Literature DB >> 35106611

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

Maryam Baniasad1, Yongseok Kim1, Michael Shaffer2, Anice Sabag-Daigle3, Ikaia Leleiwi2, Rebecca A Daly2, Brian M M Ahmer3, Kelly C Wrighton2, Vicki H Wysocki4.   

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.
© 2022. Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  Contaminant removal; Fecal sample; Protein extraction; Proteomics; Sample preparation

Mesh:

Substances:

Year:  2022        PMID: 35106611      PMCID: PMC9393048          DOI: 10.1007/s00216-022-03885-z

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.478


  44 in total

1.  Mass spectrometry-based diagnostics: the upcoming revolution in disease detection has already arrived.

Authors:  Donald H Chace
Journal:  Clin Chem       Date:  2003-07       Impact factor: 8.327

2.  The Unipept metaproteomics analysis pipeline.

Authors:  Bart Mesuere; Griet Debyser; Maarten Aerts; Bart Devreese; Peter Vandamme; Peter Dawyndt
Journal:  Proteomics       Date:  2015-02-11       Impact factor: 3.984

3.  Enrichment or depletion? The impact of stool pretreatment on metaproteomic characterization of the human gut microbiota.

Authors:  Alessandro Tanca; Antonio Palomba; Salvatore Pisanu; Maria Filippa Addis; Sergio Uzzau
Journal:  Proteomics       Date:  2015-03-19       Impact factor: 3.984

4.  Suspension trapping (STrap) sample preparation method for bottom-up proteomics analysis.

Authors:  Alexandre Zougman; Peter J Selby; Rosamonde E Banks
Journal:  Proteomics       Date:  2014-03-26       Impact factor: 3.984

Review 5.  Metaproteomics: A strategy to study the taxonomy and functionality of the gut microbiota.

Authors:  Yuqiu Wang; Yanting Zhou; Xiao Xiao; Jing Zheng; Hu Zhou
Journal:  J Proteomics       Date:  2020-03-18       Impact factor: 4.044

6.  Murine fecal proteomics: a model system for the detection of potential biomarkers for colorectal cancer.

Authors:  Ching-Seng Ang; Julie Rothacker; Heather Patsiouras; Antony W Burgess; Edouard C Nice
Journal:  J Chromatogr A       Date:  2009-10-08       Impact factor: 4.759

7.  Combine and conquer: surfactants, solvents, and chaotropes for robust mass spectrometry based analyses of membrane proteins.

Authors:  Matthew Waas; Subarna Bhattacharya; Sandra Chuppa; Xiaogang Wu; Davin R Jensen; Ulrich Omasits; Bernd Wollscheid; Brian F Volkman; Kathleen R Noon; Rebekah L Gundry
Journal:  Anal Chem       Date:  2014-01-21       Impact factor: 6.986

8.  The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition.

Authors:  Eric W Deutsch; Attila Csordas; Zhi Sun; Andrew Jarnuczak; Yasset Perez-Riverol; Tobias Ternent; David S Campbell; Manuel Bernal-Llinares; Shujiro Okuda; Shin Kawano; Robert L Moritz; Jeremy J Carver; Mingxun Wang; Yasushi Ishihama; Nuno Bandeira; Henning Hermjakob; Juan Antonio Vizcaíno
Journal:  Nucleic Acids Res       Date:  2016-10-18       Impact factor: 16.971

9.  Automated sample preparation with SP3 for low-input clinical proteomics.

Authors:  Torsten Müller; Mathias Kalxdorf; Rémi Longuespée; Daniel N Kazdal; Albrecht Stenzinger; Jeroen Krijgsveld
Journal:  Mol Syst Biol       Date:  2020-01       Impact factor: 11.429

10.  An Integrated Metagenome Catalog Reveals New Insights into the Murine Gut Microbiome.

Authors:  Till R Lesker; Abilash C Durairaj; Eric J C Gálvez; Ilias Lagkouvardos; John F Baines; Thomas Clavel; Alexander Sczyrba; Alice C McHardy; Till Strowig
Journal:  Cell Rep       Date:  2020-03-03       Impact factor: 9.423

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