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Benefits of Iterative Searches of Large Databases to Interpret Large Human Gut Metaproteomic Data Sets

  • Ariane Bassignani
    Ariane Bassignani
    Université Paris-Saclay, INRAE, MGP, 78350, Jouy-en-Josas, France
    Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350, Jouy-en-Josas, France
    Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE − Le Moulon, 91190, Gif-sur-Yvette, France
    MaIAGE, INRAE, Université Paris-Saclay, 78350 Jouy-en-Josas, France
  • Sandra Plancade*
    Sandra Plancade
    MaIAGE, INRAE, Université Paris-Saclay, 78350 Jouy-en-Josas, France
    INRAE, UR875 MIAT, F-31326 Castanet-Tolosan, France
    *Email: [email protected]
  • Magali Berland
    Magali Berland
    Université Paris-Saclay, INRAE, MGP, 78350, Jouy-en-Josas, France
  • Melisande Blein-Nicolas
    Melisande Blein-Nicolas
    Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE − Le Moulon, 91190, Gif-sur-Yvette, France
  • Alain Guillot
    Alain Guillot
    Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350, Jouy-en-Josas, France
  • Didier Chevret
    Didier Chevret
    Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350, Jouy-en-Josas, France
  • Chloé Moritz
    Chloé Moritz
    Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350, Jouy-en-Josas, France
  • Sylvie Huet
    Sylvie Huet
    MaIAGE, INRAE, Université Paris-Saclay, 78350 Jouy-en-Josas, France
    More by Sylvie Huet
  • Salwa Rizkalla
    Salwa Rizkalla
    Sorbonne Université, Inserm, UMRS Nutrition et Obésités; approches systémiques, Paris 75006, France
    Assistance Publique Hôpitaux de Paris, Service de Nutrition, CRNH Ile-de-France, Pitié-Salpêtrière Hospital, Paris 75013, France
  • Karine Clément
    Karine Clément
    Sorbonne Université, Inserm, UMRS Nutrition et Obésités; approches systémiques, Paris 75006, France
    Assistance Publique Hôpitaux de Paris, Service de Nutrition, CRNH Ile-de-France, Pitié-Salpêtrière Hospital, Paris 75013, France
  • Joël Doré
    Joël Doré
    Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350, Jouy-en-Josas, France
    More by Joël Doré
  • Olivier Langella*
    Olivier Langella
    Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE − Le Moulon, 91190, Gif-sur-Yvette, France
    *Email: [email protected]
  • , and 
  • Catherine Juste*
    Catherine Juste
    Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350, Jouy-en-Josas, France
    *Email: [email protected]
Cite this: J. Proteome Res. 2021, 20, 3, 1522–1534
Publication Date (Web):February 2, 2021
https://doi.org/10.1021/acs.jproteome.0c00669
Copyright © 2021 American Chemical Society

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    Abstract

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    The gut microbiota are increasingly considered as a main partner of human health. Metaproteomics enables us to move from the functional potential revealed by metagenomics to the functions actually operating in the microbiome. However, metaproteome deciphering remains challenging. In particular, confident interpretation of a myriad of MS/MS spectra can only be pursued with smart database searches. Here, we compare the interpretation of MS/MS data sets from 48 individual human gut microbiomes using three interrogation strategies of the dedicated Integrated nonredundant Gene Catalog (IGC 9.9 million genes from 1267 individual fecal samples) together with the Homo sapiens database: the classical single-step interrogation strategy and two iterative strategies (in either two or three steps) aimed at preselecting a reduced-sized, more targeted search space for the final peptide spectrum matching. Both iterative searches outperformed the single-step classical search in terms of the number of peptides and protein clusters identified and the depth of taxonomic and functional knowledge, and this was the most convincing with the three-step approach. However, iterative searches do not help in reducing variability of repeated analyses, which is inherent to the traditional data-dependent acquisition mode, but this variability did not affect the hierarchical relationship between replicates and all other samples.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00669.

    • Supporting Experimental Section S1. Stool sample collection and processing. Supporting Experimental Section S2. Protein digestion and peptide desalting. Supporting Experimental Section S3. LC–MS/MS analysis. Table S1. Analytical sequence of the samples. Table S2. Summary of the three search strategies. Table S3. Taxonomic diversity allowed by each of the three search strategies. Figure S1. Experimental design. Figure S2. Overview of the three interrogation methods used in this paper. Figure S3. Clustering of proteins using the X!Tandem Grouping Algorithm of X!TandemPipeline. Figure S4. Plots of the mass delta distributions for the three methods. Figure S5. Species distribution among phyla in the intersection of the three searches (A) or in the additional pool brought by the two-step (B) and the three-step strategies (C). Figure S6. iPath projection of KO entries highlighted with the three approaches. Figure S7 Distribution of metaproteins as a function of KO entries embedded. Figure S8. If present, the functional diversity of metaproteins is related to the own functional diversity of their component proteins, not to their number. Figure S9. Percentages of genes (A) and metaproteins (B, recall of Figure 4A) assigned to the different phyla within the 48 microbiomes. Figure S10. Example of reproducibility of the positioning of replicates relative to other samples in the cohort, based on relative abundances of all metaproteins. Figure S11. Metaproteins overlapping in all pairs of replicates when singletons are omitted (PDF)

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    Cited By

    This article is cited by 11 publications.

    1. Enhui Wu, Yi Yang, Jinzhi Zhao, Jianxujie Zheng, Xiaoqing Wang, Chengpin Shen, Liang Qiao. High-Abundance Protein-Guided Hybrid Spectral Library for Data-Independent Acquisition Metaproteomics. Analytical Chemistry 2024, 96 (3) , 1029-1037. https://doi.org/10.1021/acs.analchem.3c03255
    2. Simon Klaes, Shobhit Madan, Darja Deobald, Myriel Cooper, Lorenz Adrian. GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry. International Journal of Molecular Sciences 2023, 24 (21) , 15692. https://doi.org/10.3390/ijms242115692
    3. Maximilian Wolf, Kay Schallert, Luca Knipper, Albert Sickmann, Alexander Sczyrba, Dirk Benndorf, Robert Heyer. Advances in the clinical use of metaproteomics. Expert Review of Proteomics 2023, 20 (4-6) , 71-86. https://doi.org/10.1080/14789450.2023.2215440
    4. Jean Armengaud. Metaproteomics to understand how microbiota function: The crystal ball predicts a promising future. Environmental Microbiology 2023, 25 (1) , 115-125. https://doi.org/10.1111/1462-2920.16238
    5. Nobuaki Miura, Shujiro Okuda. Current progress and critical challenges to overcome in the bioinformatics of mass spectrometry-based metaproteomics. Computational and Structural Biotechnology Journal 2023, 21 , 1140-1150. https://doi.org/10.1016/j.csbj.2023.01.015
    6. Pauline Hardouin, Olivier Pible, Hélène Marchandin, Karen Culotta, Jean Armengaud, Raphaël Chiron, Lucia Grenga. Quick and wide-range taxonomical repertoire establishment of the cystic fibrosis lung microbiota by tandem mass spectrometry on sputum samples. Frontiers in Microbiology 2022, 13 https://doi.org/10.3389/fmicb.2022.975883
    7. Shahid Aziz, Faisal Rasheed, Tayyab Saeed Akhter, Rabaab Zahra, Simone König. Microbial Proteins in Stomach Biopsies Associated with Gastritis, Ulcer, and Gastric Cancer. Molecules 2022, 27 (17) , 5410. https://doi.org/10.3390/molecules27175410
    8. Céline Henry, Ariane Bassignani, Magali Berland, Olivier Langella, Harry Sokol, Catherine Juste. Modern Metaproteomics: A Unique Tool to Characterize the Active Microbiome in Health and Diseases, and Pave the Road towards New Biomarkers—Example of Crohn’s Disease and Ulcerative Colitis Flare-Ups. Cells 2022, 11 (8) , 1340. https://doi.org/10.3390/cells11081340
    9. Sandra Plancade, Magali Berland, Mélisande Blein-Nicolas, Olivier Langella, Ariane Bassignani, Catherine Juste. A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation. PeerJ 2022, 10 , e13525. https://doi.org/10.7717/peerj.13525
    10. Veronika Kuchařová Pettersen, Luis Caetano Martha Antunes, Antoine Dufour, Marie-Claire Arrieta. Inferring early-life host and microbiome functions by mass spectrometry-based metaproteomics and metabolomics. Computational and Structural Biotechnology Journal 2022, 20 , 274-286. https://doi.org/10.1016/j.csbj.2021.12.012
    11. Nicolas Nalpas, Lesley Hoyles, Viktoria Anselm, Tariq Ganief, Laura Martinez-Gili, Cristina Grau, Irina Droste-Borel, Laetitia Davidovic, Xavier Altafaj, Marc-Emmanuel Dumas, Boris Macek. An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome. Gut Microbes 2021, 13 (1) https://doi.org/10.1080/19490976.2021.1994836

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