Integrated gut microbiome and metabolome analyses identified fecal biomarkers for bowel movement regulation by Bifidobacterium longum BB536 supplementation: A RCT

Yuya Nakamura; Shinya Suzuki; Shinnosuke Murakami; Yuichiro Nishimoto; Koichi Higashi; Naoki Watarai; Junpei Umetsu; Chiharu Ishii; Yutaro Ito; Yuka Mori; Mamiko Kohno; Takuji Yamada; Shinji Fukuda

doi:10.1016/j.csbj.2022.10.026

Integrated gut microbiome and metabolome analyses identified fecal biomarkers for bowel movement regulation by Bifidobacterium longum BB536 supplementation: A RCT

Comput Struct Biotechnol J. 2022 Oct 25:20:5847-5858. doi: 10.1016/j.csbj.2022.10.026. eCollection 2022.

Authors

Yuya Nakamura^{1

2}, Shinya Suzuki^{2

3}, Shinnosuke Murakami^{1

4}, Yuichiro Nishimoto¹, Koichi Higashi⁵, Naoki Watarai², Junpei Umetsu², Chiharu Ishii⁴, Yutaro Ito⁴, Yuka Mori¹, Mamiko Kohno⁶, Takuji Yamada^{1

2}, Shinji Fukuda^{1

4

7

8

9}

Affiliations

¹ Metagen Inc., 246-2 Kakuganji, Tsuruoka, Yamagata 997-0052, Japan.
² Department of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
³ Education Academy of Computational Life Science (ACLS), 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
⁴ Institute for Advanced Biosciences, Keio University, 246-2 Kakuganji, Tsuruoka, Yamagata 997-0052, Japan.
⁵ National Institute of Genetics, Genome Evolution Laboratory, Yata 1111, Mishima 411-8540, Japan.
⁶ MORISHITA JINTAN CO., LTD, Health Care Product Department, Research & Development Division, 1-2-40 Tamatsukuri, Chuo-ku, Osaka 540-8566, Japan.
⁷ Transborder Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan.
⁸ Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, 3-25-13 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-0821, Japan.
⁹ Laboratory for Regenerative Microbiology, Juntendo University Graduate School of Medicine, Hongo, Tokyo 113-8421, Japan.

Abstract

Background: Bifidobacterium longum BB536 supplementation can be used to regulate bowel movements in various people, including healthy subjects and patients with irritable bowel syndrome (IBS); however, individuals vary in their responses to B. longum BB536 treatment. One putative factor is the gut microbiota; recent studies have reported that the gut microbiota mediates the effects of diet or drugs on the host. Here, we investigated intestinal features, such as the microbiome and metabolome, related to B. longum BB536 effectiveness in increasing bowel movement frequency.

Results: A randomized, double-blind controlled crossover trial was conducted with 24 adults who mainly tended to be constipated. The subjects received a two-week dietary intervention consisting of B. longum BB536 in acid-resistant seamless capsules or similarly encapsulated starch powder as the placebo control. Bowel movement frequency was recorded daily, and fecal samples were collected at several time points, and analyzed by metabologenomic approach that consists of an integrated analysis of metabolome data obtained using mass spectrometry and microbiome data obtained using high-throughput sequencing. There were differences among subjects in B. longum intake-induced bowel movement frequency. The responders were predicted by machine learning based on the microbiome and metabolome features of the fecal samples collected before B. longum intake. The abundances of eight bacterial genera were significantly different between responders and nonresponders.

Conclusions: Intestinal microbiome and metabolome profiles might be utilized as potential markers of improved bowel movement after B. longum BB536 supplementation. These findings have implications for the development of personalized probiotic treatments.

Keywords: 16S rRNA gene sequence; AUROC, area under the receiver operating characteristic curve; Bifidobacteria; CE-TOFMS, capillary electrophoresis time-of-flight mass spectrometry; CSA, D-camphor-10-sulfonic acid; ESVs, exact sequence variants; FDR, false discovery rate; Gut microbiota; IBD, inflammatory bowel disease; IBS, irritable bowel syndrome; ITT, intention-to-treat; MCMC, Markov Chain Monte Carlo; MDS, multidimensional scaling; Machine learning; Metabologenomics; NRs, nonresponders; PP, per-protocol population; PSRF, potential scale reduction factor; Probiotics; SCFAs, short-chain fatty acids; SRs, strong responders; WAIC, Widely Applicable Information Criterion; WRs, weak responders.