Welcome to the DQGlyco explorer where you can discover qualitative and quantitative glycoproteomics mouse and human datasets included in the DQGlyco study (Potel et al. 2024).

Deep quantitative glycoproteomics reveals gut microbiome induced remodeling of the brain glycoproteome
Click here to read the manuscript!

dataset - dataset in which the glycosite is included (details available in other tabs)
glyco_position - glycosite, N position in protein
glycoforms hit/no hit (for quantitative data) - number of glycoforms significantly changing in the given dataset
annotation - annotation evidence in different databases and studies

Labelfree glycoproteomics - mouse brains

This section summarises the identified glycopeptides in mouse brain tissue samples after PGC fractionation.

________________________________

The number of identified glycopeptides is shown per glycosite and glycan type (left). Additionally, the glycan mass distribution per site is shown (right).

________________________________

Identification overview

In this table all glycopeptides and compositions identified in this dataset are listed with their MS evidence (# psms)

glyco_position - glycosite, N position in protein
glycan_type - assigned glycan class based on classification described in Potel et al.
Modified.Peptide - peptide sequence with modifications indicated in brackets
Observed.Modifications - glycan composition and mass assigned by MSFragger
# psms - number of psms detected for this glycopeptide

TMT-based glycoproteomics - mouse brains after microbiome exposure

Three groups of 6 adult germfree C57BL/6 mice (3 males and 3 females) were either monocolonized for 2 weeks with Bacteroides uniformis, one of the most prevalent human gut microbes, by a defined 8-member community or were kept germfree. Changes in glycoform levels were quantified after normalization by protein abundance changes.

Please note that these plots might take a couple of seeconds to load.

________________________________

Normalised glycopeptide intensity per glycosite (N position in protein) coloured by mouse microbiome group. Each column represents one glycoform on the given glycosite. Points can be brushed for additional information.

________________________________

Differential expression analysis results. Protein-abundance normalised glycointensities of the three mouse microbiome groups were used as input. Points can be brushed for additional information. In light grey the complete volcano is displayed, coloured points show glycopeptides on a given site.

TMT-based glycoproteomics - functional proteomics (SPP)

Solubility proteome profiling (SPP) measures the differential solubility of a proteoform in a non-denaturing detergent (here 0.8% NP40) versus a strong denaturing detergent (here 1% SDS). When comparing the solubility of a given proteoform (here represented by a glycopeptide, measured in the enriched sample) with the solubility of the whole protein population (based on solubility of all non-modified peptides from this protein, measured in the non-enriched input), it is possible to infer the impact of a given glycoform on protein solubility. While SPP does not allow to obtain a definitive answer regarding the specific function of a given glycoform, it can pinpoint functionally important glycosylation events on a proteome-wide scale and inform on the potential role of this glycoform on protein function (e.g. if a glycoform promotes or prevents interactions with ECM, this could be reflected in an increase or decrease of solubility of this glycoform, respectively).

Please note that these plots might take a couple of seeconds to load.

________________________________

Solubility ratio (log2(NP40/SDS)) per glycosite (N position in protein) coloured by glycan type. Each dot represents one glycoform on the given glycosite. The grey line indicates the median solubility of the whole protein population. Points can be brushed for additional information.

________________________________

Differential expression analysis results. Normalised solubility ratios of a given glycoform as well as the whole protein population were used as input. Points can be brushed for additional information. In light grey the complete volcano is displayed, coloured points show glycopeptides on a given site.

TMT-based glycoproteomics - tissue profiling

Here, we conducted a proteome-wide quantitative profiling of site-specific N-glycosylation across 3 tissues - brain, liver and kidney - and 2 mice. To assess tissue specificity at the glycosite level, we calculated the pairwise Pearson correlation of glycosylation patterns (correlating the relative intensities of all glycopeptides on a given glycosite between tissues) for sites having more than 5 quantified glycopeptides.

Please note that these plots might take a couple of seeconds to load.

________________________________

Correlation of glycosylation profiles per glycosite (x-facets) and mouse (y-facets). The lower the correlation, the less tissue-specific the glycsoite. Please have a look at the manuscript for detailed information.

________________________________

Upper plot: Normalised glycopeptide intensity per glycosite (N position in protein) coloured by tissue shown for mouse1. Lower plot: Normalised protien intensity per tissue coloured by tissue shown for mouse1.

________________________________

Tissue summary table listing all glycopeptides identified per tissue and mouse.
mouse - mouse1 or mouse2
Gene - gene symbol of the selected protein
tissue - tissue in which glycopeptide has been quantified, please note the this can be several
glyco_position - glycosite, N position in protein
glycan type - assigned glycan class
Observed.Modifications - assigned glycan composition and mass
# psms - number of psms

AF2 based structural anlysis

In this section, we display the annotations that we used for the structural analysis of identified glycosites. In addition, we also show the potential pathogenicity of AA substitutions on identified glycosites as average SIFT and AlphaMissense score. The pPSE metric refers to the prediction-aware part-sphere exposure metric defined in Bludau et al. 2022 (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001636)

________________________________

dataset - dataset in which the glycosite is included (details available in other tabs)
glyco_position - glycosite, N position in protein
glycoforms hit/no hit - number of glycoforms significantly changing in the given dataset

Labelfree glycoproteomics - HEK293T/HeLa cells

This section summarises the identified glycopeptides in HeLa and HEK293T cells.

________________________________

The number of identified glycopeptides is shown per glycosite and glycan type.

________________________________

Additionally, the mass distribution of glycans per site is shown.

________________________________

In this table all glycopeptides and compositions identified in this dataset are listed with their MS evidence (# psms)

TMT-based glycoproteomics - HEK293f cells upon 2- fluorofucose treatment

We used our quantitative workflow to study the inhibition of fucosylation over time following treatment with 2-fluorofucose. 2FF readily diffuses into cells, where it is metabolized into the donor substrate GDP-2FF that blocks fucosyltransferase activity. Moreover, the intracellular accumulation of GDP-2FF has been shown to inhibit the production of endogenous GDP-fucose leading to a general suppression of protein fucosylation, which inhibits several biological processes such as tumor growth, adhesion and metastasis.

Please note that these plots might take a couple of seeconds to load.

________________________________

log2 fold-change profile for glycopeptides per site over time upon 2FF treatment. Colours correpsond to glycan class. Points can be brushed for additional information

TMT-based glycoproteomics - ProteinaseK and Pngase treatment

Here, we applied our quantitative workflow to intact living human HEK293 cells subjected to either PNGase F treatment for 30 mins (glycosidase targeting N-glycans) or proteinase K for 1 min (broad specificity enzyme used in limited proteolysis experiments. The rationale is that only cell surface-exposed glycoforms would be affected, while glycoforms present in intracellular organelles (i.e. in the ER or Golgi) would be protected, resulting in unchanged abundance

Please note that these plots might take a couple of seeconds to load.

________________________________

Differential expression results control vs enzymatic treatment. Normailsed glycopeptide intensities were used as input. Points can be brushed for additional information. Each point represents one glycoform on the given site and is coloured by assigned glycan class. The dashed line indicates the log2(1.5) fold-change cutoff. The higher the fold-cahnge the more surface-exposed a glycopeptide is.

AF2 based structural anlysis

In this section, we display the annotations that we used for the structural analysis of identified glycosites. In addition, we also show the potential pathogenicity of AA substitutions on identified glycosites as average SIFT and AlphaMissense score. The pPSE metric refers to the prediction-aware part-sphere exposure metric defined in Bludau et al. 2022 (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001636)

________________________________

Download