Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect
ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
CONTENT TYPES

In Vitro Metabolic Labeling of Intestinal Microbiota for Quantitative Metaproteomics

View Author Information
Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
CHEO Inflammatory Bowel Disease Centre and Research Institute and Department of Paediatrics, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
*E-mail: [email protected]. Phone: 613-562-5800 ext 8674.
*E-mail: [email protected]. Phone: 613-562-5800 ext 8216.
*E-mail: [email protected]. Phone: 613-737-7600 ext 4113.
Cite this: Anal. Chem. 2016, 88, 12, 6120–6125
Publication Date (Web):June 1, 2016
https://doi.org/10.1021/acs.analchem.6b01412

Copyright © 2016 American Chemical Society. This publication is available under these Terms of Use.

  • Free to Read

Article Views

2219

Altmetric

-

Citations

LEARN ABOUT THESE METRICS
PDF (1 MB)
Supporting Info (2)»

Abstract

Intestinal microbiota is emerging as one of the key environmental factors influencing or causing the development of numerous human diseases. Metaproteomics can provide invaluable information on the functional activities of intestinal microbiota and on host–microbe interactions as well. However, the application of metaproteomics in human microbiota studies is still largely limited, in part due to the lack of accurate quantitative intestinal metaproteomic methods. Most current metaproteomic microbiota studies are based on label-free quantification, which may suffer from variability during the separate sample processing and mass spectrometry runs. In this study, we describe a quantitative metaproteomic strategy, using in vitro stable isotopically (15N) labeled microbiota as a spike-in reference, to study the intestinal metaproteomes. We showed that the human microbiota were efficiently labeled (>95% 15N enrichment) within 3 days under in vitro conditions, and accurate light-to-heavy protein/peptide ratio measurements were obtained using a high-resolution mass spectrometer and the quantitative proteomic software tool Census. We subsequently employed our approach to study the in vitro modulating effects of fructo-oligosaccharide and five different monosaccharides on the microbiota. Our methodology improves the accuracy of quantitative intestinal metaproteomics, which would promote the application of proteomics for functional studies of intestinal microbiota.

This publication is licensed for personal use by The American Chemical Society.

The human body harbors hundreds of microbial species (constituting trillions of microbial cells) with the majority residing in the gastrointestinal tract constituting the intestinal microbiota. (1, 2) Accumulating evidence has associated changes in microbiota composition with many diseases such as inflammatory bowel diseases (IBD), obesity, diabetes, and cancer. (3) Moreover, modulations of intestinal microbiota using probiotics/prebiotics, drugs, or dietary intervention have also been reported to improve the host metabolic phenotypes. (4, 5) Clearly, there is considerable interest in deciphering the role of the intestinal microbiota in human diseases as it may provide avenues for therapeutic interventions.
Next-generation sequencing (NGS), such as metagenomic and metatranscriptomic sequencings, is well suited for examining the microbiota composition and predicting potential functions; however, it does not provide direct evidence on whether the genes are translated into proteins or not. Instead, metaproteomics can provide invaluable information on the functional activities of microbiome by directly profiling protein expression levels. (6, 7) In contrast to metagenomics, metaproteomics approaches have been applied to limited number of studies on the intestinal microbiota. This is due, at least in part, to challenges related to both identification and quantification of the intestinal microbial peptides/proteins. (8) Peptide/protein identification algorithms have recently been significantly improved by the use of iterative database searching of large intestinal microbial protein databases. (9) In contrast, accurate methods for peptide/protein quantification are still lacking. Most metaproteomic studies on intestinal microbiota are based on label-free quantification (LFQ), (10, 11) which may suffer from variability during the separate sample preparation and mass spectrometry (MS) runs. (12) Stable isotope labeling by amino acids in cell culture (SILAC) (13) and stable isotope labeling of mammals (SILAM) (14) are currently the most widely used metabolic-labeling approaches for accurate and robust quantitative proteomics. These methods consist in spiking a metabolically heavy labeled reference into the samples under investigation prior to sample processing and the MS run; the light-to-heavy (L/H) ratio for each protein/peptide is then calculated for comparison across samples. Although similar approaches have been applied to monocultures of bacteria, such as Staphylococcus aureus, (15) and environmental microbial communities, such as acid mine drainage biofilms, (16) their application to characterize the intestinal microbiota proteome is lacking. One of the challenges of applying these metabolic labeling approaches is the inherent high species diversity of the intestinal microbiota. This almost inevitably results in a diverse metabolic capacity to biosynthesize amino acids, (17) which hampers the full incorporation of heavy-labeled amino acids into microbial proteins. Chemical labeling approaches, such as dimethyl labeling, tandem mass tag (TMT), isobaric tag for relative and absolute quantitation (iTRAQ), and isotope-coded affinity tags (ICAT), (18-21) are also potentially applicable to intestinal microbiota studies; however, they are limited by the need to carefully control reaction conditions for consistency, and more importantly, by the variations introduced by the protein lysis and/or labeling since the chemical methods have been developed to label extracted proteins or proteolytic peptides. (22)
We therefore applied a stable isotope (15N) labeling strategy to metabolically label the human intestinal microbiota. Briefly, freshly collected human intestinal microbiota were anaerobically cultured and labeled under controlled in vitro culturing systems, and the obtained stable isotopically labeled microbiota (SILAMi) was then used as an internal standard for the analysis of unlabeled samples (Figure 1A). Upon analysis with MS and peptide/protein identification, the L/H ratios of identified peptides/proteins are automatically calculated using the Census software. (23) To our best knowledge, this report is the first to develop a metabolic labeling-based quantitative metaproteomic strategy for human intestinal microbiota studies.

Figure 1

Figure 1. Isotopic 15N metabolic labeling of human intestinal microbiota for quantitative metaproteomics. (A) Brief workflow of the stable isotope labeling of microbiota (SILAMi) and the SILAMi-based quantitative metaproteomic approaches. (B) 15N isotopic enrichment of identified intestinal microbial peptides. Mucosal–luminal interface aspirate samples from five different individuals were labeled separately with three technical replicates. The average 15N enrichment rates of all the identified peptides for each individual’s microbiota were shown.

Experimental Section

ARTICLE SECTIONS
Jump To

Metabolic labeling of human intestinal microbiota and proof-of-principle assay were performed in an anaerobic workstation (37 °C, 10% H2, 10% CO2, and 80% N2). Briefly, fresh human intestinal microbiota samples were collected from the mucosal–luminal interface (MLI) through colonoscopy according to standard protocol. (24) For metabolic labeling, the microbiota samples were inoculated at a ratio of 2% w/v into a 15N-labeled bacteria growth medium which was modified from commercially available bacteria growth medium (CGM-1000-N, U-15N, 98%; Cambridge Isotope Laboratories, Inc., MA). The in vitro cultures were passaged every 24 h into fresh medium with an inoculum rate of 10% v/v, and samples were collected at each day/passage for 15N enrichment rate calculation. For the proof-of-principle assay, microbiota samples were inoculated into basal culture medium (BCM) (25) and cultured with or without supplementation of either fructo-oligosaccharide (FOS) or monosaccharides. Culture samples were collected during the experiment at different time points.
Differential centrifugation was performed for harvesting microbial cells from the collected culture samples. (26) The resulting microbial cell pellets were used for protein extraction, in-solution trypsin digestion and MS analysis as previously described. (27) For quantification, proteins extracted from heavy-labeled and unlabeled microbiota were mixed at a 1:1 ratio followed by the same procedures as above-mentioned. A two-step database search strategy was performed for peptide/protein identification, against an in-house Human Intestinal Microbial Protein Database (HIMPD, Table S4). The 15N enrichment rate for each identified peptide was calculated using a Census software based on the algorithm reported by MacCoss et al. (28) L/H ratios of the identified peptides and protein groups for the spike-in samples were also calculated with Census as previously described. (14, 23) More experimental details are available in the Supporting Information.

Results and Discussion

ARTICLE SECTIONS
Jump To

As a complex bacteria community, such as the human intestinal microbiota, almost inevitably possesses the metabolic capacity to biosynthesize all amino acids, (17) we employed 15N labeling to comprehensively introduce the 15N isotope into microbial proteins. The commercially available 15N-labeled bacterial cell growth medium (CGM-1000-N [U-15N, 98%]; Cambridge Isotope Laboratories, Inc., MA) was used with minor modifications to support the growth of intestinal bacteria (supplementation with sodium thioglycolate and bile salts). (29) To simulate the in vivo intestinal environment, the culturing was performed in an anaerobic workstation (10% H2, 10% CO2, and 80% N2) at 37 °C. Five MLI samples collected from the distal colons of different human individuals (Table S1) were tested for 15N labeling efficiency. Briefly, freshly collected MLI samples were inoculated into modified 15N-labeled bacterial medium at an initial ratio of 2% w/v. The in vitro cultures were passaged every 24 h, samples were collected at each passage, microbial cells were harvested and processed for MS (Figure 1A). Peptide/protein identification was conducted using X!tandem pipeline, (30) which demonstrated that the number of unique peptide sequences that were completely 15N-labeled markedly increased (up to 11 800 peptides/sample after 3 days labeling), while the unlabeled peptides were barely identified (less than 100 peptides/sample; Figure S1). The Census software was employed to calculate the 15N enrichment using the algorithm developed by MacCoss et al. (28) The results showed that all five microbiota tested reached an average 15N enrichment of >95%, which is sufficient for 15N-based quantitative proteomics, (14) within three passages/days (Figure 1B). The heavy labeled microbiota cells with >95% incorporation of 15N isotopes were then used as spike-in reference, hereafter referred to as the SILAMi.
To examine the representability of the 15N-labeled SILAMi, we compared the SILAMi microbial composition to the initial inoculum (Passage 0) using both metaproteomics-based and 16S rDNA sequencing-based taxonomic methods (Supporting Materials and Methods). Briefly, for metaproteomics-based analysis, all the identified peptide sequences (i.e., 15N peptides in SILAMi and 14N peptides in Passage 0) were phylogenetically classified using Unipept, which assigns taxonomic information for peptides based on lowest common ancestor (LCA) algorithm, UniProt database, and NCBI taxonomy. (31) As shown in Figure S2, 16 of 18 microbial phyla (including those belong to Bacteria, Archaea, and Eukaryota kingdoms) and 138 of 142 genera that were detected in Passage 0 remained in the SILAMi reference (Figure S2A,B). Similarly, sequencing the bacterial 16S rDNA V6 region also showed that all of the 10 bacterial phyla and 72 of 85 bacterial genera that were detected in Passage 0 remained in the SILAMi reference (Figure S2C,D).
We then tested whether accurate ratio measurements could be obtained using SILAMi-based quantitative metaproteomics. Briefly, the same amount of SILAMi proteomes were spiked into different amounts of the unlabeled human gut metaproteomes at L/H ratios of 1:1, 1.25:1, 2:1, and 5:1, respectively. The mixtures were then processed for 4 h gradient MS analysis on an Orbitrap Elite. A total of 6 943 unique peptide sequences corresponding to 4 014 protein groups were quantified and the L/H ratios were calculated using Census. (23) The distributions of calculated L/H ratios for all the quantified protein groups are shown in Figure 2A, which demonstrated that the median L/H ratios were in great agreement with the spike-in ratios (Pearson’s r = 0.99). The fold change (FC) of each protein group between the four samples was then calculated by the “ratio of the L/H ratios” (Figure 2B), which showed that narrow FC distributions were obtained, with 81–92% of the protein groups having less than 2-fold difference to the median. This variance is comparable to the widely used super-SILAC approach. (32) Moreover, the median protein FCs (1.23-, 1.93-, and 4.39-folds; Figure 2B) were in excellent agreement with the theoretical FC values (1.25-, 2-, and 5-folds, respectively). Taken together, these results suggest accurate ratio measurements of the present SILAMi-based metaproteomic strategy for intestinal microbiota studies.

Figure 2

Figure 2. Quantitation accuracy of the SILAMi-based metaproteomics: (A) density plot showing the calculated L/H ratios of quantified protein groups in samples with different L/H spike-in ratios (1:1, 1.25:1, 2:1, and 5:1). Scatter plot shows the correlation between the calculated L/H protein ratios (median) and spike-in ratios. Pearson’s r-value was indicated; (B) density plot showing the distribution of fold changes when compared to the sample with 1:1 spike-in ratio. Log2-transformed L/H ratios or fold changes were used for generating the density plots with a bandwidth of 0.2. Dashed lines indicate median values. The percentage of proteins within a 2-fold difference to median was shown in the parentheses.

As a proof-of-principle, we applied our SILAMi approach to evaluate the effects of FOS, a known prebiotic which can stimulate the growth of anti-inflammatory short-chain fatty acid (SCFA)-producing bacteria, (33-35) on the intestinal microbiota. Briefly, freshly collected microbiota samples from one volunteer were inoculated into a modified BCM with an inoculum rate of 2% w/v (25, 36) and cultured under anaerobic conditions with or without 10 mg/mL FOS for 13 and 36 h. The metaproteomes extracted from each microbial culture were spiked with the equal amount of SILAMi proteins and processed for 2 h gradient MS analysis. A total of 2 280 protein groups were quantified. Principal component analysis (PCA) showed that FOS markedly shifted the overall metaproteome along PC1 (explaining the most variability, 37.5% of the total variance; Figure 3A). Metaproteome changes in control (NC) group during in vitro culturing were also observed along the second PC, which explains 18.5% of the total variance (Figure 3A). To identify FOS-altered microbial proteins, a two-sample t test was performed followed by Benjamin-Hochberg FDR correction on samples with and without 36 h FOS treatment. A total of 187 protein groups were identified with >2-fold changes and FDR-corrected p < 0.05 (Figure S3 and Table S2). Among those different protein groups, eight orthologs of the elongation factor Tu (EF-Tu) from different bacterial species were increased (exemplified by a quantified peptide as shown in Figure 3B; the MS/MS spectra and peptide-spectra match are shown in Figure S4). As well, nine orthologs of glyceraldehyde-3-phosphate dehydrogenase protein were also quantified, among which eight orthologs were decreased while the one from Fusobacterium varium was increased. SCFA metabolism pathways were mainly enriched by the FOS-altered proteins (Figure S5), which was in agreement with the known effects of FOS. (33, 35) We also found that the proteins involved in endotoxin synthesis (e.g., ADP-glyceromanno-heptose 6-epimerase) were decreased. Endotoxin-producing bacteria or endotoxin itself have been shown to mediate systemic chronic inflammation and to contribute to the development of metabolic diseases such as obesity and type 2 diabetes; (37, 38) thus, the decrease of endotoxin synthesis in FOS-treated microbiota may also partly explain its prebiotic activity in the intestinal tract.

Figure 3

Figure 3. Proof-of-principle examples of SILAMi-based quantitative metaproteomics for microbiota studies: (A) PCA score plot of FOS-mediated intestinal metaproteome changes. (B) Representative TICs of quantified peptides of protein EF-Tu. Both heavy (red) and light (blue) are shown. (C) Heatmap of 246 microbial proteins which were significantly modulated by different monosaccharides. Both column and row clustering were calculated based on Euclidean distance in Perseus. Blue square, GlcNAc; blue circle, glucose; yellow circle, galactose; green circle, mannose; red triangle, fucose; C, nontreated control. (D) Quantified proteins involved in bacterial fucose utilization pathway. Mean ± SD was shown in the bar charts. DHAP, dihydroxyacetone phosphate; FucP, L-fucose:H+ symporter permease; FucM, L-fucose mutarotase; FucI, L-fucose isomerase; FucA, L-fuculose-1-phosphate aldolase; FucK, L-fuculokinase; FucO, L-1,2-propanediol oxidoreductase or lactaldehyde reductase.

As another example, we applied the SILAMi approach to distinguish the effects of different monosaccharides on the microbiota. Overall, 18 human microbiota culture samples cocultured with or without 2.5 g/L of each monosaccharide (N-acetyl glucosamine, mannose, galactose, fucose, or glucose) were analyzed by SILAMi-based metaproteomics, which led to 3 158 quantified protein groups. A total of 246 protein groups were identified as being differentially abundant as compared to the nontreated control group (Table S3). Unique metaproteome patterns were observed in response to the different monosaccharide treatments (Figure 3C). The monosaccharide fucose showed the smallest effect on the metaproteome; however, cluster 206, mainly consisting of fucose utilization-related proteins, was increased only in the fucose-treated microbiota (Figure 3C). Moreover, all of the six quantified fucose utilizing proteins in the fucose utilization pathway were increased upon the supplementation of fucose (Figure 3D). (39) On the other hand, N-acetyl glucosamine (GlcNAc) resulted in the most dramatic alterations of the metaproteome with significant increase of GlcNAc degrading proteins (Figure S6A–C), which produce fructose 6-phosphate and NH3. The latter may be used for asparagine synthesis since asparagine synthase was also significantly increased (Figure S6D).

Conclusions

ARTICLE SECTIONS
Jump To

We have developed a fast and effective strategy for metabolically labeling the whole human intestinal microbiota, termed stable isotopically labeled intestinal microbiota (SILAMi). Isotopically labeled reference microbiomes are generated by inoculating fresh intestinal microbiota samples into 15N-labeled bacterial growth media for 24 h intervals until 95% 15N enrichment is reached. Once the 15N isotope is sufficiently incorporated, the labeled microbiota can be used as an internal standard for the study of unlabeled samples. To note, because of the significant differences between the in vitro growth conditions and the in vivo human intestinal environment, some of the microbial phylotypes were lost during the in vitro15N labeling. This limitation could be addressed by labeling bacteria in monocultures (i.e., those that were lacking or of interest) to generate a “super-SILAMi” that could be used similarly to the widely used super-SILAC methodology. (32) Moreover, the limitations can also be addressed using recently reported algorithms, such as hybrid LFQ and QuantFusion, (40, 41) which combine the label-free and labeled strategies to accurately quantify the proteins absent in spike-in reference. Nevertheless, SILAMi is an easy-to-implement approach to metabolically label the human intestinal microbiota. The applicability of SILAMi was demonstrated through the studies of in vitro effects of different compounds, including prebiotics and monosaccharides, on the human microbiota. The quantitative concept and downstream data processing procedures of SILAMi-based approach are completely compatible with the currently widely used SILAC/SILAM-based proteomics. Thus, the present metaproteomic approach shows a promising method for functional studies of the intestinal microbiota, which would largely promote the application of proteomics in the field of microbiota studies.

Supporting Information

ARTICLE SECTIONS
Jump To

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b01412.

  • Detailed supporting materials and methods, table of characterizations of individuals involved in sample collections, and 14N and 15N peptide identifications, MLI microbiota composition, heat map of 187 identified protein groups altered by FOS treatment, peptide-spectra match quality, metabolic pathway, and influence of monosaccharides on the relative abundance of N-acetyl glucosamine degrading related proteins (PDF)

  • Tables of 187 proteins altered by fructo-oligosaccharide; 246 key proteins modulated by the supplement of different monosaccharides; and 465 microbial species/strains included in the human intestinal microbial protein database (XLSX)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

ARTICLE SECTIONS
Jump To

  • Corresponding Authors
    • David R. Mack - CHEO Inflammatory Bowel Disease Centre and Research Institute and Department of Paediatrics, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5 Email: [email protected]
    • Alain Stintzi - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5 Email: [email protected]
    • Daniel Figeys - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5 Email: [email protected]
  • Authors
    • Xu Zhang - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
    • Zhibin Ning - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
    • Janice Mayne - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
    • Shelley A. Deeke - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
    • Jennifer Li - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
    • Amanda E. Starr - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
    • Rui Chen - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
    • Ruth Singleton - CHEO Inflammatory Bowel Disease Centre and Research Institute and Department of Paediatrics, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
    • James Butcher - Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

ARTICLE SECTIONS
Jump To

This work was supported in part by the Government of Canada through Genome Canada and the Ontario Genomics Institute (Grant OGI-067), the Canadian Institutes of Health Research (Grant GPH-129340), the Ontario Ministry of Economic Development and Innovation (Grant REG1-4450), and the Natural Sciences and Engineering Research Council of Canada (NSERC). D.F. acknowledges a Canada Research Chair in Proteomics and Systems Biology. The authors also acknowledge funding from the IBD Foundation, Crohn’s and Colitis Canada (CCC), the CHEO Research Institute, and the Faculty of Medicine of the University of Ottawa. We also acknowledge the assistance of A. Mack in making this study possible.

References

ARTICLE SECTIONS
Jump To

This article references 41 other publications.

  1. 1
    Gordon, J. I. Science 2012, 336, 1251 1253 DOI: 10.1126/science.1224686
  2. 2
    Qin, J.; Li, R.; Raes, J.; Arumugam, M.; Burgdorf, K. S.; Manichanh, C.; Nielsen, T.; Pons, N.; Levenez, F.; Yamada, T.; Mende, D. R.; Li, J.; Xu, J.; Li, S.; Li, D.; Cao, J.; Wang, B.; Liang, H.; Zheng, H.; Xie, Y.; Tap, J.; Lepage, P.; Bertalan, M.; Batto, J. M.; Hansen, T.; Le Paslier, D.; Linneberg, A.; Nielsen, H. B.; Pelletier, E.; Renault, P.; Sicheritz-Ponten, T.; Turner, K.; Zhu, H.; Yu, C.; Jian, M.; Zhou, Y.; Li, Y.; Zhang, X.; Qin, N.; Yang, H.; Wang, J.; Brunak, S.; Dore, J.; Guarner, F.; Kristiansen, K.; Pedersen, O.; Parkhill, J.; Weissenbach, J.; Bork, P.; Ehrlich, S. D. Nature 2010, 464, 59 65 DOI: 10.1038/nature08821
  3. 3
    Clemente, J. C.; Ursell, L. K.; Parfrey, L. W.; Knight, R. Cell 2012, 148, 1258 1270 DOI: 10.1016/j.cell.2012.01.035
  4. 4
    Holmes, E.; Kinross, J.; Gibson, G. R.; Burcelin, R.; Jia, W.; Pettersson, S.; Nicholson, J. K. Sci. Transl. Med. 2012, 4, 137rv6 DOI: 10.1126/scitranslmed.3004244
  5. 5
    Jia, W.; Li, H.; Zhao, L.; Nicholson, J. K. Nat. Rev. Drug Discovery 2008, 7, 123 129 DOI: 10.1038/nrd2505
  6. 6
    Verberkmoes, N. C.; Russell, A. L.; Shah, M.; Godzik, A.; Rosenquist, M.; Halfvarson, J.; Lefsrud, M. G.; Apajalahti, J.; Tysk, C.; Hettich, R. L.; Jansson, J. K. ISME J. 2009, 3, 179 189 DOI: 10.1038/ismej.2008.108
  7. 7
    Juste, C.; Kreil, D. P.; Beauvallet, C.; Guillot, A.; Vaca, S.; Carapito, C.; Mondot, S.; Sykacek, P.; Sokol, H.; Blon, F.; Lepercq, P.; Levenez, F.; Valot, B.; Carre, W.; Loux, V.; Pons, N.; David, O.; Schaeffer, B.; Lepage, P.; Martin, P.; Monnet, V.; Seksik, P.; Beaugerie, L.; Ehrlich, S. D.; Gibrat, J. F.; Van Dorsselaer, A.; Dore, J. Gut 2014, 63, 1566 1577 DOI: 10.1136/gutjnl-2012-303786
  8. 8
    Mayne, J.; Ning, Z.; Zhang, X.; Starr, A. E.; Chen, R.; Deeke, S.; Chiang, C. K.; Xu, B.; Wen, M.; Cheng, K.; Seebun, D.; Star, A.; Moore, J. I.; Figeys, D. Anal. Chem. 2016, 88, 95 121 DOI: 10.1021/acs.analchem.5b04230
  9. 9
    Jagtap, P.; Goslinga, J.; Kooren, J. A.; McGowan, T.; Wroblewski, M. S.; Seymour, S. L.; Griffin, T. J. Proteomics 2013, 13, 1352 1357 DOI: 10.1002/pmic.201200352
  10. 10
    von Bergen, M.; Jehmlich, N.; Taubert, M.; Vogt, C.; Bastida, F.; Herbst, F. A.; Schmidt, F.; Richnow, H. H.; Seifert, J. ISME J. 2013, 7, 1877 1885 DOI: 10.1038/ismej.2013.78
  11. 11
    Lichtman, J. S.; Sonnenburg, J. L.; Elias, J. E. ISME J. 2015, 9, 1908 1915 DOI: 10.1038/ismej.2015.93
  12. 12
    Usaite, R.; Wohlschlegel, J.; Venable, J. D.; Park, S. K.; Nielsen, J.; Olsson, L.; Yates Iii, J. R. J. Proteome Res. 2008, 7, 266 275 DOI: 10.1021/pr700580m
  13. 13
    Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M. Mol. Cell. Proteomics 2002, 1, 376 386 DOI: 10.1074/mcp.M200025-MCP200
  14. 14
    Rauniyar, N.; McClatchy, D. B.; Yates, J. R., 3rd. Methods 2013, 61, 260 268 DOI: 10.1016/j.ymeth.2013.03.008
  15. 15
    Schmidt, F.; Scharf, S. S.; Hildebrandt, P.; Burian, M.; Bernhardt, J.; Dhople, V.; Kalinka, J.; Gutjahr, M.; Hammer, E.; Volker, U. Proteomics 2010, 10, 2801 2811 DOI: 10.1002/pmic.201000045
  16. 16
    Mueller, R. S.; Dill, B. D.; Pan, C.; Belnap, C. P.; Thomas, B. C.; VerBerkmoes, N. C.; Hettich, R. L.; Banfield, J. F. Environ. Microbiol. 2011, 13, 2279 2292 DOI: 10.1111/j.1462-2920.2011.02486.x
  17. 17
    Metges, C. C. J. Nutr. 2000, 130, 1857S 1864S
  18. 18
    Boersema, P. J.; Raijmakers, R.; Lemeer, S.; Mohammed, S.; Heck, A. J. Nat. Protoc. 2009, 4, 484 494 DOI: 10.1038/nprot.2009.21
  19. 19
    Thompson, A.; Schafer, J.; Kuhn, K.; Kienle, S.; Schwarz, J.; Schmidt, G.; Neumann, T.; Johnstone, R.; Mohammed, A. K.; Hamon, C. Anal. Chem. 2003, 75, 1895 1904 DOI: 10.1021/ac0262560
  20. 20
    Ross, P. L.; Huang, Y. N.; Marchese, J. N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; Purkayastha, S.; Juhasz, P.; Martin, S.; Bartlet-Jones, M.; He, F.; Jacobson, A.; Pappin, D. J. Mol. Cell Proteomics 2004, 3, 1154 1169
  21. 21
    Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R. Nat. Biotechnol. 1999, 17, 994 999 DOI: 10.1038/13690
  22. 22
    Ong, S. E.; Mann, M. Nat. Chem. Biol. 2005, 1, 252 262 DOI: 10.1038/nchembio736
  23. 23
    Park, S. K.; Venable, J. D.; Xu, T.; Yates, J. R., 3rd. Nat. Methods 2008, 5, 319 322 DOI: 10.1038/nmeth.1195
  24. 24
    Jimenez-Rivera, C.; Haas, D.; Boland, M.; Barkey, J. L.; Mack, D. R. Gastroenterol Res. Pract 2009, 2009, 518932 DOI: 10.1155/2009/518932
  25. 25
    Saulnier, D. M.; Gibson, G. R.; Kolida, S. FEMS Microbiol. Ecol. 2008, 66, 516 527 DOI: 10.1111/j.1574-6941.2008.00561.x
  26. 26
    Apajalahti, J. H.; Sarkilahti, L. K.; Maki, B. R.; Heikkinen, J. P.; Nurminen, P. H.; Holben, W. E. Appl. Environ. Microbiol. 1998, 64, 4084 4088
  27. 27
    Chen, R.; Seebun, D.; Ye, M.; Zou, H.; Figeys, D. J. Proteomics 2014, 103, 194 203 DOI: 10.1016/j.jprot.2014.03.040
  28. 28
    MacCoss, M. J.; Wu, C. C.; Matthews, D. E.; Yates, J. R., 3rd. Anal. Chem. 2005, 77, 7646 7653 DOI: 10.1021/ac0508393
  29. 29
    Aries, V.; Crowther, J. S.; Drasar, B. S.; Hill, M. J. Gut 1969, 10, 575 576 DOI: 10.1136/gut.10.7.575
  30. 30
    Fenyo, D.; Beavis, R. C. Anal. Chem. 2003, 75, 768 774 DOI: 10.1021/ac0258709
  31. 31
    Mesuere, B.; Devreese, B.; Debyser, G.; Aerts, M.; Vandamme, P.; Dawyndt, P. J. Proteome Res. 2012, 11, 5773 5780 DOI: 10.1021/pr300576s
  32. 32
    Geiger, T.; Cox, J.; Ostasiewicz, P.; Wisniewski, J. R.; Mann, M. Nat. Methods 2010, 7, 383 385 DOI: 10.1038/nmeth.1446
  33. 33
    Mitsuoka, T.; Hidaka, H.; Eida, T. Nahrung 1987, 31, 427 436 DOI: 10.1002/food.19870310528
  34. 34
    Bouhnik, Y.; Vahedi, K.; Achour, L.; Attar, A.; Salfati, J.; Pochart, P.; Marteau, P.; Flourie, B.; Bornet, F.; Rambaud, J. C. J. Nutr. 1999, 129, 113 116
  35. 35
    Whelan, K.; Judd, P. A.; Preedy, V. R.; Simmering, R.; Jann, A.; Taylor, M. A. J. Nutr. 2005, 135, 1896 1902
  36. 36
    Long, W.; Xue, Z.; Zhang, Q.; Feng, Z.; Bridgewater, L.; Wang, L.; Zhao, L.; Pang, X. Sci. Rep. 2015, 5, 13469 DOI: 10.1038/srep13469
  37. 37
    Cani, P. D.; Bibiloni, R.; Knauf, C.; Waget, A.; Neyrinck, A. M.; Delzenne, N. M.; Burcelin, R. Diabetes 2008, 57, 1470 1481 DOI: 10.2337/db07-1403
  38. 38
    Fei, N.; Zhao, L. ISME J. 2013, 7, 880 884 DOI: 10.1038/ismej.2012.153
  39. 39
    Stahl, M.; Friis, L. M.; Nothaft, H.; Liu, X.; Li, J.; Szymanski, C. M.; Stintzi, A. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 7194 7199 DOI: 10.1073/pnas.1014125108
  40. 40
    Deeb, S. J.; Tyanova, S.; Hummel, M.; Schmidt-Supprian, M.; Cox, J.; Mann, M. Mol. Cell. Proteomics 2015, 14, 2947 2960 DOI: 10.1074/mcp.M115.050245
  41. 41
    Gunawardena, H. P.; O’Brien, J.; Wrobel, J. A.; Xie, L.; Davies, S. R.; Li, S.; Ellis, M. J.; Qaqish, B. F.; Chen, X. Mol. Cell. Proteomics 2016, 15, 740 751 DOI: 10.1074/mcp.O115.049791

Cited By

ARTICLE SECTIONS
Jump To

This article is cited by 30 publications.

  1. Kundi Yang, Mengyang Xu, Jiangjiang Zhu. Evaluating the Impact of Four Major Nutrients on Gut Microbial Metabolism by a Targeted Metabolomics Approach. Journal of Proteome Research 2020, 19 (5) , 1991-1998. https://doi.org/10.1021/acs.jproteome.9b00806
  2. Shahd Ezzeldin, Aya El-Wazir, Shymaa Enany, Abdelrahman Muhammad, Dina Johar, Aya Osama, Eman Ahmed, Hassan Shikshaky, Sameh Magdeldin. Current Understanding of Human Metaproteome Association and Modulation. Journal of Proteome Research 2019, 18 (10) , 3539-3554. https://doi.org/10.1021/acs.jproteome.9b00301
  3. Leyuan Li, Xu Zhang, Zhibin Ning, Janice Mayne, Jasmine I. Moore, James Butcher, Cheng-Kang Chiang, David Mack, Alain Stintzi, and Daniel Figeys . Evaluating in Vitro Culture Medium of Gut Microbiome with Orthogonal Experimental Design and a Metaproteomics Approach. Journal of Proteome Research 2018, 17 (1) , 154-163. https://doi.org/10.1021/acs.jproteome.7b00461
  4. Amanda E. Starr, Shelley A. Deeke, Leyuan Li, Xu Zhang, Rachid Daoud, James Ryan, Zhibin Ning, Kai Cheng, Linh V. H. Nguyen, Elias Abou-Samra, Mathieu Lavallée-Adam, and Daniel Figeys . Proteomic and Metaproteomic Approaches to Understand Host–Microbe Interactions. Analytical Chemistry 2018, 90 (1) , 86-109. https://doi.org/10.1021/acs.analchem.7b04340
  5. Beibei Zhang, Mingyu Zhang, Jinlong Tian, Xi Zhang, Dan Zhang, Jiabao Li, Lei Yang. Advances in the regulation of radiation-induced apoptosis by polysaccharides: A review. International Journal of Biological Macromolecules 2024, 263 , 130173. https://doi.org/10.1016/j.ijbiomac.2024.130173
  6. Changxia Yu, Qin Dong, Mingjie Chen, Ruihua Zhao, Lei Zha, Yan Zhao, Mengke Zhang, Baosheng Zhang, Aimin Ma. The Effect of Mushroom Dietary Fiber on the Gut Microbiota and Related Health Benefits: A Review. Journal of Fungi 2023, 9 (10) , 1028. https://doi.org/10.3390/jof9101028
  7. Reza Karimi, Mina Homayoonfal, Narjes Malekjani, Mohammad Saeed Kharazmi, Seid Mahdi Jafari. Interaction between β-glucans and gut microbiota: a comprehensive review. Critical Reviews in Food Science and Nutrition 2023, 56 , 1-32. https://doi.org/10.1080/10408398.2023.2192281
  8. Xu Zhang, Krystal Walker, Janice Mayne, Leyuan Li, Zhibin Ning, Alain Stintzi, Daniel Figeys. Evaluating live microbiota biobanking using an ex vivo microbiome assay and metaproteomics. Gut Microbes 2022, 14 (1) https://doi.org/10.1080/19490976.2022.2035658
  9. Veronika Kuchařová Pettersen, Antoine Dufour, Marie-Claire Arrieta. Metaproteomic profiling of fungal gut colonization in gnotobiotic mice. Animal Microbiome 2022, 4 (1) https://doi.org/10.1186/s42523-022-00163-2
  10. Xianfeng Zeng, Xi Xing, Meera Gupta, Felix C. Keber, Jaime G. Lopez, Ying-Chiang J. Lee, Asael Roichman, Lin Wang, Michael D. Neinast, Mohamed S. Donia, Martin Wühr, Cholsoon Jang, Joshua D. Rabinowitz. Gut bacterial nutrient preferences quantified in vivo. Cell 2022, 185 (18) , 3441-3456.e19. https://doi.org/10.1016/j.cell.2022.07.020
  11. Henan Zhang, Fuchun Jiang, Jinsong Zhang, Wenhan Wang, Lin Li, Jingkun Yan. Modulatory effects of polysaccharides from plants, marine algae and edible mushrooms on gut microbiota and related health benefits: A review. International Journal of Biological Macromolecules 2022, 204 , 169-192. https://doi.org/10.1016/j.ijbiomac.2022.01.166
  12. Peter S. Thuy-Boun, Ana Y. Wang, Ana Crissien-Martinez, Janice H. Xu, Sandip Chatterjee, Gregory S. Stupp, Andrew I. Su, Walter J. Coyle, Dennis W. Wolan. Quantitative Metaproteomics and Activity-based Protein Profiling of Patient Fecal Microbiome Identifies Host and Microbial Serine-type Endopeptidase Activity Associated With Ulcerative Colitis. Molecular & Cellular Proteomics 2022, 21 (3) , 100197. https://doi.org/10.1016/j.mcpro.2022.100197
  13. Caitlin M. A. Simopoulos, Daniel Figeys, Mathieu Lavallée-Adam. Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies. 2022, 319-338. https://doi.org/10.1007/978-1-0716-2124-0_22
  14. Tim Van Den Bossche, Benoit J. Kunath, Kay Schallert, Stephanie S. Schäpe, Paul E. Abraham, Jean Armengaud, Magnus Ø. Arntzen, Ariane Bassignani, Dirk Benndorf, Stephan Fuchs, Richard J. Giannone, Timothy J. Griffin, Live H. Hagen, Rashi Halder, Céline Henry, Robert L. Hettich, Robert Heyer, Pratik Jagtap, Nico Jehmlich, Marlene Jensen, Catherine Juste, Manuel Kleiner, Olivier Langella, Theresa Lehmann, Emma Leith, Patrick May, Bart Mesuere, Guylaine Miotello, Samantha L. Peters, Olivier Pible, Pedro T. Queiros, Udo Reichl, Bernhard Y. Renard, Henning Schiebenhoefer, Alexander Sczyrba, Alessandro Tanca, Kathrin Trappe, Jean-Pierre Trezzi, Sergio Uzzau, Pieter Verschaffelt, Martin von Bergen, Paul Wilmes, Maximilian Wolf, Lennart Martens, Thilo Muth. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nature Communications 2021, 12 (1) https://doi.org/10.1038/s41467-021-27542-8
  15. Xu Zhang, Zhibin Ning, Janice Mayne, Yidai Yang, Shelley A. Deeke, Krystal Walker, Charles L. Farnsworth, Matthew P. Stokes, Jean-François Couture, David Mack, Alain Stintzi, Daniel Figeys. Widespread protein lysine acetylation in gut microbiome and its alterations in patients with Crohn’s disease. Nature Communications 2020, 11 (1) https://doi.org/10.1038/s41467-020-17916-9
  16. Leyuan Li, Daniel Figeys. Proteomics and Metaproteomics Add Functional, Taxonomic and Biomass Dimensions to Modeling the Ecosystem at the Mucosal-luminal Interface. Molecular & Cellular Proteomics 2020, 19 (9) , 1409-1417. https://doi.org/10.1074/mcp.R120.002051
  17. Jing Tang, Yunxia Wang, Jianbo Fu, Ying Zhou, Yongchao Luo, Ying Zhang, Bo Li, Qingxia Yang, Weiwei Xue, Yan Lou, Yunqing Qiu, Feng Zhu. A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies. Briefings in Bioinformatics 2020, 21 (4) , 1378-1390. https://doi.org/10.1093/bib/bbz061
  18. Yuqiu Wang, Yanting Zhou, Xiao Xiao, Jing Zheng, Hu Zhou. Metaproteomics: A strategy to study the taxonomy and functionality of the gut microbiota. Journal of Proteomics 2020, 219 , 103737. https://doi.org/10.1016/j.jprot.2020.103737
  19. Jing Tang, Jianbo Fu, Yunxia Wang, Bo Li, Yinghong Li, Qingxia Yang, Xuejiao Cui, Jiajun Hong, Xiaofeng Li, Yuzong Chen, Weiwei Xue, Feng Zhu. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Briefings in Bioinformatics 2020, 21 (2) , 621-636. https://doi.org/10.1093/bib/bby127
  20. Leyuan Li, James Ryan, Zhibin Ning, Xu Zhang, Janice Mayne, Mathieu Lavallée-Adam, Alain Stintzi, Daniel Figeys. A functional ecological network based on metaproteomics responses of individual gut microbiomes to resistant starches. Computational and Structural Biotechnology Journal 2020, 18 , 3833-3842. https://doi.org/10.1016/j.csbj.2020.10.042
  21. Leyuan Li, Elias Abou-Samra, Zhibin Ning, Xu Zhang, Janice Mayne, Janet Wang, Kai Cheng, Krystal Walker, Alain Stintzi, Daniel Figeys. An in vitro model maintaining taxon-specific functional activities of the gut microbiome. Nature Communications 2019, 10 (1) https://doi.org/10.1038/s41467-019-12087-8
  22. Ngom Issa Isaac, Decloquement Philippe, Armstrong Nicholas, Didier Raoult, Chabrière Eric. Metaproteomics of the human gut microbiota: Challenges and contributions to other OMICS. Clinical Mass Spectrometry 2019, 14 , 18-30. https://doi.org/10.1016/j.clinms.2019.06.001
  23. Jing Tang, Yunxia Wang, Yi Li, Yang Zhang, Runyuan Zhang, Ziyu Xiao, Yongchao Luo, Xueying Guo, Lin Tao, Yan Lou, Weiwei Xue, Feng Zhu. Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics. Current Pharmaceutical Design 2019, 25 (13) , 1536-1553. https://doi.org/10.2174/1381612825666190618123306
  24. Danielle L. Peters, Wenju Wang, Xu Zhang, Zhibin Ning, Janice Mayne, Daniel Figeys. Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome. PROTEOMICS 2019, 19 (16) https://doi.org/10.1002/pmic.201800363
  25. Aspen T Reese, Sean M Kearney. Incorporating functional trade-offs into studies of the gut microbiota. Current Opinion in Microbiology 2019, 50 , 20-27. https://doi.org/10.1016/j.mib.2019.09.003
  26. Stefano Levi Mortera, Alessio Soggiu, Pamela Vernocchi, Federica Del Chierico, Cristian Piras, Rita Carsetti, Valeria Marzano, Domenico Britti, Andrea Urbani, Paola Roncada, Lorenza Putignani. Metaproteomic investigation to assess gut microbiota shaping in newborn mice: A combined taxonomic, functional and quantitative approach. Journal of Proteomics 2019, 203 , 103378. https://doi.org/10.1016/j.jprot.2019.103378
  27. Arash Assadsangabi, Caroline A. Evans, Bernard M. Corfe, Alan Lobo. Application of Proteomics to Inflammatory Bowel Disease Research: Current Status and Future Perspectives. Gastroenterology Research and Practice 2019, 2019 , 1-24. https://doi.org/10.1155/2019/1426954
  28. Kim H. Chuong, David R. Mack, Alain Stintzi, Kieran C. O'Doherty. Human Microbiome and Learning Healthcare Systems: Integrating Research and Precision Medicine for Inflammatory Bowel Disease. OMICS: A Journal of Integrative Biology 2018, 22 (2) , 119-126. https://doi.org/10.1089/omi.2016.0185
  29. Pey Yee Lee, Siok-Fong Chin, Hui-min Neoh, Rahman Jamal. Metaproteomic analysis of human gut microbiota: where are we heading?. Journal of Biomedical Science 2017, 24 (1) https://doi.org/10.1186/s12929-017-0342-z
  30. David R. Mack. Probiotic Therapy. 2017, 357-367. https://doi.org/10.1007/978-3-319-49215-5_28
  • Abstract

    Figure 1

    Figure 1. Isotopic 15N metabolic labeling of human intestinal microbiota for quantitative metaproteomics. (A) Brief workflow of the stable isotope labeling of microbiota (SILAMi) and the SILAMi-based quantitative metaproteomic approaches. (B) 15N isotopic enrichment of identified intestinal microbial peptides. Mucosal–luminal interface aspirate samples from five different individuals were labeled separately with three technical replicates. The average 15N enrichment rates of all the identified peptides for each individual’s microbiota were shown.

    Figure 2

    Figure 2. Quantitation accuracy of the SILAMi-based metaproteomics: (A) density plot showing the calculated L/H ratios of quantified protein groups in samples with different L/H spike-in ratios (1:1, 1.25:1, 2:1, and 5:1). Scatter plot shows the correlation between the calculated L/H protein ratios (median) and spike-in ratios. Pearson’s r-value was indicated; (B) density plot showing the distribution of fold changes when compared to the sample with 1:1 spike-in ratio. Log2-transformed L/H ratios or fold changes were used for generating the density plots with a bandwidth of 0.2. Dashed lines indicate median values. The percentage of proteins within a 2-fold difference to median was shown in the parentheses.

    Figure 3

    Figure 3. Proof-of-principle examples of SILAMi-based quantitative metaproteomics for microbiota studies: (A) PCA score plot of FOS-mediated intestinal metaproteome changes. (B) Representative TICs of quantified peptides of protein EF-Tu. Both heavy (red) and light (blue) are shown. (C) Heatmap of 246 microbial proteins which were significantly modulated by different monosaccharides. Both column and row clustering were calculated based on Euclidean distance in Perseus. Blue square, GlcNAc; blue circle, glucose; yellow circle, galactose; green circle, mannose; red triangle, fucose; C, nontreated control. (D) Quantified proteins involved in bacterial fucose utilization pathway. Mean ± SD was shown in the bar charts. DHAP, dihydroxyacetone phosphate; FucP, L-fucose:H+ symporter permease; FucM, L-fucose mutarotase; FucI, L-fucose isomerase; FucA, L-fuculose-1-phosphate aldolase; FucK, L-fuculokinase; FucO, L-1,2-propanediol oxidoreductase or lactaldehyde reductase.

  • References

    ARTICLE SECTIONS
    Jump To

    This article references 41 other publications.

    1. 1
      Gordon, J. I. Science 2012, 336, 1251 1253 DOI: 10.1126/science.1224686
    2. 2
      Qin, J.; Li, R.; Raes, J.; Arumugam, M.; Burgdorf, K. S.; Manichanh, C.; Nielsen, T.; Pons, N.; Levenez, F.; Yamada, T.; Mende, D. R.; Li, J.; Xu, J.; Li, S.; Li, D.; Cao, J.; Wang, B.; Liang, H.; Zheng, H.; Xie, Y.; Tap, J.; Lepage, P.; Bertalan, M.; Batto, J. M.; Hansen, T.; Le Paslier, D.; Linneberg, A.; Nielsen, H. B.; Pelletier, E.; Renault, P.; Sicheritz-Ponten, T.; Turner, K.; Zhu, H.; Yu, C.; Jian, M.; Zhou, Y.; Li, Y.; Zhang, X.; Qin, N.; Yang, H.; Wang, J.; Brunak, S.; Dore, J.; Guarner, F.; Kristiansen, K.; Pedersen, O.; Parkhill, J.; Weissenbach, J.; Bork, P.; Ehrlich, S. D. Nature 2010, 464, 59 65 DOI: 10.1038/nature08821
    3. 3
      Clemente, J. C.; Ursell, L. K.; Parfrey, L. W.; Knight, R. Cell 2012, 148, 1258 1270 DOI: 10.1016/j.cell.2012.01.035
    4. 4
      Holmes, E.; Kinross, J.; Gibson, G. R.; Burcelin, R.; Jia, W.; Pettersson, S.; Nicholson, J. K. Sci. Transl. Med. 2012, 4, 137rv6 DOI: 10.1126/scitranslmed.3004244
    5. 5
      Jia, W.; Li, H.; Zhao, L.; Nicholson, J. K. Nat. Rev. Drug Discovery 2008, 7, 123 129 DOI: 10.1038/nrd2505
    6. 6
      Verberkmoes, N. C.; Russell, A. L.; Shah, M.; Godzik, A.; Rosenquist, M.; Halfvarson, J.; Lefsrud, M. G.; Apajalahti, J.; Tysk, C.; Hettich, R. L.; Jansson, J. K. ISME J. 2009, 3, 179 189 DOI: 10.1038/ismej.2008.108
    7. 7
      Juste, C.; Kreil, D. P.; Beauvallet, C.; Guillot, A.; Vaca, S.; Carapito, C.; Mondot, S.; Sykacek, P.; Sokol, H.; Blon, F.; Lepercq, P.; Levenez, F.; Valot, B.; Carre, W.; Loux, V.; Pons, N.; David, O.; Schaeffer, B.; Lepage, P.; Martin, P.; Monnet, V.; Seksik, P.; Beaugerie, L.; Ehrlich, S. D.; Gibrat, J. F.; Van Dorsselaer, A.; Dore, J. Gut 2014, 63, 1566 1577 DOI: 10.1136/gutjnl-2012-303786
    8. 8
      Mayne, J.; Ning, Z.; Zhang, X.; Starr, A. E.; Chen, R.; Deeke, S.; Chiang, C. K.; Xu, B.; Wen, M.; Cheng, K.; Seebun, D.; Star, A.; Moore, J. I.; Figeys, D. Anal. Chem. 2016, 88, 95 121 DOI: 10.1021/acs.analchem.5b04230
    9. 9
      Jagtap, P.; Goslinga, J.; Kooren, J. A.; McGowan, T.; Wroblewski, M. S.; Seymour, S. L.; Griffin, T. J. Proteomics 2013, 13, 1352 1357 DOI: 10.1002/pmic.201200352
    10. 10
      von Bergen, M.; Jehmlich, N.; Taubert, M.; Vogt, C.; Bastida, F.; Herbst, F. A.; Schmidt, F.; Richnow, H. H.; Seifert, J. ISME J. 2013, 7, 1877 1885 DOI: 10.1038/ismej.2013.78
    11. 11
      Lichtman, J. S.; Sonnenburg, J. L.; Elias, J. E. ISME J. 2015, 9, 1908 1915 DOI: 10.1038/ismej.2015.93
    12. 12
      Usaite, R.; Wohlschlegel, J.; Venable, J. D.; Park, S. K.; Nielsen, J.; Olsson, L.; Yates Iii, J. R. J. Proteome Res. 2008, 7, 266 275 DOI: 10.1021/pr700580m
    13. 13
      Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M. Mol. Cell. Proteomics 2002, 1, 376 386 DOI: 10.1074/mcp.M200025-MCP200
    14. 14
      Rauniyar, N.; McClatchy, D. B.; Yates, J. R., 3rd. Methods 2013, 61, 260 268 DOI: 10.1016/j.ymeth.2013.03.008
    15. 15
      Schmidt, F.; Scharf, S. S.; Hildebrandt, P.; Burian, M.; Bernhardt, J.; Dhople, V.; Kalinka, J.; Gutjahr, M.; Hammer, E.; Volker, U. Proteomics 2010, 10, 2801 2811 DOI: 10.1002/pmic.201000045
    16. 16
      Mueller, R. S.; Dill, B. D.; Pan, C.; Belnap, C. P.; Thomas, B. C.; VerBerkmoes, N. C.; Hettich, R. L.; Banfield, J. F. Environ. Microbiol. 2011, 13, 2279 2292 DOI: 10.1111/j.1462-2920.2011.02486.x
    17. 17
      Metges, C. C. J. Nutr. 2000, 130, 1857S 1864S
    18. 18
      Boersema, P. J.; Raijmakers, R.; Lemeer, S.; Mohammed, S.; Heck, A. J. Nat. Protoc. 2009, 4, 484 494 DOI: 10.1038/nprot.2009.21
    19. 19
      Thompson, A.; Schafer, J.; Kuhn, K.; Kienle, S.; Schwarz, J.; Schmidt, G.; Neumann, T.; Johnstone, R.; Mohammed, A. K.; Hamon, C. Anal. Chem. 2003, 75, 1895 1904 DOI: 10.1021/ac0262560
    20. 20
      Ross, P. L.; Huang, Y. N.; Marchese, J. N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; Purkayastha, S.; Juhasz, P.; Martin, S.; Bartlet-Jones, M.; He, F.; Jacobson, A.; Pappin, D. J. Mol. Cell Proteomics 2004, 3, 1154 1169
    21. 21
      Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R. Nat. Biotechnol. 1999, 17, 994 999 DOI: 10.1038/13690
    22. 22
      Ong, S. E.; Mann, M. Nat. Chem. Biol. 2005, 1, 252 262 DOI: 10.1038/nchembio736
    23. 23
      Park, S. K.; Venable, J. D.; Xu, T.; Yates, J. R., 3rd. Nat. Methods 2008, 5, 319 322 DOI: 10.1038/nmeth.1195
    24. 24
      Jimenez-Rivera, C.; Haas, D.; Boland, M.; Barkey, J. L.; Mack, D. R. Gastroenterol Res. Pract 2009, 2009, 518932 DOI: 10.1155/2009/518932
    25. 25
      Saulnier, D. M.; Gibson, G. R.; Kolida, S. FEMS Microbiol. Ecol. 2008, 66, 516 527 DOI: 10.1111/j.1574-6941.2008.00561.x
    26. 26
      Apajalahti, J. H.; Sarkilahti, L. K.; Maki, B. R.; Heikkinen, J. P.; Nurminen, P. H.; Holben, W. E. Appl. Environ. Microbiol. 1998, 64, 4084 4088
    27. 27
      Chen, R.; Seebun, D.; Ye, M.; Zou, H.; Figeys, D. J. Proteomics 2014, 103, 194 203 DOI: 10.1016/j.jprot.2014.03.040
    28. 28
      MacCoss, M. J.; Wu, C. C.; Matthews, D. E.; Yates, J. R., 3rd. Anal. Chem. 2005, 77, 7646 7653 DOI: 10.1021/ac0508393
    29. 29
      Aries, V.; Crowther, J. S.; Drasar, B. S.; Hill, M. J. Gut 1969, 10, 575 576 DOI: 10.1136/gut.10.7.575
    30. 30
      Fenyo, D.; Beavis, R. C. Anal. Chem. 2003, 75, 768 774 DOI: 10.1021/ac0258709
    31. 31
      Mesuere, B.; Devreese, B.; Debyser, G.; Aerts, M.; Vandamme, P.; Dawyndt, P. J. Proteome Res. 2012, 11, 5773 5780 DOI: 10.1021/pr300576s
    32. 32
      Geiger, T.; Cox, J.; Ostasiewicz, P.; Wisniewski, J. R.; Mann, M. Nat. Methods 2010, 7, 383 385 DOI: 10.1038/nmeth.1446
    33. 33
      Mitsuoka, T.; Hidaka, H.; Eida, T. Nahrung 1987, 31, 427 436 DOI: 10.1002/food.19870310528
    34. 34
      Bouhnik, Y.; Vahedi, K.; Achour, L.; Attar, A.; Salfati, J.; Pochart, P.; Marteau, P.; Flourie, B.; Bornet, F.; Rambaud, J. C. J. Nutr. 1999, 129, 113 116
    35. 35
      Whelan, K.; Judd, P. A.; Preedy, V. R.; Simmering, R.; Jann, A.; Taylor, M. A. J. Nutr. 2005, 135, 1896 1902
    36. 36
      Long, W.; Xue, Z.; Zhang, Q.; Feng, Z.; Bridgewater, L.; Wang, L.; Zhao, L.; Pang, X. Sci. Rep. 2015, 5, 13469 DOI: 10.1038/srep13469
    37. 37
      Cani, P. D.; Bibiloni, R.; Knauf, C.; Waget, A.; Neyrinck, A. M.; Delzenne, N. M.; Burcelin, R. Diabetes 2008, 57, 1470 1481 DOI: 10.2337/db07-1403
    38. 38
      Fei, N.; Zhao, L. ISME J. 2013, 7, 880 884 DOI: 10.1038/ismej.2012.153
    39. 39
      Stahl, M.; Friis, L. M.; Nothaft, H.; Liu, X.; Li, J.; Szymanski, C. M.; Stintzi, A. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 7194 7199 DOI: 10.1073/pnas.1014125108
    40. 40
      Deeb, S. J.; Tyanova, S.; Hummel, M.; Schmidt-Supprian, M.; Cox, J.; Mann, M. Mol. Cell. Proteomics 2015, 14, 2947 2960 DOI: 10.1074/mcp.M115.050245
    41. 41
      Gunawardena, H. P.; O’Brien, J.; Wrobel, J. A.; Xie, L.; Davies, S. R.; Li, S.; Ellis, M. J.; Qaqish, B. F.; Chen, X. Mol. Cell. Proteomics 2016, 15, 740 751 DOI: 10.1074/mcp.O115.049791
  • Supporting Information

    Supporting Information

    ARTICLE SECTIONS
    Jump To

    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b01412.

    • Detailed supporting materials and methods, table of characterizations of individuals involved in sample collections, and 14N and 15N peptide identifications, MLI microbiota composition, heat map of 187 identified protein groups altered by FOS treatment, peptide-spectra match quality, metabolic pathway, and influence of monosaccharides on the relative abundance of N-acetyl glucosamine degrading related proteins (PDF)

    • Tables of 187 proteins altered by fructo-oligosaccharide; 246 key proteins modulated by the supplement of different monosaccharides; and 465 microbial species/strains included in the human intestinal microbial protein database (XLSX)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.