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. 2022 Apr 28;17(4):e0267843.
doi: 10.1371/journal.pone.0267843. eCollection 2022.

Influence of warm acupuncture on gut microbiota and metabolites in rats with insomnia induced by PCPA

Hong Yu  1   2 , Hui Yu  3 , Lengge Si  4 , Husileng Meng  4 , Wensheng Chen  4 , Zhanli Wang  3 , A Gula  1   3
Affiliations

Influence of warm acupuncture on gut microbiota and metabolites in rats with insomnia induced by PCPA

Hong Yu et al. PLoS One. .

Abstract

Background: Insomnia is the most common of the sleep disorders. Current pharmacotherapy treatment options are usually associated with adverse effects and withdrawal phenomena. Therapeutic alternatives with a more favorable safety profile for patients are needed. Mongolian medical warm acupuncture (MMWA) is an emerging therapeutic option for treating insomnia. However, the underlying mechanisms responsible for the anti-insomnia efficacy of the MMWA remain unclear. This study aims to investigate the effect of the MMWA on the alterations of the gut microbiota and serum metabolome in rats with insomnia.

Results: We found that the relative abundances of gut bacteria and the concentrations of several serum metabolites were obviously altered in PCPA-induced insomnia rats. The MMWA treatment exerted an anti-insomnia effect. In addition, the dysbiosis of the gut microbiota and the serum metabolites were ameliorated by the MMWA. Correlation analysis between the gut microbiota and metabolites suggested that the levels of Amide c18, Benzoyl chloride, Cytosine, and N, n-dimethylarginine were positively correlated with the relative abundance of Clostridium XlVa and Blautia, which characterized the insomnia rats. KEGG enrichment analysis identified the cAMP signaling pathway involving anti-insomnia effect of the MMWA. Moreover, the MMWA intervention significantly increased contents of butyrate in feces, while effectively inhibited the expression level of GAT-1 in brain tissues.

Conclusion: This study reveals that the MMWA intervention might have a major impact on the modulation of host gut microbiota and metabolites, which in turn have a crucial role in the regulation of the host's signaling pathways associated with insomnia. The present study could provide useful ideas for the study of the intervention mechanisms of the MMWA in insomnia rat models.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The therapeutic effect of the MMWA on insomnia rats.
(A) Comparison of body weight gain of rats among three groups. (B) Comparison of the movement distance, movement time, and the number of arm lifting of rats among three groups. (C) Comparison of neurotransmitters in the serum of rats among three groups. Values are expressed as the means ± SD, n = 8 rats in each group (*P < 0.05).
Fig 2
Fig 2. Influence of the MMWA treatment on gut microbiota diversity.
(A) Shannon index. (B) Simpson index. (C) Beta diversity of gut microbiota.
Fig 3
Fig 3. Influence of the MMWA treatment on gut microbiota composition.
(A) Taxonomic composition at the phylum level. (B) Taxonomic composition at the genus level.
Fig 4
Fig 4. The significantly enriched bacterial taxa in different groups as determined by LEfSe analysis.
(A) Pairwise taxonomic LEfSe analysis of the control and the model groups. (B) Pairwise taxonomic LEfSe analysis of the model and the treatment groups. (LDA score > 2.0, P < 0.05).
Fig 5
Fig 5. Influence of the MMWA treatment on the levels of serum metabolites.
(A) Volcano plot of differential metabolites between the control group and the model group. (B) OPLS-DA score plots of serum samples from the control group and the model group in positive ion mode. (C) Heatmap analysis of differential metabolites between the control group and the model group. (D) Volcano plot of differential metabolites between the model group and the treatment group. (E) OPLS-DA score plots of serum samples from the model group and the treatment group in positive ion mode. (F) Heatmap analysis of differential metabolites between the model group and the treatment group.
Fig 6
Fig 6
KEGG enrichment analysis of differential metabolites between the control group and the model group (A), and between the model group and the treatment group (B). The X-axis indicates rich factor and the Y-axis represents the terms of the KEGG pathways. The size of circle indicates the number of differential metabolites and the color of circle represents P-value.
Fig 7
Fig 7. Influence of the MMWA treatment on the level of butyrate and the expression of GAT-1.
(A) The level of butyrate in feces. (B) The expression levels of GAT-1 in brain tissues. Values are expressed as the means ± SD, n = 8 rats in each group (*P < 0.05).
Fig 8
Fig 8. Schematic illustration of potential signaling pathways regulated by the MMWA.
See this image and copyright information in PMC

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