://doi.org/10.1002/wsbm.1185

    Article  CAS  Google Scholar 

  162. Jensen ON (2006) Interpreting the protein language using proteomics. Nat Rev Mol Cell Biol 7(6):391–403. https://doi.org/10.1038/nrm1939

    Article  CAS  PubMed  Google Scholar 

  163. Venne AS, Solari FA, Faden F, Paretti T, Dissmeyer N, Zahedi RP (2015) An improved workflow for quantitative N-terminal charge-based fractional diagonal chromatography (ChaFRADIC) to study proteolytic events in Arabidopsis thaliana. Proteomics 15(14):2458–2469. https://doi.org/10.1002/pmic.201500014

    Article  CAS  PubMed  Google Scholar 

  164. Kwon SJ, Choi EY, Choi YJ, Ahn JH, Park OK (2006) Proteomics studies of post-translational modifications in plants. J Exp Bot 57(7):1547–1551. https://doi.org/10.1093/jxb/erj137

    Article  CAS  PubMed  Google Scholar 

  165. Larsen MR, Trelle MB, Thingholm TE, Jensen ON (2006) Analysis of posttranslational modifications of proteins by tandem mass spectrometry. Biotechniques 40:790–798. https://doi.org/10.2144/000112201

    Article  CAS  PubMed  Google Scholar 

  166. Seo J, Lee K-J (2004) Post-translational modifications and their biological functions: proteomic analysis and systematic approaches. J Biochem Mol Biol 37(1):35–44

    CAS  PubMed  Google Scholar 

  167. Ytterberg AJ, Jensen ON (2010) Modification-specific proteomics in plant biology. J Proteomics 73(11):2249–2266. https://doi.org/10.1016/j.jprot.2010.06.002

    Article  CAS  PubMed  Google Scholar 

  168. Beausoleil SA, Jedrychowski M, Schwartz D, Elias JE, Villén J, Li J, Cohn MA, Cantley LC, Gygi SP (2004) Large-scale characterization of HeLa cell nuclear phosphoproteins. Proc Natl Acad Sci USA 101(33):12130–12135. https://doi.org/10.1073/pnas.0404720101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Asara JM, Christofk HR, Freimark LM, Cantley LC (2008) A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen. Proteomics 8(5):994–999. https://doi.org/10.1002/pmic.200700426

    Article  CAS  PubMed  Google Scholar 

  170. Lu B, Ruse C, Xu T, Park SK, Yates J III (2007) Automatic validation of phosphopeptide identifications from tandem mass spectra. Anal Chem 79(4):1301–1310. https://doi.org/10.1021/ac061334v

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Graves JD, Krebs EG (1999) Protein phosphorylation and signal transduction. Pharmacol Ther 82(2–3):111–121. https://doi.org/10.1016/s0163-7258(98)00056-4

    Article  CAS  PubMed  Google Scholar 

  172. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP (2003) Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci 100(12):6940–6945. https://doi.org/10.1073/pnas.0832254100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422(6928):198–207. https://doi.org/10.1038/nature01511

    Article  CAS  PubMed  Google Scholar 

  174. Bendt AK, Burkovski A, Schaffer S, Bott M, Farwick M, Hermann T (2003) Towards a phosphoproteome map of Corynebacterium glutamicum. Proteomics 3(8):1637–1646. https://doi.org/10.1002/pmic.200300494

    Article  CAS  PubMed  Google Scholar 

  175. Su HC, Hutchison CA 3rd, Giddings MC (2007) Mapping phosphoproteins in Mycoplasma genitalium and Mycoplasma pneumoniae. BMC Microbiol 7(1):63. https://doi.org/10.1186/1471-2180-7-63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. O’Farrell PH (1975) High resolution two-dimensional electrophoresis of proteins. J Biol Chem 250(10):4007–4021

    PubMed  Google Scholar 

  177. Patton WF (2002) Detection technologies in proteome analysis. J Chromatogr B 771(1–2):3–31. https://doi.org/10.1016/s1570-0232(02)00043-0

    Article  CAS  Google Scholar 

  178. Boudsocq M, Droillard M-J, Barbier-Brygoo H, Laurière C (2007) Different phosphorylation mechanisms are involved in the activation of sucrose non-fermenting 1 related protein kinases 2 by osmotic stresses and abscisic acid. Plant Mol Biol 63(4):491–503. https://doi.org/10.1007/s11103-006-9103-1

    Article  CAS  PubMed  Google Scholar 

  179. Kauffmann H, Bailey JE, Fussenegger M (2001) Use of antibodies for detection of phosphorylated proteins separated by two-dimensional gel electrophoresis. Proteomics 1:194–199

    Article  Google Scholar 

  180. Bockus L, Scofield RH (2009) Phosphoprotein detection on protein electroblot using a phosphate-specific fluorophore. In: Kurien BT, Scofield RH (eds) Protein blotting and detection, vol 536. Methods in Molecular Biology. Humana Press, Totowa, pp 385–393. https://doi.org/10.1007/978-1-59745-542-8_39

    Google Scholar 

  181. Schulenberg B, Aggeler R, Beechem JM, Capaldi RA, Patton WF (2003) Analysis of steady-state protein phosphorylation in mitochondria using a novel fluorescent phosphosensor dye. J Biol Chem 278(29):27251–27255. https://doi.org/10.1074/jbc.C300189200

    Article  CAS  PubMed  Google Scholar 

  182. Nakanishi T, Ando E, Furuta M, Kinoshita E, Kinoshita-Kikuta E, Koike T, Tsunasawa S, Nishimura O (2007) Identification on membrane and characterization of phosphoproteins using an alkoxide-bridged dinuclear metal complex as a phosphate-binding tag molecule. J Biomol Tech 18(5):278–286

    PubMed  PubMed Central  Google Scholar 

  183. Aebersold R, Goodlett DR (2001) Mass spectrometry in proteomics. Chem Rev 101(2):269–296. https://doi.org/10.1021/cr990076h

    Article  CAS  PubMed  Google Scholar 

  184. Mann M, Ong S-E, Grønborg M, Steen H, Jensen ON, Pandey A (2002) Analysis of protein phosphorylation using mass spectrometry: deciphering the phosphoproteome. Trends Biotechnol 20(6):261–268. https://doi.org/10.1016/s0167-7799(02)01944-3

    Article  CAS  PubMed  Google Scholar 

  185. Simpson RJ (2003) Proteomic methods for phosphorylation site mapping. In: Simpson RJ (ed) Protein and proteomics. A laboratory manual. Cold Spring Harbour, New York, pp 597–668

    Google Scholar 

  186. Dunn JD, Reid GE, Bruening ML (2010) Techniques for phosphopeptide enrichment prior to analysis by mass spectrometry. Mass Spectrom Rev 29(1):29–54. https://doi.org/10.1002/mas.20219

    Article  CAS  PubMed  Google Scholar 

  187. Sun X, Chiu JF, He QY (2005) Application of immobilized metal affinity chromatography in proteomics. Expert Rev Proteomics 2(5):649–657. https://doi.org/10.1586/14789450.2.5.649

    Article  CAS  PubMed  Google Scholar 

  188. Thingholm TE, Jensen ON, Larsen MR (2009) Analytical strategies for phosphoproteomics. Proteomics 9(6):1451–1468. https://doi.org/10.1002/pmic.200800454

    Article  CAS  PubMed  Google Scholar 

  189. Grimsrud PA, den Os D, Wenger C, Swaney D, Schwartz DL, Sussman D, Ane MR, Coon J-M JJ (2010) Large-scale phosphorylation analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes. Plant Physiol 152(1):19–28. https://doi.org/10.1104/pp.109.149625

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. Nuhse TS, Stensballe A, Jensen ON, Peck SC (2003) Large-scale analysis of in vivo phosphorylated membrane proteins by immobilized metal ion affinity chromatography and mass spectrometry. Mol Cell Proteomics 2(11):1234–1243. https://doi.org/10.1074/mcp.T300006-MCP200

    Article  CAS  PubMed  Google Scholar 

  191. Posewitz MC, Tempst P (1999) Immobilized gallium(III) affinity chromatography of phosphopeptides. Anal Chem 71(14):2883–2892. https://doi.org/10.1021/ac981409y

    Article  CAS  PubMed  Google Scholar 

  192. Dong J, Zhou H, Wu R, Ye M, Zou H (2007) Specific capture of phosphopeptides by Zr4+-modified monolithic capillary column. J Sep Sci 30(17):2917–2923. https://doi.org/10.1002/jssc.200700350

    Article  CAS  PubMed  Google Scholar 

  193. Feng S, Ye M, Zhou H, Jiang X, Jiang X, Zou H, Gong B (2007) Immobilized zirconium ion affinity chromatography for specific enrichment of phosphopeptides in phosphoproteome analysis. Mol Cell Proteomics 6(9):1656–1665. https://doi.org/10.1074/mcp.T600071-MCP200

    Article  CAS  PubMed  Google Scholar 

  194. Wei J, Zhang Y, Wang J, Tan F, Liu J, Cai Y, Qian X (2008) Highly efficient enrichment of phosphopeptides by magnetic nanoparticles coated with zirconium phosphonate for phosphoproteome analysis. Rapid Commun Mass Spectrom 22(7):1069–1080. https://doi.org/10.1002/rcm.3485

    Article  CAS  PubMed  Google Scholar 

  195. Yu L-R, Zhu Z, Chan KC, Issaq HJ, Dimitrov DS, Veenstra TD (2007) Improved titanium dioxide enrichment of phosphopeptides from HeLa cells and high confident phosphopeptide identification by cross-validation of MS/MS and MS/MS/MS spectra. J Proteome Res 6(11):4150–4162. https://doi.org/10.1021/pr070152u

    Article  CAS  PubMed  Google Scholar 

  196. Zhou H, Xu S, Ye M, Feng S, Pan C, Jiang X, Li X, Han G, Fu Y, Zou H (2006) Zirconium phosphonate-modified porous silicon for highly specific capture of phosphopeptides and MALDI-TOF MS analysis. J Proteome Res 5(9):2431–2437. https://doi.org/10.1021/pr060162f

    Article  CAS  PubMed  Google Scholar 

  197. Heintz D, Wurtz V, High AA, Van Dorsselaer A, Reski R, Sarnighausen E (2004) An efficient protocol for the identification of protein phosphorylation in a seedless plant, sensitive enough to detect members of signalling cascades. Electrophoresis 25(7–8):1149–1159. https://doi.org/10.1002/elps.200305795

    Article  CAS  PubMed  Google Scholar 

  198. Schmidt A, Csaszar E, Ammerer G, Mechtler K (2008) Enhanced detection and identification of multiply phosphorylated peptides using TiO2 enrichment in combination with MALDI TOF/TOF MS. Proteomics 8(21):4577–4592. https://doi.org/10.1002/pmic.200800279

    Article  CAS  PubMed  Google Scholar 

  199. Hsu J-L, Wang L-Y, Wang S-Y, Lin C-H, Ho K-C, Shi F-K, Chang I-F (2009) Functional phosphoproteomic profiling of phosphorylation sites in membrane fractions of salt-stressed Arabidopsis thaliana. Proteome Sci 7(1):42. https://doi.org/10.1186/1477-5956-7-42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  200. Nawrocki J, Dunlap C, McCormick A, Carr PW (2004) Part I. Chromatography using ultra-stable metal oxide-based stationary phases for HPLC. J Chromatogr A 1028(1):1–30. https://doi.org/10.1016/j.chroma.2003.11.052

    Article  CAS  PubMed  Google Scholar 

  201. Ikeguchi Y, Nakamura H (1997) Determination of organic phosphates by column-switching high performance anion-exchange chromatography using on-line preconcentration on titania. Anal Sci 13(3):479–483. https://doi.org/10.2116/analsci.13.479

    Article  CAS  Google Scholar 

  202. Ikeguchi Y, Nakamura H (2000) Selective enrichment of phospholipids by titania. Anal Sci 16(5):541–543. https://doi.org/10.2116/analsci.16.541

    Article  CAS  Google Scholar 

  203. Larsen MR, Thingholm TE, Jensen ON, Roepstorff P, Jørgensen TJD (2005) Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol Cell Proteomics 4(7):873–886. https://doi.org/10.1074/mcp.T500007-MCP200

    Article  CAS  PubMed  Google Scholar 

  204. Pinkse MWH, Uitto PM, Hilhorst MJ, Ooms B, Heck AJR (2004) Selective isolation at the femtomole level of phosphopeptides from proteolytic digests using 2D-nanoLC-ESI-MS/MS and titanium oxide precolumns. Anal Chem 76(14):3935–3943. https://doi.org/10.1021/ac0498617

    Article  CAS  PubMed  Google Scholar 

  205. Sano A, Nakamura H (2004) Chemo-affinity of titania for the column-switching HPLC analysis of phosphopeptides. Anal Sci 20(3):565–566. https://doi.org/10.2116/analsci.20.565

    Article  CAS  PubMed  Google Scholar 

  206. Sano A, Nakamura H (2004) Titania as a chemo-affinity support for the column-switching HPLC analysis of phosphopeptides: application to the characterization of phosphorylation sites in proteins by combination with protease digestion and electrospray ionization mass spectrometry. Anal Sci 20(5):861–864. https://doi.org/10.2116/analsci.20.861

    Article  CAS  PubMed  Google Scholar 

  207. Kosmulski M (2002) The significance of the difference in the point of zero charge between rutile and anatase. Adv Colloid Interface Sci 99(3):255–264. https://doi.org/10.1016/s0001-8686(02)00080-5

    Article  CAS  PubMed  Google Scholar 

  208. Thingholm TE, Larsen MR, Ingrell CR, Kassem M, Jensen ON (2008) TiO2-based phosphoproteomic analysis of the plasma membrane and the effects of phosphatase inhibitor treatment. J Proteome Res 7(8):3304–3313. https://doi.org/10.1021/pr800099y

    Article  CAS  PubMed  Google Scholar 

  209. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127(3):635–648. https://doi.org/10.1016/j.cell.2006.09.026

    Article  CAS  PubMed  Google Scholar 

  210. McNulty DE, Annan RS (2008) Hydrophilic interaction chromatography reduces the complexity of the phosphoproteome and improves global phosphopeptide isolation and detection. Mol Cell Proteomics 7(5):971–980. https://doi.org/10.1074/mcp.M700543-MCP200

    Article  CAS  PubMed  Google Scholar 

  211. Moon JH, Shin YS, Kim MS (2009) Utility of reaction intermediate monitoring with photodissociation multi-stage (MSn) time-of-flight mass spectrometry for mechanistic and structural studies: phosphopeptides. Int J Mass Spectrom 288(1–3):16–21. https://doi.org/10.1016/j.ijms.2009.07.008

    Article  CAS  Google Scholar 

  212. DeGnore J, Qin J (1998) Fragmentation of phosphopeptides in an ion trap mass spectrometer. J Am Soc Mass Spectrom 9(11):1175–1188. https://doi.org/10.1016/s1044-0305(98)00088-9

    Article  CAS  PubMed  Google Scholar 

  213. Boersema PJ, Mohammed S, Heck AJ (2009) Phosphopeptide fragmentation and analysis by mass spectrometry. J Mass Spectrom 44(6):861–878. https://doi.org/10.1002/jms.1599

    Article  CAS  PubMed  Google Scholar 

  214. Villen J, Beausoleil SA, Gygi SP (2008) Evaluation of the utility of neutral-loss-dependent MS3 strategies in large-scale phosphorylation analysis. Proteomics 8(21):4444–4452. https://doi.org/10.1002/pmic.200800283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  215. Yocum AK, Chinnaiyan AM (2009) Current affairs in quantitative targeted proteomics: multiple reaction monitoring–mass spectrometry. Brief Funct Genomics Proteomics 8(2):145–157. https://doi.org/10.1093/bfgp/eln056

    Article  CAS  Google Scholar 

  216. Cox DM, Zhong F, Du M, Duchoslav E, Sakuma T, McDermott JC (2005) Multiple reaction monitoring as a method for identifying protein posttranslational modifications. J Biomol Tech 16(2):83–90

    PubMed  PubMed Central  Google Scholar 

  217. Domanski D, Murphy LC, Borchers CH (2010) Assay development for the determination of phosphorylation stoichiometry using multiple reaction monitoring methods with and without phosphatase treatment: application to breast cancer signaling pathways. Anal Chem 82(13):5610–5620. https://doi.org/10.1021/ac1005553

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  218. Fan J, Mohareb F, Jones AM, Bessant C (2012) MRMaid: the SRM assay design tool for Arabidopsis and other species. Front Plant Sci 3:164. https://doi.org/10.3389/fpls.2012.00164

    Article  PubMed  PubMed Central  Google Scholar 

  219. Lange V, Picotti P, Domon B, Aebersold R (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol. https://doi.org/10.1038/msb.2008.61

    Article  PubMed  PubMed Central  Google Scholar 

  220. Martinez-Marquez A, Morante-Carriel J, Selles-Marchart S, Martinez-Esteso MJ, Pineda-Lucas JL, Luque I, Bru-Martinez R (2013) Development and validation of MRM methods to quantify protein isoforms of polyphenol oxidase in loquat fruits. J Proteome Res 12(12):5709–5722. https://doi.org/10.1021/pr4006712

    Article  CAS  PubMed  Google Scholar 

  221. Wolf-Yadlin A, Hautaniemi S, Lauffenburger DA, White FM (2007) Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc Natl Acad Sci 104(14):5860–5865. https://doi.org/10.1073/pnas.0608638104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  222. Blom N, Sicheritz-Ponten T, Gupta R, Gammeltoft S, Brunak S (2004) Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 4(6):1633–1649. https://doi.org/10.1002/pmic.200300771

    Article  CAS  PubMed  Google Scholar 

  223. Xue Y, Zhou F, Zhu M, Ahmed K, Chen G, Yao X (2005) GPS: a comprehensive www server for phosphorylation sites prediction. Nucleic Acids Res 33(suppl 2):W184–W187. https://doi.org/10.1093/nar/gki393

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  224. Zanzoni A, Carbajo D, Diella F, Gherardini PF, Tramontano A, Helmer-Citterich M, Via A (2011) Phospho3D 2.0: an enhanced database of three-dimensional structures of phosphorylation sites. Nucleic Acids Res 39(suppl 1):D268–D271. https://doi.org/10.1093/nar/gkq936

    Article  CAS  PubMed  Google Scholar 

  225. Yoo P, Ho Y, Zhou B, Zomaya A (2008) SiteSeek: post-translational modification analysis using adaptive locality-effective kernel methods and new profiles. BMC Bioinform 9(1):272

    Article  Google Scholar 

  226. Basu S, Plewczynski D (2010) AMS 3.0: prediction of post-translational modifications. BMC Bioinform 11(1):210

    Article  Google Scholar 

  227. Sigrist CJA, Cerutti L, Hulo N, Gattiker A, Falquet L, Pagni M, Bairoch A, Bucher P (2002) PROSITE: a documented database using patterns and profiles as motif descriptors. Brief Bioinform 3(3):265–274. https://doi.org/10.1093/bib/3.3.265

    Article  CAS  PubMed  Google Scholar 

  228. Yaffe MB, Leparc GG, Lai J, Obata T, Volinia S, Cantley LC (2001) A motif-based profile scanning approach for genome-wide prediction of signaling pathways. Nat Biotechnol 19(4):348–353. https://doi.org/10.1038/86737

    Article  CAS  PubMed  Google Scholar 

  229. Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294(5):1351–1362. https://doi.org/10.1006/jmbi.1999.3310

    Article  CAS  PubMed  Google Scholar 

  230. Hjerrild M, Stensballe A, Rasmussen TE, Kofoed CB, Blom N, Sicheritz-Ponten T, Larsen MR, Brunak S, Jensen ON, Gammeltoft S (2004) Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry. J Proteome Res 3(3):426–433. https://doi.org/10.1021/pr0341033

    Article  CAS  PubMed  Google Scholar 

  231. Zhou S, Shoelson SE, Chaudhuri M, Gish G, Pawson T, Haser WG, King F, Roberts T, Ratnofsky S, Lechleider RJ, Neel BG, Birge RB, Fajardo JE, Chou MM, Hanafusa H, Schaffhausen B, Cantley LC (1993) SH2 domains recognize specific phosphopeptide sequences. Cell 72(5):767–778

    Article  Google Scholar 

  232. Kim JH, Lee J, Oh B, Kimm K, Koh I (2004) Prediction of phosphorylation sites using SVMs. Bioinformatics 20(17):3179–3184. https://doi.org/10.1093/bioinformatics/bth382

    Article  CAS  PubMed  Google Scholar 

  233. Plewczynski D, Tkacz A, Wyrwicz LS, Rychlewski L (2005) AutoMotif server: prediction of single residue post-translational modifications in proteins. Bioinformatics 21(10):2525–2527. https://doi.org/10.1093/bioinformatics/bti333

    Article  CAS  PubMed  Google Scholar 

  234. Wong Y-H, Lee T-Y, Liang H-K, Huang C-M, Wang T-Y, Yang Y-H, Chu C-H, Huang H-D, Ko M-T, Hwang J-K (2007) KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns. Nucleic Acids Res 35(suppl 2):W588–W594. https://doi.org/10.1093/nar/gkm322

    Article  PubMed  PubMed Central  Google Scholar 

  235. Gnad F, Gunawardena J, Mann M (2011) PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res 39(suppl 1):D253–D260. https://doi.org/10.1093/nar/gkq1159

    Article  CAS  PubMed  Google Scholar 

  236. Beausoleil SA, Villen J, Gerber SA, Rush J, Gygi SP (2006) A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat Biotechnol 24(10):1285–1292. https://doi.org/10.1038/nbt1240

    Article  CAS  PubMed  Google Scholar 

  237. Ruttenberg BE, Pisitkun T, Knepper MA, Hoffert JD (2008) PhosphoScore: an open-source phosphorylation site assignment tool for MSn data. J Proteome Res 7(7):3054–3059. https://doi.org/10.1021/pr800169k

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  238. Chen Y, Chen W, Cobb MH, Zhao Y (2009) PTMap—a sequence alignment software for unrestricted, accurate, and full-spectrum identification of post-translational modification sites. Proc Natl Acad Sci USA 106(3):761–766. https://doi.org/10.1073/pnas.0811739106

    Article  PubMed  PubMed Central  Google Scholar 

  239. Sigrist CJA, Cerutti L, de Castro E, Langendijk-Genevaux PS, Bulliard V, Bairoch A, Hulo N (2010) PROSITE, a protein domain database for functional characterization and annotation. Nucleic Acids Res 38(Database issue):D161–D166. https://doi.org/10.1093/nar/gkp885

    Article  CAS  PubMed  Google Scholar 

  240. Obenauer JC, Cantley LC, Yaffe MB (2003) Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res 31(13):3635–3641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  241. Plewczynski D, Basu S, Saha I (2012) AMS 4.0: consensus prediction of post-translational modifications in protein sequences. Amino Acids 43(2):573–582. https://doi.org/10.1007/s00726-012-1290-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  242. Xue Y, Li A, Wang L, Feng H, Yao X (2006) PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory. BMC Bioinform 7:163–163. https://doi.org/10.1186/1471-2105-7-163

    Article  CAS  Google Scholar 

  243. Hamby SE, Hirst JD (2008) Prediction of glycosylation sites using random forests. BMC Bioinform 9:500–500. https://doi.org/10.1186/1471-2105-9-500

    Article  CAS  Google Scholar 

  244. Chuang G-Y, Boyington JC, Joyce MG, Zhu J, Nabel GJ, Kwong PD, Georgiev I (2012) Computational prediction of N-linked glycosylation incorporating structural properties and patterns. Bioinformatics 28(17):2249–2255. https://doi.org/10.1093/bioinformatics/bts426

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  245. Li F, Li C, Wang M, Webb GI, Zhang Y, Whisstock JC, Song J (2015) GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome. Bioinformatics 31(9):1411–1419. https://doi.org/10.1093/bioinformatics/btu852

    Article  CAS  PubMed  Google Scholar 

  246. Chauhan JS, Bhat AH, Raghava GPS, Rao A (2012) GlycoPP: a webserver for prediction of N- and O-glycosites in prokaryotic protein sequences. PLoS ONE 7(7):e40155. https://doi.org/10.1371/journal.pone.0040155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  247. Xue Y, Liu Z, Gao X, Jin C, Wen L, Yao X, Ren J (2010) GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm. PLoS ONE 5(6):e11290. https://doi.org/10.1371/journal.pone.0011290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  248. Wen P-P, Shi S-P, Xu H-D, Wang L-N, Qiu J-D (2016) Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization. Bioinformatics 32(20):3107–3115. https://doi.org/10.1093/bioinformatics/btw377

    Article  CAS  PubMed  Google Scholar 

  249. Lee TY, Hsu JB, Lin FM, Chang WC, Hsu PC, Huang HD (2010) N-Ace: using solvent accessibility and physicochemical properties to identify protein N-acetylation sites. J Comput Chem 31(15):2759–2771. https://doi.org/10.1002/jcc.21569

    Article  CAS  PubMed  Google Scholar 

  250. Hou T, Zheng G, Zhang P, Jia J, Li J, Xie L, Wei C, Li Y (2014) LAceP: lysine acetylation site prediction using logistic regression classifiers. PLoS ONE 9(2):e89575. https://doi.org/10.1371/journal.pone.0089575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  251. Lee TY, Huang HD, Hung JH, Huang HY, Yang YS, Wang TH (2006) dbPTM: an information repository of protein post-translational modification. Nucleic Acids Res 34(Database issue):D622–D627. https://doi.org/10.1093/nar/gkj083

    Article  CAS  PubMed  Google Scholar 

  252. Dinkel H, Chica C, Via A, Gould CM, Jensen LJ, Gibson TJ, Diella F (2011) Phospho.ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res 39(suppl 1):D261–D267. https://doi.org/10.1093/nar/gkq1104

    Article  CAS  PubMed  Google Scholar 

  253. Hornbeck PV, Kornhauser JM, Tkachev S, Zhang B, Skrzypek E, Murray B, Latham V, Sullivan M (2012) PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res 40(Database issue):D261–D270. https://doi.org/10.1093/nar/gkr1122

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Director-General of the Malaysian Palm Oil Board for permission to publish this article and the funding received for the research projects.

Author information

Authors and Affiliations

  1. Malaysian Palm Oil Board, No. 6, Persiaran Institusi, Bandar Baru Bangi, 43000, Kajang, Selangor, Malaysia

    Benjamin Yii Chung Lau, Abrizah Othman & Umi Salamah Ramli

Authors
  1. Benjamin Yii Chung Lau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abrizah Othman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Umi Salamah Ramli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benjamin Yii Chung Lau.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lau, B.Y.C., Othman, A. & Ramli, U.S. Application of Proteomics Technologies in Oil Palm Research. Protein J 37, 473–499 (2018). https://doi.org/10.1007/s10930-018-9802-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10930-018-9802-x

Keywords

Search

Navigation