Investigation of the effect and mechanisms of moxa smoke in the treatment of Influenza A Virus (IAV) infection

Abstract

Influenza, primarily caused by the Influenza A virus, is a highly contagious respiratory disease. While moxa burning is a traditional method used to reduce respiratory infections, most studies have focused on the components of moxa and air disinfection, often neglecting the pharmacological effects and mechanisms of moxa smoke. This study aimed to explore the antiviral and anti-inflammatory effects of moxa smoke in vivo, as well as the underlying mechanisms involved. Utilizing multiple databases, we identified 52 components of moxa smoke that target 384 proteins, with 92 of these potentially linked to protection against H1N1. Network analysis conducted using Cytoscape revealed 16 core targets, including PPARG and STAT3. We performed molecular docking to verify the stable binding affinities of core compounds with their corresponding targets. In vivo experiments demonstrated that moxa smoke significantly decreased the number of inflammatory cells in bronchoalveolar lavage fluid (BALF), lowered the levels of H1N1 nucleoprotein (H1N1NP), and reduced the mRNA expression of cytokines with chemokines in lung tissue, including Il-6, Il-1β, Tnf-α, Cxcl1, Cxcl2, Cxcl10 and Ccl2. These results suggest a reduction in lung inflammation in mice infected with the PR8 strain of the IAV. Western blot analysis indicated that moxa smoke upregulated PPARγ and reduced phosphorylated STAT3 levels. GW9662 inhibited the reduction of recruitment of inflammatory cells by moxa smoke, but didn't inhibit the reduction of viral load after moxa smoke treatment. A four-day treatment did not cause functional injury to the lungs, kidneys, or liver of H1N1-infected mice. However, after four weeks of exposure to moxa smoke, the mice exhibited changes in organ weight and pathological damage in the lungs and kidneys. In summary, Moxa smoke suppressed influenza virus-induced inflammatory cell infiltration by upregulating PPARγ, while simultaneously reducing viral load through PPARγ-independent mechanisms. Short-term exposure to moxa smoke did not cause significant impairment of pulmonary, hepatic or renal function; however, prolonged exposure may result in respiratory and renal dysfunction, potentially leading to more severe adverse effects.

Fig 1. Interactions between moxa smoke, ingredients, targets, and influenza A.

(A) Venn diagram of MS and IAV related targets. (B) PPI network of intersection targets. The blue circle represents the drug, the green diamond signifies the active ingredient, and the orange hexagon denotes the target. A larger area signifies a larger node, while a darker color represents a higher degree of association, and a lighter color indicates a lower degree of association.

Fig 2. Topological analysis of the shared target genes of Moxa smoke related to IAV.

(A) PPI network of the Moxa smoke-IAV crossover genes. (B) Network of 34 hub target genes based on degree values. (C) Central network of 16 core target genes based on DC, BC, CC, EC, LAC, and NC. (D) Visual analysis of the central network.

Fig 3. Enrichment analysis.

(A) The GO enrichment analysis is presented, with fold enrichment represented on the y-axis and the corresponding terms on the x-axis, highlighting the ten principal results for BP, CC, and MF, respectively. (B) The KEGG pathway enrichment analysis, conducted using Metascape, is illustrated, with pathways displayed on the y-axis and false discovery rate (FDR) on the x-axis, while the color gradient indicates the P-values. The size of the bubbles corresponds to the number of genes that are enriched within each pathway.

Fig 4. Molecular docking of bioactive components with key target proteins.

(A) The docking configurations between the target proteins PPARG and STAT3 and the compounds Bis-(3,5,5-trimethylhexyl) phthalate, Lupeol acetate, and L-α-Terpineol, highlighting their respective lowest binding energies. (B) A heat map representing the molecular docking results of the top 14 bioactive components in relation to 16 core target proteins.

Fig 5. Effect of MS-exposure on body weight loss in PR8-infected mice.

Mouse body weights were expressed as the percentage change in body weight and reported as the mean ± standard error of the mean (S.E.M.), with sample sizes varying from n = 7 to 9 mice per treatment group. A one-way ANOVA was performed to assess statistical significance, with thresholds set at **p < 0.01, ***p < 0.001, and ****p < 0.0001, compare to the PR8 + sham group.

Fig 6. Modulation of BALF Leukocyte Populations by MS in Influenza PR8-Infected Mice.

Cellularity is represented by the total population of cells(A), neutrophils(B), macrophages(C), and lymphocytes(D). (E) Representative images (original magnification × 200, scale bar = 100 µm) of Kwik-Diff-stained cytospin slides of BALF from each group. The blue arrow denotes macrophages, whereas the red arrow indicates neutrophils. Data are expressed as mean ± S.E.M for n = 7-9 per treatment group. One-way ANOVA was performed to assess statistical significance; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, compare to the PR8 + sham group.

Fig 7. Histopathological Evaluation of MS-Mediated Lung Protection Against Influenza PR8 Infection.

H&E-stained lung sections were collected from mouse that were euthanized on the fifth day following infection with the PR8 strain of influenza. (A) The upper four sections present representative composite images depicting the porta pulmonis region of the left lung lobes from four different groups of mice: MEM + sham, PR8 + sham, PR8 + LMS, and PR8 + HMS, arranged from left to right. The lower four sections illustrate the presence of inflammatory infiltrates, while the final four sections depict the structural integrity and area of the pulmonary alveoli across the four groups of mice. (B) The assessment of inflammation scores in the alveolar, peribronchial, and perivascular regions. The data are expressed as mean ± standard error of the mean (S.E.M) for a sample size of n = 7-9 mice. Statistical significance was determined using one-way ANOVA, revealing significant differences among the groups (*p < 0.05, **p ＜ 0.01,***p < 0.001,****p < 0.0001).

Fig 8. MS increased the expression of PPARγ and reduced phosphorylation of STAT3.

The measurement of p-STAT3, STAT3, and PPARγ through western blot analysis (n = 6). All data are expressed as means ± standard error of the mean (SEM). Statistical significance was determined in comparison to the model group, with thresholds set at *p < 0.05, **p < 0.01, and ***p < 0.001.

Fig 9. MS alleviates the recruitment of pro-inflammatory cells by activation of PPARG in H1N1-infected mice.

(A) Total cell number and differential cell count in BALF. (B) mRNA expression of NP and cytokines. (C) p-STAT3 and STAT3 protein level. Data are expressed as mean ± S.E.M for n = 6 per treatment group. One-way ANOVA was performed to assess statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, compare to PR8 group; ^#p < 0.05, ^##p < 0.01, compare to PR8 group.

Fig 10. The Toxicological Effects of MS on Mice.

(A) The origan weight/body weight ratio (%). (B) Histopathological images of organ tissues. The red arrow denotes the macrophages within the pulmonary tissue, while the blue arrow identifies the interstitial space. Data are expressed as mean ± S.E.M for n = 6 per treatment group. One-way ANOVA was performed to assess statistical significance; *p < 0.05, ****p < 0.0001.

Fig 11. The effects of MS on MMP12 mRNA Expression, Pulmonary Collagen and Lung Tissue Injury.

(A) The mRNA expression of MMPl2 in lung tissue of normal mice after 4 days and 4 weeks MS exposure (n = 7-10). (B) Hydroxyproline level in lung tissue (n = 7-10). (C) Masson’s trichrome staining (n = 6). (D) MLI index (n = 6). Data are expressed as mean ± S.E.M. One-way ANOVA was performed to assess statistical significance; *p < 0.05, **p < 0.01, ****p < 0.0001.