A protease activity-based machine-learning approach as a complementary tool for conventional diagnosis of diarrhea-predominant irritable bowel syndrome

Autor: Kazuki Tanaka, Naoki Tanigawa, Isaiah Song, Toru Komatsu, Yugo Kuriki, Yukari Tanaka, Shin Fukudo, Yasuteru Urano, Shinji Fukuda
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Frontiers in Microbiology, Vol 14 (2023)
Druh dokumentu: article
ISSN: 1664-302X
DOI: 10.3389/fmicb.2023.1179534
Popis: Irritable bowel syndrome (IBS) has no clinically accepted biomarkers even though it affects a large number of individuals worldwide. To address this lack of understanding, we evaluated peptidase activity in fecal samples from 35 patients with diarrheal IBS without symptom exacerbation (IBS-n) and 35 healthy subjects using a library of 384 fluorescent enzymatic substrate probes. IBS-n patients had high trypsin-like peptidase activity for cleavage of C-terminal lysine and arginine residues and low elastase-like activity for cleavage of C-terminal serine and glycine residues. These fluorescent probe library data, together with diagnostic machine-learning techniques, were able to accurately predict IBS-n. This approach can be used to diagnose diseases where no clinically accepted biomarkers exist, in which fecal enzyme activity is altered and also suggests that the development of new therapies targeting enzyme activities is possible.
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