Lidocaine alleviates pulmonary fibrosis by attenuating fibroblast activation and restoring lung microbiome-metabolome homeostasis

Lina Wang 1

Chenchen Hou 1

Wenhui Sun 1

Xiahui Ge 1

Qiyun Tu 1

Huaqi Guo 1

Tianyu Zhou 1,2

Yi Wang 3✉ Emailwangyi@tjh.tjmu.edu.cn

Lifeng Yan 1✉ Emaillfyan@sibs.ac.cn

Weining Xiong 1,2 Emailxiongdoctor@hotmail.com

Department of Respiratory and Critical Care Medicine Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine 639 Zhizaoju Road 200011 Shanghai China

2 Shanghai Key Laboratory of Tissue Engineering Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine 639 Zhizaoju Road 200011 Shanghai China

3 Department of Respiratory and Critical Care Medicine, Key Laboratory of Respiratory Diseases, Tongji Hospital, Tongji Medical College National Health Commission, Huazhong University of Science and Technology 1095 Jiefang Ave 430030 Wuhan China

Lina Wang ¹ *, Chenchen Hou ¹ *, Wenhui Sun ¹ *, Xiahui Ge ¹ *, Qiyun Tu ¹, Huaqi Guo¹, Tianyu Zhou^1,2, Yi Wang^3#, Lifeng Yan^1#, Weining Xiong^1,2#

¹ Department of Respiratory and Critical Care Medicine, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China.

² Shanghai Key Laboratory of Tissue Engineering, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China.

³ Department of Respiratory and Critical Care Medicine, National Health Commission Key Laboratory of Respiratory Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave, Wuhan 430030, China

# Correspondence: Yi Wang, wangyi@tjh.tjmu.edu.cn; Lifeng Yan, lfyan@sibs.ac.cn; Weining Xiong, xiongdoctor@hotmail.com.

Lina Wang, Chenchen Hou, Wenhui Sun and Xiahui Ge contributed equally to this work.

Abstract

Pulmonary fibrosis is a devastating disease with limited treatment options. Lidocaine, a local anesthetic, has demonstrated anti-inflammatory and anti-tumor properties, but its therapeutic potential and mechanisms in pulmonary fibrosis remain largely unexplored. In this study, lidocaine administration significantly attenuated bleomycin (BLM)-induced lung injury and fibrosis. Lidocaine suppressed TGF-β1-induced human lung fibroblast differentiation, proliferation, and migration. Mechanistically, lidocaine inhibited the activation and differentiation of fibroblasts to myofibroblasts by blocking the MAPK signaling pathway. Crucially, lidocaine reversed BLM-induced lung microbiota dysbiosis and concurrently restored host metabolic changes, particularly amino acid metabolism. Integrated microbiome–metabolome analysis revealed significant correlations between lidocaine-altered bacterial genera and key amino acid metabolites, suggesting that lidocaine disrupts pathogenic bacteria–metabolite axes that drive fibrosis progression. In conclusion, our study demonstrated that lidocaine ameliorates pulmonary fibrosis by inhibiting MAPK-mediated fibroblast activation, and restoring lung microenvironment homeostasis by modulating the microbiota composition and host metabolic reprogramming. These findings position lidocaine as a novel multitarget therapeutic candidate for pulmonary fibrosis.

Key words:

Pulmonary fibrosis

Lidocaine

Fibroblast

Microbiome

Metabolome

1. Introduction

Pulmonary fibrosis is a chronic, progressive and fibrotic lung disease, with a median survival of only 3–5 years after diagnosis (1). It is characterized by repetitive alveolar epithelial injury, aberrant activation and proliferation of fibroblasts and myofibroblasts, excessive deposition of the extracellular matrix (ECM), and eventual destruction of the lung architecture (2, 3). Although Food and Drug Administration (FDA)-approved drugs such as nintedanib and pirfenidone can slow the decline in lung function and delay disease progression (4, 5), their limited efficacy and side effects limit the overall prognosis of pulmonary fibrosis (6). Therefore, there is an urgent need to develop novel therapeutic strategies against pulmonary fibrosis.

Lidocaine, a commonly used local anesthetic and voltage-gated sodium channel blocker, has shown a range of pharmacological effects beyond its analgesic role. Growing evidence indicates that lidocaine possesses immunomodulatory properties (7), which may involve the suppression of the NF-κB signaling pathway and the inhibition of downstream proinflammatory cytokines such as TNF-α and IL-6 (8). In the context of respiratory diseases, lidocaine has demonstrated therapeutic potential in models of acute lung injury (9–11), allergic airway disease (12, 13), and lung cancer (14). Given these pleiotropic actions, together with evidence supporting the role of epithelial sodium channel blockade in mitigating fibrosis (15–17), lidocaine represents a promising repurposing candidate for pulmonary fibrosis. However, its efficacy against pulmonary fibrosis and the underlying mechanisms remain poorly understood.

In this study, we investigated the therapeutic effects of lidocaine in a bleomycin (BLM)-induced murine model of pulmonary fibrosis and in TGF-β1-stimulated human lung fibroblasts in vitro. Our results demonstrate that lidocaine significantly alleviates BLM-induced lung injury and fibrosis, accompanied by the inhibition of fibroblast differentiation. Mechanistic studies further revealed that lidocaine suppresses fibroblast activation via inhibition of the MAPK signaling pathway, ameliorates lung microbiota dysbiosis, and restores amino acid metabolic homeostasis. Collectively, these findings underscore the potential of lidocaine as a multitarget therapeutic agent for pulmonary fibrosis, as it acts through integrated cellular and microenvironmental mechanisms.

2. Results

2.1 Lidocaine attenuates BLM-induced lung injury and fibrosis

To investigate the therapeutic potential of lidocaine in pulmonary fibrosis, we established a BLM-induced mouse model. The mice received intratracheal lidocaine (4 mg/kg body weight) on days 9 and 12 post-BLM administration (Fig. 1A). Severe lung injury and aberrant collagen accumulation were observed in the BLM-induced mice at 21 days, as illustrated by hematoxylin and eosin (H&E), Masson’s trichrome and Sirius red staining (Fig. 1B). In contrast, in the lidocaine-treated mice, these pathological changes were significantly attenuated (Fig. 1B). Consistently, the severity of pulmonary fibrosis was much lower in the lidocaine-treated mice than in the BLM-treated mice, as evidenced by the lower Ashcroft scores (Fig. 1C) and hydroxyproline levels (Fig. 1D) than the BLM-treated mice, confirming diminished fibrosis severity.

Furthermore, immunostaining revealed that the expression of the fibrotic markers collagen I and α-SMA was attenuated in the mice that were administered lidocaine, indicating that lidocaine significantly attenuated the differentiation of fibroblast into myofibroblast (Fig. 2A‒C). Western blot analysis confirmed a significant reduction in the expression of fibronectin and collagen I in the lungs of the lidocaine-treated mice (Fig. 2D‒F). ELISA analysis of lung homogenates revealed significantly lower levels of TGF-β1 and CXCL2 in lidocaine-treated mice than in control mice (Fig. 2G and 2H). Collectively, these results demonstrate the potential role of lidocaine in ameliorating BLM-induced lung injury and fibrosis.

2.2 Lidocaine inhibits the profibrogenic phenotype and fibroblast activation in vitro

Fibroblast activation is an essential pathogenic step in pulmonary fibrosis (18, 19). To assess the potential of lidocaine to inhibit fibroblast activation and resolve fibrosis, we treated TGF-β1-treated human pulmonary fibroblasts (HPFs and MRC5) with lidocaine. Western blotting revealed that lidocaine reversed the TGF-β1-induced upregulation of myofibroblast markers (fibronectin, collagen 1, and α-SMA) in HPF and MRC5 cells (Fig. 3A‒D). Immunostaining analysis confirmed that the expression of fibronectin and α-SMA was significantly decreased after lidocaine treatment (Fig. 3E and 3F).

Further investigations were conducted to examine the effects of lidocaine on the proliferation and migration of fibroblasts. The results of the EdU assay demonstrated that lidocaine intervention reduced the TGF-β1-induced proliferation of fibroblasts (Fig. 3G and 3H). Transwell assays revealed that lidocaine significantly suppressed the migration of fibroblasts induced by TGF-β1 (Fig. 3I and 3J). Taken together, these findings suggest that lidocaine can attenuate lung fibrosis by inhibiting fibroblast activation.

2.3 Lidocaine attenuates fibroblast activation by blocking MAPK cascade signaling

To further investigate how lidocaine regulates fibroblast differentiation, we performed proteomic analysis of lidocaine- and DMSO-treated mouse primary fibroblasts activated by TGF-β1 for 2 h. The results revealed 42 differentially expressed proteins, comprising 28 upregulated and 14 downregulated proteins, in lidocaine-treated fibroblasts (Fig. 4A and Table S1). GO enrichment analysis revealed that mitogen-activated protein kinase (MAPK) cascade signaling was significantly downregulated in lidocaine-treated primary fibroblasts (Fig. 4B). Given that MAPK signaling plays a key role in the progression of pulmonary fibrosis, we further assessed whether lidocaine attenuates fibroblast activation by regulating the MAPK signaling pathway. Western blot analysis revealed elevated levels of p-P38, p-JNK and p-Erk1/2 in the TGF-β1-induced HPF cells compared with those in the control cells, whereas the administration of lidocaine resulted in reduced expression of p-P38, p-JNK and p-Erk1/2 (Fig. 4C and 4D). To evaluate potential direct interactions between lidocaine and P38, JNK and Erk1/2, we conducted molecular docking (Fig. 4E-G). The docking affinity values were − 5.49 for P38, − 5.79 for JNK, and − 5.27 for Erk2, showing potential direct interactions. Taken together, these results showed that lidocaine alleviated fibroblast activation by altering MAPK cascade signaling.

2.4 Lidocaine modulates lung microbiota dysbiosis in BLM-exposed mice

Mounting evidence indicates that lung dysbiosis plays a key role in the pathogenesis and progression of pulmonary fibrosis (20–23). To investigate whether lidocaine regulates lung flora disorders, BALF samples from the PBS, BLM, and BLM + lidocaine groups were subjected to full-length 16S rRNA gene sequencing analysis. We identified 360 genera under 17 phyla in the BALF samples from the three groups (Table S2). Alpha-diversity analyses revealed that there was a significant difference in the richness and diversity of the lung microbiota between control and BLM-treated mice, whereas lidocaine administration reversed this change in BLM-treated mice, as evidenced by the shannon diversity index and Pielou’s evenness (Fig. 5A). Beta-diversity analysis via weighted UniFrac revealed differences in the structure of the lung microbiome composition among the three groups (ANOSIM, R = 0.266555, P = 0.033; Fig. 5B).

Stacked bar plots of relative abundances at the phylum and genus levels revealed evident differences in the lung microbiota among the three groups (Fig. 5C and 5D). Compared with the PBS control group, the BLM group presented enriched Pseudomonadota and Bacillota at the phylum level, but these increases were altered after lidocaine treatment (Fig. 5C and Table S3). At the genus level, Pseudomonas, Citrobacter, Niallia, Stutzerimonas, and Rhodoferax were significantly elevated in BLM-treated mice but reduced by lidocaine treatment (Fig. 5D and Table S4). Furthermore, random forest analysis revealed that Pseudomonas, Rhodoferax, Niallia, Stutzerimonas, and Citrobacter were enriched in the BLM-treated group compared with those in the control group, whereas lidocaine administration reduced the relative abundances of these genera compared with those in the BLM group (Fig. 5E and Table S5). Therefore, these data suggest that lidocaine has a beneficial effect on modulating the composition and structure of the lung microbiota in mice with pulmonary fibrosis.

In addition, to further assess the impact of lidocaine on the functional profiles of the lung microbiota across the three groups, we performed PICRUSt2 gene prediction analysis. A total of 33 differential KEGG pathways were identified among the groups (Fig. 5F and Table S6). After BLM exposure, the abundances of functional genes related to amino acid metabolism, carbohydrate metabolism, glycan biosynthesis and metabolism, lipid metabolism, metabolism of cofactors and vitamins, metabolism of terpenoids and polyketides, and nucleotide metabolism were significantly decreased, which was reversed by lidocaine, indicating the potential of lidocaine treatment to restore the metabolic function of the lung microbiota in pulmonary fibrosis model mice.

In summary, BLM exposure led to lung microbiota dysbiosis, which was partially attenuated by the administration of lidocaine. Moreover, the prediction of the lung microbiota function indicated the potential of lidocaine treatment to restore the lung microbiota associated metabolism function in pulmonary fibrosis model mice.

2.5 Lidocaine reverses BLM-induced lung metabolic changes

To explore the global discriminating metabolites among PBS-, BLM-, and BLM + lidocaine-treated mice, we next performed untargeted LC‒MS analysis of BALF samples. The score scatter plots for the multiPLS-DA models from both the positive and negative ion modes achieved great separation among the three mouse groups (Fig. 6A). Significant metabolites were selected using a VIP value ≥ 1 and a P value < 0.05. The total screened differentially abundant metabolites are shown in Table S7. We identified 71 dysregulated metabolites (51 upregulated and 20 downregulated) in the BLM group compared with the control group and 24 metabolites (7 upregulated and 17 downregulated) altered by lidocaine treatment (Fig. 6B). KEGG enrichment analysis revealed that the differentially abundant metabolites between the control and BLM groups were enriched mainly in the central carbon metabolism and biosynthesis of amino acids signaling pathways (Fig. 6C). For the BLM and BLM + lidocaine groups, the enrichment was mainly in the biosynthesis of amino acids, linoleic acid metabolism, and central carbon metabolism signaling pathways (Fig. 6D).

Further analysis of the differentially abundant metabolites among the three groups was conducted via Venn diagrams for the two comparison groups (Fig. 6E and Table S8). Among the 20 shared differentially abundant metabolites, shikimic acid (M173T147.neg), L-isoleucine (M131T75.neg), L-lysine (M147T42.pos), L-phenylalanine (M164T90.neg), and 2-hydroxycinnamic acid (M165T71.pos) were increased and succinic acid (M117T54.neg) and beta-alanyl-L-arginine (M245T85.pos) were decreased in BLM-treated mice compared with control mice, while lidocaine administration reversed these changes (Fig. 6F‒L). Notably, these 7 metabolites are involved in the amino acid metabolism pathway.

Therefore, consistent with the prediction of lung microbiota function, untargeted metabolomics revealed changes in pathways related to amino acid metabolism after BLM exposure or combined lidocaine treatment, indicating that lidocaine alleviated pulmonary fibrosis by normalizing BLM-induced changes in amino acid metabolism.

2.6 Integrated microbiome‒metabolome analysis identifies a lidocaine-associated network

Since several significantly regulated microbiota and metabolites are related to the beneficial effects of lidocaine in alleviating pulmonary fibrosis, which deserve further attention in terms of their system functions, we performed a Spearman correlation analysis on the basis of the differential lung microbiota and metabolites. Significant correlations (P adj<0.05) were observed between specific microbiota genera and metabolites (Fig. 7 and Table S9). In particular, Pseudomonas, Citrobacter, Niallia, Stutzerimonas, and Rhodoferax were positively correlated with the amino acids L-isoleucine, L-lysine, L-phenylalanine, and 2-hydroxycinnamic acid. These microbiota and metabolites were significantly elevated in BLM-treated mice but were reduced by lidocaine administration. Niallia and Stutzerimonas were also negatively correlated with succinic acid, which was decreased in BLM-treated mice but increased following lidocaine treatment.

Collectively, these results suggest that lung microbiota dysbiosis may influence the amino acid metabolic pathway, which contributes to the progression of pulmonary fibrosis, while lidocaine alleviates pulmonary fibrosis by regulating the lung microbiota-associated metabolic reprogramming of amino acids.

3. Discussion

In this study, we employed both in vivo and in vitro approaches to evaluate the therapeutic potential of lidocaine in pulmonary fibrosis. We demonstrated that lidocaine significantly attenuates BLM-induced pulmonary fibrosis in mice, as characterized by reduced differentiation of fibroblast into myofibroblast. Mechanistic investigations revealed that lidocaine confers protection against pulmonary fibrosis through multiple actions, including the suppression of fibroblast activation via the inhibition of MAPK signaling, the modulation of lung microbiota dysbiosis, and the restoration of amino acid metabolic homeostasis. These findings provide novel insights into the therapeutic potential of lidocaine in pulmonary fibrosis, beyond its conventional use as a local anesthetic.

Lidocaine is a widely used regional anesthetic and voltage-sensitive sodium channel blocker. Previous studies have reported its antitumor effects in breast cancer (24) and hepatocellular carcinoma (25), which are mediated through the suppression of cell proliferation, induction of apoptosis, and inhibition of migration. Additionally, lidocaine has been applied as an immunomodulatory agent in allergic airway diseases (12, 13) and acute lung injury (9, 10). Growing evidence also supports the role of epithelial sodium channel blockers as potential therapeutics for fibrotic diseases (15–17). On the basis of these properties, we hypothesize that lidocaine could serve as a promising candidate for the treatment of pulmonary fibrosis.

Our results indicate that lidocaine effectively attenuates fibrogenesis both in vivo and in vitro. Lidocaine administration markedly ameliorated BLM-induced pulmonary fibrosis, as supported by histopathological evaluation and downregulation of fibrotic markers. Furthermore, lidocaine significantly reduced Ashcroft scores and hydroxyproline levels in lung tissues. Myofibroblasts play a central role in pulmonary fibrosis by secreting ECM proteins, leading to tissue stiffening and impaired respiratory function (26). Importantly, lidocaine significantly inhibited the TGF-β1-induced myofibroblast differentiation, proliferation, and migration of human lung fibroblasts, suggesting that lidocaine mitigated lung fibrosis by inhibiting fibroblast activation.

To elucidate how lidocaine inhibits fibroblast activation, we conducted proteomic analysis, which revealed that lidocaine preferentially suppressed MAPK cascade signaling. Subsequent validation experiments confirmed that lidocaine robustly inhibits the phosphorylation of P38, Erk1/2, and JNK in TGF-β1-stimulated fibroblasts. Molecular docking analyses further supported these findings. Prior studies have shown that lidocaine attenuates P38 MAPK activation in microglia and lung epithelial cells under hypoxia/reoxygenation stress (10, 27) and inhibits both P38 and JNK phosphorylation in the lungs of an acute lung injury model (9). Thus, our data identify MAPK pathway blockade as a key mechanism underlying the antifibrotic activity of lidocaine, corroborating previous reports that MAPK activity within lung fibroblasts is critical for fibrosis progression (28).

Accumulating evidence suggests that lung microbiota dysbiosis contributes to the pathogenesis of pulmonary fibrosis (20, 22, 23). In this study, BLM exposure induced both pulmonary fibrosis and microbiota imbalance, whereas lidocaine treatment partially reversed these changes. Specifically, lidocaine reduced the relative abundance of the phyla Pseudomonadota and Bacillota as well as genera, including Pseudomonas, Citrobacter, Niallia, Stutzerimonas, and Rhodoferax, in BLM-treated mice. Notably, Pseudomonas and Citrobacter have been implicated in chronic lung inflammation and pulmonary diseases (23, 29).

PICRUSt2 analysis further linked these taxonomic shifts to functional restoration of metabolic pathways—particularly those involving amino acid, carbohydrate, and lipid metabolism—suggesting that lidocaine may help reinstate microbial metabolic functions in fibrotic lungs.

Changes in lung metabolites were investigated in the current study, post-BLM exposure or after combined lidocaine intervention through untargeted metabolomics. We found that lidocaine reversed BLM-induced alterations in key metabolites involved in amino acid metabolism, including shikimic acid, L-isoleucine, L-lysine, L-phenylalanine, 2-hydroxycinnamic acid, succinic acid and beta-alanyl-L-arginine. KEGG enrichment analysis revealed that lidocaine restored pathways related to amino acid biosynthesis. Amino acids are fundamental to numerous biological processes, including protein synthesis, redox homeostasis, and cellular signaling (30). Emerging studies highlight the role of dysregulated amino acid metabolism (e.g. glycine, glutamine, arginine, and tryptophan) in the initiation and progression of pulmonary fibrosis (31–33), supporting the concept of pulmonary fibrosis as a metabolic disorder (34). Shikimic acid is a biochemical intermediate used in plants and microorganisms for the biosynthesis of aromatic amino acids (tryptophan, phenylalanine, and tyrosine) and other key aromatic compounds, such as folates (35). Hydroxycinnamic acids, which are natural phenolic acids that are abundant in vegetal foods, alter the intestinal microbiota (36). L-phenylalanine, L-isoleucine and L-lysine are essential amino acids that are involved mainly in energy metabolism. Succinic acid is one of the main intermediates of the TCA cycle. Thus, the metabolic changes observed in this study suggest that lidocaine attenuates BLM-induced disruptions in energy metabolism.

The pulmonary microbiota and lung metabolites are interrelated. In this study, Spearman correlation analysis revealed significant associations between the levels of lidocaine-altered bacterial genera (Pseudomonas, Citrobacter, Niallia, Stutzerimonas, and Rhodoferax) and dysregulated amino acids (L-isoleucine, L-lysine, L-phenylalanine, 2-hydroxycinnamic acid, succinic acid, and beta-alanyl-L-arginine). Previous studies have reported similar correlations between the lung microbiota and amino acid metabolites following environmental exposures such as silica and PM2.5 (37, 38). Thus, our integrated microbiome‒metabolome network suggests that lidocaine may disrupt pathogenic bacteria‒metabolite axes that drive fibrosis progression.

Despite these findings, certain limitations should be acknowledged: (1) the inferred bacteria–metabolite relationships remain correlative, and further mechanistic studies are needed to establish causal links; and (2) the absence of human fibrotic lung tissue limits the clinical translatability of our results.

In conclusion, our study demonstrated that lidocaine ameliorates BLM-induced pulmonary injury and fibrosis through direct inhibition of fibroblast activation via MAPK signaling suppression, restructuring of the lung microbiota composition, and restoration of microbiota-related amino acid metabolic homeostasis. These results highlight lidocaine as a multitarget therapeutic candidate for pulmonary fibrosis that integrates cellular signaling, microbial ecology, and metabolic reprogramming.

4. Methods and Materials

4.1 Ethical statement

All animal experiments were conducted at Shanghai Ninth People's Hospital and received ethical approval from the Laboratory Animal Ethics Committee in Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Approval No. SH9H-2025-A1644-1). All methods were carried out in strict adherence to the committee's relevant guidelines and regulations. All methods are reported in accordance with ARRIVE guidelines (https://arriveguidelines.org).

4.2 Cell culture

Human lung fibroblasts MRC-5 and HPF were purchased from ScienCell Research Laboratories, Inc. (Nanjing, China), and cultured in MEM and DMEM, respectively, each supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, and 100 µg/mL streptomycin. The cells were maintained at 37°C in a humidified incubator at 37°C with 5% CO2.

4.3 BLM-induced PF mouse model

All male C57BL/6J mice (8–10 weeks) were purchased from GemPharmatech Co., Ltd. (Jiangsu, China). All the mice were housed in a temperature- and humidity-controlled environment under a 12 h light/dark cycle.

Pulmonary fibrosis was induced in mice by intratracheal administration of bleomycin (BLM) as described previously (39, 40). Briefly, on Day 0, mice were deeply anesthetized via a vaporizer (R500, Ruiwode, Shenzhen, China) with 3.5% isoflurane (Ruiwode) in an induction chamber and maintained on 1.5% isoflurane. Under anesthesia, a single dose of bleomycin (0.7 mg/kg) was administered intratracheally. Mice in the control group received an equivalent volume of PBS instead. In therapeutic experiments, lidocaine (4 mg/kg) or an equal volume of PBS was intratracheally administered on Day 9 and Day 12. On Day 21 post-BLM challenge, all mice were euthanized by prolonged exposure to a 5% isoflurane overdose for more than 5 minutes, ensuring respiratory arrest and cardiac cessation. Lung tissues were then collected for the evaluation of pulmonary fibrosis.

4.4 Histological analysis

Mouse lung tissues were dissected from the mice and fixed in 4% paraformaldehyde. Subsequently, the lungs were subjected to paraffin embedding, followed by sectioning. Hematoxylin and eosin (H&E), Sirius red, and Masson’s trichrome staining were executed on the sections as previously reported(41). These sections were observed under a microscope, and images were obtained. Fibrosis was evaluated via the Ashcroft scoring method on a scale ranging from 0 to 8. The scores from multiple microscopic fields were averaged to determine the overall degree of fibrotic alterations in each lung section. The results were visualized on a graph at 200-fold magnification. Two independent pathologists carried out the grading in a blinded fashion.

4.5 Immunofluorescence assay

Mouse lung tissues were fixed in 4% paraformaldehyde (PFA), paraffin-embedded, and sectioned. Antigen retrieval was performed using citric acid buffer (pH 6.0) in a microwave oven. After dewaxing, antigen retrieval, and endogenous peroxidase blocking, the sections were blocked with 5% bovine serum albumin (BSA) and then incubated overnight at 4°C with primary antibodies. Subsequently, species-matched fluorescent secondary antibodies were applied. Nuclei were counterstained with DAPI, and slides were mounted with antifluorescence quenched tablets. Finally, the sections were examined using a fluorescence microscope and subjected to analysis.

The cells were washed with PBS, fixed with 4% PFA, and permeabilized with 0.2% Triton X-100. After blocking with 5% goat serum, samples were incubated with primary antibodies at 4°C overnight. The following primary antibodies were used: fibronectin (1:100, Cat# 15613-1-AP, Proteintech, USA), collagen I (1:100, Cat# 91144, CST, USA), and α-SMA (1:200, Cat# 67735-1-Ig, Proteintech, USA). Nuclei were counterstained with 4′-6-diamidino-2-phenylindole (DAPI; Invitrogen, USA). Images were acquired via a laser scanning confocal microscope (Zeiss LSM 800).

4.6 Hydroxyproline assay

The hydroxyproline content in mouse lung tissue was measured via a commercial hydroxyproline assay kit (Nanjing Jiancheng Bioengineering Institute, China) following the manufacturer's instructions. Briefly, 50 mg of lung tissue was hydrolyzed with alkaline hydrolysis solution at 100°C for 20 min, followed by neutralization to pH 7.0. The supernatant was then mixed with the assay reagents, and the absorbance was detected at 560 nm via a microplate reader.

4.7 Western blot analysis

Total protein was extracted from lung tissues or fibroblasts via radioimmunoprecipitation assay (RIPA) buffer supplemented with protease inhibitor cocktail. The protein samples were separated by SDS‒PAGE gels and transferred to PVDF membranes. The membranes were then blocked and probed with primary and secondary antibodies. The signals were visualized via the ChemiDoc™ Imaging System (Bio-Rad). The following primary antibodies were used: fibronectin (Cat# 15613-1-AP, Proteintech, USA), collagen I (Cat# 91144, CST), α-SMA (Cat# 67735-1-Ig, Proteintech), p-P38 (Cat# 4511, CST), P38 (Cat# 8690, CST), p-JNK (Cat# 4668, CST), JNK (Cat# 9252, CST), p-Erk1/2 (Cat# 4370, CST), Erk1/2 (Cat# 4695, CST), Gapdh (Cat# 2118, CST), and α-Tubulin (Cat# 11224-1-AP, Proteintech).

4.8 Enzyme-linked immunosorbent assay (ELISA)

To determine the concentrations of transforming growth factor-β1 (TGF-β1) and C-X-C motif chemokine ligand 2 (CXCL2), mouse lung tissues were homogenized in PBS and centrifuged to remove insoluble debris.

The supernatants were assayed using via ELISA kits (Cat# AB119557 and Cat# AB204517; Abcam, USA) according to the manufacturers’ instructions.

4.9 Full-length 16S rRNA sequencing of the lung microbiome

Total genomic DNA was extracted from the mouse lung samples via the OMEGA Soil DNA Kit (M5635-02, Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer’s instructions. PCR amplification of the nearly full-length (V1-V9) bacterial 16S rRNA genes was performed via the forward primer 27F (5’-AGAGTTTGATCCTGGCTCAG-3’) and the reverse primer 1492R (5’-ACCTTGTTACGACTT-3’) together with sample-specific 16-bp barcodes. The PCR amplicons were purified with Agencourt AMPure Beads (Beckman Coulter, Indianapolis, IN) and quantified via the PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After the individual quantification step, amplicons were pooled in equal amounts, and single-molecule real-time (SMRT) sequencing was performed via the PacBio Sequel platform at Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China).

The obtained 16S rRNA gene sequences were analyzed via the Quantitative Insights into Microbial Ecology 2 package (QIIME2 2022.11) (42). The sequences were quality filtered, denoised, merged and chimera removed via the DADA2 plugin (43). Taxonomy assignments were performed with the sklearn feature-classifier plugin against the full-length NCBI/SILVA reference database version 138 (44).

In this study, Kruskal-Wallis rank-sum test and permutational multivariate analysis of variance were used to analyze alpha-diversity. Beta-diversity was calculated using principal coordinate analysis (PCoA) based on the Bray–Curtis distance matrix and tested by permutational multivariate analysis of variance via the analysis of similarities (ANOSIM) function. Kruskal-Wallis rank-sum test was used to analyze the abundance of the microbiota. Random forest analysis was applied to discriminate the samples from different groups via QIIME2 with default settings. Microbial functions were predicted by PICRUSt2 (Phylogenetic investigation of communities by reconstruction of unobserved states) upon KEGG(45, 46) (https://www.kegg.jp/) databases. p values were corrected for multiple inference via the Benjamin–Hochberg method.

4.10 Untargeted LC‒MS/MS metabolomics analysis

Mouse lung samples were homogenized in cold methanol/acetonitrile (1:1, v/v) to remove the protein. After centrifugation, the supernatant was vacuum-dried and reconstituted in acetonitrile/water (1:1, v/v) for LC‒MS analysis. Quality control (QC) samples were prepared by pooling 10 µL of each sample and analyzed together with the other samples to monitor instrument performance. HILIC separation was achieved on an ACQUIY UPLC BEH 1.7 µm column (waters, Ireland). Mass spectrometry was performed on a Q Exactive instrument (thermos) in both positive and negative ESI modes.

The raw data were processed via Compound Discoverer 3.0 (Thermo Fisher Scientific) for peak extraction, alignment, correction, and normalization. Metabolites were identified by matching accurate mass (< 25 ppm) and MS/MS spectra against the laboratory-built databases BioCyc, HMDB, METLIN, HFMDB, LipidMaps, and other databases. The normalized data matrix was subjected to multivariate analysis in SIMCA-P (version 14.1, Umetrics, Umea, Sweden). The 7-fold cross-validation and response permutation testing were used to evaluate the robustness of the model. Metabolites with a variable importance in projection (VIP) score > 1 from the OPLS-DA model and a p-value < 0.05 from Student's t-test were considered statistically significant. Functional analysis of differential metabolites primarily involved KEGG enrichment analysis using clusterProfiler (version 4.6.0), revealing significantly enriched metabolic pathways. The metabolomic sequencing and analysis were performed on the PacBio Sequel platform at Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China).

4.11 Proteomic analysis

Mouse primary lung cells were isolated and treated with 1mM lidocaine for 2 h, followed by TGF-β1 induction for 2 h. Protein extraction and subsequent sequencing were performed by PTM Bio Co. Ltd. (Hangzhou, China). The proteomic analysis was conducted following the protocol established in our previously published study (41).

Briefly, protein extraction was carried out using a lysis buffer with sonication on ice, followed by centrifugation at 12,000×g at 4°C for 10 min. The supernatant was collected, and the protein concentration was determined via a BCA assay (Thermo Fisher Scientific). For protein digestion, trypsin was added at a 1:50 trypsin-to-protein mass ratio and incubated overnight. The protein mixture was then reduced with 5 mmol dithiothreitol (56°C, 30 min) and alkylated with 11 mmol iodoacetamide (room temperature, 15 min).

The tryptic peptides were dissolved in solvent A (0.1% formic acid, 2% acetonitrile/in water) and loaded onto a homemade reversed-phase analytical column (25 cm length, 75/100 µm i.d.). Peptides were separated with a gradient from solvent B (0.1% formic acid in acetonitrile) at a constant flow rate of 450 nL/min on a nanoElute UHPLC system (Bruker Daltonics, USA). MS analysis was performed on a timsTOF Pro mass spectrometer (Bruker Daltonics) with an electrospray voltage of 2.3 kV. The precursors and fragments were analyzed at the TOF detector, with an MS/MS scan range from 100 to 1,700 m/z. Data processing was performed using the MaxQuant search engine (v.1.6.15.0). Proteins were identified under a false discovery rate (FDR) threshold of 1%, and at least two unique peptides were required for quantification.

4.12 Molecular docking analyse

Verification of lidocaine-target binding via molecular docking. The structure of lidocaine (ChemSpider ID: 3548) was obtained from https://www.chemspider.com/ and converted from the MOL format to Protein Data Bank (PDB) format using Open Babel software (version 3.1.1). Structures of receptor proteins were obtained from the PDB database, specifically, P38 (2FST), JNK (4L7F), and ERK2 (8AOJ). PyMOL (version 3.1.6) was used to remove water molecules and ligands from the structures, while AutoDockTools (version 1.5.7) was used for hydrogenation and charge balancing of the receptor proteins. The structures of both lidocaine and the receptor proteins were then converted to the PDBQT format, and molecular docking was performed. Docking images were visualized using PyMOL.

4.13 Statistical analysis

The experimental data are presented as the mean ± SEMs. All the statistical analyses were performed via GraphPad Prism software (version 7). Student’s t test was performed for the comparisons between two groups. One-way ANOVA was performed for the comparisons of more than two groups, followed by Tukey’s test to determine the differences among groups. Spearman correlation analysis was used to analyze the correlation between the lung microbiota and metabolites. Statistical significance was set at a P value of < 0.05.

CRediT authorship contribution statement

Lina Wang: formal analysis, investigation, data curation, writing—original draft preparation, visualization; Chenchen Hou: validation, investigation, data curation; Wenhui Sun: investigation; Xiahui Ge: investigation; Yunqi Tu, Huaqi Guo, and Tianyu Zhou: resources; Yi Wang: Conceptualization, methodology, project administration; Lifeng Yan: writing—review and editing, supervision, project administration, funding acquisition; Weining Xiong: Conceptualization, supervision, project administration, funding acquisition.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This work is funded by the Natural Science Foundation of China (grant numbers 82090015, 82330001, and 82400080) and the State Key Laboratory of Respiratory Health and Multimorbidity Special Fund (grant number 2060204).

Data Availability

All 16s rRNA gene sequences are provided and available at the National Center for Biotechnology Information Sequence Read Archive (SRP) database with accession code PRJNA1346309. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the iProX partner repository ( accession: PXD069709, https://www.iprox.cn/page/SSV024.html;url=17636458117194fwK , password: XIa4).

Electronic Supplementary Material

Below is the link to the electronic supplementary material

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Author Contribution

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Figure legends

Figure 1. Lidocaine reverses established lung fibrosis in a BLM-induced mouse model

(A) Schematic of the experimental design and timeline for BLM-treated mice administered PBS or lidocaine. (B) Histopathological evaluation of lung fibrosis in BLM-induced mice treated with PBS or lidocaine. Representative images of H&E, Masson’s trichrome, and Sirius red staining are shown at ×100 magnification. Scale bar = 100 µm. (C) Semiquantitative assessment of fibrosis severity via the Ashcroft score. (D) Quantification of hydroxyproline content in mouse lung tissues. These experiments were performed three times. One-way ANOVA was used for statistical analysis in C and D. ** P < 0.01, *** P < 0.001, **** P < 0.0001. BLM, bleomycin; Lido, lidocaine.

Fig. 2

Lidocaine attenuates the expression of fibrotic markers in a BLM-induced mouse model

Representative immunofluorescence image of Collagen 1 (green) and α-SMA (yellow) in mice after BLM induction with either PBS or lidocaine. Six mice were included in each study group. Scale bar = 100 µm. (B, C) Quantification of the relative immunofluorescence intensity from (A). (D) Western blot analysis of fibronectin and collagen 1 expression in mouse lung tissues. (E, F) Quantification of the western blot results from (D). (G, H) ELISA analysis of TGF-β1 and CXCL2 levels in lung homogenates from PBS-, BLM-, and BLM + lidocaine-treated mice. n = 7:8:9. These experiments were performed three times. Statistical analysis was performed via one-way ANOVA (B, C, E-H). * P < 0.05, ** P < 0.01, **** P < 0.0001. TGF-β1, transforming growth factor-β1; CXCL2, C-X-C motif chemokine ligand 2.

Fig. 3

Lidocaine attenuates fibroblast differentiation, proliferation and migration

Western blot analysis of fibronectin, collagen 1, and α-SMA expression in MRC5 cells pretreated with lidocaine and stimulated with TGF-β1. (B) Quantification of the western blot data from (A). (C) Western blot analysis of fibronectin, collagen 1, and α-SMA expression in HPF cells pretreated with lidocaine following TGF-β1 induction. (D) Quantification of the western blot data from (C). (E) Representative results of immunofluorescence staining of fibronectin and α-SMA in HPF cells. The nuclei were stained blue with DAPI. Images were acquired at × 200 magnification. Scale bar = 100 µm. (F) Quantification of the relative immunofluorescence intensity from (E). (G) Representative images of EdU staining in HPF cells. Images were acquired at × 200 magnification. Scale bar = 100 µm. (H) Quantification of the percentage of EdU-positive cells (%). (I) Representative results of the Transwell assay in HPF cells pretreated with lidocaine following TGF-β1 induction. Images were acquired at × 200 magnification. Scale bar = 100 µm. (J) Quantification of cell migration from (I). These experiments were performed three times. One-way ANOVA was used for statistical analysis. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Fig. 4

Lidocaine attenuates MAPK signaling in fibroblasts

The bar graph shows the differentially expressed proteins in mouse primary fibroblasts treated with lidocaine or DMSO followed by TGF-β1 stimulation. (B) GO enrichment analysis of the downregulated proteins in TGF-β1 + lidocaine-treated mouse primary fibroblasts compared with those in the TGF-β1 + DMSO-treated controls. (C) Western blot analysis of p-P38, P38, p-JNK, JNK, p-Erk1/2, and Erk1/2 expression in HPF cells pretreated with lidocaine and stimulated with TGF-β1. (D) Quantification of the western blot data from (C). (E-G) Molecular docking diagrams of lidocaine with P38 (E), JNK (F) and Erk2 (G). Lidocaine is displayed in yellow, while the target proteins are shown in purple. Binding energy is presented in units of kcal/mol. These experiments were performed three times. One-way ANOVA was used for statistical analysis. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Fig. 5

Lidocaine modulates lung microbiota dysbiosis in BLM-exposed mice

Alpha diversity of the lung microbiota ws assessed by the Shannon diversity index and Pielou’s evenness index in PBS-, BLM-, and BLM + lidocaine-treated mice. (B) Beta diversity analyzed by ANOSIM in PBS-, BLM-, and BLM + lidocaine-treated mice. (C, D) Comparison of the relative abundance (%) of the 30 most abundant phyla and genera within the lung microbiome among the three groups. (E) Random forest analysis identifying microbiota enriched in each group. (F) PICRUSt2 gene prediction analysis of KEGG metabolic pathways of the lung microbiota. n = 5:5:6. The KEGG pathway map is reproduced with permission from Kanehisa Laboratories. The Kruskal-Wallis rank-sum test was used for statistical analysis in A, C,and D. ANOSIM analysis of variance was used in B. Kruskal-Wallis and Wilcoxon-Mann-Whitney tests were used for statistical analysis in E. * P(FDR) < 0.05. ANOSIM, analysis of similarity; PICRUSt2, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.

Fig. 6

Lidocaine reverses BLM-induced metabolic alterations in BALF

MultiPLS-DA score plots from positive and negative ion modes distinguishing PBS, BLM, and BLM + lidocaine-treated mice. (B) Bar graph showing the differentially abundant metabolites in the BALF among the three groups. (C) KEGG enrichment analysis of the differentially abundant metabolites identified in PBS- and BLM-treated mice. (D) KEGG enrichment analysis of the differentially abundant metabolites identified in BLM- and BLM + lidocaine-treated mice. The KEGG pathway map is reproduced with permission from Kanehisa Laboratories. (E) Venn diagrams of overlapping differentially abundant metabolites. (F-L) The relative expression levels of the 7 shared differentially abundant metabolites involved in the amino acid metabolism pathway. n = 5:5:6. * P < 0.05. BALF, bronchoalveolar lavage fluid.

Figure 7. Integrated analysis of the lung microbiome and metabolome that were strongly associated with lidocaine

Heatmaps showing Spearman’s correlation coefficients between the 7 differential lung metabolites and the top 20 differential microbiota. * P(FDR) < 0.05, ** P(FDR) < 0.001.

Figures

Fig. 1

Lidocaine reverses established lung fibrosis in a BLM-induced mouse model

Fig. 2

Lidocaine attenuates the expression of fibrotic markers in a BLM-induced mouse model

(A) Representative immunofluorescence image of Collagen 1 (green) and α-SMA (yellow) in mice after BLM induction with either PBS or lidocaine. Six mice were included in each study group. Scale bar = 100 µm. (B, C) Quantification of the relative immunofluorescence intensity from (A). (D) Western blot analysis of fibronectin and collagen 1 expression in mouse lung tissues. The full-length original images are provided in Supplementary Information. (E, F) Quantification of the western blot results from (D). (G, H) ELISA analysis of TGF-β1 and CXCL2 levels in lung homogenates from PBS-, BLM-, and BLM + lidocaine-treated mice. n = 7:8:9. These experiments were performed three times. Statistical analysis was performed via one-way ANOVA (B, C, E-H). * P < 0.05, ** P < 0.01, **** P < 0.0001. TGF-β1, transforming growth factor-β1; CXCL2, C-X-C motif chemokine ligand 2.

Fig. 3

Lidocaine attenuates fibroblast differentiation, proliferation and migration

(A) Western blot analysis of fibronectin, collagen 1, and α-SMA expression in MRC5 cells pretreated with lidocaine and stimulated with TGF-β1. (B) Quantification of the western blot data from (A). (C) Western blot analysis of fibronectin, collagen 1, and α-SMA expression in HPF cells pretreated with lidocaine following TGF-β1 induction. The full-length original images are provided in Supplementary Information. (D) Quantification of the western blot data from (C). (E) Representative results of immunofluorescence staining of fibronectin and α-SMA in HPF cells. The nuclei were stained blue with DAPI. Images were acquired at × 200 magnification. Scale bar = 100 µm. (F) Quantification of the relative immunofluorescence intensity from (E). (G) Representative images of EdU staining in HPF cells. Images were acquired at × 200 magnification. Scale bar = 100 µm. (H) Quantification of the percentage of EdU-positive cells (%). (I) Representative results of the Transwell assay in HPF cells pretreated with lidocaine following TGF-β1 induction. Images were acquired at × 200 magnification. Scale bar = 100 µm. (J) Quantification of cell migration from (I). These experiments were performed three times. One-way ANOVA was used for statistical analysis. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Fig. 4

Lidocaine attenuates MAPK signaling in fibroblasts

(A) The bar graph shows the differentially expressed proteins in mouse primary fibroblasts treated with lidocaine or DMSO followed by TGF-β1 stimulation. (B) GO enrichment analysis of the downregulated proteins in TGF-β1 + lidocaine-treated mouse primary fibroblasts compared with those in the TGF-β1 + DMSO-treated controls. (C) Western blot analysis of p-P38, P38, p-JNK, JNK, p-Erk1/2, and Erk1/2 expression in HPF cells pretreated with lidocaine and stimulated with TGF-β1. The full-length original images are provided in Supplementary Information. (D) Quantification of the western blot data from (C). (E-G) Molecular docking diagrams of lidocaine with P38 (E), JNK (F) and Erk2 (G). Lidocaine is displayed in yellow, while the target proteins are shown in purple. Binding energy is presented in units of kcal/mol. These experiments were performed three times. One-way ANOVA was used for statistical analysis. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Fig. 5

Lidocaine modulates lung microbiota dysbiosis in BLM-exposed mice

(A) Alpha diversity of the lung microbiota ws assessed by the Shannon diversity index and Pielou’s evenness index in PBS-, BLM-, and BLM + lidocaine-treated mice. (B) Beta diversity analyzed by ANOSIM in PBS-, BLM-, and BLM + lidocaine-treated mice. (C, D) Comparison of the relative abundance (%) of the 30 most abundant phyla and genera within the lung microbiome among the three groups. (E) Random forest analysis identifying microbiota enriched in each group. (F) PICRUSt2 gene prediction analysis of KEGG metabolic pathways of the lung microbiota. n = 5:5:6. The KEGG pathway map is reproduced with permission from Kanehisa Laboratories. The Kruskal-Wallis rank-sum test was used for statistical analysis in A, C,and D. ANOSIM analysis of variance was used in B. Kruskal-Wallis and Wilcoxon-Mann-Whitney tests were used for statistical analysis in E. * P(FDR) < 0.05. ANOSIM, analysis of similarity; PICRUSt2, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.

Fig. 6

Lidocaine reverses BLM-induced metabolic alterations in BALF

(A) MultiPLS-DA score plots from positive and negative ion modes distinguishing PBS, BLM, and BLM + lidocaine-treated mice. (B) Bar graph showing the differentially abundant metabolites in the BALF among the three groups. (C) KEGG enrichment analysis of the differentially abundant metabolites identified in PBS- and BLM-treated mice. (D) KEGG enrichment analysis of the differentially abundant metabolites identified in BLM- and BLM + lidocaine-treated mice. The KEGG pathway map is reproduced with permission from Kanehisa Laboratories. (E) Venn diagrams of overlapping differentially abundant metabolites. (F-L) The relative expression levels of the 7 shared differentially abundant metabolites involved in the amino acid metabolism pathway. n = 5:5:6. * P < 0.05. BALF, bronchoalveolar lavage fluid.

Fig. 7

Integrated analysis of the lung microbiome and metabolome that were strongly associated with lidocaine

Heatmaps showing Spearman’s correlation coefficients between the 7 differential lung metabolites and the top 20 differential microbiota. * P(FDR) < 0.05, ** P(FDR) < 0.001.

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