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.

NOTE: The preparation and analysis of bacterial glycopeptide samples can be divided into four sections (Figure 1).&#160;For this study, the glycosylation of three sequenced A. baumannii&#160;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...

The authors have no conflicts of interest.

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...

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, &#34;glycan-focused searches&#34; 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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>14 G Kel-F Hub point style 3</td>
			<td>Hamilton company</td>
			<td>hanc90514</td>
			<td></td>
		</tr>
		<tr>
			<td>2-Chloroacetamide</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>C0267-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>Acetonitrile</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>34851-4L</td>
			<td></td>
		</tr>
		<tr>
			<td>Ammonium hydroxide (28%)</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>338818-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>BCA Protein Assay Reagent A</td>
			<td>Pierce</td>
			<td>23228</td>
			<td></td>
		</tr>
		<tr>
			<td>BCA Protein Assay Reagent B</td>
			<td>Pierce</td>
			<td>23224</td>
			<td></td>
		</tr>
		<tr>
			<td>C8 Empore SPE</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>66882-U</td>
			<td>An alterative vendor for C8 material is Affinisep (https://www.affinisep.com/about-us/)</td>
		</tr>
		<tr>
			<td>Formic acid</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>5.33002</td>
			<td></td>
		</tr>
		<tr>
			<td>Isopropanol</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>650447-2.5L</td>
			<td></td>
		</tr>
		<tr>
			<td>Methanol</td>
			<td>Fisher Chemical</td>
			<td>M/4058/17</td>
			<td></td>
		</tr>
		<tr>
			<td>SDB-RPS Empore SPE (Reversed-Phase Sulfonate)</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>66886-U</td>
			<td>An alterative vendor for SDB-RPS is Affinisep (https://www.affinisep.com/about-us/)</td>
		</tr>
		<tr>
			<td>Sodium Deoxycholate</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>D6750-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>ThermoMixer C</td>
			<td>Eppendorf</td>
			<td>2232000083</td>
			<td></td>
		</tr>
		<tr>
			<td>trifluoroacetic acid</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>302031-10X1ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Tris 2-carboxyethyl phosphine hydrochloride</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>C4706-2G</td>
			<td></td>
		</tr>
		<tr>
			<td>Tris(hydroxymethyl)aminomethane</td>
			<td>Sigma Aldrich Pty Ltd</td>
			<td>252859-500G</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin/Lys-C protease mixture</td>
			<td>Promega</td>
			<td>V5073</td>
			<td></td>
		</tr>
		<tr>
			<td>Vacuum concentrator</td>
			<td>Labconco</td>
			<td>7810040</td>
			<td></td>
		</tr>
		<tr>
			<td>ZIC-HILIC material</td>
			<td>Merck</td>
			<td>1504580001</td>
			<td>Resin for use in single use SPE columns can be obtain by emptying a larger form column and using the free resin</td>
		</tr>
	</tbody>
</table>

the application of open searching-based approaches for the identification of acinetobacter baumannii o-linked glycopeptides

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.

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...

Watch this Scientific Journal Video about The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides at JoVE.com

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

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.

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

Protein glycosylation is increasingly recognized as a common modification within bacterial organisms, contributing to prokaryotic physiology and optimal ...

This protocol streamlines the identification of bacterial glycop%eptides, enabling researchers to identify novel glycosylation events, as well as the differences in the glycans used for glycosylation between strains. The main advantage of this technique is that it enables non-glycosylation experts to identify potential glycosylation events in bacterial proteome samples. Open searching is a generally applicable approach that can be used for the identification of a range of modifications, not just glycosylation, and can be applied to any proteome sample.To prepare STB-RPS stop-and-go extraction tips, use a 14-gauge blunt needle to excise three STB-RPS discs from a 47-square-millimeter STB-RPS membrane for binding 50 micrograms of peptide. For larger peptide amounts, increase the number of discs accordingly. Prior to using the SDB-RPS stage tips, wash the tips by adding 150 microliters of 100%acetonitrile and spinning the buffer by centrifugation or gently applying pressure using a syringe.Next, wash the tips with 150 microliters of 30%methanol containing 1%TFA, then equilibrate the tips with 150 microliters of 90%isopropanol containing 1%TFA. After equilibration, load the proteome samples onto the STB-RPS stage tips by centrifugation or using a syringe. Then, wash the tips with 150 microliters of 90%isopropanol containing 1%TFA followed by 150 microliters of 1%TFA.To elute the peptides from the STB-RPS stage tips, use 150 microliters of 5%ammonium hydroxide and 80%acetonitrile added to a single tip and collect the eluted samples in individual tubes by gently applying pressure using a syringe. Alternatively, if eluting via centrifugation, use 150 microliters at 5%ammonium hydroxide in 80%acetonitrile added to all stage tips and collect in a clean plate. Dry the eluted peptides by vacuum centrifugation at 25 degrees Celsius.Next, to enrich the glycopeptide samples, prepare a zwitterionic hydrophilic interaction liquid chromatography, or ZIC-HILIC, stage tips. Excise one C8 disc from a 470-square-millimeter C8 membrane using a 14-gauge blunt needle and pack the disc into a P-200 tip to create a frit. Then, add approximately five millimeters of ZIC-HILIC material resuspended in 50%acetonitrile onto the frit by gently applying pressure using a syringe.Before using the tips, equilibrate the resin with 20 bed volumes of ZIC-HILIC elution buffer, leaving 10 microliters of volume above the resin to ensure that the resin does not go dry. Then, sequentially wash the resin with 20 bed volumes of ZIC-HILIC preparation buffer followed by 20 bed volumes of ZIC-HILIC loading or wash buffer. Next, resuspend the digested and dried peptides in the ZIC-HILIC loading buffer to a final concentration of four micrograms per microliter.Vortex briefly for one minute to ensure the samples are resuspended then spin them for one minute at 2000 times G and 25 degrees Celsius. After spinning, load the resuspended peptide sample onto the conditioned ZIC-HILIC column, then wash the column three times with 20 bed volumes of the ZIC-HILIC loading buffer by gently applying pressure using a syringe. Finally, elute the glycopeptides with 20 bed volumes of the ZIC-HILIC elution buffer into a 1.5-milliliter tube by gently applying pressure using a syringe, then dry the eluent by vacuum centrifugation at 25 degrees Celsius.For LC-MS analysis, resuspend the samples in Buffer A*to a final concentration of one microgram per microliter, then load the samples onto an HPLC-UPLC coupled to an MS to enable the separation and identification of glycopeptides. Monitor the resulting MS data collection, ensuring the data is being collected with the desired parameters. To analyze the proteome and glycopeptide-enriched samples, open FragPipe and click on the Workflow tab.From the Workflow pull-down menu, select the Open search option and click Add files to import the data files to be searched into FragPipe. Then, click the Database tab followed by Download to launch the download manager for downloading proteome databases from UniProt using a UniProt accession number. Within the download manager, click the Add decoys and contaminants option to incorporate decoy and contaminant proteins into this database.Next, click the MSFragger tab, then within the Peak Matching box, increase the Precursor mass tolerance from the default 500 dalton to 2, 000 dalton to identify large modifications. Click the Run tab to define the location of the outputs of FragPipe, then click the Run button to begin the search. With high-confidence glycopeptides assigned, identify commonly observed glycan-associated ions to improve the identification of glycopeptides.Next, click the MSFragger tab and add the determined delta masses of the observed glycans into the Variable modifications and Mass offsets sections with individual masses separated with a slash. Then, add the glycan-associated fragment masses of these glycans into the Glyco/Labile Mods section of MSFragger. Finally, upload all MS data associated with proteomic studies to centralized proteomic repositories, such as the PRIDE or MassIVE repositories.Open searching of the Acinetobacter baumannii strain AB307-0294 revealed two dominant delta masses corresponding to 648.25 and 692.28 daltons. The ACICU capsule is known to be composed of a tetrasaccharide K unit corresponding to a predicted mass of 843.31 dalton. This capsule structure is consistent with the most frequently observed delta mass within the ACICU.Open searching analysis of the strain D1279779 revealed the presence of multiple delta masses consistent with 203.08, 794.31, and 1588.62 daltons. Using the interactive peptide spectral annotator, glycopeptides identified across three A.baumannii strains were assessed revealing potential glycan-associated ions. Glycan-focused searches led to a notable increase in the total number of glycopeptide/peptide spectral matches identified across all three A.baumannii strains, corresponding to a 37%increase in ACICU, a 117%increase in AB307-0294, and a 363%increase in D1279779.For individual MS events, the inclusion of glycan-specific information typically increases the observed Hyperscore, albeit this increase is highly glycan-dependent. When undertaking ZIC-HILIC enrichment, ensure that the resin always remains wet, as loss of solvent may compromise the isolation of glycopeptides. Data on differences in glycosylation can be used to inform mutagenesis studies or identify gene classes responsible for glycan biosynthesis.Using open database searching, we've begun to characterize glycan diversity across bacterial genera. This has shown that bacterial glycosylation is both more more common and far more diverse than we once thought.

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2.1K vistas.  The University of Melbourne. Este protocolo agiliza la identificación de glicóp%eptidas bacterianas, lo que permite a los investigadores identificar nuevos eventos de glicosilación, así como las diferencias en los glicanos utilizados para la glicosilación entre cepas. La principal ventaja de esta técnica es que permite a los expertos en no glicosilación identificar posibles eventos de glicosilación en muestras de proteoma bacteriano. La búsqueda abierta es un enfoque generalmente aplicable que se puede utilizar ...