In silico MS/MS prediction for peptidoglycan profiling uncovers novel anti-inflammatory peptidoglycan fragments of the gut microbiota

Abstract

Peptidoglycan is an essential exoskeletal polymer across all bacteria. Gut microbiota-derived peptidoglycan fragments (PGNs) are increasingly recognized as key effector molecules that impact host biology. However, the current peptidoglycan analysis workflow relies on laborious manual identification from tandem mass spectrometry (MS/MS) data, impeding the discovery of novel bioactive PGNs in the gut microbiota. In this work, we built a computational tool PGN_MS2 that reliably simulates MS/MS spectra of PGNs and integrated it into the user-defined MS library of in silico PGN search space, facilitating automated PGN identification. Empowered by PGN_MS2, we comprehensively profiled gut bacterial peptidoglycan composition. Strikingly, the probiotic Bifidobacterium spp. manifests an abundant amount of the 1,6-anhydro-MurNAc moiety that is distinct from Gram-positive bacteria. In addition to biochemical characterization of three putative lytic transglycosylases (LTs) that are responsible for anhydro-PGN production in Bifidobacterium, we established that these 1,6-anhydro-PGNs exhibit potent anti-inflammatory activity in vitro, offering novel insights into Bifidobacterium-derived PGNs as molecular signals in gut microbiota-host crosstalk.