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Increasing the power of interpretation for soil metaproteomics data

Jouffret, Virginie, Miotello, Guylaine, Culotta, Karen, Ayrault, Sophie, Pible, Olivier, Armengaud, Jean·2021·Microbiome
DOI 10.1186/s40168-021-01139-1PMC PMC8482631
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Abstract

Background: Soil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples. Results: The number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases. Conclusions: A two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information.

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Fig. 1 Workflows assessed for metaproteomics analysis of environmental samples. The left (green) and top (blue) arrows show the experimental steps tested to obtain MS/MS spectra and metagenomics reads. The dashed-line panel presents the bioinformatics analysis, with, at the top, database construction either from metagenomics data with or without assembly step, or from generic databases. The bottom part shows the process used to refine and enhance the attribution rate through multiple search rounds. A first analysis on full databases was used to select protein-based subsets at various p-values. New searches on these subsets then identified the databases performing best
Fig. 1 Workflows assessed for metaproteomics analysis of environmental samples. The left (green) and top (blue) arrows show the experimental steps tested to obtain MS/MS spectra and metagenomics reads. The dashed-line panel presents the bioinformatics analysis, with, at the top, database construction either from metagenomics data with or without assembly step, or from generic databases. The bottom part shows the process used to refine and enhance the attribution rate through multiple search rounds. A first analysis on full databases was used to select protein-based subsets at various p-values. New searches on these subsets then identified the databases performing bestCC BY · PubMed Central
Fig. 2 Attribution rates for MS/MS spectra, obtained with sample-specific metagenomics databases. Databases were generated using the following workflows: FragGeneScan only (FGS), MEGAHIT and FragGeneScan (MGH-FGS), MEGAHIT and six-frame translation (MGH-6RF), sixgill, and PLASS. The results obtained when using the complete databases are indicated on the right (one-step search), whereas the results obtained on refined databases containing proteins selected at distinct p-values during the first-round search are shown on the left. Searches were performed at decoy-free FDR 1% and results are ordered based on the database size, using a logarithmic X-axis. The small numbers indicate the p-value threshold used for each search. Three biological replicates were used to calculate the mean and standard deviation values.
Fig. 2 Attribution rates for MS/MS spectra, obtained with sample-specific metagenomics databases. Databases were generated using the following workflows: FragGeneScan only (FGS), MEGAHIT and FragGeneScan (MGH-FGS), MEGAHIT and six-frame translation (MGH-6RF), sixgill, and PLASS. The results obtained when using the complete databases are indicated on the right (one-step search), whereas the results obtained on refined databases containing proteins selected at distinct p-values during the first-round search are shown on the left. Searches were performed at decoy-free FDR 1% and results are ordered based on the database size, using a logarithmic X-axis. The small numbers indicate the p-value threshold used for each search. Three biological replicates were used to calculate the mean and standard deviation values.CC BY · PubMed Central
Fig. 3 Attribution rates for MS/MS spectra when using generic databases. The results obtained with SGC and NCBI are shown as a function of the database size using a logarithmic X-axis. The results obtained with the whole databases are presented on the right (one-step search), whereas the results obtained on refined databases containing proteins selected at distinct p-values during the first-round search are indicated on the left (two-step search). The SGC+NCBI databases were obtained by merging the reduced NCBI and SGC protein databases at each p-value cutoff. Searches were performed at decoy-free FDR 1%. The small numbers indicate the p-value threshold used for each search. Three biological replicates were used to calculate the mean and standard deviation values.
Fig. 3 Attribution rates for MS/MS spectra when using generic databases. The results obtained with SGC and NCBI are shown as a function of the database size using a logarithmic X-axis. The results obtained with the whole databases are presented on the right (one-step search), whereas the results obtained on refined databases containing proteins selected at distinct p-values during the first-round search are indicated on the left (two-step search). The SGC+NCBI databases were obtained by merging the reduced NCBI and SGC protein databases at each p-value cutoff. Searches were performed at decoy-free FDR 1%. The small numbers indicate the p-value threshold used for each search. Three biological replicates were used to calculate the mean and standard deviation values.CC BY · PubMed Central
Fig. 4 Functional annotation of peptides and proteins identified at FDR 1%. The databases used to identify proteins are indicated on the left. For each database, the percentage of PSMs annotated are indicated in terms of GO_BP, GO_MF terms, and EC numbers obtained either by Unipept or using Diamond BLASTp queries on Uniref50. In the latter case, the GO OWL tools were used to retrieve GO slim annotations; EC numbers and KO entries were retrieved using the GhostKOALA web-service. The grey squared areas on the EC lines represent the KEGG KO annotation level, from which EC numbers were extracted
Fig. 4 Functional annotation of peptides and proteins identified at FDR 1%. The databases used to identify proteins are indicated on the left. For each database, the percentage of PSMs annotated are indicated in terms of GO_BP, GO_MF terms, and EC numbers obtained either by Unipept or using Diamond BLASTp queries on Uniref50. In the latter case, the GO OWL tools were used to retrieve GO slim annotations; EC numbers and KO entries were retrieved using the GhostKOALA web-service. The grey squared areas on the EC lines represent the KEGG KO annotation level, from which EC numbers were extractedCC BY · PubMed Central
Fig. 5 KO and taxonomical annotation of soil metagenomics data and metaproteomics data. Metagenomics data were annotated using the MG-RAST pipeline, with KO and taxonomic analysis. Peptides and sequences attributed at FDR 1% of the combined reduced sample-specific and generic database FGS_0.30+SGC_0.30+NCBI_0.30 were analyzed using Unipept and GhostKOALA to retrieve taxonomic and functional annotations, respectively. The PSMs for the three biological replicates were cumulated. A Functional annotation of three KEGG categories: metabolism (left), genetic information processing (center), and environmental information processing (right). A total of 16% of reads and 72% of PSMs were annotated in terms of function. B Taxonomic annotation of metagenomics data (MetaG) and metaproteomics data (MetaP) at Kingdom (left), Bacterial phyla (center), and Eukaryotic phyla (right) levels. Phyla represented less than 1% of the total were merged under “Other phyla”. A total of 17% of reads and 21% of PSMs were unequivocally annotated in terms of taxonomy
Fig. 5 KO and taxonomical annotation of soil metagenomics data and metaproteomics data. Metagenomics data were annotated using the MG-RAST pipeline, with KO and taxonomic analysis. Peptides and sequences attributed at FDR 1% of the combined reduced sample-specific and generic database FGS_0.30+SGC_0.30+NCBI_0.30 were analyzed using Unipept and GhostKOALA to retrieve taxonomic and functional annotations, respectively. The PSMs for the three biological replicates were cumulated. A Functional annotation of three KEGG categories: metabolism (left), genetic information processing (center), and environmental information processing (right). A total of 16% of reads and 72% of PSMs were annotated in terms of function. B Taxonomic annotation of metagenomics data (MetaG) and metaproteomics data (MetaP) at Kingdom (left), Bacterial phyla (center), and Eukaryotic phyla (right) levels. Phyla represented less than 1% of the total were merged under “Other phyla”. A total of 17% of reads and 21% of PSMs were unequivocally annotated in terms of taxonomyCC BY · PubMed Central

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Research works printed but not linked: 73

  1. Ayrault, S., M. Meybeck, J.-M. Mouchel, J. Gaspéri, L. Lestel, C. Lorgeoux and D. Boust (2019). Sedimentary archives reveal the concealed history of micropollutant contamination in the Seine River basin. Berlin, Heidelberg, Springer Berlin Heidelberg: 1-32. · DOI
  2. Ayrault S, Priadi CR, Evrard O, Lefevre I, Bonte P. Silver and thallium historical trends in the Seine River basin. J Environ Monit. 2010;12(11):2177–85. https://doi.org/10.1039/c0em00153h. · DOI · Paper page
  3. Ayrault S, Roy-Barman M, Le Cloarec MF, Priadi CR, Bonte P, Gopel C. Lead contamination of the Seine River, France: geochemical implications of a historical perspective. Chemosphere. 2012;87(8):902–10. https://doi.org/10.1016/j.chemosphere.2012.01.043. · DOI
  4. Bagnoud A, Chourey K, Hettich RL, de Bruijn I, Andersson AF, Leupin OX, et al. Reconstructing a hydrogen-driven microbial metabolic network in Opalinus Clay rock. Nat Commun. 2016;7(1):12770. https://doi.org/10.1038/ncomms12770. · DOI
  5. Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560(7717):233–7. https://doi.org/10.1038/s41586-018-0386-6. · DOI
  6. Bastida F, Jehmlich N, Martínez-Navarro J, Bayona V, García C, Moreno JL. The effects of struvite and sewage sludge on plant yield and the microbial community of a semiarid Mediterranean soil. Geoderma. 2019;337:1051–7. https://doi.org/10.1016/j.geoderma.2018.10.046. · DOI
  7. Bastida F, Torres IF, Moreno JL, Baldrian P, Ondono S, Ruiz-Navarro A, et al. The active microbial diversity drives ecosystem multifunctionality and is physiologically related to carbon availability in Mediterranean semi-arid soils. Mol Ecol. 2016;25(18):4660–73. https://doi.org/10.1111/mec.13783. · DOI
  8. Batut B, Gravouil K, Defois C, Hiltemann S, Brugere JF, Peyretaillade E, et al. ASaiM: a Galaxy-based framework to analyze microbiota data. Gigascience. 2018;7(6). https://doi.org/10.1093/gigascience/giy057. · DOI
  9. Becher D, Bernhardt J, Fuchs S, Riedel K. Metaproteomics to unravel major microbial players in leaf litter and soil environments: challenges and perspectives. Proteomics. 2013;13(18-19):2895–909. https://doi.org/10.1002/pmic.201300095. · DOI
  10. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60. https://doi.org/10.1038/nmeth.3176. · DOI · Paper page
  11. Cernava T, Erlacher A, Aschenbrenner IA, Krug L, Lassek C, Riedel K, et al. Deciphering functional diversification within the lichen microbiota by meta-omics. Microbiome. 2017;5(1):82. https://doi.org/10.1186/s40168-017-0303-5. · DOI
  12. Cheng K, Ning Z, Zhang X, Li L, Liao B, Mayne J, et al. MetaLab: an automated pipeline for metaproteomic data analysis. Microbiome. 2017;5(1):157. https://doi.org/10.1186/s40168-017-0375-2. · DOI
  13. Chourey K, Nissen S, Vishnivetskaya T, Shah M, Pfiffner S, Hettich RL, et al. Environmental proteomics reveals early microbial community responses to biostimulation at a uranium- and nitrate-contaminated site. Proteomics. 2013;13(18-19):2921–30. https://doi.org/10.1002/pmic.201300155. · DOI
  14. Cogne Y, Gouveia D, Chaumot A, Degli-Esposti D, Geffard O, Pible O, et al. Proteogenomics-guided evaluation of RNA-Seq assembly and protein database construction for emergent model organisms. Proteomics. 2020;20(10):e1900261. https://doi.org/10.1002/pmic.201900261. · DOI
  15. Coute Y, Bruley C, Burger T. Beyond target-decoy competition: stable validation of peptide and protein identifications in mass spectrometry-based discovery proteomics. Anal Chem. 2020;92(22):14898–906. https://doi.org/10.1021/acs.analchem.0c00328. · DOI
  16. Ewels P, Magnusson M, Lundin S, Kaller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8. https://doi.org/10.1093/bioinformatics/btw354. · DOI · Paper page
  17. Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15(10):579–90. https://doi.org/10.1038/nrmicro.2017.87. · DOI
  18. Gallois N, Alpha-Bazin B, Ortet P, Barakat M, Piette L, Long J, et al. Proteogenomic insights into uranium tolerance of a Chernobyl's microbacterium bacterial isolate. J Proteomics. 2018;177:148–57. https://doi.org/10.1016/j.jprot.2017.11.021. · DOI
  19. Glass JB, Yu H, Steele JA, Dawson KS, Sun S, Chourey K, et al. Geochemical, metagenomic and metaproteomic insights into trace metal utilization by methane-oxidizing microbial consortia in sulphidic marine sediments. Environ Microbiol. 2014;16(6):1592–611. https://doi.org/10.1111/1462-2920.12314. · DOI
  20. Gouveia D, Pible O, Culotta K, Jouffret V, Geffard O, Chaumot A, et al. Combining proteogenomics and metaproteomics for deep taxonomic and functional characterization of microbiomes from a non-sequenced host. NPJ Biofilms Microbiomes. 2020;6(1):23. https://doi.org/10.1038/s41522-020-0133-2. · DOI
  21. Gurdeep Singh R, Tanca A, Palomba A, Van der Jeugt F, Verschaffelt P, Uzzau S, et al. Unipept 4.0: functional analysis of metaproteome data. J Proteome Res. 2019;18(2):606–15. https://doi.org/10.1021/acs.jproteome.8b00716. · DOI
  22. Hartmann EM, Allain F, Gaillard JC, Pible O, Armengaud J. Taking the shortcut for high-throughput shotgun proteomic analysis of bacteria. Methods Mol Biol. 2014;1197:275–85. https://doi.org/10.1007/978-1-4939-1261-2_16. · DOI
  23. Henson J, Tischler G, Ning Z. Next-generation sequencing and large genome assemblies. Pharmacogenomics. 2012;13(8):901–15. https://doi.org/10.2217/pgs.12.72. · DOI
  24. Heyer R, Benndorf D, Kohrs F, De Vrieze J, Boon N, Hoffmann M, et al. Proteotyping of biogas plant microbiomes separates biogas plants according to process temperature and reactor type. Biotechnol Biofuels. 2016;9(1):155. https://doi.org/10.1186/s13068-016-0572-4. · DOI
  25. Heyer R, Schallert K, Zoun R, Becher B, Saake G, Benndorf D. Challenges and perspectives of metaproteomic data analysis. J Biotechnol. 2017;261:24–36. https://doi.org/10.1016/j.jbiotec.2017.06.1201. · DOI
  26. Hubler SL, Kumar P, Mehta S, Easterly C, Johnson JE, Jagtap PD, et al. Challenges in peptide-spectrum matching: a robust and reproducible statistical framework for removing low-accuracy, high-scoring hits. J Proteome Res. 2020;19(1):161–73. https://doi.org/10.1021/acs.jproteome.9b00478. · DOI
  27. Hultman J, Waldrop MP, Mackelprang R, David MM, McFarland J, Blazewicz SJ, et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature. 2015;521(7551):208–12. https://doi.org/10.1038/nature14238. · DOI
  28. Jagtap P, Goslinga J, Kooren JA, McGowan T, Wroblewski MS, Seymour SL, et al. A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies. Proteomics. 2013;13(8):1352–7. https://doi.org/10.1002/pmic.201200352. · DOI
  29. Jansson JK, Hofmockel KS. Soil microbiomes and climate change. Nat Rev Microbiol. 2020;18(1):35–46. https://doi.org/10.1038/s41579-019-0265-7. · DOI
  30. Johnson-Rollings AS, Wright H, Masciandaro G, Macci C, Doni S, Calvo-Bado LA, et al. Exploring the functional soil-microbe interface and exoenzymes through soil metaexoproteomics. ISME J. 2014;8(10):2148–50. https://doi.org/10.1038/ismej.2014.130. · DOI
  31. Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences. J Mol Biol. 2016;428(4):726–31. https://doi.org/10.1016/j.jmb.2015.11.006. · DOI
  32. Keiblinger KM, Wilhartitz IC, Schneider T, Roschitzki B, Schmid E, Eberl L, et al. Soil metaproteomics - comparative evaluation of protein extraction protocols. Soil Biol Biochem. 2012;54(15-10):14–24. https://doi.org/10.1016/j.soilbio.2012.05.014. · DOI
  33. Klein G, Mathe C, Biola-Clier M, Devineau S, Drouineau E, Hatem E, et al. RNA-binding proteins are a major target of silica nanoparticles in cell extracts. Nanotoxicology. 2016;10(10):1555–64. https://doi.org/10.1080/17435390.2016.1244299. · DOI
  34. Kleiner, M. (2019). "Metaproteomics: much more than measuring gene expression in microbial communities." mSystems 4(3). · DOI
  35. Kroeger ME, Delmont TO, Eren AM, Meyer KM, Guo J, Khan K, et al. New biological insights into how deforestation in Amazonia affects soil microbial communities using metagenomics and metagenome-assembled genomes. Front Microbiol. 2018;9:1635. https://doi.org/10.3389/fmicb.2018.01635. · DOI
  36. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9. https://doi.org/10.1038/nmeth.1923. · DOI · Paper page
  37. Le Cloarec MF, Bonte PH, Lestel L, Lefèvre I, Ayrault S. Sedimentary record of metal contamination in the Seine River during the last century. Physics and Chemistry of the Earth, Parts A/B/C. 2011;36(12):515–29. https://doi.org/10.1016/j.pce.2009.02.003. · DOI · Paper page
  38. Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11. https://doi.org/10.1016/j.ymeth.2016.02.020. · DOI
  39. Lin W, Wu L, Lin S, Zhang A, Zhou M, Lin R, et al. Metaproteomic analysis of ratoon sugarcane rhizospheric soil. BMC Microbiol. 2013;13(1):135. https://doi.org/10.1186/1471-2180-13-135. · DOI
  40. Liu D, Keiblinger KM, Leitner S, Wegner U, Zimmermann M, Fuchs S, et al. Response of microbial communities and their metabolic functions to drying(-)rewetting stress in a temperate forest soil. Microorganisms. 2019;7(5). https://doi.org/10.3390/microorganisms7050129. · DOI
  41. Lorgeoux C, Moilleron R, Gasperi J, Ayrault S, Bonte P, Lefevre I, et al. Temporal trends of persistent organic pollutants in dated sediment cores: chemical fingerprinting of the anthropogenic impacts in the Seine River basin, Paris. Sci Total Environ. 2016;541:1355–63. https://doi.org/10.1016/j.scitotenv.2015.09.147. · DOI
  42. May DH, Timmins-Schiffman E, Mikan MP, Harvey HR, Borenstein E, Nunn BL, et al. An alignment-free "metapeptide" strategy for metaproteomic characterization of microbiome samples using shotgun metagenomic sequencing. J Proteome Res. 2016;15(8):2697–705. https://doi.org/10.1021/acs.jproteome.6b00239. · DOI
  43. Mesuere B, Debyser G, Aerts M, Devreese B, Vandamme P, Dawyndt P. The Unipept metaproteomics analysis pipeline. Proteomics. 2015;15(8):1437–42. https://doi.org/10.1002/pmic.201400361. · DOI
  44. Meyer F, Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, et al. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics. 2008;9(1):386. https://doi.org/10.1186/1471-2105-9-386. · DOI
  45. Mirdita M, von den Driesch L, Galiez C, Martin MJ, Soding J, Steinegger M. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 2017;45(D1):D170–6. https://doi.org/10.1093/nar/gkw1081. · DOI
  46. Murray, A. E., J. Freudenstein, S. Gribaldo, R. Hatzenpichler, P. Hugenholtz, P. Kampfer, K. T. Konstantinidis, C. E. Lane, R. T. Papke, D. H. Parks, R. Rossello-Mora, M. B. Stott, I. C. Sutcliffe, J. C. Thrash, S. N. Venter, W. B. Whitman, S. G. Acinas, R. I. Amann, K. Anantharaman, J. Armengaud, B. J. Baker, R. A. Barco, H. B. Bode, E. S. Boyd, C. L. Brady, P. Carini, P. S. G. Chain, D. R. Colman, K. M. DeAngelis, M. A. de Los Rios, P. Estrada-de Los Santos, C. A. Dunlap, J. A. Eisen, D. Emerson, T. J. G. Ettema, D. Eveillard, P. R. Girguis, U. Hentschel, J. T. Hollibaugh, L. A. Hug, W. P. Inskeep, E. P. Ivanova, H. P. Klenk, W. J. Li, K. G. Lloyd, F. E. Loffler, T. P. Makhalanyane, D. P. Moser, T. Nunoura, M. Palmer, V. Parro, C. Pedros-Alio, A. J. Probst, T. H. M. Smits, A. D. Steen, E. T. Steenkamp, A. Spang, F. J. Stewart, J. M. Tiedje, P. Vandamme, M. Wagner, F. P. Wang, P. Yarza, B. P. Hedlund and A. L. Reysenbach (2020). "Roadmap for naming uncultivated Archaea and bacteria." Nat Microbiol 5(8): 987-994, DOI: https://doi.org/10.1038/s41564-020-0733-x. · DOI
  47. Muth T, Kolmeder CA, Salojärvi J, keskitalo S, Varjosalo M, Verdam FJ, Rensen SS, Reichl U, de Vos WM, Rapp E, Martens L. "Navigating through metaproteomics data: a logbook of database searching." Proteomics. 2015;15(20):3439–53. · DOI
  48. Muth T, Renard BY, Martens L. Metaproteomic data analysis at a glance: advances in computational microbial community proteomics. Expert Rev Proteomics. 2016;13(8):757–69. https://doi.org/10.1080/14789450.2016.1209418. · DOI
  49. Orellana LH, Hatt JK, Iyer R, Chourey K, Hettich RL, Spain JC, et al. Comparing DNA, RNA and protein levels for measuring microbial dynamics in soil microcosms amended with nitrogen fertilizer. Sci Rep. 2019;9(1):17630. https://doi.org/10.1038/s41598-019-53679-0. · DOI
  50. Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2(11):1533–42. https://doi.org/10.1038/s41564-017-0012-7. · DOI · Paper page
  51. Pible O, Allain F, Jouffret V, Culotta K, Miotello G, Armengaud J. Estimating relative biomasses of organisms in microbiota using "phylopeptidomics". Microbiome. 2020;8(1):30. https://doi.org/10.1186/s40168-020-00797-x. · DOI
  52. Ram RJ, Verberkmoes NC, Thelen MP, Tyson GW, Baker BJ, Blake RC 2nd, et al. Community proteomics of a natural microbial biofilm. Science. 2005;308(5730):1915–20. https://doi.org/10.1126/science.1109070. · DOI
  53. Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010;38(20):e191. https://doi.org/10.1093/nar/gkq747. · DOI
  54. Schneider T, Keiblinger KM, Schmid E, Sterflinger-Gleixner K, Ellersdorfer G, Roschitzki B, et al. Who is who in litter decomposition? Metaproteomics reveals major microbial players and their biogeochemical functions. ISME J. 2012;6(9):1749–62. https://doi.org/10.1038/ismej.2012.11. · DOI
  55. Seifert J, Muth T. Editorial for special issue: metaproteomics. Proteomes. 2019;7(1). https://doi.org/10.3390/proteomes7010009. · DOI
  56. Stamboulian M, Li S, Ye Y. Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data. Microbiome. 2021;9(1):80. https://doi.org/10.1186/s40168-021-01035-8. · DOI
  57. Starke R, Jehmlich N, Bastida F. Using proteins to study how microbes contribute to soil ecosystem services: the current state and future perspectives of soil metaproteomics. J Proteomics. 2019;198:50–8. https://doi.org/10.1016/j.jprot.2018.11.011. · DOI
  58. Starr AE, Deeke SA, Li L, Zhang X, Daoud R, Ryan J, et al. Proteomic and metaproteomic approaches to understand host-microbe interactions. Anal Chem. 2018;90(1):86–109. https://doi.org/10.1021/acs.analchem.7b04340. · DOI
  59. Tanca A, Palomba A, Deligios M, Cubeddu T, Fraumene C, Biosa G, et al. Evaluating the impact of different sequence databases on metaproteome analysis: insights from a lab-assembled microbial mixture. PLoS One. 2013;8(12):e82981. https://doi.org/10.1371/journal.pone.0082981. · DOI
  60. Tanca A, Palomba A, Fraumene C, Pagnozzi D, Manghina V, Deligios M, et al. The impact of sequence database choice on metaproteomic results in gut microbiota studies. Microbiome. 2016;4(1):51. https://doi.org/10.1186/s40168-016-0196-8. · DOI
  61. Tartaglia M, Bastida F, Sciarrillo R, Guarino C. Soil metaproteomics for the study of the relationships between microorganisms and plants: a review of extraction protocols and ecological insights. Int J Mol Sci. 2020;21(22). https://doi.org/10.3390/ijms21228455. · DOI
  62. Taubert M, Grob C, Crombie A, Howat AM, Burns OJ, Weber M, et al. Communal metabolism by methylococcaceae and methylophilaceae is driving rapid aerobic methane oxidation in sediments of a shallow seep near Elba, Italy. Environ Microbiol. 2019;21(10):3780–95. https://doi.org/10.1111/1462-2920.14728. · DOI
  63. Terrat S, Christen R, Dequiedt S, Lelievre M, Nowak V, Regnier T, et al. Molecular biomass and MetaTaxogenomic assessment of soil microbial communities as influenced by soil DNA extraction procedure. Microb Biotechnol. 2012;5(1):135–41. https://doi.org/10.1111/j.1751-7915.2011.00307.x. · DOI
  64. Trapp J, Almunia C, Gaillard JC, Pible O, Chaumot A, Geffard O, et al. Proteogenomic insights into the core-proteome of female reproductive tissues from crustacean amphipods. J Proteomics. 2016;135:51–61. https://doi.org/10.1016/j.jprot.2015.06.017. · DOI
  65. Van Den Bossche T, Kunath B, Schallert K, Schäpe S Abraham P, Armengaud J, Arntzen M, Bassignanin A, Benndorf D, Fuchs S, et al. "Critical Assessment of Metaproteome Investigation (CAMPI): A Multi-Lab Comparison of Established Workflows." BioRxiV. 2021. https://doi.org/10.1101/2021.03.05.433915. · DOI
  66. Wang HB, Zhang ZX, Li H, He HB, Fang CX, Zhang AJ, et al. Characterization of metaproteomics in crop rhizospheric soil. J Proteome Res. 2011;10(3):932–40. https://doi.org/10.1021/pr100981r. · DOI
  67. Wang Z, Wang Y, Fuhrman JA, Sun F, Zhu S. Assessment of metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences. Brief Bioinform. 2020;21(3):777–90. https://doi.org/10.1093/bib/bbz025. · DOI
  68. Wilmes P, Heintz-Buschart A, Bond PL. A decade of metaproteomics: where we stand and what the future holds. Proteomics. 2015;15(20):3409–17. https://doi.org/10.1002/pmic.201500183. · DOI
  69. Wilpiszeski RL, Aufrecht JA, Retterer ST, Sullivan MB, Graham DE, Pierce EM, et al. Soil aggregate microbial communities: towards understanding microbiome interactions at biologically relevant scales. Appl Environ Microbiol. 2019;85(14). https://doi.org/10.1128/AEM.00324-19. · DOI
  70. Wu L, Wang H, Zhang Z, Lin R, Zhang Z, Lin W. Comparative metaproteomic analysis on consecutively Rehmannia glutinosa-monocultured rhizosphere soil. PLoS One. 2011;6(5):e20611. https://doi.org/10.1371/journal.pone.0020611. · DOI
  71. Xiao J, Tanca A, Jia B, Yang R, Wang B, Zhang Y, et al. Metagenomic taxonomy-guided database-searching strategy for improving metaproteomic analysis. J Proteome Res. 2018;17(4):1596–605. https://doi.org/10.1021/acs.jproteome.7b00894. · DOI
  72. Yao Q, Li Z, Song Y, Wright SJ, Guo X, Tringe SG, et al. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nat Ecol Evol. 2018;2(3):499–509. https://doi.org/10.1038/s41559-017-0463-5. · DOI
  73. Zampieri E, Chiapello M, Daghino S, Bonfante P, Mello A. Soil metaproteomics reveals an inter-kingdom stress response to the presence of black truffles. Sci Rep. 2016;6(1):25773. https://doi.org/10.1038/srep25773. · DOI

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Metadata

Authors
Jouffret, V., Miotello, G., Culotta, K., Ayrault, S., Pible, O., & Armengaud, J.
ORCID
Armengaud, Jean
Venue
Microbiome
Publisher
Springer Science and Business Media LLC
Published
2021-09-29
Volume
9
Issue
1
ISSN
2049-2618
DOI
10.1186/s40168-021-01139-1
PMC
PMC8482631
Cited by
52
References
2
Language
en
Licence

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CoChat. (2026). Citation check: Increasing the power of interpretation for soil metaproteomics data. https://app.cochat.ai/papers/10.1186/s40168-021-01139-1
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Jouffret, V., Miotello, G., Culotta, K., Ayrault, S., Pible, O., & Armengaud, J. (2021). Increasing the power of interpretation for soil metaproteomics data. Microbiome. https://doi.org/10.1186/s40168-021-01139-1