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Cited by 4Advanced Proteomics Techniques and ApplicationsGut microbiota and healthGenomics and Phylogenetic Studies

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Peptide abundance correlations in metaproteomics enhance taxonomic and functional analysis of the human gut microbiome

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Sun, Zhongzhi, Ning, Zhibin, Wu, Qing, Li, Leyuan, Doxey, Andrew C., Figeys, Daniel·2025·npj Biofilms and Microbiomes
DOI 10.1038/s41522-025-00801-yPMC PMC12365196

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Abstract

Mass spectrometry (MS)-based proteomics is widely used for quantitative protein profiling and protein interaction studies. However, most current research focuses on single-species proteomics, while protein interactions within complex microbiomes, composed of hundreds of bacterial species, remain largely unexplored. In this study, we analyzed peptide abundance correlations within a metaproteomics dataset derived from in vitro cultured human gut microbiomes subjected to various drug treatments. Our analysis revealed that peptides from the same protein or taxon exhibited correlated abundance changes. By using t-SNE for visualization, we generated a peptide correlation map in which peptides from the same taxon formed distinct clusters. Furthermore, peptide abundance correlations enabled genome-level taxonomic assignments for a greater number of peptides. For instance, 1880 (48.9%) of the 3845 peptides initially assigned only to the family Bacteroidaceae could now be assigned to a specific genome. In species representative genome subsets, peptide correlation networks based on taxon-normalized peptide abundance (TNPA) linked functionally related peptides and provided insights into uncharacterized proteins. Altogether, our study demonstrates that analyzing peptide abundance correlations enhances both taxonomic and functional analyses in human gut metaproteomics research.

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Research works printed but not linked: 53

  1. Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016). · DOI · Paper page
  2. de Vos, W. M., Tilg, H., Van Hul, M. & Cani, P. D. Gut microbiome and health: mechanistic insights. Gut 71, 1020–1032 (2022). · DOI · Paper page
  3. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019). · DOI · Paper page
  4. Heintz-Buschart, A. & Wilmes, P. Human gut microbiome: function matters. Trends Microbiol. 26, 563–574 (2018). · DOI
  5. Lobb, B., Kurtz, D. A., Moreno-Hagelsieb, G. & Doxey, A. C. Remote homology and the functions of metagenomic dark matter. Front. Genet. https://doi.org/10.3389/fgene.2015.00234 (2015). · DOI
  6. Franzosa, E. A. et al. Sequencing and beyond: integrating molecular “omics” for microbial community profiling. Nat. Rev. Microbiol. 13, 360–372 (2015). · DOI
  7. Kleiner, M. Metaproteomics: much more than measuring gene expression in microbial communities. mSystems 4, e00115-19 (2019). · DOI
  8. Wang, J. et al. Proteome profiling outperforms transcriptome profiling for coexpression based gene Function Prediction. Mol. Cell. Proteom. 16, 121–134 (2017). · DOI
  9. Brandman, O. & Meyer, T. Feedback loops shape cellular signals in space and time. Science 322, 390–395 (2008). · DOI
  10. Garrido-Rodriguez, M., Zirngibl, K., Ivanova, O., Lobentanzer, S. & Saez-Rodriguez, J. Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks. Mol. Syst. Biol. 18, e11036 (2022). · DOI
  11. Kustatscher, G. et al. Co-Regulation map of the human proteome enables identification of protein functions. Nat. Biotechnol. 37, 1361–1371 (2019). · DOI
  12. Mateus, A. et al. The functional proteome landscape of Escherichia coli. Nature 588, 473–478 (2020). · DOI
  13. Messner, C. B. et al. The proteomic landscape of genome-wide genetic perturbations. Cell 186, 2018–2034.e21 (2023). · DOI
  14. Zaydman, M. A. et al. Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes. eLife 11, e74104 (2022). · DOI
  15. Ma, B. et al. Genetic correlation network prediction of forest soil microbial functional organization. ISME J. No. 12, 2492–2505 (2018). · DOI
  16. Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol. 39, 105–114 (2021). · DOI · Paper page
  17. Haynes, W. A., Tomczak, A. & Khatri, P. Gene annotation bias impedes biomedical research. Sci. Rep. 8, 1362 (2018). · DOI · Paper page
  18. Kustatscher, G. et al. Understudied proteins: opportunities and challenges for functional proteomics. Nat. Methods 19, 774–779 (2022). · DOI
  19. Schiebenhoefer, H. et al. Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis. Expert Rev. Proteom. 16, 375–390 (2019). · DOI
  20. Sun, Z. et al. MetaPep: a core peptide database for faster human gut metaproteomics database searches. Comput. Struct. Biotechnol. J. 21, 4228–4237 (2023). · DOI
  21. Plubell, D. L. et al. Putting Humpty Dumpty back together again: what does protein quantification mean in bottom-up proteomics?. J. Proteome Res. 21, 891–898 (2022). · DOI · Paper page
  22. Gurdeep Singh, R. et al. Unipept 4.0: functional analysis of metaproteome data. J. Proteome Res. 18, 606–615 (2019). · DOI
  23. Simopoulos, C. M. A. et al. pepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies. Bioinformatics 36, 4171–4179 (2020). · DOI
  24. Li, L. et al. Systematic metaproteomics mapping reveals functional and ecological landscapes of human gut microbiota responses to therapeutic drugs. Preprint at bioRxiv https://doi.org/10.1101/2025.02.13.637346 (2025). · DOI
  25. Li, L. et al. An in vitro model maintaining taxon-specific functional activities of the gut microbiome. Nat. Commun. 10, 4146 (2019). · DOI
  26. Nguyen, J., Lara-Gutiérrez, J. & Stocker, R. Environmental fluctuations and their effects on microbial communities, populations and individuals. FEMS Microbiol. Rev. 45, fuaa068 (2021). · DOI
  27. Li, L. et al. Revealing proteome-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics. Nat. Commun. 14, 3428 (2023). · DOI
  28. Armengaud, J. Metaproteomics to understand how microbiota function: the crystal ball predicts a promising future. Environ. Microbiol. 25, 115–125 (2023). · DOI · Paper page
  29. Cussotto, S. et al. Differential effects of psychotropic drugs on microbiome composition and gastrointestinal function. Psychopharmacology 236, 1671–1685 (2019). · DOI · Paper page
  30. Mesuere, B. et al. Unipept: tryptic peptide-based biodiversity analysis of metaproteome samples. J. Proteome Res. 11, 5773–5780 (2012). · DOI
  31. Kleiner, M. et al. Assessing species biomass contributions in microbial communities via metaproteomics. Nat. Commun. 8, 1558 (2017). · DOI · Paper page
  32. Shi, M., Evans, C. A., McQuillan, J. L., Noirel, J. & Pandhal, J. LFQRatio: a normalization method to decipher quantitative proteome changes in microbial coculture systems. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.3c00714 (2024). · DOI
  33. Lohmann, P. et al. Function is what counts: how microbial community complexity affects species, proteome and pathway coverage in metaproteomics. Expert Rev. Proteom. 17, 163–173 (2020). · DOI
  34. Sun, Z., Ning, Z. & Figeys, D. The landscape and perspectives of the human gut metaproteomics. Mol. Cell. Proteom. 23, 100763 (2024). · DOI
  35. Lloyd-Price, J., Abu-Ali, G. & Huttenhower, C. The healthy human microbiome. Genome Med. 8, 51 (2016). · DOI · Paper page
  36. Duan, H. et al. Assessing the dark field of metaproteome. Anal. Chem. 94, 15648–15654 (2022). · DOI
  37. Jin, H. et al. Hybrid, ultra-deep metagenomic sequencing enables genomic and functional characterization of low-abundance species in the human gut microbiome. Gut Microbes https://doi.org/10.1080/19490976.2021.2021790 (2022). · DOI
  38. Zhang, Y. et al. Metatranscriptomics for the human microbiome and microbial community functional profiling. Annu. Rev. Biomed. Data Sci. 4, 279–311 (2021). · DOI
  39. Spurbeck, R. R., Catlin, L. A., Mukherjee, C., Smith, A. K. & Minard-Smith, A. Analysis of metatranscriptomic methods to enable wastewater-based biosurveillance of all infectious diseases. Front. Public Health https://doi.org/10.3389/fpubh.2023.1145275 (2023). · DOI
  40. Aakko, J. et al. Data-independent acquisition mass spectrometry in metaproteomics of gut microbiota—implementation and computational analysis. J. Proteome Res. 19, 432–436 (2020). · DOI
  41. Zhao, J. et al. Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota. npj Biofilms Microbiomes 9, 4 (2023). · DOI
  42. Bonett, D. G. & Wright, T. A. Sample size requirements for estimating Pearson, Kendall and Spearman correlations. Psychometrika 65, 23–28 (2000). · DOI · Paper page
  43. Chase, L. S. S. & Priya, S. S. I’m walking into spiderwebs: making sense of protein−protein interaction data. J. Proteome Res. 23, 2723–2732 (2024). · DOI
  44. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014). · DOI · Paper page
  45. Cheng, K. et al. MetaLab 2.0 enables accurate post-translational modifications profiling in metaproteomics. J. Am. Soc. Mass Spectrom. 31, 1473–1482 (2020). · DOI
  46. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016). · DOI
  47. Yang, J. et al. DeepDigest: prediction of protein proteolytic digestion with deep learning. Anal. Chem. 93, 6094–6103 (2021). · DOI
  48. Jesse, K. & Laurens van der, M. Rtsne: T-distributed stochastic neighbor embedding using a Barnes-hut implementation. https://doi.org/10.32614/CRAN.package.Rtsne (2023). · DOI
  49. Leo, B., Adele, C., Andy, L. & Matthew, W. randomForest: Breiman and Cutlers random forests for classification and regression. https://doi.org/10.32614/CRAN.package.randomForest (2024). · DOI
  50. Gábor, C. et al. Igraph: network analysis and visualization. https://doi.org/10.32614/CRAN.package.igraph (2025). · DOI
  51. Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. ICWSM 3, 361–362 (2009). · DOI · Paper page
  52. Deutsch, E. W. et al. The ProteomeXchange Consortium in 2020: enabling `big data' approaches in proteomics. Nucleic Acids Res 48, D1145–D1152 (2020).
  53. Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022). · DOI · Paper page

Metadata

Authors
Sun, Z., Ning, Z., Wu, Q., Li, L., Doxey, A. C., & Figeys, D.
Affiliations
  • University of Ottawa (CA)
  • Beijing Proteome Research Center (CN)
  • University of Waterloo (CA)
  • University of East Anglia (GB)
Corresponding author
Figeys, Daniel
ORCID
Venue
npj Biofilms and Microbiomes
Publisher
Springer Science and Business Media LLC
Published
2025-08-19
Volume
11
Issue
1
Pages
166-166
ISSN
2055-5008 (ISSN-L)
DOI
10.1038/s41522-025-00801-y
PMC
PMC12365196
OpenAlex
W4413315983
Cited by
4
References
7
Citations by year
2026: 4
FWCI
1.01
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Biofilms and Microbiomes (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • UEA Digital Repository (University of East Anglia) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
human gut microbiome, metaproteomics, taxonomic assignment, functional annotation, Bacteroidaceae, hypothetical protein, t-SNE visualization
MeSH terms
Gastrointestinal Microbiome, Bacteria, Bacterial Proteins, Humans, Peptides, Mass Spectrometry, Proteomics

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