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

Kazuki Tanaka; Naoki Tanigawa; Isaiah Song; Toru Komatsu; Yugo Kuriki; Yukari Tanaka; Shin Fukudo; Yasuteru Urano; Shinji Fukuda

doi:10.3389/fmicb.2023.1179534

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

Front Microbiol. 2023 Jul 7:14:1179534. doi: 10.3389/fmicb.2023.1179534. eCollection 2023.

Authors

Kazuki Tanaka^{1

2

3

4}, Naoki Tanigawa¹, Isaiah Song¹, Toru Komatsu⁵, Yugo Kuriki⁵, Yukari Tanaka^{6

7}, Shin Fukudo⁷, Yasuteru Urano^{5

8}, Shinji Fukuda^{1

2

3

4

9}

Affiliations

¹ Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
² Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, Japan.
³ Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki, Japan.
⁴ Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Kanagawa, Japan.
⁵ Graduate School of Pharmaceutical Sciences, The University of Tokyo, Hongo, Tokyo, Japan.
⁶ Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Miyagi, Japan.
⁷ Department of Behavioral Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan.
⁸ Graduate School of Medicine, The University of Tokyo, Hongo, Tokyo, Japan.
⁹ Laboratory for Regenerative Microbiology, Juntendo University Graduate School of Medicine, Hongo, Tokyo, Japan.

Abstract

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.

Keywords: fluorescent probe; irritable bowel syndrome; machine-learning; prediction; trypsin.