A consortium of Hordeum vulgare and gut microbiota against non-alcoholic fatty liver disease via data-driven analysis

Abstract

Despite many recent studies on non-alcoholic fatty liver disease (NAFLD) therapeutics, the optimal treatment has yet to be determined. In this unfinished project, we combined secondary metabolites (SMs) from the gut microbiota (GM) and Hordeum vulgare (HV) to investigate their combinatorial effects via network pharmacology (NP). Additionally, we analyzed GM or barley – signalling pathways – targets – metabolites (GBSTMs) in combinatorial perspectives (HV, and GM). A total of 31 key targets were analysed via a protein-protein interaction (PPI) network, and JUN was identified as the uppermost target in NAFLD. On a bubble plot, we revealed that apelin signalling pathway, which had the lowest enrichment factor antagonize NAFLD. Holistically, we scrutinized GBSTM to identify key components (GM, signalling pathways, targets, and metabolites) associated with the Apelin signalling pathway. Consequently, we found that the primary GMs (Eubacterium limosum, Eggerthella sp. SDG-2, Alistipes indistinctus YIT 12060, Odoribacter laneus YIT 12061, Paraprevotella clara YIT 11840, Paraprevotella xylaniphila YIT 11841) to ameliorate NAFLD. The molecular docking test (MDT) suggested that tryptanthrin-JUN is an agonist, conversely, dihydroglycitein-HDAC5, 1,3-diphenylpropan-2-ol-NOS1, and (10[(Acetyloxy)methyl]-9-anthryl)methyl acetate-NOS2, which are antagonistic conformers in the apelin signalling pathway. Overall, these results suggest that combination therapy could be an effective strategy for treating NAFLD.

Graphical Abstract

Keywords:

• Nonalcoholic fatty liver disease
• secondary metabolites
• gut microbiota
• Hordeum vulgare
• network pharmacology
• apelin signalling pathway

Acknowledgements

A preliminary version of this paper appeared as a Preprint [55], which was re-elaborated during authors’ stay at Center for Microbiome, Institute for Liver and Digestive Diseases in October 2022 and May 2023 respectively. The authors wish to thank the Hallym university for exceptional working conditions.

Author contributions

Ki-Tae Suk, Dong Joon Kim and Ki-Kwang Oh had substantial contributions to the conception or design for the work. Ki-Tae Suk, Ki-Kwang Oh, Su-Been Lee, Min-Gi Cha, Goo-Hyun Kwon, Haripriya Gupta, and Raja Ganesan were involved in the acquisition, or analysis for the work. Satya Priya Sharma, Sung-Min Won, Jin-Ju Jeong, Min-Kyo Jeong, Byeong-Hyun Min, and Ji-Ye Hyun performed the interpretation of data for the work. Jung-A Eom, Hee-Jin Park, Sang-Jun Yoon, Sang Youn Lee, and Mi-Ran Choi drafted the work or conducted the revising it critically for important intellectual content. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Additional information

Funding

This study has been implemented with the support of the Hallym University, the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology[NRF2019R1I1A3A01060447 and NRF-2020R1A6A1A03043026], Korea Institute for Advancement of Technology (P0020622), and Bio Industrial Technology Development Program [20018494] funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).