Date

Wednesday 23 Sep 2026

Time

16:00 - 16:30

Location

BW018

Supervisor

Zach Armstrong

2nd reviewer

Marco van Eijk

Jury

Hermen Overkleeft

Necrotizing enterocolitis (NEC) is a life-threatening gastrointestinal disease affecting premature infants and remains a leading cause of neonatal morbidity and mortality. Although intestinal dysbiosis is strongly associated with NEC, the functional microbial activities underlying disease pathogenesis remain poorly understood. In particular, the role of bacterial mucin-degrading enzymes, such as α-L-fucosidases, has not been investigated in patient-derived samples.

The aim of this study was to detect and characterize active bacterial fucosidases in NEC-associated fecal samples using activity-based chemical biology approaches. Method development was first performed using the recombinant GH29 α-L-fucosidase BT2970 from Bacteroides thetaiotaomicron. Probe sensitivity, activity-dependent labelling, and inhibitor potency were evaluated using activity-based protein profiling (ABPP) and fluorogenic 4-methylumbelliferyl-α-L-fucopyranoside (4-MU) activity assays. The optimized workflow was subsequently applied to fecal mucin samples from NEC patients and healthy controls. Active enzymes were enriched by activity-based metaproteomics and analysed by tandem mass spectrometry, after which the proteomic data were integrated with microbiome sequencing.

The Cy5-labelled cyclophellitol aziridine probe JJB381 demonstrated the highest sensitivity, with a detection limit of 5 nM for recombinant BT2970. The iminosugar JJB272 was identified as the most potent inhibitor, producing near-complete inhibition of fucosidase activity in the nanomolar range. Activity assays confirmed active fucosidases in nearly all patient-derived mucin samples, while competitive ABPP visualized activity-dependent protein bands in complex fecal lysates. However, persistent inhibitor-insensitive labelling indicated that further optimization is required to improve probe specificity. Activity-based metaproteomics yielded only low-confidence protein identifications, preventing reliable assignment of active fucosidases to specific bacterial taxa.

This study establishes a workflow for detecting bacterial fucosidase activity in clinical samples using activity-based chemical biology. Further optimization of the metaproteomics workflow is required to achieve confident protein identification. Nevertheless, the methodology developed in this study provides a foundation for future research aimed at linking microbial enzyme activity to bacterial taxonomy and identifying functional biomarkers of necrotizing enterocolitis.