Differences of respiratory tract flora and metabonomic characteristics of lung lavage fluid in pneumoconiosis model rats

Differences of respiratory tract flora and metabonomic characteristics of lung lavage fluid in pneumoconiosis model rats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Differences of respiratory tract flora and metabonomic characteristics of lung lavage fluid in pneumoconiosis model rats Wei Gao, Han Hao, Shuyu Xiao, Shuling Yue, Xu Zhang, Peng Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7362557/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objective This study detected and analyzed the changes in the metabolomics of respiratory flora and lung lavage fluid of rats in a pneumoconiosis model. Methods Male SD rats were randomly divided into silicon dioxide group (using SiO 2 dust, SiD), coal mine dust group (using coal mine dust, CMD) and control group (using sterile physiological saline). The changes of respiratory flora in rats were analyzed by 16S rDNA gene sequencing technology, the differential metabolites of lung lavage fluid were analyzed by non targeted metabonomics of UHPLC-Q-TOF-MS. Results The lung tissue structure of SiD rats was seriously damaged, and there were obvious silicon nodules. CMD rats showed a large number of cell nodules, while the alveolar structure of the control group was normal. In the upper respiratory tract, the abundance of muris and uncultured oligotrophomonas increased, while the abundance of Pasteurella, Bacteria, Rhodobacter and uncultured Rhodobacter decreased. In SiD group, the abundance of Pasteurella and Streptococcus without milk decreased. In the lower respiratory tract, the abundance of Bacteria in CMD group rats increased, and mycoplasma γ- Proteobacteria β- The abundance of proterozoic bacteria and berberidaceae decreased. In SiD group, the abundance of Bifidobacteria, Bifidobacteriaceae and Bacteria increased, β- The abundance of proterozoic bacteria and berberidaceae decreased. Among the metabolic pathways mainly involved, pyrimidine metabolism, D-glutamine and D-glutamate metabolism may be the key metabolic pathways in the development of pneumoconiosis. Conclusion Dysregulation of Betaproteobacteriales, Burkholderiaceae, Bifidobacteriales, Bifidobacteriaceae, Streptococcus_agalactiae may lead to the occurrence of pyrimidine metabolism, D-glutamine and D-glutamate metabolism abnormalities in pneumoconiosis. Health sciences/Diseases Biological sciences/Microbiology Respiratory flora Metabolomic analysis Pneumoconiosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 What is already known on this topic. Mcrobial diversity may be closely related to cardiorespiratory health. The progress of pneumoconiosis has changed the metabolic state of the body. What this study adds . This study identified the main differential flora and main differential metabolites of pneumoconiosis. This study has proved the correlation between bacterial flora disorder and metabolic changes in pneumoconiosis. How this study might affect research, practice or policy . This study identified the main differential flora and main differential metabolites of pneumoconiosis. Introduction According to the 2021 statistical bulletin on China's health and health development, a total of 11809 cases of occupational pneumoconiosis were reported in China in 2021, and from the distribution of diseases, occupational pneumoconiosis was the most common, mainly silicosis and coal workers' pneumoconiosis [ 1 ] . Pneumoconiosis is a disease caused by the prolonged inhalation of production dust in some occupations and its retention in the lungs, leading to diffuse fibrosis of the lungs [ 2 , 3 ] . Due to the prolonged exposure to productive dust, pneumoconiosis often results in a variety of complications, such as respiratory infections (mainly of the lungs), spontaneous pneumothorax, chronic obstructive pulmonary diseases (COPD), etc. At present, the pathogenesis of pneumoconiosis has not been thoroughly studied and there is no cure for pneumoconiosis, so the main clinical treatment for pneumoconiosis today is medication or whole-lung lavage (WLL). Therefore, some scholars have suggested that a new way of thinking is needed in the clinical management of pneumoconiosis, especially in terms of new treatment strategies [ 4 ] . It has been suggested that microbial diversity may be closely related to cardiorespiratory health [ 5 ] . Many studies have shown that respiratory micro-ecological dysbiosis and changes in the structure of the flora may be associated with the development of disease, and the role of the respiratory flora on the immune system and disease is receiving increasing attention [ 6 ] . The relative abundance of Haemophilus spp., Moraxella spp. and Pseudomonas spp. has been found to increase significantly during acute exacerbations in COPD patients [ 7 ] . A similar situation exists in some chronic diseases like interstitial pneumonia [ 8 ] and bronchiectasis [ 9 ] . Metabolomics changes in organisms in disease states at a holistic level. It has been shown to not only reflect the pathological state of lung diseases, but also to have an invaluable role in the early diagnosis of diseases and the study of their pathogenesis, among other conditions. Studies have shown that in many lung diseases such as asthma, COPD, pulmonary cystic fibrosis, acute respiratory distress syndrome and lung cancer, significant changes occur in some of the major metabolites such as choline phosphate, phenylalanine, glutamine, malate, alanine, hydroxybutyrate, lactate, acetone, methanol, taurine, leukotrienes etc [ 10 – 16 ] . Meanwhile, studies have estimated that the progression of pneumoconiosis impairs the lung function of pneumoconiosis patients, destroys the lung parenchyma and also leads to abnormalities in the blood vessels of the lungs, recurrent lung infections causing narrowing of the airways, resulting in disruption of the pulmonary ventilation blood flow ratio, which accelerates the rate of ATP degradation and ultimately leads to increased uric acid production [ 17 ] . We used animal experiments to collect experimental samples and applied 16SrDNA gene sequencing technology and metabolomics research methods to detect and analyze the changes of respiratory flora and lung lavage fluid metabolites in experimental animals to explore their role in the development of pneumoconiosis. Materials and methods 1.1 Subjects and materials (1) The experimental subjects were 18 SPF-grade active SD rats, all males, weighing between 180–200 g, from the Experimental Animal Centre of North China University of Technology. The rats were housed in clean and ventilated rooms, provided with suitable clean water and feed, and their cages were cleaned regularly to avoid contamination. All animal experimental protocols were reviewed and approved by the Animal Care Welfare Committee of North China University of Science and Technology (Approval No: 2016086). All methods were carried out in accordance with the relevant guidelines and regulations of North China University of Technology. (2) Dust is taken from the Handan coal mine development area. (3) Purchase 500 g of silica dust (Sigma, USA). (4) Euthanasia was performed under deep anesthesia with intraperitoneal injection of pentobarbital sodium (150 mg/kg), followed by exsanguination to ensure death. All methods are reported in accordance with the ARRIVE guidelines ( https://arriveguidelines.org ) for the reporting of animal experiments. 1.2 Screening of dust and configuration of suspensions (1) Screening of dust: The settled dust is collected in the underground pioneering area of the Handan coal mine and is initially sieved using a sieve of appropriate aperture. The sieve (400 mesh) is placed in a device containing ultrapure water. The dust is placed on the 400 mesh sieve and the sieve is shaken to obtain a suspension. (2) Preparation of dust suspension: the dust and silica dust were weighed accurately at 2 g each and 40 ml of sterile saline was added separately to prepare a final concentration of 50 mg/mL dust suspension. The suspension is sealed with newspaper and then sterilized in a sterilizer. 1.3 Preparation of H&E-stained sections of lung tissue (1) Filming and sectioning: Lung tissues fixed in formaldehyde are dehydrated and treated with transparency, then waxed and embedded. The sections were sliced using a microtome to a thickness of 4–5 µm and placed on clean slides and dried at 60°C. (2) H&E staining: the prepared sections were placed in xylene 1–3 for 5 min, in ethanol solutions with a concentration gradient of 70%, 80%, 90% and 100% for the same 5 min, the sections were rinsed in running water for 3 min, then placed in hematoxylin for 5 min, rinsed again in running water for 2 s, divided in 1% hydrochloric acid alcohol. The sections were again rinsed in running water for 2 s and then placed in 85%, 90% and 100% ethanol solutions for dehydration, which were dried and sealed. 1.4 Respiratory flora diversity testing and analysis (1) DNA extraction from pharyngeal swabs and lung lavage fluid samples: the study was carried out by following the instructions in the Magnetic Beads Universal Genomic DNA Extraction Kit to extract DNA from pharyngeal swabs and lung lavage fluid. (2) PCR amplification The extracted DNA was used to determine its purity (OD: 1.6-2.0) and the corresponding concentration using an enzyme marker. The qualified DNA was selected for 16SrDNA PCR amplification, with amplification regions V3-V4, and needed to be re-extracted if it did not meet the requirements. Primers were used 338F: 5'-ACTCCTACGGGAGGCAGCA-3' and 806R: 5'- GGACTACHVGGGTWTCTAAT-3'. Gel electrophoresis was performed to check the integrity of the amplified fragments. (3) Library construction and sequencing assay processing The PCR amplification products were purified, quantified, homogenised and libraries were constructed and sequenced on the Illumina HiSeq 2500 platform after passing quality control. (4) Bacterial flora test data The double-ended sequence data obtained from Hiseq sequencing were spliced (merged) into one sequence Tags with reference to the Overlap linkage between PE reads, while the quality of Reads and Merge effect were given quality control filters to obtain the final valid data. A series of statistical analyses were then performed on the final validated data. 1.5 Metabolomic assay of lung lavage fluid and analysis (1) Extraction of metabolites from lung lavage fluid samples (2) Metabolomics testing The target compounds were separated on an Agilent 1290 UPLC with a Waters ACQUITY UPLC BEH Amide (2.1*100 mm, 1.7 µm) column. The aqueous phase was the A phase of the liquid chromatography, which consisted of ammonium acetate and ammonia, each at 25 mmol/L. Acetonitrile was chosen as the B phase. The gradient elution was as follows: 0 ~ 0.5 min, 95% B; 0.5 ~ 7 min, 95%~65% B; 7 ~ 8 min, 65%~40% B; 8 ~ 9 min, 40% B; 9 ~ 9.1 min, 40%~95% B; 9.1 ~ 12 min, 95% B. The flow rate was set at 0.5 mL per minute, the column temperature at 25℃ and the sample tray temperature at 4℃. The sample volume for each positive and negative ion was 2 µL. The Triple TOF 6600 high resolution mass spectrometer was selected for the mass spectrometry data collection and the mode was set to IDA (Information-dependent acquisition). The data collection software (Analyst TF 1.7, AB Sciex) was used to autonomously select ions for secondary mass spectrometry with reference to the primary mass spectrometry parameters and previously set indicators, and the 12 most intense ions with mass fractions above 100 in each cycle were scanned for secondary mass spectrometry with collision energy and cycle time set to 30 eV and 0.56 s. The ion source parameters were set as follows GS1: 60 psi, GS2: 30 psi, CUR: 35 psi, TEM: 600°C, DP: 60 V, ISVF: 5000 V(Pos)/-4000 (Neg). 1.6 Statistical analysis The data collected were processed and analyzed using the software SPSS 23.0. Data were represented as mean ± standard deviation ( \(\:\stackrel{-}{x}\pm\:s\) ), tested by one-way variance or rank sum test, and correlation analysis was performed using the Spearman statistical method, with P < 0.05 indicating a statistically significant difference. Results 1. Dust causes silicosis in rats With the prolongation of dust exposure time, the rats in the coal mine dust group and the silicon dioxide group had dull and lusterless fur, slightly decreased weight, significantly decreased activity, and slightly sluggish reaction. At the same time, the rats had symptoms of respiratory aggravation, but there was no abnormality in daily drinking water. H&E pathological sections of rats showed that the lung tissue structure of the coal mine dust group was seriously damaged, and there were a lot of inflammatory reactions, accompanied by many fibroblast aggregation, thus forming cell nodules (Fig. 1 B). In the silica group, the alveolar structure was also damaged to a certain extent, and the inflammatory reaction was aggravated, with obvious silicon nodules. The lung tissue structure of the control group is normal, with a small amount of inflammatory reaction (Fig. 1 C). 2. Analysis of SrDNA upper respiratory tract flora The sequencing length was mostly distributed between 400-440bp (Fig. 2 A). It can be seen from Venn diagram that in the control group, there are 663 Operational Taxonomic Units (OTUs) for throat swab microorganisms, and 633 and 531 OTUs for coal dust group and silicon dioxide group, respectively. In the control group and the coal dust group, there are 573 microbial OTUs in total, among which, there are 60 OTUs in the coal dust group alone, and 90 OTUs in the control group alone (Fig. 2 B). Compared with the control group, there are 502 OTUs in the two groups of rat throat swab microorganisms, 29 in the silicon dioxide group and 161 in the control group (Fig. 2 C). At other classification levels (phyla, class, order, family, genus), the number of OTUs among the three groups was not statistically significant ( Supplementary Table 1 ). Ɑ Simpson, Chao1, Ace and Shannon are commonly used indicators of diversity. Among them, Chao1 and Ace are mainly used to reflect species richness, while Simpson and Shannon focus more on community evenness (Fig. 2 D). The results showed (Table 1 ) that there was no significant difference in Chao1, Ace, Shannon and Simpson indexes of rat throat swab microbial communities among the three groups ( P > 0.05). β diversity analysis refers to indirectly reflecting the diversity of species abundance distribution according to the distance between samples. As shown in Fig. 2 E &F , when PC1 = 46.25%, the coal mine dust group and the control group are separated into two non interfering microbial communities on the left and right. When the principal coordinate PC1 = 34.65%, the silica group and the control group were separated into two non overlapping microbial communities. According to the LDA value distribution histogram and LEfSe analysis evolutionary branching diagram, the abundance of Pasteurella sp. _V6, Bacteria, and uncultured Rodentibacter in the respiratory tract flora of rat throat swabs in the coal mine dust group is lower than the control group, while that of Muribacter_and the abundance of multi and uncultured oligomonas was significantly higher than the control group (Fig. 2 G &H ). The abundance of Pasteurella sp. _V6 and Streptococcus agalactiae in the respiratory tract flora of the throat swabs of rats in the silica group was significantly lower than the control group (Fig. 2 I &J ). It can be seen from the table that Pasteurella is a respiratory tract differential flora shared by the coal dust group and the silicon dioxide group (Table 2 ). Table 1 Microbes in Swab of Rats in Three Groups ɑ Diversity index statistics ACE Chao1 Shannon Simpson NS 542.84 ± 50.60 544.94 ± 44.30 3.31 ± 1.40 0.17 ± 0.19 CMD 551.89 ± 36.87 559.02 ± 46.52 3.94 ± 0.47 0.07 ± 0.02 SiD 474.25 ± 43.85 502.50 ± 47.06 2.29 ± 1.35 0.32 ± 0.21 F 2.78 1.23 2.03 0.57 P 0.14 0.36 0.29 0.28 NS: control, CMD: coal mine dust, SiD: SiO2 dust. Table 2 Results of respiratory tract differential flora in throat swabs of rats from coal mine dust group and silicon dioxide group Common differential flora CMD SiD Pasteurella_sp._V6 Decreased Increased NS: control, CMD: coal mine dust, SiD: SiO2 dust. Table 3 Microbes in Swab of Rats in Three Groups ɑ Diversity index statistics Group ACE Chao1 Shannon Simpson NS 1005.68 ± 87.71 997.67 ± 9.16 5.12 ± 0.43 0.03 ± 0.03 CMD 1063.24 ± 84.34 1016.47 ± 22.56 4.33 ± 0.63 0.08 ± 0.05 SiD 1111.18 ± 110.53 1045.71 ± 13.29 a 4.89 ± 0.49 0.04 ± 0.02 F 0.93 6.85 1.52 1.82 P 0.45 0.03 0.29 0.24 NS: control, CMD: coal mine dust, SiD: SiO2 dust. 3. Analysis of 16SrDNA lower respiratory tract flora detection Most of the horizontal sequencing length was distributed between 400-440bp (Fig. 3 A). It can be seen from Venn diagram (Fig. 3 B &C ) that in the control group, there are 1098 OTUs in the lung lavage fluid microorganisms, 1127 and 1148 OTUs in the coal mine dust group and silicon dioxide group respectively. In the control group and the coal dust group, there are 863 microbial OTUs in total, including 264 OTUs in the coal dust group alone and 235 in the control group alone. In the control group and silica group, there are 854 OTUs with the same microorganism, among which, there are 294 OTUs in the silica group and 244 in the control group. It can be seen from the results that the number of OTUs in the silica group is higher than that in the control group at the class classification level ( P < 0.05) ( Supplementary Table 2 ). The dilution curve indicats that the number of species in the group will not increase with the increase of the sequencing amount (Fig. 3 D). There was no significant difference in Ace, Shannon, Simpson index of the lung lavage fluid microbial community among the three groups ( P > 0.05). In terms of the Chao1 index, compared with the control group, it was found that the Chao1 index in the silicon dioxide group was significantly higher, with statistical significance ( P < 0.05). As shown in Fig. 3 E &F , when the principal coordinate PC1 = 39.32%, the coal mine dust group and the control group are separated into two non interfering microbial communities on the left and right. When PC1 = 39.01%, the silica group and the control group were separated into two non intersecting microbial communities, indicating that the microbial communities of the coal dust group and the silicon dioxide group are significantly different. The abundance of Mycoplasma, Gammaproteobateria, Betaproteobateriales and Burkholderiae in the respiratory tract flora of lung lavage fluid of rats in the coal mine dust group was lower than that in the control group, but the abundance of Bacteria was significantly higher than that in the control group (Fig. 3 G &H ). The abundance of Bifidobacteriales, Bifidobacteriaceae and Bacteria in the respiratory tract flora of the lung lavage fluid in the silica group was higher than that in the control group, the abundance of Proteobacteria and Burkholderiae were significantly lower than that of the control group (Fig. 3 I &J ). β- Proteobacteria and Burkholderiaceae are respiratory tract differential flora shared by coal dust group and silica group, wherein Bacteria is up-regulated in both groups, while β- Proteobacteria and Berberidaceae were down regulated (Table 4 ). Table 4 Results of respiratory tract differential flora in coal mine dust group and silicon dioxide group in lung lavage fluid samples of rats Common differential flora CMD SiD Bacteria Increased Decreased Betaproteobacteriales Increased Decreased Burkholderiaceae Increased Decreased Table 5 Differential metabolites of lung lavage fluid in coal mine dust group and control group NO. Metabolite VIP P Fold Change Metabolic pathway 1 1-Stearoyl-2-hydroxy-sn-glycerol-3-phosphate choline 1.7236 0.0064 0.4786 2 7-oxazosterol 1.7166 0.0144 0.5976 3 Pantothenic acid 1.4486 0.0493 0.6646 4 Cer(d18:1/18:1(9Z)) 1.7597 0.0013 0.6669 5 stearic acid 1.7415 5.60962E-06 5.4589 Unsaturated fatty acid biosynthesis; Fatty acid biosynthesis Table 6 Differential metabolites of lung lavage fluid in silica group and control group No. metabolite VIP P Fold Change Metabolic pathway 1 1-Stearoyl-2-arachidonic-sn-glycerol 1.3216 0.0072 0.0571 2 Acetyl carnitine 1.2766 0.0129 0.1157 3 Uracil 1.1762 0.0215 0.1230 Pyrimidine metabolism, pantothenic acid and CoA biosynthesis β- Alanine metabolism 4 Cytidine 1.2466 0.0173 0.2000 Pyrimidine metabolism 5 N-acetylglutamine 1.2772 0.0063 0.2222 6 L-carnitine 1.2473 0.0091 0.2369 7 betaine 1.2690 0.0111 0.2927 Glycine, serine and threonine metabolism 8 Thioeramide PC 1.2327 0.0499 0.3029 9 Uridine 1.2808 0.0333 0.3055 Pyrimidine metabolism 10 (3-carboxypropyl) trimethylammonium cation 1.2579 0.0073 0.3420 11 Erucic acid amide 1.1786 0.0291 0.4228 12 l-glutamic acid 1.2187 0.0180 0.4236 Nitrogen metabolism, aminoacetyl tRNA biosynthesis, histidine metabolism, D-glutamine and D-glutamic acid metabolism, butyrate metabolism, alanine aspartic acid and glutamic acid metabolism, glutathione metabolism, porphyrin and chlorophyll metabolism, arginine and proline metabolism 13 Cyclohexylamine 1.1589 0.0431 0.4351 14 Erucic acid 1.1593 0.0427 0.4380 Unsaturated fatty acid biosynthesis 15 Phytic acid 1.1722 0.0213 0.5161 16 N-Palmitoylsphingosine 1.2536 0.0046 0.5230 17 PC(16:0/16:0) 1.2937 0.0016 0.5305 18 glycerol 1.2341 0.0045 1.8620 Glycerol metabolism, galactose metabolism 4. Analysis of the metabonomics in lung lavage fluid Most of the horizontal sequencing length was distributed between 400-440bp (Fig. 4 A). It can be seen from Venn diagramthat in the control group, there are 1098 OTUs in the lung lavage fluid microorganisms, 1127 and 1148 OTUs in the coal mine dust group and silicon dioxide group respectively (Fig. 4 B). In the control group and the coal dust group, there are 863 microbial OTUs in total, including 264 OTUs in the coal dust group alone and 235 in the control group alone. In the control group and silica group, there are 854 OTUs with the same microorganism, among which, there are 294 OTUs in the silica group and 244 in the control group. At other classification levels (phyla, order, family, genus and species), the number of OTUs among the three groups is not statistically significant (Fig. 4 C). Figure 4 D show that the curve becomes gentle when the sequencing amount is about 10000, indicating that the number of species in the group will not increase with the increase of the sequencing amount. There was no significant difference in Ace, Shannon, Simpson index of the lung lavage fluid microbial community among the three groups ( P > 0.05). In terms of the Chao1 index, compared with the control group, it was found that the Chao1 index in the silicon dioxide group was significantly higher, with statistical significance ( P < 0.05) (Fig. 4 E &F ). The abundance of Bifidobacteriales, Bifidobacteriaceae and Bacteria in the respiratory tract flora of the lung lavage fluid in the silica group was higher than that the control group. The abundance of Proteobacteria and Burkholderiae were significantly lower than that the control group. The results of respiratory tract differential flora shared by silica group and coal dust group in rat lung lavage fluid samples showed that β- Proteobacteria and Burkholderiaceae are respiratory tract differential flora shared by coal dust group and silica group (Table 4 ). 5. Correlation Analysis of Differential Microflora and Differential Metabolites Total of 510 metabolites were detected and the total ion current chromatogram of lung lavage solution QC sample ( Supplementary Fig. 1A ). The PCA score of results indicates that the sequencing data has high quality and stability and can be used for subsequent analysis ( Supplementary Fig. 1B ). The metabolites of lung lavage fluid in the coal dust group and the control group were completely separated (R 2 X = 0.585, R 2 Y = 0.985, Q 2 = 0.813), which showed that there was significant difference in the metabolic characterization of lung lavage fluid between the two groups. The lung lavage fluid samples of rats in the silicon dioxide group and the control group can be obviously distinguished (R 2 X = 0.786, R 2 Y = 0.992, Q 2 = 0.851), which shows that there is also a significant difference in the metabolic characterization of lung lavage fluid ( Supplementary Fig. 2A&B ). The metabolic network of lung lavage fluid changes significantly in these two groups. In the silica group and the control group, the lung lavage fluid samples also did not cross, indicating that the metabolic network of lung lavage fluid also changed significantly in these two groups (Fig. 5 A &B ). Among the differential flora and metabolites of coal mine dust group and control group, Pasteurella_ sp._ V6 is positively correlated with stearic acid, Rodentibacter is negatively correlated with Cer (d18:1/18:1 (9Z)), and uncultured_ bacterium_ Rodentibacter was negatively correlated with Cer (d18:1/18:1 (9Z)), 1-Stearoyl-2-hydroxy-sn-glycorol-3-phopholine, Muribacter was positively correlated with Pantothenol and Cer (d18:1/18:1 (9Z)), Muribacter_ Multis is positively correlated with Pantothenol and Cer (d18:1/18:1 (9Z)), uncultured_ bacterium_ Stenotrophomonas is negatively correlated with Stearic acid(Fig. 5 C). Among the differential microflora and metabolites of silica group and control group, Pasteurella_ sp._ V6 is positively correlated with Glycerol, Streptococcus_ Agalaciae is negatively correlated with Uracil, Phytic acid, PC (16:0/16:0), L-Carnitine, Erucamide, and (3-Carboxyproxy) trimethylammonium cation (Fig. 5 D). Among the differential bacteria and metabolites in the coal dust group and the control group, Mycoplasma were positively correlated with Stearic acid. Gammaproteobateria is negatively correlated with Cer (d18:1/18:1 (9Z)). Betaproteobateriales is negatively correlated with Pantothenol and Cer (d18:1/18:1 (9Z)), Burkholderiae is negatively correlated with Cer (d18:1/18:1 (9Z)), 7-Oxycholesterol, 1-Stearoyl-2-hydroxy-sn-glyco-3-phopholine (Fig. 5 E). Among the differential microflora and metabolites in the silica group and the control group, Bifidobacteriales were positively correlated with Uracil. Bifidobacteriaceae was positively correlated with Uracil. Betaproteobacteriales are negatively correlated with Uridine, Thioetheramide PC, N-Palmitoylsphingosine, N-Acetylglutamine, L-Glutamate, Cytidine, Cyclohexylamine, Acetylcarnitine, 1-Stearoyl-2-arachidonoyl-sn glycerol. Burkholderia cea was positively correlated with Glycerol. Burkholderiaceae is negatively correlated with Uridine, Thioetheramide PC, L-Glutamate, Cyclohexylamine, Acetylcarnitine, 1-Stearoyl-2-arachidonoyl-sn glycerol and Glycerol (Fig. 5 F). Discussion Nowadays, the microflora is increasingly becoming a hot topic of research for many diseases. Influenced by the external environment, the respiratory flora is in a long-term dynamic change and it maintains a relatively stable state, under the influence of the external environment. Furthermore, in dynamic change and maintain a relative homeostasis, which can effectively prevent the emergence of pathogenic bacteria colonization in the respiratory tract, is an important basis for respiratory health and stability. According to research, imbalances in the airway flora can cause immune dysfunction and changes in body metabolism, leading to a variety of acute and chronic respiratory diseases. As pneumoconiosis is a chronic respiratory disease, the respiratory flora may also play a important role in it. Therefore, this paper investigates the relationship between pneumoconiosis and respiratory microflora and metabolites based on the construction of a rat model of pneumoconiosis. In this study, we performed high-throughput sequencing of the respiratory flora of rats in different treatment groups after 24 w of dust exposure using 16SrDNA technology, and the sequencing results showed that the depth of sequencing met the experimental requirements. The results of our ɑ and β diversity analysis showed that the respiratory microflora of rats could be clearly distinguished between the different groups, with significant differences in microbial species diversity, suggesting that exposure of rats to dust causes disturbances in their respiratory flora and that respiratory flora may play a role in the development of pneumoconiosis. 5 differential metabolites were screened in lung lavage fluid samples and 18 differential metabolites were identified in rat lung lavage fluid from the silica group in the coal mine dust and control groups. Further topological analysis of the metabolic pathways in which these metabolic markers were located showed that there were two pathways with significant changes, for pyrimidine metabolism, D-glutamine and D-glutamate metabolism. We therefore hypothesize that these are the key metabolic pathways in the development of pneumoconiosis and will focus on these two pathways and the differential metabolites involved. Also, in conjunction with the previous findings on differential respiratory flora, we performed a Spearman correlation analysis of respiratory flora and metabolomic changes in this study. The results showed that L-glutamate, involved in D-glutamine and D-glutamate metabolism, was significantly down-regulated and negatively correlated with Betaproteobacteriales, Burkholderiaceae. Cytidine involved in pyrimidine metabolism was significantly down-regulate and negatively correlated with Betaproteobacteriales. Uridines significantly down-regulated in pyrimidine metabolism were negatively correlated with Betaproteobacteriales, Burkholderiaceae. Uridines significantly down-regulated in the metabolism of pyrimidines were negatively correlated with Streptococcus_agalactiae and positively correlated with Bifidobacteriales, Bifidobacteriaceae. These suggest that the metabolites and associated metabolic pathways in our lungs may be influenced by changes in the lung flora and subsequently have some impact on the host metabolic phenotype. Conclusions We found that exposure to dust may cause disturbance of respiratory tract flora and metabolism. This study has demonstrated that pneumoconiosis is associated with dysbiosis and metabolic disorders of the respiratory flora. Dysregulation of Betaproteobacteriales, Burkholderiaceae, Bifidobacteriales, Bifidobacteriaceae, Streptococcus_agalactiae was asscoiated with the occurrence of pyrimidine metabolism, D-glutamine and D-glutamate metabolism abnormalities in the development of pneumoconiosis. Declarations Data availability statement The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics statements Not applicable. Author Contribution Wei Gao and Han Hao: Development or design of methodology, creation of models, Data Curation, Validation, Investigation, Resources, Writing - Original Draft.Shuyu Xiao: Investigation, Software, Conceptualization.Xu Zhang, Peng Wang: Data Curation, Visualization, Investigation.Heliang Liu, Nan Liu, Yulan Jin, Jinlong Li, Xiaoming Li: Verification, Ideas, software development, Application of statistical.Fuhai Shen: Conceptualization, Methodology, Validation, Writing - Review & Editing, Supervision, Project administration, Funding acquisition. Data Availability The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. References Min, C. Y. et al. 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PLoS One . 17 (12), e0268730 (2022). Dickson, R. P., Erb-Downward, J. R. & Huffnagle, G. B. The role of the bacterial microbiome in lung disease. Expert Rev. Respir Med. 7 (3), 245–257 (2013). Salisbury, M. L. et al. Microbiome in interstitial lung disease: from pathogenesis to treatment target. Curr. Opin. Pulm Med. 23 (5), 404–410 (2017). Boyton, R. J. & Altmann, D. M. Bronchiectasis: Current Concepts in Pathogenesis, Immunology, and Microbiology. Annu. Rev. Pathol. 11 , 523–554 (2016). Callejón-Leblic, B. et al. Metabolomic study of serum, urine and bronchoalveolar lavage fluid based on gas chromatography mass spectrometry to delve into the pathology of lung cancer. J. Pharm. Biomed. Anal. 163 , 122–129 (2019). Esther, C. R. Jr. et al. Metabolomic Evaluation of Neutrophilic Airway Inflammation in Cystic Fibrosis. Chest 148 (2), 507–515 (2015). Luxon, B. A. Metabolomics in asthma. Adv. Exp. Med. Biol. 795 , 207–220 (2014). Nobakht, M. G. B. F. et al. The metabolomics of airway diseases, including COPD, asthma and cystic fibrosis. Biomarkers 20 (1), 5–16 (2015). Ren, X. et al. Comparative effects of dexamethasone and bergenin on chronic bronchitis and their anti-inflammatory mechanisms based on NMR metabolomics. Mol. Biosyst . 12 (6), 1938–1947 (2016). Rogers, A. J. & Matthay, M. A. Applying metabolomics to uncover novel biology in ARDS. Am. J. Physiol. Lung Cell. Mol. Physiol. 306 (11), L957–L961 (2014). Wendt, C. H. et al. Peptides in Bronchoalveolar Lavage in Chronic Obstructive Pulmonary Disease. PLoS One . 11 (5), e0155724 (2016). Wang, H. et al. Exploration study on serum metabolic profiles of Chinese male patients with artificial stone silicosis, silicosis, and coal worker's pneumoconiosis. Toxicol. Lett. 356 , 132–142 (2022). Additional Declarations No competing interests reported. Supplementary Files S1.jpg S2.jpg Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Editor invited by journal 09 Sep, 2025 Submission checks completed at journal 29 Aug, 2025 First submitted to journal 29 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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19:15:20","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126271,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/bb4536c4b0fd6684932ed3a4.html"},{"id":92207179,"identity":"d592ca22-89af-4692-91e5-f9209807fd64","added_by":"auto","created_at":"2025-09-25 19:07:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289065,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathological changes of lung tissue in rats of three groups after 24 weeks of exposure to dust.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e): control group. (\u003cstrong\u003eB\u003c/strong\u003e): Coal mine dust group. (\u003cstrong\u003eC\u003c/strong\u003e): Silica group.\u003c/p\u003e","description":"","filename":"Slide1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/1165d035a9a21d022eba37c1.jpg"},{"id":92207180,"identity":"23dd9164-1f09-4599-a932-8e60d51af1c5","added_by":"auto","created_at":"2025-09-25 19:07:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of 16SrDNA upper respiratory tract flora.detection results.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) : Length Distribution of Valid Sequences. (\u003cstrong\u003eB\u003c/strong\u003e): Coal mine dust group VS control group. (\u003cstrong\u003eC\u003c/strong\u003e): Silicon dioxide group VS control group.The circle indicates the number of OTUs of rat throat swab microorganisms in different groups. The overlapping position indicates the number of the same OTUs in common, and the other positions indicate the number of the OTUs in each group. (\u003cstrong\u003eD\u003c/strong\u003e): Dilution curve of three groups of throat swab samples. The abscissa and ordinate are the number of sequences randomly selected and the number of OTUs obtained by clustering, and the samples in each group are marked with different color curves. (\u003cstrong\u003eE\u003c/strong\u003e) Coal mine dust group VS control group. (\u003cstrong\u003eF\u003c/strong\u003e) Silicon dioxide group VS control group. PCoA analysis of microbial communities of three groups of throat swabs. (\u003cstrong\u003eG\u003c/strong\u003e) Distribution histogram of LDA value of respiratory tract flora of throat swabs in coal mine dust group and control group. (\u003cstrong\u003eH\u003c/strong\u003e) Evolution Branch Diagram of Respiratory Tract Microflora LEfSe of Swab in Coal Mine Dust Group and Control Group. (\u003cstrong\u003eI\u003c/strong\u003e) Histogram of LDA value distribution of respiratory tract flora of throat swabs in silica group and control group. (\u003cstrong\u003eJ\u003c/strong\u003e) Evolution Branch Diagram of Respiratory Tract Microflora LEfSe of Pharynx Swab in Silicon Dioxide Group and Control Group.\u003c/p\u003e","description":"","filename":"Slide2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/b315aa6c9e3f8df62c68cc4e.jpg"},{"id":92207819,"identity":"f92c4010-d435-4328-b7bd-fadaba65fc4a","added_by":"auto","created_at":"2025-09-25 19:15:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of 16SrDNA lower respiratory tract flora detection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) \u003cstrong\u003e\u0026nbsp;(A) \u003c/strong\u003eLength Distribution of Valid Sequences. (\u003cstrong\u003eB\u003c/strong\u003e) The common and unique OTU of respiratory tract flora of lung lavage fluid in coal mine dust group vs control group. (\u003cstrong\u003eC\u003c/strong\u003e) The common and unique OTU in respiratory tract flora of lung lavage fluid in silica group vs control group. The circle represents the number of OTUs of rat throat swab microorganisms in different groups. The overlapping position of the two circles represents the same number of OTUs in common, and the other positions represent the number of OTUs that each group has separately.(\u003cstrong\u003eD\u003c/strong\u003e): Dilution curve of three groups of throat swab samples. The abscissa and ordinate are the number of sequences randomly selected and the number of OTUs obtained by clustering, and the samples in each group are marked with different color curves. (\u003cstrong\u003eE\u003c/strong\u003e) Coal mine dust group VS control group. (\u003cstrong\u003eF\u003c/strong\u003e) Silicon dioxide group VS control group. PCoA analysis of microbial communities of three groups of throat swabs. (\u003cstrong\u003eG\u003c/strong\u003e) Distribution histogram of LDA value of respiratory tract flora of throat swabs in coal mine dust group and control group. (\u003cstrong\u003eH\u003c/strong\u003e) Evolution Branch Diagram of Respiratory Tract Microflora LEfSe of Swab in Coal Mine Dust Group and Control Group. (\u003cstrong\u003eI\u003c/strong\u003e) Histogram of LDA value distribution of respiratory tract flora of throat swabs in silica group and control group. (\u003cstrong\u003eJ\u003c/strong\u003e) Evolution Branch Diagram of Respiratory Tract Microflora LEfSe of Pharynx Swab in Silicon Dioxide Group and Control Group.\u003c/p\u003e","description":"","filename":"Slide3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/0e3e4ecf9a9e782144499d7d.jpg"},{"id":92207816,"identity":"8d1dc0ed-224f-462f-9da7-a92d3f6f5b90","added_by":"auto","created_at":"2025-09-25 19:15:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the metabonomics in lung lavage fluid. (A) \u003c/strong\u003ePCA score chart of lung lavage fluid samples from coal mine dust group vs control group\u003cstrong\u003e. (B) \u003c/strong\u003ePCA score chart of lung lavage fluid samples in silica group vs control group. (\u003cstrong\u003eC\u003c/strong\u003e) Replacement test results of OPLS-DA model of lung lavage fluid samples from coal mine dust group and control group. (\u003cstrong\u003eD\u003c/strong\u003e) Replacement test results of OPLS-DA model of rat lung lavage fluid samples in silica group and control group. (\u003cstrong\u003eE\u003c/strong\u003e) Volcanic map of differential metabolite screening of lung lavage fluid samples of rats in coal mine dust group versus control group. (\u003cstrong\u003eF\u003c/strong\u003e) Volcanic diagram of differential metabolite screening of lung lavage fluid samples of rats in silica group versus control group.The abscissa represents the multiple change of the relative content of each sample, and the ordinate represents the corresponding P value after the t-test, which is finally represented by - log P. Each dot represents the metabolite, its size represents the VIP value in the OPLS-DA model, and its color represents the final screening result: red and green represent significant increase and decrease, respectively, and gray represents no difference.\u003c/p\u003e","description":"","filename":"Slide4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/171cf25c5898028daa97b877.jpg"},{"id":92207817,"identity":"0b54ba35-cdf0-41c1-89cc-7f089cfceb1a","added_by":"auto","created_at":"2025-09-25 19:15:20","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":189213,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Analysis of Differential Microflora and Differential Metabolites.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Pathway analysis diagram of lung lavage fluid samples of rats in coal mine dust group versus control group. (\u003cstrong\u003eB\u003c/strong\u003e) Pathway analysis diagram of lung lavage fluid samples of rats in silica group vs control group. (\u003cstrong\u003eC\u003c/strong\u003e) Thermogram of correlation between differential bacterial flora of upper respiratory tract and differential metabolites of lung lavage fluid in coal mine dust group and control group. (\u003cstrong\u003eD\u003c/strong\u003e) Thermogram of correlation between differential flora of upper respiratory tract and differential metabolites of lung lavage fluid in silica group and control group. (\u003cstrong\u003eE\u003c/strong\u003e) Correlation thermogram of lower respiratory tract differential flora and pulmonary lavage fluid differential metabolite in coal mine dust group and control group. (\u003cstrong\u003eF\u003c/strong\u003e) Thermogram of correlation between lower respiratory tract differential flora and pulmonary lavage fluid differential metabolite in silica group and control group.\u003c/p\u003e","description":"","filename":"Slide5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/c9c25daeed3c2ae6db727daf.jpg"},{"id":99172291,"identity":"d16f1ed6-2dab-48ca-b356-5590c5fab0cb","added_by":"auto","created_at":"2025-12-29 16:07:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2309702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/45c8afee-008f-4ae1-9db9-9bb570b6659a.pdf"},{"id":92207178,"identity":"25e04c49-ef3d-43dc-bff1-7928a6a724a0","added_by":"auto","created_at":"2025-09-25 19:07:20","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":77044,"visible":true,"origin":"","legend":"","description":"","filename":"S1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/b6eee33f29de52eb188fdf48.jpg"},{"id":92207181,"identity":"d79b2ab6-7738-4869-929c-2e751c3c3cdc","added_by":"auto","created_at":"2025-09-25 19:07:20","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":68182,"visible":true,"origin":"","legend":"","description":"","filename":"S2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7362557/v1/a5566e82e8246fe79a8267c2.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differences of respiratory tract flora and metabonomic characteristics of lung lavage fluid in pneumoconiosis model rats","fulltext":[{"header":"What is already known on this topic.","content":"\u003cp\u003eMcrobial diversity may be closely related to cardiorespiratory health.\u003c/p\u003e\n\u003cp\u003eThe progress of pneumoconiosis has changed the metabolic state of the body.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study adds\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study identified the main differential flora and main differential metabolites of pneumoconiosis.\u003c/p\u003e\n\u003cp\u003eThis study has proved the correlation between bacterial flora disorder and metabolic changes in pneumoconiosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow this study might affect research, practice or policy\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study identified the main differential flora and main differential metabolites of pneumoconiosis.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAccording to the 2021 statistical bulletin on China's health and health development, a total of 11809 cases of occupational pneumoconiosis were reported in China in 2021, and from the distribution of diseases, occupational pneumoconiosis was the most common, mainly silicosis and coal workers' pneumoconiosis\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePneumoconiosis is a disease caused by the prolonged inhalation of production dust in some occupations and its retention in the lungs, leading to diffuse fibrosis of the lungs\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Due to the prolonged exposure to productive dust, pneumoconiosis often results in a variety of complications, such as respiratory infections (mainly of the lungs), spontaneous pneumothorax, chronic obstructive pulmonary diseases (COPD), etc. At present, the pathogenesis of pneumoconiosis has not been thoroughly studied and there is no cure for pneumoconiosis, so the main clinical treatment for pneumoconiosis today is medication or whole-lung lavage (WLL). Therefore, some scholars have suggested that a new way of thinking is needed in the clinical management of pneumoconiosis, especially in terms of new treatment strategies\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIt has been suggested that microbial diversity may be closely related to cardiorespiratory health \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Many studies have shown that respiratory micro-ecological dysbiosis and changes in the structure of the flora may be associated with the development of disease, and the role of the respiratory flora on the immune system and disease is receiving increasing attention \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The relative abundance of Haemophilus spp., Moraxella spp. and Pseudomonas spp. has been found to increase significantly during acute exacerbations in COPD patients \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. A similar situation exists in some chronic diseases like interstitial pneumonia \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e and bronchiectasis \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMetabolomics changes in organisms in disease states at a holistic level. It has been shown to not only reflect the pathological state of lung diseases, but also to have an invaluable role in the early diagnosis of diseases and the study of their pathogenesis, among other conditions. Studies have shown that in many lung diseases such as asthma, COPD, pulmonary cystic fibrosis, acute respiratory distress syndrome and lung cancer, significant changes occur in some of the major metabolites such as choline phosphate, phenylalanine, glutamine, malate, alanine, hydroxybutyrate, lactate, acetone, methanol, taurine, leukotrienes etc\u003csup\u003e[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, studies have estimated that the progression of pneumoconiosis impairs the lung function of pneumoconiosis patients, destroys the lung parenchyma and also leads to abnormalities in the blood vessels of the lungs, recurrent lung infections causing narrowing of the airways, resulting in disruption of the pulmonary ventilation blood flow ratio, which accelerates the rate of ATP degradation and ultimately leads to increased uric acid production\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe used animal experiments to collect experimental samples and applied 16SrDNA gene sequencing technology and metabolomics research methods to detect and analyze the changes of respiratory flora and lung lavage fluid metabolites in experimental animals to explore their role in the development of pneumoconiosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Subjects and materials\u003c/h2\u003e\u003cp\u003e(1) The experimental subjects were 18 SPF-grade active SD rats, all males, weighing between 180\u0026ndash;200 g, from the Experimental Animal Centre of North China University of Technology. The rats were housed in clean and ventilated rooms, provided with suitable clean water and feed, and their cages were cleaned regularly to avoid contamination. All animal experimental protocols were reviewed and approved by the Animal Care Welfare Committee of North China University of Science and Technology (Approval No: 2016086). All methods were carried out in accordance with the relevant guidelines and regulations of North China University of Technology.\u003c/p\u003e\u003cp\u003e(2) Dust is taken from the Handan coal mine development area.\u003c/p\u003e\u003cp\u003e(3) Purchase 500 g of silica dust (Sigma, USA).\u003c/p\u003e\u003cp\u003e(4) Euthanasia was performed under deep anesthesia with intraperitoneal injection of pentobarbital sodium (150 mg/kg), followed by exsanguination to ensure death.\u003c/p\u003e\u003cp\u003eAll methods are reported in accordance with the ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003cspan address=\"https://arriveguidelines.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the reporting of animal experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Screening of dust and configuration of suspensions\u003c/h2\u003e\u003cp\u003e(1) Screening of dust: The settled dust is collected in the underground pioneering area of the Handan coal mine and is initially sieved using a sieve of appropriate aperture. The sieve (400 mesh) is placed in a device containing ultrapure water. The dust is placed on the 400 mesh sieve and the sieve is shaken to obtain a suspension.\u003c/p\u003e\u003cp\u003e(2) Preparation of dust suspension: the dust and silica dust were weighed accurately at 2 g each and 40 ml of sterile saline was added separately to prepare a final concentration of 50 mg/mL dust suspension. The suspension is sealed with newspaper and then sterilized in a sterilizer.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Preparation of H\u0026amp;E-stained sections of lung tissue\u003c/h2\u003e\u003cp\u003e(1) Filming and sectioning: Lung tissues fixed in formaldehyde are dehydrated and treated with transparency, then waxed and embedded. The sections were sliced using a microtome to a thickness of 4\u0026ndash;5 \u0026micro;m and placed on clean slides and dried at 60\u0026deg;C.\u003c/p\u003e\u003cp\u003e(2) H\u0026amp;E staining: the prepared sections were placed in xylene 1\u0026ndash;3 for 5 min, in ethanol solutions with a concentration gradient of 70%, 80%, 90% and 100% for the same 5 min, the sections were rinsed in running water for 3 min, then placed in hematoxylin for 5 min, rinsed again in running water for 2 s, divided in 1% hydrochloric acid alcohol. The sections were again rinsed in running water for 2 s and then placed in 85%, 90% and 100% ethanol solutions for dehydration, which were dried and sealed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Respiratory flora diversity testing and analysis\u003c/h2\u003e\u003cp\u003e(1) DNA extraction from pharyngeal swabs and lung lavage fluid samples: the study was carried out by following the instructions in the Magnetic Beads Universal Genomic DNA Extraction Kit to extract DNA from pharyngeal swabs and lung lavage fluid.\u003c/p\u003e\u003cp\u003e(2) PCR amplification\u003c/p\u003e\u003cp\u003eThe extracted DNA was used to determine its purity (OD: 1.6-2.0) and the corresponding concentration using an enzyme marker. The qualified DNA was selected for 16SrDNA PCR amplification, with amplification regions V3-V4, and needed to be re-extracted if it did not meet the requirements. Primers were used 338F: 5'-ACTCCTACGGGAGGCAGCA-3' and 806R: 5'- GGACTACHVGGGTWTCTAAT-3'. Gel electrophoresis was performed to check the integrity of the amplified fragments.\u003c/p\u003e\u003cp\u003e(3) Library construction and sequencing assay processing\u003c/p\u003e\u003cp\u003eThe PCR amplification products were purified, quantified, homogenised and libraries were constructed and sequenced on the Illumina HiSeq 2500 platform after passing quality control.\u003c/p\u003e\u003cp\u003e(4) Bacterial flora test data\u003c/p\u003e\u003cp\u003eThe double-ended sequence data obtained from Hiseq sequencing were spliced (merged) into one sequence Tags with reference to the Overlap linkage between PE reads, while the quality of Reads and Merge effect were given quality control filters to obtain the final valid data. A series of statistical analyses were then performed on the final validated data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.5 Metabolomic assay of lung lavage fluid and analysis\u003c/h2\u003e\u003cp\u003e(1) Extraction of metabolites from lung lavage fluid samples\u003c/p\u003e\u003cp\u003e(2) Metabolomics testing\u003c/p\u003e\u003cp\u003eThe target compounds were separated on an Agilent 1290 UPLC with a Waters ACQUITY UPLC BEH Amide (2.1*100 mm, 1.7 \u0026micro;m) column. The aqueous phase was the A phase of the liquid chromatography, which consisted of ammonium acetate and ammonia, each at 25 mmol/L. Acetonitrile was chosen as the B phase. The gradient elution was as follows: 0\u0026thinsp;~\u0026thinsp;0.5 min, 95% B; 0.5\u0026thinsp;~\u0026thinsp;7 min, 95%~65% B; 7\u0026thinsp;~\u0026thinsp;8 min, 65%~40% B; 8\u0026thinsp;~\u0026thinsp;9 min, 40% B; 9\u0026thinsp;~\u0026thinsp;9.1 min, 40%~95% B; 9.1\u0026thinsp;~\u0026thinsp;12 min, 95% B. The flow rate was set at 0.5 mL per minute, the column temperature at 25℃ and the sample tray temperature at 4℃. The sample volume for each positive and negative ion was 2 \u0026micro;L.\u003c/p\u003e\u003cp\u003eThe Triple TOF 6600 high resolution mass spectrometer was selected for the mass spectrometry data collection and the mode was set to IDA (Information-dependent acquisition). The data collection software (Analyst TF 1.7, AB Sciex) was used to autonomously select ions for secondary mass spectrometry with reference to the primary mass spectrometry parameters and previously set indicators, and the 12 most intense ions with mass fractions above 100 in each cycle were scanned for secondary mass spectrometry with collision energy and cycle time set to 30 eV and 0.56 s. The ion source parameters were set as follows GS1: 60 psi, GS2: 30 psi, CUR: 35 psi, TEM: 600\u0026deg;C, DP: 60 V, ISVF: 5000 V(Pos)/-4000 (Neg).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e1.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eThe data collected were processed and analyzed using the software SPSS 23.0. Data were represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\pm\\:s\\)\u003c/span\u003e\u003c/span\u003e), tested by one-way variance or rank sum test, and correlation analysis was performed using the Spearman statistical method, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating a statistically significant difference.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Dust causes silicosis in rats\u003c/h3\u003e\n\u003cp\u003eWith the prolongation of dust exposure time, the rats in the coal mine dust group and the silicon dioxide group had dull and lusterless fur, slightly decreased weight, significantly decreased activity, and slightly sluggish reaction. At the same time, the rats had symptoms of respiratory aggravation, but there was no abnormality in daily drinking water. H\u0026amp;E pathological sections of rats showed that the lung tissue structure of the coal mine dust group was seriously damaged, and there were a lot of inflammatory reactions, accompanied by many fibroblast aggregation, thus forming cell nodules (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In the silica group, the alveolar structure was also damaged to a certain extent, and the inflammatory reaction was aggravated, with obvious silicon nodules. The lung tissue structure of the control group is normal, with a small amount of inflammatory reaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e2. Analysis of SrDNA upper respiratory tract flora\u003c/h3\u003e\n\u003cp\u003eThe sequencing length was mostly distributed between 400-440bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). It can be seen from Venn diagram that in the control group, there are 663 Operational Taxonomic Units (OTUs) for throat swab microorganisms, and 633 and 531 OTUs for coal dust group and silicon dioxide group, respectively. In the control group and the coal dust group, there are 573 microbial OTUs in total, among which, there are 60 OTUs in the coal dust group alone, and 90 OTUs in the control group alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Compared with the control group, there are 502 OTUs in the two groups of rat throat swab microorganisms, 29 in the silicon dioxide group and 161 in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). At other classification levels (phyla, class, order, family, genus), the number of OTUs among the three groups was not statistically significant (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eⱭ Simpson, Chao1, Ace and Shannon are commonly used indicators of diversity. Among them, Chao1 and Ace are mainly used to reflect species richness, while Simpson and Shannon focus more on community evenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The results showed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that there was no significant difference in Chao1, Ace, Shannon and Simpson indexes of rat throat swab microbial communities among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). β diversity analysis refers to indirectly reflecting the diversity of species abundance distribution according to the distance between samples. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u003cb\u003e\u0026amp;F\u003c/b\u003e, when PC1\u0026thinsp;=\u0026thinsp;46.25%, the coal mine dust group and the control group are separated into two non interfering microbial communities on the left and right. When the principal coordinate PC1\u0026thinsp;=\u0026thinsp;34.65%, the silica group and the control group were separated into two non overlapping microbial communities. According to the LDA value distribution histogram and LEfSe analysis evolutionary branching diagram, the abundance of Pasteurella sp. _V6, Bacteria, and uncultured Rodentibacter in the respiratory tract flora of rat throat swabs in the coal mine dust group is lower than the control group, while that of Muribacter_and the abundance of multi and uncultured oligomonas was significantly higher than the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG\u003cb\u003e\u0026amp;H\u003c/b\u003e). The abundance of Pasteurella sp. _V6 and Streptococcus agalactiae in the respiratory tract flora of the throat swabs of rats in the silica group was significantly lower than the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI\u003cb\u003e\u0026amp;J\u003c/b\u003e). It can be seen from the table that Pasteurella is a respiratory tract differential flora shared by the coal dust group and the silicon dioxide group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMicrobes in Swab of Rats in Three Groups ɑ Diversity index statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChao1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShannon\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSimpson\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e542.84\u0026thinsp;\u0026plusmn;\u0026thinsp;50.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e544.94\u0026thinsp;\u0026plusmn;\u0026thinsp;44.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e551.89\u0026thinsp;\u0026plusmn;\u0026thinsp;36.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e559.02\u0026thinsp;\u0026plusmn;\u0026thinsp;46.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSiD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e474.25\u0026thinsp;\u0026plusmn;\u0026thinsp;43.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e502.50\u0026thinsp;\u0026plusmn;\u0026thinsp;47.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNS: control, CMD: coal mine dust, SiD: SiO2 dust.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eResults of respiratory tract differential flora in throat swabs of rats from coal mine dust group and silicon dioxide group\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommon differential flora\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCMD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSiD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePasteurella_sp._V6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecreased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncreased\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNS: control, CMD: coal mine dust, SiD: SiO2 dust.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMicrobes in Swab of Rats in Three Groups ɑ Diversity index statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChao1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShannon\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSimpson\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1005.68\u0026thinsp;\u0026plusmn;\u0026thinsp;87.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e997.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1063.24\u0026thinsp;\u0026plusmn;\u0026thinsp;84.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1016.47\u0026thinsp;\u0026plusmn;\u0026thinsp;22.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSiD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1111.18\u0026thinsp;\u0026plusmn;\u0026thinsp;110.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1045.71\u0026thinsp;\u0026plusmn;\u0026thinsp;13.29\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNS: control, CMD: coal mine dust, SiD: SiO2 dust.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e3. Analysis of 16SrDNA lower respiratory tract flora detection\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMost of the horizontal sequencing length was distributed between 400-440bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). It can be seen from Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e\u0026amp;C\u003c/b\u003e) that in the control group, there are 1098 OTUs in the lung lavage fluid microorganisms, 1127 and 1148 OTUs in the coal mine dust group and silicon dioxide group respectively. In the control group and the coal dust group, there are 863 microbial OTUs in total, including 264 OTUs in the coal dust group alone and 235 in the control group alone. In the control group and silica group, there are 854 OTUs with the same microorganism, among which, there are 294 OTUs in the silica group and 244 in the control group. It can be seen from the results that the number of OTUs in the silica group is higher than that in the control group at the class classification level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe dilution curve indicats that the number of species in the group will not increase with the increase of the sequencing amount (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). There was no significant difference in Ace, Shannon, Simpson index of the lung lavage fluid microbial community among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In terms of the Chao1 index, compared with the control group, it was found that the Chao1 index in the silicon dioxide group was significantly higher, with statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e\u0026amp;F\u003c/b\u003e, when the principal coordinate PC1\u0026thinsp;=\u0026thinsp;39.32%, the coal mine dust group and the control group are separated into two non interfering microbial communities on the left and right. When PC1\u0026thinsp;=\u0026thinsp;39.01%, the silica group and the control group were separated into two non intersecting microbial communities, indicating that the microbial communities of the coal dust group and the silicon dioxide group are significantly different.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe abundance of Mycoplasma, Gammaproteobateria, Betaproteobateriales and Burkholderiae in the respiratory tract flora of lung lavage fluid of rats in the coal mine dust group was lower than that in the control group, but the abundance of Bacteria was significantly higher than that in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG\u003cb\u003e\u0026amp;H\u003c/b\u003e). The abundance of Bifidobacteriales, Bifidobacteriaceae and Bacteria in the respiratory tract flora of the lung lavage fluid in the silica group was higher than that in the control group, the abundance of Proteobacteria and Burkholderiae were significantly lower than that of the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI\u003cb\u003e\u0026amp;J\u003c/b\u003e). β- Proteobacteria and Burkholderiaceae are respiratory tract differential flora shared by coal dust group and silica group, wherein Bacteria is up-regulated in both groups, while β- Proteobacteria and Berberidaceae were down regulated (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of respiratory tract differential flora in coal mine dust group and silicon dioxide group in lung lavage fluid samples of rats\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommon differential flora\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCMD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSiD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncreased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDecreased\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetaproteobacteriales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncreased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDecreased\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurkholderiaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncreased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDecreased\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferential metabolites of lung lavage fluid in coal mine dust group and control group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetabolite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFold Change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMetabolic pathway\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1-Stearoyl-2-hydroxy-sn-glycerol-3-phosphate choline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7-oxazosterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePantothenic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.4486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCer(d18:1/18:1(9Z))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003estearic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.60962E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.4589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUnsaturated fatty acid biosynthesis;\u003c/p\u003e\u003cp\u003eFatty acid biosynthesis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferential metabolites of lung lavage fluid in silica group and control group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emetabolite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFold Change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMetabolic pathway\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1-Stearoyl-2-arachidonic-sn-glycerol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcetyl carnitine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUracil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.1762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePyrimidine metabolism, pantothenic acid and CoA biosynthesis β- Alanine metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCytidine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePyrimidine metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN-acetylglutamine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL-carnitine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebetaine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGlycine, serine and threonine metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThioeramide PC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUridine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePyrimidine metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3-carboxypropyl) trimethylammonium cation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eErucic acid amide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.1786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003el-glutamic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNitrogen metabolism, aminoacetyl tRNA biosynthesis, histidine metabolism, D-glutamine and D-glutamic acid metabolism, butyrate metabolism, alanine aspartic acid and glutamic acid metabolism, glutathione metabolism, porphyrin and chlorophyll metabolism, arginine and proline metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyclohexylamine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.1589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eErucic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.1593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUnsaturated fatty acid biosynthesis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhytic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.1722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN-Palmitoylsphingosine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC(16:0/16:0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eglycerol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.8620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGlycerol metabolism, galactose metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e4. Analysis of the metabonomics in lung lavage fluid\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMost of the horizontal sequencing length was distributed between 400-440bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). It can be seen from Venn diagramthat in the control group, there are 1098 OTUs in the lung lavage fluid microorganisms, 1127 and 1148 OTUs in the coal mine dust group and silicon dioxide group respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In the control group and the coal dust group, there are 863 microbial OTUs in total, including 264 OTUs in the coal dust group alone and 235 in the control group alone. In the control group and silica group, there are 854 OTUs with the same microorganism, among which, there are 294 OTUs in the silica group and 244 in the control group. At other classification levels (phyla, order, family, genus and species), the number of OTUs among the three groups is not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD show that the curve becomes gentle when the sequencing amount is about 10000, indicating that the number of species in the group will not increase with the increase of the sequencing amount. There was no significant difference in Ace, Shannon, Simpson index of the lung lavage fluid microbial community among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In terms of the Chao1 index, compared with the control group, it was found that the Chao1 index in the silicon dioxide group was significantly higher, with statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u003cb\u003e\u0026amp;F\u003c/b\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe abundance of Bifidobacteriales, Bifidobacteriaceae and Bacteria in the respiratory tract flora of the lung lavage fluid in the silica group was higher than that the control group. The abundance of Proteobacteria and Burkholderiae were significantly lower than that the control group. The results of respiratory tract differential flora shared by silica group and coal dust group in rat lung lavage fluid samples showed that β- Proteobacteria and Burkholderiaceae are respiratory tract differential flora shared by coal dust group and silica group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e5. Correlation Analysis of Differential Microflora and Differential Metabolites\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTotal of 510 metabolites were detected and the total ion current chromatogram of lung lavage solution QC sample (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). The PCA score of results indicates that the sequencing data has high quality and stability and can be used for subsequent analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e). The metabolites of lung lavage fluid in the coal dust group and the control group were completely separated (R\u003csup\u003e2\u003c/sup\u003eX\u0026thinsp;=\u0026thinsp;0.585, R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;=\u0026thinsp;0.985, Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.813), which showed that there was significant difference in the metabolic characterization of lung lavage fluid between the two groups. The lung lavage fluid samples of rats in the silicon dioxide group and the control group can be obviously distinguished (R\u003csup\u003e2\u003c/sup\u003eX\u0026thinsp;=\u0026thinsp;0.786, R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;=\u0026thinsp;0.992, Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.851), which shows that there is also a significant difference in the metabolic characterization of lung lavage fluid (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u0026amp;B\u003c/b\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe metabolic network of lung lavage fluid changes significantly in these two groups. In the silica group and the control group, the lung lavage fluid samples also did not cross, indicating that the metabolic network of lung lavage fluid also changed significantly in these two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e\u0026amp;B\u003c/b\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAmong the differential flora and metabolites of coal mine dust group and control group, Pasteurella_ sp._ V6 is positively correlated with stearic acid, Rodentibacter is negatively correlated with Cer (d18:1/18:1 (9Z)), and uncultured_ bacterium_ Rodentibacter was negatively correlated with Cer (d18:1/18:1 (9Z)), 1-Stearoyl-2-hydroxy-sn-glycorol-3-phopholine, Muribacter was positively correlated with Pantothenol and Cer (d18:1/18:1 (9Z)), Muribacter_ Multis is positively correlated with Pantothenol and Cer (d18:1/18:1 (9Z)), uncultured_ bacterium_ Stenotrophomonas is negatively correlated with Stearic acid(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Among the differential microflora and metabolites of silica group and control group, Pasteurella_ sp._ V6 is positively correlated with Glycerol, Streptococcus_ Agalaciae is negatively correlated with Uracil, Phytic acid, PC (16:0/16:0), L-Carnitine, Erucamide, and (3-Carboxyproxy) trimethylammonium cation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAmong the differential bacteria and metabolites in the coal dust group and the control group, Mycoplasma were positively correlated with Stearic acid. Gammaproteobateria is negatively correlated with Cer (d18:1/18:1 (9Z)). Betaproteobateriales is negatively correlated with Pantothenol and Cer (d18:1/18:1 (9Z)), Burkholderiae is negatively correlated with Cer (d18:1/18:1 (9Z)), 7-Oxycholesterol, 1-Stearoyl-2-hydroxy-sn-glyco-3-phopholine (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Among the differential microflora and metabolites in the silica group and the control group, Bifidobacteriales were positively correlated with Uracil. Bifidobacteriaceae was positively correlated with Uracil. Betaproteobacteriales are negatively correlated with Uridine, Thioetheramide PC, N-Palmitoylsphingosine, N-Acetylglutamine, L-Glutamate, Cytidine, Cyclohexylamine, Acetylcarnitine, 1-Stearoyl-2-arachidonoyl-sn glycerol. Burkholderia cea was positively correlated with Glycerol. Burkholderiaceae is negatively correlated with Uridine, Thioetheramide PC, L-Glutamate, Cyclohexylamine, Acetylcarnitine, 1-Stearoyl-2-arachidonoyl-sn glycerol and Glycerol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNowadays, the microflora is increasingly becoming a hot topic of research for many diseases. Influenced by the external environment, the respiratory flora is in a long-term dynamic change and it maintains a relatively stable state, under the influence of the external environment. Furthermore, in dynamic change and maintain a relative homeostasis, which can effectively prevent the emergence of pathogenic bacteria colonization in the respiratory tract, is an important basis for respiratory health and stability. According to research, imbalances in the airway flora can cause immune dysfunction and changes in body metabolism, leading to a variety of acute and chronic respiratory diseases. As pneumoconiosis is a chronic respiratory disease, the respiratory flora may also play a important role in it. Therefore, this paper investigates the relationship between pneumoconiosis and respiratory microflora and metabolites based on the construction of a rat model of pneumoconiosis.\u003c/p\u003e\u003cp\u003eIn this study, we performed high-throughput sequencing of the respiratory flora of rats in different treatment groups after 24 w of dust exposure using 16SrDNA technology, and the sequencing results showed that the depth of sequencing met the experimental requirements. The results of our ɑ and β diversity analysis showed that the respiratory microflora of rats could be clearly distinguished between the different groups, with significant differences in microbial species diversity, suggesting that exposure of rats to dust causes disturbances in their respiratory flora and that respiratory flora may play a role in the development of pneumoconiosis.\u003c/p\u003e\u003cp\u003e5 differential metabolites were screened in lung lavage fluid samples and 18 differential metabolites were identified in rat lung lavage fluid from the silica group in the coal mine dust and control groups. Further topological analysis of the metabolic pathways in which these metabolic markers were located showed that there were two pathways with significant changes, for pyrimidine metabolism, D-glutamine and D-glutamate metabolism. We therefore hypothesize that these are the key metabolic pathways in the development of pneumoconiosis and will focus on these two pathways and the differential metabolites involved. Also, in conjunction with the previous findings on differential respiratory flora, we performed a Spearman correlation analysis of respiratory flora and metabolomic changes in this study. The results showed that L-glutamate, involved in D-glutamine and D-glutamate metabolism, was significantly down-regulated and negatively correlated with Betaproteobacteriales, Burkholderiaceae. Cytidine involved in pyrimidine metabolism was significantly down-regulate and negatively correlated with Betaproteobacteriales. Uridines significantly down-regulated in pyrimidine metabolism were negatively correlated with Betaproteobacteriales, Burkholderiaceae. Uridines significantly down-regulated in the metabolism of pyrimidines were negatively correlated with Streptococcus_agalactiae and positively correlated with Bifidobacteriales, Bifidobacteriaceae. These suggest that the metabolites and associated metabolic pathways in our lungs may be influenced by changes in the lung flora and subsequently have some impact on the host metabolic phenotype.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe found that exposure to dust may cause disturbance of respiratory tract flora and metabolism. This study has demonstrated that pneumoconiosis is associated with dysbiosis and metabolic disorders of the respiratory flora. Dysregulation of Betaproteobacteriales, Burkholderiaceae, Bifidobacteriales, Bifidobacteriaceae, Streptococcus_agalactiae was asscoiated with the occurrence of pyrimidine metabolism, D-glutamine and D-glutamate metabolism abnormalities in the development of pneumoconiosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWei Gao and Han Hao: Development or design of methodology, creation of models, Data Curation, Validation, Investigation, Resources, Writing - Original Draft.Shuyu Xiao: Investigation, Software, Conceptualization.Xu Zhang, Peng Wang: Data Curation, Visualization, Investigation.Heliang Liu, Nan Liu, Yulan Jin, Jinlong Li, Xiaoming Li: Verification, Ideas, software development, Application of statistical.Fuhai Shen: Conceptualization, Methodology, Validation, Writing - Review \u0026amp;amp; Editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMin, C. Y. et al. [Investigation of newly diagnosed pneumoconiosis from artificial quartz stone manufacturers]. \u003cem\u003eZhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi\u003c/em\u003e. \u003cb\u003e40\u003c/b\u003e (9), 681\u0026ndash;683 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan, B. et al. Association of single nucleotide polymorphisms in the CYBA gene with coal workers' pneumoconiosis in the Han Chinese population. \u003cem\u003eInhal Toxicol.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (13\u0026ndash;14), 492\u0026ndash;497 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChair, S. Y. et al. \u003cem\u003eGenetic susceptibility in pneumoconiosis in China: a systematic review\u003c/em\u003e. \u003cem\u003eInt. Arch. Occup. Environ. Health\u003c/em\u003e, (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZou, X. X., Zhang, B. \u0026amp; Wang, H. J. 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The metabolomics of airway diseases, including COPD, asthma and cystic fibrosis. \u003cem\u003eBiomarkers\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (1), 5\u0026ndash;16 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen, X. et al. Comparative effects of dexamethasone and bergenin on chronic bronchitis and their anti-inflammatory mechanisms based on NMR metabolomics. \u003cem\u003eMol. Biosyst\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (6), 1938\u0026ndash;1947 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRogers, A. J. \u0026amp; Matthay, M. A. Applying metabolomics to uncover novel biology in ARDS. \u003cem\u003eAm. J. Physiol. Lung Cell. Mol. Physiol.\u003c/em\u003e \u003cb\u003e306\u003c/b\u003e (11), L957\u0026ndash;L961 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWendt, C. H. et al. Peptides in Bronchoalveolar Lavage in Chronic Obstructive Pulmonary Disease. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (5), e0155724 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, H. et al. Exploration study on serum metabolic profiles of Chinese male patients with artificial stone silicosis, silicosis, and coal worker's pneumoconiosis. \u003cem\u003eToxicol. Lett.\u003c/em\u003e \u003cb\u003e356\u003c/b\u003e, 132\u0026ndash;142 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Respiratory flora, Metabolomic analysis, Pneumoconiosis","lastPublishedDoi":"10.21203/rs.3.rs-7362557/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7362557/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study detected and analyzed the changes in the metabolomics of respiratory flora and lung lavage fluid of rats in a pneumoconiosis model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMale SD rats were randomly divided into silicon dioxide group (using SiO\u003csub\u003e2\u003c/sub\u003e dust, SiD), coal mine dust group (using coal mine dust, CMD) and control group (using sterile physiological saline). The changes of respiratory flora in rats were analyzed by 16S rDNA gene sequencing technology, the differential metabolites of lung lavage fluid were analyzed by non targeted metabonomics of UHPLC-Q-TOF-MS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe lung tissue structure of SiD rats was seriously damaged, and there were obvious silicon nodules. CMD rats showed a large number of cell nodules, while the alveolar structure of the control group was normal. In the upper respiratory tract, the abundance of muris and uncultured oligotrophomonas increased, while the abundance of Pasteurella, Bacteria, Rhodobacter and uncultured Rhodobacter decreased. In SiD group, the abundance of Pasteurella and Streptococcus without milk decreased. In the lower respiratory tract, the abundance of Bacteria in CMD group rats increased, and mycoplasma γ- Proteobacteria β- The abundance of proterozoic bacteria and berberidaceae decreased. In SiD group, the abundance of Bifidobacteria, Bifidobacteriaceae and Bacteria increased, β- The abundance of proterozoic bacteria and berberidaceae decreased. Among the metabolic pathways mainly involved, pyrimidine metabolism, D-glutamine and D-glutamate metabolism may be the key metabolic pathways in the development of pneumoconiosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDysregulation of Betaproteobacteriales, Burkholderiaceae, Bifidobacteriales, Bifidobacteriaceae, Streptococcus_agalactiae may lead to the occurrence of pyrimidine metabolism, D-glutamine and D-glutamate metabolism abnormalities in pneumoconiosis.\u003c/p\u003e","manuscriptTitle":"Differences of respiratory tract flora and metabonomic characteristics of lung lavage fluid in pneumoconiosis model rats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 19:07:15","doi":"10.21203/rs.3.rs-7362557/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T11:13:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T16:33:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T04:56:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326062493147036761667723079468647393180","date":"2025-10-03T14:45:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181766471355697325160505497055807906508","date":"2025-09-25T23:49:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T11:37:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T11:25:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T04:27:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-29T08:44:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-29T08:41:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0881f40a-a945-467d-b13c-fc18efefa737","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55085161,"name":"Health sciences/Diseases"},{"id":55085162,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-12-29T16:00:13+00:00","versionOfRecord":{"articleIdentity":"rs-7362557","link":"https://doi.org/10.1038/s41598-025-33203-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-23 15:57:24","publishedOnDateReadable":"December 23rd, 2025"},"versionCreatedAt":"2025-09-25 19:07:15","video":"","vorDoi":"10.1038/s41598-025-33203-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33203-3","workflowStages":[]},"version":"v1","identity":"rs-7362557","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7362557","identity":"rs-7362557","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}