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Method Article

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

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DOI:

10.3791/63242

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November 2nd, 2021

 ,  ,  , 
1Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, The University of Melbourne

* These authors contributed equally

In This Article

Summary

Open searching enables the identification of glycopeptides decorated with previously unknown glycan compositions. Within this article, a streamlined approach for undertaking open searching and subsequent glycan-focused glycopeptide searches are presented for bacterial samples using Acinetobacter baumannii as a model.

Abstract

Protein glycosylation is increasingly recognized as a common modification within bacterial organisms, contributing to prokaryotic physiology and optimal infectivity of pathogenic species. Due to this, there is increasing interest in characterizing bacterial glycosylation and a need for high-throughput analytical tools to identify these events. Although bottom-up proteomics readily enables the generation of rich glycopeptide data, the breadth and diversity of glycans observed in prokaryotic species make the identification of bacterial glycosylation events extremely challenging.

Traditionally, the manual determination of glycan compositions within bacterial proteomic datasets made this a largely bespoke analysis restricted to field-specific experts. Recently, open searching-based approaches have emerged as a powerful alternative for the identification of unknown modifications. By analyzing the frequency of unique modifications observed on peptide sequences, open searching techniques allow the identification of common glycans attached to peptides within complex samples. This article presents a streamlined workflow for the interpretation and analysis of glycoproteomic data, demonstrating how open searching techniques can be used to identify bacterial glycopeptides without prior knowledge of the glycan compositions.

Using this approach, glycopeptides within samples can rapidly be identified to understand glycosylation differences. Using Acinetobacter baumannii as a model, these approaches enable the comparison of glycan compositions between strains and the identification of novel glycoproteins. Taken together, this work demonstrates the versatility of open database-searching techniques for the identification of bacterial glycosylation, making the characterization of these highly diverse glycoproteomes easier than ever before.

Introduction

Protein glycosylation, the process of attaching carbohydrates to protein molecules, is one of the most common post-translational modifications (PTMs) in nature1,2. Across all domains of life, a range of complex machinery has evolved dedicated to the generation of glycoproteins that impact a myriad of cellular functions1,3,4,5. While protein glycosylation occurs on a range of amino acids6,7, N-linked and O-linked glycosylation events are two dominant forms observed in nature. N-linked glycosylation involves the attachment of glycans to a nitrogen atom of asparagine (Asn) residues, while in O-linked glycosylation, glycans are attached to an oxygen atom of serine (Ser), threonine (Thr), or tyrosine (Tyr) residues7. Despite the similarities in residues targeted by glycosylation systems, the differences within the glycans attached to proteins result in glycosylation being the most chemically diverse class of PTMs found in nature.

While eukaryotic glycosylation systems possess glycan diversity, these systems are typically restricted in the number of unique carbohydrates utilized. The resulting diversity stems from how these carbohydrates are arranged into glycans8,9,10,11,12. In contrast, bacterial and archaeal species possess virtually unlimited glycan diversity due to the sheer array of unique sugars generated within these systems2,10,13,14,15,16,17. These differences in the glycan diversity observed across domains of life represent a significant analytical challenge for the characterization and identification of glycosylation events. For eukaryotic glycosylation, the ability to anticipate glycan compositions has facilitated the growing interest in glycobiology; yet, the same is not true for bacterial glycosylation, which is still largely restricted to study by specialized laboratories. As the accessibility of mass spectrometry (MS) instrumentation has increased in the biosciences, MS-based approaches are now the primary method for glycoproteomic analysis.

MS has emerged as the quintessential tool for the characterization of glycosylation, with both top-down and bottom-up approaches now commonly used to characterize glycoproteins6. While top-down proteomics is used to assess global glycosylation patterns of specific proteins18,19, bottom-up approaches are used to enable the glycan-specific characterization of glycopeptides, even from complex mixtures6,20,21,22,23. For the analysis of glycopeptides, the generation of informative fragmentation information is essential for the characterization of glycosylation events24,25. A range of fragmentation approaches is now routinely accessible on instruments, including resonance ion trap-based collision-induced dissociation (IT-CID), beam-type collision-induced dissociation (CID), and electron transfer dissociation (ETD). Each approach possesses different strengths and weaknesses for glycopeptide analysis25,26, with significant progress over the last decade in applying these fragmentation approaches to analyze glycosylation6,20. However, for bacterial glycosylation analysis, the critical limitation has not been the ability to fragment glycopeptides but rather the inability to predict the potential glycan compositions within samples. Within these systems, the unknown nature of diverse bacterial glycans limits the identification of glycopeptides, even with glycosylation-focused searching tools now commonplace for the analysis of eukaryotic glycopeptides, such as O-Pair27, GlycopeptideGraphMS28, and GlycReSoft29. To overcome this issue, an alternative searching method is required, with the use of open searching tools emerging as a powerful approach for the study of bacterial glycosylation30.

Open searching, also known as blind or wildcard searching, allows the identification of peptides with unknown or unexpected PTMs21,30,31,32. Open searches utilize a variety of computational techniques, including curated modification searches, multistep database searches, or wide-mass tolerant searching33,34,35,36,37. Although open searching has great potential, its use has typically been hindered by the significant increase in analysis times and loss in sensitivity of the detection of unmodified peptides compared to restricted searches31,32. The decrease in the detection of unmodified peptide-spectral matches (PSMs) is a result of the increased false-positive PSM rates associated with these techniques, which requires increased stringent filtering to maintain the desired false discovery rates (FDRs)33,34,35,36,37. Recently, several tools have become available that significantly improve the accessibility of open searching, including Byonic31,38, Open-pFind39, ANN-SoLo40, and MSFragger21,41. These tools enable the robust identification of glycosylation events by significantly reducing analysis times and implementing approaches to handle heterogeneous glycan compositions.

This article presents a streamlined method for the identification of bacterial glycopeptides by open searching, using the Gram-negative nosocomial pathogen, Acinetobacter baumannii, as a model. A. baumannii possesses a conserved O-linked glycosylation system responsible for the modification of multiple protein substrates, known as the PglL protein glycosylation system42,43,44. While similar proteins are targeted for glycosylation between strains, the PglL glycosylation system is highly variable due to the biosynthesis of the glycan used for protein glycosylation being derived from the capsule locus (known as the K-locus)44,45,46. This results in diverse glycans (also known as a K-unit), derived from single or limited polymerized K-units, being added to protein substrates30,44,46. Within this work, the use of the open searching tool, MSfragger, within the software FragPipe, is used to identify glycans across A. baumannii strains. By combining open searching and manual curation, "glycan-focused searches" can be undertaken to further improve the identification of bacterial glycopeptides. Together, this multistep identification approach enables the identification of glycopeptides without extensive experience in the characterization of novel glycosylation events.

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Protocol

NOTE: The preparation and analysis of bacterial glycopeptide samples can be divided into four sections (Figure 1). For this study, the glycosylation of three sequenced A. baumannii strains was assessed (Table 1). Proteome FASTA databases of each of these strains are accessible via Uniprot. Refer to Table 2 for the composition of buffers used in this protocol.

1. Preparation of protein samples for proteomic analysis

  1. Isolation of proteome samples of interest
    1. If using whole cells, ensure that the cells have been washed with a phosphate-buffered saline solution (PBS) to remove potential protein contaminants present in the media. Snap-freeze whole cells after washing and store them at -80 °C until required.
    2. If fractionated samples are used (such as membrane preparations), ensure that the reagents used will not interfere with downstream liquid chromatography MS (LC-MS) analysis50.
    3. If detergents, such as sodium dodecyl-sulfate (SDS), Triton X-100, NP-40, or lauroylsarcosine, have been used, remove these detergents using acetone precipitation, SP3 sample preparation methods51, or commercial proteomic clean-up columns such as S-traps52. Alternatively, substitute incompatible detergents with an MS-compatible or removable detergent such as sodium deoxycholate (SDC) or octyl glucopyranoside.
    4. Ensure all plasticware and glassware to be used for sample preparation has not been autoclaved. Autoclaved glassware and plastics are typically heavily contaminated with small molecular weight compounds, such as polymers, which are readily detected within the MS.
  2. Solubilization of whole-cell samples
    1. Resuspend ~10 mg of washed, snap-frozen cells in 200 µL of freshly prepared sodium deoxycholate lysis buffer (SDC lysis buffer: 4% SDC in 100 mM Tris, pH 8.5).
      NOTE: Protease inhibitors can be added to the SDC lysis buffer to limit protein degradation.
    2. Boil the samples for 10 min at 95 °C with shaking (2000 rpm on a thermomixer), and then leave on ice for 10 min. Repeat this process twice to ensure efficient lysis and solubilization of the samples.
      NOTE: Samples can be stored long-term at this point at -80 °C. If stored, resolubilize by heating at 95 °C before further processing.
  3. Quantify sample protein concentrations using a bicinchoninic acid (BCA) protein assay53. Store samples on ice while undertaking quantification to limit protein degradation.
    NOTE: For total proteome analysis, 20-100 µg of protein is more than sufficient for nano LC-MS, which typically requires less than 2 µg of protein digest per analysis. The preparation of excess peptide allows for replicate analysis or further fractionation if deep proteomic coverage is required. For glycopeptide enrichment-based analysis using hydrophilic interaction chromatography (HILIC), 100-500 µg of protein is required.
  4. Reduce and alkylate samples.
    1. Add 1/10th the volume of 10x reduction/alkylation buffer (100 mM Tris 2-carboxyethyl phosphine hydrochloride; 400 mM 2-chloroacetamide in 1 M Tris, pH 8.5) to samples for a final concentration of 1x and incubate samples in the dark for 30 min at 45 °C with shaking at 1,500 rpm.
      NOTE: Check the pH of the 10x reduction/alkylation buffer to ensure a pH of approximately 7.0-8.0 before adding to the samples, as a lower pH will cause the SDC to precipitate.
  5. Briefly spin down the samples and add the proteases Trypsin/Lys-C (~10 µL, resuspended in 100 mM Tris, pH 8.5) for a final protease:protein ratio of 1:100. Incubate the digests overnight at 37 °C with shaking at 1,500 rpm (up to 18 h). To ensure complete protein digestion, use a Trypsin/Lys-C protease:protein ratio of 1:50 to 1:200.
  6. Quench digests by adding 1.25 volumes of 100% isopropanol to the samples. Vortex the samples for 1 min to mix and briefly spin them down.
    NOTE: Samples can be stored at -20 °C to be further processed later.
  7. Acidify the samples by adding 0.115 volumes of 10% trifluoroacetic acid (TFA; final concentration of ~1% TFA), vortex the samples, and briefly spin them down.

2. Processing of proteome samples

  1. Peptide clean-up of proteome samples
    1. Prepare one styrenedivinylbenzene-reverse-phase sulfonate (SDB-RPS) Stop-and-go-extraction (Stage) Tip for each sample as previously described54.
      1. Empirically, for binding 50 μg of peptide, excise three SDB-RPS discs from a 47 mm2 SDB-RPS membrane using a blunt needle (14 G). For larger peptide amounts, increase the number of discs accordingly.
    2. Prior to using SDB-RPS Stage Tips, prepare the tips by sequentially adding at least ten bed volumes of the following buffers and either spinning the buffer through the column by centrifugation (25 °C, 3 min, 500 × g) or by pushing the buffer through the column by gently applying pressure using a syringe.
      1. Wet the tips with 150 µL of 100% acetonitrile.
      2. Wash the tips with 150 µL of 30% methanol, 1% TFA in 18.2 MΩ H2O.
      3. Equilibrate the tips with 150 µL of 90% isopropanol, 1% TFA balanced with 18.2 MΩ H2O.
    3. Load the samples (containing 50% isopropanol, 1% TFA) onto the SDB-RPS Stage Tips by centrifugation (25 °C, 3 min, 500 × g) or by gently applying pressure using a syringe.
    4. Wash the SDB-RPS Stage Tips with the following buffers by centrifugation (25 °C, 3 min, 500 × g) or by gently applying pressure using a syringe.
      NOTE: Additional washes or alternative buffers can be used to remove non-peptide contaminants, such as the use of ethyl acetate instead of isopropanol55.
      1. Wash the tips with 150 µL of 90% isopropanol, 1% TFA.
      2. Wash the tips with 150 µL of 1% TFA in 18.2 MΩ H2O.
    5. Elute the peptides from the SDB-RPS Stage Tips with 150 µL of 5% ammonium hydroxide in 80% acetonitrile by centrifugation or by gently applying pressure using a syringe. Collect the samples in individual tubes.
      NOTE: Prepare 5% ammonium hydroxide in 80% acetonitrile in a plastic container immediately prior to use within a fume hood.
    6. Dry the eluted peptides by vacuum centrifugation at 25 °C.
      NOTE: If undertaking HILIC enrichment, 1-10% of the peptide eluates can be removed at this point, dried, and used as total proteome input controls.
  2. Enrichment of glycopeptide samples
    1. Prepare Zwitterionic Hydrophilic Interaction Liquid Chromatography (ZIC-HILIC) Stage Tips as previously described54,56.
      1. Briefly, excise one C8 disc from a 47 mm2 C8 membrane using a blunt needle (14 G) and pack the disc into a P200 tip to create a frit. Add approximately 5 mm of ZIC-HILIC material, resuspended in 50% acetonitrile, 50% 18.2 MΩ H2O, onto the frit by gently applying pressure using a syringe.
    2. Prior to using ZIC-HILIC Stage Tips, condition the resin by sequentially adding the following buffers and gently applying pressure using a syringe.
      ​NOTE: To ensure the integrity of the pseudo-water layer on the surface of the ZIC-HILIC resin (required to enrich glycopeptides), the resin must always remain wet. When washing the resin, always leave ~10 µL of solvent above the resin and ensure the washes/samples are pipetted directly into this residual solvent.
      1. Equilibrate the resin with 20 bed volumes (200 µL) of ZIC-HILIC elution buffer (0.1% TFA in 18.2 MΩ H2O).
      2. Wash the resin with 20 bed volumes (200 µL) of ZIC-HILIC preparation buffer (95% acetonitrile in 18.2 MΩ H2O).
      3. Wash the resin with 20 bed volumes (200 µL) of ZIC-HILIC loading/wash buffer (80% acetonitrile, 1% TFA balanced with 18.2 MΩ H2O).
    3. Resuspend the dried digested samples (from step 2.1.6) in ZIC-HILIC loading/wash buffer to a final concentration of 4 µg/µL (e.g., for 200 µg of peptide, resuspend in 50 µL of ZIC-HILIC loading/wash buffer). Vortex briefly for 1 min to ensure the samples are resuspended, and spin down for 1 min at 2,000 × g at 25 °C.
    4. Load the resuspended peptide sample onto a conditioned ZIC-HILIC column.
      1. Wash three times with 20 bed volumes (200 µL) of ZIC-HILIC loading/wash buffer (for 60 bed volume washes total) by gently applying pressure using a syringe.
      2. Elute glycopeptides with 20 bed volumes (200 µL) of ZIC-HILIC elution buffer into a 1.5 mL tube by gently applying pressure using a syringe and then dry the eluate by vacuum centrifugation at 25 °C.

3. LC-MS of proteome/glycopeptide-enriched samples

  1. Resuspend the samples in Buffer A* (2% acetonitrile, 0.1% TFA) to a final concentration of 1 µg/µL (for example, for 50 µg of peptide, resuspend in 50 µL of Buffer A*).
  2. Load the samples onto an HPLC/UPLC coupled to an MS to enable the separation and identification of glycopeptides.
    NOTE: The column parameters, including inner diameter, length, flow rates, type of chromatography resin, and required peptide injection amounts, should be optimized for the analytical setup and gradient length to be used; for an example of how to undertake optimization of analytical setups, see57.
  3. Monitor the collection of the resulting MS data ensuring the data is being collected with the desired parameters.
    NOTE: For compositional analysis, CID fragmentation is sufficient. Due to the addition of glycans to glycopeptides, glycopeptide ions are typically observed with a higher m/z and lower charge density than unglycosylated peptides. To ensure these ions are observable, allow a MS1 mass range from 400 to 2,000 m/z.
  4. Fragment selected ions using CID, ensuring the collection of low m/z fragment ions that contain oxonium ions important for the characterization of glycans.
    NOTE: Fragmentation of glycopeptides using CID is influenced by both the peptide and glycan sequences, as well as the energy applied during fragmentation25,58,59. While a range of different collision energies can be used, an optimal strategy for fragmenting glycopeptides is the use of stepped collision energies combining the use of multiple collision energies25,59,60.
  5. Use alternative fragmentation methods, if available, such as ETD for site localization, or IT-CID to aid in the determination of glycan compositions.
    ​NOTE: Neither of these fragmentation approaches are essential for compositional analysis, yet can be collected to enable further interrogation of glycopeptides of interest.

4. Analysis of proteome/glycopeptide-enriched samples

  1. Prefiltering data files to enable searching in FragPipe
    1. If ETD or IT-CID scans have been acquired within datasets, filter these scan events from the datafiles using MSConvert61 prior to searching with FragPipe.
      NOTE: For the open searching parameters outlined below, only beam-type CID data are required.
  2. Performing open searches in FragPipe
    1. Open FragPipe and click the Workflow tab. In the workflow pulldown menu, select the Open search option, and click Add files to import the data files to be searched into FragPipe (Figure 2A).
    2. Click the Database tab and launch the download manager by clicking Download. This allows proteome databases to be downloaded from Uniprot using a Uniprot Proteome ID. Click the Add decoys and contaminants option within the download manager to incorporate decoy and contaminant proteins into databases.
    3. For more stringent FDR thresholds, click the Validation tab and modify the Filter and report value from 0.01 to the required FDR.
      NOTE: The default FragPipe settings will ensure a 1% FDR at the protein level.
    4. Click the MSfragger tab. Within the Peak matching box, increase the Precursor mass tolerance from the default 500 Da to 2,000 Da to allow the identification of large modifications (Figure 2A).
    5. Click the Run tab and define the location of the outputs of FragPipe. Click the Run button to begin the search.
  3. Using the PSMs identified across datasets (contained within the psm.tsv outputs from FragPipe), identify potential glycans by plotting the frequency of observed delta masses within datasets (Figure 3). Create delta mass plots from MSfragger outputs using the R scripts accessible via PRIDE accession PXD027820. 
    NOTE: Minimal postprocessing of the open searching results is undertaken within these scripts, as the main purpose of these scripts is to aid in the visualization of delta mass profiles. Importantly, the observation of abundant delta masses alone is not proof that a modification is a potential glycan, as assigning delta masses as glycans requires further analysis of the corresponding MS2 events.
  4. To enable the characterization of glycopeptides within samples, focus on high confidence delta mass identifications, corresponding to assignments with high hyperscores.
    1. To aid in assessing glycopeptide spectra, use peptide annotation tools, such as the Interactive Peptide Spectral Annotator63, which enables the assignment of peptide-associated ions within spectra, allowing the manual identification of the glycan-associated ions (Figure 4).
      NOTE: Within the datasets presented here, hyperscores of >30 are considered high scoring, as these correspond to scores within the top 50% of all identified glycopeptides (Figure 5).
  5. With high-confidence glycopeptides assigned, identify commonly observed glycan-associated ions (Figure 4) to improve the identification of glycopeptides.
    NOTE: By incorporating glycan-associated ions within searches, known as glycan-focused searches, the quality of glycopeptide assignments can be improved.
    1. Click the MSfragger tab of FragPipe, incorporate the determined delta masses of the observed glycans into the Variable modifications and Mass Offsets sections. Add these masses by typing values into the Variable modifications and Mass Offsets sections with individual masses separated with a /. Add the glycan-associated fragment masses of these glycans into the Glyco/Labile mods section of MSFragger.
      NOTE: Figure 2B outlines the key information required for a glycan-focused search of the A. baumannii strain AB307-0294.
  6. Upload all MS data associated with proteomic studies to centralized Proteomic repositories such as the PRIDE or MASSIVE repositories.
    NOTE: All data associated with this study have been deposited into the PRIDE proteomic repository and can be accessed via the PRIDE accession: PXD027820.

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Results

To illustrate the utility of open searching for bacterial glycopeptide analysis, the chemical diversity of O-linked glycans within three strains of A. baumannii-AB307-0294, ACICU, and D1279779-was assessed. The O-linked glycoproteomes are highly variable between A. baumannii strains as the glycans used for glycosylation are derived from the highly variable capsule loci44,45,46. This chemical d...

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Discussion

Open searching is an effective and systematic method for the identification of unknown modifications. While the identification of unknown glycans within bacterial proteome samples has traditionally been a time-consuming and technically specialized undertaking, the recent developments of tools such as MSfragger21,41 and Byonic31,38 now enable the quick and effective identification of delta masses for furth...

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Disclosures

The authors have no conflicts of interest.

Acknowledgements

N.E.S is supported by an Australian Research Council Future Fellowship (FT200100270) and an ARC Discovery Project Grant (DP210100362). We thank the Melbourne Mass Spectrometry and Proteomics Facility of The Bio21 Molecular Science and Biotechnology Institute for access to MS instrumentation.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
14 G Kel-F Hub point style 3Hamilton companyhanc90514
2-ChloroacetamideSigma Aldrich Pty LtdC0267-100G
AcetonitrileSigma Aldrich Pty Ltd34851-4L
Ammonium hydroxide (28%)Sigma Aldrich Pty Ltd338818-100ML
BCA Protein Assay Reagent APierce23228
BCA Protein Assay Reagent BPierce23224
C8 Empore SPESigma Aldrich Pty Ltd66882-UAn alterative vendor for C8 material is Affinisep (https://www.affinisep.com/about-us/)
Formic acidSigma Aldrich Pty Ltd5.33002
IsopropanolSigma Aldrich Pty Ltd650447-2.5L
MethanolFisher ChemicalM/4058/17
SDB-RPS Empore SPE (Reversed-Phase Sulfonate)Sigma Aldrich Pty Ltd66886-UAn alterative vendor for SDB-RPS is Affinisep (https://www.affinisep.com/about-us/)
Sodium DeoxycholateSigma Aldrich Pty LtdD6750-100G
ThermoMixer CEppendorf2232000083
trifluoroacetic acidSigma Aldrich Pty Ltd302031-10X1ML
Tris 2-carboxyethyl phosphine hydrochlorideSigma Aldrich Pty LtdC4706-2G
Tris(hydroxymethyl)aminomethaneSigma Aldrich Pty Ltd252859-500G
Trypsin/Lys-C protease mixturePromegaV5073
Vacuum concentratorLabconco7810040
ZIC-HILIC materialMerck1504580001Resin for use in single use SPE columns can be obtain by emptying a larger form column and using the free resin

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Glycopeptide IdentificationBacterial GlycosylationZIC HILIC EnrichmentBottom Up ProteomicsGlycan DiversityLC MS AnalysisGlycoproteomic Workflow
The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides
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