Abstract
Peptidoglycan is an essential exoskeletal polymer present across all bacteria. The gut microbiota-derived peptidoglycan fragments (PGNs) are increasingly recognized as key effector molecules that impact host biology, offering attractive yet untapped potential to combat microbiome-associated diseases in humans. Unfortunately, comprehensive peptidoglycan profiling of gut bacteria has been hampered by the lack of a robust and automated analysis workflow. Currently, PGN identification still relies on manual deconvolutions of acquired tandem mass spectrometry (MS/MS) data, which are highly laborious and inconsistent. Recognizing the unique sugar and amino acid makeup of bacterial peptidoglycan and guided by the experimental MS/MS fragmentation patterns of known PGNs, we developed a computational tool PGN_MS2 that reliably simulates MS/MS spectra of PGNs. Integrating PGN_MS2 into the customizable in silico PGN database, we built an open-access PGN MS library of predicted MS/MS spectra for all molecules in the user-defined in silico PGN search space. With this library, automated searching and spectral matching can be used to identify PGN. We then performed comprehensive peptidoglycan profiling for several gut bacteria species, revealing distinct PGN structural features that may be implicated in microbiota-host crosstalk. Strikingly, the probiotic Bifidobacterium spp. has an exceedingly high proportion of anhydro-PGNs, which exhibit anti-inflammatory effects in vitro. We further identified MltG and RfpB homologs in Bifidobacterium as lytic transglycosylases (LTs), which demonstrate distinct substrate preferences to produce anhydro-PGNs. Overall, our novel PGN_MS2 prediction tool contributes to the robust and automated peptidoglycan analysis workflow, advancing efforts to elucidate the structures and functions of gut microbiota-derived PGNs in the host.
Competing Interest Statement
The authors have declared no competing interest.
