PGFinder, a novel analysis pipeline for the consistent, reproducible, and high-resolution structural analysis of bacterial peptidoglycans
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Evaluation Summary:
This manuscript presents the development and validation of a new tool for the characterization of peptidoglycan (PG), the essential cell wall polymer of bacteria. PG is a single large macromolecule that protects almost all bacterial cells. The newly developed open access tool will greatly facilitate comparative quantitative analyses and the determination of compositional diversity of PG, which might ultimately contribute to the development of new antibacterials that target this essential cell wall component.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)
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Abstract
Many software solutions are available for proteomics and glycomics studies, but none are ideal for the structural analysis of peptidoglycan (PG), the essential and major component of bacterial cell envelopes. It icomprises glycan chains and peptide stems, both containing unusual amino acids and sugars. This has forced the field to rely on manual analysis approaches, which are time-consuming, labour-intensive, and prone to error. The lack of automated tools has hampered the ability to perform high-throughput analyses and prevented the adoption of a standard methodology. Here, we describe a novel tool called PGFinder for the analysis of PG structure and demonstrate that it represents a powerful tool to quantify PG fragments and discover novel structural features. Our analysis workflow, which relies on open-access tools, is a breakthrough towards a consistent and reproducible analysis of bacterial PGs. It represents a significant advance towards peptidoglycomics as a full-fledged discipline.
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Evaluation Summary:
This manuscript presents the development and validation of a new tool for the characterization of peptidoglycan (PG), the essential cell wall polymer of bacteria. PG is a single large macromolecule that protects almost all bacterial cells. The newly developed open access tool will greatly facilitate comparative quantitative analyses and the determination of compositional diversity of PG, which might ultimately contribute to the development of new antibacterials that target this essential cell wall component.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)
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Reviewer #1 (Public Review):
This manuscript describes a freely available software tool, PGfinder, for the analysis of LC-MS datasets for bacterial peptidoglycan structure. This tool has the potential to greatly increase the speed and broad applicability of these analyses.
The authors are correct that a great limiting feature of PG structural analysis efforts has been interpretation of LC-MS datasets. Specific searches of the data using known or expected muropeptide masses produced the expected molecules, but would in some cases miss unpredicted but potentially important PG fragments. A strength of the PGfinder platform is the relatively automated generation of a database of predicted muropeptide masses with which to search the MS dataset. While the user must specify an initial set of expected monomer muropeptides, based on some …
Reviewer #1 (Public Review):
This manuscript describes a freely available software tool, PGfinder, for the analysis of LC-MS datasets for bacterial peptidoglycan structure. This tool has the potential to greatly increase the speed and broad applicability of these analyses.
The authors are correct that a great limiting feature of PG structural analysis efforts has been interpretation of LC-MS datasets. Specific searches of the data using known or expected muropeptide masses produced the expected molecules, but would in some cases miss unpredicted but potentially important PG fragments. A strength of the PGfinder platform is the relatively automated generation of a database of predicted muropeptide masses with which to search the MS dataset. While the user must specify an initial set of expected monomer muropeptides, based on some knowledge of the crude PG structure of the species/strain, a complex database is then constructed including all possible cross-linked muropeptides and molecules with a variety of known PG chemical modifications.
While this requirement for the initial muropeptide dataset might be considered a weakness if one wants to begin characterizing PG for an unstudied organism, an initial database containing monomers from a wide variety of PG chemotypes should be easily produced. This would allow an initial crude analysis to identify the basic structural PG parameters, allowing the production of a highly detailed database for that species.
As the authors point out, the vast majority of PG structure studies are targeting minor changes across environmental or growth conditions, or between mutant strains within a species, and the database construction in this version of the tool is ideal for these analyses.
A great strength of LC-MS-based PG analysis is the detection and identification of minor muropeptide components. An example is the peptide-denuded glycans thought to be present in cell poles that were shown in this study. An automated system to identify these minor components greatly increases the likelihood of their identification. The PGfinder platform was shown to produce highly reproducible results across technical replicates.
The PGfinder tool was applied to PG of three different species and was found to produce comparable results to traditional analyses, but in each case with significantly greater sensitivity in the detection and identification of minor muropeptides, often with unexpected structural modifications. This sensitivity and somewhat unbiased structure determination is a great strength.
A minor weakness inherent in MS-based analyses is the accuracy of the quantitative analysis. While the quantitative MS-data across samples is highly reproducible, some PG structural parameter values differed from those determined using other methods, such as UV detection. It is not yet clear if these differences were due to biological differences between the samples used or if the ion-count MS data is different from the UV-based data. Ion-count analyses in proteomic studies have been shown to be significantly affected by ionization efficiency of different molecules. Again, the strong PGfinder reproducibility across samples is excellent for observing differences between strains or growth conditions. The absolute accuracy of the quantitative values will remain to be seen.
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Reviewer #2 (Public Review):
The authors provide a novel and generalizable framework for the quantitative analysis of peptidoglycan (PG) composition across different bacteria. The software they develop uses MS data, which allows for high-confidence muropeptide identification across different bacteria. Authors demonstrate the reliability and utility of their methodology through analyses of cell walls first for the relatively well-characterized model species E. coli, followed by a comparative analyses between two C. difficile isolates.
In their approach, authors first applied their pipeline to analyze E. coli PG, which has been amongst the most well-studied cell walls. They achieved an unprecedented level of detail and uncovered previously unidentified low-abundance muropeptides. The authors next compared PG composition two C. difficile …
Reviewer #2 (Public Review):
The authors provide a novel and generalizable framework for the quantitative analysis of peptidoglycan (PG) composition across different bacteria. The software they develop uses MS data, which allows for high-confidence muropeptide identification across different bacteria. Authors demonstrate the reliability and utility of their methodology through analyses of cell walls first for the relatively well-characterized model species E. coli, followed by a comparative analyses between two C. difficile isolates.
In their approach, authors first applied their pipeline to analyze E. coli PG, which has been amongst the most well-studied cell walls. They achieved an unprecedented level of detail and uncovered previously unidentified low-abundance muropeptides. The authors next compared PG composition two C. difficile isolates, which has not been as well characterized. They noticed significant differences in the abundance of several muropeptides between the two strains, pointing to both new biological insights as well as an approach that allows detailed quantitative comparisons. Both results in E. coli and C. difficile could pave the way for uncovering novel biology. Finally, they analyzed an existing MS dataset of P. aeruginosa PG. The results produced by their software compared favorably to the published PG composition, reinforcing the validity of their approach.
Strengths: Overall, the paper presents solid data and the provided conclusions are within reason. One of their greatest strengths is their open access effort, especially as it relates to transparency around their methodologies (open access, Jupyter notebook) and comparisons with alternative commercial tools. This would be a considerable contribution to the field - finding a way to unify approaches or at least allow for more direct comparisons between studies by different groups using different methodologies or analysis pipelines. This alone could really enable more future discoveries in the field of bacterial cell wall biology.
Weaknesses: While their observations point to potentially interesting PG moieties and differences between conditions/strains, the experiments in the paper focus on methodologies and stop short of demonstrating any biological importance for the PG fragments and modifications identified. However, this is outside the scope of their effort. Further, it is highly likely that diversity of PG composition is, in fact, important for a wide range of cellular processes and phenotypes, based on previous studies. Thus, the biological impact of their work will depend on how widely their pipeline is adopted to explore this.
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Reviewer #3 (Public Review):
Despite knowing the general composition and structure of peptidoglycan for over 50 years as a heteropolymer of two amino sugars (N-acetylglucosamine and N-acetylmuramic acid) and attached short peptides, only recently have we discovered its true complexity and the significant differences that exist between bacterial species, and even strains. This understanding has been made possible largely by the application of high-pressure liquid chromatography to separate enzyme-generated fragments of peptidoglycan coupled with the large advances in mass spectrometric analyses. Recent reports indicate that over 100 different muropeptides (combinations of the amino sugar disaccharides and variations of the stem peptides) comprise the peptidoglycan (sacculus) of the bacteria studied to date, and this new tool promises to …
Reviewer #3 (Public Review):
Despite knowing the general composition and structure of peptidoglycan for over 50 years as a heteropolymer of two amino sugars (N-acetylglucosamine and N-acetylmuramic acid) and attached short peptides, only recently have we discovered its true complexity and the significant differences that exist between bacterial species, and even strains. This understanding has been made possible largely by the application of high-pressure liquid chromatography to separate enzyme-generated fragments of peptidoglycan coupled with the large advances in mass spectrometric analyses. Recent reports indicate that over 100 different muropeptides (combinations of the amino sugar disaccharides and variations of the stem peptides) comprise the peptidoglycan (sacculus) of the bacteria studied to date, and this new tool promises to facilitate a much greater understanding, and more importantly, the significance of this diversity. However, for this tool to be applicable to study of many important pathogenic bacteria, the muropeptide MS library would have to be expanded to include an important modification, namely O-acetylation. The manuscript has been prepared with care and attention to detail, while being clear and concise. This reviewer has only a few minor edits that the authors should consider, and one that, unfortunately, permeates the entire manuscript, including both main and supplemental figures and tables (namely, the use of J as the abbreviation for diaminopimelic acid (Dpm/Dap).
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