Elucidation of Prebiotics, Probiotics, Postbiotics, and Target from Gut Microbiota to Alleviate Obesity via Network Pharmacology Study

Ki-Kwang Oh; Haripriya Gupta; Byeong-Hyun Min; Raja Ganesan; Satya Priya Sharma; Sung-Min Won; Jin-Ju Jeong; Su-Been Lee; Min-Gi Cha; Goo-Hyun Kwon; Min-Kyo Jeong; Ji-Ye Hyun; Jung-A Eom; Hee-Jin Park; Sang-Jun Yoon; Mi-Ran Choi; Dong Joon Kim; Ki-Tae Suk

doi:10.3390/cells11182903

Elucidation of Prebiotics, Probiotics, Postbiotics, and Target from Gut Microbiota to Alleviate Obesity via Network Pharmacology Study

Cells. 2022 Sep 16;11(18):2903. doi: 10.3390/cells11182903.

Authors

Ki-Kwang Oh¹, Haripriya Gupta¹, Byeong-Hyun Min¹, Raja Ganesan¹, Satya Priya Sharma¹, Sung-Min Won¹, Jin-Ju Jeong¹, Su-Been Lee¹, Min-Gi Cha¹, Goo-Hyun Kwon¹, Min-Kyo Jeong¹, Ji-Ye Hyun¹, Jung-A Eom¹, Hee-Jin Park¹, Sang-Jun Yoon¹, Mi-Ran Choi¹, Dong Joon Kim¹, Ki-Tae Suk¹

Affiliation

¹ Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.

Abstract

The metabolites produced by the gut microbiota have been reported as crucial agents against obesity; however, their key targets have not been revealed completely in complex microbiome systems. Hence, the aim of this study was to decipher promising prebiotics, probiotics, postbiotics, and more importantly, key target(s) via a network pharmacology approach. First, we retrieved the metabolites related to gut microbes from the gutMGene database. Then, we performed a meta-analysis to identify metabolite-related targets via the similarity ensemble approach (SEA) and SwissTargetPrediction (STP), and obesity-related targets were identified by DisGeNET and OMIM databases. After selecting the overlapping targets, we adopted topological analysis to identify core targets against obesity. Furthermore, we employed the integrated networks to microbiota-substrate-metabolite-target (MSMT) via R Package. Finally, we performed a molecular docking test (MDT) to verify the binding affinity between metabolite(s) and target(s) with the Autodock 1.5.6 tool. Based on holistic viewpoints, we performed a filtering step to discover the core targets through topological analysis. Then, we implemented protein-protein interaction (PPI) networks with 342 overlapping target, another subnetwork was constructed with the top 30% degree centrality (DC), and the final core networks were obtained after screening the top 30% betweenness centrality (BC). The final core targets were IL6, AKT1, and ALB. We showed that the three core targets interacted with three other components via the MSMT network in alleviating obesity, i.e., four microbiota, two substrates, and six metabolites. The MDT confirmed that equol (postbiotics) converted from isoflavone (prebiotics) via Lactobacillus paracasei JS1 (probiotics) can bind the most stably on IL6 (target) compared with the other four metabolites (3-indolepropionic acid, trimethylamine oxide, butyrate, and acetate). In this study, we demonstrated that the promising substate (prebiotics), microbe (probiotics), metabolite (postbiotics), and target are suitable for obsesity treatment, providing a microbiome basis for further research.

Keywords: IL6; Lactobacillus paracasei JS1; equol; gut microbiota; isoflavone; obesity.

Publication types

Meta-Analysis
Research Support, Non-U.S. Gov't

MeSH terms

Butyrates
Equol
Gastrointestinal Microbiome*
Humans
Interleukin-6
Molecular Docking Simulation
Network Pharmacology
Obesity* / therapy
Prebiotics*
Probiotics*

Substances

Butyrates
Interleukin-6
Prebiotics
Equol

Grants and funding

This research was supported by the Hallym University Research Fund and the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education, Science and Technology (NRF-2020R1I1A3073530 and NRF-2020R1A6A1A03043026).