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.2022 Jun 25;23(1):71.
doi: 10.1186/s10194-022-01441-9.

Repeated inflammatory dural stimulation-induced cephalic allodynia causes alteration of gut microbial composition in rats

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Repeated inflammatory dural stimulation-induced cephalic allodynia causes alteration of gut microbial composition in rats

Shuai Miao et al. J Headache Pain..

Abstract

Background: Gut microbial dysbiosis and gut-brain axis dysfunction have been implicated in the pathophysiology of migraine. However, it is unclear whether migraine-related cephalic allodynia could induce the alteration of gut microbial composition.

Methods: A classic migraine rat model was established by repeated dural infusions of inflammatory soup (IS). Periorbital mechanical threshold and nociception-related behaviors were used to evaluate IS-induced cephalic allodynia and the preventive effect of topiramate. The alterations in gut microbial composition and potential metabolic pathways were investigated based on the results of 16 S rRNA gene sequencing. Microbiota-related short-chain fatty acids and tryptophan metabolites were detected and quantified by mass spectrometry analysis.

Results: Repeated dural IS infusions induced cephalic allodynia (decreased mechanical threshold), migraine-like behaviors (increased immobility time and reduced moving distance), and microbial composition alteration, which were ameliorated by the treatment of topiramate. Decreased Lactobacillus was the most prominent biomarker genus in the IS-induced alteration of microbial composition. Additionally, IS infusions also enhanced metabolic pathways of the gut microbiota in butanoate, propanoate, and tryptophan, while the increased tryptophan-related metabolites indole-3-acetamide and tryptophol in feces could be the indicators.

Conclusions: Inflammatory dural stimulation-induced cephalic allodynia causes the alterations of gut microbiota profile and microbial metabolic pathways.

Keywords: 16 S rRNA gene sequencing; Cephalic allodynia; Gut microbiota; Gut-brain axis; Migraine.

© 2022. The Author(s).

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of the experimental outline
Fig. 2
Fig. 2
Effects of repeated dural IS infusions on behavioral characteristics.A Comparisons of periorbital mechanical threshold among groups.B Comparisons of immobility time among groups.C Comparisons of the moving distance among groups.D Representative trajectory of each group on day 5. Data are presented as the mean ± SEM (n = 8; *P < 0.05, **P < 0.01, compared with the VEH group; ^P < 0.05, ^^P < 0.01, compared with the TO-VEH group;#P < 0.05,##P < 0.01, compared with the TO-IS group)
Fig. 3
Fig. 3
Beta-diversity of gut microbiota at the OTU level.A PCoA plot between the VEH and IS groups.B ANOSIM plot between the VEH and IS groups.C PCoA plot between the VEH and TO-VEH groups.D ANOSIM plot between the VEH and TO-VEH groups.E PCoA plot among the VEH, IS and TO-IS groups.F ANOSIM plot among the VEH, IS and TO-IS groups. In PCoA plots, each symbol represents the data of an individual sample and an R-square value (range [0, 1]) shows a given grouping factor to determine the variation of distances (Adonis test, Euclidean distance,n = 6). In ANOSIM plots, thex-axis represents the different groups and they-axis represents the distance rank of samples (ANOSIM test, Bray–Curtis distance,n = 6). An R-value (range [-1, 1]) greater than zero indicates that intergroup dissimilarities are greater than intragroup dissimilarities, while an R-value less than zero means that there are no intergroup differences
Fig. 4
Fig. 4
Microbial distributions at the phylum and family levels.A Community bar plot at the phylum level.B Relative abundance of the top 5 most abundant phyla.C Community bar plot at the family level.D Relative abundance of the top 15 most abundant families. Data are presented as the mean ± SEM (n = 6; *P < 0.05, **P < 0.05)
Fig. 5
Fig. 5
Analyses of the biomarker genus and differential pathways of carbohydrate and amino acid metabolism.A LEfSe analysis at the genus level. The bars represent the effect size (LDA score) of significantly discriminative genera.B Random forest analysis at the genus level. The y-axis displays the key genera ranked by their relative importance (MDA score) for classification.C Common biomarker genera in the LEfSe and RF analyses.D Relative abundances of the 5 biomarker genera. Data are presented as the mean ± SEM (n = 6; *P < 0.05, **P < 0.01)E Comparison of Tax4Fun2-predicted microbial functional pathways of carbohydrate and amino acid metabolism. The extended error bars plot shows differential metabolic pathways (Welch’s t-test, Benjamini–Hochberg FDR)
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