Silica dust alters fecal microbiota that contributes silicosis through the lung-gut axis in rats

Silica dust alters fecal microbiota that contributes silicosis through the lung-gut axis in 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 Silica dust alters fecal microbiota that contributes silicosis through the lung-gut axis in rats Xuejie Qi, Mingming Han, Qiang Jia, Xin Zhang, Binpeng Qu, Wenhui Yin, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2661022/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background： Silicosis is a lung disease with diffuse nodular pulmonary fibrosis because of long-term inhalation of a large number of free silica dust. Research has been reported that dysbiosis of fecal microbiota was existed in silicosis patients. However, few studies have examined the effects of silica on the intestinal tract. Objective: In this study, we aimed to investigate the change of fecal microbiota, lung and ileum tissues of rats exposed to silica dust and explore the regulatory role of fecal microbiota in rats after silica exposure. Methods: The Wistar male rats were intratracheally instilled with 50 mg/mL silicon dioxide (1 mL per rat). Hematoxylin and eosin (HE), Masson staining, enzyme-linked immunosorbent assay (ELISA) and Western blot were used to exam the pulmonary inflammation and fibrosis in rats. HE, Western blot and Transmission Electron Microscopy (TEM) were used to exam the ileac injury. The 16s rRNA gene sequences, non-targeted metabolomics, transcriptome analysis were used to exam the fecal microbiota, metabolites and ileac mRNAs respectively. Then, we employed fecal microbiota transplantation (FMT) experiment to examine whether fecal microbiota play the important roles in the change of silica-induced pulmonary inflammation, fibrosis and ileum injury. Meanwhile, Pearson correlation tests were used to detect the differential microbiota and metabolites of feces, and mRNAs of ileum on day 56. Results: The results showed that silica exposure resulted in dynamic change of pulmonary inflammation, fibrosis, fecal microbiota dysbiosis and ileum epithelial injury. FMT up-regulated the level of Bifidobacterium , restored the level of tight junction proteins of ileum. Then we found the level of Bifidobacterium was significantly down-regulated on day 56 in silica-exposed rats. Further we mainly predicted 3 potential mechanisms through conjoint analysis and KEGG analysis: (ⅰ) the change of Bifidobacterium may be related to the production of oleoyl 3-carba cyclic phosphatidic acid (3-CCPA) and the expression of Cldn8, which involved in silica-induced pulmonary inflammatory response and ileac barrier function injury; (ⅱ) Silica-induced fecal microecological dysbiosis and inflammatory respond may affect the arginine biosynthesis and utilization of arginine by regulating the level of N-Acetyl-L-Glutamic Acid and Nos2, which effect ileac architectural integrity; and (ⅲ) Silica-induced fecal microecology disorder may induce ileac injury by regulating Ido1, Kynu and Indole-3-ethanol mediated “Tryptophan metabolism” pathway. Discussion: This study provided evidence that silica could alter fecal microbiota which may in turn play an important role in silica-induced pulmonary fibrosis and ileac barrier injury in rats. Three predicted mechanistic pathways deserved further study. The findings may provide a starting roadmap to intervene in the development of silica-induced pulmonary fibrosis. Biological sciences/Microbiology/Microbial communities Health sciences/Diseases/Respiratory tract diseases Silicosis Pulmonary fibrosis Fecal microbiota Metabolomics Transcriptomics FMT Intestinal injury Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Workers at mining and stonemasonry may be exposed to high concentration of silica dust during their work. Inhalation of respirable crystalline silica (RCS) leads to the development of silicosis, a progressive pneumoconiosis with lung inflammation and fibrosis. Silicosis continued to afflict tens of millions of occupational workers all over the world [ 1 ]. Inhaled RCS can be deposited at the level of terminal bronchioles and alveoli [ 2 ]. Symptoms of accelerated silicosis include cough, hawking, dyspnea and fatigue, which seriously affects the health of the workers [ 3 ]. Importantly, the factors leading to silicosis disease progression remain incompletely characterized. The interaction between the fecal microbiota and lungs, which has been referred to as the “gut-lung axis” [ 4 ]. Increasing experimental and clinical evidence demonstrated that the gut microbiota has a crucial role in the maintenance of human health. Their functional importance for the host was undeniable, including breaking down complex dietary polysaccharide, providing essential metabolites and regulating mucosal and immune system function [ 5 , 6 ]. Disease situations such as stress, lung infections and chronic inflammatory can lead to microbial dysbiosis and influence the host stress response. Changes in intestinal bacterial communities can also influence disease outcomes even in distant organs (including the lungs) [ 7 ]. Up to now, several reports have implicated fecal microbiome plays important roles in pathogenesis of asthma/allergic diseases, chronic obstructive pulmonary disease (COPD) and lung cancer [ 8 – 10 ]. In addition, exposure to traffic-related air pollution or particulate matter significantly altered the composition of fecal microbiota both in humans and animal models [ 11 ]. The changes of fecal microbiome can increase gut permeability and induce gut barrier injury [ 12 ]. The host health would be affected adversely if the intestinal barrier was impaired [ 13 ]. Rutten et al found that COPD patients have incremental intestinal permeability, intestinal epithelial damage, and the destruction of the intestinal mucosal barrier integrity [ 14 ]. Studies also have reported that fecal microbiota transplantation (FMT) could change the microecology of the intestine and relieve injury, thereby affecting the metabolism and intestinal epithelial functions [ 15 ].So far, microbiota changes were reported to present in the fecal of patients with silicosis [ 16 ]. However, the exact role of fecal microbiota in silicosis remains unclear. Experimental animals have obvious advantages in toxicological study. The strains, age, body weight, diet and growth environment all can be strictly controlled which can affect the status of fecal microbiota. In this study, we aimed to explore the regulatory role of fecal microbiota in silica-exposed pulmonary fibrosis and intestinal injury in male rats. Moreover, we analyzed the correlation between fecal microbiota and metabolites; and metabolites and genes on day 56 after silica exposure. Multi-omics technology was used to predict potential mechanism of fecal microbiota in silica-induced pulmonary fibrosis and ileum injury in rats. Collectively, our data will constitute a comprehensive picture of fecal microbiota in silica-exposed rats and provide the roadmap for future studies of its function and biological significance in silica-exposed pulmonary fibrosis. 2. Methods 2.1. Animals and test materials Male Wistar rats (4–6 weeks of age, 150-170g) were housed in a specific pathogen-free room (22 ± 2 ℃) and a relative humidity of 55 ± 5%, with 12h light-dark cycles and free access to water and chow. Food, cage, tap water and dwelling environment were sterile. The silica crystal particles were purchased from Sigma-Aldrich (St. Louis, MO, USA). Silicon dioxide accounts for 99.7% of its chemical composition. The 80% diameter was between 1 and 5 µm. 2.2. Experimental design After 1 week of acclimatization, 100 rats were randomly divided into two groups: control group (n = 50) and silica-exposed group (n = 50). The rats in both groups were further divided into ten subgroups (n = 10) according to the time of sacrifice (day 7, 14, 28, 42 and 56). The micron-sized silica was prepared with normal saline as a 50 mg/mL silica suspension. The rats were anesthetized by Small Animal Anesthesia Machine R540IE (RWD Life Science co.Ltd) using isoflurane. The silica-exposed rats were intratracheally instilled 50 mg/mL silicon dioxide (1 mL per rat) into lung directly with an improved intubation method and the control rats were with normal saline solution (1 mL per rat) [ 17 ]. Ten rats from each control and silica-exposed group were sacrificed on day 7, 14, 28, 42 and 56, respectively. Fecal pellets, lung and ileum tissues of rats were collected. Another 30 rats were randomly divided into 3 groups to implement the FMT experiment: control, silica-exposed and silica-exposed with FMT group (n = 10). The rats in silica-exposed and silica-exposed with FMT group were intratracheally instilled with silicon dioxide in accordance with the above method and control rats were with normal saline solution. Fecal pellets from different control rats were resuspended together in saline (10 g fecal pellet/50 mL of saline) and immersion. Then the sample was filtered, centrifugated (1200 r/min, 3 minutes), and the supernatant was collected. From day 56, the rats in silica-exposed with FMT group were given four-combination antibiotics (Vancomycin, 100 mg/kg; Neomycin sulfate, metronidazole and ampicillin, 200 mg/kg) by intragastric administration for 5 days to remove the original flora. Then FMT rats were given fecal supernatant (100 µL/l0 g) by oral gavage 6 days per week for 6 consecutive weeks. The rats in silica-exposed and control group were given saline by oral gavage (100 µL/l0 g). All of rats were sacrificed on day 105. Fecal pellets, lung and ileum tissues of rats were collected. 2.3. Histological analysis The lung and ileum tissues of rats were harvested at different time points (on day 7, 14, 28, 42, 56 and 105) as above. Samples were then fixed in 10% formalin and embedded in paraffin, sectioned, and stained with hematoxylin and eosin (H&E) or Masson. All of the specimens were examined microscopically to assess the development of pulmonary fibrosis. A histopathology injury severity score for lung tissue was assigned to individual lung slides after being examined by a single pathologist who was blind to the experimental groups. Each sample was analyzed with 10 fields and the average value was taken. HE staining of lung tissues was evaluated inflammation by Szapiel’s method [ 18 ]. Masson’s trichrome staining was assessed the degree of pulmonary fibrosis according to Ashcroft’s scoring criteria [ 19 ]. 2.4. Western blot and enzyme-linked immunosorbent assay (ELISA) analyses Total proteins were extracted from lung and ileum tissues using 1% radio immunoprecipitation assay (RIPA) buffer with PMSF (Beyotime Biotechnology; C3006, Shanghai, China). Protein concentrations were calculated using a BCA Protein Assay kit (Beyotime Biotechnology, C3006, Shanghai, China). The protein extracted was separated by SDS-PAGE and then electrotransferred to PVDF membrane (Merck KGaA, Darmstadt, Germany). The membranes were incubated with primary antibodies against GAPDH (5174, 1:1000, CST), Vimentin (5741, 1:1000, CST), Collagen Ⅲ (GB111323, 1:1000, Servicebio), COL1A1(91144, 1:1000, CST), Claudin 1 (ab180158, 1:5000, abcam) and Occludin (ab131259, 1:5000, abcam) overnight at 4 ℃. Then blots were washed with Tris-Buffered Saline and 0.1% Tween 20 (TBST). Subsequently, the blots were incubated with Goat anti-rabbit IgG H&L (Alexa Fluor® 488, 1:5000, Abcam) at room temperature for 1 h. Finally, the densities were scanned by Li-Cor and quantified using the Image Studio Software [ 20 ]. The supernatant samples of rat lung tissue were analyzed for the concentrations of TNF-α (ER02-96) and IL-1β (ER01-96) using rat ELISA assay kits (Biokits Technologies Inc., Beijing, China) following manufacturer’s instructions. The absorbance was measured at 450 nm using a spectrophotometer. The results were expressed in pg/mg protein. 2.5. Transmission Electron Microscopy (TEM) After 48 h fixation in 2.5% glutaraldehyde at 4℃, ileum tissues were transferred into 1% osmium tetroxide for 1.5h post-fixation. Then the samples were dehydrated in concentrated ethanol and embedded in Epon 812. The embedding blocks were cut into ultrathin sections, stained with uranyl acetate and lead citrate. The results were observed using a Hitachi HT-7800 TEM with 80 KV [ 21 ]. 2.6. 16S rRNA gene sequences Fecal pellets were collected and stored into sterile tubes, then immediately frozen and stored at − 80 ℃. Total genome DNA from fecal samples was extracted using CTAB/SDS method. The concentration of DNA was diluted to 1 ng/µL using sterile water. The V3 and V4 hypervariable regions of the16S rRNA genes were amplified used the specific primer with the barcode. All PCR reactions were carried out in 30 µL reactions with 15 µL of Phusion®High-Fidelity PCR Master Mix (New England Biolabs), 0.2 µM of forward and reverse primers, and about 10 ng template DNA. Samples with bright main strip between 400–450 bp were chosen for further experiments. PCR products was mixed and then purified with AxyPrepDNA Gel Extraction Kit (AXYGEN). Sequencing libraries were used NEB Next®Ultra™DNA Library Prep Kit for Illumina (NEB, USA) following manufacturer’s recommendations. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina Miseq/HiSeq2500 platform and generated 250 bp/300 bp paired-end reads. The raw data are spliced, filtered and chimeric removed to obtain effective Clean Data. Sequences analysis was performed by UPARSE software package using the UPARSE-OTU and UPARSE-OTUref algorithms. Non-metric multidimensional scaling (NMDS) analysis was used to identify the dissimilarities between control and silica-exposed fecal microbiota matrixes. Linear discriminant analysis (LDA) was used to identify the significantly differentiated bacterial taxa across samples with a cutoff LDA score > 4 [ 22 ]. 2.7. Non-targeted metabolomics profiling The stool sample was thawed on ice. The 100 mg sample was taken and homogenized with 300 µL ice-cold water. The sample was centrifuged at 12,000 rpm at 4 ℃ for 10 minutes, 600 µL supernatant were sucked into another centrifuge tube. Then 100 µL 5% methanol added into the product, it was whirled at 2500 rpm for 5 minutes, and centrifuged at 12,000 rpm at 4 ℃ for 10 minutes. Finally, the supernatant was collected for LC-MS/MS analysis [ 23 ]. The original data obtained by LC-MS analysis was converted into mzML format by ProteoWizard software. Peak extraction, alignment and retention time correction were performed by XCMS program. Filter the peaks with deletion rate > 50% in each group of samples. After that, metabolic identification information was obtained by searching the laboratory’s self-built database and integrating the public database and metDNA. Multivariate statistical analysis included partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) [ 24 ]. 2.8. Transcriptome analysis Three ileum tissues from each control or silica-exposed group on day 56 were used to transcriptome analysis. Total RNA of ileum was isolated using the Trizol Reagent (Invitrogen Life Technologies). The concentration, quality and integrity of RNA were determined using a NanoDrop spectrophotometer (Thermo Scientific). Three micrograms of RNA were used. The library fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, CA, USA) to select cDNA fragments of the preferred 380 bp in length. DNA fragments with ligated adaptor molecules on both ends were selectively enriched using Illumina PCR Primer Cocktail in a 15 cycle PCR reaction. Products were purified (AMPure XP system) and quantified using the Agilent high sensitivity DNA assay on a Bioanalyzer 2100 system (Agilent). Sequencing libraries were generated using the TruSeq RNA Sample Preparation Kit (Illumina, San Diego, CA, USA). The sequencing library was then sequenced on a NovaSeq 6000 (Illumina) by Shanghai Personal Biotechnology Cp. Ltd. We use Cutadapt [ 25 ] software to filter the sequencing data to get high quality sequence (Clean Data) for further analysis. Reference genome index was built by Bowtie2 [ 26 ] and the filtered reads were mapping to the reference genome using HISAT2 [ 27 ], the default mismatch was no more than 2. The alignment region distribution of mapped reads was calculated. Then we used DESeq [ 28 ] to analyze the genes of difference expression with screened conditions as follows: expression difference multiple |log2FoldChange| > 1, significant P -value < 0.05. 2.9. Statistical analysis All results are presented as mean ± SE. All statistical analyses were performed using GraphPad Prism. GraphPad Prism (version 8.3.0) was used for all statistical analysis. Univariate statistical analysis included Student’s t-test and analysis of variance (ANOVA). The correlation was analyzed using the Pearson correlation tests. Results with P ≤ 0.05 were considered to be statistically different. 3. Result 3.1. Silica-exposed rats showed high level of pulmonary inflammation We observed significant reduction in body weight of silica-exposed rats comparing to controls at different time points ( P ≤ 0.05) (Fig. S1 A). And the lung coefficient of silica-exposed rats was significantly increased at five different time points ( P ≤ 0.05, Fig. S1 B). Lung tissues were collected at five time points respectively for HE staining (Fig. 1 A). The results demonstrated that no obvious abnormalities were found in the lungs of control rats. A large number of inflammatory cells infiltrated in the alveolar septum and destroyed alveoli integrity from day 7 to 56 after silica exposure. And inflammatory response was the most serious on day 14 with the highest Szapiel scores (Fig. 1 B). Furthermore, we detected the levels of IL-1β and TNF-α to investigate the expression of inflammation cytokine after silica exposure. As shown in Fig. 1 C and Fig. 1 D, the levels of IL-1β and TNF-α were significantly higher in the silica-exposed group than control group at different time points and remained highest on day 14 and 28 respectively. 3.2. Silica-exposed rats showed high level of pulmonary fibrosis The results of Masson staining showed that the pulmonary fibroblasts gradually increased and tiny collagen fibers were found from day 28. The cellular nodules and some fibrosis nodules were observed on day 56 in silica-exposed group (Fig. 2 A). The Ashcroft scores were progressively increased from day 7 to 56 (Fig. 2 C). To determine the degree of fibrosis at different time points after silica-exposure, we detected of the level of vimentin and collage Ⅲ proteins in lung tissues from control and silica-exposed rats using western blot (Fig. 2 B). The results demonstrated that silica induced the expression levels of vimentin and collage Ⅲ from day 28 and continuously increased until day 56 (Fig. 2 D and Fig. 2 E). 3.3. Silica-exposed rats showed ileum epithelial injury Under light microscopy, no evident pathological changes were observed in the intestinal mucosa of control rats. By contrast, structural damage to the ileum tissues were characterized by regional intestinal villi shedding and shorten in the rats from the silica-exposed group at different time points. And the degree of injury was gradually aggravated with time (Fig. 3 A). Meanwhile, the levels of tight junction proteins (including claudin 1 and occludin) were significantly reduced in the silica-exposed group on day 28, 42, 56 compared to control (Fig. 3 B- 3 D). The ultrastructure of the ileum was visualized by TEM. The results showed that microvilli and mitochondria of enterocytes had a normal structure in controls. Mitochondrial swelling, microvillus shortening and disorder were observed in the ileum on day 28, 42, 56 (Fig. 4 ). 3.4. FMT-treated rats showed the changes of Bifidobacterium , pulmonary inflammation, fibrosis and ileac injury FMT experiment was used to further validate the association between fecal microbiota dysbiosis and silica induced lung and ileum epithelial injury. The richness of fecal microbiota decreased significantly in antibiotics-treated rats (Fig. 5 A), but increased after FMT (Fig. 5 B). The results of PCoA showed that microbiota successfully separated into different groups, which was relatively concentrated in the same group (Fig. 5 C and 5 D). Based on the results of anosim analysis, significant differences were found in composition of fecal microbiota in three groups after antibiotics treatment (R = 0.891, P = 0.001) and FMT (R = 0.203, P = 0.006) (Table 1 ). The levels of Bifidobacterium was significantly increased in FMT-treated rats comparing to silica-exposed rats (Fig. 5 E). Table 1 Anosim analysis between antibiotics-treated and FMT groups Group R-value P-value 6C vs F6 vs E6 0.890677258 0.001 F6 vs E6 1 0.001 E6 vs C6 0.552517361 0.001 F6_vs_C 1 0.001 C7 vs E7 vs F7 0.20270694 0.006 F7 vs E7 0.219308036 0.034 E7 vs C7 0.217013889 0.025 F7 vs C7 0.1796875 0.049 Note: C6: The control group after antibiotics treatment. F6: The FMT group after antibiotics treatment. E6: The silica-exposed group after antibiotics treatment. C7: The control group after FMT. F7: The FMT group after FMT. E7: The silica-exposed group after FMT. HE and Masson staining of lung tissues after FMT were observed. The results demonstrated that pulmonary cellular nodules were observed in silica-exposed and FMT group. However, the pulmonary cellular nodules in silica-exposed group were larger than that in FMT group (Fig. 6 A). And the higher Szapiel and Ashcroft scores (Fig. 6 B and 6 C) were shown in silica-exposed group. We further examined inflammatory cytokines (IL-1β, TNF-α) (Fig. 6 D and 6 E) and fibrosis protein (collagen type 1, collagen Ⅲ) (Fig. 6 F, 6 G and 6 H) of lung tissues. The level of IL-1β, TNF-α, collagen type 1 and collagen Ⅲ in FMT group were lower than silica-exposed group, but no statistic difference. In addition, tight junction proteins (including claudin 1 and occludin) (Fig. 6 I, 6 J and 6 K) significantly recovered after FMT compared to silica-exposed rats. 3.5. Silica-exposed rats showed the dysbiosis of fecal microbiota To determine the change of fecal microbiota in silica-exposed rats, we collected the fecal pellets samples of rats on day 7, 14, 28, 42 and 56. The results of 16S rRNA gene sequencing analysis showed that Firmicutes and Bacteroidetes were the predominant microbes at the phylum level in the fecal microbiome of rats from control and silica-exposed group (Fig. S2A). Then, the indices (Chao 1 and Shannon) were calculated to estimate the community diversity and richness using alpha diversity metric. The Chao 1 index (Fig. S2B) wer significantly decreased on day 14 ( P = 0.0039) and increased on day 56 ( P = 0.036) in the silica-exposed group compared to control group. The Shannon index (Fig. S2C) were lower in the silica-exposed group compared to controls on day 7 and 14. Difference Alpha diversity analysis showed that microbial community diversity and richness revealed dynamic change trend. The fecal microbiome patterns in silica-exposed and control rats were differentiated using multivariate statistical analysis by the principal component analysis (PCA) plot to analysis beta diversity. We can clearly see that silica exposure resulted in marked changes in the composition of fecal bacteria at different time points (Fig. 7 A). Differences between silica-exposed rats and controls were significant on day 14 ( P = 0.016), day 28 ( P = 1.25×10 − 5 ), day 42 ( P = 8.22×10 − 3 ), day 56 ( P = 2.66×10 − 4 ). Based on the Bray-Curtis algorithm, anosim analysis were used to analyze the difference between groups and within the group, so as to determine whether the grouping was meaningful. Significant differences between groups were found on day 28 (R = 0.171, P = 0.029), day 42 (R = 0.182, P = 0.032), day 56 (R = 0.603, P = 0.001) (Table 2 ). Table 2 The results of Anosim analysis in silica-exposed group compared to control group at different time points Times (days) R-value P -value 7 0.094023324 0.082 14 0.089192708 0.123 28 0.171316964 0.029 42 0.181919643 0.032 56 0.603298611 0.001 To investigate the effect of silica exposure on fecal microbiota composition, we performed linear discriminant analysis effect size (LEfSe) analysis to identify the differentially represented fecal microbiota between control and silica-exposed groups at different times. The results showed that 42, 7, 15, 16 differential bacterial taxa were found between control and silica-exposed groups respectively on day 7, 14, 28, 42 (Fig. S2D). Twenty-five differential bacterial taxa were found between control and silica-exposed groups on day 56 when the degree of lung fibrosis was the the most severe (Fig. S2D). Specifically, in genus level, the relatively abundance of Ruminococcus2, Thermoactinomyces , Aquabacterium , Saccharopolysporarectivirgula increased in silica-exposed group, whereas the relatively abundance of Bifidobacterium , Pseudarthrobacter , Anaeroplasma , Ruminiclostridium 9 reduced on day 56. 3.6. Silica-exposed rats showed the alterations of metabolites on day 56 Then the faeces were collected to detect the alterations of metabolites on day 56. The result of metabolomic analysis showed that silica exposure induced a different metabolite profile comparing to controls on day 56. The result of Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) indicated a significant separation between the control and silica-exposed group (Fig. 7 B). Differences were present in both the positive and negative mass spectrometric ion modes. Goodness of fit values and predictive ability values (positive mode: R2X = 0.283, R2Y = 0.94, Q2 = 0.281, and P ≤ 0.05; negative mode: R2X = 0.238, R2Y = 0.957, Q2 = 0.22, and P ≤ 0.05) indicated that the OPLS-DA model possessed a satisfactory fit with good predictive power. The differential expression metabolites in silica-exposed group were further screened using variable importance in projection (VIP), an important load evaluation parameter, as a threshold in this study. Metabolites that met VIP ≥ 1.0, Fold Change ≥ 2 or ≤ 0.5, and P ≤ 0.05 were considered to be differential metabolites. There were 85 differential metabolites (57 up-regulated, 28 down-regulated) in positive mode and 21 differential metabolites (9 up-regulated, 12 down-regulated) in negative mode (Fig. 7 C) (Table S1 -2). We further validated the changed metabolites using qualitative and quantitative analysis. To further identify the relevant metabolic pathways involved in the silica exposure, we used Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation method to analyze the differential metabolites. KEGG pathway analysis showed that 16 pathways were in positive mode and 4 pathways were in negative mode (Fig. 7 D). 3.7. Silica-exposed rats showed the alterations of mRNAs of ileum tissues on day 56 In total, 900 differentially expressed mRNAs (838 up-regulated and 62 down-regulated) were identified in ileum tissue of silica-exposed rats compared to controls on day 56 (Fig. 8 ). 3.8. Correlation between fecal microbiota and metabolites;and metabolites and genes from silica-exposed rats on day 56 To explore the functional correlation between microbiota changes and metabolite perturbations, a correlation matrix was generated by calculating the Pearson’s correlation coefficient. Our data showed strong associations between fecal microbiota and metabolites (Fig. 9 A, 9 B) (Table S3). Especially, the levels of Bifidobacterium positively correlated with the relative abundance of 3-CCPA (R = 0.816, P = 0.00021) (Fig. 9 C). In addition, correlation analysis was conducted for differentially expression mRNAs and metabolites. The results with a Pearson correlation coefficient greater than 0.8 were selected. The levels of some metabolites highly correlated with the expression of mRNAs of ileum tissues. Based on the results of differential metabolites and mRNAs enrichment analysis, differential metabolites and mRNAs were involved in the same KEGG pathway (Fig. 9 D). We found that Indole-3-Ethanol, Ido1, Afmid, Kynu and Il4i1 were involved in “Tryptophan metabolism” pathway, and N-Acetyl -Glutamic Acid and Nos2 were involved in “Arginine biosynthesis” pathway. 4. Discussion Recent studies have shown that fecal microbiota was involved in regulating the homeostasis of intestinal epithelium and contributing to pathophysiological processes [ 29 ]. Pathological and disease situations such as pulmonary infections and chronic inflammatory, foreign particulate matter stimulation and metabolic disorders can also lead to the imbalance of intestinal homeostasis and intestinal microecosystem dysbiosis [ 30 ]. Understanding fecal microbiota and its associated metabolites is important to elucidate the possible mechanisms of many lung disease development. A clinical study found that some patients with advanced silicosis had peptic ulcer and gastrointestinal mucosal barrier damage [ 31 ]. This present study aimed to investigate the role of fecal microbiota in the silica-exposed pulmonary fibrosis and ileac injury in rats, and to predict the possible mechanistic pathways. Firstly, we established silicosis model by intratracheal instillation of silica particles in rats and observed dynamic changes of pulmonary inflammation and fibrosis on day 7, 14, 28, 42 and 56. Meanwhile, we observed the damage profile of ileum tissue by examining the pathological changes of ileum tissue, the level of intestinal tight junction protein and the microstructure changes of enterocyte after silica exposure at five different time points. The results showed that the inflammatory response was at a higher level on day 14. No obvious abnormality was observed in ileum tissue. On day 28, fibrocytes appeared in lung tissue, and the level of fibrin are elevated, which was consistent with our previous study [ 32 ]. At the same time, we found that intestinal villi disordered, tight junction protein levels significantly reduced, and mitochondria of intestinal epithelial cells swollen in silica-exposed rats. The level of both pulmonary fibrosis and ileum damage was gradually increased and on day 56. And the degree both of and pulmonary fibrosis gradually increased. Injury of the gut barrier and subsequent translocation of bacteria from the gut have been implicated in the development of hypermetabolism and distant organ injury [ 33 ]. These results suggested that the formation of pulmonary inflammation and fibrosis was a time-dependent and accumulative process. The occurrence of ileac injury was accompanied with pulmonary inflammation and the degree of ileac injury gradually aggravated with the pulmonary fibrosis progression. In our present study, exposure to silica dust caused pulmonary inflammation and fibrosis and ileac injury. Some studies reported that the fecal microbiota of patients with silicosis and the rats exposed to silica were significantly changed [ 16 , 34 ]. However, whether fecal microbiota play the crucial role in these changes remains unknown. Therefore, we examined the changes of fecal microbiota in silica-exposed rats from day 7 to 56. The results of 16S rRNA gene sequencing showed that community diversity was of dynamic change. The community diversity and richness were significantly decreased on day 14 after silica exposure when the level of pulmonary inflammation peaked. In recent years, studies have shown that increased inflammation could stimulate the activation of the immune system and reduce microbial diversity [ 35 ]. The result indicated that silica-induced acute inflammation in pulmonary system may disturb microbiota community structure of gut. The composition of the differential microbiota of the rats between the two groups was variable at different time points, which may be relative to disease progression. In this study, the level of Bifidobacterium was increased significantly on day 7 in silica-exposed rats. Fecal microbiota kept a dynamic balance in normal circumstances. Inflammatory response might promote the compensatory increase of the level of probiotic to induce a range of immunoregulatory responses through gut-lung axis [ 36 ]. Then we predicted that the increase of the level of Bifidobacterium might be related to dynamic regulation of fecal microbiota. The result showed that major differential microbiota Bifidobacterium was significantly decreased in silica-exposed rats with severe pulmonary fibrosis on day 56. This is possibly attributed to the change of cytokines, microbial components and metabolites induced by silica particle that result in the dysbiosis of intestinal microbiota and depleted of Bifidobacterium. Similar result was demonstrated by a study showing that long-term stimulus of inflammatory factor could destroy the intestinal homeostasis of rats [ 37 ]. Supportively, Zhou et al. found that the abundances of Bifidobacterium were decreased in the fecal samples of the silicosis patients which were consistent with our results [ 16 ]. These studies suggested that Bifidobacterium played an important role in intestinal homeostasis. The genus Bifidobacterium were gram-positive, polymorphic rod-shaped bacteria and predominating probiotic organisms in the intestinal microbiota of humans and animals [ 38 ]. The deviations in intestinal Bifidobacterium have been observed in patients with different diseases, including allergies, IBD, asthma or cystic fibrosis [ 39 , 40 ]. Our study results suggested that Bifidobacterium may play an important role in silica-induced pulmonary fibrosis. To explore the effect of changed microbiota on pulmonary fibrosis and ileac injury in rats we further conducted FMT experiment. The results showed that main compositions of microbiota were restored and the level of Bifidobacterium was increased after FMT. Meanwhile, the expression trend of pulmonary inflammation and fibrosis decreased. This indicated Bifidobacterium may be closely related to the progression of pulmonary fibrosis. However, there was no statistical difference, which may be caused by insufficient time of FMT. In addition, FMT restored the level of intestinal tight junction protein such as claudin 1 and occludin. These results indicated that fecal microbiota may play a regulatory role in silica-induced pulmonary fibrosis progression and ileac injury. The role of Bifidobacterium was worthy to further research. In addition, the results of correlation analysis showed that the levels of Bifidobacterium positively correlated with the relative abundance of 3-CCPA which was down-regulated in the profile of silica exposure. Substitution of the sn-3 oxygen with a methylene in cyclic phosphatidic acid (CPA) yielded 3-CCPA, a stabilized analog of CPA [ 41 ]. CPA is a characteristic phospholipid mediator involved in regulating many cellular processes and inhibiting cell proliferation [ 42 ]. Bacterial phospholipase D (PLD) from Actinomadurea sp. NO 362 and Streptomyces chromofuscus could be responsible for CPA production via transphosphatidylation of abundant lysophosphatidylcholin (LPC) [ 43 ]. Importantly, PLD could catalyze transphosphatidylation reactions in the presence of glycerol or short chain primary aliphatic-alcohols [ 44 ]. The lipoteichoic acids from Bifidobucterium pennsylvanicum were extracted from cytoplasmic membranes or disintegrated bacteria. Thus, our results predicted that Bifidobacterium may affect PLD catalyzing the transphosphatidylation of LCP by regulating PLD to induce 3-CCPA production. Hotta et al. demonstrated that intravenous administration of 3-CCPA has a facilitatory effect on respiration [ 45 ]. A study reported that 3-CCPA were potent inhibitors of autotoxin (ATX) which highly pertinent to chronic inflammation [ 46 ]. In chronic inflammatory disorders, ATX has been reported to be expressed from alveolar inflammatory macrophages [ 47 ]. These studies showed that 3-CCPA played an important role in pulmonary inflammatory disorders. Hence, our results suggested that the alteration of Bifidobacterium might be related to the regulation of 3-CCPA which participated in silica-induced pulmonary inflammatory response. However, the specific mechanism needs to be further studied. To screen and identify the genes related to ileac barrier injury, transcriptome analysis was performed to examine the expression of differential mRNAs of ileum tissue on day 56. And correlation analysis of differential genes and metabolites was carried out. We found that 3-CCPA positively correlated with claudin 8 (Cldn8) which was down-regulated in silica-exposed group. Cldn8 was the key proteins in building the tight junction (TJ) net, which determined the tightness of the epithelial barrier and contribute to the polarity of epithelial cells [ 48 ]. During chronic inflammatory diseases, some pathogenic microorganism can target the tight junction complex leading to TJ protein dysregulation, which results in the disruption of intestinal barrier homeostasis [ 49 ]. CPA can elevate cyclic 3',5'-adenosine monophosphate (cAMP) level through the activation of a Ca 2+ -sensitive adenylyl cyclase [ 42 ]. The activation of cAMP-dependent protein kinase (PKA) by cAMP successfully improves TJ architecture [ 50 ]. Therefore, CPA may regulate TJ protein such as Cldn8 by stimulating the production of cAMP. Bifidobacterium played the protective effects on intestinal barrier function by inhibiting secretion of proinflammatory cytokine and maintaining TJ integrity [ 51 ]. Therefore, we predicted that Bifidobacterium may be related to the production of 3-CCPA and the expression of Cldn8, which involve silica-induced pulmonary inflammatory response and further destroyed ileac barrier function. Furthermore, we found that Nitric Oxide Synthase 2 (Nos2) (up-regulated in silica-exposed rats) and N-Acetyl-L-Glutamic Acid (down-regulated in silica-exposed rats) was involved in the same “Arginine biosynthesis” pathway. Arginine supplementation could enhance intestinal barrier function [ 52 ]. The first committed step of arginine biosynthesis is acetylation of L-glutamate at the N-position, which is also a feature characteristic of many prokaryotes and fungi [ 53 ]. Our results suggested that decreased N-Acetyl-L-Glutamic Acid may inhibit arginine biosynthesis and damage intestinal barrier function. Inflammatory response results in significantly decreased L-arginine levels in the case of the upregulation of Nos2, an important mediator in pro-inflammatory in macrophages [ 54 ]. In turn, the upregulation of Nos2 enhanced utilization of arginine and impaired resynthesis of arginine from citrulline [ 55 ]. Fecal microecological dysbiosis might influence the level of N-Acetyl-L-Glutamic Acid, which is related to the inhibition of the arginine biosynthesis and the upregulation of Nos2. Therefore, we predicted that silica-induced fecal microecological disorder and inflammatory response might be related to Nos2 upregulation and then influenced the level of arginine which may finally affect ileac architectural integrity. Hence, the role of “Arginine biosynthesis” signal pathway was worth further to study in silica-exposed ileum injury. Gut tryptophan can be metabolized into indole and indole derivatives by fecal micro population, such as Bifidobacterium spp , Bacteroides spp and E. coli [ 56 ]. Indole-3-ethanol as one of the tryptophan metabolites derived from the gut microbiota and could modulate gut barrier integrity via tight junctions [ 57 ]. It has been reported that intestinal epithelial barrier function could be improved during inflammatory response in mice treated with indole-3-ethanol [ 58 ]. Our results showed that the level of Indole-3-ethanol significantly decreased after silica exposure, which may be induced by Bifidobacterium decrease and finally resulted in ileac injury. Intestinal microorganism and multiple inflammatory cytokines can stimulate the expression of indoleamine 2,3-dioxygenase 1 (Ido1). The expression levels of Ido1 was low in healthy bowel tissue [ 59 ]. Most tryptophan in tissue was metabolized to kynurenine, which was regulated by Ido1 [ 60 ]. The prominent presence of Ido1 and decreased levels of Kyn in the ileum, presumably resulted from the strong expression of Kynu in ileum tissues [ 59 ]. Our results showed that the expression of Ido1 and Kynu were significantly up-regulated in ileum tissues in silica-exposed rats. And the result of KEGG showed that Ido1 and Kynu and Indole-3-ethanol were involved in “Tryptophan metabolism” pathway. Therefore, we predicted that silica-induced fecal microecology disorder may decrease the expression of Indole-3-ethanol and increase the expression of Ido1, Kynu to promote tryptophan metabolism. “Tryptophan metabolism” pathway was worth further to study. 5. Conclusion Our results showed that the silica could induce pulmonary fibrosis, ileac barrier injury and fecal microbiota dysbiosis in rats. By 16s rRNA analysis, Bifidobacterium may affect the development of silica-induced pulmonary fibrosis and ileum injury. By conjoint analysis and KEGG analysis, 3 predicted mechanistic pathways were as follow: (ⅰ) the change of Bifidobacterium maybe related to the production of 3-CCPA and the expression of Cldn8, which involved in silica-induced pulmonary inflammatory response and ileac barrier function injury; (ⅱ) Silica-induced fecal microecological dysbiosis and inflammatory respond may affect the arginine biosynthesis and utilization of arginine by regulating the level of N-Acetyl-L-Glutamic Acid and Nos2, which effect ileac architectural integrity; and (ⅲ) Silica-induced fecal microecology disorder may induce ileac injury by regulating Ido1, Kynu and Indole-3-ethanol mediated “Tryptophan metabolism” pathway. References Krefft, S., Wolff, J., Rose, C.: Silicosis: An Update and Guide for Clinicians. Clin. Chest Med. 41 (4), 709–722 (2020) Mlika, M., Adigun, R., Bhutta, B.S.: Silicosis , in StatPearls . StatPearls Publishing Copyright © 2021, StatPearls Publishing LLC.: Treasure Island (FL). (2021) Leung, C.C., Yu, I.T., Chen, W.: Silicosis. Lancet. 379 (9830), 2008–2018 (2012) Saint-Criq, V., Lugo-Villarino, G., Thomas, M.: Dysbiosis, malnutrition and enhanced gut-lung axis contribute to age-related respiratory diseases. Ageing Res. Rev. 66 , 101235 (2021) Bingula, R., et al.: Desired Turbulence? Gut-Lung Axis, Immunity, and Lung Cancer. J Oncol, 2017: p. 5035371. (2017) Belkaid, Y., Hand, T.W.: Role of the microbiota in immunity and inflammation. Cell. 157 (1), 121–141 (2014) Karl, J.P., et al.: Effects of Psychological, Environmental and Physical Stressors on the Gut Microbiota. Front Microbiol, 9: p. 2013. (2018) Koidl, L., Untersmayr, E.: The clinical implications of the microbiome in the development of allergy diseases. Expert Rev. Clin. Immunol. 17 (2), 115–126 (2021) Lai, H.C., et al.: Gut microbiota modulates COPD pathogenesis: role of anti-inflammatory Parabacteroides goldsteinii lipopolysaccharide. Gut. 71 (2), 309–321 (2022) Zhao, Y., et al.: Role of lung and gut microbiota on lung cancer pathogenesis. J. Cancer Res. Clin. Oncol. 147 (8), 2177–2186 (2021) Ran, Z., et al.: Subchronic exposure to concentrated ambient PM2.5 perturbs gut and lung microbiota as well as metabolic profiles in mice. Environ. Pollut. 272 , 115987 (2021) Zhu, T.H., et al.: Epithelial barrier dysfunctions in atopic dermatitis: a skin-gut-lung model linking microbiome alteration and immune dysregulation. Br. J. Dermatol. 179 (3), 570–581 (2018) Generoso, S.V., et al.: Saccharomyces cerevisiae strain UFMG 905 protects against bacterial translocation, preserves gut barrier integrity and stimulates the immune system in a murine intestinal obstruction model. Arch. Microbiol. 192 (6), 477–484 (2010) Rutten, E.P.A., et al.: Disturbed intestinal integrity in patients with COPD: effects of activities of daily living. Chest. 145 (2), 245–252 (2014) Li, X., et al.: Probiotics Ameliorate Colon Epithelial Injury Induced by Ambient Ultrafine Particles Exposure. Adv. Sci. (Weinh). 6 (18), 1900972 (2019) Zhou, Y., et al.: Alterations in the gut microbiota of patients with silica-induced pulmonary fibrosis. J. Occup. Med. Toxicol. 14 , 5 (2019) Wang, H., et al.: Silicon, an important exposure marker in vivo in silicosis research. Int. Arch. Occup. Environ. Health. 94 (7), 1513–1522 (2021) Szapiel, S.V., et al.: Bleomycin-induced interstitial pulmonary disease in the nude, athymic mouse. Am. Rev. Respir Dis. 120 (4), 893–899 (1979) Ashcroft, T., Simpson, J.M., Timbrell, V.: Simple method of estimating severity of pulmonary fibrosis on a numerical scale. J. Clin. Pathol. 41 (4), 467–470 (1988) Bass, J.J., et al.: An overview of technical considerations for Western blotting applications to physiological research. Scand. J. Med. Sci. Sports. 27 (1), 4–25 (2017) Udoumoh, A.F., et al.: Light and transmission electron microscopic evaluation of the lamina epithelialis mucosae in the ileum of pre- and post-hatch broiler chicken. Anat. Histol. Embryol. 51 (1), 136–142 (2022) Johnson, J.S., et al.: Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10 (1), 5029 (2019) Li, W.W., et al.: Non-invasive urinary metabolomic profiles discriminate biliary atresia from infantile hepatitis syndrome. Metabolomics. 14 (7), 90 (2018) Shen, X., et al.: Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics. Nat. Commun. 10 (1), 1516 (2019) Kechin, A., et al.: cutPrimers: A New Tool for Accurate Cutting of Primers from Reads of Targeted Next Generation Sequencing. J. Comput. Biol. 24 (11), 1138–1143 (2017) Langmead, B., Salzberg, S.L.: Fast gapped-read alignment with Bowtie 2. Nat. Methods. 9 (4), 357–359 (2012) Sirén, J., Välimäki, N., Mäkinen, V.: Indexing Graphs for Path Queries with Applications in Genome Research. IEEE/ACM Trans. Comput. Biol. Bioinform. 11 (2), 375–388 (2014) Anders, S., Huber, W.: Differential expression analysis for sequence count data. Genome Biol. 11 (10), R106 (2010) Defois, C., et al.: Food Chemicals Disrupt Human Gut Microbiota Activity And Impact Intestinal Homeostasis As Revealed By In Vitro Systems. Sci. Rep. 8 (1), 11006 (2018) Sencio, V., Machado, M.G., Trottein, F.: The lung-gut axis during viral respiratory infections: the impact of gut dysbiosis on secondary disease outcomes. Mucosal Immunol. 14 (2), 296–304 (2021) Feng hongyi, L.: Risk factors of overt bleeding of peptic ulcer in silicosis patients. Industrial health and occupational diseases. 02 , 109–110 (1996) Sai, L., et al.: Dynamic assessing silica particle-induced pulmonary fibrosis and associated regulation of long non-coding RNA expression in Wistar rats. Genes Environ. 43 (1), 23 (2021) Deitch, E.A.: Bacterial translocation of the gut flora. J. Trauma. 30 (12 Suppl), S184–S189 (1990) Gong, G.C., Song, S.R., Su, J.: Pulmonary fibrosis alters gut microbiota and associated metabolites in mice: An integrated 16S and metabolomics analysis. Life Sci. 264 , 118616 (2021) Wesemann, D.R., Nagler, C.R.: The Microbiome, Timing, and Barrier Function in the Context of Allergic Disease. Immunity. 44 (4), 728–738 (2016) Wypych, T.P., Wickramasinghe, L.C., Marsland, B.J.: The influence of the microbiome on respiratory health. Nat. Immunol. 20 (10), 1279–1290 (2019) Budden, K.F., et al.: Probiotic Bifidobacterium longum subsp. longum Protects against Cigarette Smoke-Induced Inflammation in Mice .Int J Mol Sci, 24(1). (2022) Luo, J., et al.: The primary biological network of Bifidobacterium in the gut .FEMS Microbiol Lett, 365(8). (2018) Muhlebach, M.S., et al.: Initial acquisition and succession of the cystic fibrosis lung microbiome is associated with disease progression in infants and preschool children. PLoS Pathog. 14 (1), e1006798 (2018) Wang, W., et al.: Bifidobacterium infantis Relieves Allergic Asthma in Mice by Regulating Th1/Th2. Med. Sci. Monit. 26 , e920583 (2020) Gupte, R., et al.: Synthesis and pharmacological evaluation of the stereoisomers of 3-carba cyclic-phosphatidic acid. Bioorg. Med. Chem. Lett. 20 (24), 7525–7528 (2010) Murakami-Murofushi, K., et al.: Biological functions of a novel lipid mediator, cyclic phosphatidic acid. Biochim. Biophys. Acta. 1582 (1–3), 1–7 (2002) Mukai, M., et al.: Hepatoma cell migration through a mesothelial cell monolayer is inhibited by cyclic AMP-elevating agents via a Rho-dependent pathway. FEBS Lett. 484 (2), 69–73 (2000) McDermott, M.I., et al.: Mammalian phospholipase D: Function, and therapeutics. Prog Lipid Res. 78 , 101018 (2020) Hotta, H., Kagitani, F., Murakami-Murofushi, K.: Cyclic phosphatidic acid stimulates respiration without producing vasopressor or tachycardiac effects in rats. Eur. J. Pharmacol. 543 (1–3), 27–31 (2006) Kitakaze, K., et al.: Development of a selective fluorescence-based enzyme assay for glycerophosphodiesterase family members GDE4 and GDE7. J. Lipid Res. 62 , 100141 (2021) Magkrioti, C., et al.: Autotaxin and chronic inflammatory diseases. J. Autoimmun. 104 , 102327 (2019) Barmeyer, C., Fromm, M., Schulzke, J.D.: Active and passive involvement of claudins in the pathophysiology of intestinal inflammatory diseases. Pflugers Arch. 469 (1), 15–26 (2017) Šuligoj, T., et al.: Effects of Human Milk Oligosaccharides on the Adult Gut Microbiota and Barrier Function .Nutrients, 12(9). (2020) Patterson, C.E., et al.: Regulation of endothelial barrier function by the cAMP-dependent protein kinase. Endothelium. 7 (4), 287–308 (2000) Ling, X., et al.: Protective Effects of Bifidobacterium on Intestinal Barrier Function in LPS-Induced Enterocyte Barrier Injury of Caco-2 Monolayers and in a Rat NEC Model. PLoS One. 11 (8), e0161635 (2016) Viana, M.L., et al.: Pretreatment with arginine preserves intestinal barrier integrity and reduces bacterial translocation in mice. Nutrition. 26 (2), 218–223 (2010) Xu, Y., Labedan, B., Glansdorff, N.: Surprising arginine biosynthesis: a reappraisal of the enzymology and evolution of the pathway in microorganisms. Microbiol. Mol. Biol. Rev. 71 (1), 36–47 (2007) Hoeksema, M.A., Stöger, J.L., de Winther, M.P.: Molecular pathways regulating macrophage polarization: implications for atherosclerosis. Curr. Atheroscler Rep. 14 (3), 254–263 (2012) Elwafi, F., et al.: Endotoxemia affects citrulline, arginine and glutamine bioavailability. Eur. J. Clin. Invest. 42 (3), 282–289 (2012) Keszthelyi, D., Troost, F.J., Masclee, A.A.: Understanding the role of tryptophan and serotonin metabolism in gastrointestinal function. Neurogastroenterol Motil. 21 (12), 1239–1249 (2009) Aoki, R., et al.: Indole-3-Pyruvic Acid, an Aryl Hydrocarbon Receptor Activator, Suppresses Experimental Colitis in Mice. J. Immunol. 201 (12), 3683–3693 (2018) Scott, S.A., Fu, J., Chang, P.V.: Microbial tryptophan metabolites regulate gut barrier function via the aryl hydrocarbon receptor. Proc. Natl. Acad. Sci. U S A. 117 (32), 19376–19387 (2020) Huhn, M., et al.: Inflammation-Induced Mucosal KYNU Expression Identifies Human Ileal Crohn's Disease .J Clin Med, 9(5). (2020) Comai, S., et al.: Tryptophan in health and disease. Adv. Clin. Chem. 95 , 165–218 (2020) Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryXXMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2661022","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":183497786,"identity":"c603adb9-7e70-4ef2-be5e-2a956871dabf","order_by":0,"name":"Xuejie Qi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xuejie","middleName":"","lastName":"Qi","suffix":""},{"id":183497787,"identity":"8b651685-1c1e-45e8-8cc8-75c206659cac","order_by":1,"name":"Mingming Han","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mingming","middleName":"","lastName":"Han","suffix":""},{"id":183497788,"identity":"e9395efd-4738-4441-9d77-4bffab6009e7","order_by":2,"name":"Qiang Jia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Jia","suffix":""},{"id":183497789,"identity":"11d7630b-0387-4ce5-a249-22e974583590","order_by":3,"name":"Xin Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":183497790,"identity":"827b441c-ddc0-4f96-9e1c-78c88df635e8","order_by":4,"name":"Binpeng Qu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Binpeng","middleName":"","lastName":"Qu","suffix":""},{"id":183497791,"identity":"42de6a08-dc03-4e79-a925-3b05fe013485","order_by":5,"name":"Wenhui Yin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Yin","suffix":""},{"id":183497792,"identity":"d08ea45c-1996-4e40-af0e-9eae3d167e01","order_by":6,"name":"Cunxiang Bo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Cunxiang","middleName":"","lastName":"Bo","suffix":""},{"id":183497793,"identity":"152db67e-3661-4e34-bfc8-bba046347b68","order_by":7,"name":"Na Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Wu","suffix":""},{"id":183497794,"identity":"05b91374-c613-4579-985e-91fecc587f84","order_by":8,"name":"Yan Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liu","suffix":""},{"id":183497795,"identity":"ccef967a-1397-4865-b6f0-a0eb89941971","order_by":9,"name":"Linlin Sai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYFACxsYHCQY2dvzMzAcfEKmFudngQUVasmQ7W7IBkVrY2yQfnDnMuOE8j5kAURrMpRsbJBLbmJmNDzOYMTDU2EQT1GI552CDQWIbG5/ZYYa0BwzH0nIbCGkxuJHYkJDYxsMM1HLcgLHhMHFaDiS2STBubmYEkkRqaWxIOGPAuIGZmY04LUC/NDMkVCQkSxxmYzZIIMYv5tLtz3/+MPhvx99//uODDzU2RDhMApmXQEg5ppZRMApGwSgYBdgAAD5hQkevVycVAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Sai","suffix":""},{"id":183497796,"identity":"0a569124-b91e-4623-9e69-4be791147154","order_by":10,"name":"Cheng Peng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2023-03-06 12:56:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2661022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2661022/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34442461,"identity":"4587f678-39e4-4eb7-af49-bfca12fcb4fe","added_by":"auto","created_at":"2023-03-17 21:39:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8648327,"visible":true,"origin":"","legend":"\u003cp\u003eThe level of pulmonary inflammation and fibrosis in normal and silica-exposed rats on day 7, 14, 28, 42 and 56. A: HE staining for histopathologic changes in lung of rats. B: The scores of inflammations were assessed by Szapiel’s method. C-D: The levels of IL-1β and TNF-α. (a): \u003cem\u003eP \u003c/em\u003e≤ 0.05, compared to the control group at same time points. (b): \u003cem\u003eP \u003c/em\u003e≤ 0.05, compared to silica-exposed group on day 7. (c): \u003cem\u003eP \u003c/em\u003e≤ 0.05, compared with silica-exposed group on day 14. (d): \u003cem\u003eP \u003c/em\u003e≤ 0.05, compared to silica-exposed group on day 28. (e): \u003cem\u003eP \u003c/em\u003e≤ 0.05，compared with silica-exposed group on day 42.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/0f669310f669d05248a7d1d0.png"},{"id":34442463,"identity":"1b9d9d68-998d-4829-85f2-566343be1531","added_by":"auto","created_at":"2023-03-17 21:39:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8697087,"visible":true,"origin":"","legend":"\u003cp\u003eThe level of pulmonary fibrosis of silica-exposed and control rats on day 7, 14, 28, 42 and 56. A: Masson staining for histopathologic changes in lung of rats. B: Western blot analysis on lung tissues for collage Ⅲ and vimentin proteins C: The scores of pulmonary fibrosis by Ashcroft’s method. D-E: protein expression level of collage Ⅲ and vimentin. (a): \u003cem\u003eP \u003c/em\u003e≤ 0.05, compared to the control group at same time points. (b): \u003cem\u003eP \u003c/em\u003e≤ 0.05, compared to silica-exposed group on day 7. (c): \u003cem\u003eP \u003c/em\u003e≤ 0.05, compared to silica-exposed group on day 14. (d): \u003cem\u003eP \u003c/em\u003e≤ 0.05, compared with silica-exposed group on day 28. (e): \u003cem\u003eP \u003c/em\u003e≤ 0.05，compared with silica-exposed group on day 42.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/ffe0eff89eb7e2377fd35c13.png"},{"id":34442460,"identity":"59766ae8-2318-462c-8d1f-cdc436595fb9","added_by":"auto","created_at":"2023-03-17 21:39:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4834146,"visible":true,"origin":"","legend":"\u003cp\u003eThe profile of ileum epithelial injury in rats from the silica-exposed and control groups at different time points. A: HE staining for histological change of the ileum tissues of rats. B: Western blot analysis on ileum tissues for claudin 1 and occludin proteins. C-D: Protein expression levels of claudin 1 and occludin.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/07ce7a0f32651a6479ffa301.png"},{"id":34442464,"identity":"58a9a171-79ed-4a5a-8a68-b3d3a4153f1f","added_by":"auto","created_at":"2023-03-17 21:39:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8212571,"visible":true,"origin":"","legend":"\u003cp\u003eThe ultrastructure of the ileum epithelium by TEM. Scale bar: 5μm for panels (a1-a3, d1-d3), 2 µm for panels (b1-b3, e1-e3), 1 µm for panels (c1-c3, f1-f3).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/2eab60b943dbf0e3f0811bb3.png"},{"id":34442458,"identity":"3508d947-162e-4e8d-8d8c-a09a33e5a3d1","added_by":"auto","created_at":"2023-03-17 21:39:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":242202,"visible":true,"origin":"","legend":"\u003cp\u003eThe changes of fecal microbiota after FMT. A-B: The Shannon index of fecal microbiota after antibiotics treatment and FMT. C-D: The PCoA plot after antibiotics treatment and FMT. E: The differential microbiota between silica-exposed and FMT groups. The group of C6: The control group after antibiotics treatment. F6: The FMT group after antibiotics treatment. E6: The silica-exposed group after antibiotics treatment. C7: The control group after FMT. F7: The FMT group after FMT. E7: The silica-exposed group after FMT.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/ed98c967cadf2188f08d02f3.png"},{"id":34443439,"identity":"963a7fd4-8238-43d9-995f-101da0687473","added_by":"auto","created_at":"2023-03-17 21:47:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5372060,"visible":true,"origin":"","legend":"\u003cp\u003eThe changes of pulmonary and ileac injury after FMT. A: HE and Masson staining for histopathologic changes in lung of rats. A1-A3: HE staining in control, FMT and silica-exposed group respectively. A4-A6: Masson staining in control, FMT and silica-exposed group respectively. B: The scores of inflammations by Szapiel’s method. C: The scores of pulmonary fibrosis were assessed with Ashcroft’s method. D-E: The levels of IL-1β and TNF-α in control, FMT and silica-exposed group. F: Western blot analysis on lung tissues for collagen type 1and collagen Ⅲ proteins. G: Western blot analysis on ileum tissues for claudin 1 and occludin proteins. H-K: Protein expression level of collagen type 1, collagen Ⅲ, claudin 1 and occludin.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/2ded9e61e497abc070bf8743.png"},{"id":34442459,"identity":"6d35ef0f-de7b-4e82-aab5-1716b4cfd5c3","added_by":"auto","created_at":"2023-03-17 21:39:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":872619,"visible":true,"origin":"","legend":"\u003cp\u003eScreening and variation characteristics of various fecal microbiota and metabolites after silica exposure in rats’ faces. A: a1-a5: The PCoA plot is generated of the Weighted Unifrac Distance based on OTU counts on day 7, 14, 28, 42 and 56. B: OPLS-DA analysis of the metabolite profile showing good discrimination between the silica-exposed and control groups. C: Volcano Plot showing differential metabolites between the silica-exposed and control groups. D: The annotated significant pathways regulated by the differential metabolites in silica-exposed group.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/e066641a6a5957bae9ca0093.png"},{"id":34443440,"identity":"db85dbd9-f32c-469e-a5c1-f47a6f9259b0","added_by":"auto","created_at":"2023-03-17 21:47:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":142768,"visible":true,"origin":"","legend":"\u003cp\u003eThe volcano plot of selected differentially expressed mRNAs. The red represents up-regulated genes and the blue \u0026nbsp;represents down-regulated genes.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/f695f75e2675abc18008eb78.png"},{"id":34442465,"identity":"342e436b-74ce-42b1-8813-ce88a242d28c","added_by":"auto","created_at":"2023-03-17 21:39:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":769492,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between differential expression microbiota and metabolites, genes and metabolites. A-B: Pearson correlation analysis was performed on differential expression metabolites (positive mode(A) and negative mode (B)) and fecal microbiota (genus level). C: Scatter plots illustrating statistical association between the relative abundance of \u003cem\u003eBifidobacterium\u003c/em\u003e and 3-CCPA. D: The results of KEGG pathways analysis with differential expression metabolites and genes.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/97a9f3ef9243a0d842f78f2b.png"},{"id":56113622,"identity":"8a7215fe-9587-40ec-9b05-f5376cba10cc","added_by":"auto","created_at":"2024-05-08 17:11:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7788394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/721eeb32-44c2-4780-9669-efd39b0ebeb5.pdf"},{"id":34442467,"identity":"c0edfc61-8226-4d90-b545-87b7f4c12763","added_by":"auto","created_at":"2023-03-17 21:39:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8702499,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryXXMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-2661022/v1/5581e3b2433643c903fe9e6c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Silica dust alters fecal microbiota that contributes silicosis through the lung-gut axis in rats","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWorkers at mining and stonemasonry may be exposed to high concentration of silica dust during their work. Inhalation of respirable crystalline silica (RCS) leads to the development of silicosis, a progressive pneumoconiosis with lung inflammation and fibrosis. Silicosis continued to afflict tens of millions of occupational workers all over the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Inhaled RCS can be deposited at the level of terminal bronchioles and alveoli [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Symptoms of accelerated silicosis include cough, hawking, dyspnea and fatigue, which seriously affects the health of the workers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Importantly, the factors leading to silicosis disease progression remain incompletely characterized.\u003c/p\u003e \u003cp\u003eThe interaction between the fecal microbiota and lungs, which has been referred to as the \u0026ldquo;gut-lung axis\u0026rdquo; [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increasing experimental and clinical evidence demonstrated that the gut microbiota has a crucial role in the maintenance of human health. Their functional importance for the host was undeniable, including breaking down complex dietary polysaccharide, providing essential metabolites and regulating mucosal and immune system function [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Disease situations such as stress, lung infections and chronic inflammatory can lead to microbial dysbiosis and influence the host stress response. Changes in intestinal bacterial communities can also influence disease outcomes even in distant organs (including the lungs) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Up to now, several reports have implicated fecal microbiome plays important roles in pathogenesis of asthma/allergic diseases, chronic obstructive pulmonary disease (COPD) and lung cancer [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, exposure to traffic-related air pollution or particulate matter significantly altered the composition of fecal microbiota both in humans and animal models [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe changes of fecal microbiome can increase gut permeability and induce gut barrier injury [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The host health would be affected adversely if the intestinal barrier was impaired [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Rutten et al found that COPD patients have incremental intestinal permeability, intestinal epithelial damage, and the destruction of the intestinal mucosal barrier integrity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Studies also have reported that fecal microbiota transplantation (FMT) could change the microecology of the intestine and relieve injury, thereby affecting the metabolism and intestinal epithelial functions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].So far, microbiota changes were reported to present in the fecal of patients with silicosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the exact role of fecal microbiota in silicosis remains unclear. Experimental animals have obvious advantages in toxicological study. The strains, age, body weight, diet and growth environment all can be strictly controlled which can affect the status of fecal microbiota. In this study, we aimed to explore the regulatory role of fecal microbiota in silica-exposed pulmonary fibrosis and intestinal injury in male rats. Moreover, we analyzed the correlation between fecal microbiota and metabolites; and metabolites and genes on day 56 after silica exposure. Multi-omics technology was used to predict potential mechanism of fecal microbiota in silica-induced pulmonary fibrosis and ileum injury in rats. Collectively, our data will constitute a comprehensive picture of fecal microbiota in silica-exposed rats and provide the roadmap for future studies of its function and biological significance in silica-exposed pulmonary fibrosis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e\u003cstrong\u003e2.1.\u003c/strong\u003e Animals and test materials\u003c/h2\u003e\n \u003cp\u003eMale Wistar rats (4\u0026ndash;6 weeks of age, 150-170g) were housed in a specific pathogen-free room (22\u0026thinsp;\u0026plusmn;\u0026thinsp;2 ℃) and a relative humidity of 55\u0026thinsp;\u0026plusmn;\u0026thinsp;5%, with 12h light-dark cycles and free access to water and chow. Food, cage, tap water and dwelling environment were sterile.\u003c/p\u003e\n \u003cp\u003eThe silica crystal particles were purchased from Sigma-Aldrich (St. Louis, MO, USA). Silicon dioxide accounts for 99.7% of its chemical composition. The 80% diameter was between 1 and 5 \u0026micro;m.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Experimental design\u003c/h2\u003e\n \u003cp\u003eAfter 1 week of acclimatization, 100 rats were randomly divided into two groups: control group (n\u0026thinsp;=\u0026thinsp;50) and silica-exposed group (n\u0026thinsp;=\u0026thinsp;50). The rats in both groups were further divided into ten subgroups (n\u0026thinsp;=\u0026thinsp;10) according to the time of sacrifice (day 7, 14, 28, 42 and 56). The micron-sized silica was prepared with normal saline as a 50 mg/mL silica suspension. The rats were anesthetized by Small Animal Anesthesia Machine R540IE (RWD Life Science co.Ltd) using isoflurane. The silica-exposed rats were intratracheally instilled 50 mg/mL silicon dioxide (1 mL per rat) into lung directly with an improved intubation method and the control rats were with normal saline solution (1 mL per rat) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Ten rats from each control and silica-exposed group were sacrificed on day 7, 14, 28, 42 and 56, respectively. Fecal pellets, lung and ileum tissues of rats were collected.\u003c/p\u003e\n \u003cp\u003eAnother 30 rats were randomly divided into 3 groups to implement the FMT experiment: control, silica-exposed and silica-exposed with FMT group (n\u0026thinsp;=\u0026thinsp;10). The rats in silica-exposed and silica-exposed with FMT group were intratracheally instilled with silicon dioxide in accordance with the above method and control rats were with normal saline solution. Fecal pellets from different control rats were resuspended together in saline (10 g fecal pellet/50 mL of saline) and immersion. Then the sample was filtered, centrifugated (1200 r/min, 3 minutes), and the supernatant was collected. From day 56, the rats in silica-exposed with FMT group were given four-combination antibiotics (Vancomycin, 100 mg/kg; Neomycin sulfate, metronidazole and ampicillin, 200 mg/kg) by\u0026ensp;intragastric administration for 5 days to remove the original flora. Then FMT rats were given fecal supernatant (100 \u0026micro;L/l0 g) by oral gavage 6 days per week for 6 consecutive weeks. The rats in silica-exposed and control group were given saline by oral gavage (100 \u0026micro;L/l0 g). All of rats were sacrificed on day 105. Fecal pellets, lung and ileum tissues of rats were collected.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3. Histological analysis\u003c/h2\u003e\n \u003cp\u003eThe lung and ileum tissues of rats were harvested at different time points (on day 7, 14, 28, 42, 56 and 105) as above. Samples were then fixed in 10% formalin and embedded in paraffin, sectioned, and stained with hematoxylin and eosin (H\u0026amp;E) or Masson. All of the specimens were examined microscopically to assess the development of pulmonary fibrosis. A histopathology injury severity score for lung tissue was assigned to individual lung slides after being examined by a single pathologist who was blind to the experimental groups. Each sample was analyzed with 10 fields and the average value was taken. HE staining of lung tissues was evaluated inflammation by Szapiel\u0026rsquo;s method [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Masson\u0026rsquo;s trichrome staining was assessed the degree of pulmonary fibrosis according to Ashcroft\u0026rsquo;s scoring criteria [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.4. Western blot and enzyme-linked immunosorbent assay (ELISA) analyses\u003c/h2\u003e\n \u003cp\u003eTotal proteins were extracted from lung and ileum tissues using 1% radio immunoprecipitation assay (RIPA) buffer with PMSF (Beyotime Biotechnology; C3006, Shanghai, China). Protein concentrations were calculated using a BCA Protein Assay kit (Beyotime Biotechnology, C3006, Shanghai, China). The protein extracted was separated by SDS-PAGE and then electrotransferred to PVDF membrane (Merck KGaA, Darmstadt, Germany). The membranes were incubated with primary antibodies against GAPDH (5174, 1:1000, CST), Vimentin (5741, 1:1000, CST), Collagen Ⅲ (GB111323, 1:1000, Servicebio), COL1A1(91144, 1:1000, CST), Claudin 1 (ab180158, 1:5000, abcam) and Occludin (ab131259, 1:5000, abcam) overnight at 4 ℃. Then blots were washed with Tris-Buffered Saline and 0.1% Tween 20 (TBST). Subsequently, the blots were incubated with Goat anti-rabbit IgG H\u0026amp;L (Alexa Fluor\u0026reg; 488, 1:5000, Abcam) at room temperature for 1 h. Finally, the densities were scanned by Li-Cor and quantified using the Image Studio Software [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe supernatant samples of rat lung tissue were analyzed for the concentrations of TNF-\u0026alpha; (ER02-96) and IL-1\u0026beta; (ER01-96) using rat ELISA assay kits (Biokits Technologies Inc., Beijing, China) following manufacturer\u0026rsquo;s instructions. The absorbance was measured at 450 nm using a spectrophotometer. The results were expressed in pg/mg protein.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.5. Transmission Electron Microscopy (TEM)\u003c/h2\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003cp\u003eAfter 48 h fixation in 2.5% glutaraldehyde at 4℃, ileum tissues were transferred into 1% osmium tetroxide for 1.5h post-fixation. Then the samples were dehydrated in concentrated ethanol and embedded in Epon 812. The embedding blocks were cut into ultrathin sections, stained with uranyl acetate and lead citrate. The results were observed using a Hitachi HT-7800 TEM with 80 KV [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e2.6. 16S rRNA gene sequences\u003c/h2\u003e\n \u003cp\u003eFecal pellets were collected and stored into sterile tubes, then immediately frozen and stored at \u0026minus;\u0026thinsp;80 ℃. Total genome DNA from fecal samples was extracted using CTAB/SDS method. The concentration of DNA was diluted to 1 ng/\u0026micro;L using sterile water. The V3 and V4 hypervariable regions of the16S rRNA genes were amplified used the specific primer with the barcode. All PCR reactions were carried out in 30 \u0026micro;L reactions with 15 \u0026micro;L of Phusion\u0026reg;High-Fidelity PCR Master Mix (New England Biolabs), 0.2 \u0026micro;M of forward and reverse primers, and about 10 ng template DNA. Samples with bright main strip between 400\u0026ndash;450 bp were chosen for further experiments. PCR products was mixed and then purified with AxyPrepDNA Gel Extraction Kit (AXYGEN). Sequencing libraries were used NEB Next\u0026reg;Ultra\u0026trade;DNA Library Prep Kit for Illumina (NEB, USA) following manufacturer\u0026rsquo;s recommendations. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina Miseq/HiSeq2500 platform and generated 250 bp/300 bp paired-end reads. The raw data are spliced, filtered and chimeric removed to obtain effective Clean Data. Sequences analysis was performed by UPARSE software package using the UPARSE-OTU and UPARSE-OTUref algorithms. Non-metric multidimensional scaling (NMDS) analysis was used to identify the dissimilarities between control and silica-exposed fecal microbiota matrixes. Linear discriminant analysis (LDA) was used to identify the significantly differentiated bacterial taxa across samples with a cutoff LDA score\u0026thinsp;\u0026gt;\u0026thinsp;4 [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e2.7. Non-targeted metabolomics profiling\u003c/h2\u003e\n \u003cp\u003eThe stool sample was thawed on ice. The 100 mg sample was taken and homogenized with 300 \u0026micro;L ice-cold water. The sample was centrifuged at 12,000 rpm at 4 ℃ for 10 minutes, 600 \u0026micro;L supernatant were sucked into another centrifuge tube. Then 100 \u0026micro;L 5% methanol added into the product, it was whirled at 2500 rpm for 5 minutes, and centrifuged at 12,000 rpm at 4 ℃ for 10 minutes. Finally, the supernatant was collected for LC-MS/MS analysis [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe original data obtained by LC-MS analysis was converted into mzML format by ProteoWizard software. Peak extraction, alignment and retention time correction were performed by XCMS program. Filter the peaks with deletion rate\u0026thinsp;\u0026gt;\u0026thinsp;50% in each group of samples. After that, metabolic identification information was obtained by searching the laboratory\u0026rsquo;s self-built database and integrating the public database and metDNA. Multivariate statistical analysis included partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003e2.8. Transcriptome analysis\u003c/h2\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003cp\u003eThree ileum tissues from each control or silica-exposed group on day 56 were used to transcriptome analysis. Total RNA of ileum was isolated using the Trizol Reagent (Invitrogen Life Technologies). The concentration, quality and integrity of RNA were determined using a NanoDrop spectrophotometer (Thermo Scientific). Three micrograms of RNA were used. The library fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, CA, USA) to select cDNA fragments of the preferred 380 bp in length. DNA fragments with ligated adaptor molecules on both ends were selectively enriched using Illumina PCR Primer Cocktail in a 15 cycle PCR reaction. Products were purified (AMPure XP system) and quantified using the Agilent high sensitivity DNA assay on a Bioanalyzer 2100 system (Agilent). Sequencing libraries were generated using the TruSeq RNA Sample Preparation Kit (Illumina, San Diego, CA, USA). The sequencing library was then sequenced on a NovaSeq 6000 (Illumina) by Shanghai Personal Biotechnology Cp. Ltd. We use Cutadapt [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] software to filter the sequencing data to get high quality sequence (Clean Data) for further analysis. Reference genome index was built by Bowtie2 [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] and the filtered reads were mapping to the reference genome using HISAT2 [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e], the default mismatch was no more than 2. The alignment region distribution of mapped reads was calculated. Then we used DESeq [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] to analyze the genes of difference expression with screened conditions as follows: expression difference multiple |log2FoldChange| \u0026gt; 1, significant \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003e2.9. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eAll results are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE. All statistical analyses were performed using GraphPad Prism. GraphPad Prism (version 8.3.0) was used for all statistical analysis. Univariate statistical analysis included Student\u0026rsquo;s t-test and analysis of variance (ANOVA). The correlation was analyzed using the Pearson correlation tests. Results with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered to be statistically different.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Silica-exposed rats showed high level of pulmonary inflammation\u003c/h2\u003e \u003cp\u003eWe observed significant reduction in body weight of silica-exposed rats comparing to controls at different time points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). And the lung coefficient of silica-exposed rats was significantly increased at five different time points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eLung tissues were collected at five time points respectively for HE staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The results demonstrated that no obvious abnormalities were found in the lungs of control rats. A large number of inflammatory cells infiltrated in the alveolar septum and destroyed alveoli integrity from day 7 to 56 after silica exposure. And inflammatory response was the most serious on day 14 with the highest Szapiel scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Furthermore, we detected the levels of IL-1β and TNF-α to investigate the expression of inflammation cytokine after silica exposure. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, the levels of IL-1β and TNF-α were significantly higher in the silica-exposed group than control group at different time points and remained highest on day 14 and 28 respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Silica-exposed rats showed high level of pulmonary fibrosis\u003c/h2\u003e \u003cp\u003eThe results of Masson staining showed that the pulmonary fibroblasts gradually increased and tiny collagen fibers were found from day 28. The cellular nodules and some fibrosis nodules were observed on day 56 in silica-exposed group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The Ashcroft scores were progressively increased from day 7 to 56 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine the degree of fibrosis at different time points after silica-exposure, we detected of the level of vimentin and collage Ⅲ proteins in lung tissues from control and silica-exposed rats using western blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The results demonstrated that silica induced the expression levels of vimentin and collage Ⅲ from day 28 and continuously increased until day 56 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Silica-exposed rats showed ileum epithelial injury\u003c/h2\u003e \u003cp\u003eUnder light microscopy, no evident pathological changes were observed in the intestinal mucosa of control rats. By contrast, structural damage to the ileum tissues were characterized by regional intestinal villi shedding and shorten in the rats from the silica-exposed group at different time points. And the degree of injury was gradually aggravated with time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, the levels of tight junction proteins (including claudin 1 and occludin) were significantly reduced in the silica-exposed group on day 28, 42, 56 compared to control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eThe ultrastructure of the ileum was visualized by TEM. The results showed that microvilli and mitochondria of enterocytes had a normal structure in controls. Mitochondrial swelling, microvillus shortening and disorder were observed in the ileum on day 28, 42, 56 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. FMT-treated rats showed the changes of \u003cem\u003eBifidobacterium\u003c/em\u003e, pulmonary inflammation, fibrosis and ileac injury\u003c/h2\u003e \u003cp\u003eFMT experiment was used to further validate the association between fecal microbiota dysbiosis and silica induced lung and ileum epithelial injury. The richness of fecal microbiota decreased significantly in antibiotics-treated rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), but increased after FMT (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The results of PCoA showed that microbiota successfully separated into different groups, which was relatively concentrated in the same group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Based on the results of anosim analysis, significant differences were found in composition of fecal microbiota in three groups after antibiotics treatment (R\u0026thinsp;=\u0026thinsp;0.891, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and FMT (R\u0026thinsp;=\u0026thinsp;0.203, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The levels of \u003cem\u003eBifidobacterium\u003c/em\u003e was significantly increased in FMT-treated rats comparing to silica-exposed rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\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\u003eAnosim analysis between antibiotics-treated and FMT groups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eR-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6C vs F6 vs E6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.890677258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF6 vs E6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE6 vs C6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.552517361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF6_vs_C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC7 vs E7 vs F7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20270694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF7 vs E7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.219308036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE7 vs C7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.217013889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF7 vs C7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1796875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: C6: The control group after antibiotics treatment. F6: The FMT group after antibiotics treatment. E6: The silica-exposed group after antibiotics treatment. C7: The control group after FMT. F7: The FMT group after FMT. E7: The silica-exposed group after FMT.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHE and Masson staining of lung tissues after FMT were observed. The results demonstrated that pulmonary cellular nodules were observed in silica-exposed and FMT group. However, the pulmonary cellular nodules in silica-exposed group were larger than that in FMT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). And the higher Szapiel and Ashcroft scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) were shown in silica-exposed group. We further examined inflammatory cytokines (IL-1β, TNF-α) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE) and fibrosis protein (collagen type 1, collagen Ⅲ) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH) of lung tissues. The level of IL-1β, TNF-α, collagen type 1 and collagen Ⅲ in FMT group were lower than silica-exposed group, but no statistic difference. In addition, tight junction proteins (including claudin 1 and occludin) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK) significantly recovered after FMT compared to silica-exposed rats.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Silica-exposed rats showed the dysbiosis of fecal microbiota\u003c/h2\u003e \u003cp\u003eTo determine the change of fecal microbiota in silica-exposed rats, we collected the fecal pellets samples of rats on day 7, 14, 28, 42 and 56. The results of 16S rRNA gene sequencing analysis showed that \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e were the predominant microbes at the phylum level in the fecal microbiome of rats from control and silica-exposed group (Fig. S2A). Then, the indices (Chao 1 and Shannon) were calculated to estimate the community diversity and richness using alpha diversity metric. The Chao 1 index (Fig. S2B) wer significantly decreased on day 14 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0039) and increased on day 56 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) in the silica-exposed group compared to control group. The Shannon index (Fig. S2C) were lower in the silica-exposed group compared to controls on day 7 and 14. Difference Alpha diversity analysis showed that microbial community diversity and richness revealed dynamic change trend.\u003c/p\u003e \u003cp\u003eThe fecal microbiome patterns in silica-exposed and control rats were differentiated using multivariate statistical analysis by the principal component analysis (PCA) plot to analysis beta diversity. We can clearly see that silica exposure resulted in marked changes in the composition of fecal bacteria at different time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Differences between silica-exposed rats and controls were significant on day 14 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), day 28 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.25\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), day 42 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.22\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), day 56 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.66\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Based on the Bray-Curtis algorithm, anosim analysis were used to analyze the difference between groups and within the group, so as to determine whether the grouping was meaningful. Significant differences between groups were found on day 28 (R\u0026thinsp;=\u0026thinsp;0.171, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), day 42 (R\u0026thinsp;=\u0026thinsp;0.182, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), day 56 (R\u0026thinsp;=\u0026thinsp;0.603, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eThe results of Anosim analysis in silica-exposed group compared to control group at different time points\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimes (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.094023324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.089192708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.171316964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.181919643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.603298611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\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\u003eTo investigate the effect of silica exposure on fecal microbiota composition, we performed linear discriminant analysis effect size (LEfSe) analysis to identify the differentially represented fecal microbiota between control and silica-exposed groups at different times. The results showed that 42, 7, 15, 16 differential bacterial taxa were found between control and silica-exposed groups respectively on day 7, 14, 28, 42 (Fig. S2D). Twenty-five differential bacterial taxa were found between control and silica-exposed groups on day 56 when the degree of lung fibrosis was the the most severe (Fig. S2D). Specifically, in genus level, the relatively abundance of \u003cem\u003eRuminococcus2, Thermoactinomyces\u003c/em\u003e, \u003cem\u003eAquabacterium\u003c/em\u003e, \u003cem\u003eSaccharopolysporarectivirgula\u003c/em\u003e increased in silica-exposed group, whereas the relatively abundance of \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003ePseudarthrobacter\u003c/em\u003e, \u003cem\u003eAnaeroplasma\u003c/em\u003e, \u003cem\u003eRuminiclostridium 9\u003c/em\u003e reduced on day 56.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Silica-exposed rats showed the alterations of metabolites on day 56\u003c/h2\u003e \u003cp\u003eThen the faeces were collected to detect the alterations of metabolites on day 56. The result of metabolomic analysis showed that silica exposure induced a different metabolite profile comparing to controls on day 56. The result of Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) indicated a significant separation between the control and silica-exposed group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Differences were present in both the positive and negative mass spectrometric ion modes. Goodness of fit values and predictive ability values (positive mode: R2X\u0026thinsp;=\u0026thinsp;0.283, R2Y\u0026thinsp;=\u0026thinsp;0.94, Q2\u0026thinsp;=\u0026thinsp;0.281, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; negative mode: R2X\u0026thinsp;=\u0026thinsp;0.238, R2Y\u0026thinsp;=\u0026thinsp;0.957, Q2\u0026thinsp;=\u0026thinsp;0.22, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) indicated that the OPLS-DA model possessed a satisfactory fit with good predictive power.\u003c/p\u003e \u003cp\u003eThe differential expression metabolites in silica-exposed group were further screened using variable importance in projection (VIP), an important load evaluation parameter, as a threshold in this study. Metabolites that met VIP\u0026thinsp;\u0026ge;\u0026thinsp;1.0, Fold Change\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered to be differential metabolites. There were 85 differential metabolites (57 up-regulated, 28 down-regulated) in positive mode and 21 differential metabolites (9 up-regulated, 12 down-regulated) in negative mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-2). We further validated the changed metabolites using qualitative and quantitative analysis.\u003c/p\u003e \u003cp\u003eTo further identify the relevant metabolic pathways involved in the silica exposure, we used Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation method to analyze the differential metabolites. KEGG pathway analysis showed that 16 pathways were in positive mode and 4 pathways were in negative mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Silica-exposed rats showed the alterations of mRNAs of ileum tissues on day 56\u003c/h2\u003e \u003cp\u003eIn total, 900 differentially expressed mRNAs (838 up-regulated and 62 down-regulated) were identified in ileum tissue of silica-exposed rats compared to controls on day 56 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Correlation between fecal microbiota and metabolites;and metabolites and genes from silica-exposed rats on day 56\u003c/h2\u003e \u003cp\u003eTo explore the functional correlation between microbiota changes and metabolite perturbations, a correlation matrix was generated by calculating the Pearson\u0026rsquo;s correlation coefficient. Our data showed strong associations between fecal microbiota and metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB) (Table S3). Especially, the levels of \u003cem\u003eBifidobacterium\u003c/em\u003e positively correlated with the relative abundance of 3-CCPA (R\u0026thinsp;=\u0026thinsp;0.816, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, correlation analysis was conducted for differentially expression mRNAs and metabolites. The results with a Pearson correlation coefficient greater than 0.8 were selected. The levels of some metabolites highly correlated with the expression of mRNAs of ileum tissues. Based on the results of differential metabolites and mRNAs enrichment analysis, differential metabolites and mRNAs were involved in the same KEGG pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). We found that Indole-3-Ethanol, Ido1, Afmid, Kynu and Il4i1 were involved in \u0026ldquo;Tryptophan metabolism\u0026rdquo; pathway, and N-Acetyl -Glutamic Acid and Nos2 were involved in \u0026ldquo;Arginine biosynthesis\u0026rdquo; pathway.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eRecent studies have shown that fecal microbiota was involved in regulating the homeostasis of intestinal epithelium and contributing to pathophysiological processes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Pathological and disease situations such as pulmonary infections and chronic inflammatory, foreign particulate matter stimulation and metabolic disorders can also lead to the imbalance of intestinal homeostasis and intestinal microecosystem dysbiosis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Understanding fecal microbiota and its associated metabolites is important to elucidate the possible mechanisms of many lung disease development. A clinical study found that some patients with advanced silicosis had peptic ulcer and gastrointestinal mucosal barrier damage [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This present study aimed to investigate the role of fecal microbiota in the silica-exposed pulmonary fibrosis and ileac injury in rats, and to predict the possible mechanistic pathways.\u003c/p\u003e \u003cp\u003eFirstly, we established silicosis model by intratracheal instillation of silica particles in rats and observed dynamic changes of pulmonary inflammation and fibrosis on day 7, 14, 28, 42 and 56. Meanwhile, we observed the damage profile of ileum tissue by examining the pathological changes of ileum tissue, the level of intestinal tight junction protein and the microstructure changes of enterocyte after silica exposure at five different time points. The results showed that the inflammatory response was at a higher level on day 14. No obvious abnormality was observed in ileum tissue. On day 28, fibrocytes appeared in lung tissue, and the level of fibrin are elevated, which was consistent with our previous study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. At the same time, we found that intestinal villi disordered, tight junction protein levels significantly reduced, and mitochondria of intestinal epithelial cells swollen in silica-exposed rats. The level of both pulmonary fibrosis and ileum damage was gradually increased and on day 56. And the degree both of and pulmonary fibrosis gradually increased. Injury of the gut barrier and subsequent translocation of bacteria from the gut have been implicated in the development of hypermetabolism and distant organ injury [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These results suggested that the formation of pulmonary inflammation and fibrosis was a time-dependent and accumulative process. The occurrence of ileac injury was accompanied with pulmonary inflammation and the degree of ileac injury gradually aggravated with the pulmonary fibrosis progression.\u003c/p\u003e \u003cp\u003eIn our present study, exposure to silica dust caused pulmonary inflammation and fibrosis and ileac injury. Some studies reported that the fecal microbiota of patients with silicosis and the rats exposed to silica were significantly changed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, whether fecal microbiota play the crucial role in these changes remains unknown. Therefore, we examined the changes of fecal microbiota in silica-exposed rats from day 7 to 56. The results of 16S rRNA gene sequencing showed that community diversity was of dynamic change. The community diversity and richness were significantly decreased on day 14 after silica exposure when the level of pulmonary inflammation peaked. In recent years, studies have shown that increased inflammation could stimulate the activation of the immune system and reduce microbial diversity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The result indicated that silica-induced acute inflammation in pulmonary system may disturb microbiota community structure of gut. The composition of the differential microbiota of the rats between the two groups was variable at different time points, which may be relative to disease progression. In this study, the level of Bifidobacterium was increased significantly on day 7 in silica-exposed rats. Fecal microbiota kept a dynamic balance in normal circumstances. Inflammatory response might promote the compensatory increase of the level of probiotic to induce a range of immunoregulatory responses through gut-lung axis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Then we predicted that the increase of the level of Bifidobacterium might be related to dynamic regulation of fecal microbiota. The result showed that major differential microbiota Bifidobacterium was significantly decreased in silica-exposed rats with severe pulmonary fibrosis on day 56. This is possibly attributed to the change of cytokines, microbial components and metabolites induced by silica particle that result in the dysbiosis of intestinal microbiota and depleted of Bifidobacterium. Similar result was demonstrated by a study showing that long-term stimulus of inflammatory factor could destroy the intestinal homeostasis of rats [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Supportively, Zhou et al. found that the abundances of \u003cem\u003eBifidobacterium\u003c/em\u003e were decreased in the fecal samples of the silicosis patients which were consistent with our results [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These studies suggested that \u003cem\u003eBifidobacterium\u003c/em\u003e played an important role in intestinal homeostasis. The genus \u003cem\u003eBifidobacterium\u003c/em\u003e were gram-positive, polymorphic rod-shaped bacteria and predominating probiotic organisms in the intestinal microbiota of humans and animals [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The deviations in intestinal \u003cem\u003eBifidobacterium\u003c/em\u003e have been observed in patients with different diseases, including allergies, IBD, asthma or cystic fibrosis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our study results suggested that \u003cem\u003eBifidobacterium\u003c/em\u003e may play an important role in silica-induced pulmonary fibrosis.\u003c/p\u003e \u003cp\u003eTo explore the effect of changed microbiota on pulmonary fibrosis and ileac injury in rats we further conducted FMT experiment. The results showed that main compositions of microbiota were restored and the level of \u003cem\u003eBifidobacterium\u003c/em\u003e was increased after FMT. Meanwhile, the expression trend of pulmonary inflammation and fibrosis decreased. This indicated \u003cem\u003eBifidobacterium\u003c/em\u003e may be closely related to the progression of pulmonary fibrosis. However, there was no statistical difference, which may be caused by insufficient time of FMT. In addition, FMT restored the level of intestinal tight junction protein such as claudin 1 and occludin. These results indicated that fecal microbiota may play a regulatory role in silica-induced pulmonary fibrosis progression and ileac injury. The role of \u003cem\u003eBifidobacterium\u003c/em\u003e was worthy to further research.\u003c/p\u003e \u003cp\u003eIn addition, the results of correlation analysis showed that the levels of \u003cem\u003eBifidobacterium\u003c/em\u003e positively correlated with the relative abundance of 3-CCPA which was down-regulated in the profile of silica exposure. Substitution of the sn-3 oxygen with a methylene in cyclic phosphatidic acid (CPA) yielded 3-CCPA, a stabilized analog of CPA [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. CPA is a characteristic phospholipid mediator involved in regulating many cellular processes and inhibiting cell proliferation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Bacterial phospholipase D (PLD) from \u003cem\u003eActinomadurea sp. NO 362\u003c/em\u003e and \u003cem\u003eStreptomyces chromofuscus\u003c/em\u003e could be responsible for CPA production via transphosphatidylation of abundant lysophosphatidylcholin (LPC) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Importantly, PLD could catalyze transphosphatidylation reactions in the presence of glycerol or short chain primary aliphatic-alcohols [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The lipoteichoic acids from \u003cem\u003eBifidobucterium\u003c/em\u003e pennsylvanicum were extracted from cytoplasmic membranes or disintegrated bacteria. Thus, our results predicted that \u003cem\u003eBifidobacterium\u003c/em\u003e may affect PLD catalyzing the transphosphatidylation of LCP by regulating PLD to induce 3-CCPA production. Hotta et al. demonstrated that intravenous administration of 3-CCPA has a facilitatory effect on respiration [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. A study reported that 3-CCPA were potent inhibitors of autotoxin (ATX) which highly pertinent to chronic inflammation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In chronic inflammatory disorders, ATX has been reported to be expressed from alveolar inflammatory macrophages [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These studies showed that 3-CCPA played an important role in pulmonary inflammatory disorders. Hence, our results suggested that the alteration of \u003cem\u003eBifidobacterium\u003c/em\u003e might be related to the regulation of 3-CCPA which participated in silica-induced pulmonary inflammatory response. However, the specific mechanism needs to be further studied.\u003c/p\u003e \u003cp\u003eTo screen and identify the genes related to ileac barrier injury, transcriptome analysis was performed to examine the expression of differential mRNAs of ileum tissue on day 56. And correlation analysis of differential genes and metabolites was carried out. We found that 3-CCPA positively correlated with claudin 8 (Cldn8) which was down-regulated in silica-exposed group. Cldn8 was the key proteins in building the tight junction (TJ) net, which determined the tightness of the epithelial barrier and contribute to the polarity of epithelial cells [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. During chronic inflammatory diseases, some pathogenic microorganism can target the tight junction complex leading to TJ protein dysregulation, which results in the disruption of intestinal barrier homeostasis [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. CPA can elevate cyclic 3',5'-adenosine monophosphate (cAMP) level through the activation of a Ca\u003csup\u003e2+\u003c/sup\u003e-sensitive adenylyl cyclase [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The activation of cAMP-dependent protein kinase (PKA) by cAMP successfully improves TJ architecture [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Therefore, CPA may regulate TJ protein such as Cldn8 by stimulating the production of cAMP. \u003cem\u003eBifidobacterium\u003c/em\u003e played the protective effects on intestinal barrier function by inhibiting secretion of proinflammatory cytokine and maintaining TJ integrity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Therefore, we predicted that \u003cem\u003eBifidobacterium\u003c/em\u003e may be related to the production of 3-CCPA and the expression of Cldn8, which involve silica-induced pulmonary inflammatory response and further destroyed ileac barrier function.\u003c/p\u003e \u003cp\u003eFurthermore, we found that Nitric Oxide Synthase 2 (Nos2) (up-regulated in silica-exposed rats) and N-Acetyl-L-Glutamic Acid (down-regulated in silica-exposed rats) was involved in the same \u0026ldquo;Arginine biosynthesis\u0026rdquo; pathway. Arginine supplementation could enhance intestinal barrier function [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The first committed step of arginine biosynthesis is acetylation of L-glutamate at the N-position, which is also a feature characteristic of many prokaryotes and fungi [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Our results suggested that decreased N-Acetyl-L-Glutamic Acid may inhibit arginine biosynthesis and damage intestinal barrier function. Inflammatory response results in significantly decreased L-arginine levels in the case of the upregulation of Nos2, an important mediator in pro-inflammatory in macrophages [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In turn, the upregulation of Nos2 enhanced utilization of arginine and impaired resynthesis of arginine from citrulline [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Fecal microecological dysbiosis might influence the level of N-Acetyl-L-Glutamic Acid, which is related to the inhibition of the arginine biosynthesis and the upregulation of Nos2. Therefore, we predicted that silica-induced fecal microecological disorder and inflammatory response might be related to Nos2 upregulation and then influenced the level of arginine which may finally affect ileac architectural integrity. Hence, the role of \u0026ldquo;Arginine biosynthesis\u0026rdquo; signal pathway was worth further to study in silica-exposed ileum injury.\u003c/p\u003e \u003cp\u003eGut tryptophan can be metabolized into indole and indole derivatives by fecal micro population, such as \u003cem\u003eBifidobacterium spp\u003c/em\u003e, \u003cem\u003eBacteroides spp\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Indole-3-ethanol as one of the tryptophan metabolites derived from the gut microbiota and could modulate gut barrier integrity via tight junctions [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. It has been reported that intestinal epithelial barrier function could be improved during inflammatory response in mice treated with indole-3-ethanol [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Our results showed that the level of Indole-3-ethanol significantly decreased after silica exposure, which may be induced by \u003cem\u003eBifidobacterium\u003c/em\u003e decrease and finally resulted in ileac injury. Intestinal microorganism and multiple inflammatory cytokines can stimulate the expression of indoleamine 2,3-dioxygenase 1 (Ido1). The expression levels of Ido1 was low in healthy bowel tissue [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Most tryptophan in tissue was metabolized to kynurenine, which was regulated by Ido1 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The prominent presence of Ido1 and decreased levels of Kyn in the ileum, presumably resulted from the strong expression of Kynu in ileum tissues [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Our results showed that the expression of Ido1 and Kynu were significantly up-regulated in ileum tissues in silica-exposed rats. And the result of KEGG showed that Ido1 and Kynu and Indole-3-ethanol were involved in \u0026ldquo;Tryptophan metabolism\u0026rdquo; pathway. Therefore, we predicted that silica-induced fecal microecology disorder may decrease the expression of Indole-3-ethanol and increase the expression of Ido1, Kynu to promote tryptophan metabolism. \u0026ldquo;Tryptophan metabolism\u0026rdquo; pathway was worth further to study.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur results showed that the silica could induce pulmonary fibrosis, ileac barrier injury and fecal microbiota dysbiosis in rats. By 16s rRNA analysis, \u003cem\u003eBifidobacterium\u003c/em\u003e may affect the development of silica-induced pulmonary fibrosis and ileum injury. By conjoint analysis and KEGG analysis, 3 predicted mechanistic pathways were as follow: (ⅰ) the change of Bifidobacterium maybe related to the production of 3-CCPA and the expression of Cldn8, which involved in silica-induced pulmonary inflammatory response and ileac barrier function injury; (ⅱ) Silica-induced fecal microecological dysbiosis and inflammatory respond may affect the arginine biosynthesis and utilization of arginine by regulating the level of N-Acetyl-L-Glutamic Acid and Nos2, which effect ileac architectural integrity; and (ⅲ) Silica-induced fecal microecology disorder may induce ileac injury by regulating Ido1, Kynu and Indole-3-ethanol mediated \u0026ldquo;Tryptophan metabolism\u0026rdquo; pathway.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKrefft, S., Wolff, J., Rose, C.: Silicosis: An Update and Guide for Clinicians. Clin. Chest Med. \u003cb\u003e41\u003c/b\u003e(4), 709\u0026ndash;722 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMlika, M., Adigun, R., Bhutta, B.S.: \u003cem\u003eSilicosis\u003c/em\u003e, in \u003cem\u003eStatPearls\u003c/em\u003e. StatPearls Publishing Copyright \u0026copy; 2021, StatPearls Publishing LLC.: Treasure Island (FL). (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung, C.C., Yu, I.T., Chen, W.: Silicosis. Lancet. \u003cb\u003e379\u003c/b\u003e(9830), 2008\u0026ndash;2018 (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaint-Criq, V., Lugo-Villarino, G., Thomas, M.: Dysbiosis, malnutrition and enhanced gut-lung axis contribute to age-related respiratory diseases. Ageing Res. Rev. \u003cb\u003e66\u003c/b\u003e, 101235 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBingula, R., et al.: \u003cem\u003eDesired Turbulence? Gut-Lung Axis, Immunity, and Lung Cancer.\u003c/em\u003e J Oncol, 2017: p.\u0026nbsp;5035371. (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelkaid, Y., Hand, T.W.: Role of the microbiota in immunity and inflammation. Cell. \u003cb\u003e157\u003c/b\u003e(1), 121\u0026ndash;141 (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarl, J.P., et al.: \u003cem\u003eEffects of Psychological, Environmental and Physical Stressors on the Gut Microbiota.\u003c/em\u003e Front Microbiol, 9: p.\u0026nbsp;2013. (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoidl, L., Untersmayr, E.: The clinical implications of the microbiome in the development of allergy diseases. Expert Rev. Clin. Immunol. \u003cb\u003e17\u003c/b\u003e(2), 115\u0026ndash;126 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai, H.C., et al.: Gut microbiota modulates COPD pathogenesis: role of anti-inflammatory Parabacteroides goldsteinii lipopolysaccharide. Gut. \u003cb\u003e71\u003c/b\u003e(2), 309\u0026ndash;321 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Y., et al.: Role of lung and gut microbiota on lung cancer pathogenesis. J. Cancer Res. Clin. Oncol. \u003cb\u003e147\u003c/b\u003e(8), 2177\u0026ndash;2186 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRan, Z., et al.: Subchronic exposure to concentrated ambient PM2.5 perturbs gut and lung microbiota as well as metabolic profiles in mice. Environ. Pollut. \u003cb\u003e272\u003c/b\u003e, 115987 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, T.H., et al.: Epithelial barrier dysfunctions in atopic dermatitis: a skin-gut-lung model linking microbiome alteration and immune dysregulation. Br. J. Dermatol. \u003cb\u003e179\u003c/b\u003e(3), 570\u0026ndash;581 (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeneroso, S.V., et al.: Saccharomyces cerevisiae strain UFMG 905 protects against bacterial translocation, preserves gut barrier integrity and stimulates the immune system in a murine intestinal obstruction model. Arch. Microbiol. \u003cb\u003e192\u003c/b\u003e(6), 477\u0026ndash;484 (2010)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutten, E.P.A., et al.: Disturbed intestinal integrity in patients with COPD: effects of activities of daily living. Chest. \u003cb\u003e145\u003c/b\u003e(2), 245\u0026ndash;252 (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X., et al.: Probiotics Ameliorate Colon Epithelial Injury Induced by Ambient Ultrafine Particles Exposure. Adv. Sci. (Weinh). \u003cb\u003e6\u003c/b\u003e(18), 1900972 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Y., et al.: Alterations in the gut microbiota of patients with silica-induced pulmonary fibrosis. J. Occup. Med. Toxicol. \u003cb\u003e14\u003c/b\u003e, 5 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, H., et al.: Silicon, an important exposure marker in vivo in silicosis research. Int. Arch. Occup. Environ. Health. \u003cb\u003e94\u003c/b\u003e(7), 1513\u0026ndash;1522 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzapiel, S.V., et al.: Bleomycin-induced interstitial pulmonary disease in the nude, athymic mouse. Am. Rev. Respir Dis. \u003cb\u003e120\u003c/b\u003e(4), 893\u0026ndash;899 (1979)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshcroft, T., Simpson, J.M., Timbrell, V.: Simple method of estimating severity of pulmonary fibrosis on a numerical scale. J. Clin. Pathol. \u003cb\u003e41\u003c/b\u003e(4), 467\u0026ndash;470 (1988)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBass, J.J., et al.: An overview of technical considerations for Western blotting applications to physiological research. Scand. J. Med. Sci. Sports. \u003cb\u003e27\u003c/b\u003e(1), 4\u0026ndash;25 (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUdoumoh, A.F., et al.: Light and transmission electron microscopic evaluation of the lamina epithelialis mucosae in the ileum of pre- and post-hatch broiler chicken. Anat. Histol. Embryol. \u003cb\u003e51\u003c/b\u003e(1), 136\u0026ndash;142 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson, J.S., et al.: Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. \u003cb\u003e10\u003c/b\u003e(1), 5029 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, W.W., et al.: Non-invasive urinary metabolomic profiles discriminate biliary atresia from infantile hepatitis syndrome. Metabolomics. \u003cb\u003e14\u003c/b\u003e(7), 90 (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, X., et al.: Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics. Nat. Commun. \u003cb\u003e10\u003c/b\u003e(1), 1516 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKechin, A., et al.: cutPrimers: A New Tool for Accurate Cutting of Primers from Reads of Targeted Next Generation Sequencing. J. Comput. Biol. \u003cb\u003e24\u003c/b\u003e(11), 1138\u0026ndash;1143 (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangmead, B., Salzberg, S.L.: Fast gapped-read alignment with Bowtie 2. Nat. Methods. \u003cb\u003e9\u003c/b\u003e(4), 357\u0026ndash;359 (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSir\u0026eacute;n, J., V\u0026auml;lim\u0026auml;ki, N., M\u0026auml;kinen, V.: Indexing Graphs for Path Queries with Applications in Genome Research. IEEE/ACM Trans. Comput. Biol. Bioinform. \u003cb\u003e11\u003c/b\u003e(2), 375\u0026ndash;388 (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnders, S., Huber, W.: Differential expression analysis for sequence count data. Genome Biol. \u003cb\u003e11\u003c/b\u003e(10), R106 (2010)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDefois, C., et al.: Food Chemicals Disrupt Human Gut Microbiota Activity And Impact Intestinal Homeostasis As Revealed By In Vitro Systems. Sci. Rep. \u003cb\u003e8\u003c/b\u003e(1), 11006 (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSencio, V., Machado, M.G., Trottein, F.: The lung-gut axis during viral respiratory infections: the impact of gut dysbiosis on secondary disease outcomes. Mucosal Immunol. \u003cb\u003e14\u003c/b\u003e(2), 296\u0026ndash;304 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng hongyi, L.: Risk factors of overt bleeding of peptic ulcer in silicosis patients. Industrial health and occupational diseases. \u003cb\u003e02\u003c/b\u003e, 109\u0026ndash;110 (1996)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSai, L., et al.: Dynamic assessing silica particle-induced pulmonary fibrosis and associated regulation of long non-coding RNA expression in Wistar rats. Genes Environ. \u003cb\u003e43\u003c/b\u003e(1), 23 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeitch, E.A.: Bacterial translocation of the gut flora. J. Trauma. \u003cb\u003e30\u003c/b\u003e(12 Suppl), S184\u0026ndash;S189 (1990)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong, G.C., Song, S.R., Su, J.: Pulmonary fibrosis alters gut microbiota and associated metabolites in mice: An integrated 16S and metabolomics analysis. Life Sci. \u003cb\u003e264\u003c/b\u003e, 118616 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWesemann, D.R., Nagler, C.R.: The Microbiome, Timing, and Barrier Function in the Context of Allergic Disease. Immunity. \u003cb\u003e44\u003c/b\u003e(4), 728\u0026ndash;738 (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWypych, T.P., Wickramasinghe, L.C., Marsland, B.J.: The influence of the microbiome on respiratory health. Nat. Immunol. \u003cb\u003e20\u003c/b\u003e(10), 1279\u0026ndash;1290 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBudden, K.F., et al.: \u003cem\u003eProbiotic Bifidobacterium longum subsp. longum Protects against Cigarette Smoke-Induced Inflammation in Mice\u003c/em\u003e.Int J Mol Sci, 24(1). (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, J., et al.: \u003cem\u003eThe primary biological network of Bifidobacterium in the gut\u003c/em\u003e.FEMS Microbiol Lett, 365(8). (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhlebach, M.S., et al.: Initial acquisition and succession of the cystic fibrosis lung microbiome is associated with disease progression in infants and preschool children. PLoS Pathog. \u003cb\u003e14\u003c/b\u003e(1), e1006798 (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, W., et al.: Bifidobacterium infantis Relieves Allergic Asthma in Mice by Regulating Th1/Th2. Med. Sci. Monit. \u003cb\u003e26\u003c/b\u003e, e920583 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupte, R., et al.: Synthesis and pharmacological evaluation of the stereoisomers of 3-carba cyclic-phosphatidic acid. Bioorg. Med. Chem. Lett. \u003cb\u003e20\u003c/b\u003e(24), 7525\u0026ndash;7528 (2010)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurakami-Murofushi, K., et al.: Biological functions of a novel lipid mediator, cyclic phosphatidic acid. Biochim. Biophys. Acta. \u003cb\u003e1582\u003c/b\u003e(1\u0026ndash;3), 1\u0026ndash;7 (2002)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukai, M., et al.: Hepatoma cell migration through a mesothelial cell monolayer is inhibited by cyclic AMP-elevating agents via a Rho-dependent pathway. FEBS Lett. \u003cb\u003e484\u003c/b\u003e(2), 69\u0026ndash;73 (2000)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDermott, M.I., et al.: Mammalian phospholipase D: Function, and therapeutics. Prog Lipid Res. \u003cb\u003e78\u003c/b\u003e, 101018 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHotta, H., Kagitani, F., Murakami-Murofushi, K.: Cyclic phosphatidic acid stimulates respiration without producing vasopressor or tachycardiac effects in rats. Eur. J. Pharmacol. \u003cb\u003e543\u003c/b\u003e(1\u0026ndash;3), 27\u0026ndash;31 (2006)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitakaze, K., et al.: Development of a selective fluorescence-based enzyme assay for glycerophosphodiesterase family members GDE4 and GDE7. J. Lipid Res. \u003cb\u003e62\u003c/b\u003e, 100141 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagkrioti, C., et al.: Autotaxin and chronic inflammatory diseases. J. Autoimmun. \u003cb\u003e104\u003c/b\u003e, 102327 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarmeyer, C., Fromm, M., Schulzke, J.D.: Active and passive involvement of claudins in the pathophysiology of intestinal inflammatory diseases. Pflugers Arch. \u003cb\u003e469\u003c/b\u003e(1), 15\u0026ndash;26 (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠuligoj, T., et al.: \u003cem\u003eEffects of Human Milk Oligosaccharides on the Adult Gut Microbiota and Barrier Function\u003c/em\u003e.Nutrients, 12(9). (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatterson, C.E., et al.: Regulation of endothelial barrier function by the cAMP-dependent protein kinase. Endothelium. \u003cb\u003e7\u003c/b\u003e(4), 287\u0026ndash;308 (2000)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLing, X., et al.: Protective Effects of Bifidobacterium on Intestinal Barrier Function in LPS-Induced Enterocyte Barrier Injury of Caco-2 Monolayers and in a Rat NEC Model. PLoS One. \u003cb\u003e11\u003c/b\u003e(8), e0161635 (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViana, M.L., et al.: Pretreatment with arginine preserves intestinal barrier integrity and reduces bacterial translocation in mice. Nutrition. \u003cb\u003e26\u003c/b\u003e(2), 218\u0026ndash;223 (2010)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, Y., Labedan, B., Glansdorff, N.: Surprising arginine biosynthesis: a reappraisal of the enzymology and evolution of the pathway in microorganisms. Microbiol. Mol. Biol. Rev. \u003cb\u003e71\u003c/b\u003e(1), 36\u0026ndash;47 (2007)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoeksema, M.A., St\u0026ouml;ger, J.L., de Winther, M.P.: Molecular pathways regulating macrophage polarization: implications for atherosclerosis. Curr. Atheroscler Rep. \u003cb\u003e14\u003c/b\u003e(3), 254\u0026ndash;263 (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElwafi, F., et al.: Endotoxemia affects citrulline, arginine and glutamine bioavailability. Eur. J. Clin. Invest. \u003cb\u003e42\u003c/b\u003e(3), 282\u0026ndash;289 (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeszthelyi, D., Troost, F.J., Masclee, A.A.: Understanding the role of tryptophan and serotonin metabolism in gastrointestinal function. Neurogastroenterol Motil. \u003cb\u003e21\u003c/b\u003e(12), 1239\u0026ndash;1249 (2009)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAoki, R., et al.: Indole-3-Pyruvic Acid, an Aryl Hydrocarbon Receptor Activator, Suppresses Experimental Colitis in Mice. J. Immunol. \u003cb\u003e201\u003c/b\u003e(12), 3683\u0026ndash;3693 (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScott, S.A., Fu, J., Chang, P.V.: Microbial tryptophan metabolites regulate gut barrier function via the aryl hydrocarbon receptor. Proc. Natl. Acad. Sci. U S A. \u003cb\u003e117\u003c/b\u003e(32), 19376\u0026ndash;19387 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuhn, M., et al.: \u003cem\u003eInflammation-Induced Mucosal KYNU Expression Identifies Human Ileal Crohn's Disease\u003c/em\u003e.J Clin Med, 9(5). (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComai, S., et al.: Tryptophan in health and disease. Adv. Clin. Chem. \u003cb\u003e95\u003c/b\u003e, 165\u0026ndash;218 (2020)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Silicosis, Pulmonary fibrosis, Fecal microbiota, Metabolomics, Transcriptomics, FMT, Intestinal injury","lastPublishedDoi":"10.21203/rs.3.rs-2661022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2661022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground：\u003c/strong\u003eSilicosis is a lung disease with diffuse nodular pulmonary fibrosis because of long-term inhalation of a large number of free silica dust. Research has been reported that dysbiosis of fecal microbiota was existed in silicosis patients. However, few studies have examined the effects of silica on the intestinal tract.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eIn this study, we aimed to investigate the change of fecal microbiota, lung and ileum tissues of rats exposed to silica dust and explore the regulatory role of fecal microbiota in rats after silica exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe Wistar male rats were intratracheally instilled with 50 mg/mL silicon dioxide (1 mL per rat). Hematoxylin and eosin (HE), Masson staining, enzyme-linked immunosorbent assay (ELISA) and Western blot were used to exam the pulmonary inflammation and fibrosis in rats. HE, Western blot and Transmission Electron Microscopy (TEM) were used to exam the ileac injury. The 16s rRNA gene sequences, non-targeted metabolomics, transcriptome analysis were used to exam the fecal microbiota, metabolites and ileac mRNAs respectively. Then, we employed fecal microbiota transplantation (FMT) experiment to examine whether fecal microbiota play the important roles in the change of silica-induced pulmonary inflammation, fibrosis and ileum injury. Meanwhile, Pearson correlation tests were used to detect the differential microbiota and metabolites of feces, and mRNAs of ileum on day 56.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe results showed that silica exposure resulted in dynamic change of pulmonary inflammation, fibrosis, fecal microbiota dysbiosis and ileum epithelial injury. FMT up-regulated the level of \u003cem\u003eBifidobacterium\u003c/em\u003e, restored the level of tight junction proteins of ileum. Then we found the level of \u003cem\u003eBifidobacterium\u003c/em\u003e was significantly down-regulated on day 56 in silica-exposed rats. Further we mainly predicted 3 potential mechanisms through conjoint analysis and KEGG analysis: (ⅰ) the change of \u003cem\u003eBifidobacterium\u003c/em\u003e may be related to the production of oleoyl 3-carba cyclic phosphatidic acid (3-CCPA) and the expression of Cldn8, which involved in silica-induced pulmonary inflammatory response and ileac barrier function injury;\u003cem\u003e \u003c/em\u003e(ⅱ) Silica-induced fecal microecological dysbiosis and inflammatory respond may affect the arginine biosynthesis and utilization of arginine by regulating the level of N-Acetyl-L-Glutamic Acid and Nos2, which effect ileac architectural integrity; and (ⅲ) Silica-induced fecal microecology disorder may induce ileac injury by regulating Ido1, Kynu and Indole-3-ethanol mediated “Tryptophan metabolism” pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion:\u003c/strong\u003e This study provided evidence that silica could alter fecal microbiota which may in turn play an important role in silica-induced pulmonary fibrosis and ileac barrier injury in rats. Three predicted mechanistic pathways deserved further study. The findings may provide a starting roadmap to intervene in the development of silica-induced pulmonary fibrosis.\u003c/p\u003e","manuscriptTitle":"Silica dust alters fecal microbiota that contributes silicosis through the lung-gut axis in rats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-17 21:39:12","doi":"10.21203/rs.3.rs-2661022/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d285b751-1471-4da8-a3cf-e77bdd9fe2e8","owner":[],"postedDate":"March 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":19919803,"name":"Biological sciences/Microbiology/Microbial communities"},{"id":19919804,"name":"Health sciences/Diseases/Respiratory tract diseases"}],"tags":[],"updatedAt":"2024-05-08T17:03:45+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-17 21:39:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2661022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2661022","identity":"rs-2661022","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}