This protocol covers a detailed analysis of peptidoglycan composition using liquid chromatography mass spectrometry coupled with advanced feature extraction and bioinformatic analysis software.
Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan structure are fairly conserved across all bacteria, there is also considerable variation between Gram-positives/negatives and between species. In addition, there are numerous known variations, modifications, or adaptations to the peptidoglycan that can occur within a bacterial species in response to growth phase and/or environmental stimuli. These variations produce a highly dynamic structure that is known to participate in many cellular functions, including growth/division, antibiotic resistance, and host defense avoidance. To understand the variation within peptidoglycan, the overall structure must be broken down into its constitutive parts (known as muropeptides) and assessed for overall cellular composition. Peptidoglycomics uses advanced mass spectrometry combined with high-powered bioinformatic data analysis to examine peptidoglycan composition in fine detail. The following protocol describes the purification of peptidoglycan from bacterial cultures, the acquisition of muropeptide intensity data through a liquid chromatograph—mass spectrometer, and the differential analysis of peptidoglycan composition using bioinformatics.
Peptidoglycan (PG) is a defining characteristic of bacteria that serves to maintain cell morphology, while providing structural support for proteins and other cellular components1,2. The backbone of PG is composed of alternating β-1,4-linked N-acetyl muramic acid (MurNAc) and N-acetyl glucosamine (GlcNAc)1,2. Each MurNAc possesses a short peptide bound at the ᴅ-lactyl residue that can be crosslinked to adjacent disaccharide-linked peptides (Figure 1A,B). This crosslinking produces a mesh-like structure that encompasses the entire cell and is often referred to as a sacculus (Figure 1C). During PG synthesis, precursors are generated in the cytoplasm, and transported across the cytoplasmic membrane by flippases. Precursors are subsequently incorporated into the mature PG by transglycosylase and transpeptidase enzymes, which produce the glycosidic and peptide bonds, respectively3. However, once assembled, there are numerous enzymes produced by the bacteria that modify and/or degrade the PG to carry out a number of cellular processes, including growth and division. In addition, various modifications of the PG have been shown to confer adaptations specific to the strain, growth conditions, and environmental stress, which have been implicated in cell signalling, antimicrobial resistance, and host immune evasion4. As examples, a common modification is the addition of a C6 acetyl group on the MurNAc that confers resistance by limiting access to the glycan β-1,4 linkages to host-produced lysozyme enzymes which degrade PG4,5,6. In Enterococci, substitution of the terminal ᴅ-Ala of the peptide sidechain with ᴅ-Lac confers a greater resistance to the antimicrobial, vancomycin7,8.
The general procedure for PG isolation and purification has remained relatively unchanged since it was described in the 1960s9. Bacterial membranes are dissolved through heat treatment with SDS, followed by enzymatic removal of bound proteins, glycolipids, and remaining DNA. The purified intact sacculus can be subsequently digested into the individual components by hydrolysis of the β-1,4 linkage between GlcNAc and MurNAc. This digestion produces GlcNAc-MurNAc disaccharides with any structural modifications and/or crosslinks intact and are called muropeptides (Figure 1B).
Compositional analysis of PG was initially conducted through high pressure liquid chromatographic separation (HPLC) to purify each muropeptide followed by manual identification of muropeptides10,11. This has since been superseded by liquid chromatography tandem mass spectrometry (LC-MS), which increases detection sensitivity and decreases the manual workload of purifying each individual muropeptide. However, the time consuming and complex nature of the manual identification of muropeptides has remained a limiting factor, reducing the number of studies conducted. In recent years with the emergence of “omic” technologies, automated LC-MS feature extraction has become a powerful tool, allowing for rapid detection and identification of individual compounds in complex samples from very large datasets. Once the features are identified, bioinformatic software statistically compares the variation between samples using differential analysis isolating even minimal differences among the complex dataset and displaying them graphically to the user. The application of feature extraction software for the analysis of PG composition has only just begun to be explored12,13,14 and coupled to bioinformatic analysis12. Unlike proteomic analysis which benefits from the readily available protein databases that predict peptide fragmentation allowing for fully automated identification, no fragmentation library currently exists for peptidoglycomics. However, feature extraction can be coupled with known and predicted structural databases to predict muropeptide identification12. Here we present a detailed protocol for the use of LC-MS-based feature extraction combined with a muropeptide library for automated identification and bioinformatic differential analysis of PG composition (Figure 2).
1. Peptidoglycan sample preparation
2. Mass spectrometry data acquisition
3. Differential analysis of muropeptide abundance
Increased detection sensitivity of MS machinery coupled with high-powered peak recognition software has improved the ability to isolate, monitor, and analyze substance compositions of complex samples in very minute detail. Using these technological advancements, recent studies on peptidoglycan composition have begun to use automated LC-MS feature extraction techniques12,13,14,24 over older HPLC-based methodology11,25,26,27,28,29,30,31. Although there are numerous generic feature extraction software packages available, commercial software using recursive feature extraction is rapid and highly robust by automatically identifying and combining all the charges, isotopes, and adduct versions of each muropeptide found within the LC-MS dataset (Figure 3). In addition, initial retention times, m/z and isotopic patterns of extracted features are used to reassess (recursive) the dataset to ensure accurate identification of each feature in all data files. Therefore, the recursive algorithm aids in validating and increasing confidence in peak identification. Most generic feature extraction programs do not group charges/isotopes, etc. and will require this as an additional manual step. In addition, generic programs will be less robust as features are extracted separately within each data file and not as an entire dataset, which is part of the recursive algorithm.
The peptidoglycomic protocol presented here was recently used to examine the compositional changes of PG between two physiological growth conditions, namely, free-swimming planktonic and stationary communal biofilm12. Using a highly sensitive QTOF MS coupled with the recursive feature extraction, 160 distinct muropeptides were recognized and tracked. This represented eight times the number of muropeptides identified in this organism previously29,32, and greater than double the muropeptides identified using other methodologies in other organisms10,14,24.
Associating each m/z peak extracted from the MS data with a particular muropeptide is facilitated by cross-referencing with a database of known and predicted muropeptide structures. The fragmentation MS/MS chromatogram (Figure 4) for each extracted feature is compared to the fragmentation profile (Figure 4, gray inset) of the muropeptide proposed using the database.
Peptidoglycomic data can be viewed in a number of different ways depending on the experimental setup and the questions being asked. Such graphical analysis can include principal components analysis (PCA), scatterplots, volcano plots, heat maps, and hierarchical clustering analysis. For example, volcano plots highlight muropeptides that demonstrate a statistically significant high magnitude of abundance change between the tested conditions (Figure 5A). These selected muropeptides which represent significant abundance changes between the tested conditions can be further examined for muropeptide modifications. These modifications can include the presence of amino acid substitutions, acetylation changes, or the presence of amidase activity. When examined together, multiple muropeptides possessing the same modification can be examined for a trend toward one experimental condition (Figure 5A—highlighted points green) and the entire group assessed for significance (Figure 5B). Tracking a muropeptide modification in this way, can indicate a particular enzymatic activity that is affected by the experimental parameter. In addition, outliers from this trend may indicate enzymatic activity with a particular specificity or biological function (Figure 5A—highlighted points orange).
Figure 1: Example of a typical Gram-negative peptidoglycan structure. (A) In Gram-negative bacteria, peptidoglycan is located in the periplasm between the inner and outer membranes. (B) A single muropeptide consists of a β-1,4-linked N-acetyl glucosamine (GlcNAc) (blue) and a N-acetyl muramic acid (MurNAc) (purple) with an appended peptide sidechain (orange). The peptide sidechain can be crosslinked to the sidechain of adjacent muropeptide producing the mature mesh-like peptidoglycan (A). Purification involves the isolation of the peptidoglycan from the entire cell as a sacculus where all other cellular material has been stripped away. (C) Transmission electron micrograph of a peptidoglycan sacculi. In comparison, Gram-positive PG can consist of a greater array of variations in structure and is part of Gram-positive taxonomic classification33. Please click here to view a larger version of this figure.
Figure 2: Peptidoglycomics workflow. Sample Preparation. Step 1, grow and pellet bacterial cells (section 1.1). Step 2, purify peptidoglycan sacculi by 4% SDS boil (section 1.2). Data Acquisition. Step 3, enzymatic digestion of sacculi to produce muropeptides by breakage of the β-1,4-linkage between the N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc) of the peptidoglycan backbone (section 2.1). Step 4, analysis of muropeptide intensity through LC-MS/MS (section 2.2). Data Analysis. Step 5, recursive feature extraction identifies and collect all charges, adducts and isotopes associated with a single muropeptide (section 3.1). Step 6, identification of muropeptides by comparing predicted fragmentation with MS/MS chromatograms (section 3.3). Step 7, bioinformatic differential analysis (section 3.2) comparing peptidoglycan compositional changes between different experimental parameters. Step 8, examine the global change in muropeptide modifications within the different experimental parameters using 1D annotation (section 3.4). Please click here to view a larger version of this figure.
Figure 3: Example of a recursive feature extraction. For a muropeptide representing a peptide sidechain of alanine (A), iso-ᴅ-glutamate (E), meso-diaminopimelic acid (m), alanine (A) crosslinked to the AEmA of the adjacent muropeptide sidechain (1864.8 m/z). Included in the extracted feature for 1864.8 m/z are charges (+1, +2, and +3), adducts (e.g., sodium and potassium), loss of GlcNAc (1 or 2 GlcNAc), and multiple isotopic peaks for each variation (e.g., zoomed inset). Please click here to view a larger version of this figure.
Figure 4: Muropeptide fragmentation and identification. For annotation, each m/z peak (feature) extracted from the MS chromatogram is given a proposed muropeptide structure based on similarity to a muropeptide library. To confirm this proposed structure, predicted MS/MS fragments are generated using a chemical drawing program (gray inset). This predicted fragmentation is compared to the MS/MS chromatogram. When predicted fragments (gray inset) match the MS/MS chromatogram, the proposed muropeptide structure is confirmed. The figure was modified from Reference12. Please click here to view a larger version of this figure.
Figure 5: Differential analysis of peptidoglycan composition. (A) Volcano plot of the fold change and statistical significance of changes in muropeptide intensity between peptidoglycan purified from P. aeruginosa grown as either free-swimming planktonic or stationary biofilm culture. All muropeptides that have a modification that represented a change in the typical amino acid arrangement within the peptide sidechain are highlighted. Amino acid substituted muropeptides that showed a trend towards decreased abundance in biofilm-derived peptidoglycan are highlighted in green. Amino acid substituted muropeptides that were outliers to this trend and showed increased abundance in biofilm-derived peptidoglycan are highlighted in orange. (B) Heat map of the global fold change in abundance of all the amino acid substituted muropeptides with increased abundance (orange) and decreased abundance (green) in biofilms. These muropeptides were regrouped and assessed for whether amino acid substitution occurred on monomers, crosslinked dimers, or whether the fourth (AEm+), fifth (AEmA+) or both amino acids (AEm++) were substituted. The significance of each group of muropeptides were assessed by 1D annotation with FDR < 0.05 for significance and the associated 1D score is displayed. 1D annotation can only be performed on more than 2 muropeptides (e.g., AEm++ substitution was only found on two muropeptides). Therefore, in this case, significance must be examined for the individual muropeptides and not on the group. The figure was modified from Reference12. Please click here to view a larger version of this figure.
This protocol describes a method to purify peptidoglycan from bacterial cultures, process for LC-MS detection and analyze composition using bioinformatic techniques. Here, we focus on Gram-negative bacteria and some slight modification will be required to enable analysis of Gram-positive bacteria.
The preparation of muropeptides has remained virtually the same since it was first produced in the 1960s9,11,15. Once purified, sacculi (section 1.2.18) are digested into individual muropeptides using the muramidase enzyme mutanolysin from Streptomyces globisporus. Mutanolysin digests the PG structure by breaking the β-1,4-glycosidic linkage releasing individual muropeptides consisting of a GlcNAc-MurNAc disaccharide with appended peptide sidechain and includes any modifications or crosslinkages (Figure 1).
A limitation of previous methodology used to study PG composition has been the time-consuming manual identification of muropeptides. Due to the complexity and difficulty, adducts, charges, and/or isotopes may or may not have been included in the analysis. In addition, most studies restricted analysis to the most abundant, hence easiest to purify, muropeptides. Therefore, because of the complicated nature of the methodology, relatively few high-level detailed PG compositional analyses have been performed. The “omic”-type analyses have used recent technological improvements for the production and statistical analysis of relatively large and complex LC-MS datasets for the high-level overview of biological systems. The application of peptidoglycomics will enable the analysis of PG composition in very fine detail.
Within peptidoglycomics, recursive feature extraction reduces manual workload and increases accuracy by examining all data files at once. A recursive feature extraction algorithm is used to identify, align, and group unique spectral features (m/z peaks) across multiple LC-MS chromatographic data files making identification of muropeptide m/z peaks automated. This algorithm uses isotopic pattern matching which takes the numerous potential isotopes, ion adducts, and charge states and condenses the multiple m/z peaks into its representative single compound (or feature), which in this case would represent a single muropeptide (Figure 3). Verification of the spectral feature group is accomplished by comparing retention time, m/z, and isotopic pattern matching within each chromatographic data file to ensure robust extraction of the feature in the entire dataset. Generic feature finding algorithms may not include isotope matching or align, group, or verify m/z peaks across multiple samples and will require additional manual data processing to accomplish this feature extraction.
Once features are identified, bioinformatic differential analysis algorithms handle the very large dataset as a whole, thus allowing for useful comparisons and interpretations from the complex data. Using these bioinformatic graphical analyses is a powerful way to visualize and interpret large datasets to examine trends which may indicate biologic processes. It was only recently that these high-powered graphical analyses were used to examine peptidoglycan in very fine detail12. Differential analysis (section 3.2) assesses the changes in abundance of individual muropeptides between different experimental conditions. However, within the context of whole bacterial cells, the activity of PG modifying enzymes could result in multiple distinct muropeptide structures depending on the specificity of the catalytic activity (i.e., the addition of an acetyl group on the disaccharide could be with or without a modification of the peptide sidechain). Therefore, assessing the global abundance changes of a particular modification across all individual annotated muropeptides will give insight on the enzymatic activity acting on the PG (Figure 5) Therefore, differential analysis is used to investigate the abundance changes of individual muropeptides; whereas, 1D annotation examines abundance changes of a particular PG modification. Coupling differential analysis with 1D annotation allows PG composition to be assessed both on an individual muropeptide level and also as an indicator of overall PG enzymatic activity.
During differential analysis, it is important to note that PG is composed of a few highly abundant muropeptides and numerous low abundance muropeptides12. Therefore, baselining is very important in order to remove any bias from the high abundance muropeptides during the later steps of the analysis. Also, due to the multiple t-tests performed, a statistical correction to decrease false positives must be applied. The default is often the Benjamini-Hochberg false-discovery rate (FDR)19. Other corrections such as the more conservative Bonferroni familywise error rate (FWER)20,21 are possible.
Within the bioinformatic software, the m/z peak identified in feature extraction is also assigned a predicted structure. Other “omic”-type (e.g., proteomic) analyses benefit from the availability of large compound databases, which allow for compound identification through predictive fragmentation spectra matching. Currently, no muropeptide predicted fragmentation library exists and the confirmation of muropeptide identification remains a manual step. However, as peptidoglycomic fragmentation databases develop and become publicly available, this manual identification step will become more automated and accessible by eliminating or highly reducing sections 3.3.3 and 3.3.4.
In Escherichia coli, PG consists of ~3.5 x 106 muropeptides per cell34. Within the detection limits of the QTOF MS, even the lowest abundant muropeptides can still represent hundreds of copies of a single muropeptide within a cell12. Therefore, understanding the changes to even the lowest abundant muropeptides may provide useful insights into the biological activity of PG-targeted enzymes within the cell.
The authors have nothing to disclose.
The authors would like to thank Dr. Jennifer Geddes-McAlister and Dr. Anthony Clarke for their contributions in refining this protocol. This work was supported by operating grants from CIHR awarded to C.M.K (PJT 156111) and a NSERC Alexander Graham Bell CGS D awarded to E.M.A. Figures were created on BioRender.com.
Equipment | |||
C18 reverse phase column – AdvanceBio Peptide column (100 mm x 2.1 mm 2.7 µm) | Agilent | LC-MS data acquisition | |
Heating mantle controller, Optichem | Fisher | 50-401-788 | for 4% SDS boil |
Heating Mantle, 1000mL Hemispherical | Fisher | CG1000008 | for 4% SDS boil |
Incubator, 37°C | for sacculi purification and MS sample prep | ||
Leibig condenser, 300MM 24/40, | Fisher | CG121805 | for 4% SDS boil |
Lyophilizer | Labconco | for lyophilization of sacculi | |
Magentic stirrer | Fisher | 90-691-18 | for 4% SDS boil |
mass spectrometer Q-Tof model UHD 6530 | Aglient | LC-MS data acquisition | |
microcentrifuge filters, Nanosep MF 0.2 µm | Fisher | 50-197-9573 | cleanup of sample before MS injection |
Retort stand | Fisher | 12-000-102 | for 4% SDS boil |
Retort clamp | Fisher | S02629 | for 4% SDS boil |
round bottom flask – 1 liter pyrex | Fisher | 07-250-084 | for 4% SDS boil |
Sonicator model 120 | Fisher | FB120 | for sacculi purification |
Sonicator – micro tip | Fisher | FB4422 | for sacculi purification |
Ultracentrifuge | Beckman | sacculi wash steps | |
Ultracentrifuge bottles, Ti45 | Fisher | NC9691797 | sacculi wash steps |
Water supply | City | for water cooled condenser | |
Software | |||
Chemdraw | Cambridgesoft | molecular editor for muropeptide fragmentation prediction | |
Excel | Microsoft | viewing lists of annotated muropeptides, abundance, isotopic patterns, etc. | |
MassHunter Acquisition | Aglient | running QTOF instrument | |
MassHunter Mass Profiler Professional | Aglient | bioinformatic differential analysis | |
MassHunter Personal Compound Database and Library Manager | Aglient | muropeptide m/z MS database | |
MassHunter Profinder | Aglient | recursive feature extraction | |
MassHunter Qualitative analysis | Aglient | viewing MS and MS/MS chromatograms | |
Prism | Graphpad | Graphing software | |
Perseus | Max Plank Institute of Biochemistry | 1D annotation | |
Material | |||
Acetonitrile | Fisher | A998-4 | |
Ammonium acetate | Fisher | A637 | |
Amylase | Sigma-Aldrich | A6380 | |
Boric acid | Fisher | BP168-1 | |
DNase | Fisher | EN0521 | |
Formic acid | Sigma-Aldrich | 27001-500ML-R | |
LC-MS tuning mix – HP0321 | Agilent | G1969-85000 | |
Magnesium chloride | Sigma-Aldrich | M8266 | |
Magnesium sulfate | Sigma-Aldrich | M7506 | |
Mutanolysin from Streptomyces globisporus ATCC 21553 | Sigma-Aldrich | M9901 | |
Nitrogen gas (>99% purity) | Praxair | NI 5.0UH-T | |
Phosphoric acid | Fisher | A242 | |
Pronase E from Streptomyces griseus | Sigma-Aldrich | P5147 | |
RNase | Fisher | EN0531 | |
Sodium azide | Fisher | S0489 | |
Sodium borohydride | Sigma-Aldrich | 452890 | |
Sodium dodecyl sulfate (SDS) | Fisher | BP166 | |
Sodium hydroxide | Fisher | S318 | |
Sodium Phosphate (dibasic) | Fisher | S373 | |
Sodium Phosphate (monobasic) | Fisher | S369 | |
Stains-all | Sigma-Aldrich | E9379 |