Latilactobacillus curvatus IM01 Alleviates Allergic Airway Inflammation Through Microbial and Metabolic Crosstalk Along the Gut–Lung Axis

Abstract

Background: Gut microbiota dysbiosis is critically implicated in the pathogenesis of allergic airway inflammation (AAI) via the gut–lung axis. While Latilactobacillus curvatus is a promising probiotic candidate, its specific immunomodulatory mechanisms in respiratory diseases remain poorly understood. Objective: In this study, we investigated the protective effects and underlying mechanisms of L. curvatus IM01 in an ovalbumin (OVA)-induced murine AAI model using an integrated multi-omics approach. Results: Our results demonstrated that oral administration of L. curvatus IM01 significantly attenuated airway inflammation, suppressed Th2-type immune responses, and reduced serum IgE levels. Crucially, our multi-omics integration revealed a coherent gut–lung axis narrative driven by microbial and metabolic crosstalk. Specifically, 16S rRNA sequencing indicated that L. curvatus IM01 was closely linked to structural shifts in the gut microbial community, notably characterized by an enrichment trend for beneficial genera such as Odoribacter and Lactobacillus. This microbial restructuring was closely associated with a modulated cecal metabolic profile, as untargeted metabolomics exhibited a clear trend toward the restoration of key systemically active immunoregulatory metabolites, including indolelactic acid (ILA) and choline, which have been previously linked to the alleviation of AAI symptoms. Further linking this metabolic shift to respiratory immune tolerance, lung transcriptomic analysis showed that the treatment is strongly associated with the promotion of the differentiation of CD4⁺ T cells into Foxp3⁺ regulatory T cells (Tregs). Conclusions: Collectively, these findings suggest a novel potential pathway by which L. curvatus IM01 modulates the gut–lung axis through coordinated microbial and metabolic interventions, highlighting its potential as a therapeutic functional food ingredient for AAI.

1. Introduction

Asthma is one of the most prevalent chronic diseases worldwide, affecting over 300 million people and imposing an increasingly heavy burden on global healthcare systems [1]. Allergic asthma, a major clinical phenotype of asthma, accounts for approximately half to two-thirds of all asthma cases [2]. This condition is characterized by chronic airway inflammation driven by complex immune dysregulation, primarily resulting in bronchoconstriction and airway hyper-responsiveness. It results in recurrent episodes of airflow obstruction, which is characterized by clinical symptoms, including wheezing, shortness of breath, chest tightness, and coughing [3]. Currently, corticosteroids, antihistamines, and leukotriene receptor antagonists are used clinically to control asthma symptoms with certain efficacy; however, their long-term effectiveness is limited and accompanied by significant adverse effects [4]. Therefore, the development of novel, safe, and more effective therapeutic strategies for allergic asthma is a critical priority.

Extensive research has demonstrated that the gut microbiota and their specific metabolites can regulate host immune homeostasis, influencing the development and progression of allergic asthma. For example, Lactobacillus rhamnosus 76 alleviates airway inflammation and Th2 immune responses in ovalbumin (OVA)-sensitized mice by downregulating the STAT6/SPDEF pathway and ameliorates mucus hypersecretion in lung tissues and 16HBE cells in asthma models [5]. Faecalibacterium prausnitzii exerts anti-asthmatic effects by reducing levels of interleukins (IL-4, IL-5, IL-13) and immunoglobulin G1 (IgG1), increasing the proportion of regulatory T cells (Tregs), restoring microbial dysbiosis, and promoting the production of short-chain fatty acids (SCFAs) [6]. A recent study revealed that butyrate, a gut microbiota-derived metabolite, alleviates asthma by specifically inhibiting the Tfh13 cell subset, a pathogenic follicular helper T cell population involved in IgE production [2].

Recently, L. curvatus has gained increasing attention as a potential probiotic with immunomodulatory properties and multiple health benefits. Han et al. reported that L. curvatus exhibited lipase inhibitory activity, significantly reduced lipid accumulation in adipocytes, and downregulated key adipogenic markers, suggesting its potential as a candidate for obesity prevention [7]. Furthermore, L. curvatus has been shown to alleviate murine colitis by modulating NF-κB and extracellular signal-regulated kinase (ERK) signaling pathways, thereby inducing interleukin-10 (IL-10) production in myeloid dendritic cells [8]. Notably, heat-inactivated L. curvatus also demonstrated antioxidant and immunostimulatory effects on macrophages [9]. However, the potential therapeutic efficacy of live L. curvatus against allergic airway inflammation (AAI) remains unclear and requires further investigation.

In this research, we demonstrate that oral administration of L. curvatus IM01 significantly alleviates OVA-induced AAI in mice. Mechanistically, this protective effect is underpinned by a multi-layered regulatory network along the gut–lung axis. Within the lower gastrointestinal tract, L. curvatus IM01 intervention is accompanied by a remodeling of the microbial architecture, displaying a clear trend toward the enrichment of specific beneficial taxa such as Lactobacillus and Odoribacter. This microbial shift occurs in tandem with a favorable modulation of the cecal metabolome. Concurrently in the respiratory tract, the attenuation of pulmonary inflammation strongly correlates with the promotion of CD4⁺ T cell differentiation into Foxp3⁺ Tregs. Collectively, our findings suggest that L. curvatus IM01 exerts its protective effects against AAI via an orchestrated microbial, metabolic, and immunological crosstalk, thereby highlighting its potential as a promising probiotic candidate for the management of AAI.

2. Materials and Methods

2.1. Bacteria Strain Preparation

The strain of L. curvatus IM01 was isolated from sauerkraut fermentation liquid and preserved in the China General Microbiological Culture Collection Center (CGMCC) under preservation number CGMCC No. 30979. Following a 24 h anaerobic incubation in De Man, Rogosa and Sharpe (MRS) medium at 37 °C, log-phase bacteria cells were harvested and resuspended in sterile phosphate-buffered saline (PBS) to a concentration of 10⁹ CFU prior to oral gavage.

2.2. Animals and Ethics Statement

All animal experimental procedures were approved by the Institutional Animal Care and Use Committee in the Chinese Center for Disease Control and Prevention Laboratory Animal Center (Approval number: 2024-032) and performed in strict accordance with institutional guidelines. Female wild-type BALB/c mice (4–6 weeks old, initial body weight 15–18g) were purchased from Beijing Vital River (Certificate No.: SCXK (Beijing) 2021-0006, China). Female mice were selected for their more stable endocrine and metabolic status, lower inter-individual variation in gut microbiome and metabolome profiles, and consistency with established protocols to ensure experimental reproducibility.

Mice were housed under specific pathogen-free (SPF) conditions in standard polypropylene cages (30 cm × 20 cm × 15 cm) with autoclaved corncob bedding, at a stocking density of 3–5 mice per cage. Housing conditions were strictly controlled: temperature 22 ± 2 °C, relative humidity 50 ± 10%, 12 h light/dark cycle (lights on 08:00–20:00). Mice had ad libitum access to sterilized standard rodent chow and drinking water throughout the experiment, with a 1-week acclimation period before manipulation. Daily welfare monitoring was performed, with predefined humane endpoints: >20% body weight loss, inability to access food/water, or severe abnormal behavior. Mice were humanely euthanized at the experimental endpoint per IACUC guidelines.

2.3. OVA-Induced AAI Mouse Model

Mice were randomly allocated to three experimental groups (n = 6–7 biological replicates per group): PBS control (PBS), OVA-induced AAI (OVA), and OVA+L. curvatus IM01 treatment (OVA + LC IM01). Sample size was determined based on preliminary experimental data, peer-reviewed published protocols in the field, and animal welfare ethical principles, which provided sufficient statistical power for primary outcomes. Randomization was performed using a random number table method to eliminate selection bias: eligible mice were numbered and assigned to groups via generated random numbers, with cage positions and subsequent sample measurement order also randomized to minimize environmental and systematic detection bias.

To evaluate the preventive efficacy of L. curvatus IM01 against AAI, mice received daily oral gavage of L. curvatus IM01 suspension (1 × 10⁹ CFU in 200 μL PBS) or equal volume PBS from day 0 to day 16 (Figure 1A). The prophylactic OVA-induced AAI model was established according to a published protocol [10]. Briefly, mice were sensitized by intraperitoneal injection of 100 μg OVA (Sigma-Aldrich, St. Louis, MO, USA) emulsified with 4 mg alum adjuvant in 100 μL PBS on days 0 and 7 followed by airway challenge with 1% (w/v) aerosolized OVA for 20 min daily from day 14 to day 16. Mice were euthanized 24 h after the final challenge (day 17). All analytical measurements represent the average of three technical replicates per biological sample.

2.4. Histopathology Analysis of the Lung

Lung tissue samples were collected, fixed in 4% paraformaldehyde for 24 h, and embedded in paraffin. The paraffin-embedded tissues were cut into 4 µm-thick sections. To evaluate general airway inflammation, the sections were stained with hematoxylin and eosin (H&E). To specifically assess mucus hypersecretion and goblet cell metaplasia, adjacent tissue sections were stained with Periodic acid-Schiff (PAS). To ensure objectivity, tissue sectioning and subsequent pathological evaluations were conducted independently by three pathologists under strictly blinded conditions. To achieve this, all samples were labeled with anonymous codes, and the group allocation was concealed from not only the pathologists but also the researchers responsible for sample administration, collection, and data analysis. The group information was only unblinded after the completion of all data analysis.

The quantitative assessment of lung pathology was conducted using two specific scoring systems. For H&E staining, the severity of lung inflammation was evaluated based on four key pathological parameters: alveolitis, interstitial inflammation, perivascularitis, and peribronchiolitis. Each parameter was graded independently using an ordinal scoring system ranging from 0 to 4, where 0 represents normal lung histology (absence of detectable lesions) and 4 designates severe pathological changes. A cumulative lung inflammation score was calculated by summing these individual scores. For PAS staining, goblet cell hyperplasia was quantified based on the percentage of PAS-positive cells in the airway epithelium using a 5-point grading scale: 0 (<5% PAS-positive cells); 1 (5–25%); 2 (25–50%); 3 (50–75%); and 4 (>75%).

2.5. Flow Cytometry

Mouse lung and spleen tissues were mechanically dissociated and filtered to remove tissue debris to generate single-cell suspensions. For surface phenotyping, cell suspensions were stained with fluorochrome-conjugated antibodies against CD45 (AF700), Ly-6G (APC-Cy7), CD49b (PE-Cy7), CD170 (BV421), CD117 (BV605), FcεRI (PE), Ly-6C (PerCP/Cy5.5), CD11b (APC), CD4 (FITC), and CD25 (PE). Following surface staining, cells were fixed and permeabilized using the True-Nuclear^TM Transcription Factor Buffer Set (All from Biolegend, San Diego, CA, USA) prior to intracellular staining for FOXP3 (AF647). All antibodies and reagents were sourced from BioLegend (San Diego, CA, USA). Unstained controls and single-stained compensation controls were prepared for each fluorochrome to establish accurate compensation matrices, correct for spectral spillover, and define objective gating boundaries for all target cell populations. Flow cytometric acquisition was performed on a BD FACS Celesta flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA), and data analysis was conducted using FlowJo software (v 10.8.1). Detailed step-by-step gating workflows for all target cell populations are provided in the Supplementary Materials: Supplementary Figure S1 details the gating strategy for the identification of lung inflammatory cell subsets, and Supplementary Figure S2 illustrates the specific gating workflow for splenic Foxp3⁺ Tregs.

2.6. ELISA

Lung homogenate was prepared by dissociating whole lungs in 1 mL PBS containing protease and phosphatase inhibitor cocktail. Suspensions were passed through a 40 μm cell strainer and clarified by centrifugation. Supernatant was used for detecting cytokines by ELISA. The levels of serum IgE and pulmonary cytokines (TNF-α, IL-1β, IL-4, IL-5, IL-6, and IL-13) were measured using quantitative ELISA kits (Invitrogen, Waltham, MA, USA) according to the manufacturer’s protocols.

2.7. Quantitative Real-Time PCR (RT-qPCR)

The extracted total RNA of lung tissues was reverse-transcribed into cDNA using HiScript IV All-in-One Ultra RT SuperMix (Vazyme Biotech, Nanjing, China). To evaluate the differentiation of CD4⁺ T cell subsets, which play a central role in the immune dysregulation of AAI, we specifically selected and evaluated the mRNA expression of their master transcription factors: T-bet (Th1), Gata3 (Th2), Rorγt (Th17), and Foxp3 (Treg cells). RT-qPCR amplification was carried out with ChamQ Blue Universal SYBR qPCR Master Mix (Vazyme Biotech, Nanjing, China) on a LineGene 9600 Fluorescence Quantitative Detection System (Bioer Technology, Hangzhou, China). The gene expression levels were quantified using the 2^−ΔΔCt method, with β-actin serving as the endogenous reference gene for normalization. The specific primers were designed utilizing the NCBI Primer-BLAST tool and synthesized by Sangon Biotech (Shanghai, China). All the primer sequences used for RT-qPCR are listed in

Table 1. Primers used for quantitative real time PCR analysis.

Species/Genes	Primer Sequence
m-Tbet Forward	5′-AGCAAGGACGGCGAATGTT-3′
m-Tbet Reverse	5′-GGGTGGACATATAAGCGGTTC-3′
m-Gata3 Forward	5′-CTCGGCCATTCGTACATGGAA-3′
m-Gata3 Reverse	5′-GGATACCTCTGCACCGTAGC-3′
m-Rorγt Forward	5′-GACCCACACCTCACAAATTGA-3′
m-Rorγt Reverse	5′-AGTAGGCCACATTACACTGCT-3′
m-Foxp3 Forward	5′-CTCGTCTGAAGGCAGAGTCA-3′
m-Foxp3 Reverse	5′-TGGCAGAGAGGTATTGAGGG-3′
m-actin Forward	5′-ATATCGCTGCGCTGGTCG-3′
m-actin Reverse	5′-TTCCCACCATCACACCCTGG-3′

m: mouse.

.

The qRT-PCR amplification was carried out under the following thermal cycling conditions: an initial denaturation step of 95 °C for 10 min (1 cycle); followed by 40 cycles of denaturation at 95 °C for 15 s, annealing at 55 °C for 20 s, and extension at 72 °C for 20 s; and a final melting curve analysis consisting of 95 °C for 10 s, 65 °C for 1 min, and 97 °C for 1 s (1 cycle).

2.8. RNA Sequencing

Lung tissue transcriptome sequencing was performed by BGI Genomics (Shenzhen, China). Following total RNA extraction, mRNA was specifically enriched utilizing magnetic beads coupled with Oligo(dT) to capture polyA-tailed mRNA. The enriched mRNA was chemically fragmented using a breaking buffer and reverse-transcribed into cDNA using random N6 primers. After second-strand cDNA synthesis, the resulting double-stranded DNA underwent end-repair and 3′ adenylation, followed by the ligation of bubble-shaped adapters. The ligated products were amplified via PCR, denatured into single-stranded DNA, and subsequently circularized utilizing a bridged primer to generate single-stranded circular DNA libraries. After rigorous quality control, the constructed libraries were sequenced on the DNBSEQ platform, generating single-end 50 bp (SE50) reads. To ensure high-quality data for downstream analysis, raw sequencing reads were pre-processed using SOAPnuke software (v1.5.6) to remove adapter-contaminated reads, reads with an unknown base (‘N’) content exceeding 0.1%, and low-quality reads (defined as reads where >20% of bases have a quality score below 15). The resulting clean reads were aligned to the Mus musculus reference genome (GRCm39) using HISAT2 (v2.2.1) and to the reference gene set using Bowtie2 (v2.3.4.3). Gene expression levels were quantitatively estimated utilizing RSEM (v1.3.1) to obtain fragments per kilobase of transcript per million mapped reads (FPKM) values. Differential expression analysis between experimental groups was executed using DESeq2 (v1.40.2).

Differentially expressed genes (DEGs) were strictly defined based on statistical significance, requiring a Benjamini–Hochberg adjusted p-value (q-value) < 0.05. For hierarchical clustering and heatmap visualization, the FPKM values of DEGs were subjected to row-wise z-score normalization to standardize expression levels across samples. Hierarchical clustering was performed using the Euclidean distance metric with a complete linkage clustering algorithm, applied to both the full set of identified DEGs and the specific subset annotated to CD4⁺ T cell differentiation. For functional annotation, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the identified DEGs using hypergeometric tests. Terms and pathways with a false discovery rate (FDR)-adjusted p < 0.05 were considered significantly enriched [11].

2.9. 16S rRNA Amplicon Sequencing

The 16S rRNA gene sequencing analysis of fecal samples was conducted by BGI Genomics. Total microbial genomic DNA was extracted from fecal samples via mechanical homogenization, using the MagPure Stool DNA KF Kit B (MAGEN, Guangzhou, China) on an automated KingFisher Purification System (Thermo Fisher Scientific, Waltham, MA, USA) per the manufacturer’s instructions. DNA concentration, purity, and integrity were verified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and 1% agarose gel electrophoresis. The V3-V4 hypervariable region of the 16S rRNA gene was amplified from 30 ng of qualified genomic DNA using barcoded universal primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) with 2 × Phanta Max Master Mix (Vazyme Biotech, Nanjing, China). PCR conditions were set as follows: initial denaturation at 95 °C for 3 min; 30 cycles of 95 °C for 15 s, 56 °C for 15 s, and 72 °C for 45 s; and a final extension at 72 °C for 5 min. Amplicons were purified with BGI DNA magnetic beads (LB00V60), validated for size and concentration, pooled equimolarly for library construction, and sequenced on the DNBSEQ-G400 platform (MGI Tech, Shenzhen, China) in 2 × 250 bp paired-end (PE250) mode. Raw reads were quality-filtered to remove low-quality sequences (average Phred score < 20, > 3 ambiguous bases, or read length < 50 bp), adapters, and barcode contaminants. Valid paired-end reads were merged into consensus tags using FLASH v1.2.11 (min. 10 bp overlap, max. 0.2 mismatch rate). After chimera removal, high-quality tags were clustered into Operational Taxonomic Units (OTUs) at 97% sequence similarity using USEARCH v7.0.1090 with the UPARSE algorithm. Representative OTU sequences were taxonomically annotated against the SILVA database using the RDP Classifier v2.2 (70% confidence threshold). For α-diversity, the OTU table was rarefied to a uniform sequencing depth to eliminate coverage bias. Standard indices and Good’s coverage were calculated using Mothur v1.31.2. For β-diversity, non-metric multidimensional scaling (NMDS) was performed based on the Bray–Curtis dissimilarity matrix, and the significance of intergroup separation was assessed using analysis of similarities (ANOSIM) with 999 permutations. Furthermore, statistical differences in microbial relative abundances among groups were evaluated using the Kruskal–Wallis test, followed by the Benjamini–Hochberg FDR correction. Taxa exhibiting an unadjusted p-value < 0.05 were considered to show a clear trend of differential abundance, while their strict FDR-adjusted q-values were comprehensively reported to ensure statistical transparency.

2.10. Untargeted Metabolomics

Untargeted metabolomics was performed by BGI Genomics. Cecal content samples (25 mg) were homogenized in a pre-cooled extraction buffer containing methanol, acetonitrile, and water (2:2:1, v/v/v) spiked with isotope-labeled internal standards. Following mechanical disruption at 50 Hz for 5 min and incubation at −20 °C for 2 h, the homogenates were centrifuged at 25,000× g for 15 min at 4 °C. The supernatants were then freeze-dried, reconstituted in 50% aqueous methanol, and centrifuged again to obtain the final extracts for LC-MS/MS analysis. To monitor system stability, quality control (QC) samples were prepared by pooling 10 μL aliquots from each individual sample. Untargeted metabolomic profiling was performed using a Waters 2777c UPLC system coupled with a Q Exactive HF high-resolution mass spectrometer (Thermo Fisher Scientific). Chromatographic separation was achieved on a Waters ACQUITY UPLC BEH C18 column (1.7 μm, 2.1 mm × 100 mm) maintained at 45 °C, using a gradient elution with 0.1% formic acid in water and acetonitrile for the positive ion mode, and 10 mM ammonium formate in water and acetonitrile for the negative ion mode, at a constant flow rate of 0.35 mL/min. Mass spectrometry data were acquired in full-scan mode (m/z 70–1050, resolution 120,000) followed by data-dependent MS/MS fragmentation of the top 3 precursors (resolution 30,000) under stepped normalized collision energies of 20, 40, and 60 eV. Raw LC-MS data were processed using Compound Discoverer 3.3 for peak extraction, alignment, and compound identification against the BGI metabolome database (bmdb), mzCloud, and ChemSpider. Subsequent data preprocessing, including probabilistic quotient normalization (PQN) and quality control-based robust LOESS signal correction (QC-RLSC) to eliminate batch effects, was executed using the MetaX software package (v 1.4.16). Principal component analysis (PCA) was performed to visualize global differences in metabolic profiles among groups based on the preprocessed metabolite abundance matrix. The statistical significance of intergroup differences was validated by PERMANOVA. Orthogonal partial least squares discriminant analysis (OPLS-DA) was then applied to maximize intergroup separation, and the robustness of the models was validated through permutation tests. Differential metabolites between groups were rigorously identified using a combination of multivariate and univariate analyses, defined by a Variable Importance in Projection (VIP) score ≥ 1.0 from the OPLS-DA model, a fold change (FC) ≥ 1.2 or ≤ 0.83, and an unadjusted Student’s t-test p-value < 0.05. Finally, the biological functions of these differential metabolites were interpreted through metabolic pathway enrichment analysis utilizing the KEGG database.

2.11. Statistical Analysis

Data are expressed as means ± standard deviation (SD). All values represent the average of three technical replicates per biological sample. Data normality and variance homogeneity were evaluated using the Shapiro–Wilk test. Parametric data were analyzed via unpaired Student’s t-test (two groups) or one-way ANOVA with Tukey’s post hoc test (multiple groups). Non-normally distributed data were analyzed using the Kruskal–Wallis test. For in vivo and immunological evaluations, effect sizes (η²) and 95% Confidence Intervals (CIs) were calculated to assess the magnitude of differences. All statistical analyses were performed using GraphPad Prism software (version 9.0), and a p-value < 0.05 was considered statistically significant unless otherwise specified for high-dimensional omics data.

3. Results

3.1. L. curvatus IM01 Treatment Ameliorates OVA-Induced Allergic Response in Mice

To explore the potential role of L. curvatus IM01 in AAI, OVA-treated mice were administered L. curvatus IM01 via daily gavage (Figure 1A). Our results demonstrated that treatment with L. curvatus IM01 significantly reduced the serum level of total IgE in AAI mice compared with that in the OVA group (Figure 1B). The expression of OVA-specific IgE, a typical characteristic of AAI, was also markedly suppressed by L. curvatus IM01 (Figure 1C). Histopathological examination of lung tissues revealed that oral supplementation with L. curvatus IM01 attenuated inflammatory cell infiltration around the airways and blood vessels and reduced the number of free lymphocytes and granulocytes in the alveolar lumen (Figure 1D). Furthermore, Periodic acid-Schiff (PAS) staining indicated that L. curvatus IM01 treatment significantly decreased mucus secretion and goblet cell metaplasia in the airways (Figure 1D). Collectively, these findings suggest that L. curvatus IM01 administration markedly alleviated OVA-induced allergic immune responses in mice.

3.2. L. curvatus IM01 Suppresses OVA-Induced Inflammatory Cell Accumulation in Mice

Based on the histopathological results, we further investigated the inflammatory cell population in the lung tissues of mice treated with or without L. curvatus IM01. Quantitative analysis of absolute cell numbers revealed that OVA challenge significantly induced an increase in the total number of lung cells, characterized by a marked infiltration of absolute eosinophils and neutrophils compared to the healthy control group (Figure 2A,B). The absolute numbers of basophils and monocytes, however, showed no significant changes. Importantly, prophylactic administration of L. curvatus IM01 effectively suppressed the OVA-induced elevation in total lung cell counts and the abnormal accumulation of absolute eosinophils and neutrophils. Consistent with these absolute quantitative findings, the evaluation of cell proportions demonstrated that OVA-exposed mice exhibited a significant increase in the relative abundance of eosinophils and neutrophils in the lungs, which was similarly reversed by the administration of L. curvatus IM01 (Figure 2C,D). Moreover, although not statistically significant, a trend toward a reduction in basophil numbers was observed following L. curvatus IM01 administration in AAI mice. Interestingly, OVA or L. curvatus IM01 treatment did not result in any significant differences in the abundance of monocyte subsets among the three groups. Taken together, these findings demonstrate that prophylactic oral supplementation with L. curvatus IM01 suppresses OVA-induced inflammatory cell accumulation in mice.

3.3. L. curvatus IM01 Reduces OVA-Induced Airway Pro-Inflammatory Cytokines in Mice

Dysregulated Th2 inflammation is a central pathological process in asthma and is primarily driven by Th2 cytokines including IL-4, IL-5, and IL-13. In the present study, we observed a significant upregulation of Th2 cytokine expression in the lung tissues of OVA-challenged mice compared to the control group (Figure 3A–C). Notably, treatment with L. curvatus IM01 significantly reduced the levels of these Th2 cytokines relative to the OVA-treated group (Figure 3A–C). We further examined the expression of key pro-inflammatory cytokines. As illustrated in Figure 3, OVA induction markedly elevated the pulmonary levels of IL-6, TNF-α, and IL-1β compared to control mice (Figure 3D–F). In contrast, mice administered L. curvatus IM01 exhibited a significant reduction in the production of these inflammatory mediators compared with the OVA group (Figure 3D–F). Our results demonstrated that L. curvatus IM01 effectively attenuated the production of both Th2 and broader inflammatory cytokines induced by OVA, highlighting its potential in modulating asthma-associated immune responses.

3.4. L. curvatus IM01 Administration Is Associated with Foxp3⁺ Tregs Differentiation and Protection Against OVA-Induced AAI in Mice

To elucidate the potential mechanisms underlying the protective effects of L. curvatus IM01 on airway inflammation, we performed RNA-seq analysis of lung tissues from the three experimental groups. A Venn diagram illustrated the number of shared and unique genes across groups (Figure 4A). Compared with the control group, OVA challenge induced the upregulation of 3766 genes and the downregulation of 3307 genes (Figure 4B). In contrast, L. curvatus IM01 treatment resulted in 1183 differentially expressed genes (DEGs) compared to the OVA group, including 628 upregulated and 555 downregulated genes (Figure 4B). Clustering heatmap analysis revealed distinct transcriptomic profiles among the three groups (Figure 4C). Gene Ontology (GO) analysis indicated that L. curvatus IM01 treatment was involved in three biological processes related to immune response and three associated with inflammatory response (Figure 4D). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis demonstrated that the DEGs modulated by L. curvatus IM01 were primarily enriched in the T cell receptor signaling pathway and Th1, Th2, and Th17 cell differentiation, suggesting that its role in AAI pathogenesis may be associated with the regulation of CD4⁺ T cell differentiation (Figure 4E).

To validate these transcriptomic findings, we quantified the expression of key transcription factors governing CD4⁺ T cell differentiation using RT-qPCR (Figure 4G). The results showed that L. curvatus IM01 significantly upregulated the mRNA levels of T-bet and Foxp3. Although Gata3 expression exhibited a decreasing trend, this difference was not statistically significant. Moreover, no significant differences were observed in Rorγt expression among the three groups. Furthermore, to evaluate the systemic immune response and determine whether the immunomodulatory effects of L. curvatus IM01 extended beyond the local airway, we next examined the proportion of Foxp3⁺ Tregs in the spleen. As shown in Figure 4H, flow cytometric analysis confirmed that OVA challenge significantly reduced the abundance of Foxp3⁺ Treg cells compared to controls, whereas L. curvatus IM01 treatment restored Foxp3⁺ Treg cell frequencies to levels comparable to those in healthy mice. Collectively, these results provide compelling evidence that L. curvatus IM01 ameliorates AAI in association with modulating CD4⁺ T cell differentiation with a pronounced effect on promoting Foxp3⁺ Treg cell development, underscoring its potential as an immunobiotic therapeutic agent.

3.5. L. curvatus IM01 Modulates Gut Microbiota in OVA-Induced AAI Mice

Accumulating evidence has highlighted the crucial role of gut microbiota in regulating chronic respiratory diseases via the gut–lung axis [12,13]. To evaluate the impact of L. curvatus IM01 on gut microbiota composition, we performed 16S rRNA gene sequencing of fecal samples obtained from experimental mice. Compared to the OVA group, L. curvatus IM01 administration did not significantly alter the Chao1, Shannon, and Simpson indices, indicating that it did not affect overall microbial species richness or evenness, as assessed by α-diversity analysis (Figure 5A). In contrast, NMDS revealed a distinct clustering of microbial communities among the groups, with a reliable spatial ordination (Stress = 0.147) (Figure 5B). The statistical significance of this structural separation was rigorously validated using ANOSIM, which confirmed that the gut microbial composition was significantly distinct across the experimental groups (ANOSIM R = 0.144, p = 0.020). We further examined the relative abundance of the bacterial taxa at different taxonomic levels. At the phylum level, Bacillota (formerly Firmicutes) and Bacteroidota (formerly Bacteroidetes) were the predominant phyla in all groups (Figure 5C). Notably, L. curvatus IM01 counteracted the OVA-induced elevation in the Bacillota/Bacteroidota ratio (Figure 5C), implying a potential restoration of gut microbial homeostasis. Analysis of the top 10 genera indicated that L. curvatus IM01 administration was closely associated with structural shifts in the gut microbial community (Figure 5D). Specifically, compared to the control group, OVA-challenged mice exhibited a trend toward decreased relative abundances of Lactobacillus and Odoribacter, whereas the abundances of Lachnospiraceae_NK4A136 and Alistipes showed an increasing trend. In contrast, L. curvatus IM01 reversed these shifts, exhibiting a clear trend toward the enrichment of beneficial genera such as Lactobacillus and Odoribacter, alongside a tendency to reduce the levels of Lachnospiraceae_NK4A136 and Alistipes compared to the OVA group. Correlation heatmap analysis showed that Lactobacillus and Odoribacter were negatively correlated with asthma indicators (Figure 5E). These results demonstrated that L. curvatus IM01 is associated with the partial restoration of specific gut microbiota dysregulation, potentially contributing to its protective role against AAI.

3.6. L. curvatus IM01 Regulates Intestinal Metabolites in AAI Mice

Gut microbiota alterations have been implicated in host metabolic regulation and are associated with an increased risk of asthma [14]. To investigate whether the alleviation of airway inflammation by L. curvatus IM01 in AAI mice is linked to modulations in the metabolomic profile, we conducted non-targeted metabolomic analysis of cecal contents using liquid chromatography-mass spectrometry (LC-MS). The PCA chart revealed a distinct clustering of metabolite profiles among the three experimental groups (Figure 6A). Furthermore, orthogonal partial least squares-discriminant analysis (OPLS-DA) demonstrated a clear separation between groups based on their metabolic signatures (Figure 6B). The robustness of these OPLS-DA models was further rigorously validated through permutation tests, which confirmed that the models for both the OVA versus control and OVA + L. curvatus IM01 versus OVA comparisons were highly reliable and free from overfitting (Figure 6C). Volcano plot analysis was used to identify differentially abundant metabolites, which were rigorously defined by a VIP score ≥ 1.0, an FC score ≥ 1.2 or ≤0.83, and an unadjusted p < 0.05 (Figure 6D). Based on these criteria, our results indicate that OVA-induced AAI led to the upregulation of 167 metabolites and downregulation of 132 metabolites. Intervention with L. curvatus IM01 resulted in the upregulation of 84 and downregulation of 88 metabolites. Notably, 119 metabolites that were downregulated in the OVA group were upregulated in the OVA+L. curvatus IM01 group, suggesting their potential role as key mediators in the protective mechanism. KEGG pathway enrichment analysis was performed to elucidate the metabolic pathways affected by the OVA challenge and L. curvatus IM01 treatment (Figure 6E). The altered metabolites are predominantly involved in the biosynthesis of cofactors, ABC transporters, bile secretion, purine metabolism, and nucleotide metabolism. Importantly, metabolomic profiling demonstrated that L. curvatus IM01 administration was closely associated with a modulated metabolic landscape, exhibiting a clear trend toward restoring the relative abundances of specific beneficial metabolites, such as indolelactic acid and choline, compared to the OVA group (Figure 6F). In conclusion, our findings demonstrate that the administration of L. curvatus IM01 is strongly associated with the modulation of host metabolism, which may be closely linked to its protective effects against AAI.

4. Discussion

In this study, we evaluated the therapeutic potential of the probiotic L. curvatus IM01 on the clinical features of AAI and explored its underlying mechanisms across the gut–lung axis. Phenotypically, oral administration of L. curvatus IM01 markedly attenuated Th2-driven airway inflammation, as evidenced by reduced inflammatory cell infiltration, suppressed mucus hypersecretion, and dampened Th2 immune responses in the lung. To decipher the mechanistic basis of these effects beyond isolated phenotypic observations, we performed integrated multi-omics profiling, which revealed a coordinated, rather than independent, cascade of events underlying the probiotic-mediated protection. Specifically, L. curvatus IM01 partially restored gut microbial ecology, with a pronounced enrichment trend for beneficial commensals including Lactobacillus and Odoribacter. This microbial remodeling was tightly coupled to a reshaped cecal metabolic milieu, most notably characterized by a trend toward the restored relative abundances of bioactive metabolites ILA and choline. Ultimately, this gut-derived metabolic signature showed a strong correlation with the modulation of pulmonary immunity, particularly the enhanced differentiation of Foxp3⁺ Tregs and the restoration of immune tolerance in the lung. Taken together, our multi-omics data delineate a multi-layered regulatory network, in which L. curvatus IM01 ameliorates AAI via the coordinated modulation of gut microbial composition and its downstream metabolic pathways.

Accumulating evidence has revealed a long-distance bidirectional regulatory network between the gut and the lungs, termed the “gut–lung axis,” in which gut microbiota and their metabolites play essential roles. The development of immunomodulatory probiotic strains and therapeutic strategies targeting this axis have garnered increasing attention for the diagnosis and treatment of pulmonary diseases [15,16]. Shen et al. demonstrated that treatment with L. plantarum L168 enhanced the production of anti-inflammatory metabolites, improved lung barrier function, and attenuated pulmonary inflammation in a rat model of bronchopulmonary dysplasia [17]. Similarly, a study employing genetically engineered L. rhamnosus revealed that the recombinant strain GR-1 prevented airway inflammation and hyper-responsiveness, an effect closely associated with its ability to modulate gut microbiota composition [18]. Although orally administered heat-inactivated L. curvatus has been shown to induce immune tolerance through IL-10 production, thereby alleviating ovalbumin-induced airway hyperresponsiveness [19], its underlying mechanisms remain unclear. Consistent with these findings, viable L. curvatus IM01 significantly suppressed allergen-induced Th2 responses and inflammatory cell infiltration in the airways. Furthermore, the anti-inflammatory effects of L. curvatus IM01 were broad-spectrum, as reflected by reduced levels of IL-6, TNF-α, and IL-1β. Notably, although a decreasing trend in IL-10 was observed in the OVA group compared to controls, the administration of viable L. curvatus IM01 did not significantly elevate IL-10 expression. The balance between Th2 and Treg cells plays a critical role in the promotion and suppression of AAI. Notably, while previous studies have shown that heat-inactivated L. curvatus failed to enhance Foxp3 expression, viable L. curvatus IM01 significantly promoted the differentiation of Foxp3⁺ Treg cells in AAI mice. This observation suggests that the immunomodulatory properties of the viable strain may be linked to its ability to modulate the host metabolism.

Gut microbiota constitutes a complex and dynamic ecosystem of microorganisms that plays a vital role in maintaining host immune and metabolic homeostasis. Lactobacillus and its metabolites facilitate anti-inflammatory responses through immunological crosstalk between the gastrointestinal tract and distal organs, thereby contributing to the amelioration of conditions such as asthma, chronic obstructive pulmonary disease, and inflammatory bowel disease [20]. Odoribacter, a major butyrate-producing bacterium in the gut, is a beneficial commensal organism whose abundance is inversely correlated with the severity of non-alcoholic fatty liver disease and inflammatory bowel disease [21,22]. In our study, we observed that L. curvatus IM01 was associated with shifts in the gut microbial structure in AAI mice, leading to an enrichment trend for Lactobacillus and Odoribacter, which strongly correlated with the attenuation of the airway inflammation response. Importantly, while the administration of L. curvatus IM01 did not significantly alter the overall α-diversity of the gut microbiota, it induced significant alterations in β-diversity and promoted favorable trends in the relative abundance of specific taxonomic compositions. This suggests that the potential benefits of L. curvatus IM01 may stem from the targeted enrichment of specific beneficial commensals rather than a global restructuring of the microbiome.

Multiple metabolites drive complex interactions between the host and the microbiota. To further explore AAI-relevant metabolic pathways based on gut microbial patterns, we conducted an untargeted metabolomic analysis and found that L. curvatus IM01 was closely associated with the improvement of AAI-related metabolic disorders. Further KEGG pathway analysis indicated enrichment of ABC transporters, biosynthesis of cofactors, purine metabolism, riboflavin metabolism, nucleotide metabolism, and bile secretion following L. curvatus IM01 treatment. We further analyzed the top 50 differentially abundant metabolites that were downregulated in the OVA group and upregulated by L. curvatus IM01. Importantly, this analysis revealed that L. curvatus IM01 exhibited a clear trend toward restoring the levels of specific beneficial metabolites, most notably choline and ILA, both of which have established anti-inflammatory properties and may contribute to the mitigation of AAI through immunomodulatory mechanisms [23,24]. Supporting this, a population-based association study utilizing data from the National Health and Nutrition Examination Survey (NHANES) revealed a significant inverse correlation between dietary choline intake and asthma prevalence [24]. Furthermore, Mehta et al. demonstrated that choline supplementation significantly alleviated clinical asthma symptoms and increased PC20 FEV1 levels [25]. Similarly, tryptophan-derived indole metabolites also play crucial roles in mitigating allergic immune responses. Lactobacillus species are major producers of ILA, and exogenous ILA administration has been shown to modulate gut microbiota composition, improve survival rates, and alleviate AAI in mice [23]. Therefore, we hypothesize that L. curvatus IM01 exerts protective effects against AAI in part by modulating the host metabolic landscape, which correlates with the systemic and local immune and inflammatory responses in target organs through these bioactive metabolites.

Although our study revealed the potential contribution of L. curvatus IM01 in alleviating AAI in mice, several limitations should be noted. For instance, we did not employ antibiotic-mediated gut microbiota depletion or fecal microbiota transplantation to ascertain whether the protective effects of L. curvatus IM01 were causally dependent on the gut microbiota. Furthermore, a major limitation of this study is the lack of direct functional validation to establish strict causality. While our multi-omics data convincingly show robust associations between L. curvatus IM01 administration, increased Foxp3⁺ Tregs, and altered key metabolites, it remains unclear whether these specific mechanisms are purely causative. Future studies employing targeted functional interventions, such as in vivo Treg depletion models or direct exogenous metabolite supplementation and inhibition assays, are necessary to definitively validate these mechanistic pathways.

5. Conclusions

In summary, our findings demonstrate that L. curvatus IM01 ameliorates AAI in mice. These protective effects are closely associated with specific structural shifts in the gut microbiota composition and a favorable modulation of the cecal metabolic landscape, exhibiting a clear trend toward the restoration of beneficial metabolites. Concurrently, these gut-derived shifts strongly correlate with the promoted differentiation of Foxp3⁺ Treg cells. This study provides valuable insights into the potential development of probiotic-based interventions to mitigate AAI.