Introduction

Coal workers’ pneumoconiosis (CWP) refers to lung lesions resulting from prolonged inhalation of dust in coal mining environments1. China is the world’s largest producer of raw coal, with millions of workers involved in coal mining, washing, and beneficiation. Furthermore, it has the highest incidence rate of pneumoconiosis globally2. The 2019 Global Burden of Disease reports that CWP in China is estimated to contribute to 43.30% of global deaths and 54.95% of disability-adjusted life years worldwide3. However, there is a need for more research because our current understanding of the pathophysiology of CWP is inadequate.

Current research universally recognizes alveolar macrophages as the key players in recognizing and engulfing dust particles4. This triggers a prolonged immune-inflammatory response that activates fibroblasts, resulting in excessive extracellular matrix secretion and ultimately leading to interstitial lung fibrosis5,6. However, the intricate molecular mechanisms underlying this process remain unclear. The human lungs, being one of the body parts most exposed to the external environment, have received increased attention for their microorganisms7,8. Research indicates that alterations in the composition and abundance of the normal lung microbiota can cause various clinical manifestations. For instance, lung microbiota changes have been associated with diseases such as cystic fibrosis and chronic obstructive pulmonary disease (COPD)9,10. Elevated levels of Streptococci in the sputum of CWP patients have been observed. These shifts in microbial communities could potentially play a role in the onset and progression of CWP11. However, the exploration of lung microbiota in CWP and the specific mechanisms remain limited.

Previous research has demonstrated that silica dust activates NOD-like receptor binding protein 3 (NLRP3) inflammasomes, leading to the formation of mature interleukin-1β (IL-1β) and interleukin-18 (IL-18). The release of inflammatory factors amplifies the sustained inflammatory response and promotes the transformation of epithelial cells into fibroblasts, which secrete excessive extracellular matrix and promote the development of pulmonary fibrosis12,13,14. While the silica dust content in coal mine dust is relatively low, limited experimental studies have explored how coal mine dust affects NLRP3 inflammasome. Nevertheless, research has shown significantly elevated levels of inflammatory factors such as IL-1β and IL-18, downstream of the NLRP3 inflammasome, in the peripheral blood of CWP patients compared to healthy dust-catching controls15,16,17. To find out if coal mine dust can intensify the inflammatory response by upregulating the NLRP3 inflammasome, IL-1β and other variables, more research is necessary.

Some studies have highlighted that commensal lung microbiota affects the NLRP3 inflammasome, which is a critical mechanism via which they regulate respiratory mucosal immunity18. Moreover, research has demonstrated that deleting the NLRP3 gene could alter the lung microbiota in animals with lung cancer19. These results imply that lung microbiota and the NLRP3 inflammasome may interact and possibly have synergistic effects. Therefore, we investigate the roles and mechanisms of lung microbiota and NLRP3 inflammasome in CWP rats to offer a novel perspective on the pathogenesis of CWP and propose insights for future treatment and prevention strategies for CWP.

Methods and materials

Animal model preparation

We utilized 8-week-old specific pathogen-free (SPF) male Wistar rats weighing approximately 180–200 g, procured from the Hubei Provincial Center for Disease Control and Prevention. Following two weeks of acclimatization feeding, the rats were randomly assigned to three groups, the saline group (Control group, 18 rats), the CWP group (Experimental group, 18 rats), and the silicosis group (Positive control group, 12 rats). To improve the reliability of the modeling results, we established a silicosis group as a positive control group for the rats used in the non-exposed tracheal drip coal workers’ pneumoconiosis model. The animal room maintains a temperature range of 20–25℃. Lighting conditions consist of 150–300 lx for 12 h alternating between light and dark cycles.

Anesthesia was induced via intraperitoneal injection of a 3% pentobarbital sodium solution (50 mg/kg), followed by a single dust-staining procedure using a non-tracheal exposure method for the CWP and silicosis groups, and the control group received an equivalent volume of saline. Coal dust or silica was administered through the trachea at a concentration of 50 mg/mL in a volume of 1 mL (The coal dust particles in this experiment came from Datong, Shanxi Province, China; Silica provided by the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Ground the aforementioned silica and coal dust particles back and forth in a grinding bowl until the particles were between 1 and 5 μm in size and were sterilized at high temperatures and pressures (101 kPa, 121 ℃) for 4 h before being used in the following studies), and rats were euthanized at 7 days, 28 days, and 56 days after successful modeling. The criterion for a successful tracheal infusion was the detection of thickened lung breathing sounds or lung rales following the injection20,21. All operations and rat euthanasia in this experiment complied with ethical requirements and the code of conduct outlined in the American Veterinary Medical Association (AVMA) Guidelines for the Euthanasia of Animals (2020). The study protocol received approval from the Ethics Committee of Huazhong University of Science and Technology ([2023]IACUC Number:3602), and all experiments were conducted in an SPF-grade laboratory. All experiments were performed by relevant guidelines and regulations.

To explore the correlation between lung microbiota and NLRP3 inflammasome in CWP, we introduced NLRP3 inhibitor MCC950 (MedChemExpress, USA) and antibiotics in the CWP group (Intervention group) as follows: CWP + MCC950 rats received MCC950 (20 mg/kg) twice a week via intraperitoneal injection (CWP + MCC950, 18 rats); while CWP + antibiotic were administered a combination of broad-spectrum antibiotics (ampicillin 1 g/L, vancomycin 0.5 g/L, neomycin 1 g/L, and metronidazole 1 g/L, MedChemExpress, USA ) through nasal drip once weekly before modeling (CWP + antibiotic, 18 rats), along with antibiotic-infused drinking water from one week before modeling to 28 days after modeling (Drinking water with the same antibiotic combination should be changed every other day to keep the antibiotic levels constant), each rat received a single intranasal antibiotic drip 1 day before and on days 7, 14, 21, and 28 post-dust staining during this period (Fixed the rat and used a cotton ball soaked in alcohol to gently clean the area around the nostrils to remove secretions without getting alcohol within the nasal cavity. Once the tip of a micropipette was near the nostril but not in touch with it, slowly dripped the antibiotics to prevent sneezing, watched the animal naturally inhale it, and if the antibiotics became stuck, gently massaged the bridge of the nose to encourage inhalation. To stop the drug from leaking out, keep your head up for five to ten seconds after dripping. Dripped antibiotics were applied to both nostrils in order, for a total of 200 µl)22,23.

Histopathological examination of rat lungs with szapiel score and Ashcroft score

The left upper lung from each rat was fixed in 4% paraformaldehyde for 24 h. Following fixation, lung tissues underwent overnight rinsing in running water, dehydration with gradient alcohol, and embedding in paraffin wax. Placed the wax block on the slicer for slicing. The thickness of the slice is 5 μm. Subsequently, the tissues were deparaffinized in xylene, dehydrated in gradient ethanol, rinsed in tap water, washed in Phosphate Buffered Solution (PBS), and subjected to Hematoxylin-eosin (H&E) and Masson staining. The stained sections were then examined under a light microscope and photographed. All pathology sections were randomly numbered and examined blindly under a light microscope to grade inflammation using Szapiel’s approach24 and fibrosis using Ashcroft’s method25.

16S rRNA determination of lung microbiota

Collected rat alveolar lavage fluid for lung microbiota detection. Total microbial genomic DNA was extracted from rat alveolar lavage fluid samples using the E.Z.N.A.® DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer’s instructions. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with primer pairs 338 F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’)26 by an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA). All samples were amplified in triplicate. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions and quantified using Quantus™ Fluorometer (Promega, USA). Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Then the optimized sequences were clustered into operational taxonomic units (OTUs) using UPARSE 7.1 with a 97% sequence similarity level27,28.

Real-time fluorescence quantitative PCR

Created blocks of wax from the left lung’s upper lobe. The remaining lung tissue should be chopped and uniformly mixed before being put into an EP tube with RNA preservation solution to determine the levels of mRNA expression.10 mg of lung tissue was weighed and homogenized in 1 ml of Trizol lysis (Beyotime, Shanghai, China) solution. Total RNA extraction was carried out by sequential addition of chloroform, isopropanol, and 75% ethanol. Subsequently, 25 µL of diethylpyrocarbonate (DEPC) water was added to each tube to dissolve RNA, with repeated mixing through blowing and stirring for thorough dissolution. Qualified samples underwent real-time fluorescence quantification via PCR amplification for reverse transcription. Reverse transcription was carried out according to the instructions of the reverse transcription kit (Thermo Fisher Scientific, USA). The reverse transcription reaction was conducted using a PCR thermal cycler (Thermo Fisher Scientific, USA). GAPDH served as the internal reference gene. Set PCR reaction conditions: 50℃ for 2 min → 95℃ for 2 min → 95℃ for 15 s → 55℃ for 15 s → 72℃ for 1 min → 95℃ for 15 s, a total of 40 cycles. The target gene and internal reference underwent amplification under identical conditions, with relative expression of the target gene mRNA analyzed using the 2−ΔΔCt method. The primer sequence information is shown in Table 1.

Table 1 Primers used in the study.
Full size table

Statistical analysis

All lung microbiota data were analyzed using Mothur v1.30.1 (http://www.mothur.org/wiki/Calculators) to calculate α-diversity metrics such as Sobs and Simpson index. To assess lung microbiota diversity, we utilized the Sobs index to indicate community richness, the Simpson index for community diversity, and the Coverage index for community coverage. Between-group differences in α-diversity were assessed using the Wilcoxon rank sum test. The similarity of microbial community structures between samples was evaluated through PCoA analysis based on the Bray-Curtis distance algorithm. All data were statistically analyzed with measurement data presented as mean ± standard deviation (SD). Group differences were assessed using the Student t-test for measurement data and the chi-square test for count data, with statistical significance set at P < 0.05. Statistical analyses were performed using SPSS 22.0 and R (version 3.3.1) software. The bar chart was created using GraphPad Prism (version 9.0).

Results

Exposure to coal and silica dust causes inflammation and fibrosis in lung tissue

Histological examination using H&E staining was conducted at 7, 28, and 56 days post dust exposure. Rats in the control group exhibited structurally intact alveolar walls with minimal inflammatory cell infiltration. In contrast, rats in the CWP group and silicosis group showed disrupted alveolar structures with prominent coal dust nodules or cellular nodular hyperplasia, and significant inflammatory cell infiltration. At 7, 28, and 56 days following dust exposure, the rat lung tissues in the CWP and silicosis groups had higher inflammatory scores than the control group, and the differences were statistically significant (Fig. 1A). Masson staining revealed that the control group had an intact alveolar structure without abnormal collagen fiber distribution. The CWP group and silicosis group showed damaged alveolar structures with thickened walls and scattered blue collagen fibers in coal dust nodules. At 7, 28, and 56 days following dust exposure, the fibrosis scores of rat lung tissue in the CWP and silicosis groups were statistically significantly higher than those of the control group (Fig. 1B). Inflammatory infiltrates were most pronounced at 28 days, while blue collagen fibers were most evident at 56 days. Meanwhile, we found that after 7 days of modeling, the mRNA levels of NLRP3 inflammasome upregulation (e.g., NLRP3 and ASC), inflammatory factor IL-1β, and fibrotic factors (collagen I) in CWP rats increased. In addition, the levels of caspase-1 and fibronectin in silicotic rats also significantly increased. Furthermore, mRNA levels in CWP and silicosis groups at 28 and 56 days after modeling showed significant increases in NLRP3 inflammasome, inflammatory factors, and fibrotic factors. However, there was no significant increase in caspase-1 in CWP rats at 28 days (Fig. 1C).

Fig. 1
figure 1

Inflammatory and fibrotic responses of rat lung tissue under coal and silica dust exposure. (A and B) H&E staining (A) and Masson’s trichrome staining (B) of the representative lungs from the rats on days 7, 28, and 56 (original magnification ×100; scale: 100 μm) and Szapiel score and Ashcroft score. (C) Relative mRNA expression level of inflammatory factor and fibrotic factors in lung tissue of rats in the control group (n = 3), CWP group (n = 3), and silicosis group (n = 3) (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001).

Full size image

Exposure to coal dust altered the lung microbiota

Between the three periods of time, there were no discernible changes in the dust-stained group’s α- and β-diversities (Fig. 2A,B). Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidota were the most prevalent phylum-level microbiota in the control group and CWP group during the three time periods. Rothia, Achromobacter, and Streptococcus were the most common genus. Between the control group and the CWP group, there were differences in the abundance of dominant bacterial populations. Following coal dust exposure, changes in lung microbiota compared to the control group included an increase in Firmicutes, Staphylococcus, and Streptococcus, and a decrease in Bacteroidota, Rothia, Achromobacter, and Lactobacillus (Fig. 2C).

Fig. 2
figure 2

Characteristics of changes in lung microbiota in rats under coal dust exposure conditions. (A) Comparing the differences in α-Diversity Indices (OTU Level) between the control group (n = 5) and the CWP group (n = 5), with the group names on the horizontal axis and the average index values on the vertical axis (*P < 0.05; **P < 0.01). (B) Comparing the differences in β-Diversity Indices (OTU Level) between the control group (n = 5) and the CWP group (n = 5). The X-axis and Y-axis represent two selected primary coordinate axes, with percentages indicating the explanatory power of the primary coordinate axes for differences in sample composition. Points of different colors or shapes represent samples from different groups, with closer proximity between two sample points indicating greater similarity in species composition. (C) Comparing the microbial community composition between the control group (n = 5) and the CWP group (n = 5). The horizontal axis represents the group names, and the vertical axis represents the species names. The abundance changes of different species in the sample are displayed through the color gradient of the color blocks. The values represented by the color gradient are shown on the right side of the graph.

Full size image

Antibiotics and NLRP3 inhibitors can reduce inflammation and fibrosis in the lung tissue of CWP rats

We did observe changes in the dominant microbiota in CWP, thus we treated CWP rats with antibiotics and NLRP3 inhibitors to investigate any possible connections between these characteristic microbial communities and the onset of inflammation and fibrosis in CWP. In comparison to the CWP group, the CWP + MCC950, and CWP + antibiotic groups likewise had disrupted alveolar structures, as well as less inflammatory cell infiltration and blue collagen fibers. Although we could observe decrease in the inflammation and fibrosis scores in the CWP rats in the antibiotic and MCC950 groups, we could also see the difference in the reduction of inflammation scores in the MCC950 intervention in the 28 and 56 days was statistically significant, while the difference in the other groups was not (Fig. 3A,B). Remarkably, when compared to the CWP group, NLRP3 inhibitors and antibiotics both showed the capacity to lower NLRP3, ASC, caspase-1, IL-1β, collagen I, and fibronectin. However, after 56 days, there was less of a difference in fibrotic or inflammatory markers between the control group and the CWP + antibiotic group than there was after 28 days. This may be related to the fact that antibiotics were only used for 28 days (Fig. 3C).

Fig. 3
figure 3

Inflammatory and fibrotic responses in lung tissue of CWP rats treated with antibiotics and NLRP3 inhibitors. (A and B) H&E staining (A) and Masson’s trichrome staining (B) of the representative lungs from the rats on days 7, 28, and 56 (original magnification ×100; scale: 100 μm) and Szapiel score and Ashcroft score. (C) Relative mRNA expression level of inflammatory factor and fibrotic factors in lung tissue of rats in the CWP group (n = 3), CWP + antibiotic group (n = 3), and CWP + MCC950 group (n = 3) (*P < 0.05; **P < 0.01; ***P< 0.001; ****P < 0.0001).

Full size image

Alterations in lung microbiota in CWP rats following antibiotics treatment

Simultaneously, we examined the alterations in dominant microbial communities and microbial diversity in CWP + antibiotic. Compared to the CWP group, the Simpson index showed a statistically significant variance in the CWP + antibiotic group at 7 days and 28 days (P = 0.019, P = 0.026); the Coverage index showed a statistically significant variance in the CWP + antibiotic group at 28 days (P = 0.006), and β-diversity exhibited significant changes following antibiotics treatment at 7 and 28 days (P = 0.021, P = 0.004). The alterations in lung microbiota after 56 days were less pronounced than those after 7 and 28 days (Fig. 4A, B). The change in the Simpson index suggests that the use of antibiotics led to a decrease in pulmonary microbial diversity in CWP. When the antibiotic intervention was stopped and the rats were given 28 days of normal water, α-diversity rose and β-diversity recovered. However, levels of inflammatory and fibrotic factors continued to decline, though the rate of decrease was less pronounced than at 7 and 28 days. This suggests that the impact of microorganisms on inflammatory and fibrotic factors may be prolonged. Similarly, Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidota constituted the majority of phylum-level microbiota in the CWP + antibiotic group. Achromobacter, Streptococcus, and Rothia were common genus-level. Compared with CWP, the CWP + antibiotic group showed that Proteobacteria, Bacteroidota, and Achromobacter increased at 7 days, while Firmicutes, Actinobacteria, and Rothia decreased; at 28 days, Proteobacteria, Bacteroidota and Achromobacter increased, while Firmicutes, Streptococcus, Actinobacteria, and Rothia decreased; and at 56 days, Bacteroidota and Achromobacter increased with a little changes in the remaining microbiota. We observed that antibiotic use elevated the levels of Proteobacteria, Achromobacter, and Bacteroidota while decreasing the levels of Streptococcus, Rothia, Firmicutes, and Actinobacteriota. These changes were essentially in opposition to the trends found in the CWP and control groups (Fig. 4C). Antibiotics therefore have the potential to affect the dominant microbiota of the pulmonary microbiota, which may be connected to pulmonary inflammation and fibrosis.

Fig. 4
figure 4

Characteristics of changes in lung microbiota in CWP rats following antibiotics treatment. (A) Comparing the differences in α-Diversity Indices (OTU Level) between the control group (n = 5) and the CWP + antibiotic group (n = 5), with the group names on the horizontal axis and the average index values on the vertical axis (*P < 0.05; **P < 0.01). (B) Comparing the differences in β-Diversity Indices (OTU Level) between the control group (n = 5) and the CWP + antibiotic group (n = 5). The X-axis and Y-axis represent two selected primary coordinate axes, with percentages indicating the explanatory power of the primary coordinate axes for differences in sample composition. Points of different colors or shapes represent samples from different groups, with closer proximity between two sample points indicating greater similarity in species composition. (C) Comparing the microbial community composition between the control group (n = 5) and the CWP + antibiotic group (n = 5). The horizontal axis represents the group names, and the vertical axis represents the species names. The abundance changes of different species in the sample are displayed through the color gradient of the color blocks. The values represented by the color gradient are shown on the right side of the graph.

Full size image

Alterations in lung microbiota in CWP rats with NLRP3 inhibitors

Lung microbiota testing was done on CWP + MCC950 to investigate whether lung microbiota may be changed by lowering pulmonary inflammation and fibrosis. No significant alterations were observed in the α- and β- diversity of the pulmonary microbiota between CWP and CWP + MCC950 groups (Fig. 5A, B). The dominant microbiota at the phylum level comprised Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidota. At the genus level, Achromobacter, Streptococcus, and Rothia were prevalent. Interestingly, the dominating microbiota showed comparable tendencies of fluctuation when exposed to both MCC950 and antibiotics. Manifested as an increase in the levels of Proteobacteria, Bacteroidota, and Achromobacter, while a decrease in levels of Streptococcus, Rothia, Firmicutes, and Actinobacteriota (Except for elevated Actinobacteriota and Rothia in 56 days) (Figs. 4C and 5C). To rule out the impact of MCC950 on the pulmonary microbiota, we administered MCC950 to the rats in the control group (MCC950’s dosage and usage method are same to those previously mentioned). The findings revealed that there was no change in α- and β- diversity, but the dominating pulmonary microbiota changed: at 7 days, Proteobacteria, Bacteroidoota, and Achromobacter increased, while Streptococcus, Rothia, Firmicutes, and Actinobacteria dropped; at 28 days, Streptococcus, Actinobacteriota, Firmicutes, Bacteroidoota and Rotia reduced, while Proteobacteria and Achromobacter increased; at 56 days, Streptococcus, Achromobacter, Proteobacteria and Bacteroidoota increased, while Actinobacteriota, Rothia, Firmicutes decreased (Supplementary Figure S1). Even though MCC950 itself can have an impact on rats’ lung microbiota, we discovered that MCC950’s effects on the dominant lung microbiota (such as Streptococcus, Bacteroidota, Actinobacterota, and Rothia) differed from those of CWP rats using MCC950. This could be because of fibrotic and inflammatory factors. This implies that the abundance changes of the dominant bacterial communities in the lungs may be influenced by the lowering of pulmonary inflammation and fibrosis. Considering the similar changes in lung microbiota due to NLRP3 inhibitors and antibiotics, along with the decreased pulmonary inflammation and fibrosis with antibiotic treatment, we propose that pulmonary microbiota and inflammatory and fibrotic factors may interact and influence each other in CWP.

Fig. 5
figure 5

Characteristics of changes in lung microbiota in CWP rats following NLRP3 inhibitors. (A) Comparing the differences in α-Diversity Indices (OTU Level) between the control group (n = 5) and the CWP + MCC950 group (n = 4 or 5), with the group names on the horizontal axis and the average index values on the vertical axis. (B) Comparing the differences in β-Diversity Indices (OTU Level) between the control group (n = 5) and the CWP + MCC950 group (n = 4 or 5). The X-axis and Y-axis represent two selected primary coordinate axes, with percentages indicating the explanatory power of the primary coordinate axes for differences in sample composition. Points of different colors or shapes represent samples from different groups, with closer proximity between two sample points indicating greater similarity in species composition. (C) Comparing the microbial community composition between control group (n = 5) and the CWP + MCC950 group (n = 4 or 5). The horizontal axis represents the group names, and the vertical axis represents the species names. The abundance changes of different species in the sample are displayed through the color gradient of the color blocks. The values represented by the color gradient are shown on the right side of the graph.

Full size image

Discussion

The dynamic equilibrium of lung microbiota is believed to provide resistance against respiratory pathogens and plays a crucial role in immune tolerance regulation within the lung micro-environment29. Dust exposure does not affect the α and β diversity of lung microbiota in our study on CWP rats, but it does alter the relative abundance of dominant bacterial species (Firmicutes, Achromobacter, Proteobacteria, Rothia, Actinobacteriota, Streptococcus, Bacteroidota, Staphylococcus, and so on ), which may be related to fibrosis or inflammation. Meanwhile, our findings showed that NLRP3 inhibitors and antibiotics had similar impacts on dominant bacterial communities, and the effect of antibiotics on the relative abundance of dominant microbiota had a certain prolongation effect. This finding may be connected to the interaction that fibrosis or inflammatory factors interacted with the lung microbiota. In addition, our results indicated that the use of antibiotics and NLRP3 inhibitors produced consistently similar effects on lung microbiota across the three time periods. However, following the coal dust intervention, the lung microbiota experienced a temporary imbalance before gradually stabilizing. This may be the reason for the discrepancy in microbial changes observed at 7 days compared to 28 and 56 days following coal dust exposure.

Keeping the delicate balance between microbial migration and clearance in the body7. Existing research indicates that lung microbial settlement is dynamic rather than permanent30,31. Lung microbiota undergo significant changes in acute and chronic lung conditions. For instance, the respiratory microbiota in COPD differs notably from that in healthy individuals, with an increased abundance of lung microbial species32. Furthermore, variances in lung microbiota are linked to key clinical aspects of chronic lung diseases, leading to various clinical manifestations such as heightened bronchiectasis exacerbations and increased mortality in idiopathic pulmonary fibrosis (IPF)33. This emphasizes the involvement of lung microbiota in the development of a spectrum of respiratory diseases. Meanwhile, existing research suggested that the sputum composition of severely COPD inpatients revealed a predominance of bacteria from genera such as Acinetobacter, Wattella, Neisseria, Lactobacillus, Streptococcus, Vibrio, and Actinobacteria34,35. Gupta et al. also observed distinct microbiome variations among patients with COPD, interstitial lung disease, and sarcoidosis. Significantly, COPD patients showed higher concentrations of Firmicutes, while patients with interstitial lung disease had higher concentrations of Streptococcus and Staphylococcus, and patients with sarcoidosis showed higher Actinobacteria and lower Proteobacteria36. These studies highlight variations in dominant microbiota and specific microbiota changes across different diseases. Research has shown that Firmicutes dominate in IPF37, with an increased presence of Streptococcus in airway microbiome samples from IPF patients38,39. Moreover, specific strains of Staphylococcus and Streptococcus have been linked to the progression of IPF, particularly with adverse outcomes such as mortality in IPF patients38. As pulmonary fibrosis is a primary pathological feature of CWP, our results align with the lung microbiota characteristics observed in IPF.

The significance of inflammation in the onset and progression of pulmonary fibrosis is well-established40, while the initial pro-inflammatory scenario of NLRP3 inflammasome activation can rapidly progress to a fibrotic one41. The relationship between NLRP3 inflammasome and microbiota has been extensively studied, with most research focusing on the intestinal or lung-gut axis. Recent studies have shown that the products of the gut microbiota of NLRP3−/− mice protect against influenza A virus infection42. Additionally, in NLRP3−/− mice with lung cancer, the lung microbiota was altered, suggesting a regulatory relationship between NLRP3 inflammatory vesicles and microbiota that promotes lung cancer development19. Further, the commensal microorganisms in the lungs may regulate respiratory mucosal immunity through appropriate inflammasome activation43. Our study revealed that alterations in lung microbiota had the potential to decrease inflammation and fibrosis levels in CWP. These alterations may then have an impact on the lung microbiota, indicating a dynamic interaction between microbiota composition and inflammatory and fibrotic factors in CWP. Despite the fact that tissue inflammation and fibrosis scores did not significantly alter in the current results.

Studies on the relationship between microbes and the immune system frequently use antibiotics44,45. According to earlier studies, pretreatment of mice with broad-spectrum antibiotics decreases the activation of intestinal NLRP3 inflammatory vesicles, similar to observations in germ-free mice46,47. Antibiotics have also been found to diminish IL-1β-mediated inflammation by inhibiting the activation of NLRP3 inflammasome48. The use of antibiotics can be well studied on the intestinal and lung microbiota without using sterile mice/rats. Our findings revealed the use of broad-spectrum antibiotics in CWP rats reduced the mRNA levels of NLRP3 inflammasome (NLRP3, ASC, caspase-1), inflammatory factor (IL-1β), and fibrotic factors (collagen I, fibronectin). Even after discontinuation of antibiotics for 28 days, the lung microbiota remained altered, continuing to impact inflammatory and fibrotic factor levels. This indicated that the modified lung microbiota could influence the progression of CWP, with the effects of antibiotics persisting beyond their cessation. But according to some research, metronidazole and vancomycin interfere with symbiotic bacteria by involving bacteria that are sensitive to these drugs in maintaining NLRP3/IL-1β signaling in peritoneal macrophages49. Antibiotics have also been shown in other investigations to prevent the NLRP3 inflammasome pathway from being activated50,51. According to these findings, antibiotics may lessen inflammation in ways other than microbial depletion. As a result, the impact of antibiotics on the NLRP3 inflammasome must also take into account the potential for medicines to act through direct immune modulation in our research.

Our study has numerous limitations. First off, we just focused on the mRNA levels of fibrotic and inflammatory factors. Protein levels were not detected, and there was no longitudinal comparison of these markers to ascertain how inflammation and fibrosis developed. Second, even though coal dust was sterilized by high temperatures and pressure, the experimental results could be impacted by microorganisms that are resistant to high temperatures and pressure. Additionally, our experiment’s absence of immune cell phenotypic characterization is a limitation because NLRP3 activation and microbial sensing are mediated by innate immune cells like neutrophils and macrophages. Finally, more thorough research is still needed to determine how the MCC950 medication affects the lung microbiome.

In conclusion, the richness of some prominent bacterial communities changed as a result of coal dust exposure, which could be a contributing factor to the inflammation and fibrosis caused by coal dust. In summary, lung microbiota may serve as a key pathogenic mechanism in CWP, offering a novel microbiological perspective on the disease.