Proteomic Detection of Carbohydrate-Active Enzymes (CAZymes) in Microbial Secretomes

  • Tina R. Tuveng
  • Vincent G. H. Eijsink
  • Magnus Ø. ArntzenEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1871)


Secretomes from microorganisms growing on biomass contain carbohydrate-active enzymes (CAZymes) of potential biotechnological interest. By analyzing such secretomes, we may discover key enzymes involved in degradation processes and potentially infer the mode-of-action of biomass conversion. Some of these enzymes may have predicted functions in carbohydrate degradation, while others may not, while yet exhibiting a similar expression pattern; these latter enzymes constitute potential novel enzymes involved in the degradation process and provide a basis for further biochemical exploration. Hence, secretomes represent an important source for the study of both predicted and novel CAZymes. Here we describe a plate-based culturing technique that allows for collection of protein fractions that are highly enriched for secreted proteins, bound or unbound to the substrate, and which minimizes contamination by intracellular proteins trough unwanted cell lysis.

Key words

Secretomics Proteomics Protein secretion Carbohydrate-active enzymes CAZymes 


  1. 1.
    Himmel ME, Xu Q, Luo Y, Ding S-Y, Lamed R, Bayer EA (2010) Microbial enzyme systems for biomass conversion: emerging paradigms. Biofuels 1(2):323–341. Scholar
  2. 2.
    Payne CM, Knott BC, Mayes HB, Hansson H, Himmel ME, Sandgren M, Ståhlberg J, Beckham GT (2015) Fungal cellulases. Chem Rev 115(3):1308–1448. Scholar
  3. 3.
    Benz JP, Chau BH, Zheng D, Bauer S, Glass NL, Somerville CR (2014) A comparative systems analysis of polysaccharide-elicited responses in Neurospora crassa reveals carbon source-specific cellular adaptations. Mol Microbiol 91(2):275–299. Scholar
  4. 4.
    Suzuki K, Suzuki M, Taiyoji M, Nikaidou N, Watanabe T (1998) Chitin binding protein (CBP21) in the culture supernatant of Serratia marcescens 2170. Biosci Biotechnol Biochem 62(1):128–135. Scholar
  5. 5.
    Takasuka TE, Book AJ, Lewin GR, Currie CR, Fox BG (2013) Aerobic deconstruction of cellulosic biomass by an insect-associated Streptomyces. Sci Rep 3:1030. Scholar
  6. 6.
    Siljamäki P, Varmanen P, Kankainen M, Sukura A, Savijoki K, Nyman TA (2014) Comparative exoprotein profiling of different Staphylococcus epidermidis strains reveals potential link between nonclassical protein export and virulence. J Proteome Res 13(7):3249–3261. Scholar
  7. 7.
    Adav SS, Cheow ESH, Ravindran A, Dutta B, Sze SK (2012) Label free quantitative proteomic analysis of secretome by Thermobifida fusca on different lignocellulosic biomass. J Proteome 75(12):3694–3706. Scholar
  8. 8.
    Bengtsson O, Arntzen MØ, Mathiesen G, Skaugen M, Eijsink VGH (2016) A novel proteomics sample preparation method for secretome analysis of Hypocrea jecorina growing on insoluble substrates. J Proteome 131:104–112. Scholar
  9. 9.
    Tuveng TR, Arntzen MØ, Bengtsson O, Gardner JG, Vaaje-Kolstad G, Eijsink VGH (2016) Proteomic investigation of the secretome of Cellvibrio japonicus during growth on chitin. Proteomics 16(13):1904–1914. Scholar
  10. 10.
    Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B (2014) The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42(D1):D490–D495. Scholar
  11. 11.
    Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B (2009) The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res 37(Database):D233–D238. Scholar
  12. 12.
    Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y (2012) dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 40(Web Server issue):W445–W451. Scholar
  13. 13.
    Park BH, Karpinets TV, Syed MH, Leuze MR, Uberbacher EC (2010) CAZymes Analysis Toolkit (CAT): web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database. Glycobiology 20(12):1574–1584. Scholar
  14. 14.
    Caccia D, Dugo M, Callari M, Bongarzone I (2013) Bioinformatics tools for secretome analysis. Biochim Biophys Acta Proteins Proteom 1834(11):2442–2453. Scholar
  15. 15.
    Desvaux M, Hebraud M, Talon R, Henderson IR (2009) Secretion and subcellular localizations of bacterial proteins: a semantic awareness issue. Trends Microbiol 17(4):139–145. Scholar
  16. 16.
    Nielsen H (2017) Predicting secretory proteins with SignalP. In: Kihara D (ed) Protein function prediction: methods and protocols. Springer, New York, pp 59–73. Scholar
  17. 17.
    Nielsen H (2017) Protein sorting prediction. In: Journet L, Cascales E (eds) Bacterial protein secretion systems: methods and protocols. Springer, New York, pp 23–57. Scholar
  18. 18.
    Nielsen H (2016) Predicting subcellular localization of proteins by Bioinformatic algorithms. In: Bagnoli F, Rappuoli R (eds) Protein and sugar export and assembly in gram-positive bacteria. Springer International Publishing, Cham, pp 129–158. Scholar
  19. 19.
    Petersen TN, Brunak S, Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785. Scholar
  20. 20.
    Juncker AS, Willenbrock H, Von Heijne G, Brunak S, Nielsen H, Krogh A (2003) Prediction of lipoprotein signal peptides in Gram-negative bacteria. Protein Sci 12(8):1652–1662. Scholar
  21. 21.
    Rahman O, Cummings SP, Harrington DJ, Sutcliffe IC (2008) Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of Gram-positive bacteria. World J Microbiol Biotechnol 24(11):2377. Scholar
  22. 22.
    Bagos PG, Tsirigos KD, Liakopoulos TD, Hamodrakas SJ (2008) Prediction of lipoprotein signal peptides in Gram-positive bacteria with a Hidden Markov Model. J Proteome Res 7(12):5082–5093. Scholar
  23. 23.
    Bagos PG, Nikolaou EP, Liakopoulos TD, Tsirigos KD (2010) Combined prediction of Tat and Sec signal peptides with hidden Markov models. Bioinformatics 26(22):2811–2817. Scholar
  24. 24.
    Bendtsen J, Nielsen H, Widdick D, Palmer T, Brunak S (2005) Prediction of twin-arginine signal peptides. BMC Bioinformatics 6:167. Scholar
  25. 25.
    Krogh A, Larsson B, von Heijne G, Sonnhammer E (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580. Scholar
  26. 26.
    Käll L, Krogh A, Sonnhammer EL (2007) Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server. Nucleic Acids Res 35(Suppl 2):W429–W432. Scholar
  27. 27.
    Horton P, Park K-J, Obayashi T, Fujita N, Harada H, Adams-Collier C, Nakai K (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35(suppl_2):W585–W587. Scholar
  28. 28.
    Costa TR, Felisberto-Rodrigues C, Meir A, Prevost MS, Redzej A, Trokter M, Waksman G (2015) Secretion systems in Gram-negative bacteria: structural and mechanistic insights. Nat Rev Microbiol 13(6):343–359. Scholar
  29. 29.
    Hamilton JJ, Marlow VL, Owen RA, Costa Mde A, Guo M, Buchanan G, Chandra G, Trost M, Coulthurst SJ, Palmer T, Stanley-Wall NR, Sargent F (2014) A holin and an endopeptidase are essential for chitinolytic protein secretion in Serratia marcescens. J Cell Biol 207(5):615–626. Scholar
  30. 30.
    Bendtsen J, Kiemer L, Fausboll A, Brunak S (2005) Non-classical protein secretion in bacteria. BMC Microbiol 5(1):58. Scholar
  31. 31.
    Bendtsen J, Jensen L, Blom N, von Heijne G, Brunak S (2004) Feature based prediction of non-classical protein secretion. Protein Eng Des Sel 17:349–356. Scholar
  32. 32.
    Otto A, Becher D, Schmidt F (2014) Quantitative proteomics in the field of microbiology. Proteomics 14(4–5):547–565. Scholar
  33. 33.
    Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, Mann M, Cox J (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 13(9):731–740. Scholar
  34. 34.
    Villesen P (2007) FaBox: an online toolbox for fasta sequences. Mol Ecol Resour 7(6):965–968. Scholar
  35. 35.
    Tuveng TR, Hagen LH, Mekasha S, Frank J, Arntzen MØ, Vaaje-Kolstad G, Eijsink VGH (2017) Genomic, proteomic and biochemical analysis of the chitinolytic machinery of Serratia marcescens BJL200. Biochim Biophys Acta Proteins Proteom 1865(4):414–421. Scholar
  36. 36.
    Arntzen MO, Varnai A, Mackie RI, Eijsink VGH, Pope PB (2017) Outer membrane vesicles from Fibrobacter succinogenes S85 contain an array of carbohydrate-active enzymes with versatile polysaccharide-degrading capacity. Environ Microbiol 19(7):2701–2714. Scholar
  37. 37.
    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. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tina R. Tuveng
    • 1
  • Vincent G. H. Eijsink
    • 1
  • Magnus Ø. Arntzen
    • 1
    Email author
  1. 1.Faculty of Chemistry, Biotechnology and Food ScienceNorwegian University of Life Sciences (NMBU)ÅsNorway

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