Research ArticleCELL BIOLOGY

Mechanistic reconstruction of glycoprotein secretion through monitoring of intracellular N-glycan processing

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Science Advances  27 Nov 2019:
Vol. 5, no. 11, eaax8930
DOI: 10.1126/sciadv.aax8930
  • Fig. 1 Validation of the dynamic SILAC-PRM methodology.

    (A) Typical MS2 spectrum of an IgG glycopeptide obtained during SILAC-PRM acquisition using the broad isolation window (6 Th) and normalized collision energy (NCE) of 16% to achieve glycan-level fragmentation of light and heavy glycopeptides simultaneously (ratio, 1:5). Schematic representation of the glycopeptides of interest is shown above the corresponding peak. Gray long squares indicate the peptide backbone (with N, asparagine, indicating the N-glycosylation site; and R, arginine), blue squares indicate N-acetylglucosamine residues, green circles indicate mannose residues, and red triangles indicate fucose residues. Masses and charge states of the peak signals are reported. Mass differences between peaks that correspond to sugar moieties are indicated with the corresponding geometric representation of the sugar. (B) SILAC-PRM data acquisition of 100% heavy and 100% light IgG glycopeptides mixed in a 5:1 ratio prior to MS injection. Quantification of defined IgG glycoforms (individual bars) was conducted by averaging the peak area (black bars) or peak height (gray bars) of defined glycotransitions (fragment ions at the glycan level) (n = 3). Details about the glycoforms and the glycotransitions used for the quantification are listed in table S1. (C) N-glycan profiling analysis of purified intracellular and secreted IgGs. After PRM data acquisition, quantification was performed either on the MS1 level (light gray), by averaging the intensity of the extracted ion chromatograms, or on the MS2 level, by averaging the intensity of defined glycotransitions (dark gray) (n = 3). The relative abundance of each N-glycoform (x axis) compared with the sum of all the glycoforms is reported (y axis) for secreted (top graph) and intracellular (bottom graph) IgGs.

  • Fig. 2 Intracellular N-glycan processing.

    (A) Intracellular IgG molecules were harvested at the time indicated after the switch of the cells from light to heavy SILAC medium (x axis) and analyzed by SILAC-PRM. The fractional labeling (y axis) of intracellular pools of IgG peptides bearing different N-glycan intermediates (shown as symbols) is given over time (n = 3; except for complex sialylated structures, n = 2). The modeled turnover kinetics are shown as curves. (B) IgG fluxes through the ER processing pathway calculated by the model. The size of the arrows is proportional to the flux through each reaction indicated (numerical values predicted by the model are indicated in the figure as percentage). Upper rows reflect folded IgGs transported to the Golgi, middle rows reflect folding intermediates in the folding/ERAD pathway, and the lower rows refer to the lysosome degradation of aggregates (left) and cytoplasmic degradation by proteasome (right). Blue proteins refer to folded, and purple proteins indicate partially folded IgGs. Different N-glycan structures are shown as symbols. (C) IgG flux through the Golgi N-glycan processing pathway. The size of the arrows is proportional to the flux through each reaction indicated. The colors of the arrows indicate the different enzymes catalyzing the reaction (for the color code, see Fig. 3A). Circles highlight the major glycoforms found on secreted IgGs. Gray glycoproteins refer to IgG glycostructures that were included in the data measurements but did not provide reliable signals due to low abundance (below limit of quantification), preventing a flux calculation (no arrows).

  • Fig. 3 Empirical enzyme activities and substrates distribution along the Golgi.

    (A) The apparent activities of the different glycosyl hydrolases and transferases as calculated by the model along the normalized Golgi, from cis to trans, are shown. Inset: Enlarged y axis to reveal galactosyl- and sialyl-transferase activities. ManI, Golgi alpha-mannosidase I (light green); GnTI, N-acetylglucosamine transferase I (light blue); ManII, alpha-mannosidase II (dark green); FucT, α-1,6-fucosyl-transferase (red); GnTII, N-acetylglucosamine transferase II (dark blue); GalT, beta1,4-galactosyl-transferase (yellow); SiaT, alpha2,3 and 2,6-Sialyl transferase (purple). (B) Intracellular distribution of the different N-glycoforms of IgG calculated by the model along the normalized Golgi is shown. Abbreviations and the corresponding structures are exemplified in table S1.

  • Fig. 4 Effects of defined N-glycan processing inhibitors.

    Data acquisition and analysis under SWA (A to D) and KIF (E and F) was performed as for the control conditions (cf. Figs. 2 and 3). Fluxes through the ER processing pathway (A and E) and Golgi glycosylation networks (B and F) as calculated by the model are shown. The size of the arrows is proportional to the flux through each reaction indicated (numerical values predicted by the model are indicated in the figure as percentage). The major products in the presence of the inhibitors are circled. (C) The calculated activity of the different glycosyl hydrolases and transferases along the normalized Golgi is shown. (D) Intracellular dynamic distribution of N-glycan intermediates along the normalized Golgi is shown. See Fig. 3 for abbreviations of processing enzymes and processing intermediates.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/11/eaax8930/DC1

    Fig. S1. PRM detection and quantification limit.

    Fig. S2. Collision energy optimization and Skyline implementation into the pipeline.

    Fig. S3. Representative MS2 spectra of IgG N-glycopeptides from SILAC-PRM analysis.

    Fig. S4. Data prediction accuracy by the model: intracellular IgG N-glycan distribution.

    Fig. S5. Data prediction accuracy by the model: secreted IgG N-glycan distribution.

    Fig. S6. Effects of different perturbations on N-glycan processing.

    Fig. S7. Man4GlcNAc2 generation.

    Fig. S8. Kinetic mechanisms considered for Man4GlcNAc2 formation.

    Table S1. List of glycans selected for PRM analysis of glycopeptides.

    Table S2. ER-related parameters.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. PRM detection and quantification limit.
    • Fig. S2. Collision energy optimization and Skyline implementation into the pipeline.
    • Fig. S3. Representative MS2 spectra of IgG N-glycopeptides from SILAC-PRM analysis.
    • Fig. S4. Data prediction accuracy by the model: intracellular IgG N-glycan distribution.
    • Fig. S5. Data prediction accuracy by the model: secreted IgG N-glycan distribution.
    • Fig. S6. Effects of different perturbations on N-glycan processing.
    • Fig. S7. Man4GlcNAc2 generation.
    • Fig. S8. Kinetic mechanisms considered for Man4GlcNAc2 formation.
    • Table S1. List of glycans selected for PRM analysis of glycopeptides.
    • Table S2. ER-related parameters.

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