Introduction
Protein glycosylation is a conserved process observed across all domains
of life which allows the augmentation of protein properties including
solubility, enzymatic activity and protein stability
[1-4]. While once thought to be absent
in bacterial systems it is increasingly recognized that numerous diverse
protein glycosylation pathways exist across bacterial species ranging
from dedicated systems responsible for modifying single proteins, such
as flagellin [5] and autotransporters
[6], to those responsible for
modifying hundreds of proteins known as general glycosylation systems
[1, 2,
7]. Within Gram-negative species,
multiple general glycosylation systems have now been identified with the
PglO (also known as PglL) family of oligosaccharyltransferases emerging
as a widespread class of O- linked glycosylation systems
[8]. This family of enzymes is
increasingly recognized to be functionally diverse with multiple
variants identified that possess unique properties including glycan
specificities and substrate ranges
[9-11]. While multiple PglO enzymes
have been experimentally confirmed to drive O‑ glycosylation in
genera including Moraxella [9]Acinetobacter [11,
12], Burkholderia[13-15], Francisella[16], Pseudomonas[17], Ralstonia[18] and Neisseria[19-21] the glycoproteomes of these
bacterial species are still poorly defined with MS based approaches
increasingly used to characterize bacterial glycoproteomes.
The characterization of glycopeptides using liquid chromatography–mass
spectrometry (LC-MS) has become an indispensable tool for
glycoproteomics [22]. While isolation
of bacterial glycopeptides using offline enrichment such as hydrophilic
[11, 12,
15, 18,
23, 24],
charge [20] or affinity
[25,
26] chromatography approaches followed
by LC-MS have been ubiquitously used for bacterial glycoproteomic
studies, these approaches increase the complexity of experimental
designs and, as highlighted within comparisons of conventional
multidimensional fractionation approaches
[27], suffer from significant sample
loss. An alternative to offline fractionation techniques is the use of
online separation approaches with Ion mobility spectrometry (IMS) based
separation/fractionation emerging as a powerful technique to streamline
the analysis of biomolecules [28].
IMS enables the separation of analytes based on a combination of charge
and collisional cross-section making it uniquely suited for separating
mixtures containing diverse analytes
[29]. Previously it has been
demonstrated that IMS allows the enrichment of populations of
cross-linked peptides [30,
31], the fractionation of combinations
of post-translational modifications (PTMs) to dissect PTM crosstalk
[32] and even the ability to
distinguish glycopeptides from non-glycosylated components based on
their collisional cross-section alone
[33]. Leveraging IMS, multiple groups
have shown different IMS platforms are advantageous for glycoproteomic
analysis including trapped ion mobility separation (TIMS)
[34] as well as high-field asymmetric
waveform ion mobility spectrometry (FAIMS)
[35-38]. Using FAIMS - based
glycopeptide enrichment, we previously demonstrated that unique
glycopeptides within bacterial glycoproteomes inaccessible to
hydrophilic enrichment chromatography could be identified, allowing the
identification of novel glycosylation events without offline
glycopeptide enrichment [35]. While
we have used FAIMS - based glycopeptide enrichment to characterize both
bacterial N - and O -linked glycopeptides
[35,
39], it is still unclear if this
approach is uniformly applicable to all bacterial glycosylation systems
due to the inherent chemical diversity of bacterial glycans
[40].
Within members of the Neisseria genus, O- linked
glycosylation was first confirmed nearly 30 years ago with protein
crystallography and MS-based analysis revealing the presence of
glycosylation events within the major pilin subunit (PilE) ofNeisseria gonorrhoeae [41] andNeisseria meningitidis [42].
Future work expanded understanding of the genetics
[19, 20,
43-45] and the biochemical processes
[45-47] associated withO -linked glycosylation in Neisseria to reveal that diverse
arrays of glycans can be utilized [20,
44] to glycosylate multiple
extracytoplasmic proteins [48,
49]. Within N. meningitidis andN. gonorrhoeae, O- linked glycosylation has been shown to
contribute to antigenic variation allowing the evasion of the humoral
immune response [43,
50, 51]
yet its conservation across Neisseria species suggests that
glycosylation may play roles beyond this function. Within N.
gonorrhoeae, arguably the best characterized Neisseriaglycoproteome to date, glycan serotyping
[48] and immunoaffinity protein
enrichment [26] coupled with LC-MS
analysis have identified at least 19 glycoproteins
[26] yet the true extent of theN. gonorrhoeae glycoproteome remains unclear. A critical barrier
to glycoproteomic studies within N. gonorrhoeae has been the
dependency on bespoke affinity reagents such as monoclonal antibodies to
facilitate the identification or enrichment of glycoproteins prior to
LC-MS analysis. Recently, we demonstrated that N. gonorrhoeaeglycopeptides could be readily identified from whole cell lysates using
LC-MS allowing the assessment of the N. gonorrhoeae glycoproteome
without the need for offline glycopeptide enrichment
[52]. Notably, we also observed
proteome and glycoproteomic changes in N. gonorrhoeae in response
to the expression of an exogenous pglOO- oligosaccharyltransferase from the distantly related speciesNeisseria elongata [52]. Thus,
these findings suggest alterations in pglO alleles withinN. gonorrhoeae may drive both proteome as well as glycoproteome
alterations. However, a systematic assessment of how differentpglO alleles influence the proteome/glycoproteome of N.
gonorrhoeae is required to confirm if this is a general phenomenon.
Within this work, we explore the use of FAIMS fractionation to expand
our understanding of the glycoproteome of N. gonorrhoeae .
Leveraging FAIMS based enrichment, we explore differences in
glycosylation patterns across N. gonorrhoeae strains expressing
different pglO allelic chimeras with unique substrate targeting
activities identifying 44 unique glycoproteins. To further understand
the proteomic changes driven by alterations in PglOs, we utilized
Data-Independent Acquisition (DIA) [53,
54] revealing proteomic alterations
across N. gonorrhoeae strains expressing different pglOalleles. These findings support the expression of different pglOalleles drive glycoproteome and proteome alterations withinNeisseria species as well as that the expression of a
glycoprotein alone, even if known to be compatible with other PglO
enzymes, may be insufficient to predict glycosylation efficiency with a
given PglO. Combined, this work expands our understanding of theN. gonorrhoeae glycoproteome and supports that the distinct
targeting activities of different pglO alleles appears to drive
proteomic changes independent of the glycoproteins targeted for
glycosylation.