Guominkang formula alleviate inflammation in eosinophilic asthma by regulating immune balance of Th1/2 and Treg/Th17 cells

Abstract

The number of patients with allergic asthma is rising yearly, and hormonal drugs, such as dexamethasone, have unique advantages and certain limitations. In the treatment of allergic diseases especially allergic asthma, increasing the percentage or the function of immunosuppressive cells, such as Treg cells, may achieve a good effect. On the basis of good clinical results, we found that Guominkang (GMK) especially high-concentration GMK can achieve a similar effect with dexamethasone in controlling the symptoms of allergic asthma and inhibiting inflammation of allergic asthma. In our study, GMK can inhibit the recruitment of inflammatory cells, decrease mucus production, and reduce airway resistance. Besides, GMK can reconstruct the cellular immune balance of Th1/2 and Treg/Th17 cells. Metabolome results show that DL-glutamine, L-pyroglutamic acid, prostaglandin b1, prostaglandin e2, and 3,4-dihydroxyhydrocinnamic acid are the metabolic biomarkers and are associated with Th1/2 and Treg/Th17 cell balance. GMK can also change the gut microbiota in the allergic asthma mouse model. The genus_Muriculum, genus_(Clostridium) GCA900066575, genus_klebsiella, genus_Desulfovibrio, genus_Rikenellaceae RC9 gut group, family_Chitinophagaceae, family_Nocardioidaceae, and genus_Corynebacterium are gut microbiota biomarkers treated by GMK. Among these biomarkers, genus_Muriculum is the gut microbiota biomarker associated with Th1/2 and Treg/Th17 cell balance. Interestingly, we first found that DL-glutamine, L-pyroglutamic acid, prostaglandin b1, prostaglandin e2, and 3,4-dihydroxyhydrocinnamic acid are all associated with genus_Muriculum. GMK will be a new strategy for the treatment of eosinophilic asthma, and biomarkers will also be a new research direction.

Keywords: GMK; Th1/Th2; Treg/Th17; cellular immune balance; eosinophilic asthma.

FIGURE 1

GMK can alleviate inflammation of eosinophilic asthma in BALB/c mice model. (A) Flow chart of eosinophilic asthma model construction in BALB/c mice. (B) Allergens airway hyperresponsiveness assessment by non-invasive methods. Mice (n = 4–5/group) were sensitized and challenged with OVA. a, p < 0.05 vs. Control; aa, p < 0.01 vs. Control; aaa, p ≤ 0.001 vs. Control. b, p < 0.05 vs. GMK(H); bb, p < 0.01 vs. GMK(H); bbb, p ≤ 0.001 vs. GMK(H). c, p < 0.05 vs. GMK(M); cc, p < 0.01 vs. GMK(M); ccc, p ≤ 0.001 vs. GMK(M). d, p < 0.05 vs. GMK (L); dd, p < 0.01 vs. GMK(L); ddd, p ≤ 0.001 vs. GMK(L); e, p < 0.05 vs. DEX; ee, p < 0.01 vs. DEX; eee, p ≤ 0.001 vs. DEX. (C) HE staining and inflammation score of lung tissue. (D) Typical PAS staining and the related corresponding score of lung tissue. (E) Specific OVA-IgE antibody detection in serum, eosinophil count in bronchoalveolar lavage fluid (BALF), IL-5 and IL-13 cytokines detection in BALF (n = 5–6/group). The experiments repeated 2–3 times in this study. *p < 0.05, **p < 0.01, ***p ≤ 0.001.

FIGURE 2

Th1, Th2 cells and their related cytokines and gene detection. (A) Th1 and Th2 cells related cytokines detection in BALF and the IL-4 and IFN-γ mRNA expression in lung tissue analyzed by real-time RT-PCR. (B,C) Flow cytometry detection of Th1 and Th2 cells in spleen tissue, results was all represent as mean±SEM (n = 5–6/group). CD3⁺CD4⁺ cells were gated, then IFN-γ+ cells were gated for Th1 cells. CD3⁺CD4⁺ cells were gated, then IL-4⁺ cells were gated for Th2 cells. The experiments repeated 3 times in this study. *p < 0.05, **p < 0.01, ***p ≤ 0.001.

FIGURE 3

Treg, Th17 cells and their related cytokines and mRNA detection. (A) Treg and Th17 cells related cytokines detection in BALF. (B,C) Percentage detection of Treg and Th17 cells in spleen tissue. (D) The Foxp3 and RORγt mRNA relative expression in the lung tissue analyzed by real-time RT-PCR, results was represent as mean±SEM (n = 5–6/group). CD3⁺CD4⁺ cells were gated, then CD25⁺Foxp3⁺ cells were gated for Treg cells. CD3⁺CD4⁺ cells were gated, then IL-17⁺ cells were gated for Th17 cells. The experiments repeated 3 times in this study.*p < 0.05, **p < 0.01, ***p ≤ 0.001.

FIGURE 4

(Continued).

FIGURE 5

Correlation analysis between differential metabolites and Th1/2, Treg/Th17 cells balance treated by GMK in eosinophilic asthma. (A) Pearson correlation analysis between differential metabolites and Th1/2, Treg/Th17 immune balance, Penh value, OVA-IgE antibody and eosinophil number in BALF compared between GMK(H) and model group. (B) Metabolic biomarkers closely related to Th1/2 and Treg/Th17 cell balance in eosinophilic asthma (n = 7/group). *p < 0.05, **p < 0.01, ***p ≤ 0.001.

FIGURE 6

Gut microbiota Biomarker detection after GMK treated. (A) Principal coordinate analysis (PCoA) of gut microbiota in GMK treated group and eosinophilic asthma model group; (B) The top 10 gut microbiota at genus level between GMK treated group and model group; (C) Lefse analysis of differential gut microbiota between GMK treated group and model group (LDA >2 was used); (D) STAMP analysis of differential gut microbiota between GMK(H) treated group and model group (p < 0.05 was used), n = 7/group.

FIGURE 7

Correlation analysis between gut microbiota and Th1/2, Treg/Th17 cell balance in eosinophilic asthma. (A) Correlation analysis between differential microbiota and Th1,Th2, Treg and Th17 cell percentage, Penh value, OVA-IgE antibody and eosinophil number in BALF. (B) Correlation analysis of diffenential metabolites and gut microbiota biomarker treated by GMK (n = 5–6/group).