Deep quantitative glycoproteomics reveals gut microbiome induced remodeling of the brain glycoproteome

  1. Clément M Potel
  2. Mira Lea Burtscher
  3. Martin Garrido-Rodriguez
  4. Amber Brauer-Nikonow
  5. Isabelle Becher
  6. Athanasios Typas
  7. Michael Zimmermann
  8. Mikhail M Savitski

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Highlights

  • High throughput glycoproteomics method with multiplexed quantification

  • 25-fold improvement of the mouse brain glycoproteome coverage

  • Structural features dictate level of glycosite micro-heterogeneity

  • Gut microbiome composition extensively impacts the brain glycoproteome

  • Modulation of glycosylation is site-specific

Protein glycosylation is a highly diverse post-translational modification, modulating key cellular processes such as cell signaling, adhesion and cell-cell interactions. Its deregulation has been associated with various pathologies, including cancer and neurological diseases. Methods capable of quantifying glycosylation dynamics are essential to start unraveling the biological functions of protein glycosylation. Here we present Deep Quantitative Glycoprofiling (DQGlyco), a method that combines high-throughput sample preparation, high-sensitivity detection, and precise multiplexed quantification of protein glycosylation. We used DQGlyco to profile the mouse brain glycoproteome, in which we identify 158,972 and 15,056 unique N- and O-glycopeptides localized on 3,199 and 2,365 glycoproteins, respectively - this amounts to 25-fold more glycopeptides identified compared to previous studies. We observed extensive heterogeneity of glycoforms and determined their functional and structural preferences. The presence of a defined gut microbiota resulted in extensive remodeling of the brain glycoproteome when compared to that of germ-free animals, exemplifying how the gut microbiome may affect brain protein functions.

Article activity feed

  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/10362238.

    This review reflects comments and contributions from Femi Arogundade & Bhargy Sharma. Review synthesized by Bhargy Sharma.

    The study introduces a method called Deep Quantitative Glycoprofiling (DQGlyco), which enables the high-throughput identification and precise quantification of glycoproteins in the mouse brain. The research uncovers extensive variations in protein glycosylation, with notable differences in glycoform regulation based on protein structure and function. Additionally, the study delves into how the composition of the gut microbiome impacts changes in the brain's glycoproteome, shedding light on a potential connection between the gut microbiome and brain function through glycosylation adjustments. This comprehensive analysis enhances our understanding of the intricate role of protein glycosylation in both health and disease.

    • The methods described in the paper appear to be comprehensive and well-detailed. The paper covers various aspects of the study, from cell culture to glycopeptides enrichment and data analysis. The use of different techniques, such as Tandem Mass Tag (TMT) labeling and Porous Graphitic Carbon (PGC) fractionation, adds rigor to the experimental approach. The inclusion of gnotobiotic animal experiments and the determination of gut bacterial community composition is a crucial aspect of the study, as it explores the connection between the gut microbiome and brain glycoproteome changes.

    Major comments:

    • The discussion section of the paper provides an insightful interpretation of the results. It discusses the implications of the findings, particularly the potential link between the gut microbiome and brain physiology through glycosylation modulation. The authors acknowledge the limitations of the study and provide a balanced view of the results, which enhances the paper's credibility. However, the discussion could benefit from more context and comparisons to related research in the field.

    Minor comments:

    • The methods provided are detailed but still leave room for clarification on specific aspects. For example, what were the exact conditions and equipment used for the PGC fractionation, and how was the temperature and flow rate controlled? These details are essential for reproducibility.

    • While the glycopeptides enrichment method is described, more details on the washing and elution steps, as well as specific chromatography conditions, would provide a clearer understanding of the process.

    • In the structural analysis, there is a reference to the AlphaFold Protein Structure Database, but details about the specific structural information extracted, and how it was integrated into the study, would be beneficial.

    • Regarding the analysis of the glycosylation profiles' similarity, it would be interesting to know how the Kendall rank correlation coefficient was computed and whether the observed differences were statistically significant.

    • How much of the advantage over lectin-based enrichment ( for e.g. in Riley et al, 2019) is specifically due to the use of PGC or PBA-base in optimized buffer composition, and how much upstream filtration processes contribute? A comment on this in discussion would be informative. 

    • A brief discussion on biological relevance of same sites involved in o-glycosylation and phosphorylation

    Comments on reporting:

    • Data linked on github are not easily accessible, and some files seem to have been removed. It would be helpful to reviewers and readers if the authors include an explainer document there and archive data in Zenodo.

    Suggestions for future studies:

    • Expanding the principles used here to develop methods for lectin-based detection of HILIC detection, which are more sensitive to identification than PBA.

    • Integrate glycoproteomic data with other omics data such as transcriptomics, proteomics, and metabolomics to gain a more comprehensive view of the molecular mechanisms underlying brain function and disease.

    • Expand research on the gut-brain connection by investigating the influence of different gut microbiota compositions on brain glycoproteome changes. Comparative studies with various microbiota profiles can help pinpoint specific microbial factors that affect brain physiology.

    • Gut brain studies on relevant disease model mice to identify disease-specific glycoproteome changes. Also impact of different proportions of gut microbes on glycoproteome. 

    Competing interests

    The author declares that they have no competing interests.

  2. Version published to 10.1101/2023.09.13.557529v1 on bioRxiv