Roles of gut microbiome-associated metabolites in pulmonary fibrosis by integrated analysis

Abstract

Lung diseases often coincide with imbalances in gut microbiota, but the role of gut microbiota in pulmonary fibrosis (PF) remains unclear. This study investigates the impact of gut microbiota and their metabolites on PF. Serum and lung tissues of normal, bleomycin (BLM)- and silica-induced mice showed significant differences in gut microbiota. l-Tryptophan was upregulated within pulmonary tissue and serum metabolites both in the BLM and Silica groups. The dominant gut microbiota associated with l-tryptophan metabolism included Lachnospiraceae_NK4A136_Group, Allobaculum, Alistipes, and Candidatus_Saccharimonas. l-Tryptophan promoted BLM- and silica-induced pathological damage in PF mice. l-Tryptophan promoted TGF-β1-induced EMT and fibroblast activation in vitro via activating the mTOR/S6 pathway. In conclusion, PF mice exhibited alterations in gut microbiota and serum and lung tissue metabolites. l-Tryptophan level was associated with changes in gut microbiota, and l-tryptophan promoted PF progression in both in vivo and in vitro models, potentially through activation of the mTOR/S6 pathway.

Introduction

Pulmonary fibrosis (PF) is a severe outcome of various inflammatory lung diseases characterized by progressive interstitial damage, resulting in compromised gas exchange, breathlessness, diminished quality of life, and ultimately, respiratory failure and mortality¹. Lung injuries, including infections or exposure to toxic particles, cause damage to epithelial and endothelial cells, triggering the release of inflammatory mediators and cytokines and initiating antifibrinolytic coagulation cascades². Furthermore, inflammatory factors activate profibrotic agents that stimulate vascular and myofibroblasts, leading to extracellular matrix (ECM) production^3,4. The fibrosis-triggering factors amplify inflammatory reactions, causing abnormal wound healing, tissue damage, excessive ECM deposition, lung scar formation⁵, gas exchange impairment, respiratory insufficiency, and death⁶.

PF arises from multiple factors, including exposure to environmental and occupational toxins such as asbestos, silica, and coal dust⁵. Silicosis, one of the oldest known pulmonary diseases, results from excessive silica exposure and accounted for ~12,900 global deaths in 2019⁷. Moreover, many drugs, such as microbial drugs, radiation therapy, chemotherapy drugs, and antibiotics, contribute to PF occurrence and development⁸. Bleomycin (BLM), an anti-tumor antibiotic, commonly induces PF as a common side effect, primarily affecting the lungs, skin, and mucosal organs^9,10. Despite significant research, many aspects of PF’s mechanism remain unclear, and no effective drug or therapeutic approach exists for PF treatment^11,12.

The human body hosts numerous microorganisms, including bacteria, fungi, viruses, and protozoa, the majority of which inhabit the gastrointestinal system¹³. These microbial communities colonizing the intestines of the host are referred to as gut microbiota¹³. Recent studies reveal that the gut microbiome and its metabolites could affect the function of both local intestines and distant organs such as the brain, cardiovascular system, lungs, liver, and pancreas^14–17. Additionally, various disorders could influence the composition and abundance of gut microbiota, reflecting the close, bidirectional interaction between the intestines and distal organs^18,19. The crucial cross-talk between the gut microbiota and the lungs is termed the “gut-lung axis”²⁰. The “gut-lung axis” is implicated in various pulmonary disorders, including asthma, pulmonary carcinoma, and acute lung damage^21–23, and also plays an important role in PF²⁴. Horizontal transmission of gut microbiota has been shown to reduce mortality in lung fibrosis²⁵. Pulmonary fibrosis alters gut microbiota and related metabolites in mice²⁶. Therefore, understanding gut microbiota changes in PF might offer valuable insights into potential treatment approaches for PF.

In this study, two mouse models of PF were induced using BLM and silica. Mouse fecal samples were collected for 16S rRNA gene sequencing, while untargeted metabolomics, a comprehensive method for identifying and quantifying metabolites in biological samples, was carried out on mouse serum and lung tissues. Differential gut microbiota in PF, as well as differential metabolites in serum and lung tissues, were identified, and the role of these metabolites in PF was further investigated.

Methods

Establishment and treatment of PF mouse model

Eight-week-old male C57BL/6 mice procured from SLAC Laboratory Animal Company (Changsha, China) were cultured in an environment (24 ± 2 °C) for 7 days under regular light/dark cycles (12 h light:12 h dark). During which all mice were given unrestricted access to a normal chow diet and water. Mice were allocated into three groups in a random manner (n = 6 each): Sham, BLM, and Silica. After anesthesia, BLM mice and the Silica mice were subjected to treatments using BLM (2 mg/kg dissolved in saline, H20055883, Hanhui Pharmaceuticals Co., Ltd, Shanghai, China) and silica (100 mg/kg dissolved in saline, S5631, Sigma-Aldrich, St Louis, USA) by tracheal instillation, respectively. An equivalent amount of physiological saline (50 μL) was injected into Sham mice. Mice were anesthetized and euthanized with isoflurane on the 28th day of modeling. Mouse blood, lung tissues, and intestinal tissues were collected. The intestinal contents were harvested and placed in sterile centrifuge tubes, which were subsequently kept at −80 °C. Mouse serum and lung tissue samples were frozen and then dispatched to APExBIO Co. Ltd. (Shanghai, China) for liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics assay. The colon tissues were collected and allocated into two parts: one was formalin-fixed for histological examination, and the other was kept at −80 °C for enzyme-linked immunosorbent assay (ELISA).

For l-tryptophan treatment, mice were assigned into five groups at random (n = 6 each): Sham, BLM, Silica, BLM + Try, and Silica + Try. PF modeling was conducted as previously performed. The day after PF modeling, the BLM + Try mice and the Silica + Try mice were given a daily intraperitoneal injection of l-tryptophan (50 mg/kg/d, T0011, Solarbio, Beijing, China) for 4 weeks. Subsequently, mouse weight was recorded. Next, isoflurane-anesthetized mice were euthanized. Mouse blood was harvested, and their bilateral lung tissues were collected and measured. The lung coefficients were calculated based on the following formula: lung coefficient = lung wet weight/body weight × 100%. All protocols of this study were carried out under the approval of the Animal Ethics Committee of Jiangxi Chest Hospital, Jiangxi Province.

Preparation of fecal hydration liquid and fecal microbiota transplantation (FMT)

Fresh feces from sham, BLM, and Silica mice were harvested in the morning using the stress defecation method. About three to four complete feces were collected from each mouse and stored in a sterilized centrifuge tube. The collected fresh feces were added to sterilized saline at a ratio of 100 mg: 1 mL, mixed by vigorous vortexing. After being filtered with an 800-mesh filter sieve to remove large particles, the filtrate was collected and centrifuged at 4 °C for 5 min at 600 × g. About 200 μL supernatant was subsequently taken for oral administration to ABX-treated mice. The pseudo germ-free (PGF) mice were established as previously described²⁷. Mice were fed with drinking water mixed with ABX [ampicillin (1 g/L), vancomycin (0.5 g/L), and neomycin (1 g/L)] for 2 weeks. The mice were subsequently randomly divided into the following four groups (n = 6 each): PGF-BLM + FMT (sham), PGF-Silica + FMT (sham), PGF-BLM + FMT (BLM), and PGF-BLM + FMT (Silica). FMT was performed thrice weekly from 7 to 28 days after BLM and silica treatment (as mentioned above). On the 28^th day, mice were euthanized, and blood and lung tissue samples were collected for subsequent experiments.

Histopathological examination

After being fixed with 4% paraformaldehyde and paraffin-embedded, the left lung of mice was cut into 4-μm cross-sections. Slices were dewaxed, hydrated, and stained with hematoxylin, eosin (HE), and Masson’s trichrome. First, slices were subjected to 5-min staining with hematoxylin and 5-min staining eosin staining solution (Servicebio Co., Ltd., Wuhan, China), then dehydrated with increasing ethanol concentrations and cleared using xylene. Lastly, neutral resin was used to seal the cross-sections, and a microscope (Olympus, Tokyo, Japan) was applied to observe the cross-sections.

For Masson’s trichrome staining, the Masson trichrome stain kit (Solarbio) was utilized as directed by the manufacturer to stain the lung tissue slices. Next, a microscope was used to observe the cross-sections.

Immunohistochemistry (IHC)

After being dewaxed and hydrated, mouse lung and colon tissue slices were heated with ethylene diaminetetra-acetic acid (EDTA). The slices were subsequently subjected to 25-min incubation with 3% H₂O₂ in light-deprived conditions, followed by the addition of 3% bovine serum albumin (BSA) for a further 30-min incubation at room temperature (RT). The slices were subsequently were then subjected to an overnight incubation at 4 °C using the antibodies: anti-alpha-smooth muscle actin (α-SMA) (1:100; #AF1032; Affinity, Wuhan, China), anti-E-cadherin (1:10000; 20874-1-AP; Proteintech, Wuhan, China), Collagen I (1:500, ab270993; Abcam, Cambridge, USA), tumor necrosis factor-α (TNF-α) (1:1000, ab307164; Abcam), and interleukin-1β (IL-1β) (1:500, ab283818; Abcam). After three phosphate-buffered saline (PBS) washes, horseradish peroxidase (HRP)-conjugated IgG (1:1000; GAR007; MultiSciences, Hangzhou, China) was supplemented for 1-h incubation at RT. The cross-sections were subsequently rinsed in PBS thrice, added with 2,4-diaminobutyric acid (DAB) solution, and subjected to 3-min counterstaining with hematoxylin. Lastly, the neutral resin was employed to seal dehydrated and permeabilized slices, and a microscope was applied to observe the slices²⁸.

Detection of hydroxyproline

The hydroxyproline detection kit (A030-2-1; Nanjing JianCheng Bioengineering Institute, Nanjing, China) was employed as directed by the manufacturer to detect the hydroxyproline level within mouse serum. In short, a 500 μL serum sample was added and mixed with 1 mL of hydrolysate, followed by hydrolysis (20 min) at 95 °C or boiling water. The corresponding reagents were added for reaction following the instructions. After the reaction, centrifugation (3500 r/min, 10 min) was performed, and the supernatant was collected. The optical density (OD) measurement at 550 nm of the supernatant was then conducted.

ELISA

TNF-α, IL-1β, and lipopolysaccharide (LPS) expression levels within mouse serum, pulmonary tissues, colon tissues and MLE-12 and WML2 cells were detected using the corresponding ELISA kits (Ruixin Biotech, Quanzhou, China) (TNF-α: RXW202412M; IL-1β: RX203063M; LPS: RX202425M) as directed by the manufacturer. A microplate reader (Bio-Rad, Hercules, USA) was applied to measure the OD value at 450 nm. After homogenizing lung and intestinal tissues, the bicinchoninic acid (BCA) method (Beyotime, Shanghai, China) was used to measure the protein content. The level of cytokines in intestinal tissues and lung tissues were normalized to per mg total protein.

Cell culture and treatment

Mouse lung epithelial cells (MLE-12, #CRL-2110) were supplied by the American Type Culture Collection (ATCC, Manassas, USA). Mouse pulmonary fibroblast cell line WML2 was purchased from Minzhou Bio, Co., Ltd. (Ningbo, China). MLE-12 and WML2 cells were cultivated within 10% fetal bovine serum (FBS)-contained DMEM (Procell Life Science & Technology Co., Ltd., Wuhan, China), respectively added with 1% penicillin and streptomycin. All cells were cultivated in a humid incubator (37 °C, 5% CO₂).

MLE-12 and WML2 cells were subjected to 12-h treatment with L-Tryptophan of various concentrations (5, 50, 500, and 1000 μM) (HY-N0623, Purity: 99.87%, MedChem Express, Monmouth Junction, USA). Next, 10 ng/mL mouse TGF-β1 (Sangon Biotech, Shanghai, China) was added into cells for a further 48 h. In the meantime, MLE-12 and WML2 cells were treated with 10 nM mTOR inhibitor ridaforolimus (also named MK-8669; HY-50908, MedChem Express) for 24 h²⁹ or 20 μg/mL TNF-α receptor antagonist, R-7050 (HY-110203, MedChem Express) for 24 h³⁰. Finally, cells were collected for future analysis.

Cell counting kit-8 (CCK-8) assay

CCK-8 assay was performed to detect MLE-12 and WML2 cell viability. Briefly, cells were seeded in a 96-well plate (5 × 10³ cells/well) overnight, then treated with different concentrations of l-tryptophan (5, 50, 500, and 1000 μM), TGF-β1, MK-8669 or R-7050. Following 0, 24, and 48-h further cultures, 10 μL CCK-8 solution was supplemented into the 96-well plates for 2-h incubation at 37 °C. A microplate reader (Bio-Rad) was used to measure the OD value at 450 nm.

Scratch assay

Cells were seeded (5 × 10⁵ cells/well) onto six-well plates to grow to a confluence of 90%. A 200 μL sterile pipette tip was applied to scratch cells, followed by three washes with PBS. Next, the cells were treated with 50 μM l-tryptophan, 10 ng/mL TGF-β1, MK-8669 or R-7050. Cells were photographed at 0 and 48 h.

Western blot

RIPA lysis buffer (Beyotime) was applied to extract the total protein of mouse lung tissues, MLE-12, and WML2 cells. The BCA reagent kit (Beyotime) was used to determine the total protein content. Following electrophoresis by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), the separated proteins were electroblotted from the gel onto polyvinylidene fluoride (PVDF) membranes (Merck Millipore, Billerica, USA). The membranes were blocked for 2 h using 5% nonfat milk in TBST solution (Sangon Biotech), followed by overnight incubation at 4 °C with the primary antibodies: anti-Collagen I (1:1000; AF7001; Affinity), anti-E-Cadherin (1:20,000; 20874-1-AP; Proteintech), and anti-α-SMA (1:1000; AF1032; Affinity), anti-Vimentin (1:2000; 10366 1-AP; Proteintech), anti-N-cadherin (1:1000; AF4039; Affinity), anti-Fibronectin (1:1000; AF5335; Affinity), anti-mTOR (1:5000; 66888-1-Ig; Proteintech), anti-p-mTOR (1:5000; 67778-1-Ig; Proteintech), anti-S6 (1:10,000; 80208-1-RR; Proteintech), anti-p-S6 (1:5000; 80206-1-RR; Proteintech), and anti-GAPDH (1:3000; AF7021; Affinity). After three PBS washes, HRP-conjugated IgG antibodies (1:5000; GAR0072; Multisciences) were added for 2-h incubation at RT, and then washed thrice using TBST solution. GAPDH was utilized as an internal reference. A highly sensitive enhanced chemiluminescence (ECL) kit (New Cell & Molecular Biotech Co., Ltd., Suzhou, China) was used to visualize the protein bands. Image J (National Institutes of Health, Bethesda, USA) was applied to analyze the band intensity.

DNA extraction, PCR amplification, and 16S rRNA gene sequencing

After PF modeling, the fecal contents of the Sham, BLM, and Silica groups mice were harvested. The genomic DNA of fecal samples was extracted using the magnetic bead method (Beijing TianGen Biotech Co., Ltd., Beijing, China). 1% agarose gel was employed to determine extracted DNA content and purity. The primers 388 F (5′-ACTCCTACGGGGAGCAG-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′) were used to amplify gene fragments from the conserved V3-V4 region of the 16S rRNA gene from the extracted DNA. The thermal cycling parameters were: 95 °C for 3 min, then a total of 27 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, with the final extension at 72 °C for 10 min.

Library construction and sequencing

The Agencourt AMPure XP magnetic beads were used as directed by the manufacturer to collect and purify PCR products. A TruSeq DNA PCR-Free Sample Preparation kit (Illumina, San Diego, USA) was applied to prepare the sequencing library. All samples were sequenced upon the Illumina NovaSeq platform. Raw sequences were processed using UCLUST in QIIME software (v1.9). One amplicon sequence variants (ASV) set was created with denoising methods and gut microbiota was measured using the observed number of ASVs, and the dominant gut microbiota was analyzed through a heat map of fecal microbiota structure.

Alpha and beta diversity analysis

The diversity within a particular area or ecosystem is called Alpha diversity. In this study, Observed, Chao1, and abundance-based coverage estimator (ACE) indices were used to evaluate species richness, and Shannon, Simpson, and J were employed to evaluate diversity. The spatiotemporal variation within species composition could be analyzed using Beta diversity. Analysis of similarities (ANOSIM) is a nonparametric test used to test whether inter-group differences are dramatically higher compared to intra-group differences and to determine whether the groups are meaningful. R software was used for non-metric multidimensional scaling (NMDS) analysis. NMDS is a data analysis approach that lowers multidimensional research items to low-dimensional space for location, analysis, and classification while preserving the initial relationships between items.

Pre-processing of serum and lung tissue samples for untargeted metabolomics analysis

The peripheral blood was centrifuged (3500 rpm, 10 min), with the serum harvested and promptly kept at −80 °C. After being defrosted upon ice, the specimens were filtered through a 0.22-μm filter membrane. Next, 100 μL of each specimen was taken out and supplemented with 300 μL of methanol (added with 5 μg/mL 2-chloro-l-phenylalanine as internal reference). After 1-min vortex mixing, the mixture was subjected to 10-min centrifugation at 13,000 rpm at 4 °C. The supernatant was transferred into sampler vials for analysis.

Untargeted LC-MS metabolomics

Samples were analyzed on an Agilent 1290 HPLC, Agilent 6545UHD, and Accurate-Mass Q-TOF MS (Agilent Technologies, Santa Clara, USA) as previously described³¹. Waters XSelect ® HSS T3 (2.5 μm, 100*2.1 mm) was used as the chromatographic column. The mobile phases were composed of phase A (0.1% formic acid solution in water) and phase B (acetonitrile plus 0.1% formic acid). The flow rate was set to 0.4 mL/min and maintained at a temperature of 40 °C. The volume of injection was 4 μL. The gradient for elution was: 0–3 min, 20% B; 3–9 min, 20–95% B; 9–13 min, 95% B; 13–13.1 min, 95–5% B; 13.1–16 min, 5% B.

MS analysis was performed in either positive ion or negative ion mode. The optimization parameters were as follows: capillary voltage (positive ion mode: 4.5 kV; negative ion mode: 3.5 kV); drying gas flow (positive ion mode: 8 L/min; negative ion mode: 10 L/min); gas temperature: 325 °C; nebulizer pressure: 20 psig; fragmentor voltage: 120 V; skimmer voltage:45 V; mass range: m/z 50–1500.

Agilent Massachunter Qualitative Analysis B.08.00 software (Agilent Technologies) was used to convert raw data to the common (mz. data) format. The XCMS program on the R software platform was used for peak detection, retention time correction, and automatic integration pretreatment. After the metabolite structure identification and pre-processing of XCMS-extracted data, experimental data quality was evaluated through total ion current (TIC) plots of quality control (QC) samples, principal component analysis (PCA) on overall samples, and correlation analysis on QC samples, followed by data analysis. The SIMCA14.1 software (Umetrics, MKS Instruments Inc., Andover, USA) was applied to conduct PCA and orthogonal partial least-square discriminant analyses (OPLS-DA) for data modeling, and the reliability of the model was verified. Fold change (FC), variable importance of the project (VIP) ≥1, and independent sample t-test (p < 0.05) within the OPLS-DA model were used to screen differential metabolites.

Statistical analysis

The cell experiments were performed for three biological replicates. The animal experiments were performed for six biological replicates. All experimental data were represented in form of mean ± standard deviation (SD), and GraphPad Prism 8.0 software and SPSS software were employed for data analysis. The Kolmogorov–Smirnov test showed whether the data were in normal distribution. The one-way analysis of variance (ANOVA) followed by the LSD test or Dunnett T3 test was employed for comparisons among multi-groups. Data that were not normally distributed were analyzed using the Kruskal–Wallis test. The Pearson correlation coefficient was used to analyze the relationship between metabolites, microbiota, and inflammatory cytokines. The threshold for statistical significance was p < 0.05.

Results

PF mouse model was induced by BLM and silica

BLM and silica were used to induce the PF mouse model (Supplementary Fig. 1A). BLM- and silica-induced mice showed noticeably higher serum levels of hydroxyproline than the control mice (Supplementary Fig. 1B). The mice in the BLM and Silica groups were found to exhibit alveolar enlargement, lung interstitium thickening, and inflammatory cell infiltration in lung tissues, along with notable collagen deposition and fibrosis (Supplementary Fig. 1C–E). Furthermore, BLM- and silica-induced mice showed significantly elevated Collagen I and α-SMA proteins, and decreased E-cadherin proteins (Supplementary Fig. 1F, G). In summary, both BLM and silica successfully induced the PF mouse model.

BLM and silica-induced inflammatory response in PF mice

The inflammation level in mice was further detected. TNF-α, IL-1β, and LPS levels within mouse serum showed to be remarkably elevated within the BLM mice and silica mice than those in the Sham mice (Supplementary Fig. 2A). Moreover, in comparison with the Sham mice, mouse lung tissues showed notably increased levels of TNF-α, IL-1β, and LPS in BLM mice and silica mice (Supplementary Fig. 2B); within colon tissues, the levels of TNF-α and IL-1β showed to be increased in BLM mice and silica mice, but LPS level exhibited no remarkable change (Supplementary Fig. 2C). Furthermore, IHC staining results indicated that the TNF-α and IL-1β positive rate in colon tissues of the BLM and silica groups showed to be compared to the Sham group (Supplementary Fig. 2D). In summary, BLM and silica-induced inflammatory responses in mice.

The diversity of gut microbiota in PF mice was analyzed

Subsequently, alterations in gut microbiota in mice were detected. Firstly, the amplicon sequence variants (ASVs) accumulation curve was used to determine the species richness and evenness within the specimens. According to the results, each group of samples showed rich species composition, with a relatively high evenness of species (Fig. 1A). Microbial differences between samples were analyzed. As indicated by the Venn plot, there were 391 overlapping ASVs between the Sham and BLM groups, 347 overlapping ASVs between the Sham mice and the Silica mice, and 448 overlapping ASVs between the BLM mice and the Silica mice. A total of 268 common ASVs among the three sets of samples were identified (Fig. 1B). Additionally, the inter-group diversity differences of gut microbiota samples were analyzed through Alpha diversity analysis. It was observed that α-diversity of gut microbiota samples of the BLM and Silica groups were higher than that of the Sham group (Fig. 1C). Beta diversity analysis was used to examine the diversity between groups and assess the meaningfulness of grouping (Fig. 1D); differences among the three groups of samples were shown to be significant (p < 0.05), and the inter-group differences showed to be higher compared to intra-group differences (R > 0). NMDS analysis results also revealed significant differences among the three groups of samples (Fig. 1E). Taken together, differences were present in gut microbiota samples among different groups.

Fig. 1. The diversity of gut microbiota in PF mice was analyzed.

Mice were allocated into 3 groups in a random manner (n = 6 each): Sham, BLM, and Silica groups. Then, the fecal contents of the Sham, BLM, and Silica groups mice were harvested and applied for 16S rRNA gene sequencing. A ASVs accumulation distribution curve was displayed; B the overlap of ASVs in different groups was shown by a Venn plot; C alpha diversity analysis was performed; D beta diversity analysis was performed; E NMDS analysis was performed. *p < 0.05, **p < 0.01.

The abundance differences in mouse gut microbiota samples were analyzed

The top 10 gut microbiota in terms of relative abundance (in descending order) at the species level were selected as the dominant microbiota, including Muribaculaceae, Lactobacillus, Lachnospiraceae_NK4A136_group, Alloprevotella, Clostridia_UCG-014, Prevotellaceae_NK3B31_group, Helicobacter, Allobaculum, Alistipes, and Candidatus_Saccharimonas. The abundance distribution trend of each group of samples was displayed using a box plot and heat map; it was found that four microbiota (Lachnospiraceae_NK4A136_group, Alloprevotella, Clostridia_UCG-014, and Alistipes) showed significant differences among the three sample groups (Fig. 2A, B and Supplementary Table 1). Compared to those in the Sham mice, eight dominant microbiotas (except for Lactobacillus and Helicobacter) in the BLM and Silica groups have a greater abundance, among which Lachnospiraceae_NK4A136_group and Alloprevotella showed notable abundance differences (p < 0.05) (Supplementary Table 2). Compared with the BLM mice, the Silica mice exhibited a decreased abundance of Lachnospiraceae_NK4A136_group and Alistipes, and an increased abundance of Clostridia_UCG-014 (Fig. 2A, B and Supplementary Table 2).

Fig. 2. The abundance differences in mouse gut microbiota samples were analyzed.

The abundance of the TOP10 dominant gut microbiota in mice was shown by A the box plot and B the heat map. n = 6. *p < 0.05, **p < 0.01, ***p < 0.005.

Untargeted metabolomics analysis was conducted on mouse lung tissues and serum

Next, mouse lung tissues were subjected to untargeted metabolomics analysis. PCA analysis results showed significant separation between different groups, indicating metabolic differences in the currently established PCA_scores model among the three sample groups. Moreover, significant inter-group and small intra-group differences indicated that the extracted peak area data were stable and acceptable throughout the run, which can be used for subsequent analysis (Fig. 3A). OPLS-DA analysis results showed that samples in the same group were concentrated and gathered together, while showing significant separation trend differences between groups (Fig. 3B). This demonstrated the stability and the reliability of the OPLS-DA model, which can be used to distinguish three groups of samples (Fig. 3B). Next, the OPLS-DA model was proven to effectively distinguish samples between groups by random permutation tests, suggesting the metabolic differences between groups (Fig. 3C). Differential metabolites analysis results revealed 314 and 190 differential metabolites were screened in the NEG ion mode and POS ion mode under the conditions of VIP ≥1 and an independent sample t-test (p < 0.05), respectively. Subsequently, 15 common metabolites with significant differences in the NEG and POS ion modes were identified (Supplementary Table 3), such as l-tryptophan and Arachidonic acid (Fig. 3D and Supplementary Fig. 3).

Fig. 3. Untargeted metabolomics analysis was conducted on mouse lung tissues.

A PCA analysis was conducted (left: NEG; right: POS); B OPLS-DA analysis was conducted (left: NEG; right: POS); C oplsda_permutation analysis was conducted (up: NEG; down: POS); D metabolites with significant differences in mouse lung tissues were shown by heat map (left: NEG; right: POS).

Untargeted metabolomics analysis was also performed on mouse serum. Similarly, the PCA analysis results demonstrated significant separation between different groups (Fig. 4A). The OPLS-DA analysis results showed notable inter-group differences and slight intra-group differences, as well as a significant separation trend difference between groups (Fig. 4B). According to the oplsda_permutation results, the current model could efficiently distinguish specimens between groups, indicating that there were metabolic differences between groups (Fig. 4C). The 229 and 123 differential metabolites were screened in NEG and POS ion modes, respectively. Following screening, 8 common differential metabolites (such as L-Tryptophan and Octadecanedioic acid) in NEG and POS ion modes were identified (Fig. 4D, Supplementary Fig. 4, and Supplementary Table 4).

Fig. 4. Untargeted metabolomics analysis was conducted on mouse serum.

A PCA analysis was conducted (left: NEG; right: POS); B OPLS-DA analysis was conducted (left: NEG; right: POS); C oplsda_permutation analysis was conducted (up: NEG; down: POS); D metabolites with significant differences in mouse serum were shown by heat map (left: NEG; right: POS).

The correlation between gut microbiota and metabolites in mice

A common differential metabolite, l-tryptophan, was identified after intersecting differential metabolites in the lung tissues and serum. As indicated by the results, l-tryptophan exhibited significant differences in metabolites in NEG and POS ion modes in mouse lung tissues and serum of Sham, BLM, and Silica samples. Moreover, l-tryptophan was noticeably upregulated within the pulmonary tissue samples and serum of mice within the BLM and Silica mice (Fig. 5A). The correlation analysis results indicated that l-tryptophan metabolic level was related to the abundance of Lachnospiraceae_NK4A136_group, Allobaculum, Alistipes, and Candidatus_Saccharimonas (Fig. 5B). Specifically, the abundance of Lachnospiraceae_NK4A136_group within the BLM and Silica mice showed to be remarkably increased compared to the Sham mice. Moreover, its abundance in the BLM group showed to be markedly increased compared to the Silica mice. Allobaculum abundance in the BLM and Silica mice was also higher compared to the Sham mice, but there was no notable difference between the Silica mice and the Sham mice. Alistipes abundance in the BLM group was markedly elevated relative to that in the Sham and Silica groups, and no remarkable difference in Alistipes abundance between the Silica mice and the Sham mice was found. The abundance of Candidatus_Saccharimonas within the BLM and Silica mice was higher compared to the Sham mice, but there was no notable difference between the BLM mice and the Sham mice (Fig. 5C). Taken together, l-tryptophan was a metabolite associated with differential gut microbiota in PF.

Fig. 5. The association between gut microbiota and metabolites within mice was analyzed.

A The abundance of l-tryptophan in metabolites in mouse lung tissues and serum in NEG and POS ion modes was analyzed; B the correlation between the metabolic level of l-tryptophan and the abundance of gut microbiota was analyzed; C the abundance of l-tryptophan-related gut microbiota in the samples was analyzed. n = 6. *p < 0.05, **p < 0.01, ***p < 0.005.

The correlation analysis of l-tryptophan and its associated gut microbiota with inflammatory factors

Subsequently, the correlation of l-tryptophan and the associated gut microbiota with TNF-α, IL-1β, and LPS within mouse pulmonary tissue samples and serum was analyzed. It was found that in mouse lung tissues, NEG_L-Tryptophan, PSO_L-Tryptophan, Lachnospiraceae_NK4A136_group, and Alistipes exhibited a positive correlation with LPS and TNF-α; Allobaculum exhibited a positive correlation with IL-1β and TNF-α (Fig. 6A). In mouse serum, NEG_L-Tryptophan and POS_L-Tryptophan were significantly positively correlated with proinflammatory cytokines (TNF-α, IL-1β, and LPS); Lachnospiraceae_NK4A136_group exhibited a positive correlation with IL-1β; Candidatus_Saccharimonas was positively correlated with LPS; Allobaculum was positively correlated with LPS and TNF-α (Fig. 6B).

Fig. 6. The correlation of l-tryptophan and its associated gut microbiota with inflammatory factors was analyzed.

The correlation of l-tryptophan and its associated gut microbiota with inflammatory cytokines (TNF-α, IL-1β, and LPS) within mouse pulmonary tissue samples (A), and serum (B) was displayed using a heat map. *p < 0.05, **p < 0.01.

FMT from PF mice aggravated the development of PF

To further address the cause-and-effect relationship between gut microbiota and PF, the FMT experiment with feces from sham and PF mice was performed on pseudo germ-free (PGF) mice. FMT treatment from BLM- and silica-induced mice showed notably increased lung/body weight ratio and serum hydroxyproline levels in BLM- and silica-induced PGF mice (Fig. 7A, B). Compared with the PGF-BLM + FMT (sham) or PGF-Silica + FMT (sham) mice, the mice treated FMT from BLM- and silica-induced mice have been found to exhibit more serious pathological damage, and the increased collagen fiber deposition in the ECM (Fig. 7C, D). Additionally, mice in the PGF-BLM + FMT (BLM) and PGF-BLM + FMT (Silica) groups showed significantly increased α-SMA levels and decreased E-cadherin levels within pulmonary tissue samples relative to PGF-sham mice (Fig. 7E, F). Taken together, FMT with feces from BLM- and silica-induced mice promoted PF progression.

Fig. 7. Fecal microbiota transplantation (FMT) from PF mice aggravated the development of PF.

The pseudo germ-free (PGF) mice were established using antibiotics, and then FMT from sham mice or BLM- and silica-induced mice were performed daily from 7 to 28 days after BLM and silica treatment. Mice were randomly divided into four groups: PGF-BLM + FMT (sham), PGF-Silica+FMT (sham), PGF-BLM + FMT (BLM), and PGF-BLM + FMT (Silica), and then examined for A the mouse lung/body weight; B serum hydroxyproline level by the hydroxyproline detection kit; C the pathological damages in mouse lung tissue samples using HE staining; D the fibrotic degree within mouse pulmonary tissue samples using Masson’s trichrome staining; E, F α-SMA and E-cadherin levels within mouse pulmonary tissue samples were detected using (E) Western blot and (F) IHC assay. n = 6. **p < 0.01, vs. the PGF-BLM + FMT (sham) mice, ##p < 0.01, vs. the PGF-Silica + FMT (sham) mice.

l-Tryptophan promoted TGF-β1-induced EMT

Next, the role of l-tryptophan in PF was investigated in vitro. Firstly, to find the optimal concentration of l-tryptophan in cells, the MLE-12 cells were treated with various concentrations of l-tryptophan (5, 50, 500, and 1000 μM) for 12 h, and cell viability was detected. According to the results, l-tryptophan treatment increased cell viability in a concentration-dependent manner, and l-tryptophan at 50 μM concentration significantly enhanced cell viability (Fig. 8A). Therefore, 50 μM l-tryptophan was selected for subsequent experiments. Next, MLE-12 cells were pretreated with 50 μM l-Tryptophan for 12 h, prior to 48 h treatment with 10 ng/mL TGF-β1. TGF-β1 or l-tryptophan alone treatment notably increased cell viability, and combined treatment of TGF-β1 and l-tryptophan could further promote cell viability (Fig. 8B). Furthermore, TGF-β1 or l-tryptophan alone treatment-induced MLE-12 cells were transformed into spindle-shaped mesenchymal cells, along with the notably elevated levels of α-SMA, Vimentin, and N-cadherin and reduced E-cadherin protein level. The combined treatment of TGF-β1 and l-tryptophan could further promote these trends (Fig. 8C, D). Moreover, TGF-β1 or l-tryptophan alone treatment notably facilitated TNF-α and IL-1β levels in MLE-12 cells; and the combined treatment of TGF-β1 and l-tryptophan further promoted TNF-α and IL-1β levels (Fig. 8E). To determine whether the effects of l-tryptophan on TGF-β1-induced EMT by stimulating inflammatory pathways, the TNF-α receptor antagonist (R-7050) along with l-tryptophan treatment were applied to MLE-12 cells. As Supplementary Fig. 5A showed, compared to l-tryptophan alone treatment or the combined treatment of TGF-β1 and l-tryptophan, R-7050 treatment notably inhibited TNF-α and IL-1β levels in MLE-12 cells. Moreover, under l-tryptophan alone treatment or l-tryptophan along with TGF-β1 treatment, R-7050 treatment markedly suppressed cell viability (Supplementary Fig. 5B), inhibited cell transformation into spindle-shaped mesenchymal cells (Supplementary Fig. 5C) and reduced α-SMA, Vimentin, and N-cadherin protein levels and promoted E-cadherin protein level (Supplementary Fig. 5D). Taken together, l-tryptophan boosted TGF-β1-caused EMT within lung epithelial cells by stimulating inflammatory pathway.

Fig. 8. L-Tryptophan promoted TGF-β1-induced EMT.

A MLE-12 cell line was subjected to 12-h pretreatment with various concentrations of l-Tryptophan (5, 50, 500, and 1000 μM), prior to 24-h or 48-h treatment with 10 ng/mL TGF-β1 for EMT induction. Then, the viability of MLE-12 cells was detected using CCK-8 assay. B–D MLE-12 cells were pretreated with 50 μM l-tryptophan for 12 h, prior to 48 h treatment with 10 ng/mL TGF-β1 and then examined for B cell viability by CCK-8 assay; C cell morphological changes by a microscope; D E-cadherin, α-SMA, Vimentin, and N-cadherin protein contents using Western blot; E TNF-α and IL-1β levels by ELISA kits. N = 3, *p < 0.05, **p < 0.01, vs. Control group; #p < 0.05, ##p < 0.01, vs. the TGF-β1 group.

l-Tryptophan promoted TGF-β1-induced pulmonary fibroblast activation

The effects of l-tryptophan on fibroblast activation were further investigated. In short, WML2 cells were subjected to 12-h treatment with various concentrations of l-tryptophan (5, 50, 500, and 1000 μM), and cell viability was detected. According to the results, following 48 h of treatment, TGF-β1 stimulus increased WML2 cell viability in a concentration-dependent manner (Fig. 9A). l-Tryptophan at 50 μM concentration significantly enhanced WML2 cell viability; hence, 50 μM l-tryptophan was chosen for subsequent experiments. TGF-β1 or l-tryptophan alone treatment markedly promoted cell viability and combined treatment of TGF-β1 and l-tryptophan further facilitated cell viability (Fig. 9B). Additionally, α-SMA, Collagen I, and Fibronectin protein levels, as well as cell migratory ability, were promoted by TGF-β1 or l-tryptophan alone treatment, which could be further enhanced by the combined treatment of TGF-β1 and l-tryptophan (Fig. 9C, D). Moreover, the TNF-α and IL-1β levels in WML2 cells were increased by TGF-β1 or l-tryptophan alone treatment; and further enhanced by the combined treatment of TGF-β1 and l-tryptophan (Fig. 9E). Also, to determine whether the effects of l-tryptophan on TGF-β1-induced pulmonary fibroblast activation by stimulating inflammatory pathways, the TNF-α receptor antagonist (R-7050) along with l-tryptophan treatment were applied to WML2 cells. As Supplementary Fig. 6A showed, R-7050 treatment notably inhibited TNF-α and IL-1β levels in WML2 cells when compared with the Try group or TGF-β1+ Try group. Moreover, when compared to the Try group or TGF-β1+ Try group, R-7050 treatment dramatically suppressed cell viability (Supplementary Fig. 6B), inhibited α-SMA, Collagen I, and Fibronectin protein levels (Supplementary Fig. 6C) and reduced cell migratory ability (Supplementary Fig. 6D) in WML2 cells. The above findings indicated that l-tryptophan promoted TGF-β1-induced fibroblast activation by stimulating an inflammatory pathway.

Fig. 9. L-Tryptophan promoted TGF-β1-induced fibroblast activation.

WML2 cells were subjected to 12-h pretreatment with various concentrations of l-tryptophan (5, 50, 500, and 1000 μM), A the viability of WML2 cells was detected using CCK-8 assay. B–D WML2 cells were pretreated with 50 μM l-tryptophan for 12 h, prior to 48 h treatment with 10 ng/mL TGF-β1 and then examined for B cell viability by CCK-8 assay; C α-SMA, Collagen I, and Fibronectin protein levels using Western blot; D the migratory ability using plate scratch assay; E TNF-α and IL-1β levels by ELISA kits. N = 3, *p < 0.05, **p < 0.01, vs. Control group; #p < 0.05, ##p < 0.01, vs. the TGF-β1 group.

l-Tryptophan promoted BLM- and silica-induced PF in vivo

Subsequently, the effect of l-tryptophan on PF progression was validated in vivo. BLM- and silica-induced mice showed notably increased lung/body weight ratio and serum hydroxyproline levels; l-tryptophan treatment further facilitated these increases (Fig. 10A, B). Compared with the Sham mice, the BLM and Silica mice have been found to exhibit severe inflammatory reactions, thickened alveolar septum, and inflammatory cell infiltration, along with a large area of lung tissues that were stained blue, and the increased collagen fiber deposition in the ECM. l-Tryptophan treatment further aggravated inflammatory cell infiltration, disrupted alveolar structure, and increased collagen fiber deposition in the ECM (Fig. 10C, D). Additionally, mice in the BLM and Silica groups showed notably increased α-SMA levels and decreased E-cadherin levels within pulmonary tissue samples relative to sham mice. l-Tryptophan further increased α-SMA levels while reducing E-cadherin levels in mice lung tissues (Fig. 10E, F). Moreover, l-tryptophan further increased levels of LPS, TNF-α, and IL-1β in mouse lung tissues (Fig. 10G). Taken together, l-tryptophan promoted BLM- and silica-induced PF progression.

Fig. 10. l-Tryptophan promoted BLM- and silica-induced PF in vivo.

The PF mouse models were induced using BLM and silica, and then mice were treated with l-tryptophan on the second day for 4 weeks. Then, the mice were randomly divided into five groups: Sham, BLM, Silica, BLM + Try, and Silica + Try groups. A The mouse lung/body weight was detected; B serum hydroxyproline level was detected by the hydroxyproline detection kit; C the pathological damages in mouse lung tissue samples were observed using HE staining; D the fibrotic degree within mouse pulmonary tissue samples was evaluated using Masson’s trichrome staining; E, F α-SMA and E-cadherin levels within mouse pulmonary tissue samples were detected using E western blot and F IHC; G the levels of LPS, TNF-α, and IL-1β in mouse lung tissues were detected using ELISA. n = 6. **p < 0.01, vs. the Sham mice, ##p < 0.01, vs. the BLM or Silica mice.

l-Tryptophan promoted the mTOR/S6 signaling activation

Subsequently, the downstream mechanism of l-tryptophan in regulating PF was further explored. As previously reported, the mTOR pathway is activated within PF³². Moreover, l-tryptophan has been shown to promote the activation of the mTOR signaling pathway³³. S6 is a downstream effector protein of the mTOR pathway, which could be activated via mTOR in PF. Therefore, l-tryptophan may activate the mTOR/S6 pathway to enhance PF development. It was found that p-mTOR/mTOR and p-S6/S6 levels within MLE-12 and WML2 cells after TGF-β1 or l-tryptophan alone treatment treatment were remarkably increased. The combined treatment of TGF-β1 and l-tryptophan further increased p-mTOR/mTOR and p-S6/S6 levels within MLE-12 cells and WML2 cells (Fig. 11A, B). Mice in the BLM and Silica groups showed notably increased p-mTOR/mTOR and p-S6/S6 levels within pulmonary tissue samples relative to sham mice. l-Tryptophan further increased p-mTOR/mTOR and p-S6/S6 levels in mice lung tissues (Fig. 11C). Moreover, to ascertain whether inhibiting mTOR/S6 signaling could mitigate l-tryptophan-induced fibrosis, mTOR inhibitor ridaforolimus (also named MK-8669) along with l-tryptophan treatment were applied to MLE-12 cells (Supplementary Fig. 7) and WML2 cells (Supplementary Fig. 8). Compared to Try group or TGF-β1+ Try group, MK-8669 treatment significantly restrained p-mTOR/mTOR and p-S6/S6 levels (Supplementary Fig. 7A), inhibited cell viability (Supplementary Fig. 7B), inhibited cell transformation into spindle-shaped mesenchymal cells (Supplementary Fig. 7C), reduced α-SMA, Vimentin, and N-cadherin protein levels and promoted E-cadherin protein level (Supplementary Fig. 7D), suppressed TNF-α and IL-1β levels (Supplementary Fig. 7E) in MLE-12 cells. Besides, MK-8669 treatment notably inhibited p-mTOR/mTOR and p-S6/S6 levels (Supplementary Fig. 8A), suppressed cell viability (Supplementary Fig. 8B), inhibited α-SMA, Collagen I, and Fibronectin protein levels (Supplementary Fig. 8C), reduced cell migratory ability (Supplementary Fig. 8D) and inhibited TNF-α and IL-1β levels (Supplementary Fig. 8E) in WML2 cells when compared with Try group or TGF-β1+ Try group. From all the above, l-tryptophan promoted TGF-β1-induced EMT and fibroblast activation by the mTOR/S6 signaling activation.

Fig. 11. l-Tryptophan promoted the mTOR/S6 signaling activation.

A, B p-mTOR, mTOR, p-S6, and S6 levels in A MLE-12 cells and B WML2 cells was detected using Western blot. n = 3. ** p < 0.01, vs. Control group; ## p < 0.01, vs. the TGF-β1 group; & &p < 0.01, vs. the Try group. C p-mTOR, mTOR, p-S6, and S6 levels within mouse pulmonary tissue samples were detected using Western blot. n = 6. **p < 0.01, vs. the Sham mice, ##p < 0.01, vs. the BLM or Silica mice.

Discussion

Pulmonary fibrosis (PF) is a progressive and chronic interstitial lung disease with multiple contributing factors, including drugs, dust, viruses, and air pollution³⁴. Despite extensive research, the pathogenesis of PF remains incompletely understood. As previously reported, gut microbiota and the associated ‘gut-lung axis’ play crucial roles in regulating pulmonary disorders²⁷, suggesting that gut microbiota influence lung disorders. Nevertheless, the characteristics of gut microbiota and lung tissue metabolome in PF, and their potential connections, remain unclear. This study investigated the composition of gut microbiota and metabolomic profiles in PF and explored their potential roles in disease progression.

The gut microbiota composition of PF mice was first analyzed using 16S rRNA gene sequencing, identifying significant alterations in Lachnospiraceae_NK4A136_group, Alloprevotella, Clostridia_UCG-014, and Alistipes. Among these, Lachnospiraceae_NK4A136_Group and Alloprevotella were notably elevated in both BLM- and silica-treated groups, Clostridia_UCG-014 increased in silica-treated mice, and Alistipes in BLM-treated mice. Lachnospiraceae_NK4A136_Group, known for producing short-chain fatty acids (SCFAs), is typically associated with maintaining intestinal barrier integrity in mice³⁵. However, it has also been linked to adverse outcomes in other diseases, including primary sclerosing cholangitis, asthma, and knee osteoarthritis^36–38. Similarly, Alloprevotella, which contributes to SCFA production, has been implicated in both beneficial and harmful roles. Alloprevotella increases diamine oxidase and trimethylamine N-oxide values that are detrimental to health³⁹, whereas Alloprevotella abundance is downregulated after colitis treatment⁴⁰. Clostridia_UCG-014 abundance has been found to increase in colitis-associated colorectal cancer⁴¹. Alistipes abundance is increased in fibrotic liver injury, which is decreased after drug treatment⁴². These findings suggest that changes in specific gut bacteria might influence PF progression. Previous studies have shown that modulating gut microbiota, such as through probiotics or FMT, can regulate gut microecology and impact PF²⁵. For example, FMT from polydatin-treated BLM mice effectively alleviated lung fibrosis in antibiotic-treated PF models⁴³, while FMT from healthy mice can alleviate pulmonary fibrosis in STZ-induced models through anti-inflammatory and anti-apoptotic effects⁴⁴. In our study, FMT from BLM- and silica-treated mice promoted PF, further supporting the influence of gut microbiota on PF progression.

To gain a deeper understanding of the mechanisms through which gut microbiota might affect PF, we conducted untargeted metabolomics screening to identify metabolites in serum and lung tissues that might be involved in disease progression. Herein, metabolomic analysis identified 8 and 15 significant differential metabolites in serum and lung tissues, respectively, with l-tryptophan showing significant upregulation in both serum and lung tissues of PF mice. l-Tryptophan has been associated with disease progression in various conditions, including nonalcoholic fatty liver disease⁴⁵, scleroderma-like lesions⁴⁶, and renal fibrosis⁴⁷. Subsequently, correlation analysis revealed associations between l-tryptophan levels and gut bacteria such as Lachnospiraceae_NK4A136_Group, Allobaculum, Alistipes, and Candidatus_Saccharimonas, further suggesting an interaction between gut microbiota and l-tryptophan metabolism. In addition, l-tryptophan positively correlated with pro-inflammatory cytokines (LPS, IL-1β, and TNF-α) levels, implicating it as a potential driver of inflammation in PF. These observations suggest that elevated l-tryptophan levels might contribute to PF pathogenesis through inflammatory and fibrotic mechanisms.

Furthermore, the role of l-tryptophan in PF progression was validated in vitro and in vivo. Lung epithelial cells and fibroblasts are known to play critical roles in PF^48,49, as they contribute to disease progression through proliferation, aging, and epithelial-to-mesenchymal transition (EMT) under conditions of chronic inflammation and oxidative stress⁵⁰. Fibroblasts differentiate into myofibroblasts, causing excessive lung pathological injury and furthering PF development⁵¹. TGF-β1 is a well-established inducer of EMT and fibroblast activation in PF^52,53. Therefore, herein, TGF-β1 was used to stimulate lung epithelial cells and fibroblasts, establishing an in vitro PF model. Consistent with previous findings, TGF-β1 treatment enhanced alveolar epithelial cell viability and induced EMT, as manifested by cell transformation into spindle-shaped mesenchymal cells, an increase in mesenchymal markers (α-SMA, Vimentin, and N-cadherin) levels, and a reduction in the epithelial marker E-cadherin⁵⁴. In fibroblast, TGF-β1 treatment similarly enhanced fibroblast vitality, migration ability, and expression of fibrotic markers, including α-SMA, Collagen I, and Fibronectin⁵⁵. In our study, treatment with l-tryptophan further amplified these TGF-β1-induced effects in both epithelial cells and fibroblasts, supporting its role in exacerbating PF. To investigate whether L-Tryptophan directly stimulates inflammatory pathways involved in fibrosis, we evaluated its effect on pro-inflammatory cytokine expression in lung epithelial and fibroblast cells. Results indicated that L-Tryptophan enhanced inflammation-related markers, particularly when combined with TGF-β1, suggesting a pro-inflammatory role in fibrosis progression. Additionally, inhibition of TNF-α signaling reduced the impact of L-Tryptophan on both inflammatory and fibrotic markers, providing mechanistic insights into how L-Tryptophan might contribute to TGF-β1-induced fibrosis through the activation of inflammatory pathways.

In addition, the in vivo mouse experiments also showed that l-tryptophan aggravated PF in BLM- and silica-induced models, supporting its profibrotic role in PF. Notably, tryptophan metabolism produces diverse metabolites with varied effects on fibrosis. For instance, tryptophan metabolite 5-methoxytryptophan has been shown to inhibit pulmonary fibrosis⁵⁵ and inflammation⁵⁶, while kynurenine displays anti-fibrotic activity by antagonizing fibroblast differentiation and promoting collagen degradation⁵⁷. Conversely, another tryptophan metabolite, serotonin (also known as 5-hydroxytryptamine, 5-HT), exacerbates PF by promoting inflammation, exudation of proteins and cells, and oxidative stress⁵⁸. In our study, l-tryptophan promoted BLM- and silica-induced PF, suggesting that tryptophan metabolites may have distinct roles in fibrosis progression, with some metabolites potentially exacerbating and others mitigating fibrotic responses.

mTOR, a serine/threonine-specific protein kinase belonging to the PI3K family⁵⁹, is tightly associated with cell growth, survival, and apoptosis, contributing to various disorders, including malignancy, organ fibrosis, and aging^60,61. Activation of mTOR has been observed in silica-induced PF³², and l-tryptophan is also known to promote the activation of the mTOR pathway³³. p-S6, a downstream effector of mTOR, is activated by mTOR within PF⁶². In this study, TGF-β1-stimulated alveolar epithelial cells and fibroblasts showed significantly elevated levels of p-mTOR/mTOR and p-S6/S6, consistent with previous reports indicating that mTOR/S6 signaling participates in TGF-β1-induced EMT⁶³ and fibrosis^62,64. l-Tryptophan further increased p-mTOR/mTOR and p-S6/S6 levels, suggesting that it may enhance TGF-β1-induced fibrotic signaling through mTOR/S6 activation. To investigate whether mTOR/S6 signaling mediates L-Tryptophan-induced fibrosis, we co-administered the mTOR inhibitor ridaforolimus with l-tryptophan in MLE-12 and WML2 cells. Ridaforolimus effectively reduced phosphorylated mTOR and S6 levels, suppressed l-tryptophan-induced cell viability, and inhibited mesenchymal transformation and fibrosis markers. Additionally, it decreased pro-inflammatory cytokine expression and cell migration. These results suggest that l-tryptophan promotes fibrosis through mTOR/S6 activation, and mTOR inhibition could counteract this effect, indicating a potential therapeutic role for targeting mTOR/S6 in fibrosis.

The limitations of this study should also be noted. Firstly, 16S rRNA sequencing only identifies microbes at the genus or family level, and in some cases, it may not provide species-level resolution. However, shotgun metagenomics is a powerful technique that involves sequencing all the DNA in a sample, providing a comprehensive view of the entire microbial community⁶⁵. In the future, we may employ shotgun sequencing to further reveal the detailed alterations of gut microbiota in PF mice. Secondly, in the present study, microbiome and metabolite levels of PF mice are reported as single events rather than dynamic processes, making it difficult to track progression and causal changes over time. In future experiments, we may conduct longitudinal analyses of the microbiome and metabolite profiles at multiple time points during disease progression in PF mice. Furthermore, as our experiments were conducted in mouse models and cell cultures, these findings might not be directly applicable to humans. Instead, our results provide a foundational basis for future research into the role of gut microbiota in PF.

In summary, this study investigated the differential changes in gut microbiota and metabolites in PF by establishing BLM- and silica-induced PF models. Representative differential gut microbiota Lachnospiraceae_NK4A136_Group and Alloprevotella were identified, and the associated metabolite l-tryptophan was found upregulated in BLM- and silica-induced PF. Additionally, l-tryptophan promoted the progression of PF in vivo and in vitro, which may be achieved through mediating the mTOR/S6 signaling pathway. These findings offer new insights into the role of the “gut-lung axis” in PF. Although these findings suggest that gut microbiota might influence PF progression, further research is needed to determine the therapeutic relevance of microbiota modulation. Future studies incorporating probiotic or FMT interventions in animal models and human subjects could help clarify whether gut microbiota modification might contribute to PF management. Collectively, our findings provide initial insights into the potential role of gut microbiota in PF progression, suggesting directions for future research. Further studies are essential to evaluate the therapeutic applicability of targeting gut microbiota in PF.

Author contributions

J.L. and Y.L. made substantial contributions to the conception and design of the work; W.W., X.K., and K.L. were involved in the experimental conducting; X.Y. and C.Z. analysed and interpreted the data; J.L. and Z.J. drafted the manuscript; J.Z. and Y.L. revised the work critically for important intellectual content; J.L. and Y.L. collected grants; Final approval of the work: all authors.

Data availability

We confirm that all information is included in the manuscript or supporting files. Raw data on gut microbiota and metabolites from “Roles of gut microbiome-associated metabolites in pulmonary fibrosis by integrated analysis” (https://figshare.com/s/96bb031b9efde2a294c0). Raw data on the “Roles of gut microbiome-associated metabolites in pulmonary fibrosis by integrated analysis,” including Figs. 1–11 and Supplementary Figs. 1–8. (https://figshare.com/s/c9663a233870ed519142).

Competing interests

The authors declare no competing interests.

Ethical approval and consent to participate

The guidelines for the care and use of animals were approved by the Medicine Animal Welfare Committee of Jiangxi Chest Hospital.

Supplementary information

The online version contains supplementary material available at 10.1038/s41522-024-00631-4.