Gut microbiota intervention alleviates pulmonary inflammation in broilers exposed to fine particulate matter from broiler house

Abstract

The gut microbiota of poultry is influenced by a variety of factors, including feed, drinking water, airborne dust, and footpads, among others. Gut microbiota can affect the immune reaction and inflammation in the lungs. To investigate the effect of gut microbiota variation on lung inflammation induced by PM_2.5 (fine particulate matter) in broilers, 36 Arbor Acres (AA) broilers were randomly assigned to three groups: control group (CON), PM_2.5 exposure group (PM), and PM_2.5 exposure plus oral antibiotics group (PMA). We used non-absorbable antibiotics (ABX: neomycin and amikacin) to modify the microbiota composition in the PMA group. The intervention was conducted from the 18th to the 28th day of age. Broilers in the PM and PMA groups were exposed to PM by a systemic exposure method from 21 to 28 days old, and the concentration of PM_2.5 was controlled at 2 mg/m³. At 28 days old, the lung injury score, relative mRNA expression of inflammatory factors, T-cell differentiation, and dendritic cell function were significantly increased in the PM group compared to the CON group, and those of the PMA group were significantly decreased compared to the PM group. There were significant differences in both α and β diversity of cecal microbiota among these three groups. Numerous bacterial genera showed significant differences in relative abundance among the three groups. In conclusion, gut microbiota could affect PM_2.5-induced lung inflammation in broilers by adjusting the capacity of antigen-presenting cells to activate T-cell differentiation.

Importance: Gut microbes can influence the development of lung inflammation, and fine particulate matter collected from broiler houses can lead to lung inflammation in broilers. In this study, we explored the effect of gut microbes modified by intestinal non-absorbable antibiotics on particulate matter-induced lung inflammation. The results showed that modification in the composition of gut microbiota could alleviate lung inflammation by attenuating the ability of dendritic cells to stimulate T-cell differentiation, which provides a new way to protect lung health in poultry farms.

Keywords: T cell; dendritic cell; gut microbiota; particulate matter; pulmonary inflammation.

Fig 1

Effect of gut microbiota intervention on the lung inflammation of broilers exposed to fine particulate matter. (A) Flowchart of the gut microbiota intervention and PM_2.5 exposure. (B) Relative organ weight (%) for 28-day-old broilers in three groups (n = 6). (C) Histopathological examination of lungs from three groups (Bar = 500 or 50 µm). (D) Lung injury score from three groups. Data are expressed as the mean ± SEM (n = 6). *P < 0.05, **P < 0.01.

Fig 2

Effect of gut microbiota intervention on the lung inflammatory factor of broilers exposed to fine particulate matter. (A–F) The mRNA expression of an inflammatory factor in the lungs of broilers. (G–H) Inflammatory factor levels measured in the lungs of broilers. Data are expressed as the mean ± SEM (n = 6). *P < 0.05; **P < 0.01.

Fig 3

Effect of gut microbiota intervention on the mRNA expression of T-cell differentiation and costimulatory molecules of DCs in the lungs of broilers exposed to fine particulate matter. (A–D) The mRNA expression of T-cell differentiation markers in the lungs of broilers. (E–G) The mRNA expression of costimulatory molecules of DCs in the lungs of broilers. Data are expressed as the mean ± SEM (n = 6). *P < 0.05; **P < 0.01.

Fig 4

The cecum bacterial diversity of broilers. (A)A Venn diagram was drawn to reveal the number of standard and unique ASVs in three groups. (B) Tukey’s multiple comparisons analysis evaluated cecum content bacterial richness (Chao1, ACE, and Sobs) and diversity (Shannon, Simpson, and Pielou). Data are expressed as the mean ± SEM, *P < 0.05; **P < 0.01. (C) Cluster dendrogram of unweighted_unifrac in three groups. (D) Principal coordinates analysis (PCoA) of weighted_unifrac in three groups. (E) Partial least squares discriminant analysis (PLS-DA) in three groups. n = 5 per group.

Fig 5

The relative abundances of cecum bacterial taxa of chicken. (A) Composition of the gut microbiota community (top 10) at the phylum level. (B) Composition of the gut microbiota community (top 20) at the genus level. (C) The distribution of Desulfobacterota, Cyanobacteria, and Actinobacteriota among three groups. (D) The distribution of Bacteroides, Alistipes, Parabacteroides, Lachnoclostridium, Phascolarctobacterium, Intestinimonas, and Bilophila among three groups. (E) A Venn diagram of common and unique genera among three groups. (F) The top five unique dominant genera in the CON, PM, and PMA groups. Data are expressed as the mean ± SEM, n = 5 per group. *P < 0.05; **P < 0.01.

Fig 6

The linear discriminant analysis (LDA) effect size (LEfSe) assessed the contribution of cecum bacterial taxa to the differences between the CON, PM, and PMA groups. (A) Cladogram revealed significantly enriched bacterial taxa. (B) Bar chart showing the LDA score of bacterial taxa among three groups. Bacterial taxa covered from the domain to the genus level. Significant differences were defined as P < 0.05 and LDA score >2.0. n = 5 per group.

Fig 7

Heatmap of Spearman’s correlations between inflammatory factor, T-cell-related transcriptional factors, costimulatory molecules of DCs, and differential cecum microbiota. (A) The top 20 genera of abundance. (B) The top 20 species of abundance.