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Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces

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Abstract

Bottom-up proteomics is a powerful method for the functional characterization of mouse gut microbiota. To date, most of the bottom-up proteomics studies of the mouse gut rely on limited amounts of fecal samples. With mass-limited samples, the performance of such analyses is highly dependent on the protein extraction protocols and contaminant removal strategies. Here, protein extraction protocols (using different lysis buffers) and contaminant removal strategies (using different types of filters and beads) were systematically evaluated to maximize quantitative reproducibility and the number of identified proteins. Overall, our results recommend a protein extraction method using a combination of sodium dodecyl sulfate (SDS) and urea in Tris–HCl to yield the greatest number of protein identifications. These conditions led to an increase in the number of proteins identified from gram-positive bacteria, such as Firmicutes and Actinobacteria, which is a challenging task. Our analysis further confirmed these conditions led to the extraction of non-abundant bacterial phyla such as Proteobacteria. In addition, we found that, when coupled to our optimized extraction method, suspension trap (S-Trap) outperforms other contaminant removal methods by providing the most reproducible method while producing the greatest number of protein identifications. Overall, our optimized sample preparation workflow is straightforward and fast, and requires minimal sample handling. Furthermore, our approach does not require high amounts of fecal samples, a vital consideration in proteomics studies where mice produce smaller amounts of feces due to a particular physiological condition. Our final method provides efficient digestion of mouse fecal material, is reproducible, and leads to high proteomic coverage for both host and microbiome proteins.

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Data availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange [29] Consortium via the PRIDE [30] partner repository with the dataset identifier PXD027788.

References

  1. Finehout EJ, Lee KH. An introduction to mass spectrometry applications in biological research. Biochem Mol Biol Educ. 2004;32:93–100. https://doi.org/10.1002/bmb.2004.494032020331.

    Article  CAS  PubMed  Google Scholar 

  2. Chace DH, Petricon EF, Liotta LA. Mass spectrometry-based diagnostics: the upcoming revolution in disease detection has already arrived [3] (multiple letters). Clin Chem. 2003;49:1227–9. https://doi.org/10.1373/49.7.1227.

    Article  CAS  PubMed  Google Scholar 

  3. Zhang Y, Fonslow BR, Shan B, Baek MC, Yates JR. Protein analysis by shotgun/bottom-up proteomics. Chem Rev. 2013;113:2343–94. https://doi.org/10.1021/cr3003533.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gundry RL, White MY, Murray CI, Kane LA, Fu Q, Stanley BA, et al. Preparation of proteins and peptides for mass spectrometry analysis. 2010;77:342–55. https://doi.org/10.1002/0471142727.mb1025s88.Preparation.

    Article  Google Scholar 

  5. Gillet LC, Leitner A, Aebersold R. Mass spectrometry applied to bottom-up proteomics: entering the high-throughput era for hypothesis testing. Annu Rev Anal Chem. 2016;9:449–72. https://doi.org/10.1146/annurev-anchem-071015-041535.

    Article  Google Scholar 

  6. Lichtman JS, Ferreyra JA, Ng KM, Smits SA, Sonnenburg JL, Elias JE. Host-microbiota interactions in the pathogenesis of antibiotic-associated diseases. Cell Rep. 2016;14:1049–61. https://doi.org/10.1016/j.celrep.2016.01.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Deatherage Kaiser BL, Li J, Sanford JA, Kim YM, Kronewitter SR, Jones MB, et al. A Multi-omic view of host-pathogen-commensal interplay in Salmonella-mediated intestinal infection. PLoS One 2013;8. https://doi.org/10.1371/journal.pone.0067155.

  8. Thursby E, Juge N. Introduction to the human gut microbiota. Biochem J. 2017;474:1823–36. https://doi.org/10.1042/BCJ20160510.

    Article  CAS  PubMed  Google Scholar 

  9. Hao WL, Lee YK. Microflora of the gastrointestinal tract: a review. Methods Mol Biol. 2004;268:491–502. https://doi.org/10.1385/1-59259-766-1:491.

    Article  PubMed  Google Scholar 

  10. Hillman ET, Lu H, Yao T, Nakatsu CH. Microbial ecology along the gastrointestinal tract. Microbes Environ. 2017;32:300–13. https://doi.org/10.1264/jsme2.ME17017.

    Article  PubMed  PubMed Central  Google Scholar 

  11. 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. https://doi.org/10.1016/j.jprot.2017.07.001.

    Article  CAS  PubMed  Google Scholar 

  12. Wu J, Zhu J, Yin H, Liu X, An M, Pudlo NA, et al. Development of an integrated pipeline for profiling microbial proteins from mouse fecal samples by LC-MS/MS. J Proteome Res. 2016;15:3635–42. https://doi.org/10.1021/acs.jproteome.6b00450.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Waas M, Bhattacharya S, Chuppa S, Wu X, Jensen DR, Omasits U, et al. Combine and conquer: surfactants, solvents, and chaotropes for robust mass spectrometry based analyses of membrane proteins. Anal Chem. 2014;86:1551–9. https://doi.org/10.1021/ac403185a.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang Y, Zhou Y, Xiao X, Zheng J, Zhou H. Metaproteomics: a strategy to study the taxonomy and functionality of the gut microbiota. vol. 219. Elsevier B.V; 2020. https://doi.org/10.1016/j.jprot.2020.103737.

  15. Chen EI, Cociorva D, Norris JL, Yates JR. Optimization of mass spectrometry-compatible surfactants for shotgun proteomics. J Proteome Res. 2007;6:2529–38. https://doi.org/10.1021/pr060682a.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Botelho D, Wall MJ, Vieira DB, Fitzsimmons S, Liu F, Doucette A. Top-down and bottom-up proteomics of SDS-containing solutions following mass-based separation. J Proteome Res. 2010;9:2863–70. https://doi.org/10.1021/pr900949p.

    Article  CAS  PubMed  Google Scholar 

  17. Ilavenil S, Al-Dhabi NA, Srigopalram S, Kim YO, Agastian P, Baaru R, et al. Removal of SDS from biological protein digests for proteomic analysis by mass spectrometry. Proteome Sci. 2016;14:1–6. https://doi.org/10.1186/s12953-016-0098-5.

    Article  CAS  Google Scholar 

  18. Baniasad M, Reed AJ, Lai SM, Zhang L, Schulte KQ, Smith AR, et al. Optimization of proteomics sample preparation for forensic analysis of skin samples. J Proteomics. 2021;249: 104360. https://doi.org/10.1016/j.jprot.2021.104360.

    Article  CAS  PubMed  Google Scholar 

  19. Ludwig KR, Schroll MM, Hummon AB. Comparison of in-solution, FASP, and S-Trap based digestion methods for bottom-up proteomic studies. J Proteome Res. 2018;17:2480–90. https://doi.org/10.1021/acs.jproteome.8b00235.

    Article  CAS  PubMed  Google Scholar 

  20. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6:359–62. https://doi.org/10.1038/nmeth.1322.

    Article  CAS  PubMed  Google Scholar 

  21. Hailemariam M, Eguez RV, Singh H, Bekele S, Ameni G, Pieper R, et al. S-Trap, an ultrafast sample-preparation approach for shotgun proteomics. J Proteome Res. 2018;17:2917–24. https://doi.org/10.1021/acs.jproteome.8b00505.

    Article  CAS  PubMed  Google Scholar 

  22. Hughes CS, Moggridge S, Müller T, Sorensen PH, Morin GB, Krijgsveld J. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat Protoc. 2019;14:68–85. https://doi.org/10.1038/s41596-018-0082-x.

    Article  CAS  PubMed  Google Scholar 

  23. Müller T, Kalxdorf M, Longuespée R, Kazdal DN, Stenzinger A, Krijgsveld J. Automated sample preparation with SP 3 for low-input clinical proteomics . Mol Syst Biol 2020;16:1–19. https://doi.org/10.15252/msb.20199111.

  24. Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–8. https://doi.org/10.1093/bioinformatics/bts174.

    Article  CAS  PubMed  Google Scholar 

  25. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6. https://doi.org/10.1093/bioinformatics/btv033.

    Article  CAS  PubMed  Google Scholar 

  26. Doug Hyatt, Gwo-Liang Chen, Philip F LoCascio, Miriam L Land, FWL, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010;6:1–8.

  27. Lesker TR, Durairaj AC, Gálvez EJC, Lagkouvardos I, Baines JF, Clavel T, et al. An integrated metagenome catalog reveals new insights into the murine gut microbiome. Cell Rep. 2020;30:2909-2922.e6. https://doi.org/10.1016/j.celrep.2020.02.036.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Steinegger M, Söding J. Clustering huge protein sequence sets in linear time. Nat Commun 2018;9. https://doi.org/10.1038/s41467-018-04964-5.

  29. Deutsch EW, Csordas A, Sun Z, Jarnuczak A, Perez-Riverol Y, Ternent T, et al. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res. 2017;45:D1100–6. https://doi.org/10.1093/nar/gkw936.

    Article  CAS  PubMed  Google Scholar 

  30. Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47:D442–50. https://doi.org/10.1093/nar/gky1106.

    Article  CAS  PubMed  Google Scholar 

  31. McIlwain S, Mathews M, Bereman MS, Rubel EW, MacCoss MJ, Noble WS. Estimating relative abundances of proteins from shotgun proteomics data. BMC Bioinformatics. 2012;13:308. https://doi.org/10.1186/1471-2105-13-308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lichtman JS, Marcobal A, Sonnenburg JL, Elias JE. Host-centric proteomics of stool: a novel strategy focused on intestinal responses to the gut microbiota. Mol Cell Proteomics. 2013;12:3310–8. https://doi.org/10.1074/mcp.M113.029967.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ang CS, Rothacker J, Patsiouras H, Burgess AW, Nice EC. Murine fecal proteomics: a model system for the detection of potential biomarkers for colorectal cancer. J Chromatogr A. 2010;1217:3330–40. https://doi.org/10.1016/j.chroma.2009.10.007.

    Article  CAS  PubMed  Google Scholar 

  34. Mesuere B, Debyser G, Aerts M, Devreese B, Vandamme P, Dawyndt P. The Unipept metaproteomics analysis pipeline. Proteomics. 2015;15:1437–42. https://doi.org/10.1002/pmic.201400361.

    Article  CAS  PubMed  Google Scholar 

  35. Mesuere B, Devreese B, Debyser G, Aerts M, Vandamme P, Dawyndt P. Unipept: tryptic peptide-based biodiversity analysis of metaproteome samples. J Proteome Res. 2012;11:5773–80. https://doi.org/10.1021/pr300576s.

    Article  CAS  PubMed  Google Scholar 

  36. Forster SC, Kumar N, Anonye BO, Almeida A, Viciani E, Stares MD, et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat Biotechnol. 2019;37:186–92. https://doi.org/10.1038/s41587-018-0009-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhang X, Ning Z, Mayne J, Moore JI, Li J, Butcher J, et al. MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota. Microbiome. 2016;4:1–12. https://doi.org/10.1186/s40168-016-0176-z.

    Article  CAS  Google Scholar 

  38. Borton MA, Sabag-Daigle A, Wu J, Solden LM, O’Banion BS, Daly RA, et al. Chemical and pathogen-induced inflammation disrupt the murine intestinal microbiome. Microbiome. 2017;5:1–15. https://doi.org/10.1186/s40168-017-0264-8.

    Article  Google Scholar 

  39. Jiang X, Jiang X, Feng S, Tian R, Ye M, Zou H. Development of efficient protein extraction methods for shotgun proteome analysis of formalin-fixed tissues. J Proteome Res. 2007;6:1038–47. https://doi.org/10.1021/pr0605318.

    Article  CAS  PubMed  Google Scholar 

  40. 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. https://doi.org/10.1002/pmic.201400573.

    Article  CAS  PubMed  Google Scholar 

  41. Isaacson T, Damasceno CMB, Saravanan RS, He Y, Catalá C, Saladié M, et al. Sample extraction techniques for enhanced proteomic analysis of plant tissues. Nat Protoc. 2006;1:769–74. https://doi.org/10.1038/nprot.2006.102.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  43. Feist P, Hummon AB. Proteomic challenges: sample preparation techniques for microgram-quantity protein analysis from biological samples. Int J Mol Sci. 2015;16:3537–63. https://doi.org/10.3390/ijms16023537.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Erde J, Loo RRO, Loo JA. Improving proteome coverage and sample recovery with enhanced FASP (eFASP) for quantitative proteomic experiments. Methods Mol Biol 2017;1551:11–8. https://doi.org/10.1007/978-1-4939-6747-6_2.

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Acknowledgements

We thank the OSU Campus Chemical Instrument Center for access to the timsTOF Pro purchased with grant S10 OD026945 from the National Institutes of Health.

Funding

This work was funded by the grant number 5R01AI43288 from the National Institutes of Health.

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Authors and Affiliations

  1. Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA

    Maryam Baniasad, Yongseok Kim & Vicki H. Wysocki

  2. Department of Soil and Crop Sciences, The Colorado State University, Fort Collins, CO, USA

    Michael Shaffer, Ikaia Leleiwi, Rebecca A. Daly & Kelly C. Wrighton

  3. Department of Microbiology, The Ohio State University, Columbus, OH, USA

    Anice Sabag-Daigle & Brian M. M. Ahmer

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  1. Maryam Baniasad
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Correspondence to Vicki H. Wysocki.

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Mouse experiments in this study were performed in accordance with protocols approved by The Ohio State University Institutional Animal Care and Use Committee (IACUC; OSU 2009A0035-R4).

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Baniasad, M., Kim, Y., Shaffer, M. et al. Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces. Anal Bioanal Chem 414, 2317–2331 (2022). https://doi.org/10.1007/s00216-022-03885-z

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