Skip to main content

Metaproteomics: Sample Preparation and Methodological Considerations

Part of the Advances in Experimental Medicine and Biology book series (PMISB,volume 1073)

Abstract

Meta-omic techniques have progressed rapidly in the past decade and are frequently used in microbial ecology to study microorganisms in their natural ecosystems independent from culture restrictions. Metaproteomics, in combination with metagenomics, enables quantitative assessment of expressed proteins and pathways from individual members of the consortium. Together, metaproteomics and metagenomics can provide a detailed understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize nutrients, and these insights can be obtained directly from environmental samples. Here, we outline key aspects of sample preparation, database generation, and other methodological considerations that are required for successful quantitative metaproteomic analyses and we describe case studies on the integration with metagenomics for enhanced functional output.

Keywords

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

Buying options

Chapter
EUR   29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR   85.59
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR   105.49
Price includes VAT (France)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
EUR   105.49
Price includes VAT (France)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Wilmes P, Heintz-Buschart A, Bond PL (2015) A decade of metaproteomics: where we stand and what the future holds. Proteomics 15(20):3409–3417. https://doi.org/10.1002/pmic.201500183

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  2. Xiong W, Abraham PE, Li Z, Pan C, Hettich RL (2015) Microbial metaproteomics for characterizing the range of metabolic functions and activities of human gut microbiota. Proteomics 15(20):3424–3438. https://doi.org/10.1002/pmic.201400571

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, Richardson PM, Solovyev VV, Rubin EM, Rokhsar DS, Banfield JF (2004) Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428(6978):37–43. https://doi.org/10.1038/nature02340

    CrossRef  CAS  PubMed  Google Scholar 

  4. Aliaga Goltsman DS, Comolli LR, Thomas BC, Banfield JF (2015) Community transcriptomics reveals unexpected high microbial diversity in acidophilic biofilm communities. ISME J 9(4):1014–1023. https://doi.org/10.1038/ismej.2014.200

    CrossRef  CAS  PubMed  Google Scholar 

  5. Wilmes P, Bond PL (2004) The application of two-dimensional polyacrylamide gel electrophoresis and downstream analyses to a mixed community of prokaryotic microorganisms. Environ Microbiol 6(9):911–920. https://doi.org/10.1111/j.1462-2920.2004.00687.x

    CrossRef  CAS  PubMed  Google Scholar 

  6. Dettmer K, Aronov PA, Hammock BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26(1):51–78. https://doi.org/10.1002/mas.20108

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hagen LH, Frank JA, Zamanzadeh M, Eijsink VG, Pope PB, Horn SJ, Arntzen MO (2017) Quantitative metaproteomics highlight the metabolic contributions of uncultured phylotypes in a thermophilic anaerobic digester. Appl Environ Microbiol 83(2). https://doi.org/10.1128/AEM.01955-16

  8. Lee PY, Chin S-F, Neoh H-M, Jamal R (2017) Metaproteomic analysis of human gut microbiota: where are we heading? J Biomed Sci 24:36. https://doi.org/10.1186/s12929-017-0342-z

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  9. Urich T, Lanzén A, Stokke R, Pedersen RB, Bayer C, Thorseth IH, Schleper C, Steen IH, Øvreas L (2014) Microbial community structure and functioning in marine sediments associated with diffuse hydrothermal venting assessed by integrated meta-omics. Environ Microbiol 16:2699–2710. https://doi.org/10.1111/1462-2920.12283

    CrossRef  CAS  PubMed  Google Scholar 

  10. Püttker S, Kohrs F, Benndorf D, Heyer R, Rapp E, Reichl U (2015) Metaproteomics of activated sludge from a wastewater treatment plant – a pilot study. Proteomics 15:3596–3601. https://doi.org/10.1002/pmic.201400559

    CrossRef  CAS  PubMed  Google Scholar 

  11. Hultman J, Waldrop MP, Mackelprang R, David MM, McFarland J, Blazewicz SJ, Harden J, Turetsky MR, McGuire AD, Shah MB, VerBerkmoes NC, Lee LH, Mavrommatis K, Jansson JK (2015) Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521:208–212. https://doi.org/10.1038/nature14238

    CrossRef  CAS  PubMed  Google Scholar 

  12. Wang D-Z, Xie Z-X, Zhang S-F (2014) Marine metaproteomics: current status and future directions. J Proteomics 97:27–35. https://doi.org/10.1016/j.jprot.2013.08.024

    CrossRef  CAS  PubMed  Google Scholar 

  13. Keiblinger KM, Fuchs S, Zechmeister-Boltenstern S, Riedel K (2016) Soil and leaf litter metaproteomics – a brief guideline from sampling to understanding. FEMS Microbiol Ecol 92. https://doi.org/10.1093/femsec/iw180

  14. Zhang X, Li L, Mayne J, Ning Z, Stintzi A, Figeys D (2017) Assessing the impact of protein extraction methods for human gut metaproteomics. J Proteomics 180:120. https://doi.org/10.1016/j.jprot.2017.07.001

    CrossRef  CAS  PubMed  Google Scholar 

  15. Méndez-García C, Peláez AI, Mesa V, Sánchez J, Golyshina OV, Ferrer M (2015) Microbial diversity and metabolic networks in acid mine drainage habitats. Front Microbiol 6:475. https://doi.org/10.3389/fmicb.2015.00475

    CrossRef  PubMed  PubMed Central  Google Scholar 

  16. Wiśniewski JR, Zougman A, Mann M (2009) Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome. J Proteome Res 8:5674–5678. https://doi.org/10.1021/pr900748n

    CrossRef  CAS  PubMed  Google Scholar 

  17. Zougman A, Selby PJ, Banks RE (2014) Suspension trapping (STrap) sample preparation method for bottom-up proteomics analysis. Proteomics 14:1006–1000. https://doi.org/10.1002/pmic.201300553

    CrossRef  CAS  PubMed  Google Scholar 

  18. Hernandez-Valladares M, Aasebø E, Mjaavatten O, Vaudel M, Bruserud Ø, Berven F, Selheim F (2016) Reliable FASP-based procedures for optimal quantitative proteomic and phosphoproteomic analysis on samples from acute myeloid leukemia patients. Biol Proced Online 18:13. https://doi.org/10.1186/s12575-016-0043-0

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wisniewski JR, Zougman A, Mann M (2009) Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome. J Proteome Res 8(12):5674–5678. https://doi.org/10.1021/pr900748n

    CrossRef  CAS  PubMed  Google Scholar 

  20. Keiblinger KM, Wilhartitz IC, Schneider T, Roschitzki B, Schmid E, Eberl L, Riedel K, Zechmeister-Boltenstern S (2012) Soil metaproteomics – comparative evaluation of protein extraction protocols. Soil Biol Biochem 54:14–24. https://doi.org/10.1016/j.soilbio.2012.05.014

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  21. Speda J, Johansson MA, Carlsson U, Karlsson M (2017) Assessment of sample preparation methods for metaproteomics of extracellular proteins. Anal Biochem 516:23–36. https://doi.org/10.1016/j.ab.2016.10.008

    CrossRef  CAS  PubMed  Google Scholar 

  22. Wessel D, Flugge UI (1984) A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Anal Biochem 138(1):141–143

    CrossRef  CAS  PubMed  Google Scholar 

  23. Wang W, Vignani R, Scali M, Cresti M (2006) A universal and rapid protocol for protein extraction from recalcitrant plant tissues for proteomic analysis. Electrophoresis 27:2782–2786. https://doi.org/10.1002/elps.200500722

    CrossRef  CAS  PubMed  Google Scholar 

  24. Arenella M, D’Acqui LP, Pucci A, Giagnoni L, Nannipieri P, Renella G (2014) Contact with soil-borne humic substances interfere with the prion identification by mass spectrometry. Biol Fertil Soils 50:1009–1013. https://doi.org/10.1007/s00374-014-0922-y

    CrossRef  CAS  Google Scholar 

  25. Arenella M, Giagnoni L, Masciandaro G, Ceccanti B, Nannipieri P, Renella G (2014) Interactions between proteins and humic substances affect protein identification by mass spectrometry. Biol Fertil Soils 50:447–454. https://doi.org/10.1007/s00374-013-0860-0

    CrossRef  CAS  Google Scholar 

  26. Piccolo A, Spiteller M (2003) Electrospray ionization mass spectrometry of terrestrial humic substances and their size fractions. Anal Bioanal Chem 377:1047–1059. https://doi.org/10.1007/s00216-003-2186-5

    CrossRef  CAS  PubMed  Google Scholar 

  27. Benndorf D, Balcke GU, Harms H, von Bergen M (2007) Functional metaproteome analysis of protein extracts from contaminated soil and groundwater. ISME J 1:224–234. https://doi.org/10.1038/ismej.2007.39

    CrossRef  CAS  PubMed  Google Scholar 

  28. Giagnoni L, Magherini F, Landi L, Taghavi S, Modesti A, Bini L, Nannipieri P, Van der lelie D, Renella G (2011) Extraction of microbial proteome from soil: potential and limitations assessed through a model study. Eur J Soil Sci 62:74–81. https://doi.org/10.1111/j.1365-2389.2010.01322.x

    CrossRef  CAS  Google Scholar 

  29. Qian C, Hettich RL (2017) Optimized extraction method to remove humic acid interferences from soil samples prior to microbial proteome measurements. J Proteome Res 16:2537–2546. https://doi.org/10.1021/acs.jproteome.7b00103

    CrossRef  CAS  PubMed  Google Scholar 

  30. Dowell JA, Frost DC, Zhang J, Li L (2008) Comparison of two-dimensional fractionation techniques for shotgun proteomics. Anal Chem 80:6715–6723. https://doi.org/10.1021/ac8007994

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lee C-L, Hsiao H-H, Lin C-W, Wu S-P, Huang S-Y, Wu C-Y, Wang AH-J, Khoo K-H (2003) Strategic shotgun proteomics approach for efficient construction of an expression map of targeted protein families in hepatoma cell lines. Proteomics 3:2472–2486. https://doi.org/10.1002/pmic.200300586

    CrossRef  CAS  PubMed  Google Scholar 

  32. Weston LA, Bauer KM, Hummon AB (2013) Comparison of bottom-up proteomic approaches for LC-MS analysis of complex proteomes. Anal Methods 5:4615. https://doi.org/10.1039/C3AY40853A

    CrossRef  CAS  Google Scholar 

  33. Heyer R, Kohrs F, Reichl U, Benndorf D (2015) Metaproteomics of complex microbial communities in biogas plants. J Microbial Biotechnol 8:749–763. https://doi.org/10.1111/1751-7915.12276

    CrossRef  CAS  Google Scholar 

  34. Yang F, Shen Y, Camp DG 2nd, Smith RD (2012) High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis. Expert Rev Proteomics 9(2):129–134. https://doi.org/10.1586/epr.12.15

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhang X, Fang A, Riley CP, Wang M, Regnier FE, Buck C (2010) Multi-dimensional liquid chromatography in proteomics – a review. Anal Chim Acta 664(2):101–113. https://doi.org/10.1016/j.aca.2010.02.001

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kohrs F, Heyer R, Magnussen A, Benndorf D, Muth T, Behne A, Rapp E, Kausmann R, Heiermann M, Klocke M, Reichl U (2014) Sample prefractionation with liquid isoelectric focusing enables in depth microbial metaproteome analysis of mesophilic and thermophilic biogas plants. Anaerobe 29:59–67. https://doi.org/10.1016/j.anaerobe.2013.11.009

    CrossRef  CAS  PubMed  Google Scholar 

  37. Pirmoradian M, Budamgunta H, Chingin K, Zhang B, Astorga-Wells J, Zubarev RA (2013) Rapid and deep human proteome analysis by single-dimension shotgun proteomics. Mol Cell Proteomics 12(11):3330–3338. https://doi.org/10.1074/mcp.O113.028787

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bilbao A, Varesio E, Luban J, Strambio-De-Castillia C, Hopfgartner G, Muller M, Lisacek F (2015) Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics 15(5–6):964–980. https://doi.org/10.1002/pmic.201400323

    CrossRef  CAS  PubMed  Google Scholar 

  39. Tanca A, Palomba A, Fraumene C, Pagnozzi D, Manghina V, Deligios M, Muth T, Rapp E, Martens L, Addis MF (2016) The impact of sequence database choice on metaproteomic results in gut microbiota studies. Microbiome 4(1):51

    CrossRef  PubMed  PubMed Central  Google Scholar 

  40. Muth T, Renard BY, Martens L (2016) Metaproteomic data analysis at a glance: advances in computational microbial community proteomics. Expert Rev Proteomics 13(8):757–769. https://doi.org/10.1080/14789450.2016.1209418

    CrossRef  CAS  PubMed  Google Scholar 

  41. Bragg L, Tyson GW (2014) Metagenomics using next-generation sequencing. In: Paulsen IT, Holmes AJ (eds) Environmental microbiology: methods and protocols. Humana Press, Totowa, pp 183–201. https://doi.org/10.1007/978-1-62703-712-9_15

    CrossRef  Google Scholar 

  42. Heintz-Buschart A, May P, Laczny CC, Lebrun LA, Bellora C, Krishna A, Wampach L, Schneider JG, Hogan A, de Beaufort C, Wilmes P (2016) Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol 2:16180. https://doi.org/10.1038/nmicrobiol.2016.180

    CrossRef  CAS  PubMed  Google Scholar 

  43. Peng Y, Leung HC, Yiu SM, Chin FY (2012) IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28(11):1420–1428. https://doi.org/10.1093/bioinformatics/bts174

    CrossRef  CAS  PubMed  Google Scholar 

  44. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31(10):1674–1676. https://doi.org/10.1093/bioinformatics/btv033

    CrossRef  CAS  PubMed  Google Scholar 

  45. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19(5):455–477. https://doi.org/10.1089/cmb.2012.0021

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sangwan N, Xia F, Gilbert JA (2016) Recovering complete and draft population genomes from metagenome datasets. Microbiome 4:8. https://doi.org/10.1186/s40168-016-0154-5

    CrossRef  PubMed  PubMed Central  Google Scholar 

  47. Frank JA, Pan Y, Tooming-Klunderud A, Eijsink VG, McHardy AC, Nederbragt AJ, Pope PB (2016) Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data. Sci Rep 6:25373. https://doi.org/10.1038/srep25373

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kang DD, Froula J, Egan R, Wang Z (2015) MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3:e1165. https://doi.org/10.7717/peerj.1165

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW (2014) MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome 2(1):26. https://doi.org/10.1186/2049-2618-2-26

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  50. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, Lahti L, Loman NJ, Andersson AF, Quince C (2014) Binning metagenomic contigs by coverage and composition. Nat Methods 11(11):1144–1146. https://doi.org/10.1038/nmeth.3103

    CrossRef  CAS  PubMed  Google Scholar 

  51. Gregor I, Dröge J, Schirmer M, Quince C, McHardy AC (2016) PhyloPythiaS+: a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes. PeerJ 4:e1603. https://doi.org/10.7717/peerj.1603

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  52. Huson DH, Auch AF, Qi J, Schuster SC (2007) MEGAN analysis of metagenomic data. Genome Res 17(3):377–386. https://doi.org/10.1101/gr.5969107

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  53. Dröge J, Gregor I, McHardy AC (2015) Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods. Bioinformatics 31(6):817–824. https://doi.org/10.1093/bioinformatics/btu745

    CrossRef  CAS  PubMed  Google Scholar 

  54. McHardy AC, Rigoutsos I (2007) What’s in the mix: phylogenetic classification of metagenome sequence samples. Curr Opin Microbiol 10(5):499–503. https://doi.org/10.1016/j.mib.2007.08.004

    CrossRef  CAS  PubMed  Google Scholar 

  55. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Droege J, Gregor I, Majda S, Fiedler J, Dahms E, Bremges A, Fritz A, Garrido-Oter R, Sparholt Jorgensen T, Shapiro N, Blood PD, Gurevich A, Bai Y, Turaev D, DeMaere MZ, Chikhi R, Nagarajan N, Quince C, Hestbjerg Hansen L, Sorensen SJ, Chia BKH, Denis B, Froula JL, Wang Z, Egan R, Kang DD, Cook JJ, Deltel C, Beckstette M, Lemaitre C, Peterlongo P, Rizk G, Lavenier D, Wu Y-W, Singer SW, Jain C, Strous M, Klingenberg H, Meinicke P, Barton M, Lingner T, Lin H-H, Liao Y-C, Gueiros Z, Silva G, Cuevas DA, Edwards RA, Saha S, Piro VC, Renard BY, Pop M, Klenk H-P, Goeker M, Kyrpides N, Woyke T, Vorholt JA, Schulze-Lefert P, Rubin EM, Darling AE, Rattei T, McHardy AC (2017) Critical Assessment of Metagenome Interpretation − a benchmark of computational metagenomics software. bioRxiv. https://doi.org/10.1101/099127

  56. Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, Delmont TO (2015) Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3:e1319. https://doi.org/10.7717/peerj.1319

    CrossRef  PubMed  PubMed Central  Google Scholar 

  57. Zhu Z, Niu B, Chen J, Wu S, Sun S, Li W (2013) MGAviewer: a desktop visualization tool for analysis of metagenomics alignment data. Bioinformatics 29(1):122–123. https://doi.org/10.1093/bioinformatics/bts567

    CrossRef  CAS  PubMed  Google Scholar 

  58. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW (2015) CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25(7):1043–1055. https://doi.org/10.1101/gr.186072.114

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  59. Rodriguez-R LM, Konstantinidis KT (2016) The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. PeerJ Preprints 4:e1900v1901. https://doi.org/10.7287/peerj.preprints.1900v1

    CrossRef  Google Scholar 

  60. Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, Schulz F, Jarett J, Rivers AR, Eloe-Fadrosh EA, Tringe SG, Ivanova NN, Copeland A, Clum A, Becraft ED, Malmstrom RR, Birren B, Podar M, Bork P, Weinstock GM, Garrity GM, Dodsworth JA, Yooseph S, Sutton G, Glockner FO, Gilbert JA, Nelson WC, Hallam SJ, Jungbluth SP, Ettema TJG, Tighe S, Konstantinidis KT, Liu W-T, Baker BJ, Rattei T, Eisen JA, Hedlund B, McMahon KD, Fierer N, Knight R, Finn R, Cochrane G, Karsch-Mizrachi I, Tyson GW, Rinke C, The Genome Standards C, Lapidus A, Meyer F, Yilmaz P, Parks DH, Eren AM, Schriml L, Banfield JF, Hugenholtz P, Woyke T (2017) Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol 35(8):725–731. https://doi.org/10.1038/nbt.3893

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  61. Trimble WL, Keegan KP, D’Souza M, Wilke A, Wilkening J, Gilbert J, Meyer F (2012) Short-read reading-frame predictors are not created equal: sequence error causes loss of signal. BMC Bioinformatics 13:183. https://doi.org/10.1186/1471-2105-13-183

    CrossRef  PubMed  PubMed Central  Google Scholar 

  62. Zhu W, Lomsadze A, Borodovsky M (2010) Ab initio gene identification in metagenomic sequences. Nucleic Acids Res 38(12):e132–e132. https://doi.org/10.1093/nar/gkq275

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  63. Frank JA, Arntzen MØ, Sun L, Hagen LH, McHardy AC, Horn SJ, Eijsink VGH, Schnürer A, Pope PB (2016) Novel syntrophic populations dominate an ammonia-tolerant methanogenic microbiome. mSystems 1(5). https://doi.org/10.1128/mSystems.00092-16

  64. Tang H, Li S, Ye Y (2016) A graph-centric approach for metagenome-guided peptide and protein identification in metaproteomics. PLoS Comput Biol 12(12):e1005224. https://doi.org/10.1371/journal.pcbi.1005224

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  65. Weimann A, Mooren K, Frank J, Pope PB, Bremges A, McHardy AC (2016) From genomes to phenotypes: traitar, the microbial trait analyzer. mSystems 1(6). https://doi.org/10.1128/mSystems.00101-16

  66. Muth T, Benndorf D, Reichl U, Rapp E, Martens L (2013) Searching for a needle in a stack of needles: challenges in metaproteomics data analysis. Mol Biosyst 9(4):578–585. https://doi.org/10.1039/c2mb25415h

    CrossRef  CAS  PubMed  Google Scholar 

  67. Nesvizhskii AI, Aebersold R (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol Cell Proteomics 4(10):1419–1440. https://doi.org/10.1074/mcp.R500012-MCP200

    CrossRef  CAS  PubMed  Google Scholar 

  68. Perkins DN, Pappin DJ, Creasy DM, Cottrell JS (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20(18):3551–3567

    CrossRef  CAS  PubMed  Google Scholar 

  69. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10(4):1794–1805. https://doi.org/10.1021/pr101065j

    CrossRef  CAS  PubMed  Google Scholar 

  70. Craig R, Beavis RC (2003) A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Commun Mass Spectrom 17(20):2310–2316. https://doi.org/10.1002/rcm.1198

    CrossRef  CAS  PubMed  Google Scholar 

  71. Eng JK, Searle BC, Clauser KR, Tabb DL (2011) A face in the crowd: recognizing peptides through database search. Mol Cell Proteomics 10(11):R111.009522. doi:https://doi.org/10.1074/mcp.R111.009522

    CrossRef  CAS  Google Scholar 

  72. Vaudel M, Burkhart JM, Sickmann A, Martens L, Zahedi RP (2011) Peptide identification quality control. Proteomics 11(10):2105–2114. https://doi.org/10.1002/pmic.201000704

    CrossRef  CAS  PubMed  Google Scholar 

  73. Elias JE, Gygi SP (2007) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 4(3):207–214. https://doi.org/10.1038/nmeth1019

    CrossRef  CAS  PubMed  Google Scholar 

  74. Jagtap P, Goslinga J, Kooren JA, McGowan T, Wroblewski MS, Seymour SL, Griffin TJ (2013) A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies. Proteomics 13(8):1352–1357. https://doi.org/10.1002/pmic.201200352

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  75. Chatterjee S, Stupp GS, Park SK, Ducom JC, Yates JR 3rd, Su AI, Wolan DW (2016) A comprehensive and scalable database search system for metaproteomics. BMC Genomics 17(1):642. https://doi.org/10.1186/s12864-016-2855-3

    CrossRef  PubMed  PubMed Central  Google Scholar 

  76. Wang Y, Ahn TH, Li Z, Pan C (2013) Sipros/ProRata: a versatile informatics system for quantitative community proteomics. Bioinformatics 29(16):2064–2065. https://doi.org/10.1093/bioinformatics/btt329

    CrossRef  CAS  PubMed  Google Scholar 

  77. Gonnelli G, Stock M, Verwaeren J, Maddelein D, De Baets B, Martens L, Degroeve S (2015) A decoy-free approach to the identification of peptides. J Proteome Res 14(4):1792–1798. https://doi.org/10.1021/pr501164r

    CrossRef  CAS  PubMed  Google Scholar 

  78. Shevchenko A, Sunyaev S, Loboda A, Shevchenko 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

    CrossRef  CAS  PubMed  Google Scholar 

  79. Pevtsov S, Fedulova I, Mirzaei H, Buck C, Zhang X (2006) Performance evaluation of existing de novo sequencing algorithms. J Proteome Res 5(11):3018–3028. https://doi.org/10.1021/pr060222h

    CrossRef  CAS  PubMed  Google Scholar 

  80. Frank A, Pevzner P (2005) PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal Chem 77(4):964–973

    CrossRef  CAS  PubMed  Google Scholar 

  81. Ma B, Zhang K, Hendrie C, Liang C, Li M, Doherty-Kirby A, Lajoie G (2003) PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom 17(20):2337–2342. https://doi.org/10.1002/rcm.1196

    CrossRef  CAS  PubMed  Google Scholar 

  82. Allmer J (2011) Algorithms for the de novo sequencing of peptides from tandem mass spectra. Expert Rev Proteomics 8(5):645–657. https://doi.org/10.1586/epr.11.54

    CrossRef  PubMed  Google Scholar 

  83. Muth T, Kolmeder CA, Salojarvi J, Keskitalo S, Varjosalo M, Verdam FJ, Rensen SS, Reichl U, de Vos WM, Rapp E, Martens L (2015) Navigating through metaproteomics data: a logbook of database searching. Proteomics 15(20):3439–3453. https://doi.org/10.1002/pmic.201400560

    CrossRef  CAS  PubMed  Google Scholar 

  84. Craig R, Cortens JC, Fenyo D, Beavis RC (2006) Using annotated peptide mass spectrum libraries for protein identification. J Proteome Res 5(8):1843–1849. https://doi.org/10.1021/pr0602085

    CrossRef  CAS  PubMed  Google Scholar 

  85. Lam H, Deutsch EW, Eddes JS, Eng JK, King N, Stein SE, Aebersold R (2007) Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7(5):655–667. https://doi.org/10.1002/pmic.200600625

    CrossRef  CAS  PubMed  Google Scholar 

  86. Frewen B, MacCoss MJ (2007) Using BiblioSpec for creating and searching tandem MS peptide libraries. Curr Protoc Bioinformatics Chapter 13:Unit 13 17. doi:https://doi.org/10.1002/0471250953.bi1307s20

  87. Liu H, Sadygov RG, Yates JR 3rd (2004) A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 76(14):4193–4201. https://doi.org/10.1021/ac0498563

    CrossRef  CAS  PubMed  Google Scholar 

  88. Ishihama Y, Oda Y, Tabata T, Sato T, Nagasu T, Rappsilber J, Mann M (2005) Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol Cell Proteomics 4(9):1265–1272. https://doi.org/10.1074/mcp.M500061-MCP200

    CrossRef  CAS  PubMed  Google Scholar 

  89. Paoletti AC, Parmely TJ, Tomomori-Sato C, Sato S, Zhu D, Conaway RC, Conaway JW, Florens L, Washburn MP (2006) Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors. Proc Natl Acad Sci U S A 103(50):18928–18933. https://doi.org/10.1073/pnas.0606379103

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  90. Nahnsen S, Bielow C, Reinert K, Kohlbacher O (2013) Tools for label-free peptide quantification. Mol Cell Proteomics 12(3):549–556. https://doi.org/10.1074/mcp.R112.025163

    CrossRef  CAS  PubMed  Google Scholar 

  91. Muth T, Behne A, Heyer R, Kohrs F, Benndorf D, Hoffmann M, Lehteva M, Reichl U, Martens L, Rapp E (2015) The MetaProteomeAnalyzer: a powerful open-source software suite for metaproteomics data analysis and interpretation. J Proteome Res 14(3):1557–1565. https://doi.org/10.1021/pr501246w

    CrossRef  CAS  PubMed  Google Scholar 

  92. 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

    CrossRef  CAS  PubMed  Google Scholar 

  93. Jagtap PD, Blakely A, Murray K, Stewart S, Kooren J, Johnson JE, Rhodus NL, Rudney J, Griffin TJ (2015) Metaproteomic analysis using the Galaxy framework. Proteomics 15(20):3553–3565. https://doi.org/10.1002/pmic.201500074

    CrossRef  CAS  PubMed  Google Scholar 

  94. Argentini A, Goeminne LJ, Verheggen K, Hulstaert N, Staes A, Clement L, Martens L (2016) moFF: a robust and automated approach to extract peptide ion intensities. Nat Methods 13(12):964–966. https://doi.org/10.1038/nmeth.4075

    CrossRef  CAS  PubMed  Google Scholar 

  95. Huson DH, Beier S, Flade I, Gorska A, El-Hadidi M, Mitra S, Ruscheweyh HJ, Tappu R (2016) MEGAN community edition – interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol 12(6):e1004957. https://doi.org/10.1371/journal.pcbi.1004957

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  96. Mesuere B, Devreese B, Debyser G, Aerts M, Vandamme P, Dawyndt P (2012) Unipept: tryptic peptide-based biodiversity analysis of metaproteome samples. J Proteome Res 11(12):5773–5780. https://doi.org/10.1021/pr300576s

    CrossRef  CAS  PubMed  Google Scholar 

  97. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O’Donovan C, Redaschi N, Yeh LS (2004) UniProt: the universal protein knowledgebase. Nucleic Acids Res 32(Database issue):D115–D119. https://doi.org/10.1093/nar/gkh131

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  98. Schneider T, Schmid E, de Castro JV Jr, Cardinale M, Eberl L, Grube M, Berg G, Riedel K (2011) Structure and function of the symbiosis partners of the lung lichen (Lobaria pulmonaria L. Hoffm.) analyzed by metaproteomics. Proteomics 11(13):2752–2756. https://doi.org/10.1002/pmic.201000679

    CrossRef  CAS  PubMed  Google Scholar 

  99. Penzlin A, Lindner MS, Doellinger J, Dabrowski PW, Nitsche A, Renard BY (2014) Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics. Bioinformatics 30(12):i149–i156. https://doi.org/10.1093/bioinformatics/btu267

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  100. Heyer R, Schallert K, Zoun R, Becher B, Saake G, Benndorf D (2017) Challenges and perspectives of metaproteomic data analysis. J Biotechnol 261:24. https://doi.org/10.1016/j.jbiotec.2017.06.1201

    CrossRef  CAS  PubMed  Google Scholar 

  101. 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. https://doi.org/10.1038/nmeth.3901

    CrossRef  CAS  PubMed  Google Scholar 

  102. Luo W, Brouwer C (2013) Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29(14):1830–1831. https://doi.org/10.1093/bioinformatics/btt285

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  103. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

    CrossRef  CAS  PubMed  Google Scholar 

  104. Finn RD, Clements J, Eddy SR (2011) HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 39(Web Server issue):W29–W37. https://doi.org/10.1093/nar/gkr367

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  105. Prakash T, Taylor TD (2012) Functional assignment of metagenomic data: challenges and applications. Brief Bioinform 13(6):711–727. https://doi.org/10.1093/bib/bbs033

    CrossRef  PubMed  PubMed Central  Google Scholar 

  106. Overbeek R, Fonstein M, D’Souza M, Pusch GD, Maltsev N (1999) The use of gene clusters to infer functional coupling. Proc Natl Acad Sci U S A 96(6):2896–2901

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  107. Tatusov RL, Koonin EV, Lipman DJ (1997) A genomic perspective on protein families. Science 278(5338):631–637

    CrossRef  CAS  PubMed  Google Scholar 

  108. 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

    CrossRef  CAS  PubMed  Google Scholar 

  109. Haft DH, Selengut JD, White O (2003) The TIGRFAMs database of protein families. Nucleic Acids Res 31(1):371–373

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  110. Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28(1):304–305

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

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

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  112. Krieger CJ, Zhang P, Mueller LA, Wang A, Paley S, Arnaud M, Pick J, Rhee SY, Karp PD (2004) MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res 32(Database issue):D438–D442. https://doi.org/10.1093/nar/gkh100

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  113. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25(1):25–29. https://doi.org/10.1038/75556

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  114. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550. https://doi.org/10.1073/pnas.0506580102

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  115. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(5):P3

    CrossRef  PubMed  Google Scholar 

  116. Reimand J, Kull M, Peterson H, Hansen J, Vilo J (2007) g:Profiler – a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res 35(Web Server issue):W193–W200. https://doi.org/10.1093/nar/gkm226

    CrossRef  PubMed  PubMed Central  Google Scholar 

  117. Finn RD, Attwood TK, Babbitt PC, Bateman A, Bork P, Bridge AJ, Chang HY, Dosztanyi 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 SC, Wu CH, Xenarios I, Yeh LS, Young SY, Mitchell AL (2017) InterPro in 2017-beyond protein family and domain annotations. Nucleic Acids Res 45(D1):D190–D199. https://doi.org/10.1093/nar/gkw1107

    CrossRef  CAS  PubMed  Google Scholar 

  118. Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweiler R, Lopez R (2005) InterProScan: protein domains identifier. Nucleic Acids Res 33(Web Server issue):W116–W120. https://doi.org/10.1093/nar/gki442

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  119. Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Jacob B, Huang J, Williams P, Huntemann M, Anderson I, Mavromatis K, Ivanova NN, Kyrpides NC (2012) IMG: the Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res 40(Database issue):D115–D122. https://doi.org/10.1093/nar/gkr1044

    CrossRef  CAS  PubMed  Google Scholar 

  120. Kanehisa M, Sato Y, Morishima K (2016) BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol 428(4):726–731. https://doi.org/10.1016/j.jmb.2015.11.006

    CrossRef  CAS  PubMed  Google Scholar 

  121. Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B (2014) The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42(Database issue):D490–D495. https://doi.org/10.1093/nar/gkt1178

    CrossRef  CAS  PubMed  Google Scholar 

  122. 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. https://doi.org/10.1093/nar/gks479

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  123. 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. https://doi.org/10.1093/glycob/cwq106

    CrossRef  CAS  PubMed  Google Scholar 

  124. Rawlings ND, Barrett AJ, Finn R (2016) Twenty years of the MEROPS database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res 44(D1):D343–D350. https://doi.org/10.1093/nar/gkv1118

    CrossRef  CAS  PubMed  Google Scholar 

  125. Huberts DH, van der Klei IJ (2010) Moonlighting proteins: an intriguing mode of multitasking. Biochim Biophys Acta 1803(4):520–525. https://doi.org/10.1016/j.bbamcr.2010.01.022

    CrossRef  CAS  PubMed  Google Scholar 

  126. Mayers MD, Moon C, Stupp GS, Su AI, Wolan DW (2017) Quantitative metaproteomics and activity-based probe enrichment reveals significant alterations in protein expression from a mouse model of inflammatory bowel disease. J Proteome Res 16(2):1014–1026. https://doi.org/10.1021/acs.jproteome.6b00938

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  127. Jehmlich N, Schmidt F, Taubert M, Seifert J, Bastida F, von Bergen M, Richnow HH, Vogt C (2010) Protein-based stable isotope probing. Nat Protoc 5(12):1957–1966. https://doi.org/10.1038/nprot.2010.166

    CrossRef  CAS  PubMed  Google Scholar 

  128. Campanaro S, Treu L, Kougias PG, Francisci D, Valle G, Angelidaki I (2016) Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy. Biotechnol Biofuels 9(1):1

    CrossRef  CAS  Google Scholar 

  129. Zhou Y, Pope PB, Li S, Wen B, Tan F, Cheng S, Chen J, Yang J, Liu F, Lei X (2014) Omics-based interpretation of synergism in a soil-derived cellulose-degrading microbial community. Sci Rep 4:5288. https://doi.org/10.1038/srep05288

  130. Hartmann H, Ahring BK (2005) Anaerobic digestion of the organic fraction of municipal solid waste: influence of co-digestion with manure. Water Res 39(8):1543–1552. https://doi.org/10.1016/j.watres.2005.02.001

    CrossRef  CAS  PubMed  Google Scholar 

  131. Westerholm M, Moestedt J, Schnürer A (2016) Biogas production through syntrophic acetate oxidation and deliberate operating strategies for improved digester performance. Appl Energy 179:124–135

    CrossRef  CAS  Google Scholar 

  132. Gallert C, Winter J (1997) Mesophilic and thermophilic anaerobic digestion of source-sorted organic wastes: effect of ammonia on glucose degradation and methane production. Appl Microbiol Biotechnol 48(3):405–410

    CrossRef  CAS  Google Scholar 

  133. McInerney MJ, Struchtemeyer CG, Sieber J, Mouttaki H, Stams AJM, Schink B, Rohlin L, Gunsalus RP (2008) Physiology, ecology, phylogeny, and genomics of microorganisms capable of syntrophic metabolism. In: Wiegel J, Maier RJ, Adams MWW (eds) Incredible anaerobes: from physiology to genomics to fuels, Annals of the New York Academy of Sciences, vol 1125. Blackwell Publishing, Oxford, pp 58–72. https://doi.org/10.1196/annals.1419.005

    CrossRef  Google Scholar 

  134. Westerholm M, Roos S, Schnürer A (2011) Tepidanaerobacter acetatoxydans sp. nov., an anaerobic, syntrophic acetate-oxidizing bacterium isolated from two ammonium-enriched mesophilic methanogenic processes. Syst Appl Microbiol 34(4):260–266

    CrossRef  CAS  PubMed  Google Scholar 

  135. Schnürer A, Schink B, Svensson BH (1996) Clostridium ultunense sp. nov., a mesophilic bacterium oxidizing acetate in syntrophic association with a hydrogenotrophic methanogenic bacterium. Int J Syst Bacteriol 46(4):1145–1152

    CrossRef  PubMed  Google Scholar 

  136. Hattori S, Kamagata Y, Hanada S, Shoun H (2000) Thermacetogenium phaeum gen. nov., sp. nov., a strictly anaerobic, thermophilic, syntrophic acetate-oxidizing bacterium. Int J Syst Evol Microbiol 50(4):1601–1609

    CrossRef  CAS  PubMed  Google Scholar 

  137. Llewellyn MS, Boutin S, Hoseinifar SH, Derome N (2014) Teleost microbiomes: the state of the art in their characterization, manipulation and importance in aquaculture and fisheries. Front Microbiol 5:207. https://doi.org/10.3389/fmicb.2014.00207

    CrossRef  PubMed  PubMed Central  Google Scholar 

  138. Karlsen C, Ottem KF, Brevik OJ, Davey M, Sorum H, Winther-Larsen HC (2017) The environmental and host-associated bacterial microbiota of Arctic seawater-farmed Atlantic salmon with ulcerative disorders. J Fish Dis 40:1645. https://doi.org/10.1111/jfd.12632

    CrossRef  CAS  PubMed  Google Scholar 

  139. Ángeles Esteban M (2012) An overview of the immunological defenses in fish skin. ISRN Immunol 2012:29. https://doi.org/10.5402/2012/853470

    CrossRef  Google Scholar 

  140. Martens EC, Chiang HC, Gordon JI (2008) Mucosal glycan foraging enhances fitness and transmission of a saccharolytic human gut bacterial symbiont. Cell Host Microbe 4(5):447–457

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  141. Roberts SD, Powell MD (2005) The viscosity and glycoprotein biochemistry of salmonid mucus varies with species, salinity and the presence of amoebic gill disease. J Comp Physiol B 175(1):1–11

    CAS  PubMed  Google Scholar 

  142. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460–2461. https://doi.org/10.1093/bioinformatics/btq461

    CrossRef  CAS  PubMed  Google Scholar 

  143. Kuczynski J, Stombaugh J, Walters WA, González A, Caporaso JG, Knight R (2012) Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Microbiol 1E. 5.1–1E. 5.20

    Google Scholar 

  144. Cordero H, Morcillo P, Cuesta A, Brinchmann MF, Esteban MA (2016) Differential proteome profile of skin mucus of gilthead seabream (Sparus aurata) after probiotic intake and/or overcrowding stress. J Proteomics 132:41–50. https://doi.org/10.1016/j.jprot.2015.11.017

    CrossRef  CAS  PubMed  Google Scholar 

  145. Jurado J, Fuentes-Almagro CA, Guardiola FA, Cuesta A, Esteban MA, Prieto-Alamo MJ (2015) Proteomic profile of the skin mucus of farmed gilthead seabream (Sparus aurata). J Proteomics 120:21–34. https://doi.org/10.1016/j.jprot.2015.02.019

    CrossRef  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Magnus Ø. Arntzen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kunath, B.J. et al. (2019). Metaproteomics: Sample Preparation and Methodological Considerations. In: Capelo-Martínez, JL. (eds) Emerging Sample Treatments in Proteomics. Advances in Experimental Medicine and Biology(), vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-12298-0_8

Download citation

Publish with us

Policies and ethics