Role of nasal microbiota in regulating host anti-influenza immunity in dogs

Geng, Jinzhu; Dong, Yuhao; Huang, Hao; Wen, Xia; Xu, Ting; Zhao, Yanbing; Liu, Yongjie

doi:10.1186/s40168-025-02031-y

Research
Open access
Published: 27 January 2025

Role of nasal microbiota in regulating host anti-influenza immunity in dogs

Jinzhu Geng¹,
Yuhao Dong¹,
Hao Huang¹,
Xia Wen¹,
Ting Xu¹,
Yanbing Zhao¹ &
…
Yongjie Liu¹

Microbiome volume 13, Article number: 27 (2025) Cite this article

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Abstract

Background

Numerous studies have confirmed a close relationship between the pathogenicity of influenza and respiratory microbiota, but the mechanistic basis for this is poorly defined. Also, the majority of these studies have been conducted on murine models, and it remains unclear how far these findings can be extrapolated from murine models to other animals. Considering that influenza A virus is increasingly recognized as an important canine respiratory pathogen, this study investigated the cross-talk between nasal and lung tissues mediated by microbes and its association with influenza susceptibility in a beagle dog model.

Results

Using 16S rRNA gene sequencing, combined with comparative transcriptomic, anatomical, and histological examinations, we investigated viral presence, gene expression profiles, and microbiota in the nasal cavity and lung after influenza infection in the beagles with antibiotic-induced nasal dysbiosis. Our data showed that dysbiosis of the nasal microbiome exacerbates influenza-induced respiratory disease and the epithelial barrier disruption, and impairs host antiviral responses in the nasal cavity and lung. Moreover, dysregulation of nasal microbiota exacerbates the influenza-induced disturbance in lung microbiota. Further, we also identified a strain of Lactobacillus plantarum isolated from canine nasal cavity with a significant antiviral effect in vitro, and found that its antiviral activity might be associated with the activation of the interferon (IFN) pathway and modulation of the impaired autophagy flux induced by influenza infection.

Conclusions

Our investigation reveals that nasal microbiota dysbiosis exerts a prominent impact on host antiviral responses, inflammation thresholds, and mucosal barrier integrity during influenza infection. Lactobacilli, as part of the nasal microbiota, may contribute to host antiviral defenses by modulating the IFN and autophagy pathways. Collectively, this study underscores the importance of nasal microbiota homeostasis in maintaining respiratory health.

Video Abstract

Background

Respiratory tract infections (RTIs) persist as a substantial global health challenge [1]. An expanding body of research underscores the connection between bacterial communities or microbiota in the respiratory tract and the susceptibility to, as well as the severity of, RTIs [2, 3]. This link may be elucidated through direct microbe-microbe interactions, pathogen exclusion, or intricate host-microbe cross-talk [4]. For the majority of respiratory bacterial pathogens, initial colonization of the upper respiratory tract (URT) is a prerequisite before progressing to an upper, lower, or disseminated respiratory infection [5]. The hindrance of this initial stage of pathogenesis in respiratory infections by the resident microbiota, known as “colonization resistance,” could play a crucial role in maintaining respiratory health [6]. Furthermore, numerous studies have demonstrated the intricate and complex interactions between microbes and hosts in different ecological niches, such as the brain-gut axis, lung-gut axis, and liver-gut axis. These findings enhance our understanding of symbiotic microbes within the body [7,8,9]. The URT is closely linked anatomically and physiologically with the lower respiratory tract (LRT) including the lung, and the influence of upper respiratory microbes on the lung microbiota is increasingly being recognized [10]. Previous studies have reported correlations between microbiome composition and disease manifestations in the nasal cavity and lung [4, 10]. Historically, lungs were considered sterile because it is necessary to maintain a thin bronchial epithelial fluid lining for efficient gas exchange[11]. However, subsequent research indicates that the lungs of healthy individuals harbor a microbiota [12, 13]. Although this community is quantitatively less abundant than what has been found in the oral and gut microbiota, these lung microorganisms are pivotal in maintaining respiratory health and modulating immune responses [14].

The unique environment within the lower airways fosters a dynamic homeostatic state for the lung microbiome, and the active involvement of microbes from the nasal and oral cavities in shaping the lung microbiota significantly contributes to maintaining this dynamic equilibrium [10, 15]. The dysbiosis of the airway microbiota has been linked to the acceleration of lung function decline in chronic obstructive pulmonary diseases [16]. The transient colonization of the lungs by oral and airway microbiota can modulate lung immune responses and inflammation thresholds, reducing susceptibility to Streptococcus pneumoniae and expediting its clearance [17, 18]. Several studies have indicated an association between coronavirus disease (COVID-19) [19], respiratory syncytial virus (RSV) [20] or influenza A virus (IAV) [21] infection, and the URT microbiome. Intranasal administration of Bifidobacterium longum has been shown to provide protection against virus-induced lung inflammation and injury in a murine model of lethal influenza infection [22]. However, the direct association between nasal microbiota and susceptibility to influenza viruses, along with its underlying mechanisms, remains unclear.

In this study, we established a model of nasal microbiota dysbiosis in 3-month-old beagles by locally applying a combination of mupirocin and neomycin ointments to the nasal cavity, and revealed the impact of nasal microbiota dysbiosis on antiviral immunity and lung microbiota. Interestingly, we have identified two commensal bacterial genera, Lactobacillus and Moraxella, that exhibit a completely opposite correlation with host antiviral responses, inflammation thresholds, and barrier maintenance in both the nasal and lung compartments during influenza infection. Further study reveals a key role of Lactobacillus plantarum C123, which was isolated from the nasal cavity of a healthy beagle, in modulating the antiviral response. As far as we know, such correlation has not been reported to date.

Methods

Viral, bacterial, and cell strains

The viral strain of the H3N2 subtype, A/Canine/Jiangsu/06/2010 (JS/10), was used as a challenge virus in this study. This virus was isolated from nasopharyngeal swabs of a dog with severe respiratory syndrome, and the nucleotide sequences for its eight genes have been deposited in GenBank (accession numbers JN247616 to JN247623). The virus was grown in Madin-Darby canine kidney (MDCK, ATCC CCL-34) cells and titered by plaque assay as previously reported [23]. Ten strains of lactic acid bacteria (LAB) were isolated from the nasal cavity, oral cavity, or rectum of healthy beagles. Among five strains of Lactobacillus animalis, C122, C132, and A113 were isolated from the oral cavity, C133 from the rectum, and B223 from the nasal cavity. Among four strains of L. plantarum, C123 and A112 were isolated from the nasal cavity, and B222 and B321 from the oral cavity. One strain of Lactococcus lactis, C222, was isolated from the oral cavity. Additionally, L. plantarum Wcsf-1, isolated from human saliva, was from National Collection of Industrial and Marine Bacteria (NCBIM). A549 human lung carcinoma cells (ATCC CCL-185) were cultured in F-12 K medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS), while MDCK and 293 T human embryonic kidney cells (ATCC CRL-3216) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% FBS.

Animal grouping and treatment

Three-month-old beagle puppies ( Anlimao Biotechnology, Yizheng, China) were housed in a BSL-2 isolation facility at the experimental animal center of Nanjing Agricultural University under standard husbandry conditions. These beagles have not received any drug or vaccine treatment, and were confirmed to be negative for current circulating influenza viruses by serology as determined by haemagglutination inhibition (HI) assays as described previously [24]. After 1 week of acclimatization, they were randomly divided into three groups (Nor, WT, and Abx) and placed in individual rooms. Each group comprises 9 dogs, consisting of 4 females and 5 males. The Nor group functions as an experimental negative control, receiving no interventions (neither antibiotic treatment nor virus inoculation). The WT group received virus inoculation, but no antibiotic treatment. The Abx group received not only antibiotic treatment but also virus inoculation.

The neomycin/mupirocin ointment was selected to reproduce antibiotic-induced nasal dysbiosis in the beagles, as previously described [25, 26]. Before nasal treatment, equal volume of 10 mg neomycin ointment (Jiangmen HengJian, China) and 10 mg mupirocin ointment (SmithKline & French, China) were mixed into a 1.5-mL microcentrifuge tube. For administration, a sterile polyester-tipped swab was used to collect all the mixed ointments at the bottom of the tube. The ointment-coated swab was gently applied to exterior of nostrils and interior of anterior nares up to the first turbinate with a polyester applicator swab, approximately 3–4 cm deep, taking care to avoid mucosal damage [25]. The ointment with a tube was only used for one nostril of a dog in the Abx group. For the Nor and WT groups, a sterile swab was dipped into the pre-aliquoted phosphate-buffered saline (PBS) and applied to dog nostrils, following the same procedure as described above for the Abx group. The nasal treatment was performed twice daily. Three days later by application of the antibiotic ointment, intranasal virus inoculation was performed with 1 mL of JS/10 at a concentration of 10⁷ plaque-forming units per mL (PFU/mL) for the Abx and WT groups, while the Nor group received 1 mL of PBS. Dogs were observed daily to monitor body weight and clinical symptoms. Samples were collected on days 1, 3, 6, and 8 following the challenge. Euthanasia and tissue sampling were performed under sodium pentobarbital anesthesia to minimize the suffering of animals, which was complied with the guidelines of the Animal Welfare Council of China and were approved by the Ethics Committee for Animal Experiments of Nanjing Agricultural University (approval number PT2020022).

Sample collection and 16S rRNA gene sequencing

Nasal samples for sequencing were collected at three distinct time points. The first time point (T1) involved nasal swabs were collected 7 days post-group housing, prior to antibiotic treatment. The second time point (T2) involved samples were collected 3 days post-antibiotic treatment, immediately before the onset of viral infection. The third time point (T3) involved samples were collected 8 days post-infection (dpi), just before the animals were euthanized.

Nasal microbiota collection at three stages (T1, T2, and T3) was performed as described previously [25]. A flexible minitip flocked polyester swab (Sigma-Aldrich) was gently inserted into each beagle’s nares up to the depth of the first turbinate. Each nostril was swabbed for 15 s, using the same swab for both nostrils to ensure consistency in sampling. After collection, the swab was placed in a dedicated storage tube and temporarily stored on dry ice to preserve the microbial integrity. Swabs were subsequently stored dry at − 80 °C until DNA extraction for sequencing analysis. For the extraction of genomic DNA from the nasal microbiota, nasal swabs were vortexed for 2 min in 1 mL of modified liquid Amies transport medium (Thermo Scientific, USA). Subsequently, 500-μL samples from each swab were centrifuged at 13,000 × g at 4 °C for 10 min. The resulting pellets were then dissolved in Buffer ALT with lysozyme (Sigma, USA) and lysostaphin (Sigma, USA) and incubated for 30 min at 37 °C. DNA was then extracted using the Bacterial DNA Extraction Kit (Omega Bio-tek, USA) according to the manufacturer’s protocol. The V3–V4 region of the 16S rRNA gene was amplified using the primer pair 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACHVGGGTATCTAATCC). The amplicons were further sequenced in a single run on the NovaSeq6000 (Illumina, USA) sequencing platform. For the lung microbiota collection, the lung was sampled using a biopsy punch plunger with a diameter of 4.0 mm (Miltex, USA) according to a pre-designated experimental protocol. The sampled tissues were promptly placed into sterile 1.5-mL microcentrifuge tubes and stored at − 80 °C until retrieval for 16S rRNA gene sequencing. For the extraction of genomic DNA from the lung microbiota, the lung tissues were homogenized for 10 min in 1 mL of modified liquid Amies transport medium. Subsequently, the homogenates were centrifuged at 1000 × g at 4 °C for 10 min to remove large tissue fragments. Following this, the supernatant was centrifuged at 13,000 × g at 4 °C for 10 min. DNA extraction and 16S rRNA gene sequencing were performed followed the same steps as described above for nasal microbiota.

Nasal bacterial load analysis

Bacterial loads were quantified in triplicate by real-time quantitative PCR (RT-qPCR) as previously described [27, 28]. Briefly, RT-qPCR was performed using the StepOnePlus (Applied Biosystems, USA) and the AceQ qPCR SYBR Green Master Mix (Vazyme, China). A 253 bp product was generated using the primer pairs: 520F, 5′- AYTGGGYDTAAAGNG and 820R, 5′-TACNVGGGTATCTAATCC. All reactions were performed in triplicate and included standards and non-template controls. Standard curves were established using near full-length 16S rRNA gene amplicons of Escherichia coli DH5α in the pUC19 plasmids (Vazyme, China). Plasmids were quantified using the Quant-iT Picogreen dsDNA Assay Kit (Promega, USA) and then serially diluted in tenfold increments to create standards ranging from 1 × 10⁸ to 1 × 10⁴ copies. Reactions consisted of 10 μL of SYBR Fast qPCR Master mix, 0.4 μL of each primer (10 μM), and 7.2 μL of nuclease-free PCR water (Biosharp, China). Then 2 μL of template DNA was added to each reaction. The RT-qPCR program was as follows: 90 °C for 5 min, followed by 40 cycles of 95 °C for 30 s, 50 °C for 15 s, and 72 °C for 15 s. Melt curves were run as default from 60 to 95 °C over 15 min.

Microbiota sequence data analysis

The downstream amplicon bioinformatic analyses were performed with EasyAmplicon pipeline (v1.15) [29]. The nonredundant sequences were denoised into amplicon sequence variants (ASVs) via the -unoise3 command of USEARCH (v10.0) [30]. The feature table was created with VSEARCH (v2.15) [31]. Taxonomic classification of ASVs was achieved using the sintax algorithm of USEARCH based on the Ribosomal Database Project (RDP) training set v16. The sequences of all samples were rarefied to 20,000 for the downstream diversity analysis by R package vegan (v2.6–4) [32]. α- and β- diversity analyses were conducted using EasyAmplicon (v1.15). Differences in Richness index and the Chao1 index between groups were assessed using Tukey’s HSD test. For difference comparisons, R package DESeq2 (v1.40.2) and GraphPad Prism (v2.1.441.0) were utilized to identify significantly differential features between groups, and the Benjamini–Hochberg or Tukey–Kramer method was used to control the FDR [33]. The R package igraph (v1.5.1, https://github.com/igraph) was utilized for differential ASV correlation analysis, followed by visualization using Cytoscape (v3.8.2). The R package ggcor (v0.9.8.1, https://github.com/Github-Yilei/ggcor) was employed for conducting Mantel test correlation analysis between the transcription levels of cytokines and differential ASVs. GraphPad Prism (v2.1.441.0) and the R package ggplot2 (v3.4.3) were employed for the analysis of ASVs and for visualizing the results [34].

Metagenomic pathway prediction

PICRUSt2 was utilized to predict the metagenomic functional compositions. Pathways that were different in abundance between the WT and the Abx groups were obtained using R package DESeq2 (v1.40.2), and the Benjamini–Hochberg FDR was used to correct for multiple tests. The R package ggplot2 (v3.4.3) was utilized for visualization of the identified pathways. The Pearson correlation between functional pathways and differential ASVs was calculated with R package igraph (v1.5.1) and visualized using the ggplot2 (v3.4.3) and pheatmap (v1.0.12).

Histological examination

Tissues were fixed in 4% paraformaldehyde at 4 °C for 3 days and then embedded in paraffin, and were sectioned at 4 μm. The sections were deparaffinized in xylol and rehydrated in a graded series of ethanol and water, and then stained with hematoxylin and eosin. The tracheal and nasal mucosae were measured at thirty random unilateral points using SlideViewer imaging software and PANNORAMIC® 250 Flash III DX (3DHISTECH Ltd, Hungary), and the mean values of the thickness were calculated.

Immunofluorescence analysis

Immunofluorescence staining was performed on paraffin-embedded sections. The sections were deparaffinized in a xylene gradient and rehydrated in an ethanol gradient. Antigen retrieval was performed by steaming in citrate antigen retrieval solution (Beyotime, China) for 20 min. Then samples were blocked with the blocking buffer (Beyotime, China) for 10 min. The sections were sequentially incubated with the primary antibody at 4 °C overnight and anti-rabbit secondary antibody conjugated with Alexa Fluor 488 (Proteintech, China) or anti-mouse secondary antibody conjugated with Alexa Fluor 555 (Proteintech, China) at room temperature for 1 h. In this study, the primary antibodies targeting the following proteins were used at 1:100 dilution: influenza virus nucleoprotein (NP; laboratory preparation), tight junction protein 1 (TJP1/ZO-1; Bioss, China), microtubule-associated protein 1 light chain 3 beta (MAP1LC3B/LC3B; Abmart, China), or sequestosome 1 (SQSTM1/p62; Abmart, China). After washing in PBS, nuclei were counterstained with 4',6-diamidino-2-phenylindole (DAPI) and coverslips were mounted with Antifade Mounting Medium (Beyotime, China). Images were acquired using a Zeiss LSM 970(Zeiss, Germany) microscope, and morphometric analysis was performed employing ZEISS ZEN (v3.9) and ImageJ [35].

Western blot analysis

Western blot was performed as described earlier [36]. Briefly, total protein samples were extracted from tissues, and the concentration of protein was determined spectrophotometrically at 562 nm using a BCA protein assay kit (Epizyme Biomedical Technology, China). All samples were diluted to the same concentration and added to 5 × SDS-PAGE sample loading buffer (Beyotime, China). Thermic denaturation was promoted at 99 °C for 5 min. For western blot assays, proteins were separated on an electrophoretic run and transferred on a 0.22-µm PVDF membrane (Merck Millipore, USA). The membranes were blocked in 5% (w/v) non-fat milk in Tris-buffered saline with Tween-20 (TBST) and incubated overnight with the primary antibodies at 4 °C. The primary antibodies targeting the following proteins were used at 1:1000 dilution: influenza virus matrix protein 1 (M1) and NP, and TANK-binding kinase 1 (TBK1), phospho-TBK1 (Ser172), interferon regulatory factor 3 (IRF3), phospho-IRF3 (S386), nuclear factor-Kappa B/p65 (NF-κB/p65), phospho-NF-κB/p65 (Ser536), MAP1LC3B/LC3B, and SQSTM1/p62. Except for anti-M1 and anti-NP antibodies, which were produced from laboratory preparations, all other primary antibodies were purchased from Abmart, (Shanghai, China). The expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a loading control. After washing in TBST, the horseradish peroxidase (HRP)-conjugated anti-rabbit or anti-mouse secondary antibody (Proteintech, China) were used at 1:5000 dilution for 90 min at room temperature. Relative protein expression was quantified by an enhanced chemiluminescence (ECL) assay kit (Epizyme Biomedical Technology, China) and visualized on the ChemiDoc Imaging system (Bio-Rad, USA). Band densitometry was performed on the ImageJ software and normalized based on the control group.

Quantification of mRNA expression

Total RNA was isolated from tissue samples or whole blood with Total RNA Kit (Omega Bio-tek, USA), according to the manufacturer’s instructions. After purity and quality checks, mRNA was converted into complementary DNA (cDNA) with a High-Capacity cDNA Reverse Transcriptase kit (Vazyme, China). The cDNA was diluted 1:10 (v/v) by RNase-free water (Biosharp, China). Relative mRNA expression was quantified by RT-qPCR analyses on a StepOnePlus (Applied Biosystems, USA), using AceQ qPCR SYBR Green Master Mix (Vazyme, China). Each biological sample was analyzed in triplicate, using the GAPDH as the housekeeping gene. For each gene of interest, the sequences of the forward and reverse primers used are listed in Supplementary Table 1.

RNA sequencing (RNA-seq) analysis

Total RNA from nasal and lung tissues was extracted using TRIzol (Invitrogen, USA). After purity assessment via NanoDrop One (Thermo Fisher Scientific, USA), integrity of the extracted RNA was evaluated employing the Agilent 2100 systems (Agilent Technologies, USA). The mRNA from the total RNA pool was enriched and purified with the VAHTS mRNA Capture Beads reagent kit (Vazyme, China), and then was fragmented via ion shearing to achieve fragments within the range of 250–450 bp, serving as templates for the initiation of cDNA’s first-strand synthesis. Subsequently, the first-strand cDNA was employed as a template for the synthesis of the second cDNA strand, followed by terminal repair and dA-tailing of the double-stranded cDNA. After universal adapter ligation, magnetic bead purification and size selection (250–350 bp) were executed. PCR amplification was then performed with dual-end indexing primers, and a 0.9X magnetic bead purification yielded a refined library. The library’s quality was assessed through ABI QuantStudio 12 K (Applied Biosystems, China) fluorescence quantification assays. Ultimately, second-generation sequencing was conducted on the Illumina NovaSeq 6000 platform (Illumina, China), with paired-end (PE) sequencing for comprehensive analysis. Following high-throughput sequencing, raw data were processed into Fastq format using Illumina bcl2fastq software. Subsequent refinement included data curation with the fastp tool (v0.23.), and exclusion of ribosomal RNA using sortmerna (v4.3.4). Using STAR (v2.7.10), sequencing data were aligned to the reference genome (Dog10K_Boxer_Tasha, GCF_000002285.5) to remove non-host RNA from the results. Quality assessment was performed by RSeQC v4.0.0, QualiMap (v2.2.2), featureCounts (v2.0.1), and Preseq (v3.1.1). Quantitative analysis was performed by Salmon (v1.9.0) and DESeq2 (v1.40.2). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA) were conducted via clusterProfiler (v4.8.2) [37]. Differential gene clustering based on k-means clustering occurred through the Search Tool for the Retrieval of Interacting Genes/Protein (STRING) online platform (https://cn.string-db.org/) and ClusterGVis (v0.0.2) [38]. All plots were generated using ggplot2 (v3.4.3) and pheatmap (v1.0.12).

Co-infection assay in vitro

A549 cells were cultured in F-12 K medium. Seeded at a density of 1.0 × 10⁶ cells/mL in 6-well plates with 2 mL/well, cells were incubated at 37 °C with 5% CO₂ until attaining 80% confluency. Following PBS washes, cells were infected with JS/10 at a multiplicity of infection (MOI) of 1 and incubated for 1 h at 37 °C with 5% CO₂, then washed thrice with PBS and replenished with 2 mL of Opti-MEM medium containing 0.125 μg/mL TPCK-trypsin (Sigma-Aldrich, USA). Different LAB strains were statically incubated at 37 °C in MRS broth (Sigma-Aldrich, USA) until reaching an OD₆₀₀ of 0.5. After washed by resuspending in PBS, the bacterial suspension was adjusted to an OD₆₀₀ of 1.0 in Opti-MEM medium, and subsequently added to A549 cells previously infected with JS/10 at an MOI of 10. At specific time points, cell lysates were collected using RIPA buffer (Solarbio, China) containing 1% protease inhibitor and phosphatase inhibitor (MedChemExpress, USA), and the total protein concentration was determined using a BCA protein assay kit (Epizyme Biomedical Technology, China). Lysates were stored at − 80 °C until further analysis by SDS-PAGE and western blotting.

Luciferase reporter assays

A549 cells were co-transfected with 10 ng of the pIFN-β-Fluc plasmid (encoding firefly luciferase) and 2 ng of the pGL4.75 plasmid (encoding Renilla luciferase for normalization). At 24 h post-transfection, cells were washed with PBS and then infected with JS/10 at an MOI of 1. After a 1-h incubation at 37 °C with 5% CO₂, cells were washed three times with PBS and replenished with 0.5 mL of the Opti-MEM medium containing 0.125 μg/mL TPCK-trypsin. Different LAB strains were statically incubated at 37 °C in MRS broth until reaching an OD₆₀₀ of 0.5. The cultures were then washed with PBS and resuspended in the Opti-MEM medium containing 0.125 μg/mL TPCK-trypsin, adjusting the OD₆₀₀ to 1. Then the bacterial suspension was added to A549 cells previously infected with JS/10. At 24 h post-infection, the cells were lysed with lysis buffer, and luciferase activities were measured using a dual luciferase assay kit (Promega, USA) according to the manufacturer’s instructions. Poly(I: C) (Sigma-Aldrich, USA) at a concentration of 0.1 μg/mL was used as a positive control for IFN promoter activation, while 1 μg/mL of E. coli lipopolysaccharide (LPS O111:B4, Sigma-Aldrich, USA) was used as a positive control for NF-κB activation. The Opti-MEM medium with TPCK-trypsin served as a negative control.

Cytotoxicity assay

Cell viability was detected using CCK-8 assay (Beyotime, China). In brief, A549 cells were seeded at a density of 0.5 × 10⁵ cells/mL in 96-well plates with 100 μL/well, and incubated at 37 °C with 5% CO₂ until attaining 80% confluency. Then cells were infected with bacterial suspension at an MOI of 10 and virus suspension at an MOI of 1 for 12 h or 24 h at 37 °C with 5% CO₂. Positive controls were prepared by adding 10 μM Cisplatin (MedChemExpress, USA) instead of viral infection, while wells treated only with the Opti-MEM medium served as negative controls. At the indicated time points, 10 µL CCK-8 solution was added into each well and incubated at 37 °C for 1 h in the dark. The absorbance was measured at a wavelength of 450 nm by a microplate reader. The results were normalized by the control wells of uninfected cells.

Lentiviral transduction

293 T cells were seeded at a density of 1.0 × 10⁶ cells/mL in 6-well plates with 2 mL/well. Subsequently, the plasmids plvx-GFP-RFP-hLC3B, pMD2.G, and pSPAX2 were co-transfected into the 293 T cells using Lipofectamine 2000 (Thermo Fisher Scientific, USA). At 48 h and 72 h post-transfection, cell supernatants were filtered through a 0.45-μm membrane to remove cell debris, and then used to infect A549 cells for 24 h. Following infection, a selection pressure of 2 μg/mL puromycin (MedChemExpress, USA) was applied, and single-cell clones were obtained using the limiting dilution method. The cells were cultured in F-12 K medium containing 10% FBS, supplemented with 5 μM/mL rapamycin (MedChemExpress, USA) for 12 h. Autophagosome formation was then observed under a fluorescence microscope (Zeiss Axio Vert A1, Germany).

Results

Dysbiosis of the nasal microbiome exacerbates influenza-induced respiratory disease

To investigate the potential connection between nasal microbiota and influenza susceptibility, we created a model of nasal microbiota dysbiosis in 3-month-old beagles by locally applying a combination of mupirocin and neomycin ointment to the nasal cavity. As expected, antibiotic treatment significantly reduced the microbial absolute abundance in the nasal cavity (Fig. S1A). Subsequently, relative abundance data obtained through equal-weight resampling showed a notable decrease in α-diversity, as indicated by both the Chao1 and Richness indices (Fig. 1A). Principal coordinate analysis (PCoA) based on Bray–Curtis distances revealed a significant shift in the nasal microbiome structure following antibiotic treatment (Fig. 1B). This disruption was further characterized by an expansion of the phylum Proteobacteria (Fig. S1B), a hallmark commonly associated with gut dysbiosis [39]. We also observed a significant increase in the ratio of Psychrobacter, Achromobacter, Ralstonia, Blautia, and Escherichia-Shigella, along with a decrease in the abundance of Bacteroides, Leucobacter, Lactobacillus, and Lachnoclostridium at the genus level (Fig. S1C, p < 0.05, Benjamini-Hochberg).

To explore the impact of antibiotic-induced nasal microbiome dysbiosis on host functionality, we predicted functional pathways influenced by microbial composition using PICRUSt2 [40]. Differential analysis at the third level of KEGG pathways revealed significant changes in viral infection and apoptosis pathways after antibiotic treatment (Fig. 1C). These findings suggest that nasal microbiome disturbances might compromise the host’s ability to resist influenza infection. To demonstrate this, we created a model of nasal microbiota dysbiosis in beagles by locally applying a combination of antibiotics and then inoculated them intranasally with 10⁷ PFU of H3N2 virus (Abx group). A detailed overview of the experimental workflow was provided in Fig. 1D. On the second day post-virus infection, both the WT and Abx groups exhibited symptoms such as runny nose, sneezing, and poor appetite. Furthermore, the Abx group demonstrated more severe clinical symptoms compared to the WT group, including elevated body temperature (Fig. 1E) and weight loss (Fig. 1F). By the third day post-infection, the Abx group presented with distinct wet rales in the lungs, rapid breathing, and persistent high fever. Clinical symptoms during the infection process were scored according to the criteria established by John [41]. The data indicated significant differences in clinical score among the three groups, with the Abx group presenting the highest clinical score (Fig. 1G, p < 0.001, Tukey’s HSD). Through plaque assay, we observed that the Abx group exhibited higher viral titers in both the nasal cavity and lungs compared to the WT groups (Fig. 1H, p < 0.001, Tukey’s HSD).

Considering that more pronounced pathological changes occurred at 8 dpi according to our preliminary experiments, we selected day 8 as the sampling time point. Gross anatomy revealed extensive hemorrhagic spots in the lungs of the Abx group. Histopathological examination of the turbinate mucosa revealed that in the WT group, the pseudostratified columnar ciliated epithelium was partially necrotic and exfoliated, whereas in the Abx group, severely altered pseudostratified columnar ciliated epithelium was observed (Fig. 1I). The quantification for the thickness of nasal and trachea epithelia by SlideViewer indicated that antibiotic treatment-mediated dysbiosis in the nasal microbiome exacerbates the disruption of the nasal epithelium and tracheal mucosal epithelial barrier during influenza infection (Fig. S2A, p < 0.001, Tukey’s HSD). The histopathological examination of the lung tissue in the WT group showed widespread alveolar wall thickening, accompanied by scattered infiltration of lymphocytes and neutrophils; in contrast, the lung of the Abx group showed severe alveolar wall thickening, accompanied by the infiltration of large numbers of lymphocytes and neutrophils and a small number of macrophages (Fig. S2B). In addition, a significant appearance of epithelial proliferation was exhibited in the lung of the Abx group, characterized by enlarged nuclei and mitotic patterns and an observable amount of cell necrosis and nuclear fragmentation (Fig. 1I, Fig. S2B). Then we scored the histopathological changes in the nasal, tracheal, and lung tissues based on the evaluation criteria outlined and described elsewhere [42]. The data indicated that the Abx group obtained the highest scores across nasal, tracheal, and lung, with significant differences observed among the three groups (Fig. 1J, p < 0.05, Tukey’s HSD). In the Nor group, no obvious histopathological changes were observed in the turbinate bone, trachea, and lung tissues. Similarly, the immunofluorescence detection for influenza virus nucleoprotein (NP) across the nasal, tracheal, and lung regions among the groups showed a consistent trend with histological scoring (Fig. 1K, L, p < 0.05, Tukey’s HSD). These results imply that dysbiosis of the nasal microbiome enhances susceptibility of influenza infections and exacerbates pathophysiology in the affected dogs.

Community dynamics and functional changes of nasal microbiota during influenza infection

Microbiota residing in the nasal cavity has been reported to be associated with susceptibility to and severity of RTIs [1, 4, 43]. To explore which microbial keystone taxa play a pivotal role in host resistance to influenza infection, we characterized the bacterial compositions to reveal differences in the microbial communities among the Nor, WT, and Abx groups. The leveling of rarefaction curves indicates sufficient sequencing depth to capture most microbial diversity in the samples (Fig. S3). The Chao1 and Richness diversity indices in the Abx group consistently remained lower than those in the WT group throughout the experimental period (Fig. S4A, B; p < 0.001). The results of the PCoA suggested that the divergence of the samples from the Abx group became distinct compared to the Nor and WT groups both before and after virus infection (Fig. S4C, p < 0.001, Wilcoxon rank-sum test). However, regardless of virus infection, no significant differences were observed between the Nor and WT groups (Fig. S4C, p > 0.05, Wilcoxon rank-sum test). Meanwhile, the ternary plot indicates that regardless of influenza infection, the high-abundance microbial communities (genus level: relative abundance > 0.5%) in the Abx group showed a significant loss after antibiotic treatment (Fig. 2A, Fig. S5A).

To further explore the bacterial genus enriched in the Nor, WT, and Abx groups at different experimental time point (T1, T2, and T3), a differential enrichment analysis was conducted using DESeq2. Relative abundance analysis revealed that compared to the WT group, a diminished proportion of bacterial genus Lactobacillus was identified in the Abx group, while an inverse trend was observed for Moraxella (Fig. 2B, Fig. S5B, p < 0.05, Tukey’s HSD). To further ascertain whether this change was attributable to antibiotic treatment or influenza infection, we conducted an intra-group comparison for Abx and WT groups at the T2 and T3. Interestingly, we found that, irrespective of the Abx or WT group, the relative abundance of Lactobacillus showed no significant difference at the T2 and T3. In contrast, for Moraxella, both the Abx and WT groups exhibited a notable increase, with the relative abundance in the Abx group significantly surpassing that in the WT group at the T3 (Fig. S6, p < 0.05, Tukey’s HSD). Then we conducted a correlation analysis on the microbial communities at T2 and T3 in both the Abx and WT groups. After antibiotic treatment (T2), the proportions of Lactobacillus were observed to be negatively associated with Moraxella. Additionally, after virus infection (T3), the proportions of Lactobacillus, Megamonas, Prevotella_9, and Lachnoclostridium were observed to have a negative association with Moraxella (Fig. 2C, Fig. S7, p < 0.05, Benjamini-Hochberg). Therefore, we further speculate that these changes in microbial communities may also correlate with the viral titers in the nasal and lung tissues. As expected, a significant correlation was observed between nasal microbiota and virus titers in the nasal and lung tissues (Fig. 2D). The virus titers presented negative correlations to the bacterial genus Lactobacillus, Megamonas, Prevotella_9, and Lachnoclostridium, but a positive association with Moraxella. Similarly, such changes of Lactobacillus and Moraxella were also observed in dogs with antibiotic treatment but not infected with influenza virus (Fig. S8).

Functional analysis of nasal microbiota based on the PICRUSt2 and KEGG database revealed substantial differences among the Nor, WT, and Abx groups before virus infection. Notably, these differences encompass pathways associated with the infection of pathogenic microorganisms, including Kaposi sarcoma-associated herpesvirus (KSHV), herpes simplex virus 1 (HSV-1), hepatitis C virus (HCV), human cytomegalovirus (HCMV), human immunodeficiency virus 1 (HIV-1), Epstein-Barr virus (EBV), hepatitis B virus (HBV), and IAV, as well as cell junctions (tight, adherens, and gap junctions), autophagy, Toll, and Imd signaling pathways (Fig. S9). After viral infection, the WT and Abx groups demonstrated functional distinctions primarily in pathogenic microbial infection-related pathways and autophagy, with no significant variances in cell junction pathways, and the Toll and Imd signaling pathway. However, the Nor group exhibited noteworthy differences in cell communication pathways compared to both the Abx and WT groups (Fig. 2E). The data led us to speculate that these differences in functional pathways might correlate with changes in Lactobacillus and Moraxella. Therefore, we conducted a correlation analysis between the abundance of the two bacterial genera before and after infection and their contribution to pathways. Our data indicate that after antibiotic treatment, Lactobacillus had a significant negative correlation with the pathway associated with pathogenic infections; in contrast, Moraxella exhibited a significant positive correlation with the pathway associated with pathogenic infections (Fig. S10, p < 0.05, Benjamini-Hochberg). Given these data, we speculate that the abundance of Lactobacillus and Moraxella in the nasal microbiome may be associated with susceptibility to influenza infection.

Dysbiosis of the nasal microbiome diminishes the antiviral response within the nasal cavity

To better understand the host-microbiota interplay and its potential connection to influenza susceptibility, we conducted a transcriptomic profiling of nasal tissues collected from the Nor, WT, and Abx groups at 8 dpi. Our transcriptome data revealed 947 and 950 differentially expressed genes (DEGs) from WT versus Nor and Abx versus Nor comparisons, respectively. Also, when compared to the WT group, we identified 723 genes upregulated and 308 genes downregulated (Fig. 3A). A total of 2784 DEGs yielded by inter-group comparisons (Fig. 3B) were subjected to clustering analysis based on the Fuzzy C-means (FCM) algorithm, resulting in the identification of 5 distinct clusters (Fig. 3C). Notably, Cluster 3 and Cluster 5 displayed contrasting trends. Specifically, Cluster 3 predominantly comprised inflammation-related genes (e.g., NLRP3, IL1β), while Cluster 5 was enriched with genes associated with innate immunity (e.g., Mx1, OASL, OAS1, OAS2, OAS3, ISG15, ISG20, IFIH1). This observation suggests that dysbiosis of nasal microbiota may attenuate host innate immune antiviral responses to some extent. Subsequently, we performed the KEGG enrichment analysis and found that the DEGs were mainly enriched in 9 modules, including viral infectious disease, bacterial infectious disease, signal transduction, transport and catabolism, signaling molecules and interaction, immune system, cellular processes, cellular community, and cell growth and death (Fig. S11A, p < 0.05, Benjamini-Hochberg). Further, GSEA for the Abx group showed a significant enrichment of the genes associated with viral disease, including coronavirus disease (COVID-19), and EBV, HCV, HSV-1, HIV-1, HBV, and human T-cell leukemia virus 1 (HTLV-1) infections. Additionally, some inflammation-related pathways were enriched, including IL-17, tumor necrosis factor (TNF), RIG-I-like receptor, Toll-like receptor (TLR), cytosolic DNA-sensing, NOD-like receptor, and Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathways (Fig. S11B, p < 0.05, Benjamini-Hochberg). The protein–protein interaction (PPI) networks for DEGs were subsequently constructed using STRING (https://string-db.org/) with a minimum required interaction score of 0.4. We can effectively categorize the DEGs into five distinct clusters (Fig. 3D). Within these clusters, genes are primarily associated with natural immune response and antiviral defense (Cluster 1), mucin formation on mucosal surfaces (Cluster 2), epithelial barrier function (Cluster 3), inflammation (Cluster 4), and biological processes related to autophagy (Cluster 5) (Fig. 3E). In Cluster 1, notably, genes involved in regulating IFN production (IFIH1, IRF1, IRF9, STAT1 and STAT2) and IFN-mediated antiviral proteins (Mx1, OAS1, OAS2, OAS3, OASL, IFI27L2, IFI35, IFI44, IFI44L, IFIT2, IFIT3, ISG15, ISG20, TRIM14, TRIM22, and TRIM25) exhibit a significant reduction in expression levels in the Abx group compared to the WT group (Fig. 3E, p < 0.05, Benjamini-Hochberg). Notably, the mRNA transcript levels of cytokines in the blood following viral infection (Fig. 3F) are generally consistent with those observed in the nasal cavity (Fig. 3E). Therefore, we speculate that the disruption of nasal microbiota may, to some extent, attenuate the host’s antiviral immune response.

Our transcriptome data also indicate a significant increase in the expression levels of mucin-associated genes (MUC1, MUC4, MUC15, and MUC20) in the Abx group compared to the WT group (Fig. 3E, Cluster 2, p < 0.01, Benjamini-Hochberg). It is known that mucin acts as a frontline defense, forming a protective barrier against viruses and bacteria. However, excessive mucus production contributes to complications in respiratory diseases, such as heightened susceptibility to infections, compromised lung function and increased mortality [44, 45]. Following influenza infection, the Abx group exhibited pronounced rhinorrhea and a higher frequency of sneezing. These findings suggest, to some extent, that the heightened transcriptional levels of mucins in the Abx group may exacerbate influenza infection. Correspondingly, our histopathological examination indicated that the dysbiosis of nasal microbiota exacerbated the disruption of mucosal barrier following influenza infection (Fig.S2A). However, at the transcriptional level of barrier-related genes, no consistent trend was observed in the Abx group compared to the WT and Nor groups (Fig. 3C, Cluster 3). Therefore, we further conducted an immunofluorescence analysis of the tight junction protein ZO-1 (TJP1), and demonstrated that its expression at the infection site was lower in the Abx group than in the WT group (Fig. 3G).

Disruption of nasal microbiota exacerbates the dysbiosis of lung microbiota following influenza infection

It is well-known that the upper airway accommodates the most substantial biomass and stable microbial communities, and the lungs are continually exposed to these bacteria through micro-aspiration. Given this, we pose a question whether disruption of the nasal microbiota leads to a change in lung microbiota composition, and thus exacerbates influenza infection. To answer this, we performed the 16S rRNA gene amplicon sequencing of lung samples. The specific sampling and analysis procedure is depicted in Fig. 4A. To investigate the composition and distribution characteristics of lung microbiota in the Nor, WT, and Abx groups, we utilized the EasyAmplicon package for taxonomy analysis. Our results reveal that at the phylum level, Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Fusobacteria, and Acidobacteria dominate the bacterial taxa in the canine lung (Fig. 4B). At the genus level, Bifidobacterium, Lactobacillus, Bacillus, Moraxella, Streptococcus, Bacteroides, and Nitratireductor constitute the predominant microbial communities in the canine lung (Fig. 4C). This composition bears resemblance to the microbiota found in the human lung [14, 46]. Additionally, the Abx group exhibited significantly lower relative abundances of Firmicutes and Bacteroidetes compared to the Nor and WT groups (Fig. S12A, p < 0.05, Tukey–Kramer). However, no significant differences were noted in Proteobacteria, Firmicutes and Bacteroidetes between the Nor and WT groups (Fig. S12A, p > 0.05, Tukey–Kramer). The differential analysis at the genus level revealed that, compared to the WT group, the relative abundances of Moraxella, Nitratireductor, Mesorhizobium, Marvinbryantia, and Mycobacterium were significantly higher in the Abx group, but the opposite was true for Lactobacillus and Odoribacter (Fig. 4D, Fig. S12B, C, p < 0.05, Tukey–Kramer). When compared with the Nor group, similar changes in abundance of Moraxella and Lactobacillus were observed in the Abx group (Fig. S12D). Except for the genus Odoribatcter, the abundance of other microbial communities did not show significant differences between the WT and Nor groups (Fig. S12E). The rarefaction curve indicated that the sequencing depth was sufficient for diversity analysis (Fig. S12F). In the analysis of species diversity, we observed significant differences in both α-diversity and β-diversity between the Abx and the WT or Nor groups (p < 0.05, Tukey–Kramer), but no significant difference was found between the Nor and WT groups (Fig. 4E, F). Further, we determine the contribution of lung microbiota to host pathways. The functional analysis by PICRUSt2 indicates significant differences in several pathways between the Abx group and the WT group. These include the viral infection-related pathway (IAV, HBV, HCV, HCMV, EBV, KSHV, HSV1, and HIV), autophagy-related pathway (mTOR), apoptosis pathway, cell junctions (tight junction, adherens junction and gap junction), and Toll and Imd signaling pathways (Fig. 4G, p < 0.05, Benjamini-Hochberg). Additionally, the two bacterial genera, Lactobacillus and Veillonella, have a notable positive correlation with the signaling pathways including mTOR, and Toll and Imd, while a significant negative correlation with viral infection-related pathways. In contrast, Mycobacterium, Mesorhizobium, and Nitratireductor exhibited a distinct positive correlation with viral infection-related pathways (Fig. 4H, Fig. S13, p < 0.05). Collectively, these findings indicate that disruption in the nasal microbiota exacerbates the dysbiosis of lung microbiota during influenza infection. Furthermore, microbial communities of the lung exhibit homogeneous alterations that have been observed in the nasal microbiota.

Disruption of lung microbiota exacerbates inflammatory response and barrier damage during influenza infection

To better evaluate the potential role of lung microbiota in lung health and disease, we analyzed the differential mRNA expression in the lungs from the Nor, WT, and Abx groups. Our data revealed 909 and 920 DEGs from WT versus Nor and Abx versus Nor comparisons, respectively; in comparison to the WT group, 905 genes showed upregulation while 305 genes exhibited downregulation in the Abx group (Fig. S14A). The union of DEGs from inter-group comparisons yielded a total of 2225 genes (Fig. S14B). The clustering analysis for the DEGs based on the Fuzzy-c means (FCM) algorithm identified four distinct clusters (Fig. S14C). Interestingly, we observed the emergence of two distinct clusters (Cluster 2 and Cluster 4) among the DEGs in the lung transcriptome. Cluster 2 includes several canonical IFN-stimulated genes (ISGs), such as Mx1, OASL, OAS1, OAS2, OAS3, and ISG15. In contrast, Cluster 4 comprises inflammation-related genes, including IL1α, IL1β, NLRP3, and IL18. These DEGs are also present in the nasal tissue transcriptome data. Subsequently, the KEGG and GSEA results showed significant enrichment in 7 modules, including viral infectious disease, bacterial infectious disease, transport and catabolism, immune system, signal transduction, cellular community, and cell growth and death in the Abx group (Fig. S14D, E). Remarkably, these enrichment patterns closely resembled those observed in the nasal transcriptome (Fig. 3C). Furthermore, we also conducted STRING clustering analysis on the DEGs, utilizing protein interaction scores as the criteria, resulting in the classification of four distinct clusters. It was found that MUC1, IL6, TLR6, TLR2, TLR4, IL1B, CCL2, OCLN, TJP1/ZO-1, CLDN1, and RHOA1 were interconnected within Clusters 1, 2, and 3 (Fig. 5A). Normalization of the expression levels of these genes based on the Nor group showed that the ISGs, including OAS1, OAS2, Mx1, Mx2, ISG15, IFIT2, IFIT3, and TRIM25, were significantly downregulated in the Abx group (Fig. 5B, p < 0.05, Benjamini-Hochberg). Furthermore, the Abx group exhibited higher expression levels of inflammatory factors (IL1β, IL6, IL17β, IL18, NLRP3, CCL2, CXCL8, and CXCL14) and various TLRs (TLR1, TLR2, TLR3, TLR6, TLR7, and TLR8) compared to the WT group (Fig. 5B, p < 0.05, Benjamini-Hochberg). We also utilized RT-qPCR to assess the transcription levels of inflammatory and IFN-related cytokines, and the results were generally consistent with the transcriptomic data (Fig. 5C). Moreover, our transcriptomic analysis also revealed that genes related to RhoA signaling (Rac1, RHOA1, LIMK1, and LIMK2), which primarily contribute to the destabilization of adherens junctions (AJs) and increase in endothelial permeability [47], were consistently upregulated in the Abx group (Fig. 5B, p < 0.05, Benjamini-Hochberg). Notably, the transcription levels of nearly all MUCIN genes, especially MUC4 (109.51 folds), MUC5B (57.01 folds), and MUC16 (12.27 folds), showed significant upregulation in the Abx group compared to the WT group (Fig. 5B, p < 0.05, Benjamini-Hochberg).

To evaluate whether the DEGs are correlated with the distinct distribution of lung microbiota, we conducted a Mantel test correlation analysis on cytokines and differential lung microbiota. Our data indicate a significant positive correlation between the mRNA levels of antiviral (IFNβ1, IFNα, OAS1, Mx1, PKR, ISG15, Myd88, Mx2) and inflammation-related genes (IL6, TNFα, Bax, Caspase 3) and the relative abundances of Lactobacillus in lung tissues (Fig. 5D, p < 0.01, Mantel’s R ≥ 0.4). Conversely, there was a notable negative correlation between these genes and the relative abundances of Moraxella (Fig. 5D, p < 0.01, Mantel’s R ≥ 0.4). Pearson correlation coefficient analysis revealed that Lactobacillus exhibited a significant positive correlation with the mRNA levels of IFNβ1 and Myd88 (Fig. 5E, Pearson’s R > 0.7, p < 0.001, Benjamini-Hochberg), but a significant negative correlation with TNFα and Caspase 3 mRNA levels (Fig. 5E, Pearson’s R < − 0.7, p < 0.001, Benjamini-Hochberg). In contrast, Moraxella demonstrated an opposite trend to Lactobacillus. Then we assessed the phosphorylation status of key proteins in the inflammatory and IFN pathways through western blot analysis. Compared with the WT group, the phosphorylation levels of IRF3 and TBK1 were higher, while the phosphorylation levels of inflammation-related proteins, for example, NF-κB/p65, were lower in the Abx group (Fig. 5F). This finding further confirms that microbial dysbiosis in the lung diminishes the host antiviral response.

Canine-derived Lactobacillus spp. exerts a significant in vitro antiviral effect and activates IFN-mediated pathways

In this study, we used ten LAB strains isolated from healthy beagles, including 5 strains of L. animalis, 4 strains of L. plantarum, and 1 strain of L. lactis, to conduct an in vitro antiviral assay. L. plantarum Wcsf-1, a previously reported strain capable of activating IFN response, serves as a control [48]. As shown in Fig. 6A, we conducted two schemes for virus and Lactobacillus infection experiments. Pre-treatment with L. animalis strains C122 and B223, and L. plantarum C123, B321, and Wcfs-1 significantly reduced the expression of viral NP at 24 h after influenza infection, when compared with the other LAB pre-treatments and the virus-only group; similarly, following co-infection with the influenza virus, L. animalis strains A113 and B223, and L. plantarum strains C123, A112, B321, and Wcfs-1 also reduced NP levels (Fig. 6B). Additionally, in vitro cytotoxicity assessment showed that introducing L. plantarum strains did not result in significant cytotoxic effects compared to the virus-only group (Fig. S15). The dual-luciferase reporter assay revealed that compared with the virus-only group, L. plantarum strains C123, B321, and Wcfs-1, and L. animalis B223, significantly enhanced IFN-β promoter activation, and all the LAB strains did not affect NF-κB/p65 promoter activity (Fig. 6C).

Considering that L. plantarum C123 exerts the strongest antiviral response, next we investigate whether the antiviral activity of L. plantarum C123 is related to upstream regulation of the IFN pathway. It has been known that the TBK1-IRF3 signaling cascade, which integrates RNA- and DNA-sensing pathways during viral infection, plays a critical role in the production of type I IFNs [49]. Therefore, we assessed TBK1 and its phosphorylation levels in A549 cells following exposure to L. plantarum C123 and influenza infection. Our data showed that compared to the virus-only group, total and phospho-TBK1 levels were increased in the early stages of infection (1 h, 4 h, 8 h) whether the L. plantarum C123-treated or co-infection groups, but at later stages (12 h, 24 h), their levels were significantly decreased in the co-infection group. Interestingly, influenza virus NP levels were consistently lower in the co-infection group (Fig. 6D). Thus, we speculate that L. plantarum C123 may activate additional antiviral pathways beyond the IFN response.

L. plantarum C123 is capable of interfering with influenza-induced incomplete autophagy

Influenza viruses employ various strategies to enhance self-replication, including initiating the autophagy process and subsequently blocking autophagosome-lysosome fusion [50,51,52]. Notably, our nasal transcriptome data, as shown in Fig. 3C, revealed significantly elevated expression of autophagy-related genes (ATG9B, GABARAPL2, MAP1LC3B/LC3B, SQSTM1/p62) in the Abx group compared to the WT group. Therefore, we hypothesize that nasal microbiota might play significant roles in regulating autophagy. To validate this hypothesis, we further analyzed the autophagy-associated protein levels in the nasal tissue by immunofluorescence staining and found increased expression of MAP1LC3B/LC3B and SQSTM1/p62 in the Abx group compared to the WT group, accompanied by substantial SQSTM1/p62 accumulation in tissues (Fig. 7A). Also, autophagosome formation was significantly elevated in influenza-infected cells compared to uninfected cells (Fig. 7B). This was further validated by western blot, which showed increased lipidated LC3B and SQSTM1/p62 levels following influenza infection (Fig. 7C).

Considering that L. plantarum C123 was isolated from canine nasal cavity, and its coinfection with influenza virus resulted in significantly altered expression of TBK1 (Fig. 6D), an important component of multiple signaling pathways including autophagy [53, 54], we speculate that L. plantarum C123 might play a vital role in modulating influenza-induced autophagy. To test this idea, we performed an in vitro co-infection assay with L. plantarum C123 and influenza virus and examined the expression levels of autophagy-related proteins and viral proteins. As expected, we found that SQSTM1/p62 accumulation decreased significantly compared to the virus-only group, along with a marked reduction in viral NP and M1 proteins at 24 h post-infection (Fig. 7D, E). In parallel, we performed cytotoxicity tests (CCK-8 assays) and confirmed that L. plantarum C123 was nontoxic to A549 cells (Fig. 7F). We also stably transfected influenza-infected A549 cells with the GFP-RFP-LC3B, and observed a significant retention of green fluorescence in the virus-only infected cells compared to the co-infected cells with L. plantarum C123 and influenza virus at 24 h post-infection (Fig. 7G). These findings indicate that nasal microbiota may activate autophagy during influenza infection, potentially reversing virus-induced autophagic block and enhancing viral clearance. Although our data support this regulatory logic, it requires further investigation by the use of inhibitors or knockout techniques to block the autophagy pathway and demonstrate its significance.

Discussion

Over the years, a wealth of evidence suggests the role of bacterial communities in the respiratory tract in preventing respiratory pathogens from establishing an infection [1, 18, 21]. The nasal cavity, an essential component of the URT, serves as the primary site for the initial contact of the influenza virus [55]. A previous study in human populations indicates that the microbial composition of the nasal cavity is associated with influenza susceptibility [21], but the underlying mechanisms remain unclear. At present, most of what we know about the protection mechanisms of commensal bacteria stems from studies using mouse models, and relatively little information is available in other animals. In this study, we created a model of nasal microbiota dysbiosis in 3-month-old beagles by locally applying a combination of mupirocin and neomycin ointments to the nasal cavity [5, 56]. Our data indicated that the antibiotic-treated dogs exhibited significantly exacerbated influenza infection in the respiratory tract compared with the vehicle-treated animals. And disruption of the nasal microbiome results in significant variations in microbiota-linked functional pathways, particularly those associated with viral infection, inflammation, and barrier functions. This provides us with a hint that maintaining the stability of the respiratory ecosystem is crucial for effectively controlling viral diseases. To our knowledge, this is the first canine study to prospectively explore the relationship between the nasal microbiome and influenza infection.

The mechanistic basis for how the microbiota protects against the respiratory infection is limited, but a previous study on the gut microbiome in pigs reported that microbial imbalances directly contribute to dysfunction in the intestinal mucosal barrier [57]. Corresponding with this, our study found that antibiotic treatment in the nasal cavity exacerbates the disruption of the nasal mucosal epithelial barrier after influenza infection. Interestingly, in the Abx group, almost all genes encoding the mucin, a major structural and functional component of mucus [58], have shown significant upregulation, especially MUC4, MUC5B, and MUC16, compared to the WT group. Mucus hypersecretion has been reported to contribute to pathophysiology of a number of severe respiratory conditions in human [59]. Therefore, it is hypothesized that dysregulation of mucins in this study might contribute to uncontrolled inflammation and result in abnormal airway function. This speculation can be supported by evidence-based clinical (sneezing and pulmonary crackles) and pathological (interstitial pneumonia) features observed in the Abx group.

Additionally, numerous studies highlight the role of the microbiota in regulating the IFN-I response by promoting constitutive IFN-I production and maintaining basal ISG expression levels [60, 61]. Antibiotic-induced depletion of gut microbiota has been shown to reduce IFN-β-mediated antiviral activity [62]. Notably, in our study, the Abx group exhibited low transcription levels of antiviral ISGs, along with an increase in inflammatory cytokines in both nasal and lung compartments when compared with the WT group. Therefore, we speculate that disturbance in nasal microbiota may attenuate the host’s IFN-mediated antiviral immune response. One thing should be pointed out is that we have not excluded the direct impact of antibiotics on influenza virus replication. While nasal application of mupirocin has not been reported to affect IAV replication, neomycin may inhibit the viral replication by inducing the expression of the ISGs in the nasal mucosa [58], or binding to the IAV RNA promoter [63]. However, our study did not establish the neomycin or mupirocin as control treatments due to ethical considerations for animal welfare. Despite this limitation, we believe nasal microbiota dysbiosis is a major contributing factor to deduced antiviral immunity, as our data showed that the transcription level of ISGs in the nasal and lung tissues was significantly lower in the Abx group compared to the WT group, and on the contrary, the viral load was significantly higher in the Abx group, suggesting that the direct inhibition effect of neomycin or mupirocin on viral replication can be ignored in this study.

It is unclear how nasal microbiota dysbiosis exacerbates lung damage caused by influenza. Given the anatomical continuity between the nasal cavity and the LRT, it is likely that the nasal microbiome plays a crucial role in shaping the lung microbiome [64, 65]. In this study, the phylogenetic relationship based on the primary microbial ASV sequences showed a homology of 100% at genus level, including Haemophilus, Moraxella, Streptococcus, Lactobacillus, Prevotella, and Bifidobacterium between the lung and the nasal cavity. During the viral infection, we also observed homogeneous changes in the nasal and lung microbiota. These findings provide additional evidence for the idea that a portion of the lung microbiota is derived from the nasal cavity. Of note, microbiome analysis revealed that influenza virus (H3N2) infection alone did not significantly result in imbalance of lung microbiota; in contrast, intranasal treatment with antibiotics prior to virus infection altered the diversity and composition of lung microbiota. This finding suggests that disruption of lung microbiota in the Abx group is only related to nasal antibiotic treatment and not to viral infection. Contrary to this, a previous study has shown that infection with H7N9 influenza virus leads to respiratory microbiota disruption [66]. We speculate that this difference might be due to variations in animal models, infection doses, sampling methods, viral subtypes, or environmental factors. Similar propositions can be found in other studies [67, 68].

Currently, there are few studies to identify the microbial groups in the microbiota that are directly responsible for affecting respiratory defenses. In this study, we sought to identify members of the microbiota that are closely associated with respiratory infection, and to define the mechanistic basis for their effect. Our data demonstrated the relative abundance of Moraxella was markedly elevated in both the nasal cavity and lungs of the Abx group following influenza infection, and showed a near-perfect negative correlation with that of Lactobacillus, Prevotella, Megamonas, and Lachnoclostridium. We speculate that changes in the relative abundance of these bacterial genera might contribute to influenza severity. Massive colonization by Moraxella has been demonstrated to induce a mixed proinflammatory immune response [69]. Moraxella can excessively activate the TLR signaling pathway, compromising the innate immune response of alveolar macrophages and contributing to exacerbations of chronic obstructive pulmonary disease (COPD) [70]. Interestingly, our lung transcriptomic analysis demonstrated the transcription levels of various TLRs (TLR1, TLR2, TLR3, TLR6, TLR7, and TLR8) in the Abx group were consistently higher than those of the WT group. The excessive and prolonged TLR activation can induce expression of pro-inflammatory cytokines, resulting in inflammatory tissue damage [71]. In line with this, our transcriptomic data also revealed elevated expression of inflammatory cytokines in the Abx group. Currently, it is not clear the role played by Moraxella in influenza severity. Nevertheless, our ongoing experiments suggest that co-infection with Moraxella and influenza virus exacerbates viral infection and compromises epithelial barrier integrity (data not shown), although the underlying mechanisms are still under investigation.

A previous study suggests that Lactobacillus is capable of upregulating tight junction protein ZO-1, thereby enhancing epithelial barrier integrity [72]. This led us to speculate that such a protective mechanism might be compromised in the Abx group due to the lower abundance of Lactobacillus, potentially exacerbating viral pathogenesis. The finding that the Abx group had a significantly lower expression of TJP1 compared to the WT group further supports the involvement of microbial community in barrier integrity. A recent study has indicated that L. paracasei modulates lung immunity and enhances the ability to combat influenza infection [73]. In our study, the relative abundance of Lactobacillus in both the nasal cavity and lung was significantly negatively correlated with the viral titers in these tissues, while it was significantly positively correlated with the mRNA levels of IFN-I and ISGs such as ISG15, OAS1, and Mx2 in the lungs. This provides us with a tantalizing hint that Lactobacillus might play significant roles in regulating respiratory immunity and antiviral defenses. To verify this, we isolated ten strains of LAB from healthy beagles and assessed their antiviral activities in vitro. One strain of L. plantarum, named C123, exhibited powerful antiviral effects, which caught our attention. This bacterium can activate IFN promoter activity, and its co-infection with influenza virus results in significantly reduced levels of TBK1, a key link between IFN-I response and autophagy [74], compared to the virus-only infection. Considering that influenza viruses have been reported to evade host defense mechanisms by blocking autophagic flow [51], we examined the expression levels of autophagy-related proteins (LC3B, p62), and demonstrated that L. plantarum could activate autophagy. Further autophagic fluorescence reporter assays indicated that compared to the virus-only group, the autophagic flow was apparently restored due to the presence of L. plantarum in the coinfection group. Based on these findings, we speculate that L. plantarum may exert antiviral effects by activating IFN and modulating autophagic flow during influenza infection. However, the precise mechanisms by which L. plantarum balances IFN activation and autophagy to mediate antiviral effects require further investigation.

Certainly, our study has several limitations. Firstly, we used 16S rRNA amplicon sequencing for all microbial sequencing samples. The sample abundance and sequencing depth may introduce additional biases. Secondly, each lung sample for sequencing was collected from six regions within an individual lung. It is likely that microbiome heterogeneity (and thus pneumotype categorization) varies over time and across segments of the lung. Furthermore, different cell types across tissues contribute to varied and complex immune responses, and transcriptomic analysis may not accurately reflect the immune reactions of distinct ecological niches. Thirdly, the in vitro cellular environments used for the investigation of antiviral effect exerted by Lactobacillus may be insufficient to simulate the complexity of the in vivo environment. Though there are some limitations and deficiencies, we believe this discovery will yield insights into how symbiotic bacteria modulate host antiviral defenses by balancing innate immunity, maintaining mucosal barriers, and regulating host autophagy.

Data availability

All raw data used in this study are available in the NCBI Sequence Read Archive (SRA), accession numbers PRJNA1125627, PRJNA1126008.

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Funding

The study is supported by the National Natural Science Foundation of China (32273094) and Jiangsu Provincial Science and Technology Plan Special Fund (Innovation Support Plan International Science and Technology Cooperation) (BZ2023048).

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Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China
Jinzhu Geng, Yuhao Dong, Hao Huang, Xia Wen, Ting Xu, Yanbing Zhao & Yongjie Liu

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Jinzhu Geng
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Yuhao Dong
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Hao Huang
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Xia Wen
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Ting Xu
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Yanbing Zhao
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Yongjie Liu
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Contributions

JZ.G. and YJ.L. designed the experiments. JZ.G. wrote the paper. YH.D. H.H., X.W., BY.Z., and T.X. helped with sample collection and data presentation. JZ.G. performed the majority of the experiments and analyzed the data. YJ.L. supervised the study. YJ.L. helped revise the manuscript. JZ.G. drafted the original paper. All authors read and approved the final manuscript.

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Correspondence to Yongjie Liu.

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The Animal Protection and Ethics Committee of Nanjing Agricultural University approved (approval number PT2020022) and oversaw all experimental procedures, ensuring that they were carried out in accordance with established protocols.

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The authors declare no competing interests.

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Supplementary Material 1.

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Geng, J., Dong, Y., Huang, H. et al. Role of nasal microbiota in regulating host anti-influenza immunity in dogs. Microbiome 13, 27 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-025-02031-y

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Received: 20 June 2024
Accepted: 07 January 2025
Published: 27 January 2025
DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-025-02031-y

Role of nasal microbiota in regulating host anti-influenza immunity in dogs

Abstract

Background

Results

Conclusions

Background

Methods

Viral, bacterial, and cell strains

Animal grouping and treatment

Sample collection and 16S rRNA gene sequencing

Nasal bacterial load analysis

Microbiota sequence data analysis

Metagenomic pathway prediction

Histological examination

Immunofluorescence analysis

Western blot analysis

Quantification of mRNA expression

RNA sequencing (RNA-seq) analysis

Co-infection assay in vitro

Luciferase reporter assays

Cytotoxicity assay

Lentiviral transduction

Results

Dysbiosis of the nasal microbiome exacerbates influenza-induced respiratory disease

Community dynamics and functional changes of nasal microbiota during influenza infection

Dysbiosis of the nasal microbiome diminishes the antiviral response within the nasal cavity

Disruption of nasal microbiota exacerbates the dysbiosis of lung microbiota following influenza infection

Disruption of lung microbiota exacerbates inflammatory response and barrier damage during influenza infection

Canine-derived Lactobacillus spp. exerts a significant in vitro antiviral effect and activates IFN-mediated pathways

L. plantarum C123 is capable of interfering with influenza-induced incomplete autophagy

Discussion

Data availability

References

Acknowledgements

Funding

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Additional information

Publisher’s Note

Supplementary Information

Supplementary Material 1.

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About this article

Cite this article

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Keywords

Microbiome

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