Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics

Peptoglycomics uses advanced mass spectrometry combined with high-powered bioinformatic data analysis to examine peptidoglycan composition in fine detail. Using bioinformatic algorithms enables the analysis, extraction, and comparison of mass spec data across a collection of data files simultaneously, decreasing the workload and increasing the accuracy. Understanding the diverse variations and modifications of peptidoglycan has implications in understanding cellular processes, including growth and division, host immune evasion, and antimicrobial resistance.

We recommend preparing and analyzing a small subset of samples first to become familiar with the protocol and to act as a pilot project to assess the experimental parameters. As mass spectrometry data analysis software often varies in the definitions that are used when describing features and functions, visual demonstration will aid users in applying this protocol across platforms. For peptidoglycan sacculi extraction, resuspend frozen cell pellets at approximately one to 10 of the original culture volume in 20 millimolar sodium phosphate at four degrees Celsius before adding the cell suspension dropwise to boiling 8%SDS in 20 millimolar sodium phosphate to a final one-to-one volume.

Maintain a gentle boil for 30 minutes to three hours with stirring to ensure complete membrane dissociation. The resulting mixture should be completely clear with no remaining cell clumps or viscosity. When the solution has cooled to room temperature, collect the sacculi by ultra centrifugation, followed by five to seven washes in approximately 50 milliliters of room temperature 20 millimolar sodium phosphate per wash until the wash buffer has an SDS concentration of approximately 0.001%as determined by colorimetric dye.

After the last wash, resuspend the sacculi in 5 to 10 milliliters of room temperature 20 millimolar sodium phosphate and supplement the sample with 50 micrograms per milliliter of amylase, DNase, and RNase in 10 millimolar magnesium sulfate for a one-hour incubation with agitation or nutation at 37 degrees Celsius. At the end of the incubation, digest the reaction overnight with 100 micrograms per milliliter of pronase at 37 degrees Celsius. The next morning after ultra centrifugation, boil the sample for one hour in 25 milliliters of 2%SDS and 20 millimolar sodium phosphate in a steamer.

At the end of the incubation, wash the sample five to seven times with 50 milliliters of room temperature double distilled water as demonstrated. When the SDS concentration reaches approximately 0.001%resuspend the pellet in a sufficient quantity of double distilled water to obtain a sacculi suspension and wash the container. After overnight storage at minus 80 degrees Celsius, lyophilize the frozen sample before diluting the sacculi to a 10 milligrams of peptidoglycan per milliliter of double distilled water concentration.

For LC-MS analysis, start a new project in the instrument software and assign the appropriate LC-MS QTOF data files to the experimental condition of interest. Run the data processing wizard batch recursive feature extraction and set the data processing filters to match the parameters of the LC-MS conditions and instrumentation to accurately identify group and verify mass-to-charge peaks representing individual muropeptides. Review the results at the end of the feature extraction.

If a significant number of features failed to align in a group, adjust the recursive filtering parameters to broaden or restrict the detection window as necessary. Then export the data as a compound exchange file. For differential spectral feature analysis, start a new project in the system software and follow the instructions for data import.

During the data analysis, select the significance and fold change for differential analysis and set the baseline data to the median intensity across all of the data files. Once the analysis is complete, examine the resulting graphical and statistical analyses to identify muropeptides that demonstrate a significant abundance change between the tested experimental conditions. Under the project navigator, right-click on the various analyses and select an export option to save the feature details as a tsv file.

Ensure to save multiple analyses to obtain all the relevant data. Within the differential analysis software, under results interpretation, select ID browser, add a library of expected muropeptide structures, and select similar parameters as demonstrated for the feature extraction to produce a predicted muropeptide annotation for each identified feature. To manually confirm the predicted muropeptide annotation, compare the mass-to-charge ratio peaks of the tandem mass spectrometry chromatogram to the predicted mass-to-charge ratio of all of the possible fragmentations of a known muropeptide structure.

Use a molecular editor to draw the predicted muropeptide structure using the mass fragmentation tool to show the mass-to-charge ratio of the mass spectrometry fragments when each bond is broken either individually or in combination, then compare all of the possible fragmentations of the muropeptide structure to the tandem mass spectrometry chromatogram. To confirm the muropeptide annotation, the mass-to-charge ratio peaks of multiple fragments should be found in the tandem mass spectrometry chromatogram with a very minimal mass-to-charge ratio alignment window. To assess the global changes in muropeptide modifications, edit the tsv file of the statistically significant high-fold change muropeptides to include a single column for each muropeptide modification.

Populate this column with a designation for each muropeptide annotated and upload the modified tsv file into Perseus. Under annotate rows, click categorically annotate rows and add data files to each experimental parameter. Then under test, click on two sample tests to perform a student's T test and click 1D to perform 1D annotation.

An increased detection sensitivity of mass spectrometry 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. Associating each mass-to-charge peak extracted from the mass spectrometry data with a particular muropeptide is facilitated by cross-referencing with a database of known and predicted muropeptide structures. The fragmentation tandem mass spectrometry chromatogram for each extracted feature can be compared to the fragmentation profile of the muropeptide proposed using the database.

Volcano plots can be used to highlight muropeptides that demonstrate a statistically significant high magnitude of abundance change between the tested conditions. When examined together, multiple muropeptides possessing the same modification can be examined for a trend toward one experimental condition and the entire group can be assessed for significance. Tracking a muropeptide modification in this way can identify a particular enzymatic activity that is affected by the experimental parameter.

In addition, outliers from this trend may reveal enzymatic activities with a particular specificity or biological function. This procedure examines biochemical changes and biological variations in polymers, thereby providing insight into the enzymatic activity working on PG in vivo. Examining PG composition in verifying detail will aid in the understanding of complex cellular structures which has implications for many critical physiological processes.