REFERENCES

1. Hou K, Wu ZX, Chen XY, et al. Microbiota in health and diseases. Signal Transduct Target Ther 2022;7:135.

2. Guzzo GL, Andrews JM, Weyrich LS. The neglected gut microbiome: fungi, protozoa, and bacteriophages in inflammatory bowel disease. Inflamm Bowel Dis 2022;28:1112-22.

3. Hrncir T. Gut microbiota dysbiosis: triggers, consequences, diagnostic and therapeutic options. Microorganisms 2022;10:578.

4. Zhang Z, Tang H, Chen P, Xie H, Tao Y. Demystifying the manipulation of host immunity, metabolism, and extraintestinal tumors by the gut microbiome. Signal Transduct Target Ther 2019;4:41.

5. Ogunrinola GA, Oyewale JO, Oshamika OO, Olasehinde GI. The human microbiome and its impacts on health. Int J Microbiol 2020;2020:8045646.

6. Dinakaran V, Rathinavel A, Pushpanathan M, Sivakumar R, Gunasekaran P, Rajendhran J. Elevated levels of circulating DNA in cardiovascular disease patients: metagenomic profiling of microbiome in the circulation. PLoS One 2014;9:e105221.

7. Kinross JM, Darzi AW, Nicholson JK. Gut microbiome-host interactions in health and disease. Genome Med 2011;3:14.

8. Fekete EE, Figeys D, Zhang X. Microbiota-directed biotherapeutics: considerations for quality and functional assessment. Gut Microbes 2023;15:2186671.

9. Ferrocino I, Rantsiou K, McClure R, et al; MicrobiomeSupport Consortium. The need for an integrated multi-OMICs approach in microbiome science in the food system. Compr Rev Food Sci Food Saf 2023;22:1082-103.

10. Berg G, Rybakova D, Fischer D, et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome 2020;8:103.

11. Zhang X, Li L, Butcher J, Stintzi A, Figeys D. Advancing functional and translational microbiome research using meta-omics approaches. Microbiome 2019;7:154.

12. Creskey M, Li L, Ning Z, et al. An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis. Proteomics 2023;23:e2200116.

13. Pietilä S, Suomi T, Elo LL. Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples. ISME Commun 2022;2:51.

14. Fernández-Costa C, Martínez-Bartolomé S, McClatchy DB, Saviola AJ, Yu NK, Yates JR 3rd. Impact of the identification strategy on the reproducibility of the DDA and DIA results. J Proteome Res 2020;19:3153-61.

15. Zhang F, Ge W, Ruan G, Cai X, Guo T. Data-independent acquisition mass spectrometry-based proteomics and software tools: a glimpse in 2020. Proteomics 2020;20:e1900276.

16. Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 2020;17:41-4.

17. Meier F, Brunner AD, Frank M, et al. diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nat Methods 2020;17:1229-36.

18. Guzman UH, Martinez-Val A, Ye Z, et al. Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition. Nat Biotechnol 2024.

19. Zhao J, Yang Y, Xu H, et al. Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota. NPJ Biofilms Microbiomes 2023;9:4.

20. Dumas T, Martinez Pinna R, Lozano C, et al. The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes. Microbiome 2024;12:46.

21. Gómez-Varela D, Xian F, Grundtner S, Sondermann JR, Carta G, Schmidt M. Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics. Front Microbiol 2023;14:1258703.

22. Zhang X, Deeke SA, Ning Z, et al. Metaproteomics reveals associations between microbiome and intestinal extracellular vesicle proteins in pediatric inflammatory bowel disease. Nat Commun 2018;9:2873.

23. Karaduta O, Dvanajscak Z, Zybailov B. Metaproteomics-an advantageous option in studies of host-microbiota interaction. Microorganisms 2021;9:980.

24. Starr AE, Deeke SA, Li L, et al. Proteomic and metaproteomic approaches to understand host-microbe interactions. Anal Chem 2018;90:86-109.

25. Sberro H, Fremin BJ, Zlitni S, et al. Large-scale analyses of human microbiomes reveal thousands of small, novel genes. Cell 2019;178:1245-59.e14.

26. Petruschke H, Schori C, Canzler S, et al. Discovery of novel community-relevant small proteins in a simplified human intestinal microbiome. Microbiome 2021;9:55.

27. Ma Y, Guo Z, Xia B, et al. Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nat Biotechnol 2022;40:921-31.

28. Su M, Ling Y, Yu J, Wu J, Xiao J. Small proteins: untapped area of potential biological importance. Front Genet 2013;4:286.

29. Leary DH, Hervey WJ 4th, Deschamps JR, Kusterbeck AW, Vora GJ. Which metaproteome? The impact of protein extraction bias on metaproteomic analyses. Mol Cell Probes 2013;27:193-9.

30. Tanca A, Palomba A, Pisanu S, Addis MF, Uzzau S. Enrichment or depletion? The impact of stool pretreatment on metaproteomic characterization of the human gut microbiota. Proteomics 2015;15:3474-85.

31. Blackburn JM, Martens L. The challenge of metaproteomic analysis in human samples. Expert Rev Proteomics 2016;13:135-8.

32. Zhang X, Li L, Mayne J, Ning Z, Stintzi A, Figeys D. Assessing the impact of protein extraction methods for human gut metaproteomics. J Proteomics 2018;180:120-7.

33. Xiao L, Feng Q, Liang S, et al. A catalog of the mouse gut metagenome. Nat Biotechnol 2015;33:1103-8.

34. Chi H, Liu C, Yang H, et al. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat Biotechnol 2018;36:1059-61.

35. Kong AT, Leprevost FV, Avtonomov DM, Mellacheruvu D, Nesvizhskii AI. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods 2017;14:513-20.

36. Cheng K, Ning Z, Zhang X, et al. MetaLab 2.0 enables accurate post-translational modifications profiling in metaproteomics. J Am Soc Mass Spectrom 2020;31:1473-82.

37. Santos-Júnior CD, Der Torossian Torres M, Duan Y, et al. Computational exploration of the global microbiome for antibiotic discovery. bioRxiv 2023.

38. Xian F, Brenek M, Krisp C, Ravi Kumar RK, Schmidt M, Gómez-Varela D. Ultra-sensitive metaproteomics (uMetaP) redefines the dark field of metaproteome, enables single-bacterium resolution, and discovers hidden functions in the gut microbiome. bioRXiv. [Preprint] Apr 22, 2024. [accessed on 2024 Jun 28]. Available from: https://www.biorxiv.org/content/10.1101/2024.04.22.590295v1.

39. Fuchs S, Engelmann S. Small proteins in bacteria - Big challenges in prediction and identification. Proteomics 2023;23:e2200421.

40. Nalpas N, Hoyles L, Anselm V, et al. An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome. Gut Microbes 2021;13:1994836.

41. Lagkouvardos I, Lesker TR, Hitch TCA, et al. Sequence and cultivation study of Muribaculaceae reveals novel species, host preference, and functional potential of this yet undescribed family. Microbiome 2019;7:28.

42. Arumugam M, Raes J, Pelletier E, et al; MetaHIT Consortium. Enterotypes of the human gut microbiome. Nature 2011;473:174-80.

43. Duan H, Ning Z, Sun Z, Guo T, Sun Y, Figeys D. MetaDIA: a novel database reduction strategy for DIA human gut metaproteomics. bioRxiv. [Preprint] Mar 16, 2024. [accessed on 2024 Jun 28]. Available from: https://www.biorxiv.org/content/10.1101/2024.03.14.585104v1.

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