Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometry based glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Corticosteroid-binding globulin (CBG) delivers anti-inflammatory cortisol to inflamed tissues through proteolysis of an exposed reactive center loop (RCL) by neutrophil elastase (NE). We previously demonstrated that RCL-localized Asn347-linked N-glycans impact NE proteolysis, but a comprehensive structure-function characterization of the RCL glycosylation is still required to better understand CBG glycobiology. Herein, we first performed RCL-centric glycoprofiling of serum-derived CBG to elucidate the Asn347-glycans and then used molecular dynamics simulations to study their impact on NE proteolysis. Importantly, we also identified O-glycosylation (di/sialyl T) across four RCL sites (Thr338/Thr342/Thr345/Ser350) of serum CBG close to the NE-targeted Val344-Thr345 cleavage site. A restricted N- and O-glycan co-occurrence pattern on the RCL involving exclusively Asn347 and Thr338 glycosylation was experimentally observed and supported in silico by modeling of a CBG-GalNAc-transferase (GalNAc-T) complex with various RCL glycans. GalNAc-T2 and GalNAc-T3 abundantly expressed by liver and gall bladder, respectively, showed in vitro a capacity to transfer GalNAc (Tn) to multiple RCL sites suggesting their involvement in RCL O-glycosylation. Recombinant CBG was then used to determine roles of RCL O-glycosylation through longitudinal NE-centric proteolysis experiments, which demonstrated that both sialoglycans (disialyl T) and asialoglycans (T) decorating Thr345 inhibit NE proteolysis. Synthetic RCL O-glycopeptides expanded on these findings by showing that Thr345-Tn and Thr342-Tn confer strong and moderate protection against NE cleavage, respectively. Molecular dynamics substantiated that short Thr345-linked O-glycans abrogate NE interactions. In conclusion, we report on biologically relevant CBG RCL glycosylation events, which improve our understanding of mechanisms governing cortisol delivery to inflamed tissues.

Septic shock, the excessive immune response to pathogen infection, accounts globally for ∼20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific, and insensitive, hindering timely and effective treatment. Aiming to establish easy-to-measure glyco-signatures that may identify the most critically unwell patients, we applied comparative glycomics and glycoproteomics to sera longitudinally collected from septic shock survivors (n = 29) and nonsurvivors (n = 8). Glycomics of all 134 serum samples (sampled daily until recovery/death) revealed significant N-glycome dynamics across both patient groups. Unsupervised clustering of the serum N-glycome measured upon intensive care unit (ICU) admission (day 1) indicated survivorship-specific glyco-signatures. We therefore employed machine learning to train a random forest model using the serum N-glycome data. The model accurately classified survivorship outcomes of 35 of 37 patients (accuracy 94.6%) and correctly predicted 29 of 29 survivors (specificity 100%) and six of eight nonsurvivors (sensitivity 75%). Interrogation of the serum N-glycome data revealed that Lewis x (Le^x)-type N-glycans are elevated in nonsurvivors relative to survivors at ICU admission, a finding recapitulated by glycoproteomics. Among the 58 other Le^x-containing serum glycoproteins that were strongly associated with acute phase response and stress pathways, alpha-1-acid-glycoprotein (AGP-1) was identified as a principal carrier of Le^x glycoepitopes with a potential to stratify septic shock survivors from nonsurvivors (AUC 0.90). This study lays a foundation for risk stratification of septic shock patients by uncovering easy-to-assay AGP-1-Le^x glycoforms that identify individuals experiencing poor survival outcomes already upon ICU admission, with the potential to translate to early individualized clinical care at the bedside.

Septic shock, the excessive immune response to pathogen infection, poses a major health concern accounting globally for ∼20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific and insensitive, hindering timely and effective treatment. Aiming to identify easy-to-assay glyco-signatures that may identify and guide the clinical management of the most critically unwell patients, we applied quantitative glycomics and glycoproteomics to sera longitudinally collected from septic shock survivors (n=29) and non-survivors (n=8). Glycomics of all 134 serum samples (sampled daily until recovery/death) revealed significant N -glycome dynamics across both patient groups. Unsupervised clustering of the serum N -glycome upon ICU admission (day 1) indicated survivorship-specific glyco- signatures. We therefore employed machine learning to train a random forest model using the serum N -glycome data. The model accurately classified survivorship outcomes of 35 of 37 patients (specificity 94.6%) and correctly predicted 6 of 8 non-survivors (sensitivity 75%) based on ICU day 1 data. Further interrogation of the serum N -glycome data revealed elevated Lewis fucosylation in non-survivors relative to survivors at ICU admission, a finding recapitulated by glycoproteomics. Amongst 58 other serum proteins strongly linked to acute phase response and stress pathways, alpha-1-acid-glycoprotein was identified as a principal carrier of Lewis fucosylation with a potential to stratify septic shock survivors from non- survivors (AUC 0.935). This study lays a foundation for risk stratification of septic shock patients by uncovering easy-to-assay glyco-signatures that identify individuals with poor survival outcomes upon ICU admission, with the potential to translate to early individualised clinical care at the bedside.